Processes doi: 10.3390/pr14081291

Authors: Wangyang Hu Yuheng Luo Jianxun Huang Juntao Liang Jiajia Wu Yifei Wang Gang Jin Qiang Xu

Non-invasive temperature estimation during online operation is a critical challenge in enclosed micro-reaction systems, particularly when the thermal mass of the working fluid varies dynamically or is uncertain. Conventional model-based approaches typically rely on fixed thermal parameters, leading to significant estimation errors when the actual reagent volume deviates from nominal conditions. To address this limitation, this study proposes a volume-adaptive temperature estimation framework applied to an ultrafast quantitative polymerase chain reaction (qPCR) system. By modeling the heat-transfer pathways via a simplified resistance–capacitance (RC) network, a nonlinear least squares (NLS) algorithm within an output-error (OE) framework is employed to identify key thermal parameters online. The framework separates the estimation into an offline calibration stage—where a thermocouple-equipped chip provides ground-truth data—and an online deployment stage that relies solely on non-invasive external measurements. This approach allows the system to explicitly compensate for volume-induced variations in thermal inertia. Validation experiments on an ultrafast qPCR platform with reagent volumes ranging from 100 to 250 μL and heating rates exceeding 20 °C/s demonstrate that the method achieves robust performance, maintaining a mean absolute error (MAE) of reagent temperature at 0.24 ℃ and restricting the average volume estimation error to within 1.37 μL. DNA gel electrophoresis results further confirm the biological reliability of the temperature prediction strategy by verifying amplification specificity. This work provides a generalised solution for precise thermal management in micro-systems subject to variable thermal loads.

Processes doi: 10.3390/pr14081290

Authors: Hossein Naderi Ioannis Xanthis Theofilos Giannopoulos Efstratios Kroustis Elias P. Koumoulos

This work explores a sustainable route for producing recycled AlNiCo-based magnetic composites by incorporating end-of-life AlNiCo-5 particles into a polyamide 12 (PA12) matrix, thereby eliminating conventional debinding requirements. The study emphasizes material circularity through the reuse of mechanically recovered magnetic waste and polymeric residues. Virgin PA12 powder was used as the matrix material for high magnetic filler loadings of 40, 60, and 70 wt.% AlNiCo-5, while stearic acid was introduced to enhance interfacial compatibility and overall processability. The resulting composites were shaped into filaments and processed via material extrusion additive manufacturing, demonstrating that commercially available fused filament fabrication systems can successfully handle highly filled metal-polymer blends when supported by appropriate formulation and process parameter optimization. The findings confirm the feasibility of manufacturing flexible, functional, and resource-efficient magnetic components using widely accessible equipment, highlighting a promising pathway toward the cost-effective recycling and reuse of AlNiCo magnetic materials.

Processes doi: 10.3390/pr14081289

Authors: Ying Wang Deqian Cui

Coking coal flotation is a typical nonlinear, multi-variable, and multi-objective process in which concentrate quality and combustible matter recovery must be balanced under fluctuating feed and operating conditions. To improve both predictive reliability and decision support, this study proposes an integrated data-driven framework that combines particle swarm optimization-back propagation (PSO-BP) prediction, SHapley Additive exPlanations (SHAP) based interpretation, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization, and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (Entropy-TOPSIS) decision-making. After three-sigma outlier screening, 2000 valid distributed control system (DCS) samples were retained for model development and temporal holdout evaluation, and an additional 200 later-period industrial samples were used for independent validation. The data were partitioned chronologically, with months 1–4, month 5, and month 6 used for training, validation, and temporal holdout testing, respectively, while the months 7–8 dataset was reserved for later-period validation. The results show that PSO-BP consistently outperformed conventional BP under both temporal holdout and later-period validation. SHAP analysis identified raw coal ash and collector dosage as the dominant factors for product-quality prediction, while collector dosage and frother dosage contributed most strongly to tailing heat of combustion. NSGA-II further revealed the trade-off among clean coal ash, clean coal sulfur, and tailing heat of combustion, and Entropy-TOPSIS converted the Pareto-optimal candidate set into a practically balanced operating recommendation. Sensitivity and robustness analyses indicated acceptable stability of both the optimization process and the final decision result. Overall, the proposed framework provides an interpretable prediction–optimization–decision workflow for coking coal flotation and offers a practical basis for future DCS-assisted intelligent regulation.

Processes doi: 10.3390/pr14081286

Authors: Siyuan Lin Yunhu Lu Jia Wei

The fractures of Weixinan oil shale simultaneously influence drilling fluid invasion and solid strength and makes the collapse pressure difficult to predict. The indentation–NMR combined experiments were conducted to analyze the collapse characteristics, and the relationship between water porosity and shale strength was established. The experiment results show that water infiltration still occurs in oil-based drilling fluids in the short term and leads to a significant strength decrease. 3D numerical modeling was used to analyze water migration and shale strength weakening under fluid–solid coupling. It was found that fractures act as seepage channels and this aggravates the clay hydration and the shale instability. This causes high collapse pressure under specific well inclination angles and azimuth angles. The research results provide important references for shale wellbore stability analysis and engineering practice with complex geostress conditions and hydration.

Processes doi: 10.3390/pr14081287

Authors: Zeyang Zhang Jianhong Man Qingwen Li

Existing methods for assessing the stability of deep tunnel face rarely account for the weakening effect of rock mass parameters caused by excavation disturbance. This paper employs a vertical discretization method to divide the rigid failure body into vertical strip elements with fixed horizontal widths. By considering the weakening effect of rock mass parameters, a stability analysis model for the tunnel face is established. The equivalent cohesion and internal friction angle of the rock mass are obtained using the Hoek–Brown criterion and the equivalent Mohr–Coulomb transformation. Combined with the disturbance weakening factor, these yield the equivalent rock mass parameters after disturbance. Stability is solved using limit analysis and the principle of virtual power. The accuracy of the established model is verified through numerical simulation, demonstrating that the proposed analytical approach requires only about 90 s per run compared to approximately 7 h for 3D numerical models. The results indicate that the importance of parameters, in descending order under the specified reference conditions for deep-buried tunnels, is GSI > Dr > h1 > mi, where GSI play a dominant role. Excavation disturbance significantly reduces rock mass strength numerically. Assessing GSI and controlling the blasting disturbance are key to ensuring the stability of deep tunnels.

Processes doi: 10.3390/pr14081285

Authors: Kai Yang Hanhong Yu Shanshan Fu Hualei Xu Jie Wang Houshun Jiang

Permeability evolution in hydrate-bearing porous media is a key factor controlling gas production efficiency during natural gas hydrate exploitation. In this study, laboratory experiments were conducted using sand-packed tubes filled with quartz sand and glass beads to systematically investigate the variation of liquid-phase permeability with hydrate saturation. The effects of pore structure, particle size, and initial gas injection pressure on hydrate formation and permeability reduction were analyzed. Furthermore, experimental results were compared with four commonly used permeability models, including the Kozeny model, the Dai model, the Masuda model, and the parallel capillary model. The results show that permeability decreases continuously with increasing hydrate saturation in both porous media, and the most rapid decline occurs at low saturation levels between 0 and 9%. Under the same conditions of 20–40 mesh and an initial pressure of 6.0 MPa, the pressure drop rate in the quartz-sand-packed tube reaches 1.062 kPa per minute, which is about 2.35 times higher than the 0.451 kPa per minute observed in the glass-bead-packed tube, indicating a faster hydrate formation rate and stronger permeability reduction in quartz sand. In addition, both increasing particle mesh size and raising the initial gas injection pressure significantly promote methane consumption and hydrate formation. Model comparison results demonstrate that permeability reduction is strongly dependent on pore structure. The Kozeny pore-filling model, the Dai model (M = 3), and the Masuda model (N = 8) show good agreement with the glass-bead data, whereas the Dai model (M = 8), the Masuda model (N = 15), and the pore-center form of the parallel capillary model better describe the quartz-sand system. In contrast, models based on particle-surface coating show poor agreement in both media. These findings indicate that permeability reduction is primarily controlled by pore-space occupation and flow-path restriction rather than uniform surface coverage. The results suggest that hydrate growth is more likely to occur in pore centers and critical pore-throat regions, although this conclusion is based on macroscopic model comparison and requires further validation by pore-scale observations. This study provides a quantitative basis for model selection and improves the understanding of permeability evolution in hydrate-bearing porous media.

Processes doi: 10.3390/pr14081284

Authors: Novozhilov Ilyushina Martirosyan

The energy-intensive nature of primary aluminum production necessitates advanced computational tools for process optimization. This study presents a coupled electro-thermal model of an aluminum reduction cell, developed within the framework of smart manufacturing. Using the finite difference method (FDM) implemented in MATLAB R2025b, the model resolves the three-dimensional configuration of a cell with eight prebaked anodes across four distinct physical domains (electrolyte, anodes, cathode, and gas phase). The computational grid comprises approximately 45,000 nodes with refined vertical resolution (Δz = 0.025 m) in the interelectrode gap. The electrostatic solution converges within 150–200 iterations using successive over-relaxation (SOR, ω = 1.5), with a total runtime under 15 min for 30,000 s of simulated physical time on a standard desktop workstation. Simulation results reveal characteristic temperature profiles with maxima reaching 1150 °C and a thermal uniformity index of approximately 130 °C across the central cross-section. The predicted specific energy consumption of 14.0 MWh/t Al aligns with industrial benchmarks. This computationally accessible virtual testbed enables rapid assessment of design modifications and process parameters, supporting the goals of energy efficiency and enhanced operational stability in primary aluminum production.

Processes doi: 10.3390/pr14081283

Authors: Xiaopeng Yue Chongwang Yue Yayun Fu

To optimize the stiffness matrix structure for frequency-domain elastic wave forward modeling in 2D VTI (transversely isotropic with a vertical symmetry axis) media—thereby reducing memory consumption and improving computational efficiency—we simplify the conventional 25-point finite-difference scheme to derive a 17-point frequency-domain finite-difference scheme. This approach reformulates the finite-difference operators for the partial derivatives and acceleration terms in the elastic wave equations, reducing the number of grid points involved in the computation by 30% compared to the 25-point scheme. The optimized matrix construction leverages sparse matrix storage techniques, decreasing memory usage by approximately 27%. Numerical validation, conducted using a double-layer VTI medium model and the Marmousi model with three major faults and an anticline containing limestone layers at the base of the faults, demonstrates that the 17-point finite-difference scheme maintains comparable accuracy while requiring 14% less computation time and featuring a 25% reduction in nonzero elements within the impedance matrix. Comparisons of wavefield snapshots and receiver components (horizontal component U and vertical component V) support this conclusion. These improvements enable the use of more efficient iterative solvers.

Processes doi: 10.3390/pr14081282

Authors: Houxue Xia Zhenyu Sun Huagang Tong Liusan Wu

In flexible manufacturing systems, the composite mobile manipulator (CMM) is subject to nonlinear inertial disturbances arising from the dynamic coupling between the mobile platform and the robotic arm. These disturbances significantly impair positioning precision during grasping tasks. This paper addresses the dynamic decoupling of multi-body nonlinear inertial disturbances within CMM systems. Departing from the conventional “stop-then-plan” serial execution paradigm, we propose a full-cycle spatiotemporally coupled trajectory optimization method. The operation cycle is bifurcated into two synergistic stages: “dynamic calibration” and “static execution.” The dynamic calibration trajectory is pre-planned and executed synchronously during platform movement to actively compensate for inertial-induced pose deviations. Concurrently, the static execution trajectory is optimized and then triggered immediately upon platform standstill, ensuring a seamless and precise transition to the “Grasping Pose”. It is worth noting that the temporal characteristic central to this framework lies in the concurrent execution of static trajectory optimization and platform transit: by the time the platform reaches its destination, the pre-planned trajectory is already available for immediate triggering, achieving zero task-switching wait time at the planning layer. The term “zero-latency” here does not imply a fixed-cycle real-time response at the control layer, but rather the complete elimination of decision latency afforded by the parallel planning architecture. This framework eliminates computational latency, markedly enhancing operational efficiency. Key innovations include two novel constraints. First, the Adaptive Task-space Bounded Search Constraint (ATBSC) framework restricts optimization to a geometry-inspired search region, thereby enhancing search efficiency and ensuring controllable deviations. Second, the Multi-Rigid-Body Coupling Constraint (MRBCC) system explicitly models inertial transmission across motion phases to suppress pose fluctuations. The proposed framework is developed and validated within an obstacle-free workspace. In simulation-based validation on a UR10 6 degree-of-freedom manipulator model, experimental results indicate that ATBSC increases valid solution density to 84.7% and reduces average deviation by 72.8%. Furthermore, under the tested conditions, MRBCC mitigates end-effector position errors by 79.7–81.0% with a 97.5% constraint satisfaction rate. The improved Cuckoo Search algorithm (ICSA), serving as the solver component of the proposed framework, achieves an 11.9% lower fitness value and a 13.1% faster convergence rate compared to the standard Cuckoo Search algorithm in the tested scenarios, suggesting its effectiveness as a reliable solver for the constrained multi-objective trajectory optimisation problem.

Processes doi: 10.3390/pr14081281

Authors: Raju Buchanahalli Thimmaiah Shobha Visweswara S. Suresh Kumar Raju Fatemah H. H. Al Mukahal Abeer Al Elaiw Sibyala Vijayakumar Varma

The study addresses a selected issue in industrial cooling, that is, how to transport heat more efficiently when the process involves fiber spinning and extrusion, in which conventional fluids usually cannot work. We considered a ternary nanofluid that passed around a porous stretching cylinder and particularly considered the synergistic effect of quadratic thermal buoyancy, and the thermally generated double-diffusive heat and solute (TGDHS) effect. Through the Casson fluid model and considering the magnetic fields, radiations, and nonlinear chemical reactions, we reduced complex PDEs to simple ODEs. The results were evident using the BVP4C numerical method. Although in reality, magnetic fields and thermal radiation become a retarding force, the quadratic thermal buoyancy is the driving force behind accelerating the flow. An important trade-off that we discovered is that a heavier Casson fluid reduces heat and mass transfer. The addition of Nimonic 80A, AA7072, and AA7075 nanoparticles to ethylene glycol consistently enhances heat transfer, outperforming the base fluid by 7.8% even at low concentrations. While AA7072 and AA7075 drive significant increases of over 16%, Nimonic 80A offers a much more marginal contribution of 1.23%. Consequently, the Nusselt number is far more sensitive to the concentration of the aluminum alloys than to the Nimonic 80A. Finally, this work demonstrates that the most significant parameter in intensifying convective heat and mass transfer in such industrial systems is the strong forces of buoyancy.

Processes doi: 10.3390/pr14081278

Authors: Maolin He Dehua Liu Hao Lei Jiawei Hu Jiayan Chen

Currently, China boasts abundant shale gas resources. However, in the process of flowing production, there remain significant discrepancies in our understanding of the flow patterns of gas and water, and many challenges persist in gas–water measurement. Given the dense pore structure and complex micro-features of shale gas reservoirs, this study proposes a method to estimate the fractal dimension by utilizing shale mercury injection curves based on experimentally determined relative permeability curves, thereby enabling a more accurate fitting of these curves. Experimental results show that the two-phase co-infiltration zone in the shale is narrow overall, with bound water saturation exceeding 50%. The findings indicate that the experimentally measured relative permeability curves closely match those fitted using the fractal dimension approach. Moreover, the lower the permeability, the more the equal-permeability points of the fitted curves shift toward the lower-right quadrant. Overall, the fitting performance is satisfactory, providing additional research directions and insights for determining relative permeability curves of gas and water in shale gas reservoirs.

Processes doi: 10.3390/pr14081280

Authors: Wenhui Dang Yingjie Wang Zhen Zhong Xin Wang Hao Chen Yuqiang Xu Lei Yang Hailong He

Accurate prediction of formation pore pressure is of great significance for drilling safety, the efficient development of oil and gas resources, and engineering risk control. Traditional methods based on empirical parameters or mechanical models are difficult to fully adapt to complex geological conditions. Although intelligent models have strong nonlinear modeling capabilities, they are highly dependent on large-scale and high-quality training data, and tend to suffer from poor generalization ability and insufficient adaptability in blocks with limited samples or significant differences in geological characteristics. To improve the adaptability of the model between different blocks, this study introduces a heterogeneous transfer learning method to construct a formation pore pressure prediction model suitable for scenarios with inconsistent feature spaces. This method can effectively transfer knowledge from the source domain to the target domain, alleviating the prediction difficulties caused by differences in data distribution. Experimental results show that the proposed method still maintains excellent prediction accuracy and stability under the conditions of limited training samples and complex geological conditions, and has better generalization ability and cross-block applicability compared with traditional models.

Processes doi: 10.3390/pr14081279

Authors: Yongjun Xiao Yu Lu Chunlin Wu Lei Liu Hao Zhao Ran Wen Jian Zheng Xin Luo Hong Liu Hengbao Li

The evaluation of sweet spots for infill wells is critical to identifying premium reservoir zones, avoiding fracture hits, and achieving safe, efficient development with maximum production potential. Firstly, considering that geological and engineering factors—such as high fracability and good reservoir quality—are conducive to the formation of complex fracture networks and sufficient gas production after fracturing, quantitative evaluation indicators for fracability and geological properties have been established. Secondly, a classification method for different sweet spot tiers in infill wells was proposed. Lastly, taking the Changning infill pilot wells as an example, for sections not affected by fracture interference, higher sweet spot evaluation scores show a strong correlation with improved predictive performance of tracer-based gas production forecasts. Conversely, in fracture-interfered zones, a discrepancy was observed between the sweet spot evaluation results and actual gas production volumes. The horizontal wellbores were classified into a six-tier system (L1–L6), with tailored fracturing design recommendations provided accordingly. This study offers scientific guidance for the precise evaluation of sweet spots in infill wells and the design of customized staged fracturing, thereby significantly enhancing fracturing effectiveness.

Processes doi: 10.3390/pr14081277

Authors: Simla D. Maharaj Charles Rashama Riann Christian Tracy Masebe Melissa Inderpal-Pillay Tonderayi S. Matambo

This study evaluated the anaerobic digestibility of primary sludge from two wastewater treatment plants (WWTPs), Leeuwkuil and Rietspruit. Anaerobic biodegradation produces biogas as an energy carrier. Sludge from the primary settling tanks was tested in batch mode as a mono-substrate, without pretreatment or external inoculum. Proximate and ultimate analyses were used to estimate theoretical methane production. Anaerobic digestibility tests were then performed using an Automatic Methane Potential System (AMPTS® II, Bioprocess Control). The volatile-to-total solid (VS/TS) ratios were 71 for Leeuwkuil and 13 for Rietspruit. Theoretical methane yields for Leeuwkuil sludge were 257–293 L/kg VS. For Rietspruit, the Buswell and Dulong methods gave negative theoretical BMP values (−76 and −15 L/kg VS), suggesting these models may be unsuitable for high-oxygen-content substrates. Measured methane production was 11.3 L/kg VS for Leeuwkuil and 4.8 L/kg VS for Rietspruit, indicating low anaerobic digestibility relative to solid content. Leeuwkuil primary sludge nevertheless showed better potential as a co-substrate for methane production than Rietspruit sludge. Rietspruit sludge may pose challenges for anaerobic digestion, though pretreatment or co-digestion could improve performance. Based on measured methane productivities, each WWTP could generate about 0.5 MWh of electricity per day from biogas. The study shows that primary sludge digestibility depends strongly on the physico-chemical characteristics of the influent wastewater. Primary sludge can often be improved for digestion through chemical/physical pretreatment and co-digestion with secondary sludge or suitable agro-industrial organic residues.

Processes doi: 10.3390/pr14081276

Authors: Petr Trávníček Pavel Rössner Jan Pokorný Igor Laštůvka Petr Junga Tomáš Vítěz Juraj Ružbarský Jozef Maščenik

Fire incidents in municipal waste management facilities remain a persistent safety issue, complicated by high variability and limited reliability of available data. This study presents a process-oriented evaluation of 86 fire incidents recorded between 2013 and 2022 in the South Moravian Region of the Czech Republic, based on a verified non-public database of the Fire Rescue Service. Most incidents (approximately 76%) were associated with municipal solid waste landfills, confirming their dominant role within the sector. Spontaneous combustion was identified as the most frequent ignition mechanism; however, in nearly 78% of cases, the exact cause could not be conclusively determined, indicating a high level of uncertainty in incident reporting. Key quantitative indicators, including extinguishing water consumption (mean 32 m3, median 10 m3) and affected fire area, exhibited substantial variability, limiting their direct use for quantitative evaluation. To address these limitations, representative fire scenarios were systematically identified and analysed using the ARIA 3 framework in combination with the Bow-Tie methodology. This approach enables the interpretation of fire incidents as disturbances in operational processes and supports the identification of scenario-specific preventive and mitigation barriers. The results show that, despite data uncertainty, incident records provide a robust basis for identifying recurring fire patterns and facility-specific vulnerabilities, supporting scenario-based risk management and informed decision-making in municipal waste management systems.

Processes doi: 10.3390/pr14081275

Authors: Wei Xu Yulin Wang Lihong Peng Zixuan Wang Sheng Zhang Hongyi Lai Yongjia Hu Huankun Zheng

The inherent intermittency and uncertainty of wind power generation pose significant challenges to grid security and the integration of renewable energy. Accurate and reliable short-term wind power forecasting is crucial for enhancing wind energy usage and ensuring the safe operation of power systems. Current mainstream forecasting methods inadequately model spatial correlations among regional wind farms. Additionally, wind power generation is susceptible to sudden changes in weather conditions and environmental factors, limiting the robustness of existing forecasting methods when confronting dynamically changing prediction environments. This poses major challenges for accurate and reliable regional wind power forecasting. This paper employs Graph Convolutional Networks (GCN) to model spatial connections between wind farms while introducing a combined TCN-Transformer model for temporal feature extraction and dependency modeling. Furthermore, to enhance prediction accuracy and reliability, Deep Q-Network (DQN) is incorporated to dynamically correct model prediction errors. Experimental results demonstrate that the proposed short-term wind power forecasting method achieves an RMSE of 60.14 and an MAE of 45.98, showing significant improvement over predictions from models without DQN error correction and other comparative models. Future work may extend the forecasting horizon to provide more information support for grid supply security decisions.

Processes doi: 10.3390/pr14081274

Authors: Yue Liu Qinglin Cheng Yuchun Li Jinwei Yang Shaosong Zhao Zhengsong Huang

High-penetration renewable energy significantly increases uncertainty, dynamic network coupling, and the need for secure and coordinated smart-grid dispatch. To address the limitations of conventional forecasting-based and static graph-based methods, this paper proposes a unified dispatch framework that integrates topology-informed dynamic graph learning, privacy-aware multi-agent symbiotic reinforcement learning, and structural causal intervention analysis. The dispatch problem is formulated as a constrained partially observable stochastic game, in which multiple agents coordinate generation adjustment, reserve allocation, and congestion-aware corrective actions under engineering constraints. A physics-informed dynamic graph convolutional module captures both fixed physical topology and stress-dependent operational couplings, while a KL-regularized multi-agent reinforcement learning scheme improves cooperative task allocation under renewable fluctuations. Federated optimization with Rényi differential privacy is introduced to protect sensitive local operational information during training. In addition, a structural causal module provides intervention-based interpretation of how wind variation, load escalation, and line stress affect dispatch cost, congestion risk, and renewable curtailment. Experiments on a public-trace-driven benchmark based on a modified IEEE 30-bus system show that the proposed method achieves the best overall performance among the compared baselines, reducing dispatch-cost RMSE to 3.82, locational-price MAE to 2.95, renewable curtailment to 4.8%, and the constraint-violation rate to 0.30%. Overall, the framework shows favorable performance on the test benchmark, provides post hoc intervention-based interpretation of dispatch outcomes, and is evaluated under a reproducible benchmark construction and assessment protocol.

Processes doi: 10.3390/pr14081273

Authors: Shichao Yang Yibo Cui Xulong Yao Lin Sun Yanbo Zhang Bin Guo

Accurate classification of rock fracture modes is essential for understanding rock mass instability mechanisms. To address the limitation of traditional acoustic emission (AE) classification methods that treat a single AE signal as a single fracture event, overlooking its composite nature from multiple fracture events and leading to misclassification, this study proposes a novel rock fracture mode classification method based on AE spectral analysis. This study details the development framework, theoretical model, classification criteria, application process, and experimental validation of the new rock fracture mode classification method. Uniaxial compression tests on granite, marble, and limestone, along with rockburst simulation tests on granite, were conducted to validate the classification of fracture modes. In rockburst simulations, shear fracture signals accounted for 48% on average, composite signals 40%, and tensile signals 12%. The method effectively distinguishes multiple fracture events within a single AE signal, accurately classifies fracture modes, and elucidates the dynamic evolution of fracture modes during the rockburst precursor stage, offering significant advantages for rock fracture mode classification and mechanistic insight.

Processes doi: 10.3390/pr14081272

Authors: Yanhao Liang Lei Shao Hao Sun Ze Wang Han Zhang

Based on the waterflooding development practice of thick carbonate reservoirs in the Middle East, aiming at the practical problems such as complex water invasion types, rapid water breakthrough of oil wells and poor development performance in such reservoirs, this study takes the MB1 reservoir of H Oilfield as the research object and establishes a multi-scale hierarchical screening scheme for the main controlling factors of water cut rise covering the reservoir-block-well group levels. Firstly, the target reservoir is divided into several typical development blocks by means of numerical simulation technology. On this basis, the dynamic development characteristics of the reservoir, typical blocks and well groups are analyzed respectively. The multi-sequence grey correlation method is adopted to screen the common influencing factors of water cut rise in typical blocks, and then the multi-factor sensitivity analysis of the screened key factors is carried out by numerical simulation. Finally, it is determined that the main controlling factors affecting the water cut rise in the reservoir are the development degree of high-permeability layers, the rationality of well pattern layout, and the injection–production intensity, and the corresponding development adjustment strategies are proposed accordingly. Guided by the multi-scale hierarchical screening of main controlling factors for water cut rise, this study improves the traditional grey correlation method and proposes a multi-sequence grey correlation analysis method. This method for determining the controlling factors, which combines mathematical approaches with reservoir numerical simulation techniques, gives full play to the advantages of both. It reduces the range of variables in numerical simulation analysis, avoids the sharp increase in simulation complexity caused by multi-factor coupling, and greatly improves work efficiency while ensuring research depth.

Processes doi: 10.3390/pr14081271

Authors: Antonina P. Malyushevskaya Olena Mitryasova Michał Koszelnik Ivan Šalamon Andrii Mats Andżelika Domoń Eleonora Sočo

Electric discharge cavitation is an effective method for water treatment that combines physical and chemical effects within a single process. It enables water disinfection, extraction acceleration, dispersion of solid particles, and enhancement of porous material permeability. Compared to conventional chemical treatment, it reduces the demand for reagents and minimizes secondary pollution. This new and developing technology significantly contributes to the preservation of natural aquatic ecosystems by providing a sustainable alternative to traditional decontamination methods, thereby reducing the overall anthropogenic pressure on the environment. This study focuses on developing a reliable method for assessing electric discharge cavitation intensity and controlling water purification processes. The proposed approach is based on the oxidation of iodide ions to molecular iodine by reactive species generated during electric discharge cavitation. The adapted iodometric method is sensitive, reproducible, and does not require complex optical or acoustic equipment. Experimental results confirmed that iodometry provides an accurate evaluation of cavitation intensity, allowing control of specific energy consumption and optimization of treatment parameters. Optimal operating conditions were established to control the water processing by electric discharge cavitation: stainless-steel electrodes, specific input energy not exceeding 280 kJ·L−1, the presence of a free liquid surface in the working chamber, and a discharge pulse frequency below 10 Hz. The proposed method supports the development of energy-efficient, low-waste technologies for wastewater and natural water treatment and facilitates the integration of electric discharge systems into existing water treatment infrastructure, particularly under resource-limited conditions.

Processes doi: 10.3390/pr14081270

Authors: Haoming Sun Yuan Liang

In recent decades, triazine herbicides (THs), one of the most widely used agrochemicals, have been extensively applied to enhance crop yields. However, their persistent nature and high mobility have resulted in pervasive contamination of aquatic ecosystems, posing significant risks to non-target organisms and human health through bioaccumulation and endocrine disruption. Addressing THs pollution in water bodies has thus emerged as a critical environmental challenge. This study reviews the efficacy of biochar, a carbon-rich material derived from biomass pyrolysis, for TH removal due to its high surface area, hierarchical porosity, and tunable surface functionality. The maximum reported adsorption capacities are up to 260.5 mg·g−1; with degradation efficiencies, they can exceed 99.5% in advanced oxidation systems. Mechanistic investigations reveal that TH removal primarily involves π–π interactions, hydrogen bonding, pore filling, and electrostatic attraction during adsorption, while degradation proceeds via radical pathways (e.g., •OH, SO4•−) and nonradical routes (e.g., 1O2, direct electron transfer) in processes such as persulfate activation, photocatalysis, and Fenton-like reactions. By analyzing degradation intermediates and pathways, this review underscores the necessity of coupling adsorption with advanced oxidation to achieve complete mineralization and mitigate secondary ecological risks. Furthermore, it emphasizes the importance of tailoring biochar’s physicochemical properties through feedstock selection, pyrolysis conditions, and chemical modifications to optimize THs’ removal performance. This work advocates for the integration of biochar-based technologies into sustainable water treatment frameworks, aligning with carbon neutrality goals and circular economy principles. Future research should prioritize scalable synthesis methods, long-term stability assessments, and field-scale validations to translate laboratory insights into practical solutions for safeguarding global water resources. However, realizing this potential requires that we overcome challenges related to matrix interference, catalyst deactivation, and incomplete mineralization, which are often overlooked in laboratory-scale studies.

Processes doi: 10.3390/pr14081269

Authors: Dong Jiang Wei Yuan Bo Qi Huawei Yu Li Zhang

Neutron logging-while-drilling is a nuclear logging technique within the logging-while-drilling (LWD) system, characterized by high sensitivity to hydrogen formation. With the increasing complexity of well trajectories and the development of unconventional oil and gas, it has evolved from a traditional porosity measurement tool into a critical source of real-time information for geosteering and engineering decision-making. From a systems engineering perspective, this paper reviews the physical basis, tool system configuration, data processing methods, and typical engineering applications of LWD neutron logging. It discusses key technical bottlenecks and development trends. The results indicate that multiple interacting factors, including the neutron source, detector configuration, measurement geometry, environmental suppression capability, and interpretation strategy, constrain its performance. The transition from chemical neutron sources to pulsed neutron generators (PNG) represents a critical turning point, improving measurement safety and expanding the range of measurable parameters while simultaneously introducing new engineering challenges such as target material lifetime and long-term stability. Field practice further demonstrates that the main value of LWD neutron logging lies in providing real-time porosity and related information that overcomes physical limitations during drilling, supporting geosteering and real-time reservoir evaluation decisions. Based on current progress, future work will focus on enhancing the reliability of PNG-based neutron sources and developing data processing and intelligent interpretation workflows that integrate physical models with data-driven methods.

Processes doi: 10.3390/pr14081268

Authors: Ruilong Suo Jingong Zhang Tao Zhang Feng Zhang Bolong Wang Zhaoyu Zhang Dawei Ren Yitao Lei

The automatic extraction of seismic reflection events is fundamental to seismic interpretation and structural identification in oil and gas exploration, particularly for large-scale regional surveys and preliminary basin-scale assessments. Although the B-COSFIRE (Bar-Combination of Shifted Filter Responses) method has demonstrated strong capability in detecting ridge-like structures, its application in large-scale seismic processing is limited by high computational cost and complex filter bank configuration. Conventional edge detectors such as the Canny operator are computationally efficient but often produce fragmented and noise-sensitive results in low signal-to-noise ratio (SNR) seismic data because they rely solely on local gradient information and ignore the spatial continuity of geological horizons. To overcome these limitations, this study proposes a lightweight and computationally efficient framework for rapid seismic event extraction. The method simplifies the B-COSFIRE architecture by replacing its configurable filter bank with a Difference-of-Gaussian (DoG) operator, which enhances ridge-like reflection features while suppressing background interference through a center–surround mechanism. Furthermore, a Spatial Consistency Constraint (SCC) module is introduced to enforce lateral continuity using directional morphological closing operations. This strategy reconstructs disrupted reflection segments and converts isolated detection responses into spatially coherent linear structures. Adaptive thresholding and skeletonization are then applied to obtain single-pixel-wide reflection contours suitable for geological interpretation and regional structural analysis. The proposed method was evaluated using both synthetic seismic models (Ricker wavelet convolution with Gaussian noise, σ = 0.15) and real post-stack seismic profiles characterized by low SNR conditions. Experimental results demonstrate that the proposed method achieves a Precision of 0.9527, Recall of 1.0000, and F1-score of 0.9758 on synthetic data, outperforming both the standard Canny detector (F1: 0.8972) and B-COSFIRE (F1: 0.7311). The Continuity Index reaches 261.00 pixels, substantially higher than Canny (223.67 pixels) and B-COSFIRE (66.86 pixels). Notably, B-COSFIRE exhibits a severely imbalanced detection profile (Precision: 0.5762, Recall: 1.000), indicating excessive false positives that undermine its practical utility. The proposed method additionally achieves the lowest runtime (0.024 s per profile), representing a 44× speedup over B-COSFIRE (1.039 s), while requiring no training data. Overall, the proposed framework provides a practical and efficient solution for automated seismic event extraction. With only a small number of geologically interpretable parameters and strong robustness across different datasets, the method is well-suited for large-scale seismic data processing and preliminary structural assessment in underexplored regions, enabling rapid first-pass evaluation of extensive survey areas before detailed interpretation and reservoir characterization. These characteristics make the method particularly suitable for computer-assisted interpretation workflows in industrial oil and gas exploration. Unlike prior approaches that treat seismic event extraction as a generic edge detection problem, the proposed framework explicitly encodes geological prior knowledge—specifically, the lateral continuity of stratigraphic interfaces—as a morphological constraint, bridging the gap between image processing methodology and geophysical interpretation requirements.

Processes doi: 10.3390/pr14081267

Authors: Fei Wen Xiaochen Li Leilei Wang Jiahui Shi Junxiong Zhao Taiheng Yin

Coal is inherently soft, characterized by well-developed cleat systems, low strength, and significant anisotropy. Existing models that treat coal as a continuous medium or consider only a single plane of weakness fail to capture the synergistic effects of multiple weaknesses on wellbore instability. This study addresses this gap by integrating the strength criteria of weakness planes with wellbore stress theory. First, in situ stresses were transformed into the coordinate system of the weakness planes to derive the acting stress components. A strength criterion incorporating multiple structural planes—accounting for the coal matrix, bedding, face cleats, and butt cleats—was then applied to establish a coupled wellbore stability criterion. A corresponding collapse pressure program was developed using Visual Basic to analyze the effects of stress state, wellbore trajectory, and weakness orientation. The results show that the presence of multiple weakness planes significantly increases the sensitivity of wellbore stability to trajectory. Drilling parallel to the direction of minimum horizontal stress minimizes shear stress and collapse pressure, whereas drilling at high angles or parallel to the maximum horizontal stress activates the weakness planes, leading to a sharp increase in collapse pressure. The presence of these weaknesses results in a highly non-uniform and direction-dependent collapse pressure distribution, with their synergistic interactions further exacerbating the risk of localized failure.

Processes doi: 10.3390/pr14081266

Authors: Shiue-Der Lu Chin-Tsung Hsieh Hwa-Dong Liu Shao-Tang Xu

Chiller systems account for a substantial proportion of building energy consumption, where their operational efficiency and start–stop cycling frequency directly influence overall system energy use and equipment lifespan. In practical applications, load fluctuations and improper control settings often cause chillers to experience frequent cycling, leading to decreased efficiency and increased mechanical wear. While existing studies predominantly focus on real-time control or model predictive approaches, fewer investigations systematically identify stable operating regions and optimal control thresholds using historical operational data. This study proposes a data-driven method for identifying an operational threshold. Long-term historical data are analyzed to establish a start–stop event detection mechanism. A normalized power index is introduced, and multi-scenario classification—incorporating seasonal conditions and peak/off-peak periods—is employed to evaluate system behavior across different contexts. Furthermore, a quantile scanning approach combined with hysteresis simulation is utilized to identify optimal operational threshold intervals. Stability evaluation indices, based on cycling frequency, power variation rate, and load deviation magnitude, are constructed to quantify stability performance. To verify the robustness of these thresholds, K-fold cross-validation is performed. Results indicate that the identified thresholds effectively reduce cycling frequency and power fluctuations, thereby enhancing system stability. Specifically, the start–stop cycling frequency is reduced by approximately 75–90%, and the power variation rate decreases by up to 85% across various scenarios. This study provides an offline decision-support framework to assist operators in optimizing control parameters and strategies. These outcomes serve as a reference for chiller energy management and provide empirical evidence for the future design of control strategies.

Processes doi: 10.3390/pr14081265

Authors: Bruna Naiara Silva de Oliveira Almeida Rafael Agra Dias Pamela Thainara Vieira da Silva Renê Anisio da Paz Bruna Aline Araujo Carlos Bruno Barreto Luna Renate Maria Ramos Wellen Edcleide Maria Araújo

The global shift toward sustainable industrial processes has increased the demand for advanced materials capable of performing under harsh conditions, with high-temperature polymer nanocomposites emerging as a key development area. This study investigates the fabrication of sustainable polysulfone (PSU)/medium-density fiberboard (MDF) nanocomposites through phase inversion, using PSU—a matrix known for its high glass transition temperature—as the base. Membranes were created by adding MDF residue at 1, 3, 5, 7, and 10 phr (parts per hundred resin). Characterization included analyzing polymer solution viscosity, ATR-FTIR, contact angle, SEM, porosity, equilibrium water content, average pore radius, tensile testing, and permeation performance. Incorporating MDF residue increased solution viscosity and affected porosity and the structure of the top layer. Mechanical testing showed MDF acted as a functional additive, improving the elastic modulus and tensile strength, and supporting overall structural stability under hydraulic stress. The membranes exhibited competitive water flux and maintained high selectivity (80–92% rejection; over 95% turbidity removal) at 1.0 and 2.0 bar. The 3 and 5 phr levels optimized performance, demonstrating that repurposing industrial waste within high-performance matrices is a practical approach for producing durable materials that meet the needs of energy systems and complex industrial separation processes.

Processes doi: 10.3390/pr14081263

Authors: Xuebin Luan Zhiyu Jiao Haoran Liu Yujia Tang Jing Ding Jiaze Ma Yufei Wang

This study develops a high-resolution techno-economic optimization framework to assess the feasibility of green hydrogen production in 100% renewable, off-grid systems. Utilizing 5-minute interval meteorological data aggregated to hourly resolution spanning 5 years across seven geographically diverse sites, this study co-optimizes the integration of hybrid wind–solar power generation, flexible electrolyzer operation, and a multi-timescale energy storage portfolio, incorporating short-duration, long-duration, and seasonal storage. On the generation side, a hybrid wind–solar configuration achieves the lowest levelized cost of hydrogen (LCOH). For energy storage, no single storage technology can economically address demand fluctuations across short-term, medium-term, long-term, and seasonal timescales. Instead, a coordinated multi-timescale storage strategy incorporating energy-to-energy mechanisms reduces the LCOH by up to 40%. Increasing hydrogen tank capacity and enabling flexible electrolyzer operation further lowers the LCOH. Significant regional resource variability leads to substantial cost disparities, with the most favorable region achieving a low LCOH of $2.45/kg. Several regions are projected to reach the $3/kg target by 2030, while areas with limited resources require large-scale hydrogen storage to ensure supply reliability. These results represent deterministic lower-bound estimates under perfect foresight; accounting for forecast uncertainty and real-world operational constraints would likely increase actual costs by approximately 5–15%.

Processes doi: 10.3390/pr14081264

Authors: Karla Ramos Amin Karmali

S. Tomé and Principe (STP) islands have been studied in recent years for their wide range of medicinal plants which exhibit several biological activities of great medicinal interest for some diseases. Experimental design for optimization of several parameters was carried out by a full-factorial test of two levels of three factors for secondary metabolite extraction from Rauvolfia caffra leaves. The best conditions for highest extraction of phenolic compounds (i.e., 89.90 μmoles gallic acid equivalent/g leaves) were obtained at 25 °C in H2O and at 5 days of incubation. Several phytochemical assays were performed for characterization of these plant extracts, and the highest levels of TFC, DPPH and reducing power were obtained with aqueous plant extraction at 25 °C and for 5 days of incubation, whereas leaf extraction with water at 40 °C for 5 days of incubation revealed the highest levels of ABTS scavenging activity. The levels of SOD and superoxide radical scavenging activities were highest in plant extraction, with hexane at 25 and 40 °C for 5 days of incubation, respectively. The present report consists of a novel and intrinsic synchronous fluorescence and phosphorescence characterization of secondary metabolites from this plant extract. Intrinsic and non-destructive synchronous fluorescence was carried out in the range of 250 to 750 nm with a Δλ range of 5–30 nm, which exhibited several fluorescence peaks in hexane and aqueous plant extracts. On the other hand, intrinsic and non-destructive synchronous phosphorescence was also performed which also exhibited several peaks in aqueous and hexane extracts. 3D spectra of secondary metabolites confirmed the fluorescence peaks observed in SFS in plant extracts. FTIR spectroscopy was selected to investigate the structural properties of secondary metabolites in these plant extracts. Therefore, the present work describes a novel characterization of secondary metabolites by a non-destructive and intrinsic synchronous fluorescence technique for plant extracts.

Processes doi: 10.3390/pr14081262

Authors: Chao Lv Ning Wei Hongliang Zhao Ming Wang Hanwen Zhang Hongru Qiu

With the decreasing availability of high-quality mineral resources and the increasing complexity of ore properties, efficient and sustainable flotation technology has become a research focus in the field of mineral processing. To optimize the taper angle of the air distributor in a KYF flotation machine, numerical simulation was used in this study to investigate its influence on the internal flow field, gas-phase characteristics, structural pressure distribution, and stirring power consumption. The results show that the peak turbulent kinetic energy and gas holdup are concentrated in the shear zone between the impeller and stator. Under the +5° condition, the peak turbulent kinetic energy is the lowest, while its vertical distribution is the most uniform. The peak gas holdup in the impeller–stator region reaches 19.2%, and the number of efficient bubbles with a diameter of 0.5 mm reaches 3.8 × 106 per m3, which is significantly higher than under the other conditions. During stable operation, this condition exhibits the lowest stirring power consumption at 126.0 W, which is 7.557% and 4.255% lower than under the −5° and 0° conditions, respectively. The optimal taper angle is therefore determined to be +5°. However, the associated large pressure gradient on the impeller surface may accelerate blade wear, indicating that surface strengthening measures should be considered to balance performance and durability.

Processes doi: 10.3390/pr14081261

Authors: Tianyu Gong Youhang Zhou Xuan Tang Zhenhai Liu Ding Li Yuqin Xiao

Curvature ratio (δ) governs secondary flows in gas–solid two-phase flow through bends, thereby affecting particle dynamics and leading to non-uniform wall deposition and increased erosion risk. In this study, a coupled Reynolds stress model (RSM) and Discrete phase model (DPM) framework was employed. A wall contact model incorporating adhesion, rebound, and removal mechanisms was implemented via a User-Defined Function (UDF). The spatial distribution and deposition characteristics of particles with different inertia (Stokes number range: 0.020 ≤ St ≤ 30.176) were systematically investigated in the range of δ = 2.0~3.5. The results reveal a distinct inertial dependence in particle spatial distribution: particles with St < 1 exhibit a “high-dispersion, weak-aggregation” pattern, whereas those with St > 1 form an “outer-wall agglomeration, inner-wall cavity” characteristic. As δ increases, the secondary flow intensity decreases while the effective centrifugal path lengthens. Governed by the combined effects of the effective collision coefficient (Rc) and effective adhesion rate (ηa), particle deposition is inhibited for St < 1 but enhanced for St > 1. This study advances the understanding of deposition under geometric constraints and provides a basis for optimizing pipeline design.

Processes doi: 10.3390/pr14081260

Authors: Pei Gao Hemiao Yu

Employing eco-friendly and low-carbon methods to restore petroleum-polluted soil is a growing trend. However, the low-carbon remediation theories and methods for petroleum-polluted soil are still in their early stages. Herein, the carbon footprint and environmental impacts of different petroleum-polluted soil remediation methods were studied based on life cycle assessment (LCA). It was found that the carbon footprint and environmental impacts of the solidification/stabilization (S/S) method were much lower than those of pyrolysis and chemical oxidation methods. Moreover, compared with other S/S materials, the carbon footprint of lime–fly ash solidification for petroleum-polluted soil was the lowest, at only 12.72 kg CO2 eq. Moreover, its unconfined compressive strength (UCS) increased by 700% compared to the untreated petroleum-polluted soil. On this basis, the response surface method was further employed to optimize remediation parameters using carbon footprint and UCS growth rate as response variables. The results showed that the optimal parameters for solidifying petroleum-polluted soil were lime content of 10.41%, fly ash content of 21.89%, and a curing time of 27 days. This study provides the important theoretical basis and practical guidance for the low-carbon and efficient remediation of petroleum-polluted soil.

Processes doi: 10.3390/pr14081259

Authors: Qi Dang Chun-Gong Li Jin-Chun Mao Xiang Wang

Surface modification can regulate surface properties through the design of surface structures and chemical compositions. Heparin has good anticoagulant properties and is widely used in the surface modification of blood-contact materials. However, the molecular structure of heparin has often been overlooked in terms of its application and further utilization in the design process. In this study, heparin was grafted onto substrate materials through polydopamine/polyethyleneimine co-deposition coating and connected with cysteine to obtain PP-Hep-Cys. The sulfhydryl content of PP-Hep-Cys reached about 2.06 nmol/cm2, and the NO generation was about 0.2415 nmol/cm2. In the absence of NO donors, the introduction of heparin contributed to the inhibition of platelets, but when NO donors were added, the inhibition of platelets was caused by the synergistic effect of heparinization and NO generation. In addition, the clotting time of PP-Hep-Cys was significantly prolonged compared with PP, and the hemolysis rate declined to 0.33 ± 0.01%. Regarding the adsorption performance of Cu2+, the adsorption capacity of PP-Hep-Cys was higher than that of PP-Hep and PP-Cys, and monolayer adsorption was dominant. Overall, these results indicated that heparin, as a spacer, and cysteine, as a ligand, exerted a synergistic effect on blood compatibility and adsorption.

Processes doi: 10.3390/pr14081258

Authors: Guiping Liao Hongmei Tang Jiayan Wu Quanyun Ye Yihao Li Zhongbo Shang Leiye Sun Pingxiao Wu

Schwertmannite (Sch) is a widespread iron oxyhydroxysulfate mineral in acid mine drainage (AMD) systems, and its transformation strongly influences the environmental fate of chromium (Cr). However, the role of Ca(II), which is commonly introduced during alkaline neutralization of AMD, in regulating the transformation of Cr(VI)-adsorbed schwertmannite (Cr-Sch) and subsequent Cr redistribution remains insufficiently understood. In this study, transformation experiments were conducted under various pH conditions (3.0, 7.0, and 10.0) to investigate the effects of Ca(II) speciation on mineral transformation and Cr behavior. The results demonstrated that the transformation of Cr-Sch was predominantly pH-dependent. Under acidic conditions, Cr-Sch transformed into goethite via dissolution–recrystallization, resulting in transient Cr release followed by partial refixation. The presence of Ca(II) exerted only a minor influence due to weak interactions between Ca2+ and positively charged mineral surfaces. Under alkaline conditions, Cr-Sch preferentially transformed into hematite through dehydroxylation and cation rearrangement, leading to the sustained release of adsorbed Cr(VI). In contrast, Ca(II) predominantly precipitated as CaCO3 precipitate (calcite, aragonite, and vaterite) under alkaline conditions, which coated mineral surfaces and inhibited phase transformation and Cr release. These findings reveal that Ca(II) regulates Cr redistribution primarily through pH-dependent speciation and mineral–surface interactions, highlighting coupled geochemical processes governing iron mineral transformation and contaminant mobility in AMD environments. This study provides mechanistic insights for predicting Cr behavior and optimizing alkaline remediation strategies in mining-impacted systems.

Processes doi: 10.3390/pr14081257

Authors: Murat Yılmaz M. Erdem Isenkul Nil Vural Atiye Tugrul

The durability of limestone aggregates is a critical factor affecting the long-term performance of concrete, particularly under aggressive environmental conditions. However, conventional durability tests such as the magnesium sulfate soundness test are time-consuming and labor-intensive. In this study, a transformer-based deep learning model, namely the FT-Transformer, was employed to predict the magnesium sulfate soundness loss of limestone aggregates using standard aggregate index properties. The dataset used in the modeling stage consisted of 108 limestone aggregate samples, each characterized by particle density, water absorption, Los Angeles fragmentation, and magnesium sulfate soundness loss. Although four standardized laboratory tests were conducted for each sample, yielding 432 individual test results in total, the prediction dataset comprised 108 complete observations. The predictive performance of the FT-Transformer was evaluated and compared with Linear Regression, Polynomial Regression, and Support Vector Regression models. Under the single-split evaluation, the FT-Transformer achieved a test R2 value of 0.6473 and a test MSE value of 0.0212. In addition, a repeated random-split statistical analysis demonstrated that the FT-Transformer achieved better average predictive performance than Linear Regression across 50 repeated train, validation and test partitions. These findings indicate that transformer-based tabular learning can provide an effective and practically applicable framework for aggregate durability prediction and may support preliminary material assessment and engineering decision-making processes.

Processes doi: 10.3390/pr14081256

Authors: He Liu Yusheng Yang Haowen Yuan Suling Wang Kangxing Dong

In the extraction of shale gas and oil, the vibration characteristics of the drill string significantly influence wellbore quality, potentially leading to wellbore instability, excessive tool wear, and diminished drilling efficiency. This study tackles the challenges associated with drill string vibrations by developing an integrated technical framework of multi-field coupled dynamic modeling, Sobol-based key parameter identification, and NSGA-II-driven multi-objective structural optimization, and proposes a synergistic vibration suppression strategy combining structural parameter adjustment and hydraulic damper configuration based on multibody dynamics and finite element analysis. Initially, a dynamic model that accounts for the coupling between the wellbore and the drill string is developed to scrutinize the impact of various vibration modes on wellbore quality. Subsequently, detrimental vibrations are mitigated through the optimization of structural parameters, including but not limited to stiffness distribution and the strategic placement of vibration absorbers. Finally, the efficacy of the optimized design is substantiated through numerical simulations and field experiments. The results demonstrate that the optimized drill string achieves a simulation average reduction of 30% in lateral vibration amplitude across the rotational speed range of 60–120 RPM and a simulation average improvement of 25% in the attenuation of axial vibration energy. These enhancements notably bolster drilling stability and elevate wellbore quality. This research furnishes both theoretical and technical underpinnings for the efficient development of shale gas and oil resources.

Processes doi: 10.3390/pr14081255

Authors: Carlos Castang Daniela Chavarro Santiago Laín

This study presents the characterization of the aerodynamic forces and moments acting on irregular particles of prescribed sphericity, generated through truncated spherical harmonic expansions and immersed in a uniform flow at intermediate Reynolds numbers (1 ≤ Re ≤ 200). Particle-resolved direct numerical simulations are conducted using the commercial solver ANSYS Fluent to quantify the statistical behavior of drag, transverse lift, and transverse torque coefficients, along with the corresponding force and moment components, as a function of Reynolds number. Deviations from spherical geometry are shown to induce persistent flow asymmetries, leading to finite transverse lift and torque components even under uniform inflow conditions, effects that cannot be captured by models based on dynamically equivalent spheres. For a sphericity of 0.93, represented by six particle realizations, irregular particles exhibit mean drag values approximately 10% higher than those of spheres with the same equivalent diameter. In addition, both the magnitude and the statistical characteristics of the aerodynamic coefficients are strongly modulated by the combined effects of particle shape irregularity and flow regime. These results provide new insight into the role of geometric complexity in fluid–particle interactions and represent a step forward toward improved predictive capability beyond conventional spherical and quasi-spherical approximations. Furthermore, the present findings provide a physically grounded basis for the development of fluid–particle interaction models for irregular particles, suitable for implementation within Euler–Lagrange simulations of turbulent dispersed flows.

Processes doi: 10.3390/pr14081254

Authors: Naiara Jacinta Clerici Ryan dos Santos Silva Daniel Tibério Ferreira Fabio Patrício Sanchez Vera Maria Ismenia Sodero Toledo Faria Júlio César dos Santos Sílvio Silvério da Silva

The enzymatic saccharification of cellulose remains a key step in biomass conversion processes, often influenced by enzyme stability, distribution, and accessibility at solid–liquid interfaces. Immobilization of cellulolytic enzymes on nanostructured supports has been proposed as a strategy to modulate catalytic behavior; however, the relationship between enzyme loading and catalytic response remains insufficiently understood. In this study, CuO-based nanobiocatalysts were prepared through controlled cellulase immobilization and systematically evaluated under defined experimental conditions. Structural and physicochemical characterization was performed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and integrated thermal analysis (TGA–DTG–DSC), enabling a comparative assessment of the analyzed systems. SEM analysis showed that the average particle diameter increased from 39.5 ± 14.8 nm (CuO nanoparticles) to 95.6 ± 21.8 nm (NPI10), 106.6 ± 27.7 nm (NPI15), and 113.5 ± 23.1 nm (NPI20), indicating progressive variations in particle organization with increasing enzyme loading. Catalytic performance was evaluated through enzymatic hydrolysis of cellulose filter paper as a model substrate, with products quantified by HPLC at a representative reaction time. The system prepared at lower enzyme loading (NPI10) exhibited product formation comparable to that of the free enzyme, with apparent average glucose formation values of 1.054 and 1.047 mg·mL−1·h−1, respectively. In contrast, higher immobilization levels were associated with reduced catalytic output. Across all systems, glucose was the predominant product, with negligible accumulation of intermediate oligomers under the evaluated conditions. These results indicate that increasing enzyme loading does not correspond to proportional increases in product formation and highlight the influence of enzyme distribution and accessibility within the system. The combined structural and catalytic observations provide a controlled framework for evaluating how immobilization conditions influence system behavior in nanobiocatalytic systems.

Processes doi: 10.3390/pr14081252

Authors: Huimin Zhang Xiao Feng Ying Kang Dingxun Ye Zucheng Wu Shanwen Tao

Permeable reactive barrier (PRB) is an in situ remediation of contaminated sites mostly suitable for halogenated pollutants like halo-hydrocarbons reduced by zero-valent irons (ZVI) developed during early 1990’s. However, remediation of some nitrogen-pollutants like ammonia and urea is unsuccessful due to lack of reactants. Most recent advanced direct ammonia/urea fuel cells utilize indirect hydrogen within ammonia/urea molecules to generate electricity. Herein, a comprehensive study based on the chosen design, working principles, advantages and disadvantages of direct ammonia fuel cells for new approach of PRBs for denitrifying nitrogen-contaminant is summarized. Most surveys are carrying out in our laboratories and this work aims to review the most recent advances in ammonia fuel cells integrated with PRBs and demonstrates the proximity of this technology to future applications. Meanwhile, several challenges such as how to accumulate ammonia and urea in order to achieve satisfying energy recovery, oxidants formation, power densities and long term stability are also summarized in this review.

Processes doi: 10.3390/pr14081253

Authors: Wook Kwon Dahye Choi Soungwoo Park Jinkyu Kim

In the publication as published [...]

Processes doi: 10.3390/pr14081249

Authors: Qianqian Tian Weiliang Xiong Junhong Jia Futeng Feng Huilin Wang Lili Wang Yueheng Cheng Lei Liu Changhua Yang

To address early water breakthrough and poor residual-oil mobilization in low-permeability reservoirs, a core–shell nanosphere/surfactant composite system was developed for profile control and enhanced oil recovery. The core–shell nanospheres were prepared by a semi-continuous seed-growth method, and a target particle-size window of 100–200 nm was selected based on pore-throat/particle matching. The representative sample, HK-0417, had an average particle size of about 120 nm and showed good dispersion stability in formation brine at 45 °C. After blending with the surfactant ALT-603, the system achieved an ultralow oil–water interfacial tension on the order of 10−3 mN/m and reduced the water contact angle of the oil-aged surface from 125° to 70°, indicating a shift toward near-neutral wettability. Core-flooding tests further showed that, under the same chemical dosage, slug injection (HK-0417 followed by ALT-603) demonstrated better performance than co-injection, with higher incremental oil recovery (15.49% vs. 13.17%) and higher plugging efficiency during subsequent water flooding (81.25% vs. 78.46%). The novelty of this work lies in integrating particle-size-window design, controllable preparation of core–shell nanospheres, and direct comparison of injection strategies within one system. The results provide practical guidance for formulation design and injection-mode selection for enhanced oil recovery in low-permeability reservoirs.

Processes doi: 10.3390/pr14081251

Authors: Yanara Tamarit-Pino Ociel Muñoz-Fariña José Miguel Bastías-Montes Roberto Quevedo-León Olga García-Figueroa Horacio Fraguela-Meissimilly Marcia María Cabrera-Pérez Carla Vidal-San Martín

The effects of thermal and non-thermal pretreatments combined with different drying methods on the drying kinetics, physicochemical properties, and bioactive compounds of the Chilean wild mushroom Morchella conica were investigated. Fresh samples were subjected to hot-air drying (HAD, 60 °C), freeze-drying (FD), and a hybrid process (FD–HAD), applied directly or after pretreatments including thermal pre-drying (55 and 75 °C), ultrasound (US, 10 and 20 min), and high hydrostatic pressure (HHP, 600 MPa). Drying curves were successfully fitted using the Weibull model (R2 > 0.987), showing that HAD combined with thermal and ultrasound pretreatments increased drying rates, while FD–HAD reduced total drying time. Freeze-drying better preserved color (ΔE < 2) and minimized shrinkage (<8%), whereas HAD produced darker samples and greater structural deformation. Water activity decreased below 0.30 in most treatments, ensuring microbiological stability. Thermal pretreatments enhanced total phenolic content, while FD preserved antioxidant capacity. Principal component analysis explained 62.2% of the total variance, revealing distinct quality profiles among drying methods. Overall, FD and hybrid FD–HAD combined with moderate pretreatments showed the best balance between drying efficiency and quality preservation, while HHP improved antioxidant properties under specific conditions. These findings highlight the potential of integrating innovative pretreatments with drying technologies to optimize processing of Morchella conica.

Processes doi: 10.3390/pr14081250

Authors: Walid Zenasni Ismail Hakkı Tekiner Hanaa Abdelmoumen Rachid Nejjari Abdelhak Chergui Said Ennahli El Amine Ajal

As demand for sustainable plant protein rises, pumpkin seeds emerge as a promising but underutilized source. Conventional alkaline extraction (ALK) often impairs protein functionality, prompting interest in non-thermal alternatives. This study systematically compared the functional, colloidal, and structural properties of pumpkin seed protein isolates obtained via ALK (conducted at 50 °C), ultrasound-assisted (UAE), and microwave-assisted extraction (MAE). UAE produced the highest extraction yield (50.07%), superior overall solubility, greatest water and fat absorption capacities, and lowest least gelation concentration (12%). Furthermore, UAE best preserved native protein secondary structure (retaining 43.45% alpha-helix), as quantified by FTIR peak deconvolution, and maintained an intact, flake-like morphology under scanning electron microscopy (SEM), yielding the most uniform particle size distribution. Conversely, MAE achieved the highest protein content (73.53%) and the most negative zeta potential, leading to the highest emulsifying and foaming capacities despite inducing a bimodal particle size and irregular, porous surface morphology. ALK performed the poorest across structural and functional metrics, severely denaturing the proteins due to combined alkaline and thermal stress. UAE is recommended for applications requiring optimal solubility and gelation, whereas MAE is highly effective for emulsion- and foam-based food systems, reinforcing pumpkin seeds as a viable sustainable protein ingredient.

Processes doi: 10.3390/pr14081248

Authors: Vaibhav V. Khedekar Abdul R. A. N. Memon Mayur Pal

Accurate prediction of subsurface temperature distributions is essential for geothermal reservoir assessment, thermal performance evaluation, and decision support in reservoir management. However, repeated high-resolution numerical simulations are computationally expensive, particularly when multiple scenarios, heterogeneous petrophysical fields, and varying grid resolutions must be analyzed. This study presents a U-Net-based surrogate modeling framework for fast geothermal temperature field prediction on structured grids, coupled with interpolation strategies for handling unseen grid resolutions and intermediate time instances. Training and evaluation data are generated using the MATLAB Reservoir Simulation Toolbox (MRST) (24.1.0.2578822 (R2024a) Update 2) under multiple porosity–permeability realizations and at several grid resolutions (130 × 73, 67 × 37, 36 × 19, and 20 × 11) on a 2D grid. Data preprocessing and reshaping techniques are used to preserve spatial correspondence across resolutions. For fixed trained grids, the surrogate directly predicts temperature fields from porosity, permeability, and time inputs. For unseen grids, a grid interpolation strategy combines predictions from neighboring trained resolutions using weighted blending based on target grid cell count, followed by spatial resizing to the requested resolution. In addition, time interpolation is used to estimate temperature maps at intermediate time steps between predicted/simulated snapshots. The proposed framework enables rapid generation of temperature maps while maintaining spatial structure, making it suitable for efficient geothermal screening and multiscale scenario analysis.

Processes doi: 10.3390/pr14081247

Authors: Carlos Antonio Padilla-Esquivel Gema Báez-Barrón Carlos Daniel Gil-Cisneros Diana Karen Zavala-Vega Eduardo García-García Vanessa Villazón-León Heriberto Alcocer-García Fabricio Nápoles-Rivera César Ramírez-Márquez José María Ponce-Ortega

Optimization has played a fundamental role in the evolution of chemical engineering, enabling systematic decision-making under technical, economic, and environmental constraints. This review presents a structured and comparative analysis of the historical development and current state of optimization methodologies applied to chemical engineering, covering the transition from early linear and nonlinear programming approaches to advanced data-driven and artificial intelligence-based frameworks. A systematic literature review was conducted following the PRISMA guidelines, through which a total of 101 articles were retained for analysis. The results indicate that mixed-integer programming and decomposition-based methods remain widely adopted for structured industrial problems, while metaheuristic and hybrid data-driven approaches have experienced significant growth in recent years. In particular, a clear trend toward the integration of machine learning and surrogate modeling techniques is observed, driven by the need to address large-scale, non-convex, and highly nonlinear systems. The analysis reveals a clear methodological shift from classical linear optimization frameworks toward hybrid optimization strategies capable of addressing large-scale, non-convex, and highly nonlinear problems. Finally, current challenges and future research directions are identified, emphasizing the need for robust hybrid approaches that combine mathematical programming and intelligent algorithms to effectively manage complexity in next-generation chemical systems.

Processes doi: 10.3390/pr14081246

Authors: Li Ma Qinian Yan Hao Hu Zihe Xu Lina Fan Hongxia Jia Lixin Li

Water quality prediction in non-stationary environmental systems requires not only high predictive accuracy but also structural robustness under physical, ecological, and operational constraints. This study reframes multi-model fusion as a constraint-governed inference architecture and synthesizes advances in rule-based adjudication, reliability-aware aggregation, post-fusion projection, dual-track adaptation, and hierarchical backoff control. By establishing a taxonomy of boundary constraints—specifically mass conservation, reaction kinetics, hydraulic transport, and ecological tipping points—an admissible prediction manifold identifies key structural limitations in existing paradigms, particularly their vulnerability to physical inconsistency and diminished reliability during non-stationary distribution shifts. A unified end-to-end robust framework is proposed in which candidate predictions are separated from admissibility validation, uncertainty is directly coupled to aggregation logic, and degradation pathways are explicitly defined under distribution shift. Furthermore, a multidimensional robustness evaluation matrix is introduced, incorporating structural consistency, ecological compliance, calibration quality, and adaptive stability alongside conventional accuracy metrics. The study advances water quality forecasting from model-centric optimization toward architecture-level governance, demonstrating that constraint-aware designs improve structural consistency, robustness under distribution shifts, and early warning reliability, providing a systematic reference for developing resilient, transparent, and operationally deployable environmental prediction systems.

Processes doi: 10.3390/pr14081245

Authors: Jie Zhong Huirong Huang Zihan Guo Chen Wu Xi Chen Shangfei Song Qian Huang Yuan Tian Xueyuan Long

This paper investigates the mechanical transfer mechanism of bimetallic composite pipes used in highly sour gas fields located in high-steep mountainous areas. It systematically analyzes the mechanical response behavior of these pipes under the coupled effects of complex geological conditions and operational loads. By establishing and validating a finite element model that accounts for material nonlinearity and pipe–soil interaction, the study examines the influence of key factors—including internal pressure, landslide displacement, and base pipe wall thickness—on the stress distribution and transfer mechanism within the pipeline. The results demonstrate that increased internal pressure significantly elevates both circumferential and axial stresses: when internal pressure increases from 7 MPa to 9 MPa, the liner hoop stress increases by 35.5% and the base pipe axial stress increases by 27.5%. When landslide displacement exceeds a critical threshold of 3 m, the stress in the base pipe rises sharply, with axial stress increasing by 39.7% when displacement increases from 3 m to 5 m; conversely, increasing the base pipe wall thickness from 12 mm to 15 mm effectively reduces the overall stress level, decreasing base pipe axial stress by 40.4% and liner axial stress by 86.9%. Stress transfer exhibits a dual-path characteristic, which can be described as “bidirectional transfer induced by internal pressure” and “progressive transfer caused by landslide”. These quantitative findings provide a theoretical basis for the safe design and operation of bimetallic composite pipes in high-steep mountainous regions.

Processes doi: 10.3390/pr14081244

Authors: Zhe Hu Fei Liu Zhiguo Wang

The single-heat-exchanger dual-channel water circulation structure is a critical process configuration in laboratory-scale bioreactors. However, frequent switching between heating and cooling modes and the difficulty of establishing an accurate mechanistic model make precise temperature regulation challenging. To address this issue, a model-free adaptive temperature control scheme based on a second-order universal model is proposed, together with a real-time implementation algorithm. Separate controllers are designed for the heating and cooling processes to ensure accurate regulation under different operating conditions. Pulse-width modulation is employed to achieve equivalent continuous actuation of switching-type actuators, and a temperature dead-zone mechanism is introduced to suppress excessive actuator switching. For practical implementation, controller parameters are initialized offline using particle swarm optimization based on experimental data. Experimental results demonstrate that the proposed method satisfies the ±0.1 °C process requirement while achieving small steady-state fluctuations, low overshoot, and short settling time, thereby verifying its effectiveness for bioreactor temperature regulation under mode-switching conditions.

Processes doi: 10.3390/pr14081243

Authors: Yijun Zhang Jiaqi Li Zhipeng Xiang Hui Zhang Boyuan Yang Xinrui Wang Baokang Wu

Buckling of variable-diameter tubing strings in ultra-high-temperature and high-pressure (UHTHP) deviated wells presents challenges that cannot be addressed by existing uniform-tubing models. This study develops a segmented Euler–Bernoulli buckling model that accounts for stiffness discontinuities at diameter transitions, temperature–pressure-coupled effective axial force, and wellbore-constraint effects. The model is developed for packer-constrained variable-diameter production tubing under UHTHP gas-production conditions. A global transfer matrix formulation is introduced to derive buckling characteristic conditions and critical loads. Results show that reduced stiffness at diameter transitions facilitates localized buckling and promotes the shift from sinusoidal to helical modes as the effective axial force increases. Variations in tubing or casing inner diameter significantly alter buckling-zone lengths and induce abrupt changes in dogleg severity. The proposed model provides a practical analytical framework for predicting the buckling behavior of variable-diameter tubing strings in UHTHP wells and offers guidance for tubing design and well integrity assessment.

Processes doi: 10.3390/pr14081242

Authors: Jun Zhang Sheng Fan Zhong He Xin Zheng Shifeng Zhang

To overcome polymer-based plugging materials’ disadvantage of being prone to degradation and failure under hydrothermal conditions, an epoxy resin plugging particle with a high-pressure-bearing capacity under high temperatures was prepared by optimizing the curing process. Bisphenol A Epoxy Resin E51 and Diethyltoluenediamine (DETDA) were selected as raw materials for sample preparation. Due to the high viscosity of the system, 1,2-cyclohexanediol diglycidyl ether was introduced as a diluent, and an optimal concentration of 20% was determined through experimental optimization. Non-isothermal differential scanning calorimetry, bottle testing, and infrared spectroscopy were employed to investigate the variation laws of curing temperature, curing time and curing degree during the epoxy resin curing process via one-step and multi-step methods. The compressive strength of the epoxy resin prepared using the two processes was evaluated. After comprehensively comparing the preparation time, process complexity, and compressive strength of the final samples of the one-step and two-step curing methods, the one-step process (90 °C/5 h) was determined to be superior. In addition, the results of the fracture plugging experiment showed that after the bulk epoxy resin prepared using the optimized process was made into particles through a mechanical method and treated under hydrothermal conditions at 120 °C, the maximum breakthrough pressure reached 4.2 MPa, which was 950% and 135.96% higher than that of Particle 1 (Poly(2-acrylamido-2-methylpropanesulfonic acid)/acrylamide (PAMPS/AM) gel) and Particle 2 (PAMPS/AM gel treated with Polyethylene glycol (PEG)), respectively, which were used as control groups. This result indicates that epoxy resin can be used as a high-temperature-resistant plugging material and should be further researched.

Processes doi: 10.3390/pr14081241

Authors: Danyang Li Jie Song Hui Liu

With the rise of computer-related fields, data centers have become essential infrastructure. Thermal analysis helps to improve data center performance and reduce data center energy consumption. Due to the variable load, the scheduling of the data center is frequent, and the thermal state also changes frequently. However, existing thermal analysis methods have a high cost regarding mesh division and thermal calculation and cannot provide dynamic thermal simulation for data centers. To address this challenge, this paper proposes a cost-compensated spatial–temporal meshing method for transient thermal simulation (TranSim) of the data center. TranSim adaptively adjusts the mesh boundaries according to the workload gradient of a location, and it can adaptively adjust the meshing step time according to the workload change frequency in order to achieve transient simulation. Cost-compensated thermal calculation replaces the CFD model, considering air flow, by adding the thermal source, thermal medium, thermal radiation and thermal lagging in order to gain a simple thermal calculation. This paper designs an experiment for comparing TranSim with several popular data center thermal simulation methods, such as a structured mesh with a CFD model, regarding their transient effect, time cost, and error cost. The results show that TranSim has a good transient effect, low error cost (the simulation error decreases by 13.5% compared with the average error) and low time cost (the simulation time is only about 7% that of the most accurate data center thermal simulation method).

Processes doi: 10.3390/pr14081240

Authors: Liwei Wu Ziyue Zhang Chengkai Zhang Gensheng Li Xianzhi Song Mengmeng Zhou Xuezhe Yao

Managed pressure drilling (MPD) is the core technology for developing formations with high pressure and narrow density windows. It precisely maintains the bottomhole pressure (BHP) within the safe operating window defined by formation pore pressure and fracture pressure by actively regulating the wellbore pressure profile. If pressure control becomes unstable, it can easily trigger gas kicks or lost circulation, posing a severe threat to operational safety. However, existing model predictive control (MPC) schemes have significant limitations: pure data-driven models exhibit poor generalization under complex conditions, while control algorithms based on traditional mechanistic models struggle to meet the stringent real-time requirements of field control cycles due to high-complexity numerical iteration processes. To balance control precision and real-time performance, this paper proposes a physics-informed model predictive control framework (PINC-MPC). During the training phase, physical prior knowledge such as the law of mass conservation is embedded into the neural network as constraints to construct a physically consistent deep surrogate model, enabling it to characterize complex wellbore characteristics. In the control phase, this surrogate model replaces the time-consuming numerical solving process of the mechanistic model within the MPC loop, achieving near-real-time state prediction and rolling optimization while ensuring physical fidelity. Experimental results indicate that PINC-MPC demonstrates superior control performance. Its median single-step solving time is only 16.81 ms, achieving an 11.1-fold acceleration compared to the mechanistic model-based scheme (187.3 ms). In a 5000 s full-cycle closed-loop control experiment, the total time required for the former is only 1.68 s, while the latter reaches 18.73 s, representing an efficiency improvement of approximately 91%. In terms of control accuracy, the integrated absolute error (IAE), reflecting the total deviation of the control process, significantly decreased from 63.40 MPa·s for the industrial successive linearization MPC (SLMPC) to 12.90 MPa·s, an improvement of 79.7%. Especially in extreme dynamic conditions such as simulated pump shutdowns for pipe connections and sudden gas kicks, the framework demonstrates excellent predictive ability and response efficiency. It can proactively trigger compensation actions to keep BHP fluctuations within 0.30 MPa, significantly outperforming the traditional SLMPC method. The research results prove that PINC-MPC provides an efficient, precise, and robust nonlinear control strategy for MPD systems, offering important engineering reference value for enhancing the automation level of intelligent drilling systems.

Processes doi: 10.3390/pr14081239

Authors: Elena Ovchinnikova Yuriy Kozhubaev Elina Sitzhanova Vyacheslav Potekhin Irina Kim Vsevolod Chentsov

Equipment selection is a critical decision in mining operations, directly influencing production efficiency, maintenance requirements, and operational costs. However, this decision is complicated by significant uncertainty surrounding equipment performance and remaining service life. This paper presents a hybrid decision support framework that integrates Fuzzy Logic, Pareto Optimality, and a Genetic Algorithm (GA) to address the challenge of roadheader selection under such uncertainty. The proposed Fuzzy–Pareto–GA approach applies fuzzy logic to model the inherent uncertainty in performance data; employs Pareto optimization to identify optimal trade-offs between multiple, often conflicting criteria; and utilizes a genetic algorithm to efficiently navigate the solution space. The framework is validated using real-world data from an operating mining company, considering three key criteria: operating time, remaining service life, and the remaining service life ratio. The results demonstrate that the fuzzy–Pareto approach effectively identifies a set of non-dominated solutions, and the robustness of these rankings is confirmed through a comprehensive sensitivity analysis. The proposed framework offers mining engineers a transparent and uncertainty-aware tool for equipment selection, a decision that serves as a critical foundation for effective production process optimization.

Processes doi: 10.3390/pr14081238

Authors: Jianhua Bai Zheng Chen Wei Zhang Zhaochuan Zhou Liu Wang Yuande Xu Shaojiu Jiang Chengtao Zhu Zhijun Liu Le Zhang Zechao Huang Qiang Wang Zhixiong Zhang Chenwei Zou Xiaodong Tang Yukun Du

During offshore oilfield development, traditional injection–production processes commonly suffer from delayed regulation, low operational efficiency, and heavy reliance on manual intervention. Achieving real-time diagnosis of injection–production anomalies and dynamic optimization under complex geological conditions and harsh marine environments represents a core scientific challenge. This study presents the development and field deployment of an intelligent cable-controlled injection–production integrated management system. The work is positioned as an application- and system-oriented study, focusing on addressing practical challenges in offshore oilfield operations through the integration of established machine learning techniques into a cohesive operational platform. The system employs a cloud-native microservice architecture and integrates nine functional modules, enabling closed-loop management from data acquisition to intelligent decision making. Key methodological contributions include: (1) a weighted ensemble model combining Random Forest and SVM for blockage diagnosis, balancing global feature learning with boundary sample discrimination to achieve 92% diagnostic accuracy; (2) a Bayesian fusion framework that integrates static geological priors with dynamic sensitivity analysis for probabilistic quantification of injector–producer connectivity, achieving 85% identification accuracy with rigorous uncertainty propagation; and (3) a three-stage human–machine collaborative mechanism that substantially reduces anomaly response latency while ensuring field safety. Field application in Bohai oilfields demonstrates that the system shortens the injection–production response cycle by approximately 42%, reduces anomaly response time from over 72 h to less than 2 h (a 97% reduction), decreases water consumption per ton of oil by 27.6%, and increases injection–production uptime by 11.3 percentage points. This study provides an interpretable, extensible, and closed-loop technical solution for intelligent offshore oilfield development, with future directions including digital twin predictive simulation and reinforcement learning for real-time optimization.

Processes doi: 10.3390/pr14081237

Authors: Ying Zhao Ting Sun Yuan Chen Wenxing Wang

In ultra-deep drilling environments, downhole measurement tools often fail or cannot be deployed due to extreme high-temperature and high-pressure (HPHT) conditions. Consequently, mud-logging data become one of the few reliable real-time information sources for evaluating drilling performance and identifying abnormal conditions. This study proposes a data-driven framework for automatic identification of drilling operation statuses using machine learning, with a particular focus on ultra-deep and HPHT wells. A support vector machine (SVM)-based classification workflow was established to recognize nine representative drilling operation statuses from mud-logging data. Through systematic model optimization, the proposed method achieved a classification accuracy of 91.33%. By incorporating a sliding window-based time-series optimization strategy, the overall accuracy was further improved to 95.22%, while the recognition accuracy of HPHT-related operations increased from 77.67% to 89.33%. These results demonstrate that the optimized model possesses strong adaptability and stability under extreme HPHT conditions. This study specifically targets HPHT environments with limited downhole data and incorporates time-series optimization to enhance model robustness. The proposed framework provides a reliable approach with potential for generalization for intelligent operation recognition in ultra-deep drilling, supporting real-time decision-making and improving operational safety and efficiency in challenging environments.

Processes doi: 10.3390/pr14081236

Authors: Zefeng Li Long Chai Yining Zhou Jianping Lan Mian Zhang Yuchen Tian Linghong Tang

During the CO2 fracturing of unconventional oil and gas resources, the structural and operational parameters significantly influence the fracturing effectiveness. To quantitatively reveal the influence mechanisms of key parameters on the CO2 jet flow field through perforations, this study employed computational fluid dynamics (CFD) via Ansys Fluent to simulate and compare the effects of the nozzle contraction angle, injection rate, confining pressure, and fluid temperature. The results indicate that the contraction angles and injection rates have a more significant influence on the jet temperature, pressure, and velocity than the confining pressures and fluid temperatures. As the contraction angle increases, the average velocity of the jet core region increases by 5.0% (with the most significant growth at 35°), and the length of the potential core increases correspondingly. The flow through the perforations is characterized by an instantaneous drop of 2.5 °C in temperature and 2.7 MPa in pressure, then transitions to a regime of temperature recovery and dynamical pressure decay along the fracture. Increasing the fracturing displacement raises the maximum jet velocity to 104.7 m/s (an average increase of 15.5%), extends the potential core length, and amplifies the temperature and pressure drops across the perforation from 1.1 °C and 1.2 MPa to 4.2 °C and 4.8 MPa, respectively. Conversely, higher confining pressure reduces the average jet velocity by 4.3%, shortens the potential core, and diminishes the perforation temperature and pressure drops from 5 °C and 3 MPa to 2 °C and 2.5 MPa. In contrast, elevating the fluid temperature increases the jet velocity by an average of 6.3% but exerts minimal influence on the potential core length; the temperature drop at the perforation remains at approximately 2 °C, while the pressure drop rises from 2.2 MPa to 2.9 MPa. Collectively, both the confining pressure and fluid temperature significantly affect the density and velocity characteristics of the jet. An increase in confining pressure enhances the density of the CO2 jet fluid, which may potentially improve the fracturing impact in actual engineering applications. Quantitatively, the influence of each parameter on the temperature, pressure, and velocity of the CO2 jet is ranked from the most significant to the least as follows: nozzle contraction angle > fracturing injection displacement > formation confining pressure > fluid temperature. The findings of this research have direct implications for practical application, informing the optimization of the fracturing design to achieve greater efficiency and lower risk in CO2 fracturing operations.

Processes doi: 10.3390/pr14081235

Authors: Bo Wang Bing Zhang Jiahao Wang Dawei Liu Hai Huang Ping Wang Liming Lin

The lithology of multilayer superposed coal-measure reservoirs is highly interbedded, and the mechanical contrast between adjacent layers is significant, resulting in strong uncertainty in the initiation and propagation behavior of hydraulic fractures. To address the problem that the fracture-propagation mechanism under multi-lithology assemblages remains insufficiently understood, typical layered composite specimens were constructed, and large-scale true triaxial hydraulic fracturing physical simulation tests were performed to systematically investigate the effects of coal seam thickness, interlayer thickness, injection rate, and fracturing-fluid viscosity on fracturing pressure, fracture propagation path, and propagation capacity. The results show that when the coal seam thickness does not exceed 90 mm, cross-layer connectivity at the fracture breakthrough interface is more likely to occur. Interlayer thickness directly controls fracture-height growth. When the mudstone interlayer thickness is 40 mm, the fracture still retains the ability to propagate across layers, whereas this ability decreases significantly as the interlayer becomes thicker. When the injection rate is increased from 20 mL min−1 to 30 mL min−1, the overall pump-pressure platform rises, accompanied by a simultaneous increase in fracture extension scale and connectivity. As the fracturing-fluid viscosity increases from 3 mPa·s to 24 mPa·s, both the fracturing pressure and platform pressure increase significantly, and the fracture morphology gradually changes from dispersed propagation to more concentrated extension. The results further indicate that structural constraint factors (coal seam thickness and interlayer thickness) and dynamic driving factors (injection rate and fracturing-fluid viscosity) jointly control the spatial structure and pressure-response characteristics of fractures. Among these factors, interlayer thickness determines the conditions for cross-layer fracture propagation, injection rate and fluid viscosity control the ability to maintain net pressure within the fracture, and coal seam thickness constitutes an important geometric constraint. These findings provide an experimental basis for fracturing-parameter optimization and cross-layer stimulation design in multilayer superposed reservoirs.

Processes doi: 10.3390/pr14081234

Authors: Xingyi Wang Shutong Wang Shengbin Zhao Lufan Qi Quan Chen Chenyu Guo Guoliang Deng

Sensitivity and effective sensing range are core performance metrics of flexible pressure sensors, directly dictating their practical applicability. A key challenge in sensor design is sensitivity degradation with elevated pressure, hindering synergistic optimization of high sensitivity and broad sensing range, while cumbersome electrode fabrication further impedes facile preparation and large-scale deployment of high-performance devices. Herein, this work proposes a novel fabrication strategy for flexible iontronic pressure sensors via direct laser writing (DLW) technology. A controllable ultraviolet laser patterns polyimide substrates to fabricate hierarchical stepped conoid-like microstructural templates, which are transferred to ion gels through reverse molding. The DLW-enabled precise geometric control and hierarchical conical architectures efficiently amplify interfacial contact area variation under pressure, significantly boosting sensitivity. The resultant sensor achieves a high sensitivity of 118.4 kPa−1 and a broad detection range up to 2000 kPa, with fast response/recovery times of 38.4 ms and 47 ms and excellent mechanical stability enduring 2000 loading–unloading cycles at 850 kPa. Multi-scenario physiological signal monitoring validates its accurate capture of laryngeal vibrations and joint movements. This work establishes a straightforward, efficient microfabrication route for high-performance flexible iontronic sensors, accelerating their practical application in wearable health monitoring and related fields.

Processes doi: 10.3390/pr14081233

Authors: Bin Xu Luyang Wang Tingting Xiang Rui Gu

Decarbonizing downstream steel logistics remains underexplored in sustainable supply chain management. This study proposes a machine learning-enhanced tri-objective optimization framework for the ship stowage planning problem (SSPP). The framework handles heterogeneous finished steel products, including coils, plates, ingots, tubes, and sections. The model simultaneously maximizes deadweight utilization and minimizes a novel Adaptive Weighted Moment Balance (AWMB) index. It also minimizes voyage carbon emissions through a trim-and-heel resistance penalty. A spatial-to-sequential discretization strategy transforms the NP-hard placement problem into a tractable permutation optimization. A deep neural network (DNN) surrogate achieves a 3.57-fold speedup with only 1.52% hypervolume degradation. An improved NSGA-III algorithm with adaptive operators ensures Pareto front exploration. Embedded step-wise moment verification guarantees dynamic stability throughout loading and unloading. Validated on real data from a Chinese steel enterprise, the framework achieves 99.88% deadweight utilization, reduces transverse and longitudinal imbalance by 48.27% and 90.54%, and cuts CO2 emissions by 95.5% per voyage. SOLAS constraints, load line limits, and CII/FuelEU targets are addressed through embedded stability and capacity constraints. Multi-route and weather-dependent validation remains necessary before fleet-scale deployment.

Processes doi: 10.3390/pr14081232

Authors: Guangfeng Qi Yuqi Dong Jiehua Feng Chenghan Zhu Yingqiang Yan Fei Li Dongya Zhao

As reservoir development enters the middle and late stages, variations in formation pressure and water cut lead to significant changes in liquid supply capacity. Under conventional fixed stroke-per-minute (SPM) operation, the production capacity of beam pumping wells often fails to match the dynamically varying inflow, resulting in severe dynamic fluid level fluctuations and subsequent pump-off, gas locking, and abnormal rod string loading. To address these issues, this paper develops a dynamic fluid level model based on the operating mechanism of beam pumping wells, explicitly incorporating system uncertainties and reservoir disturbances. On this basis, a tube-based robust model predictive control (Tube-RMPC) strategy is proposed, in which nominal predictions are combined with local feedback compensation to effectively mitigate model uncertainties and external disturbances. Simulation results demonstrate that, compared with conventional PID control and traditional MPC methods, the proposed approach achieves superior performance in dynamic fluid level tracking accuracy, disturbance rejection, and closed-loop stability.

Processes doi: 10.3390/pr14081231

Authors: Demirbağ Erim Köse

Heat and mass transfer behaviour of frozen cylindrical potato croquettes was investigated during the deep-fat frying process at different oil temperatures (150, 160, 170, and 180 °C) and times (540, 420, 300, and 240 s), using the product’s actual geometry to better represent real frying conditions. The apparent effective heat transfer coefficient (he), effective moisture diffusivity (De), and effective mass transfer coefficient (ke) were estimated experimentally. Increasing oil temperature led to a gradual decrease in he, whereas De and ke increased consistently across the investigated range. The calculated he values ranged from 82.980 to 119.86 W m−2 °C−1, while De and ke varied between 1.237 × 10−6–1.331 × 10−6 m2s−1 and 6.189 × 10−6–13.312 × 10−6 m s−1, respectively. The time-dependent behaviour of the transport parameters was further analyzed using pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetic models. Statistical evaluation based on R2 and RMSE showed that PFO best described he, whereas PSO provided superior agreement for De and ke. These results demonstrate that heat and mass transport during frying are dynamic processes that can be quantitatively characterized using simplified kinetic formulations, offering a practical engineering tool for process prediction and optimization.

Processes doi: 10.3390/pr14081230

Authors: Piotr Rudnicki Przemysław Wiewiórski Adam Kowalik Jerzy Kaleta

The aim of this study was to design and validate an automated 5 L prototype Stirred-Tank Photobioreactor (ST-PBR) dedicated to the two-stage cultivation of the microalga Haematococcus pluvialis. The classic limitations of stirred-tank reactors (such as high shear stress and suboptimal light penetration) were overcome through precise phase-controlled illumination (60 and 300 μmol m−2 s−1) and the implementation of an advanced embedded control system integrated with Keysight VEE Pro 9.33 software. The design features an innovative mixing system utilizing a dual marine impeller driven by a brushless motor—operating at a mathematically defined tip speed of 0.48 m/s to preserve cellular integrity—alongside a precise gas dosing strategy (pH-stat) employing medical-grade components. Process verification demonstrated highly stable operation, maintaining a dry biomass concentration of 1.315 g/L with no recorded sedimentation, while achieving a highly competitive astaxanthin biosynthesis yield of 4.12% dry weight (DW). Furthermore, enzymatic extraction facilitated the recovery of a product with high biological activity, as confirmed by an increase in equine adipocyte viability up to 128.1 ± 3.1% in in vitro MTS assays, highlighting its potential for veterinary nutraceutical applications. The developed solution represents a scalable, cost-effective, and viable alternative to advanced tubular photobioreactors.

Processes doi: 10.3390/pr14081229

Authors: Edgar E. Tibaduiza-Rincón Walter M. Villa-Acevedo Jesús M. López-Lezama

This paper provides a comprehensive analysis of the Optimal Reactive Power Dispatch (ORPD) problem, focusing on its mathematical formulations and the methodologies employed to solve it. This paper systematically categorizes the problem into single-objective and multi-objective formulations, as well as single-period and multi-period models, and addresses both single-area and multi-area operational frameworks. It explores a broad range of optimization techniques used to tackle the ORPD problem, including classical optimization methods, metaheuristic algorithms, and hybrid approaches. Additionally, this paper discusses the incorporation of uncertainty in ORPD models, highlighting methods to account for the stochastic nature of power systems. A critical assessment of the current literature identifies existing knowledge gaps and outlines promising future research directions. This paper aims to provide researchers with a thorough understanding of the ORPD problem, offering insights into emerging trends and areas for further exploration.

Processes doi: 10.3390/pr14081228

Authors: Kunzi Wang Zongxing Li Qiankai Gao Liming Xu

Machining stability in profile grinding directly affects surface quality and form accuracy, while the variation in local contact conditions induced by complex contour geometries makes its stability behavior more complicated than that of conventional grinding. This study investigates chatter stability under the coupled effects of contour geometric features and process parameters. A dynamic grinding force model is developed based on a tool nose micro-element method, explicitly considering the coupled effects of contour geometric parameters, wheel–workpiece contact, and regenerative effects. A chatter stability model is then established, and an iterative method is proposed to predict stability limits under different contour features. The results indicate that wheel speed and grinding depth dominate system stability. Under the same curvature radius, convex contours exhibit the highest stability, followed by straight and concave contours. As the curvature radius increases, the stability boundaries gradually converge toward that of the straight contour. Increasing the contour normal angle (CNA) significantly enhances stability and promotes the transition of the dominant unstable mode from single-direction to multi-directional coupling. Grinding experiments on a composite curved workpiece validate the model, showing strong agreement between predicted stability regions and measured chatter marks and spectra. The proposed model provides a basis for parameter selection and chatter suppression in complex profile grinding.

Processes doi: 10.3390/pr14081227

Authors: Diandong Hou Yiming Yang Yan Liu Guoli Bian Zhenan Li Mingchao Du Lehua Zhao Peng Zhang Xizhai Zhang

The sorting of coal gangue is of great significance for improving coal quality, avoiding environmental pollution, and reducing labor costs. The image-based coal gangue sorting method has been proposed by a large number of researchers, but the complexity of the environment, the speed and accuracy of coal gangue detection and recognition methods, and the performance of hardware equipment all pose challenges to the accuracy of coal gangue sorting. This paper discusses the research and application of deep-learning methods in the field of coal gangue detection and proposes an improved YOLOv7 coal gangue detection model for ordinary GPU devices with large computing power and memory. In response to the feature redundancy problem of the YOLOv7 model in coal gangue detection tasks, FasterNet was introduced to improve the backbone network of YOLOv7, reducing redundant calculations and memory access, making the model more effective in extracting features. In response to the requirements for detection speed in high-speed motion of belt conveyors, VoVGSCSP was introduced to improve the efficient layer aggregation network (ELAN) of YOLOv7 neck, further enhancing the detection speed of the model. The experimental results show that when the belt speed is 0.6 m/s, the improved model’s mAP0.5 is similar to YOLOv7, FPS increases from 9 frames per second to 18 frames per second, coal gangue sorting rates reach 91.1%, and coal misselection rates are 4.8%. The proposed coal gangue detection and recognition method based on improved YOLOv7 has increased the detection speed of the recognition model and promoted the improvement of coal gangue sorting efficiency.

Processes doi: 10.3390/pr14081225

Authors: Bagdaulet Kenzhaliyev Almagul Ultarakova Nina Lokhova Arailym Mukangaliyeva Azamat Yessengaziyev Kaisar Kassymzhanov

Depletion of reserves of rich copper–porphyry ore deposits necessitates the development of highly efficient methods for Mo (VI) extraction from complex, corrosive hydro-metallurgical media. The present study undertakes a comprehensive assessment of sorptive concentration of Mo (VI) from strongly acidic sulfate solutions (120 g/L H2SO4) by employing a spectrum of commercially available strong- and weak-base anion-exchange resins. It has been established that the macroporous weak-base anion exchanger Purolite A-100 demonstrates decisive superiority over gel-type analogs (Lewatit M-800, AB-17), facilitating unimpeded intra-gel diffusion of bulky molybdenyl sulfato-complexes anions, thereby circumventing the obstructive “sieve effect.” Thermodynamic and kinetic investigations revealed that the sorption process exhibits pronounced concentration- and pH-dependent characteristics. Peak extraction efficiency (up to 95.91%) is achieved at pH ≈ 1, a finding that correlates with the region of maximal protonation of tertiary amino groups within the resin matrix. Kinetic acceleration of mass transfer upon heating to 80 °C has been experimentally confirmed, yielding 94.6% extraction within 60 min. The obtained results corroborate the prospective integration of macroporous weak-base anion exchangers into operational hydro-metallurgical schemes as an environmentally benign and efficacious alternative to conventional solvent extraction of molybdenum.

Processes doi: 10.3390/pr14081226

Authors: Na Liu Xuan Yu Jianhua Zhang Xinxin Wang Cheng Siong Chin

Chaotic behavior in power systems that are integrated with permanent-magnet synchronous generators (PMSGs) poses a significant threat to stability and security. Existing control methods often suffer from slow convergence, reliance on precise system models, or the inability to guarantee convergence within a predefined time. To address these issues, this paper develops a predefined-time synchronization control scheme for chaotic PMSG systems under unknown nonlinearities and external disturbances. First, an adaptive neural network with variable exponent coefficients is constructed to approximate unknown system dynamics online. Second, a predefined-time stability criterion is established, ensuring global convergence of synchronization errors within a user-specified time, independently of initial conditions. Third, the proposed controller achieves superior disturbance rejection without requiring prior knowledge of disturbance bounds. Numerical simulations demonstrate that the proposed method outperforms conventional finite-time control in convergence speed, control smoothness, and robustness to parameter variations—offering a practical and theoretically guaranteed solution for enhancing the stability of PMSG-based power systems.

Processes doi: 10.3390/pr14081224

Authors: Guanghua Yang Yanchen Li Zihang Chen Wenlong Yue Da Ao Zhiqiang Cai

The novel strain TD-94 with higher ammonia degradation efficiency was isolated from the activated sludge of SINOPEC and belongs to the family of Alcaligenes faecalis (A. faecalis) based on its 16sRNA sequence and physio-biochemical characteristics. It is a Gram-negative, highly heterotrophic aerobic ammoxidation bacterium that is capable of effectively treating ammonia-nitrogen wastewater. The genome size of strain TD-94 was 4,361,949 bp with a GC content of 56.47% and a total of 4101 genes, which accounted for 89.54% of the total genome length. Analysis of various databases showed that 649 genes were annotated in the GO database; a total of 2712, 4095 and 12 genes were annotated in the KEGG, COG, and ADRB databases, respectively; and there were 24 types of cytochrome P450, 477 signal peptides, and eight secondary metabolites. All these data provide a theoretical basis for the mechanism of action of the strain TD-94. Based on the whole-genome sequencing results, functional genes related to nitrogen metabolism in A. faecalis TD-94, including aerobic ammonia oxidation (AOB), hydroxylamine oxidoreductase (HAO), and pyruvic oxime dioxygenase (POD) were identified. Through growth curve analysis and the identification of functional genes, the nitrogen metabolism pathway of A. faecalis TD-94 was proposed, demonstrating that the strain TD-94 has good denitrification capabilities and a novel degradative pathway.

Processes doi: 10.3390/pr14081223

Authors: Jing Li Bo Ning Lele Yang Fenlan Ou Bo Li Dezhi Qiu Xuemei Jin

Ocean Thermal Energy Conversion (OTEC) is characterized by its abundant reserves and pollution-free nature, enabling stable power generation around the clock. Since the power output of an OTEC system is significantly influenced by the energy available from cold and warm seawater, the accurate evaluation of the outlet temperature of the cold-water pipe (CWP) is crucial. To analyze the heat transfer performance of the CWP, this paper investigates the temperature field of the OTEC CWP and employs numerical simulation methods to conduct finite element analysis of the temperature field under different discharge conditions. The results indicate that during the pumping of deep-sea cold water through the CWP, heat is absorbed from the warmer upper seawater layers. When the pumping discharge rate is higher, the shorter fluid residence time due to higher flow velocity results in a lower outlet temperature. Compared to steel CWPs, high-density polyethylene (HDPE) is more suitable for OTEC systems due to its lower thermal conductivity and density.

Processes doi: 10.3390/pr14081222

Authors: Zhiheng Shen Yumei Li Xinrui Li Haoyuan Zheng Yan Xi Liwei Yu

The cement plug-casing interface is critical for long-term wellbore integrity in well abandonment to prevent fluid channeling. However, traditional cement easily debonds under long-term in situ stress and fluid exposure, causing seal failure and safety risks. To address this issue and overcome the limitations of conventional cement, a three-dimensional finite element model was established based on stress-seepage coupling theory. A systematic comparative analysis of the interface debonding mechanisms for three materials—cement, resin, and alloy—and their different combination sequences was conducted. The entire process of interface damage was quantified. The effects of material combination, formation elastic modulus, and injection rate on sealing performance were analyzed. Results show that the stiffness gradient dominates the failure mode, and the “cement–resin–alloy” configuration best suppresses damage propagation, reducing failure height by about 30%. Additionally, interface integrity is sensitive to formation constraints and operational parameters: the interface failure height decreases as the formation elastic modulus increases, and increases as the injection rate rises. The findings of this study can provide a theoretical basis and engineering reference for the optimal design of composite plugging barriers in demanding operational conditions, such as those encountered in carbon sequestration wells.

Processes doi: 10.3390/pr14081221

Authors: Dalne Sinclair Armin Mirzapour-Kouhdasht Juan A. Velasquez Da Chen Senay Simsek Jen-Yi Huang

The global transition towards sustainable food systems has intensified the search for alternative protein sources that can meet human nutritional demands with reduced environmental impacts. Although microalgae are rich in protein, their applications in food remain limited due to thick cell walls and intense green color. The aim of this study is to modify Chlorella vulgaris by high-pressure homogenization (HPH) and decolorization to improve its processability for extrusion-based 3D printing. Microalgal biomass was pretreated by HPH at different pressures (10,000, 15,000, 20,000 psi) for one to three passes, followed by pigment removal using ethanol of different concentrations (70, 85, 100%). Microscopic imaging shows that HPH effectively disrupted microalgal cell walls and caused cell disintegration, resulting in increased foaming stability (22–28%) but lower solubility (up to 24%), with other functional properties largely preserved. Ethanol treatments markedly decolored microalgae and increased their water-holding capacity (10–45%) and solubility (6–11%). The formulation of HPH-treated decolorized microalgae with soy protein isolate and xanthan gum increased the viscosity (66–179%) and elasticity (78–235%) of printing inks. The resulting 3D prints show higher hardness (47–128%), springiness (up to 155%) and chewiness (47–408%). The information obtained from this study provides guidance for modifying the functional and rheological properties of microalgae and contributes to advancing the formulation and manufacturing of microalgae-based foods.

Processes doi: 10.3390/pr14081220

Authors: Andżelika Krupińska Zuzanna Kamińska Sylwia Włodarczak Magdalena Matuszak Marek Ochowiak

This study presents the results of research on the modification of gypsum with bio-waste to improve its thermal insulation properties and to evaluate the influence of the type and amount of the additive on the physical, mechanical, and microstructural properties of the composite. Various fractions of plant-based bio-waste were used in amounts ranging from 0.75 to 10% by weight. The thermal conductivity coefficient and thermal diffusivity were determined. Additionally, analyses of dimensional stability over time, visual appearance, and phase distribution uniformity were conducted. Mechanical tests included surface hardness measurements. In order to determine the material’s durability, water absorption and frost resistance tests were performed, and structural changes and properties after these cycles were analyzed. It was found that selecting the appropriate type and proportion of additive makes it possible to obtain composites with a favorable balance between thermal insulation, dimensional stability, and mechanical performance. The conducted research confirms the potential for effective use of bio-waste as a gypsum-modifying raw material, contributing to the development of sustainable building materials with a reduced environmental footprint and improved functional parameters.

Processes doi: 10.3390/pr14081216

Authors: Jialisen Yimanhazi Keji Wan Mingqiang Gao Qiongqiong He Zhenyong Miao

High-alkali coal can cause slagging and fouling and impact the operational lifespan of the boilers. Traditional single-indicator methods often yield inconsistent results when evaluating the slagging risk of high-alkali coal. In this study, six coal samples were selected and systematically analyzed for their slagging characteristics using scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD), and ash morphology analysis. Furthermore, a comprehensive evaluation model was constructed by integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the entropy weight method. Additionally, based on images of ash morphology, the fractal dimension (D) was introduced as a quantitative indicator to predict slagging tendency through crack characteristics. The results show that TF, ZD, and KB samples, which are rich in alkaline oxides (CaO, Fe2O3, Na2O, K2O), form low-melting-point eutectic silicates during combustion, resulting in significant melting and agglomeration with wide cracks between aggregates, indicating a strong slagging tendency. Their fractal dimensions (D) range from 1.81 to 1.92. In contrast, HM and WQ samples, dominated by SiO2 and Al2O3, form high-melting-point mullite and quartz, showing loose ash morphology with uniformly distributed cracks and a weak slagging tendency, with D values of 1.68 and 1.75, respectively. A significant negative correlation was observed between D and the E-TOPSIS model (y = 3.54 − 1.72x). Therefore, fractal analysis allows for rapid assessment of slagging risk without the need for complex chemical testing. This study provides valuable insights for predicting the slagging tendency of high-alkali coal during combustion.

Processes doi: 10.3390/pr14081218

Authors: Hongsheng Wang Wenrui Zhao

Large-scale surface subsidence induced by extra-thick seam longwall top-coal caving (LTCC) is strongly amplified by thick unconsolidated overburden, posing serious serviceability risks to overlying linear infrastructure. Taking the S103 Provincial Highway above Panel 6118 in Inner Mongolia, China, as the engineering background, this study integrates theoretical analysis, numerical simulation, and in situ monitoring to investigate the subsidence-control mechanism of directional presplitting roof cutting. The results show that roof cutting mitigates surface subsidence by reconstructing the overburden structural system and weakening the stress-transfer chain, thereby transforming key-stratum deformation from integral bending to segmented block movement and narrowing the subsidence-affected zone. An equivalent mining-depth model for subsidence-boundary convergence is proposed to characterize the inward migration of the subsidence-basin boundary under thick unconsolidated cover, and a segmented probability-integral model is developed to explain the kink-like high-gradient feature in the post-cut subsidence profile. Parametric simulations of roof-cutting positions (p = 0, 2, 4, …, 32 m) show that the most effective mitigation occurs in the range p = 4–12 m; using minimum–maximum highway subsidence together with profile flattening as the optimization criteria, the representative optimum is identified at p ≈ 10 m, for which the maximum highway subsidence is approximately 57 mm, about 76% lower than that in the non-cutting case. The results further indicate that, although roof cutting significantly reduces subsidence and deformation gradients, fissure localization and possible discontinuous deformation near the pre-split weak plane still require careful field monitoring.

Processes doi: 10.3390/pr14081219

Authors: Ben Logan Tao Sun

Developmental engineering (DE) is a bottom-up strategy for generating functional tissues from modular tissues (MTs), offering potential advantages over conventional top-down approaches. However, scalable MT production remains constrained by limited understanding of scaffold aggregation, oxygen transport, and hydrodynamic effects in bioreactors. This study integrates theoretical simulations with empirical correlations to analyze these factors and provide a systematic basis for MT production. Microcarrier aggregates were modelled to evaluate minimum oxygen concentration (Cmin). Results indicate that larger microcarrier diameters (dmc) are associated with increased Cmin due to longer diffusion distances. Aggregate geometry and packing configuration, including hexagonal close packing and the “kissing number,” influenced oxygen distribution and may explain observed Cmin plateaus. Hydrodynamic behaviour was assessed using the Zwietering correlation and Kolmogorov turbulence scaling. Denser microcarrier aggregates required higher minimum stirring speeds (Nmin), while larger dmc increased susceptibility to shear. Increased agitation intensity and more aggressive impeller designs reduced Nmin but were associated with potential cell damages. Higher medium density (e.g., 20% FBS) reduced shear stress and energy dissipation. A unified framework integrating oxygen diffusion, aggregate geometry, microcarrier properties, and hydrodynamics is proposed to estimate oxygen limitation and cell damage, highlighting trade-offs relevant to MT production in DE.

Processes doi: 10.3390/pr14081215

Authors: Chuanxu Wan Lu Xiao Guolei Chen Qingwei Kang Peng Zhou Gang Zhou Guocong Lin

Wide-body dump trucks in open-pit mines frequently operate under high loads and severe road conditions, demanding superior dynamic performance from their suspension systems. Existing studies tend to focus only on the influence of individual parameters on the dynamic characteristics of hydro-pneumatic suspensions, lacking systematic analysis of parameter coupling effects and optimal parameter combinations. Taking the two-stage pressure hydro-pneumatic suspension of a wide-body dump truck as the research object, this paper theoretically analyzes its working characteristics and establishes an AMESim model under multiple excitation conditions to reveal how parameter interactions affect the dynamic performance of the suspension. With peak liquid pressure, maximum liquid pressure fluctuation, and maximum vehicle body vertical acceleration as optimization objectives, a multi-objective optimization algorithm is employed to determine the optimal suspension parameters. The results indicate that the interactive responses of damping orifice diameter and check valve diameter with respect to peak pressure and body vertical acceleration exhibit strong nonlinearity. Compared with the original parameter scheme, the optimized design reduces peak liquid pressure, maximum pressure fluctuation, and peak body vertical acceleration by 8.76%, 29.1%, and 11.7%, respectively, significantly improving vehicle ride comfort and mitigating pressure oscillations in the hydro-pneumatic suspension. The research results can provide theoretical support and engineering reference for intelligent operation and maintenance of mine heavy equipment, optimization design of suspension systems and efficient and reliable operation.

Processes doi: 10.3390/pr14081217

Authors: Maria-Alexandra Pricop Camelia Oprean Alexandra Teodora Lukinich-Gruia Alexandra Ivan Virgil Păunescu Călin Adrian Tatu

(1) Background: Aristolochia spp. are plants spread around the world and are cautiously used for medicinal purposes due to their toxic compounds. Because of their content of aristolochic acid I (AAI), a major carcinogenic compound, these plant preparations can cause acute and chronic kidney disease, which is associated with cancer. These compounds also contaminate the environment where Aristolochia plants grow, leading to indirect exposure of the population. (2) Methods: The study provides a practical solution for minimizing the toxic effects of AAI using activated charcoal (AC). An ultra-high-pressure liquid chromatography (UHPLC) coupled with a diode array detector (DAD) was used for the AAI qualitative and quantitative evaluation at different time points. Also, the greenness of the chromatographic analysis was evaluated with the AGREE method. (3) Results: A medical pill of 250 mg AC removed 125 µg/mL AAI from a methanolic solution in 30 min with 97.65% efficiency. The greenness for the analytical evaluation was 58%. (4) Conclusions: This study offers, for the first time, a low-cost medical and environmental solution for AAI contamination. The UHPLC–DAD method seems to be an environmentally responsible platform for the AAI routine analysis. AC shows efficient removal, which could be used both for Aristolochia sp. pharmaceutical preparations as well as in environmental decontamination.

Processes doi: 10.3390/pr14081212

Authors: Zhanghua Zou Shizhong Yang Yankai Chen Zhibin Chen Jianli Huang Yuan Xie Fatih Evrendilek Wuming Xie Sheng Zhong Zuoyi Yang Jingyong Liu

Secondary aluminum dross (SAD) is classified as hazardous waste (HW48) due to its content of toxic (e.g., heavy metals, fluorides) and highly reactive phases (e.g., aluminum nitride, AlN). This review systematically synthesizes the sources, heterogeneous composition, and environmental risks of SAD, and critically evaluates state-of-the-art hydrometallurgical and pyrometallurgical detoxification and resource-utilization technologies. Comparative, mechanism-oriented analyses are used to elucidate the respective advantages, limitations, and scalability of wet versus thermal routes. Particular emphasis is placed on the migration, transformation, and ultimate fate of key hazardous species (AlN, fluorides, chlorides, and heavy metals) during treatment and product valorization. An integrated hydro–pyro nexus is conceptualized as synergistic hybrid processing that transcends the trade-offs between efficiency, energy consumption, and product purity that currently limit standalone technologies. Emerging hybrid process concepts, advanced additives, and circular-economy-oriented product pathways are evaluated to address current technological bottlenecks. Finally, critical knowledge gaps and research priorities are identified to accelerate safe, low-carbon, and high-value utilization of SAD.

Processes doi: 10.3390/pr14081214

Authors: Sandeep Jain Rahul Singh Mourya Reliance Jain Sheetal Kumar Dewangan Saurabh Tiwari

Understanding the depth and severity of corrosion is vital for evaluating the long-term durability and economic performance of Zn-based structures. In this study, a machine learning (ML) framework was applied to forecast the corrosion depth of zinc under varying environmental circumstances. A dataset consisting of 300 samples compiled from previously published atmospheric corrosion studies under various environmental conditions was used to develop and evaluate the machine learning models. Seven ML algorithms were developed by integrating different environmental constraints such as temperature, time of wetness (TOW), SO2 concentration, Cl− concentration, and exposure time as input parameters. The models were trained using cross-validation and hyperparameter optimization to ensure robust predictive performance and minimize overfitting. The Random Forest (RF) model confirmed superior predictive performance with an R2 of 96.4% and RMSE of 0.642 µm among all used models. The predictive ability of the optimized RF model was further confirmed using five new environmental systems, attaining excellent agreement with predicted values (R2 = 97.9%, RMSE = 0.87 µm). Model interpretability analysis using SHAP (SHapley Additive exPlanations) discovered that exposure time and SO2 concentration are the most significant parameters leading zinc corrosion behaviour. The developed ML framework provides interpretable insights into the influence of environmental parameters on atmospheric zinc corrosion behaviour and provides a reliable tool for forecasting corrosion depth. These findings highlight the potential of ML approaches to support corrosion mitigation strategies and accelerate materials design by reducing reliance on conventional trial-and-error experimentation.

Processes doi: 10.3390/pr14081213

Authors: Weiwei Jia Youcheng Ding Xilong Ye Xinyi Huang Maofa Wang Chenglong Miao

Reliable sensor fault detection is critical for the safe and efficient operation of complex industrial systems, such as thermal power plants. However, traditional data-driven methods and standard deep learning models often struggle to detect incipient gradual drift faults under severe environmental noise, primarily because they ignore the inherent physical correlations among multivariate sensor signals. To address this challenge, this paper proposes a novel Physics-Coupled Deep Long Short-Term Memory Autoencoder (PC-Deep-LSTM-AE). Specifically, we integrate a deep LSTM architecture with an explicit non-linear information compression bottleneck and layer normalization to enhance robust feature extraction in high-noise environments. Furthermore, we innovatively introduce a Physics-Coupling Loss (PCC Loss) that jointly optimizes the mean squared reconstruction error and the Pearson correlation coefficient, forcing the model to strictly preserve the dynamic physical relationships among multivariable signals. Extensive experiments were conducted on a real-world thermal power plant dataset with severe noise injection. The results demonstrate that the proposed PC-Deep-LSTM-AE achieves an outstanding F1-score of over 0.98, significantly outperforming mainstream baseline models, including Vanilla LSTM-AE, GRU-AE, Bi-LSTM-AE, and CNN-AE. The proposed method exhibits exceptional robustness and high interpretability for root-cause analysis, highlighting its immense potential for real-world industrial deployment.

Processes doi: 10.3390/pr14081211

Authors: Yiqiang Ren Vladimir Vasilievich Glazunov Natalya Nikolaevna Efimova

Electrical resistivity tomography (ERT) serves as an auxiliary tool for marine engineering geological investigation. Through modeling, the effectiveness of this method was evaluated in areas affected by hydrological and underwater environmental changes, with a focus on the submarine geological structure in nearshore environments. The effects of pore water mineralization and cation exchange capacity on the resistivity of seabed sedimentary layers were investigated via rock physics modeling, and the corresponding relationship curves were obtained. Physical simulation experiments were also conducted to validate the rock physics modeling results. This process quantitatively analyzed the factors influencing the resistivity of nearshore seabed sediments, obtained the resistivity of each sedimentary layer, and interpreted the causes of resistivity variations. Resistivity models of different terrains were established for sandy clay seabed sediments with varying water salinities. The innovative use of submarine electrical resistivity tomography was proposed, and its feasibility and advantages were confirmed through numerical simulations. Field tests along the Baltic Sea coast demonstrated that, compared with previous methods, submarine electrical resistivity tomography offers higher resolution and improved exploration performance.

Processes doi: 10.3390/pr14081210

Authors: Zhicong Lv Zhiwei Wang Qifu Lin Jiawei Gong Yong Li Yuchen Zhang Longwei Chen

In the original publication [...]

Processes doi: 10.3390/pr14081209

Authors: Xiangyun Kong Lei Huang Jie Zhou Hainan He Dong Xu Bingji Li Pei Yan Xiaochen Wang Quan Yang Xianghong Ma Yuchun Xu

Equipment wear and assembly clearances can change the longitudinal stiffness of hot strip mills and further affect roll-gap levelling accuracy and asymmetric strip profile control. In this study, the longitudinal stiffness of a 1580 mm four-high hot strip finishing mill was investigated by combining the analytical calculation of the hydraulic press-down system with a three-dimensional mill–strip finite element model. The effects of typical horizontal and vertical gap forms, including work-roll offset, same-side deflection, roll crossing, and unilateral vertical clearance caused by step-pad wear, on total longitudinal stiffness and stiffness difference between the two sides were analysed systematically. The results show that work-roll horizontal offset changes the longitudinal stiffness in a nonlinear manner, whereas work-roll rotation and roll crossing generally reduce the longitudinal stiffness and increase the stiffness asymmetry between the two sides. Unilateral vertical clearance also causes nonlinear variation in both total stiffness and side-to-side stiffness difference. The proposed method was further applied to the stiffness prediction module of the Guangxi BG 1700 mm hot strip mill production line, providing support for equipment maintenance, roll-gap levelling, and stable strip production.

Processes doi: 10.3390/pr14081208

Authors: Matthew S. Emerson Sharon I. Lall-Ramnarine Jasmine L. Hatcher-Lamarre Marie F. Thomas Masao Gohdo Boning Wu Min Liang Sharon Ramati Fei Wu Claudio J. Margulis Edward W. Castner Robert R. Engel James F. Wishart

A diverse range of 4-dimethylaminopyridinium (DMAP) bis(trifluoromethylsulfonyl)-amide ionic liquids with specific functionalities (alkyl, alkoxy, hydroxyalkyl and benzyl) were designed, characterized and compared with their pyridinium analogs in terms of their physical and radiolytic properties. The influence of the dimethylamino group on ionic liquid structure was investigated by X-ray diffraction and molecular dynamics simulations. The influence of the electron-donating ability of the dimethylamino-substituted cation is evident in the differences in the electronic density of states between the DMAP and pyridinium ILs. This leads to substantial changes in the radical transients observed in pulse radiolysis of the neat ILs. It was found that the DMAP salts were higher melting, more viscous and less conducting than their pyridinium analogs. However, the DMAP salts exhibited higher thermal stabilities and could therefore be useful for high-temperature applications.

Processes doi: 10.3390/pr14081207

Authors: Jianping Zhou Zhaocai Pan Yu Zhang Hongjun Wu Guang Wu Jianyi Liu

Wax deposition occurs to varying degrees in most oil and gas wells. The basic data of existing wax precipitation prediction models are mainly single-component wax experimental data based on the melting process of wax crystals during heating, which is quite different from the cooling crystallization process of wax in oil and gas production. Moreover, the published solubility test data of binary n-alkanes are mainly concentrated in the range of nC10–nC36, leaving existing thermodynamic models without available data for predicting the behavior of high-carbon alkanes. Based on the idea of wax crystallization and precipitation during cooling, this study experimentally determined the solid–liquid equilibrium solubilities of high-carbon n-alkanes (nC38, nC40, nC44, nC48 and nC50) with different concentrations in n-heptane, n-nonane and n-dodecane, as well as the crystallization parameters of pure substances, by using a DSC instrument. This effectively fills the gap in the basic physical property data of long-chain alkanes (more than nC36) and the cooling process in existing studies. In addition, we measured the crystallization parameters of pure high-carbon n-alkanes (nC38, nC40, nC44, nC48 and nC50) during cooling, including crystallization temperature, transition temperature, crystallization enthalpy and transition enthalpy under cooling conditions. The experimental data are in good agreement with the solubility predicted by the ideal solution model for the cooling process, with an average absolute percentage error of less than 10% and average solubility deviation generally within 0.078 mol%. This indicates that the ideal solution model has good accuracy for predicting the precipitation of n-alkane wax and n-alkane solvents. This study provides basic data for the prediction theory of paraffin precipitation.

Processes doi: 10.3390/pr14081206

Authors: Giuseppe Gargano Ainoa Morillas España Hounaida Kefi Francisco Gabriel Acién Fernández Joaquín Pozo-Dengra

Microalgae dewatering is a major bottleneck for the industrial deployment of microalgal biorefineries due to its high energy and water requirements. This study investigates the optimization and modeling of an industrial-scale aerated submerged ultrafiltration (UF) system for microalgae pre-concentration under real operating conditions. A submerged hollow-fibre Koch LE8 UF module (348 m2, 0.03 µm) was operated directly on Chlorella sp. cultures produced in an 800 m2 outdoor photobioreactor. Filtration–backwash cycles were experimentally optimized, identifying an optimal sequence of 8.33 min filtration and 1 min backwash, enabling up to 80% net water removal per cycle while maintaining fouling largely reversible under the tested conditions. Long-term trials (6–7 h) achieved stable concentration factors of 3.6–4.3 with complete biomass retention and sustained permeate flux despite increasing solids concentration. Reuse of permeate for backwashing eliminated freshwater consumption without compromising membrane performance. A dynamic resistance-in-series (RIS) model, incorporating mass balances and an empirically derived concentration-polarisation resistance, accurately reproduced permeate flux and biomass concentration dynamics (R2 > 0.83) using a single fitted parameter. The validated model was further applied as a digital twin to simulate operation up to the theoretical concentration factor of 10, quantifying the associated energy and water demands. The system exhibited a low estimated specific energy consumption of 1.25 kWh·kg−1 biomass and a water demand of 0.30 m3·kg−1, demonstrating that aerated submerged UF is a robust, scalable, and energy-efficient solution for industrial microalgae harvesting.

Processes doi: 10.3390/pr14081205

Authors: Zhenyu Wang Wei Xiao Tianxiang Cheng Haitao Zhu Shiming Wei

Fine migration is widely recognized as a primary cause of production decline in unconsolidated sandstone reservoirs. Migrated fines may accumulate within pore throats and obstruct flow channels, or they may be transported into the wellbore with the produced fluids, leading to operational issues such as wellbore plugging, pump sticking, and equipment abrasion. Despite extensive studies on fine migration, the role of particle wettability has received limited attention. In this study, the mineralogical composition of formation particles was first characterized using X-ray diffraction (XRD) and quantitative clay analysis. Surface modification experiments were then conducted to investigate the effect of hexadecylamine (HDA) on particle wettability and to determine the optimal reaction conditions. Surface characterization techniques were employed to elucidate the modification mechanism. Subsequently, sand-packed tube displacement experiments were performed to evaluate the influence of wettability alteration on fine migration behavior. The underlying mechanisms were further interpreted through interfacial thermodynamic analysis. Two potential field application schemes are proposed to facilitate practical implementation in oilfield operations. The results indicate that the water contact angle of formation particles increased from 0° to 150° when treated with 0.8 wt% HDA for 24 h. Surface characterization confirms that HDA molecules were physically adsorbed onto the particle surfaces. Displacement experiments demonstrate that the permeability reduction rate decreases significantly with increasing particle hydrophobicity. Thermodynamic analysis suggests that the work of adhesion on the modified particle surface was reduced by 93.3%, thereby weakening fluid–particle interfacial coupling and suppressing fine mobilization. This study provides a wettability-based approach for mitigating permeability damage caused by fine migration in unconsolidated sandstone reservoirs.

Processes doi: 10.3390/pr14081204

Authors: Chun Liu Yongchi Lian Junsheng Du Yiying Xiong Heng Liu Wenting Du Yuruo Duan

In karst tunnel engineering, water-filled cavities located above the tunnel crown, under the combined effects of excavation disturbance and hydraulic pressure, are prone to triggering water and mud inrush disasters. The thickness of the water-resisting rock layer is therefore a key factor controlling the stability of the surrounding rock. To address the difficulty in accurately characterizing the mechanical behavior of the crown of horseshoe-shaped tunnels using conventional circular plate or beam models, this study innovatively develops an explicit analytical model for the minimum safe thickness of the water-resisting rock layer based on clamped elliptical thin plate theory and Kirchhoff plate theory, incorporating the influence of cross-sectional geometry. Parametric sensitivity analysis indicates that both karst water pressure and tunnel crown height significantly amplify the required minimum safe thickness, whereas an increase in the tensile strength of the surrounding rock effectively reduces the thickness demand. Specifically, when the karst water pressure increases from 2.5 MPa to 4.5 MPa, the minimum safe thickness rises from 7.5 m to 10.0 m, showing an approximately linear growth trend. The analytical model is further validated through numerical simulations under different “water pressure–thickness” conditions. The results demonstrate that at the calculated recommended thickness, the surrounding rock achieves stable convergence after excavation. High tensile stress and elevated pore pressure zones are mainly concentrated near the tunnel crown, without the formation of through-going tensile failure. Engineering application indicates that the proposed model can provide a quantitative basis for the design of water-resisting rock layer thickness and the assessment of water inrush risk in karst tunnels.

Processes doi: 10.3390/pr14081202

Authors: Cheng Tang Bo Liang Chongjing Wang Xinbin He Peng Zhang Jun Peng Yuangui Zhang

Shale gas exploration in the Shixi block, Guizhou, faces significant challenges due to complex geological structures and normal pressure. To reduce exploration risk, we propose an integrated “Four-in-One” evaluation workflow that combines geological sweet spots, engineering feasibility, preservation conditions, and paleogeomorphology. The workflow features a ‘cap-constraint’ velocity model to reduce structural uncertainty and a tiered multi-scale discontinuity detection strategy for low-SNR seismic data. Application of this workflow in the Shixi block delineated two Class I favorable zones (42.61 km2) with estimated resources of 8.33 billion cubic meters. Drilling results from 56 horizontal wells validate the accuracy of our prediction model, confirming that preservation condition is the primary controlling factor for gas accumulation in this normally pressured setting. This study provides a practical reference for shale gas assessment in structurally complex, normally pressured regions.

Processes doi: 10.3390/pr14081203

Authors: David F. Nettleton Iria Naveira-Souto Elisabet Rosell-Vives Andrés Cruz-Conesa Roger Fàbrega Alsina Alexandra Poch

Background/Objectives: The question addressed in the current work is to develop a simulation of a pharmaceutical process (DNA encapsulation within lipid nanoparticles using a microfluidic micromixer) which will be of utility to the end users (laboratory-scale formulation development). The simulation and the microfluidic approach also address sustainability issues, such as reducing the environmental impact of the process itself, and reducing the need for physical testing. The paper details the implementation and validation, taking into account key performance indicators and control parameters. Methods: The main method applied for simulation development is a novel multi-agent approach to incorporate stochastic probabilistic behavior, combined with theoretical definitions from the process experts and relevant literature, and data/results from laboratory-scale experiments with different parameter configurations. Results: The simulation was implemented as a representation of the real physical process, reproducing the relationships between process parameters (flow rates) and experimental key performance indicators (capsule diameter, poly dispersion index, encapsulation efficiency). The simulation results demonstrated a general agreement with the empirical results and provided useful predictive insights for the laboratory experiments. Conclusions: The simulation has potential as a support tool for laboratory experiments to reduce physical testing and indicate the most promising configurations on which to focus, with potential savings in time, resources and other costs.

Processes doi: 10.3390/pr14081201

Authors: Xinlong Hao Yue Zhao Xilai Zhao Xu Zhou Lihong Mu Youlong Tuo Wenxi Qian

Previous studies on velvet antler processing have mainly evaluated single techniques, and systematic comparisons of processing combinations are limited. This study investigated the effects of different processing combinations on the nutritional composition and physicochemical properties of velvet antler from red deer and sika deer. A 2 × 2 factorial design was applied: Blood-Retained vs. Blood-Removed and Boiled/Fried (zhuzha; no deep-frying) vs. Vacuum Freeze-Dried. In this study, Boiled/Fried was treated as a single processing method. The four processing combinations were analyzed as independent groups using one-way ANOVA. Additionally, two-way ANOVA was conducted to evaluate the main effects of pretreatment, dehydration method, and their interaction on the measured indices. To account for species background, a three-way ANOVA (species × pretreatment × dehydration) was further conducted for key indices. Moisture, crude protein, ash, and crude fat contents were determined. All composition-related indices were evaluated on both wet-weight and dry-weight bases to distinguish moisture-driven concentration or dilution effects from processing-related retention changes. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted for multivariate evaluation. Spearman’s rank correlation was used for association analysis, and Pearson’s correlation with linear regression was applied to quantify linear relationships (reported as r). Freeze-drying significantly reduced moisture content (p < 0.01) and increased crude protein content (p < 0.05). PCA and OPLS-DA demonstrated clear compositional separation among the four processing combinations, with moisture and crude protein as the main contributors (cumulative explained variance > 83%). The effects of Blood-Retained and Blood-Removed treatments differed between species. Three-way ANOVA indicated significant species-dependent effects (e.g., species × pretreatment and or species × dehydration interactions), while the pretreatment × dehydration interaction was significant for TAAs. In the Boiled/Fried groups, total amino acid content (TAA) decreased with increasing moisture. In the Freeze-Dried groups, moisture was significantly negatively correlated with TAAs in the Blood-Retained treatment (Pearson r = −0.886, p < 0.05), whereas no significant correlation was observed in the Blood-Removed treatment (r = 0.429, p > 0.05). Wet- versus dry-basis comparisons indicated that some between-treatment differences were attributable to moisture-related concentration or dilution effects, whereas differences persisting on a dry basis more directly reflected processing-related nutrient retention. Processing combinations produced species-dependent effects in velvet antler. The three-way ANOVA supported species-dependent pretreatment effects and confirmed that the influence of blood retention or removal on amino acid outcomes was contingent on the dehydration regime (pretreatment × dehydration for TAAs). From an application standpoint, no single processing route is universally optimal across all quality attributes; freeze-drying provides a robust baseline, whereas the choice of blood retention or removal should be made in a target-oriented manner (e.g., physicochemical stability versus protein and amino acid retention) while accounting for species background and interaction effects. Therefore, these findings provide a scientific basis for improving product quality, processing efficiency, and standardization in China’s velvet antler industry.

Processes doi: 10.3390/pr14081200

Authors: Luisa F. Medina-Ganem Neali Valencia-Espinoza Eduardo Bautista-Peñuelas Raul E. Medina-Ganem Alejandro Vega-Rios Manuel I. Peña-Cruz Erick R. Bandala Alberto Quevedo-Castro Martin Pacheco-Álvarez Oscar M. Rodriguez-Narvaez

Hydrothermal carbonization (HTC) of agricultural waste is a promising waste management technique. However, the use of different raw materials may produce hydrochars with varying efficiencies, both in yield and application, and environmental impacts, due to differences in composition and required processing conditions. To understand the influence of biomass type and acid-assisted HTC conditions, this study used sugarcane and agave bagasse to produce functionalized hydrochars and evaluated them for the removal of Reactive Orange 84; an azo dye used in the textile industry. Material characterization was performed using FT-IR, TGA, BET, and XRD analyses. In addition, a life cycle assessment was conducted to evaluate environmental impacts associated with hydrochars produced using H2SO4 at concentrations of 0.2 and 0.5 M. TGA and XRD results indicate that agave bagasse hydrochars (HBA) retain more crystalline lignocellulosic structures, whereas sugarcane bagasse hydrochars (HBS) exhibit predominantly amorphous structures after HTC. FT-IR analysis confirmed the presence of –SO3H functional groups; however, HBA samples showed greater availability of these groups with increasing acid concentration. Adsorption experiments and LCA results demonstrated that the most favorable treatment, in terms of emission reduction and dye removal, was agave bagasse functionalized with 0.5 M H2SO4, achieving 75.7% mass yield and 94.5% dye removal.

Processes doi: 10.3390/pr14081199

Authors: Kaituo Li Lin Zhu Fei Xiong Jia Liu Yi Xue Zhengzheng Cao Yuejin Zhou Xin Liang Ming Ji Guannan Liu Faning Dang

Enhanced geothermal systems (EGS) are a key technology for developing deep geothermal resources, yet they face significant challenges in constructing efficient thermal reservoirs within high-stress, high-strength, and low-permeability crystalline rock formations. Traditional hydraulic fracturing (HF) techniques encounter deep challenges in these environments, including excessively high fracturing pressures, limited fracture network patterns, and the risk of induced seismicity. This paper reviews the multi-scale thermal-mechanical mechanisms, fracture evolution patterns, and control strategies associated with thermal stimulation and permeability enhancement in the modification of deep geothermal reservoirs. Research indicates that thermally induced fracturing triggers intergranular and transgranular cracks at the microscopic scale due to mineral thermal expansion mismatches, which macroscopically manifests as nonlinear degradation of rock strength and modulus. The redistribution of the thermal elastic stress field significantly lowers the breakdown pressure, while matrix thermal contraction increases fracture aperture, leading to an exponential enhancement of permeability following a cubic law. However, the high confining pressure constraints, true triaxial stress anisotropy, and thermal short-circuiting risks present substantial suppression and challenges to the effectiveness of thermal stimulation in deep in situ environments. Different fracturing media, such as water, liquid nitrogen (LN2), and supercritical CO2, exhibit varying advantages in thermal stimulation efficiency due to their unique thermal-flow characteristics. Future research should focus on the thermal-mechanical coupling mechanisms under true triaxial stress conditions, and develop intelligent control strategies for permeability enhancement and thermal short-circuiting risk mitigation. This study synthesizes existing analyses and proposes potential engineering strategies for stimulating deep EGS reservoirs, offering significant strategic value for the development of geothermal energy as a baseload renewable resource.

Processes doi: 10.3390/pr14081198

Authors: Xiaoyi Jiang Shuhai Jiang Binyang Sun Lei Tan Qimeng Li Hu Xu

Reservoir water level fluctuations alter the saturation line in earth-rock dams, thereby affecting the accuracy of electrical leakage detection. To quantitatively investigate this influence, a three-dimensional (3D) geoelectric model of a concentrated leakage pathway was constructed using COMSOL Multiphysics based on parameters from a reservoir in Zhejiang Province. Numerical simulations were performed under unsaturated, partially saturated, and fully saturated conditions with respect to the leakage zone, and a fixed-electrode monitoring system was deployed for in situ validation. The results show that 3D resistivity slices can approximately delineate the leakage hazard center. Under fully saturated conditions, the low-resistivity anomaly center shifts upward by 0.7 m. Under unsaturated conditions, the high-resistivity anomaly center shifts upward by 1.7 m. Under partially saturated conditions, the high-resistivity anomaly center exhibits the most pronounced upward shift (3.0 m). Notably, under partially saturated conditions, the boundary point between the high- and low-resistivity anomalies is located close to the central depth of the leakage pathway (deviation of approximately 0.7 m above the center), serving as a useful diagnostic indicator. In situ tests corroborate these findings, with identified anomaly zones matching the actual leakage points. This study provides a quantitative framework for interpreting geoelectrical data in earth-rock dams under fluctuating reservoir levels.

Processes doi: 10.3390/pr14081196

Authors: Yue Zhang Hui Liao Song Zhu Yanting Zhao Fei Li Xiang Li Yue Li

Compound sugars (Cs) and creatine (Cr) have the potential to enhance exercise endurance; however, the mechanisms underlying their effects and the stability of their formulations still require further investigation. This study investigated the effects of Cs and Cr supplementation on exercise performance in C57BL/6 mice, as well as the processing properties of Cs and Cr powder. The exhaustion time, serum fatigue indices, creatine contents, the morphology of muscle tissue in mice were determined. The results demonstrated that combined supplementation of sugar and creatine (Cs-Cr, Cs 6.2 mg/g + Cr 1.0 mg/g) could significantly increase exhaustion time and forelimb grip strength and reduce the levels of lactate and blood urea nitrogen by 22.3% and 25.86%, respectively. In addition, Cs-Cr supplementation increased muscle mass and muscle fiber density in exercise-trained mice and thus alleviated muscle damage caused by exercise. However, Cs-Cr powder exhibits poor stability during processing. Xanthan gum and locust bean gum (m/m = 6:4) has been demonstrated to increase the stability and viscosity of Cs-Cr beverages. Moreover, the addition of 1.5% CaSiO3 also reduced the caking of the powder and increased the stability of the product. This study provides a theoretical basis for the application of Cs-Cr in a functional solid beverage.

Processes doi: 10.3390/pr14081197

Authors: Harryson Guimarães de Lima Clériston Moura Vieira Júnior Humberto Santos Adalberto Freire do Nascimento Júnior Antônio Celso Dantas Antonino Sérgio Peres Ramos da Silva

The improper disposal of end-of-life tires poses significant environmental challenges due to their petroleum-based composition and slow degradation, while simultaneously representing an underutilized energy resource. This study investigates the slow pyrolysis of shredded waste tires in a fixed-bed electrically heated reactor to evaluate the production and fuel properties of gaseous, liquid, and solid fractions. Experiments were conducted with 100 g samples under nitrogen at final temperatures of 400, 500, and 600 °C, with residence times of 40, 25, and 10 min, respectively. Higher temperatures promoted gas formation, increasing yields from 27% to 32% and achieving a maximum lower heating value of 30.54 MJ m−3 at 600 °C, with enhanced H2 and CH4 contents. Solid yields decreased slightly (41% to 37%), while char maintained stable heating values (~29 MJ kg−1). Liquid yields remained near 33% and showed high calorific values (~41 MJ kg−1), densities of 700–770 kg m−3, low acidity, low ash content, and increased viscosity at higher temperatures. Energy conversion efficiency reached 74.4% at 500 °C. The integrated evaluation of all fractions under identical conditions highlights fixed-bed pyrolysis as a promising pathway for waste-tire valorization and decentralized fuel production.

Processes doi: 10.3390/pr14081195

Authors: Wenyan Yang Dayi Qian Xiaona Wang Haishu Sun Jianguo Liu Qunhui Wang

Corn bran is a major by-product of corn starch processing. Due to its high cellulose and hemicellulose contents and relatively low lignin abundance, it represents a promising feedstock for biorefineries. However, efficiently deconstructing corn bran cell wall to maximize fermentable sugar yield while minimizing inhibitor formation remains a challenge due to the complex cross-linked structure of its lignocellulosic matrix that hinders substrate accessibility and prone to side reactions during deconstruction. This study systematically evaluated various pretreatment strategies and identified dilute sulfuric acid as the optimal method to maximize hemicellulose dissolution and total sugar recovery while maintaining low levels of refractory phenolic inhibitors (1.03 g/L, far lower than alkaline and sulfite-based pretreatment). Under optimal conditions (0.80% v/v sulfuric acid, 129 °C, and 23 min), the hemicellulose dissolution rate reached 99.58%, with a pentose yield of 0.38 g/g corn bran and hexose yield of 0.16 g/g corn bran. Subsequent enzymatic hydrolysis of the solid residue (20 FPU/g initial dry weight cellulase) further released hexose-rich sugars. The integrated process achieved a significant total reducing sugar yield of 0.79 g/g corn bran. These findings demonstrate an effective pathway for the high-value utilization of corn bran and provide a scalable process strategy applicable to other lignocellulosic agricultural wastes for sustainable bioenergy production.

Processes doi: 10.3390/pr14081194

Authors: Xiankang Xin Xuecheng Jiang Saijun Liu Gaoming Yu Xujian Jiang

With the continuous development of the petroleum industry, bottomhole pressure prediction technology, which exerts a significant impact on oil production and recovery, has become a key research direction in the current oil and gas field. To enhance the accuracy and robustness of bottomhole pressure prediction under transient and variable operating conditions, a method based on data augmentation strategies and hyperparameter optimization was proposed in this paper. Addressing challenges such as limited data volume and significant disturbances in actual oilfield production, a data augmentation strategy incorporating noise perturbation and sliding windows was introduced to expand training samples and improve model generalization. In terms of model architecture, a deep network integrating CNN, BiGRU, and Multi-Head Attention mechanisms was proposed in this paper, which is referred to as the CNN-BiGRU-Multi-Head Attention model. By introducing Bayesian optimization for automatic hyperparameter search, the performance of the temporal model was further enhanced, achieving efficient extraction and dynamic focusing of wellbore pressure temporal features. Prediction results demonstrated that the proposed method outperforms existing mainstream forecasting models in metrics such as Mean Absolute Error (MAE) and Coefficient of Determination (R2), with R2 reaching 0.9831, which confirms its strong generalization capability and engineering applicability. Practical guidance for intelligent oilfield production management and bottomhole pressure forecasting, along with a novel prediction method, is provided by this study, which holds significant importance for extending well life and stabilizing hydrocarbon production.

Processes doi: 10.3390/pr14081193

Authors: Ivan Pavlenko Vadym Baha Marek Ochowiak Magdalena Matuszak Oleh Chekh

The use of confuser–diffuser nozzles in power machines enables efficient conversion of gas energy into mechanical work. However, traditional Laval, Venturi, and Vitoszynski nozzles are associated with shock wave formation, causing energy losses, noise, and structural loading. This study proposes innovative jet nozzles with an internal streamlined body that forms annular flow rather than a classical diffusor. A rational computational design methodology based on the Venturi effect criterion and equality of cross-sectional area variation laws was developed. A couple of configurations with spindle-toroidal and ellipsoidal streamlined bodies were generated analytically, studied numerically, and confirmed experimentally. Based on the SST turbulence model, CFD simulations for a compressible flow (air) show that the proposed designs reduce the pressure jump from 60 kPa (traditional nozzle) to 20 kPa for the spindle-toroidal configuration and eliminate it for the ellipsoidal configuration. The Reynolds number in the throat decreases by a factor of 2.6, reducing turbulence. The outlet velocity increases by 3.0% for the spindle-toroidal design, while the ellipsoidal nozzle provides expansion with slightly lower velocity but a smoother velocity profile. Experimental thrust measurements agree with simulations within 2.6–6.7%. The proposed designs enhance energy efficiency, reduce erosion and vibration, and enable adaptive flow control via axial displacement of the streamlined body.

Processes doi: 10.3390/pr14081192

Authors: Yue Wang Qinghua Cheng Jianchao Li Yunwei Kang Hui Liu Qian Wei Dali Guo Zixi Guo

Tight conglomerate reservoirs are characterized by strong heterogeneity, significant in-situ stress differences, and unbalanced fracturing stimulation, which make fracture pressure prediction challenging and severely restrict the effectiveness of reservoir stimulation and ultimate recovery. Although acid pretreatment is an effective means to reduce fracture pressure, its quantitative relationship with fracture pressure remains unclear. There is an urgent need to establish a systematic method that integrates reservoir heterogeneity characterization, data augmentation, and intelligent prediction. Aiming at the tight conglomerate reservoir in the MH Block, this study proposes an intelligent fracture pressure prediction and acid pretreatment optimization method that integrates Self-Organizing Maps (SOMs), Generative Adversarial Networks (GANs), and Transformer models. First, SOM is used to perform unsupervised clustering of logging parameters to identify different geological feature categories and achieve fine-scale characterization of reservoir heterogeneity. Second, to address the issue of limited samples within each cluster, GAN is employed for high-quality data augmentation to expand the training sample set. Finally, a fracture pressure prediction model is constructed based on the Transformer architecture, and the influence of acid treatment parameters on fracture pressure is quantitatively analyzed using the SHAP method and laboratory experiments. The results show that the proposed model achieves a coefficient of determination (R2) of 0.93, a root mean square error (RMSE) of 2.38 MPa, and a mean absolute percentage error (MAPE) of 2.02% on the test set, with prediction accuracy significantly outperforming benchmark models such as BPNN, XGBoost, and LSTM. Ablation experiments verify that both the SOM clustering and GAN augmentation modules effectively enhance model performance. Analysis of acid treatment parameters indicates that hydrofluoric acid (HF) concentration is the dominant factor influencing fracture pressure reduction, and the mud acid system exhibits a stronger synergistic effect compared to the single hydrochloric acid system. Reasonable optimization of acid concentration and dosage can significantly reduce fracture pressure (3.14–5.28 MPa). This method provides a theoretical basis and engineering guidance for accurate fracture pressure prediction and optimal design of acid pretreatment in tight conglomerate reservoirs.

Processes doi: 10.3390/pr14081191

Authors: Milica Aćimović Djorđe Djatkov Aleksandar Nesterović Stanko Milić Nikolina Dizdar Nebojša Kladar Zorica Tomičić Slađana Rakita Ivana Čabarkapa

The aim of this study was to comprehensively characterize lavender pellets produced from post-distillation residues and evaluate their multifunctional valorization potential. Physicochemical properties, including moisture, ash, heating value, organic matter, total and organic carbon, macro- and micronutrients, potentially toxic heavy metals, polyphenols, microbiological safety, and nutritive composition, were assessed. The pellets demonstrated an energy content comparable to other agricultural residues, with a higher heating value of 18,900 kJ/kg and a lower heating value of 16,603 kJ/kg. High organic matter (87%) and a slightly acidic pH support soil moisture retention, while favorable macronutrient levels enhance their suitability as a soil amendment. Water-based extractions (infusion and decoction) achieved higher yields (15.60–21.66%) than ethanol (13.04%) and more effectively recovered bioactive polyphenols, particularly rosmarinic and chlorogenic acids. Low moisture and water activity ensured storage stability and minimal microbial growth, which was confirmed by microbiological safety tests. Nutritionally, pellets contained moderate protein (9.38%), high cellulose (33.38%), and low fat (2.18%), with total amino acids of 8.91 g/100 g and 36.7% essential amino acids, along with a favorable fatty acid profile rich in polyunsaturated fractions. Overall, these findings highlight lavender pellets as a sustainable resource for energy, soil improvement, bioactive compound recovery, and complementary animal feed within circular economy frameworks. However, future research should focus on investigating whether residual compounds remain in lavender residues that could exert antifeedant or phytotoxic effects. Additionally, the potential for the sequential valorization of lavender residues should be explored, initially through the extraction of bioactive phenols, followed by pellet production for use as fuel or soil amendments. This approach would enable multiple cascading uses and maximize their contribution to comprehensive circular economy strategies.