Processes, Volume 12, Issue 3 (March 2024) – 203 articles

Rapid detection and sensitive analysis of MMZ is of great importance for food safety. Herein, a fluorescent molecularly imprinted sensor based on upconversion nanoparticles (UCNPs) grafted onto covalent organic frameworks (COFs) was designed for the detection of MMZ. COFs with a high specific surface area and excellent affinity serve as substrates for grafting of UCNPs, which can inhibit the aggregation burst of UCNPs and improve the mass transfer rate of the sensor. Through a series of characterizations, it was found that the proposed UCNP-grafted COFs@MIP-based sensor had good optical stability, high adsorption efficiency, strong anti-interference ability, and high sensitivity owing to the integration of the advantages of UCNPs, COFs and MIPs. Under the optimal conditions, a good linear relationship was presented between the fluorescence intensity of UCNP-grafted COFs@MIPs and the methimazole concentration in the range of 0.05–3 mg L⁻¹, and the detection limit was 3 μg L⁻¹. The as-prepared UCNP-grafted COFs@MIPs were successfully applied for the detection of MMZ in actual samples, and the results were relevant with those determined by high-performance liquid chromatography. The sensor has good sensitivity, reusability, and high selectivity, which are highly valuable in the rapid analysis and detection of food safety. Full article

Internal recycle quadruple fluidized bed pyrolyzer (IR-QFBP) consists of a dual fluidized bed pyrolyzer and a dual fluidized bed combustor and is proposed in this work. It is a new kind of efficient fluidized bed with high pyrolysis and energy efficiency. IR-QFBP may attract extensive attention because of its compact structure. Cold hydrodynamic characteristics of IR-QFBP are the bases of modeling and designing for the hot one. To fully understand the hydrodynamic characteristics of IR-QFBP, a cold flow model on a laboratory scale was designed and set up; furthermore, the two-fluid model (TFM) based simulation was also carried out. The pressure profiles, fluidization states, velocity profiles, and circulation rates of a solid powder at different operation conditions in IR-QFBP were investigated. The results showed that the stable internal circulation of solid powder can be achieved in IR-QFBP. And different circulation characteristics can be obtained by adjusting the operating conditions. Full article

Enzymatic additives, particularly alkaline proteases, play a crucial role in enhancing detergent effectiveness against protein-based stains. Despite advancements in enzyme stabilization techniques, there is a need for innovative strategies to further improve protease stability in laundry detergents. However, research exploring the utilization of substrate imprinting technology to achieve this objective remains limited. Therefore, this study aims to enhance the stability of alkaline proteases in laundry detergents by employing casein as an imprinting substrate and utilizing transglutaminase-mediated (TGase) crosslinking to modify proteases 102 and 306. The optimal temperature, pH, and thermal stability of the modified alkaline proteases 102 and 306 showed no significant changes. However, these two modified alkaline proteases exhibited varying degrees of improvement in stability among the 14 detergent additives tested. Under 40 °C incubation for 24 h, the relative enzyme activity of modified alkaline protease 102 increased approximately 1.4–15-fold in AEO-9, BS-12, CMI, APG, FMEE, and SOE, while the relative enzyme activity of modified alkaline protease 306 increased approximately 1.2–3.7-fold across different additives (FMEE, AEO-9, BS-12, SOE, FAA, and AEC-9Na). These modified proteases demonstrated improved stability and wider applicability across commercial detergent formulations available. Integrated into standard laundry detergent at a 1:7 ratio before and after modification, they effectively removed protein stains from the cotton fabric after 24 h of 40 °C incubation. These findings provide insights into more effective stain-removal techniques. Full article

The objective of this inquiry is to illuminate the attributes of heavy metal contamination and evaluate the potential ecological hazards inherent in the surface soil of Chenzhou City. A comprehensive analysis was conducted on 600 systematically collected soil samples within the study area, utilizing enrichment factors, geo-accumulation indices, comprehensive pollution indices, potential ecological hazard indices, and health risk assessment models to evaluate the degree of heavy metal contamination in the soil, potential ecological risks, and associated health hazards. The findings reveal that the average enrichment factor (EF) for each heavy metal is below 2, with the hierarchy from highest to lowest being Hg > Cd > Cu > Pb > Ni > Zn > Cr > As. Approximately 78.67% of soil samples exhibit no pollution to weak pollution levels based on heavy metal enrichment factors. Moreover, the comprehensive pollution index (IPI_N) indicates that 95.17% of soil samples are within safe and pollution-free levels, indicating an overall environmentally secure setting. However, 2.67% of samples display heightened potential ecological risk levels, primarily concentrated in the southwestern region of the study area, influenced by nearby industrial activities. Additionally, it is noteworthy that both the non-carcinogenic and carcinogenic health hazards emanating from soil heavy metals to adult individuals lie within tolerable thresholds. Among these, arsenic (As), chromium (Cr), and lead (Pb) have been discerned as the principal non-carcinogenic agents. It is of particular significance that only a solitary soil specimen, located in the southwestern quadrant of the investigative region, manifests detectable health perils for children. Full article

An afterburner encounters two primary features: high incoming flow velocity and low oxygen concentration in the incoming airflow, which pose substantial challenges and contribute significantly to the deterioration of combustion performance. In order to research the influence of oxygen content on the dynamic combustion characteristics of the afterburner under various inlet velocities, the effect of oxygen content (14–23%) on the field structure of reacting bluff body flow, flame morphology, temperature pulsation, and pressure pulsation of the afterburner at different incoming flow velocities (0.1–0.2 Ma) was investigated in this study by using a large eddy simulation method. The results show that two different instability features, BVK instability and KH instability, are observed in the separated shear layer and wake, and are influenced by changes in the O₂ mass fraction and Mach number. The oxygen content and velocity affected the oscillation amplitude of the downstream flow. As the O₂ mass fraction decreases, the flame oscillation amplitude increases, the OH concentration in the combustion chamber decreases, and the flame temperature decreases. Additionally, the amplitude of the temperature pulsation in the bluff body flame was primarily influenced by the temperature intensity of the flame and BVK instability. Moreover, the pressure pulsation is predominantly affected by the dynamic characteristics of the flow field behind the bluff body. When the BVK instability dominated, the primary frequency of the pressure pulsation aligned with that of the temperature pulsation. Conversely, under the dominance of the KH instability, the temperature pulsation did not exhibit a distinct main frequency. At present, the influence of oxygen content and incoming flow rate on the combustion performance of the combustion chamber is not clear. The study of the effect of oxygen content on the combustion characteristics of the combustion chamber at different incoming flow rates provides a reference for improving the performance of the combustion chamber and enhancing the combustion stability. Full article

This study investigates the degradation kinetics of polycyclic aromatic hydrocarbons (PAHs) in contaminated soil using hydrogen peroxide (H₂O₂) and the Fenton process (H₂O₂/Fe²⁺). The effect of oxidant concentration and the Fenton molar ratio on PAH decomposition efficiency is examined. Results reveal that increasing H₂O₂ concentration above 25 mmol/samples leads to a slight increase in the rate constants for both first- and second-order reactions. The Fenton process demonstrates higher efficiency in PAH degradation compared to H₂O₂ alone, achieving decomposition yields ranging from 84.7% to 99.9%. pH evolution during the oxidation process influences PAH degradation, with alkaline conditions favoring lower elimination rates. Fourier-transform infrared (FTIR) spectroscopy analysis indicates significant elimination of PAHs after treatment, with both oxidants showing comparable efficacy in complete hydrocarbon degradation. The mechanisms of PAH degradation by H₂O₂ and the Fenton process involve hydroxyl radical formation, with the latter exhibiting greater efficiency due to Fe²⁺ catalysis. Gaussian process regression (GPR) modeling accurately predicts reduced concentration, with optimized ARD-Exponential kernel function demonstrating superior performance. The Improved Grey Wolf Optimizer algorithm facilitates optimization of reaction conditions, yielding a high degree of agreement between experimental and predicted values. A MATLAB 2022b interface is developed for efficient optimization and prediction of C/C₀, a critical parameter in PAH degradation studies. This integrated approach offers insights into optimizing the efficiency of oxidant-based PAH remediation techniques, with potential applications in contaminated soil remediation. Full article

In order to address the problem of the real-time scheduling and control of batch chemical systems, this work proposes a model predictive control method based on Petri nets. First, a method is presented to construct a batch chemical system’s timed Petri net model. Second, a control structure is designed to augment the Petri net model to control the valves. This results in timed Petri nets that formally represent the process specifications of a batch chemical system. Third, a model predictive control method is developed to schedule and control timed Petri nets, where a proposed heuristic function is utilized to perform the optimization computation. The model parameters are dynamically adjusted using online data, and both scheduling and valve control instructions are calculated in real time. Finally, a series of experiments is carried out in a beer canning plant to verify the proposed method. According to the experimental results, the scheduling and control problem can be solved in real time, where the online computations can be performed in milliseconds, and the resulting scheduling strategies are optimal or near-optimal. Full article

Northern European nations are at the forefront of renewable energy adoption but face challenges in optimizing energy conversion efficiency. There is a lack of detailed understanding of how behavioral factors affect the efficiency of renewable energy conversion in these countries. This study aims to evaluate and compare the renewable energy conversion efficiency of Northern European countries, intending to inform strategic policy making and identify best practices for technology deployment in the renewable energy sector. Employing a Data Envelopment Analysis (DEA) model, the study integrates behavioral economic parameters—specifically, the aversion loss and gain significance coefficients—to assess the efficiency of renewable energy conversion, accounting for psychological factors in decision making. A comprehensive sensitivity analysis was conducted, varying the gain significance coefficient while maintaining the aversion loss coefficient at constant levels. This experiment was designed to observe the impact of behavioral parameters on the efficiency ranking of each country. The analysis revealed that Latvia consistently ranked highest in efficiency, irrespective of the gain significance valuation, whereas Iceland consistently ranked lowest. Other countries demonstrated varying efficiency rankings with changes in gain significance, indicating different behavioral economic influences on their renewable energy sectors. Theoretically, the study enhances the DEA framework by integrating behavioral economics, offering a more holistic view of efficiency in renewable energy. Practically, it provides a benchmarking perspective that can guide policy and investment in renewable energy, with sensitivity analysis underscoring the importance of considering behavioral factors. The research offers a practical tool for policymakers and energy stakeholders to align renewable energy strategies with behavioral incentives, aiming to improve the adoption and effectiveness of these initiatives. Full article

The increasing demand for efficient wastewater treatment technologies, driven by global population growth and industrialisation, highlights the necessity for advanced, reliable solutions. This study investigated the efficacy of a slurry photocatalytic membrane reactor (PMR) for the advanced removal of organic pollutants, quantified via chemical oxygen demand (COD), under natural and simulated solar light irradiation. Employing two variants of iron-doped titania as photocatalysts and a polysulfone-based polymeric membrane for the separation process, the investigation showcased COD removal efficiencies ranging from 66–85% under simulated solar light to 52–81% under natural sunlight over a 7 h irradiation period. The overall PMR system demonstrated COD removal efficiencies of 84–95%. The results confirmed the enhanced photocatalytic activity afforded by iron doping and establish solar-powered slurry PMRs as an effective, low-energy, and environmentally friendly alternative for the advanced treatment of municipal wastewater, with the research providing valuable insights into sustainable water management practices. Full article

Coalbed methane (CBM) pilot wells typically exhibit a short production period, necessitating evaluation of their estimated ultimate recovery (EUR) through numerical simulation. Utilizing limited geological data from the pilot areas to finish history matching and subsequent production forecasting presents substantial challenges. This paper introduces a comprehensive numerical simulation workflow for CBM pilot wells, encompassing the following steps. Initially, geological parameters are categorized into two groups based on their statistical distribution trends: trend parameters (i.e., gas content, permeability, Langmuir volume, and Langmuir pressure) and non-trend parameters (i.e., fracture porosity, gas–water relative permeability, and rock compressibility). The probability method is employed to ascertain the probable high and low limits for trend parameter distributions, while empirical or analogous methods are applied to define the boundaries for non-trend parameters. Subsequently, the parameter sensitivity analysis is conducted to understand the influence of varying parameters on cumulative gas and water production. Conclusively, experimental design algorithms generate over 100 simulation cases using the identified sensitive parameters, from which the top ten optimal cases are chosen for EUR prediction. This workflow features two technological innovations: (1) considering the most comprehensive set of reservoir parameters for uncertainty and sensitivity analyses, and (2) considering the matching accuracy of both cumulative production and dynamic production trends when selecting optimal matching cases. This approach was successfully implemented in the C pilot area of the Bowen Basin, Australia. In addition, it offers valuable insights for numerical simulation of unconventional natural gases, such as shale gas. Full article

Pectinase, a kind of hydrolase, mainly contains polygalacturonase, pectinase, and pectin lyase, which can hydrolyze pectin to generate galacturonide and is widely used in industry. At present, pectinase’s activity is still relatively low. Hyperactive pectinase was produced with solid-phase fermentation and a tray bioreactor using the novel strain Aspergillus sp. CM96 in this study. This pectinase’s activity can reach 17,000 U·g⁻¹ after fermentation with a tray bioreactor, an increase of 86% compared to that obtained using flask liquid fermentation. The pectinase was purified and its characteristics were explored. Additionally, during pectinase fermentation, the activities of protease, glucanase, and cellulase were also determined to reach 7000, 8000, and 3000 U·g⁻¹. The enzyme mixture was used to improve substrate digestion efficiency in 144 Soviet white pigs after adding a 0.05% cocktail enzyme for 38 days. The results showed that the average daily gain (ADG) increased by 139.41 ± 1.04 g·day⁻¹, while the average daily feed intake (ADFI) and the feed conversion rate (FCR) decreased by 19.82 ± 1.64 g·day⁻¹ and 0.07 ± 0.01 in 38 days, which indicated that the addition of hyperactive pectinase from the strain CM96 can increase nutrient digestibility and improve feed efficiency. Full article

There are many natural fractures in shale reservoirs, changes in hydraulic fracture extension patterns. In the paper, a multi-fracture extension finite element model for horizontal wells in shale reservoirs under the disturbance of natural fractures is established by combining the actual geological parameters and construction parameters of a horizontal well multi-fracturing operation in X oilfield to analyze the effects of the difference in geostress, elastic modulus, angle of natural fractures, and the number of natural fracture groups on the hydraulic fracture extension. The results show that with the increase in ground stress difference and natural fracture angle, hydraulic fractures are more likely to penetrate the natural fractures; with the increase in elastic modulus, the fracture stress and tip stress increase, the volume of rupture unit, the fracture extension width and the pore pressure concentration area decrease, and it is easy to form a long and narrow fracture; with the increase in the number of fracture groups, the connectivity of reservoir fractures increases, the extension of fractures is stronger, and it is easier to form a complex fracture network. Full article

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO₂ emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC’s environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system’s technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO₂ generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region. Full article

This study investigated the characteristics of radiofrequency, middle-pressure argon plasma used in the atomic layer deposition (ALD) of Al₂O₃ films. Based on the electrical characteristics—the current, voltage, and phase shift between them—and the stability of the plasma plume, the optimum plasma power, allowing reliable switching on of the plasma for any step of an ALD cycle, was determined. Spectral measurements were performed to determine the gas temperature and reactive species that could be important in the ALD process. The density of metastable argon atoms was estimated using tunable laser absorption spectroscopy. It was concluded that plasma heating of substrates did not affect film growth. The crystallization-enhancing effect of plasma observed in these experiments was due to the action of OH radicals produced in the plasma. Full article

Nanomaterials play a beneficial role in enhancing the rheological behavior of fracturing (frac) fluid by reacting with intermolecular structures. The inclusion of these materials into the fluid improves its stability, increases the viscosity of polymers, and enhances its resistance to high temperature and pressure. In this investigation, multi-walled carbon nanotubes (CNTs), nano-zinc oxides (N-ZnO), and nano-copper oxides (N-CuO) have been utilized to ameliorate the rheological properties of water-based fracturing fluid. Different concentrations of these aforementioned nanomaterials were prepared to determine their effects on the rheological behavior of the fluid. The results revealed that the size of nanoparticles ranged from 10 to 500 nm, 300 nm, and 295 nm for CNTs, N-ZnO, and N-CuO, respectively. Moreover, employing CNTs exhibited a resistance of 550 cp at 25 °C and reached 360 cp at 50 °C with a CNT concentration of 0.5 g/L. In contrast, N-CuO and N-ZnO showed a resistance of 206 cp at 25 °C and significantly decreased to 17 cp and 16 cp with higher concentrations of 10 g/L and 1 g/L, respectively. Based on these findings, this study recommends utilizing CNTs to enhance fracking fluid’s chemical and physical properties, which need to be highly viscous and stable under reservoir conditions. Full article

Total organic carbon (TOC) content is a key indicator for determining the hydrocarbon content of shale. The current model for calculating the TOC content of shale is relatively simplistic, the modeling process is cumbersome, and the parameters involved are influenced by subjective factors, which have certain shortcomings. To address this problem, a time-domain convolutional neural network (TCN) model for predicting total organic carbon content based on logging sequence information was established by starting from logging sequence information, conducting logging parameter sensitivity analysis experiments, prioritizing logging-sensitive parameters as model feature vectors, and constructing a TCN network. Meanwhile, to overcome the problem of an insufficient sample size, a five-fold cross-validation method was used to train the TCN model and obtain the weight matrix with the minimum error, and then a shale reservoir TOC content prediction model based on the TCN model was established. The model was applied to evaluate the TOC logging of the Lianggaoshan Formation in the Sichuan Basin, China, and the predicted results were compared with the traditional ΔlogR model. The results indicate that the TCN model predicts the TOC content more accurately than the traditional model, as demonstrated by laboratory tests. This leads to a better application effect. Additionally, the model fully explores the relationship between the logging curve and the total organic carbon content, resulting in improved accuracy of the shale TOC logging evaluation. Full article

The Marangoni effect involves a mass transfer along an interface between two phases owing to the gradient of the interfacial tension. The flow caused by this phenomenon is called Marangoni convection, a complex phenomenon that involves mass transfer processes, such as surfactant adsorption/desorption processes, solvent dissolution phenomena, and viscous dissipation processes. Therefore, the strength of the convection depends on the various thermodynamic and physical properties of the fluids. In this study, we experimentally investigated the relationship between the Marangoni convection generated inside a hanging oil droplet and the interfacial tension of the oil droplet in an aqueous phase by the particle image velocimetry method. This convection velocity depended on the initial value of the interfacial tension in the oil–water interfacial tension oscillation phenomenon accompanied by the expansion and contraction of the hanging drop. Additionally, the droplet oscillation frequency decreased as the Marangoni convection velocity increased. Furthermore, continuous convection, which is unlike Marangoni convection, was observed within this spontaneously expanding and contracting hanging-droplet system. This buoyant convection was caused by the mutual dissolution of the hanging-droplet oil phase and the surrounding aqueous phase. Full article

With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy. Full article

Large-scale volumetric fracturing is generally used during shale gas development. The return rate of fracturing fluid is low, and a large amount of slickwater is retained in the reservoir. The adsorption and desorption of partially hydrolyzed polyacrylamide (HPAM), an additive commonly used in slickwater, on the surface of shale was studied using Longmaxi shale from the Sichuan Basin. The experimental results showed that the mass ratio of the HPAM solution to shale reached saturation adsorption at 20:1 when the concentration of HPAM solution was 1000 mg/L and 25:1 when the concentration of HPAM solution was 500 mg/L. The mass ratio of the HPAM solution to shale was fixed at 25:1, and the adsorption equilibrium was reached at a HPAM concentration of 1000 mg/L when the aqueous solution temperature was 30 °C and 800 mg/L when the aqueous solution temperature was 60 °C. The Langmuir adsorption model yielded a better fit than the Freundlich adsorption model. The adsorption equilibrium time at 30 °C was at 60 min for a HPAM concentration of 500 mg/L, while for a concentration of 1000 mg/L, it was at 90 min. The adsorption equilibrium time at 60 °C was 40 min for a HPAM concentration of 500 mg/L, whereas it was 60 min for a HPAM concentration at 1000 mg/L. The pseudo-second order (PSO) kinetics model yielded better fits than the pseudo-first order (PFO) kinetics model. The adsorption of HPAM on shale was strong, and the adsorbed HPAM resembled cobwebs adhering to the shale surface. HPAM on the surface of shale after adsorption was able to resist the desorption capacity of water. However, when the amount of adsorbed HPAM on shale increased significantly, the amount of residual HPAM on the surface of the shale decreased rapidly during desorption in deionized water. The desorption of HPAM on the shale surface followed a modified desorption model. The higher the concentration of HPAM adsorbed on the shale surface was, the easier it was to desorb and the easier it was to be removed from the shale. Full article

In this paper, a waste heat recovery compound system for series hybrid electric vehicles is established. The existing components of vehicle air conditioning are used in the organic Rankine cycle (ORC) to realize miniaturization. The waste heat recovery compound system is constructed using GT-SUITE, and the objective of the analysis is to increase the power output and engine thermal efficiency increase ratio (ETEIR). The effects of the expander speed, pump speed, working fluid mass flow rate, and working fluid type on the waste heat recovery compound system are analyzed. The simulation results show that the optimal schemes for the ORC system and compound system corresponding to the expander speed and pump speed are 1000 pm, 2500 rpm, 1200 rpm, and 2500 rpm, respectively. Compared with the ORC system, the maximum power output of the compound system with the same working fluid in three states (1500 rpm, 2500 rpm, and 3500 rpm) of the engine is increased by 21.67%, 24.05%, and 28.23%, respectively. Working fluid supplies of 0.4 kg/s, 0.4 kg/s, and 0.6 kg/s in the three engine states are also considered the best solutions. The working fluid R1234yf and R1234ze are the preferred choices for a waste heat recovery compound system, which have a high system power output and ETEIR and are environmentally friendly. Full article

An efficient and accurate method for optimizing capsule-type plate heat exchangers is proposed in this paper. This method combines computational fluid dynamics simulation, a backpropagation algorithm and multi-objective optimization to obtain better heat transfer performance of heat exchanger structures. For plate heat exchangers, the heat transfer coefficient j and friction coefficient f are a pair of contradictory objectives. The optimization of capsule-type plate heat exchangers is a multi-objective optimization problem. In this paper, a backpropagation neural network was used to construct an approximate model. The plate shape was optimized by a multi-objective genetic algorithm. The optimized capsule-type plate heat exchanger has lower flow resistance and higher heat exchange efficiency. After optimization, the heat transfer coefficient is increased by 8.3% and the friction coefficient is decreased by 14.3%, and the heat transfer effect is obviously improved. Further, analysis of flow field characteristics through field co-ordination theory provides guidance for the further optimization of plates. Full article

This article explores the impact of operating parameters on the classification efficiency of a rotor classifier. Based on the experimental data of calcined petroleum coke classification, a single-factor experimental analysis is conducted to find the relationship between operating parameters and classification performance. The cut size becomes progressively smaller as the rotor speed and feeding speed increase, and progressively larger as the inlet air volume increases. Newton’s classification efficiency and classification accuracy decreased with the increase in feeding speed. The range analysis of the orthogonal experiment shows that the rotor speed and inlet air volume have significant effects on the classification performance, but the effect of feed speed is relatively weak. In addition, the optimal combination of operating parameters is obtained by optimizing the operating parameters. Newton’s classification efficiency under this combination is estimated, and the estimated value is 82%. The verification experiment reveals that the Newton’s classification efficiency is 83.5%, which is close to the estimated value. Meanwhile, the classification accuracy is 0.626. This study provides theoretical guidance for the industrial production of calcined petroleum coke and accumulates basic experimental data for the development of air classifiers. Full article

The parameters of coalbed methane reservoirs have large differences, and the precise values cannot represent the resource and production characteristics of the whole block. In order to address these problems, an index system for evaluating the production potential of coalbed methane blocks was constructed, the weights of evaluation parameters were determined, and a model for the preferential selection of coalbed methane blocks based on the subjective–objective combination of weights method was established. The main coal seams (No. 2-1 and No. 4-2) of the Pingdingshan-Shoushan I Mine Block were taken as the research objects to rank the development potential of CBM blocks in a preferential way. The results show that the six resource and production parameters of No. 2-1 coal are gas content, top and bottom rock properties, coal seam thickness, coal seam depth, coal body structure, and tectonic conditions, in descending order of importance, and the parameters of No. 4-2 coal are gas content, coal body structure, coal seam thickness, top and bottom rock properties, coal seam depth, and tectonic conditions, in descending order of importance. It is predicted that the favorable CBM gas development sweet spot areas of the No. 2-1 coal seam and No. 4-2 coal seam will be located along the exploration wells W15–W29 and W31, respectively. This paper aims to make a multi-dimensional and more comprehensive evaluation of coalbed methane mining potential in the Shoushan I mine, and provide a technical basis for the next step of coalbed methane mining in the study area. Full article

To address the issue of low accuracy in detecting defects of battery cell casings with low space ratio and small object characteristics, the low space ratio feature and small object feature are studied, and an object detection algorithm based on dual-coordinate attention and small object loss feedback is proposed. Firstly, the EfficientNet-B1 backbone network is employed for feature extraction. Secondly, a dual-coordinate attention module is introduced to preserve more positional information through dual branches and embed the positional information into channel attention for precise localization of the low space ratio features. Finally, a small object loss feedback module is incorporated after the bidirectional feature pyramid network (BiFPN) for feature fusion, balancing the contribution of small object loss to the overall loss. Experimental comparisons on a battery cell casing dataset demonstrate that the proposed algorithm outperforms the EfficientDet-D1 object detection algorithm, with an average precision improvement of 4.23%. Specifically, for scratches with low space ratio features, the improvement is 13.21%; for wrinkles with low space ratio features, the improvement is 9.35%; and for holes with small object features, the improvement is 3.81%. Moreover, the detection time of 47.6 ms meets the requirements of practical production. Full article

Driven by growing concerns about food supply and the environment, research on alternative protein sources has become increasingly important. In this context, de-oiled seed cakes, particularly soybean cakes, have emerged as a promising option. However, the conventional methods, such as organic solvent extraction, from which these cakes are obtained present several limitations. This study aims to evaluate the efficiency of supercritical fluid extraction (SFE) as an alternative method for de-oiling soybean seeds and obtaining related protein isolates. By using SFE for de-oiling, it was possible to achieve 19% more protein isolates from soybean cakes than the conventional de-oiling method using hexane. Moreover, protein isolates from the SFE de-oiled cake reported significantly improved (p < 0.05) emulsifying abilities and water absorption capacity. Gel electrophoresis and differential scanning calorimetry indicated the presence of a higher concentration of proteins in their native state in the SFE de-oiled flour. Finally, results from the sulfhydryl group content, surface hydrophobicity, and protein dispersibility index also supported these conclusions. The SFE process produced de-oiled soybean cakes with superior functional characteristics and lower environmental impact. Thus, this study provided important information for the food industry to develop more sustainable and healthier production methods. Full article

The equipment in a factory will gradually deteriorate during production, leading to the production of defective products. Without appropriate maintenance, the defect rate will increase over time. Consequently, the production cost will rise, the inventory quality will be affected, the profit will decrease, and the risk of carbon emissions will increase, leading to more customer complaints and damaging the corporate image. In addition to focusing on preventive maintenance to ensure the quality of products, companies should also take carbon emissions into consideration. Furthermore, the frequency of maintenance must be carefully considered, as both carbon emissions and maintenance costs will increase if the frequency is too high; conversely, if the maintenance frequency is too low or non-existent, the defect rate may increase cumulatively, or production may be suspended due to equipment failure. Therefore, this research explores preventive maintenance and inventory management issues within an imperfect production system and develops an extended economic production quantity model that incorporates defective products as well as taking carbon tax and preventive maintenance into consideration. The main purpose is to determine the optimal maintenance frequency, production, and replenishment cycle length, so as to maximize the total profit under the carbon tax policy. This study demonstrates a computing process with relatively impractical product data based on the actual business situation of a disposable diaper manufacturer. Furthermore, a sensitivity analysis is implemented to the model parameters in the proposed model. The managemental insights are illustrated based on the results of theoretical analysis to provide a reference to policy makers during decision making, hence, to secure the sustainability and green transitions of corporates. The results of this study not only help to reduce environmental impact but can also improve the competitiveness and sustainable development of enterprises. Full article

Heat-transfer enhancement and entropy generation were investigated for a double-helical-type-channel heat sink with different rib structures set on the upper wall. Based on available experimental data, a series of simulations with various turbulence models were conducted to find the best numerical model. Five different rib structures were considered, which were diamond (FC-DR), rectangular (FC-RR), drop-shaped (FC-DSR), elliptic (FC-ER) and frustum (FC-FR). The research was carried out under turbulent flow circumstances with a Reynolds number range of 10,000–60,000 and a constant heat-flow density. The numerical results show that the thermal performance of the flow channel set with a rib structure is better than that of the smooth channel. FC-ER offers the lowest average temperature and the highest temperature uniformity, with a Nusselt number improvement percentage ranging from 15.80% to 30.77%. Overall, FC-ER shows the most excellent performance evaluation criteria and lowest augmentation entropy-generation number compared with the other reinforced flow channels. Full article

The importance of biocatalysis for ecologically sustainable syntheses in the chemical industry and for applications in everyday life is increasing. To design efficient applications, it is important to know the related enzyme kinetics; however, the measurement is laborious and error-prone. Flow reactors are suitable for rapid reaction parameter screening; here, a novel workflow is proposed including digital image processing (DIP) for the quantification of product concentrations, and the use of structured data acquisition with EnzymeML spreadsheets combined with ontology-based semantic information, leading to rapid and smooth data integration into a simulation tool for kinetics evaluation. One of the major findings is that a flexibly adaptive ontology is essential for FAIR (findability, accessibility, interoperability, reusability) data handling. Further, Python interfaces enable consistent data transfer. Full article