Journal Description
Processes
Processes
is an international, peer-reviewed, open access journal on processes/systems in chemistry, biology, material, energy, environment, food, pharmaceutical, manufacturing, automation control, catalysis, separation, particle and allied engineering fields published monthly online by MDPI. The Systems and Control Division of the Canadian Society for Chemical Engineering (CSChE S&C Division) and the Brazilian Association of Chemical Engineering (ABEQ) are affiliated with Processes and their members receive discounts on the article processing charges. Please visit Society Collaborations for more details.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, Inspec, AGRIS, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Chemical) / CiteScore - Q2 (Chemical Engineering (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 13.7 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.5 (2022);
5-Year Impact Factor:
3.4 (2022)
Latest Articles
Investigating Salt Precipitation in Continuous Supercritical Water Gasification of Biomass
Processes 2024, 12(5), 935; https://doi.org/10.3390/pr12050935 - 03 May 2024
Abstract
The formation of solid deposits in the process of supercritical water gasification (SCWG) is one of the main problems hindering the commercial application of the process. Seven experiments were conducted with the grass Reed Canary Grass with different preheating temperatures, but all ended
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The formation of solid deposits in the process of supercritical water gasification (SCWG) is one of the main problems hindering the commercial application of the process. Seven experiments were conducted with the grass Reed Canary Grass with different preheating temperatures, but all ended early due to the formation of solid deposits (maximum operation of 3.8 h). The position of solid deposits in the lab plant changed with the variation in the temperature profile. Since the formation of solid deposits consisting of salts, coke, and corrosion products is a severe issue that needs to be resolved in order to enable long-time operation, inner temperature measurements were conducted to determine the temperature range that corresponds with the zone of solid formation. The temperature range was found to be 400 to 440 °C. Wherever this temperature was first reached solid deposits occurred in the system that led to blockage of the flow. Additional to the influence of the temperature, the influence of the flow direction (up-flow or down-flow) on the operation of the continuous SCWG plant was examined. If salts are not separated from the system sufficiently, up-flow reactors should be avoided because they amplify the accumulation of solid deposits leading to a shortened operation time. The heating concept coupled with the salt separation needs to be redesigned in order to separate the salts before entering the gasification reactors. Outside of the determined temperature zone no deposition was visible. Thus, even though the gasification efficiency was low it could be shown that the operation was limited to the deposits forming in the heating section and not by incomplete gasification in the reactor where T > 600 °C.
Full article
(This article belongs to the Special Issue Supercritical Technology Applied to Food, Pharmaceutical and Chemical Industries—2nd Edition)
Open AccessArticle
Influence of a Long Flexible Fiber on the Transport Capability of a Non-Clogging Pump
by
Jing Liu, Jingwei Xu, Suguo Zhuang and Kai Wang
Processes 2024, 12(5), 934; https://doi.org/10.3390/pr12050934 - 03 May 2024
Abstract
During the operation of non-clogging pumps, the flexible long fiber is prone to clogging and winding during the flow process, which can result in damage to the non-clogging pump, so a numerical simulation method of a solid–liquid two-phase flow in a non-clogging pump
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During the operation of non-clogging pumps, the flexible long fiber is prone to clogging and winding during the flow process, which can result in damage to the non-clogging pump, so a numerical simulation method of a solid–liquid two-phase flow in a non-clogging pump with a flexible long fiber is proposed in this paper. The unsteady numerical simulation of the two-phase flow of a single flexible fiber with different densities, lengths and diameters in a double-blade non-clogging pump was carried out to study the influence of fiber parameters on fiber transport capability. The results show that at a density of 920 kg/m3, 300 kg/m3 and 732 kg/m3, the transport capability of flexible fibers decreases successively, and the transport time T0 is 0.32 s, 0.36 s and 0.48 s, respectively. The transport capability of flexible fibers with a length of 150 mm, 200 mm and 250 mm decreases successively, and the transport time T0 is 0.34 s, 0.48 s and 0.96 s, respectively. The transport time T0 is 0.48 s when the fiber diameter dp is 5 mm. When the fiber diameter dp is 7.5 mm, the transport time T0 is 0.51 s. When the fiber diameter dp is 10 mm, the fiber transport capability of the non-clogging pump decreased significantly, and the transport time T0 is 0.68 s. The fiber length has the most obvious effect on fiber transport capability, followed by the fiber diameter and fiber density.
Full article
(This article belongs to the Section Advanced Digital and Other Processes)
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Open AccessArticle
Plasma Technology Applied to Improve Wettability for Emerging Mycelium-Based Materials
by
Paz Aragón Chivite, Núria Portolés Gil, Ruth Garcia Campà, Lorenzo Bautista Pérez and Paula Félix de Castro
Processes 2024, 12(5), 933; https://doi.org/10.3390/pr12050933 - 03 May 2024
Abstract
Plasma technology is increasing its applications in the textile industry for conferring surface functionalities through greener processes. In this study, plasma treatments are studied to improve the wettability of mycelium-based material, an emerging material with a lot of potential in the near future.
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Plasma technology is increasing its applications in the textile industry for conferring surface functionalities through greener processes. In this study, plasma treatments are studied to improve the wettability of mycelium-based material, an emerging material with a lot of potential in the near future. The plasma effect was characterized by assessing the added functionality (wettability) and inspecting surface modifications with different techniques, such as scanning electron microscopy (SEM) and X-Ray photoelectron spectroscopy (XPS). Low pressure plasma (LPP) treatments were successfully applied into the mycelium-based material and optimal power of discharge and treatment time were set for this material (750 W, 17.5 min). With the optimized LPP treatments, the water absorption capacity of mycelium-based material was improved by 2000% and some surface morphological modifications were observed by SEM analysis. On the other hand, XPS analysis demonstrated how the plasma treatment changes the surface composition.
Full article
(This article belongs to the Special Issue Microscale Processing with Non-thermal Plasma Discharges and Its Application)
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Open AccessArticle
Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data
by
Minjing Chen, Zhan Qu, Wei Liu, Shanjie Tang, Zhengkai Shang, Yanfei Ren and Jinliang Han
Processes 2024, 12(5), 932; https://doi.org/10.3390/pr12050932 - 03 May 2024
Abstract
The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual
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The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual field applications. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some limitations. Only learning from data leads to the poor generalization ability and anti-interference ability of prediction models. To solve this problem, a production prediction method of tight gas wells based on the decline curve and data-driven neural network is established in this paper. Based on the actual production data of fractured horizontal wells in three tight gas reservoirs in the Ordos Basin, the prediction effect of the Arps decline curve model, the SPED decline curve model, the MFF decline curve model, and the combination of the decline curve and data-driven neural network model is compared and analyzed. The results of the case analysis show that the MFF model and the combined data-driven model have the highest accuracy, the average absolute percentage error is 14.11%, and the root-mean-square error is 1.491, which provides a new method for the production prediction of tight gas wells in the Ordos Basin.
Full article
(This article belongs to the Special Issue Advanced Reservoir Simulation and Modelling, Thermal and Enhanced Oil Recovery Processes)
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Open AccessArticle
SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion
by
Hongli Li, Zhiqi Yi, Liye Mei, Jia Duan, Kaimin Sun, Mengcheng Li, Wei Yang and Ying Wang
Processes 2024, 12(5), 931; https://doi.org/10.3390/pr12050931 - 02 May 2024
Abstract
The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited
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The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similarity among inter-class features. Moreover, limited computing capability makes it difficult for small and medium-sized enterprises to deploy and utilize networks effectively. Therefore, we propose a novel lightweight steel detection network (SCFNet), which is based on spatial channel reconstruction and deep feature fusion. The network adopts a lightweight and efficient feature extraction module (LEM) for multi-scale feature extraction, enhancing the capability to extract blurry features. Simultaneously, we adopt spatial and channel reconstruction convolution (ScConv) to reconstruct the spatial and channel features of the feature maps, enhancing the spatial localization and semantic representation of defects. Additionally, we adopt the Weighted Bidirectional Feature Pyramid Network (BiFPN) for defect feature fusion, thereby enhancing the capability of the model in detecting low-contrast defects. Finally, we discuss the impact of different data augmentation methods on the model accuracy. Extensive experiments are conducted on the NEU-DET dataset, resulting in a final model achieving an mAP of 81.2%. Remarkably, this model only required 2.01 M parameters and 5.9 GFLOPs of computation. Compared to state-of-the-art object detection algorithms, our approach achieves a higher detection accuracy while requiring fewer computational resources, effectively balancing the model size and detection accuracy.
Full article
(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)
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Open AccessArticle
YOLOv8-LMG: An Improved Bearing Defect Detection Algorithm Based on YOLOv8
by
Minggao Liu, Ming Zhang, Xinlan Chen, Chunting Zheng and Haifeng Wang
Processes 2024, 12(5), 930; https://doi.org/10.3390/pr12050930 - 02 May 2024
Abstract
In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining production safety and equipment reliability. This research introduces an improved bearing
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In industrial manufacturing, bearings are crucial for machinery stability and safety. Undetected wear or cracks can lead to severe operational and financial setbacks. Thus, accurately identifying bearing defects is essential for maintaining production safety and equipment reliability. This research introduces an improved bearing defect detection model, YOLOv8-LMG, which is based on the YOLOv8n framework and incorporates four innovative technologies: the VanillaNet backbone network, the Lion optimizer, the CFP-EVC module, and the Shape-IoU loss function. These enhancements significantly increase detection efficiency and accuracy. YOLOv8-LMG achieves a [email protected] of 86.5% and a [email protected]–0.95 of 57.0% on the test dataset, surpassing the original YOLOv8n model while maintaining low computational complexity. Experimental results reveal that the YOLOv8-LMG model boosts accuracy and efficiency in bearing defect detection, showcasing its significant potential and practical value in advancing industrial inspection technologies.
Full article
(This article belongs to the Special Issue Fault Diagnosis Process and Evaluation in Systems Engineering)
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Open AccessEditorial
Special Issue Entitled “Immune Regulatory Properties of Natural Products”
by
Jai-Eun Kim and Wansu Park
Processes 2024, 12(5), 929; https://doi.org/10.3390/pr12050929 - 02 May 2024
Abstract
Although the immunomodulatory effects of natural products have not yet been completely elucidated, attempts to use natural products in the treatment of immune-mediated inflammatory diseases such as autoimmune diseases, chronic inflammatory diseases, mutant viral infections, and even immunosenescence-related cancers are ongoing [...]
Full article
(This article belongs to the Special Issue Immune Regulatory Properties of Natural Products)
Open AccessArticle
Growth Substrate Geometry Optimization for the Productive Mechanical Dry Transfer of Carbon Nanotubes
by
Andre Butzerin, Sascha Weikert and Konrad Wegener
Processes 2024, 12(5), 928; https://doi.org/10.3390/pr12050928 - 01 May 2024
Abstract
The selection of growth substrate geometries for the mechanical dry transfer of carbon nanotubes to device substrates depends on the precision of the assembly equipment. Since these geometries play a decisive role in the overall efficiency of the process, an investigation of the
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The selection of growth substrate geometries for the mechanical dry transfer of carbon nanotubes to device substrates depends on the precision of the assembly equipment. Since these geometries play a decisive role in the overall efficiency of the process, an investigation of the most important geometry parameters is carried out. The substrate geometry affects the number of carbon nanotubes suspended during the growth process and the speed of mechanical assembly at the same time. Since those two criteria are interlinked and affect productivity, a meta-model for the growth and selection of the nanotubes is simulated and a time study of the resulting assembly motions is subsequently performed. The geometry parameters are then evaluated based on the total number of suspended carbon nanotubes and the throughput rate, measured in transfers per hour. The accuracy specifications are then taken into account. Depending on the overall accuracy that can be achieved, different offset angles and overlaps between the growth and receiving substrate can be reached, which affect productivity differently for different substrate geometries. To increase the overall productivity, growth substrate designs are adapted to allow fully automated operation. This measure also reduces the frequency of substrate exchanges once all carbon nanotubes have been harvested. The introduction of substrates with multiple, polygonally arranged edges increases the total number of nanotubes that can be harvested. The inclusion of polygonally arranged edges in the initial analysis shows a significant increase in overall productivity.
Full article
(This article belongs to the Special Issue Micro/Nano Manufacturing Processes: Theories and Optimization Techniques)
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Open AccessFeature PaperReview
Textiles for Very Cold Environments
by
Tomasz Blachowicz, Maciej Malczyk, Ilda Kola, Guido Ehrmann, Eva Schwenzfeier-Hellkamp and Andrea Ehrmann
Processes 2024, 12(5), 927; https://doi.org/10.3390/pr12050927 - 01 May 2024
Abstract
Textiles are often used to protect people from cold environments. While most garments are designed for temperatures not far below 0 °C, very cold regions on the earth near the poles or on mountains necessitate special clothing. The same is true for homeless
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Textiles are often used to protect people from cold environments. While most garments are designed for temperatures not far below 0 °C, very cold regions on the earth near the poles or on mountains necessitate special clothing. The same is true for homeless people who have few possibilities to warm up or workers in cooling chambers and other cold environments. Passive insulating clothing, however, can only retain body heat. Active heating, on the other hand, necessitates energy, e.g., by batteries, which are usually relatively heavy and have to be recharged regularly. This review gives an overview of energy-self-sufficient textile solutions for cold environments, including energy harvesting by textile-based or textile-integrated solar cells; piezoelectric sensors in shoes and other possibilities; energy storage in supercapacitors or batteries; and heating by electric energy or phase-change materials.
Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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Open AccessArticle
Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling
by
Nurullah Güleç and Özgür Kabak
Processes 2024, 12(5), 926; https://doi.org/10.3390/pr12050926 - 01 May 2024
Abstract
This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization
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This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.
Full article
(This article belongs to the Section Energy Systems)
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Open AccessArticle
Study on the Damage Mechanism of Coal under Hydraulic Load
by
Hongyan Li, Yaolong Li, Weihua Wang, Yang Li, Zhongxue Sun, Shi He and Yongpeng Fan
Processes 2024, 12(5), 925; https://doi.org/10.3390/pr12050925 - 01 May 2024
Abstract
Hydraulic fracturing is extensively utilized for the prevention and control of gas outbursts and rockbursts in the deep sections of coal mines. The determination of fracturing construction parameters based on the coal seam conditions and stress environments merits further investigation. This paper constructs
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Hydraulic fracturing is extensively utilized for the prevention and control of gas outbursts and rockbursts in the deep sections of coal mines. The determination of fracturing construction parameters based on the coal seam conditions and stress environments merits further investigation. This paper constructs a damage analysis model for coal under hydraulic loads, factoring in the influence of the intermediate principal stress, grounded in the unified strength theory analysis approach. It deduces the theoretical analytical equation for the damage distribution of a coal medium subjected to small-flow-rate hydraulic fracturing in underground coal mines. Laboratory experiments yielded the mechanical parameters of coal in the study area and facilitated the fitting of the intermediate principal stress coefficient. Leveraging these datasets, the study probes into the interaction between hydraulic loads and damage radius under assorted influence ranges, porosity, far-field crustal stresses, and brittle damage coefficients. The findings underscore that hydraulic load escalates exponentially with the damage radius. Within the variable range of geological conditions in the test area, the effects of varying influence range, porosity level, far-field stress, and brittle damage coefficient on the outcomes intensify one by one; a larger hydraulic load diminishes the impact of far-field stress variations on the damage radius, inversely to the influence range, porosity, and brittle damage. The damage radius derived through the gas pressure reduction method in field applications corroborates the theoretical calculations, affirming the precision of the theoretical model. These findings render pivotal guidance for the design and efficacy assessment of small-scale hydraulic fracturing in underground coal mines.
Full article
(This article belongs to the Special Issue Monitoring, Process Control, Simulation, and Optimization in Coal Mining)
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Open AccessArticle
Human–Robot Cooperation Control Strategy Design Based on Trajectory Deformation Algorithm and Dynamic Movement Primitives for Lower Limb Rehabilitation Robots
by
Jie Zhou, Yao Sun, Laibin Luo, Wenxin Zhang and Zhe Wei
Processes 2024, 12(5), 924; https://doi.org/10.3390/pr12050924 - 01 May 2024
Abstract
Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of
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Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI.
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(This article belongs to the Section Automation Control Systems)
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Open AccessFeature PaperArticle
Influence of Interfacial Tribo-Chemical and Mechanical Effect on Tribological Behaviors of TiN Film in Different Environments
by
Yu Cao, Guizhi Wu, Yunfeng Wang, Yongjun Li and Huijing Xu
Processes 2024, 12(5), 923; https://doi.org/10.3390/pr12050923 - 30 Apr 2024
Abstract
A series of experiments has been conducted to investigate the tribological properties of a TiN film sliding against GCr15 steel balls in ambient air, low vacuum and high vacuum environments. Various friction loads and sliding velocities were also applied. The TiN film displays
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A series of experiments has been conducted to investigate the tribological properties of a TiN film sliding against GCr15 steel balls in ambient air, low vacuum and high vacuum environments. Various friction loads and sliding velocities were also applied. The TiN film displays a steady-state friction stage after the running-in stage in all the above environments, while the durations of running-in stages are different. The steady-state friction coefficients of the TiN film were around 0.56 in ambient air and 0.3 in the high vacuum environment (1 × 10−5 mbar). In the low vacuum (1 × 10−2 mbar) environment, a low friction coefficient (around 0.19) was attained for all the friction tests on TiN film, irrespective of the applied load and sliding velocity. In the meantime, it was noticed that the applied loads and the sliding velocities would change the duration of the running-in stage before reaching the low friction coefficient. It is revealed by the analysis of wear tracks that the metal oxides induced by the tribo-chemical effect at the friction interface play an important role in affecting the tribological behaviors of the TiN films in different environments. The Raman results show that the main component of the metal oxides is hematite (α-Fe2O3), and the amount of iron oxide is related to the friction environment. The composition and quantity of iron oxides produced by the interfacial tribo-chemical effect affect the tribological behavior. The results also show that the mechanical wear process at the friction interface displays a polishing effect, which would reduce the surface roughness. The mechanical wear performance varies under different loads and velocities. The tribological tests results indicate that the interfacial tribo-chemical effect and mechanical wear process should be considered together rather than individually to interpret the tribological behaviors of TiN films in different environments.
Full article
(This article belongs to the Special Issue Latest Research on Advanced Material Surface Treatment Processing)
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Open AccessArticle
Innovative Plant-Derived Biomaterials for Sustainable and Effective Removal of Cationic and Anionic Dyes: Kinetic and Thermodynamic Study
by
El Mokhtar Saoudi Hassani, Dounia Azzouni, Mohammed M. Alanazi, Imane Mehdaoui, Rachid Mahmoud, Atul Kabra, Abdeslam Taleb, Mustapha Taleb and Zakia Rais
Processes 2024, 12(5), 922; https://doi.org/10.3390/pr12050922 - 30 Apr 2024
Abstract
The aim of this study is to purify industrial textile effluents by treating two types of commonly encountered dyes: blue maxilon (BM), of cationic nature, and black eriochrome (NE), of anionic nature. We intend to employ an innovative approach based on the adsorption
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The aim of this study is to purify industrial textile effluents by treating two types of commonly encountered dyes: blue maxilon (BM), of cationic nature, and black eriochrome (NE), of anionic nature. We intend to employ an innovative approach based on the adsorption of these dyes onto a novel vegetal biomaterial derived from Aleppo pine fibers (FPAs). A kinetic and thermodynamic study was conducted. The effect of some physicochemical parameters on both dye adsorption and FPAs was also evaluated. The modeling of the adsorption results was performed using Langmuir, Freundlich, Temkin, and Dubinin Radushkevich (D-R) isotherms. The results indicate that the equilibrium time strongly depends on the initial concentration of the two dyes, being 60 min with pseudo-second-order adsorption kinetics for both dyes. Adsorption isotherms under the optimal conditions of adsorbent mass, temperature, medium pH, and dye concentration were used to determine the maximum adsorption efficiency, which was close to 93% and 98% for BM and NE, respectively. The results also show that the adsorption of both dyes on FPAs fits well with Langmuir’s model. The thermodynamic study indicates that the adsorption of both dyes on FPAs is spontaneous and exothermic in nature for BM and endothermic for NE.
Full article
(This article belongs to the Topic Advanced Processes and Technologies for Wastewater: Collection, Treatment, and Resource)
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Open AccessArticle
Enhancing Data Preservation and Security in Industrial Control Systems through Integrated IOTA Implementation
by
Iuon-Chang Lin, Pai-Ching Tseng, Pin-Hsiang Chen and Shean-Juinn Chiou
Processes 2024, 12(5), 921; https://doi.org/10.3390/pr12050921 - 30 Apr 2024
Abstract
Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional data storage paradigms, tethered to physical hard disks, are fraught with inherent
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Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional data storage paradigms, tethered to physical hard disks, are fraught with inherent susceptibilities, underscoring the pressing need for the deployment of resilient preservation frameworks. This study delves into the transformative potential offered by distributed ledger technology (DLT), with a specific focus on IOTA, within the expansive landscape of the Internet of Things (IoT). Through a meticulous examination of the intricacies inherent to data transmission protocols, we present a novel paradigm aimed at fortifying data security. Our approach advocates for the strategic placement of IOTA nodes on lower-level devices, thereby streamlining the transmission pathway and curtailing vulnerabilities. This concerted effort ensures the seamless preservation of data confidentiality and integrity from inception to storage, bolstering trust in the convergence of IoT and DLT technologies. By embracing proactive measures, organizations can navigate the labyrinthine terrain of data management, effectively mitigate risks, and cultivate an environment conducive to innovation and progress.
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(This article belongs to the Special Issue Process Automation and Smart Manufacturing in Industry 4.0/5.0)
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Open AccessArticle
Research on the Multifactor Synergistic Corrosion of N80 and P110 Steel Tubing in Shale Gas Wells in Sichuan Basin
by
Yufei Li, Dajiang Zhu, Jian Yang, Qiang Liu, Lin Zhang, Linfeng Lu, Xiangkang Liu and Shuai Wang
Processes 2024, 12(5), 920; https://doi.org/10.3390/pr12050920 - 30 Apr 2024
Abstract
We aimed to investigate the corrosion patterns and the main controlling factors of N80 steel and P110 steel tubing under different sections. Conducting weight loss corrosion experiments for 168 h using high-temperature and high-pressure autoclaves to simulate the corrosion behavior of two types
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We aimed to investigate the corrosion patterns and the main controlling factors of N80 steel and P110 steel tubing under different sections. Conducting weight loss corrosion experiments for 168 h using high-temperature and high-pressure autoclaves to simulate the corrosion behavior of two types of casing materials, N80 steel and P110 steel, in different well sections under specific conditions of CO2 content, chloride ion concentration, temperature, pressure, and sulfate-reducing bacteria population in highly mineralized formation water. X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) were used to analyze the corrosion products, surface morphology, and elemental composition of the two steel pipes. Additionally, 3D microscopy was employed to observe the morphology and measure the dimensions of localized corrosion pits. Under different well sections, the corrosion products formed on N80 steel and P110 steel mainly consist of FeCO3, and crystalline salts of chlorides present in the solution medium. Under low-water-cut conditions, narrow and deep corrosion defects were observed, while narrow and shallow corrosion defects were found under high-water-cut conditions. In the upper wellbore section, both steel pipes exhibited dispersed and thin corrosion product films that suffered from rupture and detachment, resulting in severe localized corrosion. In the middle wellbore section, the corrosion product film on N80 steel comprised irregularly arranged polygonal grains, some of which exhibited significant gaps, leading to extremely severe corrosion. For P110 steel, the corrosion product film was also dispersed and thin, with extensive detachment and extremely severe corrosion. In the lower wellbore section, both steel pipes were covered with a dense layer of grains, with smaller gaps between them, effectively protecting the metal matrix from corrosion. Consequently, the corrosion rate decreased compared to the middle section but still exhibited severe corrosion. In low-water-cut conditions, attention should be given to the risk of column safety due to corrosion from condensate water and CO2, as well as the size of narrow and deep corrosion defects in the middle wellbore section. In high-water-cut conditions, it is recommended to use corrosion inhibitors in combination while focusing on SRB bacteria corrosion in the upper wellbore section, condensate water in the middle section, CO2 content and chloride ion coupling in the lower section, and the size of narrow and shallow corrosion defects causing column safety risks.
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(This article belongs to the Special Issue Advances in Technology for Enhancing Oil and Gas Recovery in Shale Reservoirs)
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Open AccessArticle
Investigation on Synergism and Its Influence Parameters between Coal and Biomass during Co-Gasification Based on Aspen Plus
by
Jinbo Chen, Peng Jiang, Yipei Chen and Shuai Liu
Processes 2024, 12(5), 919; https://doi.org/10.3390/pr12050919 - 30 Apr 2024
Abstract
The co-gasification of coal and biomass offers numerous benefits, including improved gasification efficiency, reduced pollution emissions, and the utilization of renewable resources. However, there is a lack of comprehensive research on the synergistic effects of, and influence parameters on, coal–biomass co-gasification. This study
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The co-gasification of coal and biomass offers numerous benefits, including improved gasification efficiency, reduced pollution emissions, and the utilization of renewable resources. However, there is a lack of comprehensive research on the synergistic effects of, and influence parameters on, coal–biomass co-gasification. This study employs Aspen Plus simulations to investigate the co-gasification behavior of coal and corn straw, focusing on the synergistic effects and the impact of various operating conditions. A synergistic coefficient is defined to quantify the interactions between the feedstocks. Sensitivity analyses explore the effects of gasification temperature (800–1300 °C), coal rank (lignite, bituminous, anthracite), biomass mass fraction (0–50%), oxygen-to-carbon ratio, and steam-to-carbon ratio on the synergistic coefficients of effective syngas content (CO + H2), specific oxygen consumption, specific fuel consumption, and cold gas efficiency. The results reveal an optimal biomass mass fraction of 10% for maximizing cold gas efficiency, with the syngas primarily consisting of H2 (36.8%) and CO (61.6%). Higher gasification temperatures (up to 1200 °C) improve syngas quality and process efficiency, while higher-rank coals exhibit better gasification performance compared to lignite. Optimal oxygen-to-carbon and steam-to-carbon ratios are identified for maximizing syngas yield and quality. These findings provide valuable guidance for the design and optimization of industrial coal–biomass co-gasification processes, enabling the maximization of syngas quality, process efficiency, and resource utilization.
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(This article belongs to the Section Chemical Processes and Systems)
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Open AccessArticle
Optimization of Abnormal Hydraulic Fracturing Conditions of Unconventional Natural Gas Reservoirs Based on a Surrogate Model
by
Su Yang, Jinxuan Han, Lin Liu, Xingwen Wang, Lang Yin and Jianfa Ci
Processes 2024, 12(5), 918; https://doi.org/10.3390/pr12050918 - 30 Apr 2024
Abstract
Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based
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Abnormal conditions greatly reduce the efficiency of hydraulic fracturing of unconventional gas reservoirs. Optimizing the fracturing scheme is crucial to minimize the likelihood of abnormal operational conditions, such as pressure channeling, casing deformation, and proppant plugging. This paper proposes a novel machine learning-based method for optimizing abnormal conditions during hydraulic fracturing of unconventional natural gas reservoirs. Firstly, the main controlling factors of abnormal conditions are selected through a hybrid controlling analysis, upon which a surrogate model is established for predicting the occurrence probability of abnormal conditions, rather than whether abnormal conditions happen or not. Subsequently, a machine learning-based optimization algorithm is developed to minimize the occurrence probability of abnormal conditions, acknowledging their inevitability during the fracturing process. The optimal results demonstrate the proposed method outperforms traditional methods, on average. The proposed methodology is more in line with the needs of practical operation in an environment full of uncertainty.
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(This article belongs to the Special Issue Data-Based Prediction Models in Energy Systems: From Principles to Applications)
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Open AccessArticle
Quantitative Description of Pore and Fracture Distribution Heterogeneity Using Mercury Removal Curve and Applicability of Fractal Models
by
Huasheng Chong, Xiao Liu, Danyang Xi, Junjian Zhang, Veerle Vandeginste, Dongdong Wang and Peng Yao
Processes 2024, 12(5), 917; https://doi.org/10.3390/pr12050917 - 30 Apr 2024
Abstract
Many studies have used fractal theory to characterize pore structure distribution heterogeneity through mercury intake curves. However, there is relatively little research on the fractal model calculation of mercury removal curves. In this study, a high-pressure mercury intrusion test is used to describe
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Many studies have used fractal theory to characterize pore structure distribution heterogeneity through mercury intake curves. However, there is relatively little research on the fractal model calculation of mercury removal curves. In this study, a high-pressure mercury intrusion test is used to describe the pore and fracture distribution heterogeneity (PFDH). The fractal physical meaning of the mercury removal curve was determined by calculating the change in the curve’s fractal dimension value. The results are as follows. (1) According to mercury removal efficiency and porosity, samples can be divided into types A (mercury removal efficiency above 35%) and B (mercury removal efficiency below 35%). In general, type A sample belongs to micro-pore-developed types, and type B samples belong to the macro-pore-developed type. (2) The Menger model (M) represents the complexity of a specific surface area, while the Sierpinski model (S) represents the roughness of the pore volume. Among all the samples, the lower-pore-volume region controls PFDH. (3) According to the calculation results of the single fractal model, it can be seen that the PFDH of type B is stronger than that of type A, which is similar to the results of mercury intrusion. According to the calculation structure of the multifractal model, it can be seen that the volume distribution heterogeneity of type B under various pores is significantly stronger than that of type A. This is opposite to the result of mercury injection. (4) DM has a relationship with the pore volume percentage at different stages, so the M model at the mercury inlet stage can better characterize PFDH at the mercury inlet stage.
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(This article belongs to the Section Chemical Processes and Systems)
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Open AccessArticle
Numerical Investigation on Alkaline-Surfactant-Polymer Alternating CO2 Flooding
by
Weirong Li, Xin Wei, Zhengbo Wang, Weidong Liu, Bing Ding, Zhenzhen Dong, Xu Pan, Keze Lin and Hongliang Yi
Processes 2024, 12(5), 916; https://doi.org/10.3390/pr12050916 - 29 Apr 2024
Abstract
For over four decades, carbon dioxide (CO2) has been instrumental in enhancing oil extraction through advanced recovery techniques. One such method, water alternating gas (WAG) injection, while effective, grapples with limitations like gas channeling and gravity segregation. To tackle the aforementioned
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For over four decades, carbon dioxide (CO2) has been instrumental in enhancing oil extraction through advanced recovery techniques. One such method, water alternating gas (WAG) injection, while effective, grapples with limitations like gas channeling and gravity segregation. To tackle the aforementioned issues, this paper proposes an upgrade coupling method named alkaline-surfactant-polymer alternating gas (ASPAG). ASP flooding and CO2 are injected alternately into the reservoir to enhance the recovery of the WAG process. The uniqueness of this method lies in the fact that polymers could help profile modification, CO2 would miscible mix with oil, and alkaline surfactant would reduce oil–water interfacial tension (IFT). To analyze the feasibility of ASPAG, a couples model considering both gas flooding and ASP flooding processes is established by using the CMG-STARS (Version 2021) to study the performance of ASPAG and compare the recovery among ASPAG, WAG, and ASP flooding. Our research delved into the ASPAG’s adaptability across reservoirs varying in average permeability, interlayer heterogeneity, formation rhythmicity, and fluid properties. Key findings include that ASPAG surpasses the conventional WAG in sweep and displacement efficiency, elevating oil recovery by 12–17%, and in comparison to ASP, ASPAG bolsters displacement efficiency, leading to a 9–11% increase in oil recovery. The primary flooding mechanism of ASPAG stems from the ASP slug’s ability to diminish the interfacial tension, enhancing the oil and water mobility ratio, which is particularly efficient in medium-high permeability layers. Through sensitivity analysis, ASPAG is best suited for mid-high-permeability reservoirs characterized by low crude oil viscosity and a composite reverse sedimentary rhythm. This study offers invaluable insights into the underlying mechanisms and critical parameters that influence the alkaline-surfactant-polymer alternating gas method’s success for enhanced oil recovery. Furthermore, it unveils an innovative strategy to boost oil recovery in medium-to-high-permeability reservoirs.
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(This article belongs to the Section Energy Systems)
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