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
Data-Driven Method for Vacuum Prediction in the Underwater Pump of a Cutter Suction Dredger
Processes 2024, 12(4), 812; https://doi.org/10.3390/pr12040812 - 17 Apr 2024
Abstract
Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of
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Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of the dredger under abnormal working conditions. In this paper, a data-driven method for predicting the vacuum of the underwater pump of the cutter suction dredger (CSD) is proposed with the help of big data, machine learning, data mining, and other technologies, and based on the historical data of “Hua An Long” CSD. The method eliminates anomalous data, standardizes the data set, and then relies on theory and engineering experience to achieve feature extraction using the Spearman correlation coefficient. Then, six machine learning methods were employed in this study to train and predict the data set, namely, lasso regression (lasso), elastic network (Enet), gradient boosting decision tree (including traditional GBDT, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM)), and stacking. The comparison of the indicators obtained through multiple rounds of feature number iteration shows that the LightGBM model has high prediction accuracy, a good running time, and a generalization ability. Therefore, the methodological framework proposed in this paper can help to improve the efficiency of underwater pumps and issue timely warnings in abnormal working conditions.
Full article
(This article belongs to the Special Issue AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design)
Open AccessArticle
Procedure for Aggregating Indicators of Quality and Life-Cycle Assessment (LCA) in the Product-Improvement Process
by
Andrzej Pacana and Dominika Siwiec
Processes 2024, 12(4), 811; https://doi.org/10.3390/pr12040811 - 17 Apr 2024
Abstract
Sustainable product development requires combining aspects, including quality and environmental. This is a difficult task to accomplish. Therefore, procedures are being sought to combine these aspects in the process of product improvement. Therefore, the objective of the investigation was to develop a procedure
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Sustainable product development requires combining aspects, including quality and environmental. This is a difficult task to accomplish. Therefore, procedures are being sought to combine these aspects in the process of product improvement. Therefore, the objective of the investigation was to develop a procedure that supports the integration of quality-level indicators and life-cycle assessment (LCA) to determine the direction of product improvement. The procedure involves determining the quality indicators based on the expectations of the customer, which are subsequently processed using the formalised scoring method (PS). A life-cycle assessment index is determined for the main environmental impact criterion. According to the proposed mathematical model, these indicators are aggregated, and this process takes into account their importance in terms of product usefulness and environmental friendliness. Interpretations of the results and the direction of product improvement are from the results obtained from the modified IPA model (importance–performance analysis). The procedure is used in the verification of product prototypes, wherein the proposed approach, and its test, was carried out for a self-cooling beverage can (and its alternatives) with a “chill-on-demand” system, which is a technology supporting rapid cooling on demand. The life-cycle assessment was carried out to assess the carbon footprint, which is crucial for activities to reduce greenhouse gases. The direction of improvement of this product was shown to concern the selection of transport means, the reduction of energy use in the production phase, or the change of the method of opening the can. What is original is the proposal of a procedure for integrating the quality indicator and the life-cycle assessment indicator, taking into account the key environmental burden. The procedure can be used in manufacturing companies when designing and improving products in terms of their sustainable development.
Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
Open AccessArticle
Properties of Carbonic Anhydrase-Containing Active Coatings for CO2 Capture
by
Xiaobo Li, Rui Zhou, Haoran Yang, Zimu Liang, Yuxiang Yao, Zhipeng Yu, Mingsai Du, Diming Lou and Ke Li
Processes 2024, 12(4), 810; https://doi.org/10.3390/pr12040810 - 17 Apr 2024
Abstract
Carbonic anhydrase (CA)-based biological CO2 capture is emerging as a prominent carbon capture and storage (CCS) technology. We developed a tagged CA–Ferritin chimera, resulting in a high-purity, high-activity, micrometer-sized CA aggregate, SazF, with a yield of 576.6 mg/L (protein/medium). SazF has an
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Carbonic anhydrase (CA)-based biological CO2 capture is emerging as a prominent carbon capture and storage (CCS) technology. We developed a tagged CA–Ferritin chimera, resulting in a high-purity, high-activity, micrometer-sized CA aggregate, SazF, with a yield of 576.6 mg/L (protein/medium). SazF has an optimum temperature of 50 °C and demonstrates thermal stability between 40 and 60 °C. It operates efficiently in Tris–HCl buffer (pH = 8–9), making it compatible with ship exhaust conditions. For enhanced stability and reusability, SazF was encapsulated in SiO2 and integrated into an epoxy resin to produce a corrosion-active coating. This coating, applied to foam metal fillers, showed less than 3% protein leakage after ten days and retained over 70% activity after a month at 60 °C. This simple preparation method and the cost-effective production of these biomaterials that can continuously and efficiently absorb CO2 in high-temperature environments are suitable for most CO2 capture devices. They have a broad application prospect in the field of industrial carbon capture.
Full article
(This article belongs to the Section Environmental and Green Processes)
Open AccessArticle
A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM
by
Ziyi Chen, Weiguo Zhao, Pingping Shen, Chengli Wang and Yanfu Jiang
Processes 2024, 12(4), 809; https://doi.org/10.3390/pr12040809 - 17 Apr 2024
Abstract
To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows
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To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the four flow meters, respectively. Compared with the Bayesian classifier diagnostic algorithm, this has been increased by 4.18%. And compared with support vector machine diagnostic algorithms improved by the SSA, it has increased by 2.28%.
Full article
(This article belongs to the Special Issue AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design)
Open AccessArticle
Study of Acid Fracturing Strategy with Integrated Modeling in Naturally Fractured Carbonate Reservoirs
by
Xusheng Cao, Jichuan Ren, Shunyuan Xin, Chencheng Guan, Bing Zhao and Peixuan Xu
Processes 2024, 12(4), 808; https://doi.org/10.3390/pr12040808 - 17 Apr 2024
Abstract
Natural fractures and wormholes strongly influence the performance of acid fracturing in naturally fractured carbonate reservoirs. This work uses an integrated model to study the effects of treatment parameters in acid fracturing in different reservoir conditions. Hydraulic fracture propagation, wormhole propagation, complex fluid
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Natural fractures and wormholes strongly influence the performance of acid fracturing in naturally fractured carbonate reservoirs. This work uses an integrated model to study the effects of treatment parameters in acid fracturing in different reservoir conditions. Hydraulic fracture propagation, wormhole propagation, complex fluid leak-off mediums, and heat transfer are considered in the modeling. The model is validated in several steps by analytical solutions. The simulation results indicated that natural fractures and wormholes critically impact acid fracturing and can change the predicted outcomes dramatically. The high permeability reservoirs with conductive natural fractures or low permeability reservoirs with natural fracture networks showed the highest stimulation potential in applying acid fracturing technology. The optimal acid injection rate depends on natural fracture geometry and reservoir permeability. This study also observed that obtaining a high production index is difficult because natural fractures and wormholes reduce the acid efficiency during acid fracturing. Building an acid-etched fracture system consisting of acid-etched natural fractures and hydraulic fractures may help us better stimulate the naturally fractured carbonate reservoirs. The paper illustrates a better understanding of the effects of the treatment design parameters on productivity. It paves a path for the optimal design of acid fracturing treatment for heterogeneous carbonate reservoirs.
Full article
(This article belongs to the Special Issue Advanced Fracturing Technology for Oil and Gas Reservoir Stimulation)
Open AccessArticle
Reliability Analysis of Dynamic Sealing Performance in the Radial Hydraulic Drilling Technique
by
Lin Chai, Yongsheng Liu, Guoqiang Chen, Qiang Sun, Wenlong Gao and Zijun Dou
Processes 2024, 12(4), 807; https://doi.org/10.3390/pr12040807 - 17 Apr 2024
Abstract
Traditional coiled tubing radial drilling with the same diameter cannot support deep and ultra-deep wells for high-pressure hydraulic jet drilling due to small diameter and sizeable hydraulic loss over long distances. The novel downhole movable pipe radial hydraulic drilling technique extracts a small
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Traditional coiled tubing radial drilling with the same diameter cannot support deep and ultra-deep wells for high-pressure hydraulic jet drilling due to small diameter and sizeable hydraulic loss over long distances. The novel downhole movable pipe radial hydraulic drilling technique extracts a small diameter high-pressure injection pipe from the (tubing pipe) oil pipe and then drills it horizontally into the formation to form a radial hole. Dynamic sealing is the core of this technology, which achieves high-pressure fluid sealing while ensuring the injection pipe smoothly slides out of the oil pipe. A sealing tool is designed between the tubing and the injection pipe to prevent the leakage of high-pressure fluid. In this paper, the finite element model of the sealing tool was established, and the deformation and stress of the sealing tool under different interference and fluid pressure were simulated and analyzed. The relationship between stress distribution and contact pressure under the corresponding conditions was obtained. The results show that the von Mises stress increases significantly with the increase in fluid pressure under certain interference conditions. When the fluid pressure was 35 MPa, 52 MPa, and 70 MPa, the maximum von Mises stress was 29.65 MPa, 30.87 MPa, and 32.47 MPa, respectively, within a reasonable range. The stress peak area changes simultaneously, indicating that the possible damage location changes with the fluid pressure change. The maximum contact pressure between the sealing ring and the smooth rod increases with interference and fluid pressure, which always meets the sealing conditions. A laboratory test bench was built to test the high-pressure sealing performance of the sealing tool. Combined with the simulation data and test results, the downhole continuous rod dynamic sealing tool was modified to provide theoretical guidance for practical application.
Full article
(This article belongs to the Special Issue Risk Assessment and Reliability Engineering of Process Operations)
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Open AccessFeature PaperArticle
Scaling Fed-Batch and Perfusion Antibody Production Processes in Geometrically Dissimilar Stirred Bioreactors
by
Vivian Ott, Jan Ott, Dieter Eibl and Regine Eibl
Processes 2024, 12(4), 806; https://doi.org/10.3390/pr12040806 - 17 Apr 2024
Abstract
Modern production processes for biopharmaceuticals often work with very high cell densities. Moreover, there is a trend towards moving from fed-batch to continuous perfusion processes; a development that is influencing the requirements for bioreactor design and process control. In this study, the transfer
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Modern production processes for biopharmaceuticals often work with very high cell densities. Moreover, there is a trend towards moving from fed-batch to continuous perfusion processes; a development that is influencing the requirements for bioreactor design and process control. In this study, the transfer of fed-batch and perfusion experiments between different cylindrical stirred lab-scale bioreactors and Thermo Scientific’sTM (Waltham, MA, USA) cubical HyPerformaTM DynaDriveTM Single-Use Bioreactor was investigated. Different scaling parameters were used, which were selected based on the requirements of the respective processes. Peak cell densities of up to 49 × 106 cells mL−1 and antibody titers of up to 5.2 g L−1 were achieved in 15- to 16-day fed-batch experiments. In 50-day perfusion cultivations, a viable cell volume of >100 mm3 mL−1 was maintained and more than 1 g L−1 d−1 of antibodies were harvested. The perfusion processes were automated with both cell bleed control and glucose concentration control. Cell retention was performed using Repligen’s (Waltham, MA, USA) XCell® ATF perfusion systems and single-use devices. In summary, approaches for successfully scaling highly productive fed-batch and perfusion processes between geometrically dissimilar lab and pilot scale bioreactors were demonstrated. The advantages of perfusion in comparison to fed-batch processes were also observed.
Full article
(This article belongs to the Topic Bioreactors: Control, Optimization and Applications - 2nd Volume)
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Open AccessReview
Advancements in Bioelectrochemical Systems for Solid Organic Waste Valorization: A Comprehensive Review
by
Shivani Maddirala, Sudipa Bhadra, Md. Salatul Islam Mozumder, Vijay Kumar Garlapati and Surajbhan Sevda
Processes 2024, 12(4), 805; https://doi.org/10.3390/pr12040805 - 17 Apr 2024
Abstract
Environmental pollution and energy scarcity are the two significant issues that could substantially impede the sustainable growth of our civilization. Microbial fuel cells (MFCs) are an emerging technique for converting the chemical energy of organic wastes directly into electric energy, allowing for both
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Environmental pollution and energy scarcity are the two significant issues that could substantially impede the sustainable growth of our civilization. Microbial fuel cells (MFCs) are an emerging technique for converting the chemical energy of organic wastes directly into electric energy, allowing for both energy recovery and environmental rehabilitation. Solid organic waste decomposition is generally more challenging compared to organic wastewater due to several factors, including the nature of the waste, the decomposition process, and the associated environmental and logistical considerations. With rapid population expansion and acceleration of urbanization, waste generation continues to rise globally, causing complicated environmental, socioeconomic, and energy problems and a growing demand for public health globally. Bioelectrochemical systems (BES) are promising solid waste management options. However, BES may not be the most effective solution on its own for certain types of waste or may be incapable of treating all waste components. In many circumstances, combining BES with other solid treatment technologies can increase overall treatment efficiency and waste management. Combining BES with other solid treatment methods can have synergistic effects, boosting waste treatment efficiency, resource recovery, and environmental sustainability. However, to guarantee the successful integration and optimization of these combined approaches, site-specific factors, waste characteristics, and system compatibility must be considered.
Full article
(This article belongs to the Section Environmental and Green Processes)
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Open AccessArticle
Synergistic Effects of Plastid Terminal Oxidases 1 and 2 in Astaxanthin Regulation under Stress Conditions
by
Jun Chen, Jiangxin Wang, Hui Li, Ming Xiao, Yihong Zheng, Jiancheng Li, Jinxia Wu and Guanqin Huang
Processes 2024, 12(4), 804; https://doi.org/10.3390/pr12040804 - 17 Apr 2024
Abstract
Plastid terminal oxidases (PTOXs) are essential for maintaining photosynthetic efficiency and cellular redox homeostasis. Astaxanthin, a carotenoid pigment with antioxidant properties, is synthesized and accumulates in response to oxidative stress induced by high-light intensity or nutrient limitation. It suggests that PTOX may impact
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Plastid terminal oxidases (PTOXs) are essential for maintaining photosynthetic efficiency and cellular redox homeostasis. Astaxanthin, a carotenoid pigment with antioxidant properties, is synthesized and accumulates in response to oxidative stress induced by high-light intensity or nutrient limitation. It suggests that PTOX may impact astaxanthin biosynthesis under environmental stress conditions due to its involvement in ROS regulation. The ptox1 gene is thought to have a conserved role in safeguarding the photosynthetic apparatus from over-reduction and participating in energy dissipation. On the other hand, the ptox2 gene seems to be involved in the evolution of astaxanthin synthesis and adaptive responses to diverse environmental stressors. Efficient gene silencing strains were developed in Chlamydomonas reinhardtii CC849 for ptox1 and ptox2. The study found that the ptox2 gene correlates highly with resistance to intense light stress. Furthermore, the ptox2 gene showed increased activity under high salt stress conditions, indicating its importance in stress coping mechanisms. The quantification of astaxanthin in the gene-silenced strains revealed that ptox1 acts as a positive regulator, while ptox2 functions as a negative regulator of astaxanthin accumulation. Understanding the coordination between ptox1 and ptox2 could clarify the synergistic actions of these genes in maintaining photosynthetic performance and redox balance under fluctuating environmental conditions.
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(This article belongs to the Special Issue Advances in Chemical Characterization, Pharmacological Applications and Synthetic Biology of Natural Products)
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Open AccessArticle
Occurrence and Removal of Microplastics in Tertiary Wastewater Treatment Plants: A Case Study of Three Plants in Zhengzhou, China
by
Yang Li, Tongtong Qin, Xinjie Bai, Wenjing Wu, Xudong Chen, Minghui Shen, Liwen Qin, Yanyan Dou and Xuejun Duan
Processes 2024, 12(4), 803; https://doi.org/10.3390/pr12040803 - 16 Apr 2024
Abstract
Microplastics have been widely detected in wastewater treatment plants, but there is still a significant dearth of research data on the removal efficiency of microplastics in such plants. The present study focused on three wastewater treatment plants situated in Zhengzhou, China. On-site sampling
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Microplastics have been widely detected in wastewater treatment plants, but there is still a significant dearth of research data on the removal efficiency of microplastics in such plants. The present study focused on three wastewater treatment plants situated in Zhengzhou, China. On-site sampling and Raman spectrum detection techniques were employed to identify microplastics in both wastewater and sludge samples, while the removal efficiency of microplastics was quantified for each plant. Results showed that the abundance of microplastics in influent exhibited ranging from 147.5 ± 2.6 to 288.8 ± 11.8 n/L, while the range in sludge samples was from 12,024.7 ± 1737.0 n/kgdw to 20,818.4 ± 5662.0 n/kgdw. The removal efficiencies of microplastics in the three WWTPs ranged from 76.2% to 91.2%. The primary components of microplastics were generally identified as fibers ranging in size from 10 to 100 μm. The samples collectively exhibited a total of seven distinct colors, with the predominant proportion being transparent. Polypropylene was the polymer type with the highest proportion. The sludge in WWTPs plays a pivotal role in the accumulation of MPs from wastewater bodies, necessitating increased attention toward its proper disposal in future endeavors.
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(This article belongs to the Section Environmental and Green Processes)
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Open AccessArticle
Simulation of Solidification Structure in the Vacuum Arc Remelting Process of Titanium Alloy TC4 Based on 3D CAFE Method
by
Zhenquan Jing, Rui Liu, Naitao Geng, Ying Wang and Yanhui Sun
Processes 2024, 12(4), 802; https://doi.org/10.3390/pr12040802 - 16 Apr 2024
Abstract
Vacuum arc remelting is the main production method of titanium alloy ingots at present. In order to obtain good quality ingots, it is of great significance to study the formation of the solidification structure of ingots via vacuum arc remelting. In order to
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Vacuum arc remelting is the main production method of titanium alloy ingots at present. In order to obtain good quality ingots, it is of great significance to study the formation of the solidification structure of ingots via vacuum arc remelting. In order to select and optimize the nucleation parameters for the solidification microstructure simulation of an ingot, a 3D CAFE model for microstructure evolution during vacuum arc remelting was established, taking into account heat transfer, flow, and solute diffusion. The Gaussian distribution continuous nucleation model and extended KGT model were used to describe the grain nucleation and dendrite tip growth rates, respectively. The multi-point mass source and moving boundary method were used to simulate the ingot growth. The results show that there are three typical crystal regions in the solidification structure of vacuum arc remelting titanium alloy ingots, namely the surface fine crystal region, columnar crystal region, and central equiaxed crystal region. The proportion of the columnar crystal region in the solidification structure of an ingot increases gradually with the increase in the undercooling of the maximum bulk nucleation. With an increase in the maximum bulk nucleation density, the equiaxed grain zone gradually increases, and the grain size gradually decreases. The proportion of the columnar crystal region in the solidification structure of an ingot increases gradually with an increase in the undercooling of the maximum bulk nucleation. The maximum volume nucleation variance has no obvious effect on the change in the solidification structure. When the maximum volume nucleation undercooling is 5.5 K, the maximum volume nucleation standard deviation is 4 K, and the maximum volume nucleation density is 5 × 108. The solidification structure simulation results are in good agreement with the experimental results.
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(This article belongs to the Special Issue Metallurgical Process: Optimization and Control)
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Open AccessArticle
Security Assessment of Industrial Control System Applying Reinforcement Learning
by
Mariam Ibrahim and Ruba Elhafiz
Processes 2024, 12(4), 801; https://doi.org/10.3390/pr12040801 - 16 Apr 2024
Abstract
Industrial control systems are often used to assist and manage an industrial operation. These systems’ weaknesses in the various hierarchical structures of the system components and communication backbones make them vulnerable to cyberattacks that jeopardize their security. In this paper, the security of
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Industrial control systems are often used to assist and manage an industrial operation. These systems’ weaknesses in the various hierarchical structures of the system components and communication backbones make them vulnerable to cyberattacks that jeopardize their security. In this paper, the security of these systems is studied by employing a reinforcement learning extended attack graph to efficiently reveal the subsystems’ flaws. Specifically, an attack graph that mimics the environment is constructed for the system using the state–action–reward–state–action technique, in which the agent is regarded as the attacker. Attackers may cause the greatest amount of system damage with the fewest possible actions if they have the highest cumulative reward. The worst-case assault scheme with a total reward of 42.9 was successfully shown in the results, and the most badly affected subsystems were recognized.
Full article
(This article belongs to the Special Issue Challenges and Advances of Process Control Systems)
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Open AccessArticle
Optimization of Anti-Skid and Noise Reduction Performance of Cement Concrete Pavement with Different Grooved and Dragged Textures
by
Biyu Yang, Songli Yang, Zhoujing Ye, Xiaohua Zhou and Linbing Wang
Processes 2024, 12(4), 800; https://doi.org/10.3390/pr12040800 - 16 Apr 2024
Abstract
Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise.
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Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. This study addressed the critical need to optimize pavement textures to balance improved skid resistance with noise reduction. Tests were conducted to assess the influence of surface texture on skid resistance and noise, exploring the relationship between texture attributes and their performance in these areas. The investigation examined the effects of texture representation methods, mean profile depth, and the high-speed sideway force coefficient (SFC) on noise intensity and pavement skid resistance. The findings revealed that transverse grooves significantly improved the SFC, enhancing skid resistance. In contrast, longitudinal burlap drag, through its micro- and macro-texture adjustments, effectively reduced vibration frequencies between the tire and pavement, thus mitigating noise. Utilizing the TOPSIS multi-objective optimization framework, an optimization model for pavement textures was developed to augment skid resistance and noise reduction at varying speeds. The results indicated that at 60 km/h, an optimal balance of groove width, depth, and spacing yielded superior skid resistance with a minimal noise increase. At 80 km/h, increased groove spacing and depth were shown to effectively decrease noise while maintaining efficient water evacuation. The optimal pavement texture design must consider the specific context, including traffic volume, vehicle types, and operating speeds. This study provides essential guidance for optimizing urban cement concrete pavement textures, aiming to diminish traffic noise and bolster road safety.
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(This article belongs to the Special Issue Industrial Process Operation State Sensing and Performance Optimization)
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Open AccessArticle
Automatic Detection of Banana Maturity—Application of Image Recognition in Agricultural Production
by
Liu Yang, Bo Cui, Junfeng Wu, Xuan Xiao, Yang Luo, Qianmai Peng and Yonglin Zhang
Processes 2024, 12(4), 799; https://doi.org/10.3390/pr12040799 - 16 Apr 2024
Abstract
With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness
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With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness recognition model was proposed based on a convolutional neural network and transfer learning. Banana photos at different ripening stages were collected as a dataset, and data augmentation was applied. Then, weights and parameters of four models trained on the original ImageNet dataset were loaded and fine-tuned to fit our banana dataset. To investigate the learning rate’s effect on model performance, fixed and updating learning rate strategies are analyzed. In addition, four CNN models, ResNet 34, ResNet 101, VGG 16, and VGG 19, are trained based on transfer learning. Results show that a slower learning rate causes the model to converge slowly, and the training loss function oscillates drastically. With different learning rate updating strategies, MultiStepLR performs the best and achieves a better accuracy of 98.8%. Among the four models, ResNet 101 performs the best with the highest accuracy of 99.2%. This research provides a direct effective model and reference for intelligent fruit classification.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Food Processing and Food Industries)
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Open AccessArticle
Diesel Adulteration Detection with a Machine Learning-Enhanced Laser Sensor Approach
by
Bachar Mourched, Tariq AlZoubi and Sabahudin Vrtagic
Processes 2024, 12(4), 798; https://doi.org/10.3390/pr12040798 - 16 Apr 2024
Abstract
This paper introduces a novel and cost-effective method for detecting adulterated diesel, specifically targeting contamination with kerosene, by leveraging machine learning and the refractive index values of mixed diesel samples. It proposes a laser-based sensor, employing COMSOL simulations for synthetic data generation to
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This paper introduces a novel and cost-effective method for detecting adulterated diesel, specifically targeting contamination with kerosene, by leveraging machine learning and the refractive index values of mixed diesel samples. It proposes a laser-based sensor, employing COMSOL simulations for synthetic data generation to facilitate machine learning training. This innovative approach not only streamlines the detection process by eliminating the need for expensive equipment and specialized personnel but also enables on-site testing without extensive sample preparation. The sensor’s design, utilizing light refraction and reflection principles, allows for the accurate measurement of diesel adulteration levels. Validation results showcase the machine learning models’ high precision in predicting adulteration percentages, as evidenced by an R-squared value of 0.999 and a mean absolute error of 0.074. This research signifies a leap in sensor technology, offering a practical solution for rapid diesel adulteration detection, especially in developing countries, by minimizing reliance on advanced laboratory analyses. The sensor’s design aligns with the requirements for low-cost IoT technology, presenting a versatile tool for various applications.
Full article
(This article belongs to the Special Issue Clean Combustion and Emission in Vehicle Power System, 2nd Edition)
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Open AccessArticle
Chitosan-Based Grafted Cationic Magnetic Material to Remove Emulsified Oil from Wastewater: Performance and Mechanism
by
Sicong Du, Chuang Liu, Peng Cheng and Wenyan Liang
Processes 2024, 12(4), 797; https://doi.org/10.3390/pr12040797 - 16 Apr 2024
Abstract
In order to remove high-concentration emulsified oil from wastewater, a chitosan-based magnetic flocculant, denoted as FS@CTS-P(AM-DMC), was employed in this present study. The effects of factors including the magnetic flocculant dose, pH values, and coexisting ions were investigated. A comparative dosing mode with
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In order to remove high-concentration emulsified oil from wastewater, a chitosan-based magnetic flocculant, denoted as FS@CTS-P(AM-DMC), was employed in this present study. The effects of factors including the magnetic flocculant dose, pH values, and coexisting ions were investigated. A comparative dosing mode with the assistance of polyacrylamide (PAM) was also included. The evolution of floc size was studied using microscopic observation to investigate the properties of flocs under different pH values and dosing modes. Particle image velocimetry (PIV) and extended Deryaguin–Landau–Verwey–Overbeek models were utilized to illustrate the distribution and velocity magnitude of the particle flow fields and to delve into the mechanism of magnetic flocculation. The results showed that FS@CTS-P(AM-DMC) achieved values of 96.4 and 74.5% for both turbidity and COD removal for 3000 mg/L of simulated emulsified oil. In the presence of PAM, the turbidity and COD removal reached 95.7 and 71.6%. In addition, FS@CTS-P(AM-DMC) demonstrated remarkable recycling and reusability performances, maintaining effective removal after eight cycles. The strength and recovery factors of magnetic flocs without PAM reached 69.3 and 76.8%, respectively. However, with the addition of PAM, they decreased to 46.73 and 51.47%, respectively. During the magnetophoretic processes, FS@CTS-P(AM-DMC) and oil droplets continuously collided and aggregated, forming three-dimensional network aggregates. Moreover, the magnetic floc generated a swirling motion, and the residual emulsified oil droplets could be further captured. Emulsified oil droplets were primarily removed through charge neutralization under acidic conditions. Under neutral and alkaline conditions, magnetic interactions played a major role in magnetic flocculation.
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(This article belongs to the Section Environmental and Green Processes)
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Open AccessArticle
Research on the Analysis of and Countermeasures for the Eutrophication of Water Bodies: Waihu Reservoir as a Case Study
by
Yiting Qi, Xin Cao, Ruisi Cao, Mingjie Cao, Ailan Yan, Erpeng Li and Dong Xu
Processes 2024, 12(4), 796; https://doi.org/10.3390/pr12040796 - 15 Apr 2024
Abstract
Water quality deterioration and eutrophication have become a global concern, while reservoir pollution caused by multiple factors has led to frequent algal blooms, posing a serious threat to rural drinking water security and urban water supply. The purpose of this paper is to
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Water quality deterioration and eutrophication have become a global concern, while reservoir pollution caused by multiple factors has led to frequent algal blooms, posing a serious threat to rural drinking water security and urban water supply. The purpose of this paper is to analyze the current water quality of Waihu Reservoir and use the single index method, the weighted comprehensive scoring method, and the nutrient level index method (TLI) to evaluate eutrophication. On this basis, the pollution sources of the reservoir are comprehensively analyzed and discussed, and effective control strategies are proposed. The evaluation results indicate that the reservoir is of moderate eutrophication type. Therefore, reducing the input of nutrients such as nitrogen and phosphorus in water is the main goal of alleviating exogenous pollution. The combination of engineering intervention and ecological restoration strategies to remove nutrients from the aquatic environment is an effective strategy to manage endogenous pollution. From the point of view of the source of pollution, this study provides an in-depth analysis of exogenous and endogenous pollution, respectively, and the proposed treatment is instructive for the control and routine management of eutrophication in the Waihu Reservoir, as well as for the management of similar problems in different reservoirs.
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(This article belongs to the Special Issue Processes of Pollution Control and Resource Utilization)
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Open AccessArticle
Development and Process Optimization of a Steamed Fish Paste Cake Prototype for Room Temperature Distribution
by
Jin-Hwa Lee, Sang In Kang, Sana Mansoor, Inhwan Lee, Do Youb Kim, Ye Youl Kim, Yongjoon Park, Jae-Hak Sohn, Khawaja Muhammad Imran Bashir and Jae-Suk Choi
Processes 2024, 12(4), 795; https://doi.org/10.3390/pr12040795 - 15 Apr 2024
Abstract
Surimi-based products typically demand cold storage and a cold chain distribution system, which not only affects their physical properties and flavor but also escalates production costs. In this study, we introduced a novel high-temperature and high-pressure retort processing method to enable room temperature
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Surimi-based products typically demand cold storage and a cold chain distribution system, which not only affects their physical properties and flavor but also escalates production costs. In this study, we introduced a novel high-temperature and high-pressure retort processing method to enable room temperature storage and distribution of a surimi-based product, a fish paste cake. Our optimization efforts focused on refining the processing conditions for the fish paste cake. This included incorporating transglutaminase, sugar additives, natural herbal or seaweed extracts, and optimizing retort processing conditions to enhance textural properties, minimize browning and off flavor, and extend the shelf-life of the product. Our results demonstrated that the addition of 0.3% ACTIVA TG-K, 1.0% trehalose, and 0.5% sea tangle extract during the production process significantly enhanced the gel strength, minimized browning, and improved the overall flavor of the fish paste cake prototype. Importantly, the developed prototype exhibited favorable biochemical, textual, nutritional, and sensory properties, extending the shelf-life up to 160 days without compromising physical, chemical, or sensory attributes. In addition, the developed prototype exhibited improved elasticity, compared to control groups. The innovative process not only facilitates room temperature storage and distribution of surimi-based products but also holds potential for generating additional profits.
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(This article belongs to the Section Food Process Engineering)
Open AccessArticle
Optimizing the Mixing Ratios of Source-Separated Organic Waste and Thickened Waste Activated Sludge in Anaerobic Co-Digestion: A New Approach
by
Anahita Rabii, Ahmed El Sayed, Amr Ismail, Saad Aldin, Yaser Dahman and Elsayed Elbeshbishy
Processes 2024, 12(4), 794; https://doi.org/10.3390/pr12040794 - 15 Apr 2024
Abstract
Anaerobic co-digestion (AnCoD) presents several advantages over conventional mono-digestion. Various factors can impact the efficiency of the co-digestion process, including the mixing ratio of the feedstocks. This study primarily investigates the effects of different mixing ratios on methane production during the co-digestion of
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Anaerobic co-digestion (AnCoD) presents several advantages over conventional mono-digestion. Various factors can impact the efficiency of the co-digestion process, including the mixing ratio of the feedstocks. This study primarily investigates the effects of different mixing ratios on methane production during the co-digestion of source-separated municipal organic waste (SSO) with thickened waste activated sludge (TWAS). While the C/N or COD/N ratio has generally been used for optimizing the mixing ratios of co-digested feedstocks, a new approach is introduced in this study to evaluate the effects of the lipid, protein, and carbohydrate (L:P:C) ratios on the efficiency of AnCoD with respect to methane production, kinetics, and synergism at mixing ratios of TWAS:SSO of 10:90, 30:70, 50:50, 70:30, and 10:90. AnCoD improved methane production and kinetics relative to TWAS at all mixing ratios, the highest of which was at the 10:90 ratio, corresponding to a methane yield, maximum methane production rate, and an L:P:C ratio of 353 mL CH4/g COD, 25 mL CH4/g COD/d, and 8:1:18, respectively. Improvements in methane yields and kinetics due to synergy were evident at all mixing ratios, with improvements in methane yields ranging from 11 to 23% and improvements in kinetics ranging from 18 to 58%. Improvements in methane yields and kinetics were insensitive to the feedstock composition beyond the 50:50 mixing ratio.
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(This article belongs to the Special Issue Anaerobic Digestion Process: Design, Optimization and Application)
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Open AccessArticle
A Deep Learning Approach Based on Novel Multi-Feature Fusion for Power Load Prediction
by
Ling Xiao, Ruofan An and Xue Zhang
Processes 2024, 12(4), 793; https://doi.org/10.3390/pr12040793 - 15 Apr 2024
Abstract
Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid
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Adequate power load data are the basis for establishing an efficient and accurate forecasting model, which plays a crucial role in ensuring the reliable operation and effective management of a power system. However, the large-scale integration of renewable energy into the power grid has led to instabilities in power systems, and the load characteristics tend to be complex and diversified. Aiming at this problem, this paper proposes a short-term power load transfer forecasting method. To fully exploit the complex features present in the data, an online feature-extraction-based deep learning model is developed. This approach aims to extract the frequency-division features of the original power load on different time scales while reducing the feature redundancy. To solve the prediction challenges caused by insufficient historical power load data, the source domain model parameters are transferred to the target domain model utilizing Kendall’s correlation coefficient and the Bayesian optimization algorithm. To verify the prediction performance of the model, experiments are conducted on multiple datasets with different features. The simulation results show that the proposed model is robust and effective in load forecasting with limited data. Furthermore, if real-time data of new energy power systems can be acquired and utilized to update and correct the model in future research, this will help to adapt and integrate new energy sources and optimize energy management.
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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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