As one of the main chili varieties in Mexico, Yahualica chili requires year-round availability. This study examines the feasibility of five drying methods (open-air, solar, microwave, freeze-drying and shade drying) used to preserve this culturally and economically valuable product. The results show the drying duration and rate for solar drying with varying air temperatures (40, 50, 60, and 70 °C) and airflows (150, 200, 250, and 300 m3/h) and microwave drying with varying power levels (90, 160, 360, and 600 W). Convection drying efficiency increased with temperature and airflow, according to the findings. Microwave drying significantly reduced drying time, and higher powers further accelerated moisture removal. Open sun and shade drying was the slowest, and open sun drying was also susceptible to factors compromising quality. Total Phenolic Content (TPC), Total Capsaicinoids Content (TCC), and antioxidant activity had a positive effect, since the drying methodologies favored the release of these compounds.
{"title":"Evaluation of Various Drying Methods for Mexican Yahualica chili: Drying Characteristics and Quality Assessment","authors":"Diana Paola García-Moreira, Neith Pacheco, Harumi Hernández-Guzmán, Younes Bahammou, Zakaria Tagnamas, Ivan Moreno, Erick César López-Vidaña","doi":"10.3390/pr12091969","DOIUrl":"https://doi.org/10.3390/pr12091969","url":null,"abstract":"As one of the main chili varieties in Mexico, Yahualica chili requires year-round availability. This study examines the feasibility of five drying methods (open-air, solar, microwave, freeze-drying and shade drying) used to preserve this culturally and economically valuable product. The results show the drying duration and rate for solar drying with varying air temperatures (40, 50, 60, and 70 °C) and airflows (150, 200, 250, and 300 m3/h) and microwave drying with varying power levels (90, 160, 360, and 600 W). Convection drying efficiency increased with temperature and airflow, according to the findings. Microwave drying significantly reduced drying time, and higher powers further accelerated moisture removal. Open sun and shade drying was the slowest, and open sun drying was also susceptible to factors compromising quality. Total Phenolic Content (TPC), Total Capsaicinoids Content (TCC), and antioxidant activity had a positive effect, since the drying methodologies favored the release of these compounds.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms.
{"title":"Enhancing Active Disturbance Rejection Control for a Vehicle Active Stabiliser Bar with an Improved Chicken Flock Optimisation Algorithm","authors":"Zhenglin Tang, Qiang Zhao, Duc Truong Pham, Xuesong Zhang","doi":"10.3390/pr12091979","DOIUrl":"https://doi.org/10.3390/pr12091979","url":null,"abstract":"An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study employs thermodynamic-diagram analysis to investigate component ratios within the Fe-Al-Si-Cr system, focusing on the behavior of homogeneous liquid states. Through comprehensive modeling, a phase diagram is constructed, elucidating the interplay of iron, aluminum, silicon, and chromium components. This study identifies stable elementary tetrahedra within the system, providing insights into phase compositions and distribution. Key findings reveal the significance of tetrahedral geometry in understanding and optimizing alloy compositions, particularly in the context of complex chromium alloys. This research underscores the utility of thermodynamic analysis in advancing our understanding of alloy systems and facilitating the optimization of production processes.
{"title":"Exploring Alloy Composition Dynamics: Thermodynamic Analysis of Fe-Al-Si-Cr System in Homogeneous Liquid State","authors":"Yerbol Shabanov, Yerlan Zhumagaliyev, Nurzhan Nurgali, Murat Dossekenov, Karlyga Almuratova, Raigul Orynbassar, Tursyngul Kainenova, Botagoz Bakirova, Fatima Kanapiyeva, Elvira Zhunussova","doi":"10.3390/pr12091947","DOIUrl":"https://doi.org/10.3390/pr12091947","url":null,"abstract":"This study employs thermodynamic-diagram analysis to investigate component ratios within the Fe-Al-Si-Cr system, focusing on the behavior of homogeneous liquid states. Through comprehensive modeling, a phase diagram is constructed, elucidating the interplay of iron, aluminum, silicon, and chromium components. This study identifies stable elementary tetrahedra within the system, providing insights into phase compositions and distribution. Key findings reveal the significance of tetrahedral geometry in understanding and optimizing alloy compositions, particularly in the context of complex chromium alloys. This research underscores the utility of thermodynamic analysis in advancing our understanding of alloy systems and facilitating the optimization of production processes.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Numerically controlled (NC) machine tools, as vital production equipment in manufacturing, have been widely applied across various sectors and have become a core competitive advantage for enterprises in the global market. Therefore, ensuring the normal and efficient operation of NC machine tool groups and promptly diagnosing faults have become critical concerns for many enterprises and scholars today. This paper focuses on bearing fault diagnosis, utilizing the vibration signals from the Case Western Reserve University Bearing Data Center as the input dataset. This study constructed a dual-stream convolutional neural network (CNN) fault diagnosis model, where the first stream processes one-dimensional vibration signal spectra and the second stream handles two-dimensional time-frequency maps derived from the same signals. The model uniquely integrates convolutional attention mechanisms to enhance feature extraction along with dropout algorithms and batch normalization to prevent overfitting and improve training stability. The proposed approach enables a comprehensive learning of both temporal and spatial features, effectively identifying bearing faults with high accuracy. The model’s performance was validated against this widely recognized dataset, demonstrating superior accuracy compared to traditional methods.
{"title":"Enhanced Bearing Fault Diagnosis in NC Machine Tools Using Dual-Stream CNN with Vibration Signal Analysis","authors":"Zhen Ni, Yifei Tong, Yixuan Song, Ruikang Wang","doi":"10.3390/pr12091951","DOIUrl":"https://doi.org/10.3390/pr12091951","url":null,"abstract":"Numerically controlled (NC) machine tools, as vital production equipment in manufacturing, have been widely applied across various sectors and have become a core competitive advantage for enterprises in the global market. Therefore, ensuring the normal and efficient operation of NC machine tool groups and promptly diagnosing faults have become critical concerns for many enterprises and scholars today. This paper focuses on bearing fault diagnosis, utilizing the vibration signals from the Case Western Reserve University Bearing Data Center as the input dataset. This study constructed a dual-stream convolutional neural network (CNN) fault diagnosis model, where the first stream processes one-dimensional vibration signal spectra and the second stream handles two-dimensional time-frequency maps derived from the same signals. The model uniquely integrates convolutional attention mechanisms to enhance feature extraction along with dropout algorithms and batch normalization to prevent overfitting and improve training stability. The proposed approach enables a comprehensive learning of both temporal and spatial features, effectively identifying bearing faults with high accuracy. The model’s performance was validated against this widely recognized dataset, demonstrating superior accuracy compared to traditional methods.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yanbo Zhang, Lei Zhang, Yulin Gao, Ping Shi, Yu Wang, Lingrong Kong
The bypass valve of a positive displacement motor is a key component for regulating the bottom hole pressure and ensuring the normal circulation of drilling fluid during the drilling process. Severe erosion damage to the bypass valve significantly affects the service life of the positive displacement motor, yet there is currently a lack of related research. In this research, the flow characteristics of drilling fluid inside the valve core were analyzed through flow field simulation, and the main factors influencing erosion damage to the valve core were investigated. The results indicate that the side holes and flow channel structure of the valve core are the main causes of erosion. Based on this, two optimization schemes are proposed, namely, reducing the number of bypass side holes to 4 and optimizing the flow channel cone angle to 45°. The simulation results show that the erosion rate of the optimized valve core is significantly reduced, and the service life is effectively improved. Finally, a valve core life prediction model is established using a back propagation (BP) neural network to evaluate the optimization effect. The results show that the applicable flow range and maximum service life of the optimized valve core are increased by approximately 60% and 75.4%, respectively, validating the effectiveness of the optimization scheme.
{"title":"Research on Erosion Damage Laws and Structural Optimization of Bypass Valve for Positive Displacement Motors","authors":"Yanbo Zhang, Lei Zhang, Yulin Gao, Ping Shi, Yu Wang, Lingrong Kong","doi":"10.3390/pr12091953","DOIUrl":"https://doi.org/10.3390/pr12091953","url":null,"abstract":"The bypass valve of a positive displacement motor is a key component for regulating the bottom hole pressure and ensuring the normal circulation of drilling fluid during the drilling process. Severe erosion damage to the bypass valve significantly affects the service life of the positive displacement motor, yet there is currently a lack of related research. In this research, the flow characteristics of drilling fluid inside the valve core were analyzed through flow field simulation, and the main factors influencing erosion damage to the valve core were investigated. The results indicate that the side holes and flow channel structure of the valve core are the main causes of erosion. Based on this, two optimization schemes are proposed, namely, reducing the number of bypass side holes to 4 and optimizing the flow channel cone angle to 45°. The simulation results show that the erosion rate of the optimized valve core is significantly reduced, and the service life is effectively improved. Finally, a valve core life prediction model is established using a back propagation (BP) neural network to evaluate the optimization effect. The results show that the applicable flow range and maximum service life of the optimized valve core are increased by approximately 60% and 75.4%, respectively, validating the effectiveness of the optimization scheme.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Menglong Wang, Lin Tian, Jinghao Wu, Yunxing Cao, Li Wang, Bin Shi, Mingyue Sun, Shimin Liu, Yunbing Hu
Nitrogen–slick water composite fracturing is a novel, recently developed fracturing technology. Due to its impact on increasing permeability, this technology outperforms hydraulic fracturing. This study adopted the horizontal well XJ-1L, Xinjing coal mine, Qinshui Basin, China, as a study area to statistically analyze the fracture properties, stress drop, and b-value distribution characteristics of 1217 effective micro-seismic events generated during nitrogen–water composite fracturing. The results show that: (1) gradually reducing the proportion of gas in fracturing fluid reduced the proportion of tensile fractures at a ratio of between 15.6% and 0.8%, whereas the proportion of strike-slip fractures gradually increased by between 1.6% and 15.2%; (2) the stress drop and b-values in the nitrogen fracturing (NF) stage, representative of stress disturbance, exceeded those in the hydraulic fracturing (HF) stage, consistent with greater numbers of tensile fractures formed in the NF stage; (3) the greater number of tensile fractures and their increasing permeability could be explained based on the influences of gas compressibility and pore pressure on coal fractures. This study provides a theoretical and practical basis for optimizing the exploitation of low-permeability coal reservoirs.
氮-粘滑水复合压裂技术是最近开发的一种新型压裂技术。由于其对提高渗透率的作用，该技术优于水力压裂。本研究以中国沁水盆地新井煤矿 XJ-1L 水平井为研究区域，统计分析了氮水复合压裂过程中产生的 1217 次有效微震事件的压裂性质、应力降和 b 值分布特征。结果表明(1）压裂液中气体比例逐渐降低，拉伸裂缝比例降低了 15.6%至 0.8%，而走向滑动裂缝比例逐渐增加了 1.6%至 15.2%；（2）氮压裂阶段代表应力扰动的应力降和 b 值超过了水力压裂阶段，这与氮压裂阶段形成的张拉裂缝数量较多相一致；（3）基于气体可压缩性和孔隙压力对煤裂缝的影响，可以解释张拉裂缝数量较多及其渗透率增加的原因。这项研究为优化低渗透煤储层的开采提供了理论和实践依据。
{"title":"Fracture Properties of Nitrogen–Slick Water Composite Fracturing in Coal Reservoir","authors":"Menglong Wang, Lin Tian, Jinghao Wu, Yunxing Cao, Li Wang, Bin Shi, Mingyue Sun, Shimin Liu, Yunbing Hu","doi":"10.3390/pr12091949","DOIUrl":"https://doi.org/10.3390/pr12091949","url":null,"abstract":"Nitrogen–slick water composite fracturing is a novel, recently developed fracturing technology. Due to its impact on increasing permeability, this technology outperforms hydraulic fracturing. This study adopted the horizontal well XJ-1L, Xinjing coal mine, Qinshui Basin, China, as a study area to statistically analyze the fracture properties, stress drop, and b-value distribution characteristics of 1217 effective micro-seismic events generated during nitrogen–water composite fracturing. The results show that: (1) gradually reducing the proportion of gas in fracturing fluid reduced the proportion of tensile fractures at a ratio of between 15.6% and 0.8%, whereas the proportion of strike-slip fractures gradually increased by between 1.6% and 15.2%; (2) the stress drop and b-values in the nitrogen fracturing (NF) stage, representative of stress disturbance, exceeded those in the hydraulic fracturing (HF) stage, consistent with greater numbers of tensile fractures formed in the NF stage; (3) the greater number of tensile fractures and their increasing permeability could be explained based on the influences of gas compressibility and pore pressure on coal fractures. This study provides a theoretical and practical basis for optimizing the exploitation of low-permeability coal reservoirs.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luiz Eduardo Zani de Moraes, Felipe Augusto Olivo Marcoti, Marco Antônio Naves Lucio, Bianca Caroline da Silva Rocha, Lucas Bonfim Rocha, Adriano Lopes Romero, Evandro Bona, Ana Paula Peron, Osvaldo Valarini Junior
Phenyl urea herbicides such as diuron and linuron are commonly used in agriculture to eliminate weeds. Their uncontrolled use can cause environmental problems. In this study, the adsorption of these herbicides was evaluated using activated carbon from coffee grounds, activated with zinc chloride (AC-ZnCl2, 100% purity), nitric acid (AC-HNO3, 65% purity), and commercially activated (AC-C) carbon for comparison purposes. The spent coffee grounds were transformed into activated carbon through the calcination process. The highest removal efficiency for diuron 40 mg∙L−1 and linuron 31 mg∙L−1 was obtained using the ZnCl2-activated adsorbent, being 100% and 45%, respectively. The best pH range was between 4 and 6. Adsorption kinetic studies showed that pseudo-first and second-order models fit the experimental data, with the adsorption rate increasing rapidly within 60 min for the concentrations tested. Adsorption isotherms indicated that the Langmuir model provided the best fit for diuron, while the Freundlich model was more appropriate for linuron. The efficiency of the adsorption process using activated carbon (AC) was confirmed by the toxicity analysis of diuron and linuron solutions before and after adsorption with AC.
{"title":"Analysis and Simulation of Adsorption Efficiency of Herbicides Diuron and Linuron on Activated Carbon from Spent Coffee Beans","authors":"Luiz Eduardo Zani de Moraes, Felipe Augusto Olivo Marcoti, Marco Antônio Naves Lucio, Bianca Caroline da Silva Rocha, Lucas Bonfim Rocha, Adriano Lopes Romero, Evandro Bona, Ana Paula Peron, Osvaldo Valarini Junior","doi":"10.3390/pr12091952","DOIUrl":"https://doi.org/10.3390/pr12091952","url":null,"abstract":"Phenyl urea herbicides such as diuron and linuron are commonly used in agriculture to eliminate weeds. Their uncontrolled use can cause environmental problems. In this study, the adsorption of these herbicides was evaluated using activated carbon from coffee grounds, activated with zinc chloride (AC-ZnCl2, 100% purity), nitric acid (AC-HNO3, 65% purity), and commercially activated (AC-C) carbon for comparison purposes. The spent coffee grounds were transformed into activated carbon through the calcination process. The highest removal efficiency for diuron 40 mg∙L−1 and linuron 31 mg∙L−1 was obtained using the ZnCl2-activated adsorbent, being 100% and 45%, respectively. The best pH range was between 4 and 6. Adsorption kinetic studies showed that pseudo-first and second-order models fit the experimental data, with the adsorption rate increasing rapidly within 60 min for the concentrations tested. Adsorption isotherms indicated that the Langmuir model provided the best fit for diuron, while the Freundlich model was more appropriate for linuron. The efficiency of the adsorption process using activated carbon (AC) was confirmed by the toxicity analysis of diuron and linuron solutions before and after adsorption with AC.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Petroleum drilling sludge (PDS) is one of the most significant waste products generated during drilling activities worldwide. The disposal of this waste must be carried out using the most cost-effective methods available. The objective of this manuscript is to mathematically model the parameters of drying processes experimentally applied to PDS. For this purpose, this study employed two different artificial intelligence techniques: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs). These methods were used to predict the parameters. In the calculations, the inputs included petroleum drilling mud with varying quantities (50 g, 100 g, and 150 g) and drying times, using a 120 W microwave drying power. The results indicated that the coefficient of determination (R2) and the root mean square error (RMSE) obtained during the test phase for ANFIS were 0.999965 and 0.005425, respectively, while for ANN, the R2 and RMSE were 0.999973 and 0.004774, respectively. Analysis of the evaluation results revealed that both methods provided predictions for moisture content that were closer to experimental values compared to drying rate values.
{"title":"Modeling Drying Process Parameters for Petroleum Drilling Sludge with ANN and ANFIS","authors":"Aytaç Moralar","doi":"10.3390/pr12091948","DOIUrl":"https://doi.org/10.3390/pr12091948","url":null,"abstract":"Petroleum drilling sludge (PDS) is one of the most significant waste products generated during drilling activities worldwide. The disposal of this waste must be carried out using the most cost-effective methods available. The objective of this manuscript is to mathematically model the parameters of drying processes experimentally applied to PDS. For this purpose, this study employed two different artificial intelligence techniques: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs). These methods were used to predict the parameters. In the calculations, the inputs included petroleum drilling mud with varying quantities (50 g, 100 g, and 150 g) and drying times, using a 120 W microwave drying power. The results indicated that the coefficient of determination (R2) and the root mean square error (RMSE) obtained during the test phase for ANFIS were 0.999965 and 0.005425, respectively, while for ANN, the R2 and RMSE were 0.999973 and 0.004774, respectively. Analysis of the evaluation results revealed that both methods provided predictions for moisture content that were closer to experimental values compared to drying rate values.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept in the model-free adaptive control (MFAC) framework, the discrete-time nonlinear model is converted into a full-form dynamic linearization (FFDL) model. Secondly, using the FFDL data model, a discrete sliding mode controller is designed. A discrete integral sliding mode surface is chosen to mitigate chattering during the reaching phase, and a hyperbolic tangent function with minimal slope variation is selected for smoother switching control. Furthermore, a DESO is designed to estimate uncertainties in the discrete system, enabling real-time compensation for the controller. Finally, a genetic optimization algorithm is employed for parameter tuning to minimize the time cost associated with selecting control parameters. The design process of this scheme relies solely on the data of the controlled system, without depending on a mathematical model. The proposed DESO-MFASMC scheme is tested through simulations using a typical numerical equation and the existing EFG-BC/320 electric heavy-duty forklift from the Quzhou Special Equipment Inspection Center. Simulation results show that the proposed method is significantly superior to the traditional MFAC and PID control methods in tracking accuracy and robustness when dealing with nonlinear disturbance of the system. The DESO-MFASMC scheme proposed in this paper not only shows its advantages in theory but also verifies its effectiveness and practicability in engineering through practical application.
{"title":"Model-Free Adaptive Sliding Mode Control Scheme Based on DESO and Its Automation Application","authors":"Xiaohua Wei, Zhen Sui, Hanzhou Peng, Feng Xu, Jianliang Xu, Yulong Wang","doi":"10.3390/pr12091950","DOIUrl":"https://doi.org/10.3390/pr12091950","url":null,"abstract":"This paper addresses a class of uncertain nonlinear systems with disturbances that are challenging to model by proposing a novel model-free adaptive sliding mode control (MFASMC) scheme based on a discrete-time extended state observer (DESO). Initially, leveraging the pseudo partial derivative (PPD) concept in the model-free adaptive control (MFAC) framework, the discrete-time nonlinear model is converted into a full-form dynamic linearization (FFDL) model. Secondly, using the FFDL data model, a discrete sliding mode controller is designed. A discrete integral sliding mode surface is chosen to mitigate chattering during the reaching phase, and a hyperbolic tangent function with minimal slope variation is selected for smoother switching control. Furthermore, a DESO is designed to estimate uncertainties in the discrete system, enabling real-time compensation for the controller. Finally, a genetic optimization algorithm is employed for parameter tuning to minimize the time cost associated with selecting control parameters. The design process of this scheme relies solely on the data of the controlled system, without depending on a mathematical model. The proposed DESO-MFASMC scheme is tested through simulations using a typical numerical equation and the existing EFG-BC/320 electric heavy-duty forklift from the Quzhou Special Equipment Inspection Center. Simulation results show that the proposed method is significantly superior to the traditional MFAC and PID control methods in tracking accuracy and robustness when dealing with nonlinear disturbance of the system. The DESO-MFASMC scheme proposed in this paper not only shows its advantages in theory but also verifies its effectiveness and practicability in engineering through practical application.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142187994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nandhya K. P. Prikusuma, Muhammad G. Algifari, Rafiandy A. Harahap, Zulfiadi Zulhan, Taufiq Hidayat
Knowledge of the phase equilibria in the MgO–CaO–SiO2–Al2O3 slag system is crucial for the nickel laterite smelting process. The phase equilibria of this slag system were experimentally investigated, focusing on the olivine and tridymite/cristobalite primary phase fields, using high-temperature equilibration and quenching methods, followed by Scanning Electron Microscopy–Energy Dispersive X-Ray analysis. The phase equilibria of the MgO–CaO–SiO2 slag system at 1400 °C and 1500 °C were first determined in the absence of ferronickel alloy. The phase equilibria between 1400 °C, 1450 °C, and 1500 °C were then determined under a reducing condition, i.e., at equilibrium with ferronickel alloy and solid carbon. Finally, the effect of Al2O3 addition on the liquidus and solidus compositions in the slag system under the reducing condition was investigated at 1400 °C and 1450 °C. Comparisons between the experimentally constructed diagram, previous data, and FactSage-predicted phase diagrams have been provided and discussed. The present study identified the liquid slag both in the absence and presence of ferronickel alloy and solid carbon, as well as in the presence of Al2O3 impurity, within the formation boundaries of olivine and tridymite/cristobalite solids. Identifying the liquid slag area is essential to ensure that the nickel laterite smelting slag can be tapped from the furnace.
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