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Data-Driven Modeling, Control and Optimization of Complex Industrial Processes
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Data-Driven Modeling, Control and Optimization of Complex Industrial Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 25009

Special Issue Editors

Prof. Dr. Rey-Chue Hwang

E-Mail Website
Guest Editor
Department of Electrical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
Interests: artificial intelligent system; signal processing; fuzzy control
Prof. Dr. Huixin Tian

E-Mail Website
Guest Editor
School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
Interests: modeling, control and optimization of complex industrial processes; data mining, machine learning, pattern recognition, big data analytics and the application of artificial intelligence in different fields, including intelligent manufacturing, intelligent energy, intelligent civil aviation, etc.

Special Issue Information

Dear Colleagues,

The last decade has seen a radical step-change in the scale and complexity of engineering systems in industrial processes such as manufacturing technologies, equipment used, and production processes in the petrochemical industry, iron and steel metallurgy, the light industry, and other industries. Complexity arises from a number of factors, such as strong nonlinearities, multi-variable coupling, and variations in operation conditions, together with unknown model structures and parameters. So, it is hard to establish mathematical models using the first principle techniques and to further control and optimize them using traditional theory. Moreover, the rapid development and application of information and communication technologies make it possible to collect massive data for industrial processes. Such data contain comprehensive knowledge and information about the operation and control of industrial processes. The question of how to deal with complex industrial systems using industrial data has attracted an increasing amount of interest. As the core technologies, the development of new modeling, control, and optimization techniques for large-scale and complex industrial process based on industrial data has become a multidiscipline theme that brings together the modern control theory, computer modeling, intelligent optimization, powerful data real-time processing, and networking technology.

The main focus of this Special Issue is new theories and their applications in data-based modeling, control, and optimization for complex industrial processes, especially in industry applications. Topics include, but not are limited to:

  • Advanced data-driven simulation and modeling methods for complex industrial systems and processes;
  • Data-driven control theory, approaches, and applications;
  • Data-driven fault diagnosis, health maintenance, and performance evaluation;
  • Data-driven modeling, optimization, scheduling, decision making, and simulation;
  • Intelligent transport systems and electric vehicles;
  • Statistical learning, machine learning, data mining, and practical applications in the automation field.

Prof. Dr. Rey-Chue Hwang
Prof. Dr. Huixin Tian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven modeling and simulation
  • control theory and application
  • industrial optimization
  • fault diagnosis
  • complex industrial processes

Published Papers (17 papers)

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Research

24 pages, 11727 KiB  
Article
Application of Intelligent Control in Chromatography Separation Process
by Chao-Fan Xie, Hong Zhang and Rey-Chue Hwang
Processes 2023, 11(12), 3443; https://doi.org/10.3390/pr11123443 - 16 Dec 2023
Viewed by 589
Abstract
Chromatographic separation plays a pivotal role in the manufacturing of chemical products and biopharmaceuticals. This technique exploits differences in distribution between stationary and mobile phases to separate mixtures, impacting final product quality. Simulated moving bed (SMB) technology, recognized for its continuous feed, enhances [...] Read more.
Chromatographic separation plays a pivotal role in the manufacturing of chemical products and biopharmaceuticals. This technique exploits differences in distribution between stationary and mobile phases to separate mixtures, impacting final product quality. Simulated moving bed (SMB) technology, recognized for its continuous feed, enhances efficiency and increases production capacity while reducing solvent and water consumption. Despite its complexity in controlling variables like flow rates and valve switching times, traditional control theories fall short. This study introduces an intelligent fuzzy controller resembling an approximate neural network (NN) for SMB control. Simulation results demonstrate the controller’s effectiveness in achieving desirable outcomes for the SMB system. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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21 pages, 5215 KiB  
Article
A Timestep-Adaptive-Diffusion-Model-Oriented Unsupervised Detection Method for Fabric Surface Defects
by Shancheng Tang, Zicheng Jin, Ying Zhang, Jianhui Lu, Heng Li and Jiqing Yang
Processes 2023, 11(9), 2615; https://doi.org/10.3390/pr11092615 - 01 Sep 2023
Cited by 1 | Viewed by 1062
Abstract
Defect detection is crucial in quality control for fabric production. Deep-learning-based unsupervised reconstruction methods have been recognized universally to address the scarcity of fabric defect samples, high costs of labeling, and insufficient prior knowledge. However, these methods are subject to several weaknesses in [...] Read more.
Defect detection is crucial in quality control for fabric production. Deep-learning-based unsupervised reconstruction methods have been recognized universally to address the scarcity of fabric defect samples, high costs of labeling, and insufficient prior knowledge. However, these methods are subject to several weaknesses in reconstructing defect images into defect-free images with high quality, like image blurring, defect residue, and texture inconsistency, resulting in false detection and missed detection. Therefore, this article proposes an unsupervised detection method for fabric surface defects oriented to the timestep adaptive diffusion model. Firstly, the Simplex Noise–Denoising Diffusion Probabilistic Model (SN-DDPM) is constructed to recursively optimize the distribution of the posterior latent vector, thus gradually approaching the probability distribution of surface features of the defect-free samples through multiple iterative diffusions. Meanwhile, the timestep adaptive module is utilized to dynamically adjust the optimal timestep, enabling the model to flexibly adapt to different data distributions. During the detection, the SN-DDPM is employed to reconstruct the defect images into defect-free images, and image differentiation, frequency-tuned salient detection (FTSD), and threshold binarization are utilized to segment the defects. The results reveal that compared with the other seven unsupervised detection methods, the proposed method exhibits higher F1 and IoU values, which are increased by at least 5.42% and 7.61%, respectively, demonstrating that the proposed method is effective and accurate. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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13 pages, 2493 KiB  
Article
Detection of Large Foreign Objects on Coal Mine Belt Conveyor Based on Improved
by Kaifeng Huang, Shiyan Li, Feng Cai and Ruihong Zhou
Processes 2023, 11(8), 2469; https://doi.org/10.3390/pr11082469 - 16 Aug 2023
Cited by 2 | Viewed by 1066
Abstract
An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and [...] Read more.
An algorithm based on the YOLOv5 model is proposed to address safety incidents such as tearing and blockage at transfer points on belt conveyors in coal mines caused by foreign objects mixed in with the coal flow. Given the tough underground conditions and images acquired with low quality, recursive filtering and MSRCR image enhancement algorithms were utilized to preprocess the dynamic images collected by underground monitoring devices, substantially enhancing image quality. The YOLOv5 model has been improved by introducing a multi-scale attention module (MSAM) during the channel map slicing, thereby increasing the model’s resistance to interference from redundant image features. Deep separable convolution was utilized in place of conventional convolution to detect, identify, and process large foreign objects on the belt conveyor as well as to increase detection speed. The MSAM-YOLOv5 model was trained before being installed on the NVIDIA Jetson Xavier NX platform and utilized to identify videos gathered from the coal mine belt conveyor. According to the experimental findings, the upgraded MSAM-YOLOv5 model has a greater recognition accuracy than YOLOv5L, with an average recall rate for different foreign objects of 96.27%, an average detection accuracy of 97.35%, and a recognition speed of 44 frames/s. The algorithm assures detection accuracy while increasing detection speed, satisfying the requirements for large foreign object detection on belt conveyors in coal mines. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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15 pages, 2731 KiB  
Article
Application of an Improved Link Prediction Algorithm Based on Complex Network in Industrial Structure Adjustment
by Yixuan Ma, Rui Zhao and Nan Yin
Processes 2023, 11(6), 1689; https://doi.org/10.3390/pr11061689 - 01 Jun 2023
Cited by 2 | Viewed by 1165
Abstract
For a healthy industrial structure (IS) and stable economic development in China, this study proposes an improved link prediction algorithm (LP) based on complex networks. The algorithm calculates the similarity by constructing a mixed similarity index. A regional IS network model is built [...] Read more.
For a healthy industrial structure (IS) and stable economic development in China, this study proposes an improved link prediction algorithm (LP) based on complex networks. The algorithm calculates the similarity by constructing a mixed similarity index. A regional IS network model is built in the study, and the direction of IS adjustment is calculated with the mixed similarity indicators. In this study, the prediction accuracy of the proposed improved LP algorithm in the real network dataset is up to 0.944, which is significantly higher than that of the other algorithms. In the reality of IS optimization, industries of high similarity could be obtained through similarity algorithms, and reasonable coordinated development strategies are proposed. In addition, the simulated IS adjustment strategy in this study shows that it is highly sustainable in development, which is reflected in its lower carbon emissions. The optimization of IS adjustment could be achieved through IS network model and the improved LP algorithm. This study provides valuable suggestions for China’s regional industrial structure adjustment. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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14 pages, 3619 KiB  
Article
Parameter Optimization and Control Strategy of Hybrid Electric Vehicle Transmission System based on Improved GA Algorithm
by Daobao Luo, Wujun Ji and Xin Hu
Processes 2023, 11(5), 1554; https://doi.org/10.3390/pr11051554 - 18 May 2023
Viewed by 1224
Abstract
Most of the traditional hybrid electric vehicles (HEVs) choose to optimize the transmission ratio parameters, and the parameter changes of the whole vehicle and other components are only calculated as fixed values. It is difficult to give consideration to the optimization of the [...] Read more.
Most of the traditional hybrid electric vehicles (HEVs) choose to optimize the transmission ratio parameters, and the parameter changes of the whole vehicle and other components are only calculated as fixed values. It is difficult to give consideration to the optimization of the economy and power of hybrid vehicles. Therefore, the research proposes to build the transmission ratio, the required power of the vehicle’s working mode, and other models through the dynamic analysis. The parameters of the whole vehicle are optimized on the basis of parameter matching. At the same time, this paper chooses to adopt a hybrid optimization algorithm, combining particle swarm optimization (PSO) and genetic algorithm (GA). The weighted average method and constraint method are used to design the fitness function. The simulation experiment is carried out by Cruise software and MATLAB. Compare the iterative fitness of the PSO-GA algorithm with the traditional PSO and GA algorithm. It can be concluded that PSO-GA converges at the 12th iteration, with an average optimal fitness of 0.5239, which is higher than the traditional algorithm. At the same time, the parameter optimization of PSO-GA and the simulated annealing algorithm is compared. It is found that in the same task, the gasoline consumption after SA algorithm optimization is 0.561 L, while the fuel consumption under PSO-GA algorithm optimization is 0.475 L. The method proposed in this study has improved the power and economy of the HEV model and is effective. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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17 pages, 4784 KiB  
Article
The Application of the Gesture Analysis Method Based on Hybrid RF and CNN Algorithms in an IoT–VR Human–Computer Interaction System
by Xin Li and Shuli He
Processes 2023, 11(5), 1348; https://doi.org/10.3390/pr11051348 - 27 Apr 2023
Viewed by 852
Abstract
With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human–machine interaction module for IoT–VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used [...] Read more.
With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human–machine interaction module for IoT–VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used in this study to build an intelligent gesture recognition model. The experiments were conducted to test the application performance of the design model. The test results show that the qualification rate of the analytical model designed in this study is significantly higher than that of the comparative model. When the threshold is determined to be 43.26 mm, the analytical qualification rates of the RF-CNN (the method of combining RF with CNN algorithms), faster regions with CNN features (Faster-RCNN), and RF models are 82.41%, 76.10%, and 59.10%, respectively. The calculation time of the RF–CNN model is between the two comparative models. From the test data, it can be observed that the research results have certain significance for improving the accuracy of gesture machine recognition technology in China’s VR Internet of Things (IoT) system. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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29 pages, 1506 KiB  
Article
Evaluation Methodology of Interoperability for the Industrial Domain: Standardization vs. Mediation
by Yuhan Chen, David Annebicque, Alexandre Philippot, Véronique Carré-Ménétrier and Thierry Daneau
Processes 2023, 11(4), 1274; https://doi.org/10.3390/pr11041274 - 19 Apr 2023
Cited by 1 | Viewed by 1159
Abstract
With the arrival of Industry 4.0, interoperability has become a major subject for companies worldwide. It is a crucial asset that enables new technologies and possibilities (Industrial Internet of Things, predictive maintenance or traceability solutions). With the increasing importance of data in business [...] Read more.
With the arrival of Industry 4.0, interoperability has become a major subject for companies worldwide. It is a crucial asset that enables new technologies and possibilities (Industrial Internet of Things, predictive maintenance or traceability solutions). With the increasing importance of data in business use cases, companies are faced with a choice between two interoperability approaches to deal with the challenge of reconciling different domains: standardization and mediation. This paper presents an analysis of each approach and proposes a decision-making methodology based on the Analytic Hierarchy Process (AHP) that aims to help companies in choosing the most suitable solution to resolve interoperability challenges. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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13 pages, 1708 KiB  
Article
Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique
by Kunhao Chen, Zhendong Huang, Cheng Chen, Yijia Cheng, Yuanbiao Shang, Pengcheng Zhu, Haoye Jv, Lanlan Li, Weili Li and Shuyi Wang
Processes 2023, 11(4), 1208; https://doi.org/10.3390/pr11041208 - 14 Apr 2023
Viewed by 1403
Abstract
A method of steel structure surface crack identification based on artificial intelligence technology is proposed to solve the problem that steel cracks can not be detected and forewarned in time when they appear in the railway industrial environment. The appearance of steel cracks [...] Read more.
A method of steel structure surface crack identification based on artificial intelligence technology is proposed to solve the problem that steel cracks can not be detected and forewarned in time when they appear in the railway industrial environment. The appearance of steel cracks greatly weakens the stability of steel structures, and will seriously endanger the safety of the railway industry if it is not detected and repaired in time. However, the common steel crack detection methods cannot achieve real-time monitoring of steel structures. In order to monitor the surface of steel structure in real-time and explore the recognition effect and model the advantages of common classification neural network models for surface cracks of railway industrial steel, this study evaluates the network model with multiple indicators and parameters under two experimental conditions. In this study, the steel surface cracks in the railway industrial environment are taken as samples, and the steel cracks are identified through the neural network model. For large-volume datasets, the recognition accuracy of the three network models has reached 97%, of which the YOLOv5 model has the best comprehensive recognition ability, and the C-Alex model has the best performance and convergence speed in small-volume datasets. This study explores the application prospects of models under different scenarios, proving that the three models can effectively detect steel surface cracks in real-time, and at the same time, it will pave the way for the development and application of artificial intelligence multi-model fusion technology in the field of the railway industry. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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15 pages, 1713 KiB  
Article
Design and Evaluation of Regenerated Landscapes of Factory Sites Based on Evaluation Factors
by Kejia Zhang, Yue Han, Tianlong Chai, Yanyan Xu and Hao Wang
Processes 2023, 11(3), 681; https://doi.org/10.3390/pr11030681 - 23 Feb 2023
Cited by 2 | Viewed by 1208
Abstract
Research in the field of industrial heritage regeneration suffers from high subjectivity and low reliability in design and evaluation. The study takes seven regeneration projects of the Kailuan family of industrial heritage as examples, designs four optimisation and improvement strategies and constructs an [...] Read more.
Research in the field of industrial heritage regeneration suffers from high subjectivity and low reliability in design and evaluation. The study takes seven regeneration projects of the Kailuan family of industrial heritage as examples, designs four optimisation and improvement strategies and constructs an evaluation system containing four intermediate layers and 23 indicator layers based on the evaluation factor method to realise the evaluation of design solutions. The average evaluation value of the four intermediate layers was approximately 0.65, and the average evaluation value of the 23 evaluation indicators was approximately 0.68. The evaluation values of the four intermediate layers for the seven Kailuan projects showed that the evaluation of the park and the evaluation of the participants’ perceptions were roughly higher than the standard values, while the evaluation of the buildings and the evaluation of the environmental image had lower evaluation values. The correlations between the park evaluation and the other three intermediate level evaluation indicators were all over 0.500, and all had positive correlations, while the correlations between any two of the remaining indicators were weak and not statistically significant. The regenerative landscape design and evaluation of the Kailuan system of factory heritage enriches the current landscape design evaluation system and provides corresponding optimisation strategies for landscape optimisation design. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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12 pages, 1946 KiB  
Article
Application of Parametric Design in the Optimization of Traditional Landscape Architecture
by Yue Han, Kejia Zhang, Yanyan Xu, Hao Wang and Tianlong Chai
Processes 2023, 11(2), 639; https://doi.org/10.3390/pr11020639 - 20 Feb 2023
Cited by 6 | Viewed by 2156
Abstract
Parametric design, with its unique scientific and logical nature, is gradually applied in the field of landscape design. Therefore, the GIS (geographic information systems) technology of parametric software was applied to the optimization of traditional landscape architecture, and its practical application quality was [...] Read more.
Parametric design, with its unique scientific and logical nature, is gradually applied in the field of landscape design. Therefore, the GIS (geographic information systems) technology of parametric software was applied to the optimization of traditional landscape architecture, and its practical application quality was discussed. The actual analysis results showed that the evaluation result of parametric design had the highest score of 7.71 in behavioral perception. The overall score was 7.28, showing a high scientific nature. In the evaluation of landscape environmental benefits, after the optimization of landscape greening by parametric design, the air cleanliness and living comfort were generally improved, compared with those before optimization, and the highest values were 11.97 ± 6.01 and 5.86 ± 2.11 respectively. In the evaluation of the economic benefits of gardens, the economic income of gardens in the past 8 years generally increased, reaching the highest of 3.5795 billion yuan, with a growth rate of 78.92%. At the same time, the return on investment reached 26.17%, far exceeding the expected 20%. Among the social benefits, the weight of increasing employment opportunities was the largest at 0.36. In summary, parameterized optimization of traditional landscape design can effectively improve its social, environmental, and economic benefits and has good practical value. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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22 pages, 5646 KiB  
Article
Predictive Control Method of Reaming up in the Raise Boring Process Using Kernel Based Extreme Learning Machine
by Guoye Jing, Wei Yan and Fuwen Hu
Processes 2023, 11(1), 277; https://doi.org/10.3390/pr11010277 - 14 Jan 2023
Viewed by 1681
Abstract
Raise boring is an important method to construct the underground shafts of mines and other underground infrastructures, by drilling down the pilot hole and then reaming up to the desired diameter. Seriously different from the drilling operations of the mechanical parts in mechanized [...] Read more.
Raise boring is an important method to construct the underground shafts of mines and other underground infrastructures, by drilling down the pilot hole and then reaming up to the desired diameter. Seriously different from the drilling operations of the mechanical parts in mechanized mass production, it is very difficult to obtain a good consistency in the construction environments of each raise or shaft, to be more exact, every construction process is highly customized. The underground bottom-up reaming process is impossible to be observed directly, and the rock breaking effect is very difficult to be measured in real-time, due to the rock debris freely falling under the excavated shaft. The optimal configurations of the operational parameters in the drilling and working pressures, torque, rotation speed and penetration speed, mainly depend on the accumulation of construction experience or empirical models. To this end, we presented a machine learning method, based on the extreme learning machine, to determine in real-time, the relationships between the working performance and the operational parameters, and the physical-mechanical properties of excavated geologic zones, aiming at a higher production or excavation rate, safer operation and minimum ground disturbance. This research brings out new possibilities to revolutionize the process planning paradigm of the raise boring method that traditionally depends on experience or subject matter expertise. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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20 pages, 6024 KiB  
Article
Two-Dimensional Age Replacement Decision for Structural Dependence Parallel Systems via Intelligent Optimization Algorithm
by Enzhi Dong, Zhonghua Cheng, Rongcai Wang and Shuai Yue
Processes 2022, 10(12), 2519; https://doi.org/10.3390/pr10122519 - 28 Nov 2022
Cited by 1 | Viewed by 990
Abstract
From large-scale aerospace systems to household appliances and other systems in daily life, the application of parallel systems is involved. A parallel system is a typical structural dependence multi-component system, in addition to a series system and hybrid system. This paper takes a [...] Read more.
From large-scale aerospace systems to household appliances and other systems in daily life, the application of parallel systems is involved. A parallel system is a typical structural dependence multi-component system, in addition to a series system and hybrid system. This paper takes a parallel system as the research object and minimizes the expected cost rate or maximizes the availability by determining the optimal two-dimensional age replacement interval. The structural dependence of the components is described by the copula function, and the system life model is established. Based on the system life model, the two-dimensional age replacement expected cost rate model and availability model are proposed. In case analysis, the simulated annealing algorithm (SAA), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to find the optimal warranty scheme for the engine fuel fine filter. SAA can converge faster and find a warranty scheme that makes the warranty cost rate lower or the availability higher. Compared with one-dimensional age replacement, two-dimensional age replacement strategy has more advantages in saving warranty costs and improving system availability. Finally, rationalization suggestions are put forward for managers to make maintenance decisions through comparative analysis and sensitivity analysis. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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18 pages, 7942 KiB  
Article
Purity Control Based on a Type-II Fuzzy Controller for a Simulated Moving Bed
by Chao-Fan Xie and Rey-Chue Hwang
Processes 2022, 10(11), 2437; https://doi.org/10.3390/pr10112437 - 17 Nov 2022
Viewed by 1186
Abstract
The control of a simulated moving bed (SMB) is always a challenging chemical control topic due to its complexity and nonlinearity. Its mathematical model must undergo an affine transformation and digitization before it can be controlled. Basically, there are three aspects that need [...] Read more.
The control of a simulated moving bed (SMB) is always a challenging chemical control topic due to its complexity and nonlinearity. Its mathematical model must undergo an affine transformation and digitization before it can be controlled. Basically, there are three aspects that need to be considered in the nonlinear control of an SMB. First, the nonlinear characteristics are more complicated due to the switching time parameters of discrete events. Second, the control objective is not to minimize the control output error, but to make the separated concentrations between the components of the substance reach a certain ratio. Finally, the control variables are highly coupled. So far, the vast majority of the industry still uses relatively simple PLC controls; a few use specific controllers based on materials to be separated such as model predictive controls and PID controllers. Therefore, there is no unified intelligent processing mode. In this paper, a type-II fuzzy controller is presented and used as an SMB control. The interference of the related parameters was tested to observe the stability and robustness of the controller. The type-II fuzzy control was based on type-II fuzzy sets, which resulted in the type-II fuzzy controller having more flexible attribution function values. The results showed that the type-II fuzzy controller was not only more accurate in the control, but also better for robustness and adaptability than an ordinary fuzzy controller and PID controller. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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14 pages, 581 KiB  
Article
Causal Plot: Causal-Based Fault Diagnosis Method Based on Causal Analysis
by Yoshiaki Uchida, Koichi Fujiwara, Tatsuki Saito and Taketsugu Osaka
Processes 2022, 10(11), 2269; https://doi.org/10.3390/pr10112269 - 03 Nov 2022
Cited by 2 | Viewed by 2001
Abstract
Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does [...] Read more.
Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are identified as causes of faults. In this study, the proposed causal plot was applied to fault diagnosis problems of a vinyl acetate monomer (VAM) manufacturing process. The application results showed that the proposed causal plot diagnosed appropriate causes of faults even when conventional contribution plots could not do the same. In addition, we discuss the effects of the presence of a recycle flow on fault diagnosis results based on the analysis result of the VAM process. The proposed causal plot contributes to realizing safe and efficient process operations. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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16 pages, 5974 KiB  
Article
The Estimation of Centrifugal Pump Flow Rate Based on the Power–Speed Curve Interpolation Method
by Yuezhong Wu, Denghao Wu, Minghao Fei, Gang Xiao, Yunqing Gu and Jiegang Mou
Processes 2022, 10(11), 2163; https://doi.org/10.3390/pr10112163 - 22 Oct 2022
Cited by 3 | Viewed by 2240
Abstract
During the global energy crisis, it is essential to improve the energy efficiency of pumps by adjusting the pump’s control strategy according to the operational states. However, monitoring the pump’s operational states with the help of external sensors brings both additional costs and [...] Read more.
During the global energy crisis, it is essential to improve the energy efficiency of pumps by adjusting the pump’s control strategy according to the operational states. However, monitoring the pump’s operational states with the help of external sensors brings both additional costs and risks of failure. This study proposed an interpolation method based on PN curves (power–speed curves) containing information regarding motor shaft power, speed, and flow rate to achieve high accuracy in predicting the pump’s flow rates without flow sensors. The impact factors on the accuracy of the estimation method were analyzed. Measurements were performed to validate the feasibility and robustness of the PN curve interpolation method and compared with the QP and back-propagation neural network (BPNN) methods. The results indicated that the PN curve interpolation method has lower errors than the other two prediction models. Moreover, the average absolute errors of the PN curve interpolation method in the project applications at 47.5 Hz, 42.5 Hz, 37.5 Hz, and 32.5 Hz are 0.1442 m3/h, 0.2047 m3/h, 0.2197 m3/h, and 0.1979 m3/h. Additionally, the average relative errors are 2.0816%, 3.2875%, 3.6981%, and 2.9419%. Hence, this method fully meets the needs of centrifugal pump monitoring and control. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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16 pages, 6987 KiB  
Article
Analysis of the Electromagnetic Coupling Characteristics of Dual-Load Wireless Power Transmission Channels
by Ming Xue, Hu Hou, Longnv Li, Jianwu Guo, Pengcheng Zhang and Gaojia Zhu
Processes 2022, 10(10), 1931; https://doi.org/10.3390/pr10101931 - 24 Sep 2022
Viewed by 1166
Abstract
This paper studies the characteristics of four dual-load systems, coaxial different sides, coaxial same side, different axis same side, and coplanar coaxial, based on the principle of near-field resonance wireless power transmission. Firstly, a dual-load cooperative coupling equivalent circuit model is established, the [...] Read more.
This paper studies the characteristics of four dual-load systems, coaxial different sides, coaxial same side, different axis same side, and coplanar coaxial, based on the principle of near-field resonance wireless power transmission. Firstly, a dual-load cooperative coupling equivalent circuit model is established, the mathematical expression of system energy efficiency is derived, and the influence of the cross-coupling of the receiving coils in the system on the performance of the system is compared and analyzed in four coupling situations. Secondly, the magnetic field distribution characteristics and the influence of transmission distance and load impedance on the transmission characteristics are studied by electromagnetic field simulation software. Finally, the experimental results show that the maximum efficiency of the dual-load wireless power transmission system is only 0.43 in the case of coaxial and coplanar coupling, which is not suitable for power transmission. In the case of coaxial coupling on the same side, the received power of dual loads has a large difference, which is suitable for electrical equipment with different power requirements. When the coupling distance between the transmitting and receiving coils changes synchronously in the case of coaxial opposite-side and different axis same-side coupling, the efficiency of the former persisting declination and the efficiency of the latter rise first and then drop. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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16 pages, 4084 KiB  
Article
Research on Evaluation Method of Electric Vehicle Wireless Charging Interoperability Based on Two Parameter Representation
by Lin Sha, Jiangang Liu and Zhixin Chen
Processes 2022, 10(8), 1591; https://doi.org/10.3390/pr10081591 - 12 Aug 2022
Cited by 3 | Viewed by 2017
Abstract
The interoperability of wireless charging for electric vehicles refers to the radio energy transmission that meets the performance and function requirements of different manufacturers and different models of electric vehicles on the premise of meeting the relevant requirements. If it fails to meet [...] Read more.
The interoperability of wireless charging for electric vehicles refers to the radio energy transmission that meets the performance and function requirements of different manufacturers and different models of electric vehicles on the premise of meeting the relevant requirements. If it fails to meet the requirements, the wireless charging system of electric vehicles has difficulty to realize interconnection and low charging efficiency, Therefore, how to evaluate the interoperability is a key issue in the promotion of electric vehicle wireless charging. In this paper, an interoperability evaluation method based on two parameters is proposed. The interoperability impedance plane is constructed by the system detuning coefficient A. The comprehensive evaluation of different compensation networks and coupling coils is realized; the power characteristic impedance ε is obtained by analyzing and calculating the relationship between the transmission power of the system while the system impedance, and the transmission power evaluation of the wireless power transmission system is realized. At the same time, according to simulation and experiment, it was verified that A meets the interoperability requirements when A is in the range of (−0.62, 0.62) in the aligned position and (−0.75, 0.75) in the offset position. When the input voltage is 200 V, when ε satisfies 0.1925 ≥ ε > 0.0925, the system WPT2 power level transmission interoperability requirements are met. The method in this paper can guide the interoperability evaluation of electric vehicle wireless charging. Full article
(This article belongs to the Special Issue Data-Driven Modeling, Control and Optimization of Complex Industrial Processes)
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