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Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for...
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Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 40074

Special Issue Editors

Dr. Jie Zhang

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: advanced process control; process fault detection and diagnosis; neural networks and neuro-fuzzy systems; multivariate statistical process control; optimal control of batch processes
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Meihong Wang

E-Mail Website
Guest Editor
Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S1 3JD, S Yorkshire, UK
Interests: process modelling/simulation; process optimisation; process control; carbon capture, utilisation and storage (CCUS); energy storage; bioenergy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) techniques have been developing very fast during the past two decades, with many new methods emerging. Novel machine learning techniques, such as deep learning, convolutional neural networks, deep belief networks, long short-term memory networks, and reinforcement learning, have been successfully applied to solve many complicated problems ranging from image processing to natural language processing. These novel CI techniques have also been applied to process systems engineering areas with many successful applications reported, such as data-driven modelling of nonlinear processes, inferential estimation and softsensors, intelligent process monitoring, and process optimisation based on CI techniques.

This Special Issue on “Neural Networks, Fuzzy Systems, and Other Computational Intelligence Techniques for Advanced Process Control” aims to curate novel advances in the development and application of computational inteligence to address longstanding challenges in process systems engineering. Topics include but are not limited to:

  • Data-driven modelling of industrial processes using machine learning techniques such as neural networks;
  • Intelligent process control using neural networks, fuzzy systems, and other computational intelligence techniques;
  • Intelligent image analysis in process systems engineering;
  • Inteligent process monitoring using computational intelligence techniques; and
  • Process optimisation using computational intelligence techniques.

Dr. Jie Zhang
Prof. Dr. Meihong Wang
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

  • neural networks
  • fuzzy systems
  • computational intelligence
  • machine learning
  • process modelling
  • process monitoring
  • intelligent control

Published Papers (18 papers)

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Editorial

Jump to: Research

5 pages, 191 KiB  
Editorial
Special Issue: Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control
by Jie Zhang and Meihong Wang
Processes 2023, 11(8), 2278; https://doi.org/10.3390/pr11082278 - 28 Jul 2023
Cited by 1 | Viewed by 532
Abstract
Computational intelligence (CI) techniques have developed very fast over the past two decades, with many new methods emerging [...] Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)

Research

Jump to: Editorial

35 pages, 1181 KiB  
Article
A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks
by Koon Meng Ang, Cher En Chow, El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Faten Khalid Karim, Doaa Sami Khafaga, Sew Sun Tiang and Wei Hong Lim
Processes 2022, 10(12), 2579; https://doi.org/10.3390/pr10122579 - 3 Dec 2022
Cited by 6 | Viewed by 2174
Abstract
Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used [...] Read more.
Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the algorithm’s robustness in handling various optimization problems. Finally, the proposed algorithm is formulated as a training algorithm of ANN to optimize its neuron weights, biases, and selection of activation function based on the given classification dataset. The ANN model trained by the proposed algorithm is reported to outperform those trained by existing PSO variants in terms of classification accuracy when solving the majority of selected datasets, suggesting its potential applications in challenging real-world problems, such as intelligent condition monitoring of complex industrial systems. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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10 pages, 985 KiB  
Article
Deep Reinforcement Learning for Traffic Light Timing Optimization
by Bin Wang, Zhengkun He, Jinfang Sheng and Yu Chen
Processes 2022, 10(11), 2458; https://doi.org/10.3390/pr10112458 - 20 Nov 2022
Cited by 3 | Viewed by 3079
Abstract
Existing inflexible and ineffective traffic light control at a key intersection can often lead to traffic congestion due to the complexity of traffic dynamics, how to find the optimal traffic light timing strategy is a significant challenge. This paper proposes a traffic light [...] Read more.
Existing inflexible and ineffective traffic light control at a key intersection can often lead to traffic congestion due to the complexity of traffic dynamics, how to find the optimal traffic light timing strategy is a significant challenge. This paper proposes a traffic light timing optimization method based on double dueling deep Q-network, MaxPressure, and Self-organizing traffic lights (SOTL), namely EP-D3QN, which controls traffic flows by dynamically adjusting the duration of traffic lights in a cycle, whether the phase is switched based on the rules we set in advance and the pressure of the lane. In EP-D3QN, each intersection corresponds to an agent, and the road entering the intersection is divided into grids, each grid stores the speed and position of a car, thus forming the vehicle information matrix, and as the state of the agent. The action of the agent is a set of traffic light phase in a signal cycle, which has four values. The effective duration of the traffic lights is 0–60 s, and the traffic light phases switching depends on its press and the rules we set. The reward of the agent is the difference between the sum of the accumulated waiting time of all vehicles in two consecutive signal cycles. The SUMO is used to simulate two traffic scenarios. We selected two types of evaluation indicators and compared four methods to verify the effectiveness of EP-D3QN. The experimental results show that EP-D3QN has superior performance in light and heavy traffic flow scenarios, which can reduce the waiting time and travel time of vehicles, and improve the traffic efficiency of an intersection. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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16 pages, 3964 KiB  
Article
Dual Input Fuzzy Logic Controllers for Closed Loop Hemorrhagic Shock Resuscitation
by David Berard, Saul J. Vega, Guy Avital and Eric J. Snider
Processes 2022, 10(11), 2301; https://doi.org/10.3390/pr10112301 - 5 Nov 2022
Cited by 4 | Viewed by 1057
Abstract
Hemorrhage remains a leading cause of preventable death in emergency situations, including combat casualty care. This is partially due to the high cognitive burden that constantly adjusting fluid resuscitation rates can require, especially in austere or mass casualty situations. Closed-loop control systems have [...] Read more.
Hemorrhage remains a leading cause of preventable death in emergency situations, including combat casualty care. This is partially due to the high cognitive burden that constantly adjusting fluid resuscitation rates can require, especially in austere or mass casualty situations. Closed-loop control systems have the potential to simplify hemorrhagic shock resuscitation if properly tuned for the application. We have previously compared 4 different controller types using a hardware-in-loop test platform that simulates hemorrhagic shock conditions, and we found that a dual input—(1) error from target and (2) rate of error change—fuzzy logic (DFL) controller performed best. Here, we highlight a range of DFL designs to showcase the tunability the controller can have for different hemorrhage scenarios. Five different controller setups were configured with different membership function logic to create more and less aggressive controller designs. Overall, the results for the different controller designs ranged from reaching the setup rapidly but often overshooting the target to more conservatively approaching the target, resulting in not reaching the target during high active hemorrhage rates. In conclusion, DFL controllers are well-suited for hemorrhagic shock resuscitation and can be tuned to meet the response rates set by clinical practice guidelines for this application. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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13 pages, 1991 KiB  
Article
SOC Estimation of E-Cell Combining BP Neural Network and EKF Algorithm
by Yun Gao, Wujun Ji and Xin Zhao
Processes 2022, 10(9), 1721; https://doi.org/10.3390/pr10091721 - 29 Aug 2022
Cited by 12 | Viewed by 1650
Abstract
Power lithium battery is an important core component of electric vehicles (EV), which provides the main power and energy for EV. In order to improve the estimation accuracy of the state of charge (SOC) of the electric vehicle battery (E-cell), the extended Kalman [...] Read more.
Power lithium battery is an important core component of electric vehicles (EV), which provides the main power and energy for EV. In order to improve the estimation accuracy of the state of charge (SOC) of the electric vehicle battery (E-cell), the extended Kalman filter (EKF) algorithm, and backpropagation neural network (BPNN) are used to build the SOC estimation model of the E-cell, and the self-learning characteristic of BP neural network is used to correct the error and track the SOC of the E-cell. The results show that the average error of SOC estimation of BP-EKF model is 0.347%, 0.0231%, and 0.0749%, respectively, under the three working conditions of constant current discharge, pulse discharge, and urban dynamometer driving schedule (UDDS). Under the influence of different initial value errors, the average estimation errors of BP-EKF model are 0.2218%, 0.0976%, and 0.5226%. After the noise interference is introduced, the average estimation errors of BP-EKF model under the three working conditions are 1.2143%, 0.2259%, and 0.5104%, respectively, which proves that the model has strong robustness and stability. Using the BP-EKF model to estimate and track the SOC of E-cell can provide data reference for vehicle battery management and is of great significance to improve the battery performance and energy utilization of EV. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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20 pages, 6490 KiB  
Article
Multi-Objective Optimization of a Crude Oil Hydrotreating Process with a Crude Distillation Unit Based on Bootstrap Aggregated Neural Network Models
by Wissam Muhsin and Jie Zhang
Processes 2022, 10(8), 1438; https://doi.org/10.3390/pr10081438 - 22 Jul 2022
Cited by 3 | Viewed by 2506
Abstract
This paper presents the multi-objective optimization of a crude oil hydrotreating (HDT) process with a crude atmospheric distillation unit using data-driven models based on bootstrap aggregated neural networks. Hydrotreating of the whole crude oil has economic benefit compared to the conventional hydrotreating of [...] Read more.
This paper presents the multi-objective optimization of a crude oil hydrotreating (HDT) process with a crude atmospheric distillation unit using data-driven models based on bootstrap aggregated neural networks. Hydrotreating of the whole crude oil has economic benefit compared to the conventional hydrotreating of individual oil products. In order to overcome the difficulty in developing accurate mechanistic models and the computational burden of utilizing such models in optimization, bootstrap aggregated neural networks are utilized to develop reliable data-driven models for this process. Reliable optimal process operating conditions are derived by solving a multi-objective optimization problem incorporating minimization of the widths of model prediction confidence bounds as additional objectives. The multi-objective optimization problem is solved using the goal-attainment method. The proposed method is demonstrated on the HDT of crude oil with crude distillation unit simulated using Aspen HYSYS. Validation of the optimization results using Aspen HYSYS simulation demonstrates that the proposed technique is effective. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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18 pages, 457 KiB  
Article
Event-Triggered Filtering for Delayed Markov Jump Nonlinear Systems with Unknown Probabilities
by Huiying Chen, Renwei Liu, Weifeng Xia and Zuxin Li
Processes 2022, 10(4), 769; https://doi.org/10.3390/pr10040769 - 14 Apr 2022
Cited by 3 | Viewed by 1345
Abstract
This paper focuses on the problem of event-triggered H∞ asynchronous filtering for Markov jump nonlinear systems with varying delay and unknown probabilities. An event-triggered scheduling scheme is adopted to decrease the transmission rate of measured outputs. The devised filter is mode dependent [...] Read more.
This paper focuses on the problem of event-triggered H∞ asynchronous filtering for Markov jump nonlinear systems with varying delay and unknown probabilities. An event-triggered scheduling scheme is adopted to decrease the transmission rate of measured outputs. The devised filter is mode dependent and asynchronous with the original system, which is represented by a hidden Markov model (HMM). Both the probability information involved in the original system and the filter are assumed to be only partly available. Under this framework, via employing the Lyapunov–Krasovskii functional and matrix inequality transformation techniques, a sufficient condition is given and the filter is further devised to ensure that the resulting filtering error dynamic system is stochastically stable with a desired H∞ disturbance attenuation performance. Lastly, the validity of the presented filter design scheme is verified through a numerical example. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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18 pages, 2684 KiB  
Article
Identification of Control Parameters for Converters of Doubly Fed Wind Turbines Based on Hybrid Genetic Algorithm
by Linlin Wu, Hui Liu, Jiaan Zhang, Chenyu Liu, Yamin Sun, Zhijun Li and Jingwei Li
Processes 2022, 10(3), 567; https://doi.org/10.3390/pr10030567 - 14 Mar 2022
Cited by 6 | Viewed by 1640
Abstract
The accuracy of doubly fed induction generator (DFIG) models and parameters plays an important role in power system operation. This paper proposes a parameter identification method based on the hybrid genetic algorithm for the control system of DFIG converters. In the improved genetic [...] Read more.
The accuracy of doubly fed induction generator (DFIG) models and parameters plays an important role in power system operation. This paper proposes a parameter identification method based on the hybrid genetic algorithm for the control system of DFIG converters. In the improved genetic algorithm, the generation gap value and immune strategy are adopted, and a strategy of “individual identification, elite retention, and overall identification” is proposed. The DFIG operation data information used for parameter identification considers the loss of rotor current, stator current, grid-side voltage, stator voltage, and rotor voltage. The operating data of a wind farm in Zhangjiakou, North China, were used as a test case to verify the effectiveness of the proposed parameter identification method for the Maximum Power Point Tracking (MPPT), constant speed, and constant power operation conditions of the wind turbine. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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17 pages, 2917 KiB  
Article
A Novel Fault Detection Scheme Based on Mutual k-Nearest Neighbor Method: Application on the Industrial Processes with Outliers
by Jian Wang, Zhe Zhou, Zuxin Li and Shuxin Du
Processes 2022, 10(3), 497; https://doi.org/10.3390/pr10030497 - 1 Mar 2022
Cited by 9 | Viewed by 2259
Abstract
The k-nearest neighbor (kNN) method only uses samples’ paired distance to perform fault detection. It can overcome the nonlinearity, multimodality, and non-Gaussianity of process data. However, the nearest neighbors found by kNN on a data set containing a lot of outliers or noises [...] Read more.
The k-nearest neighbor (kNN) method only uses samples’ paired distance to perform fault detection. It can overcome the nonlinearity, multimodality, and non-Gaussianity of process data. However, the nearest neighbors found by kNN on a data set containing a lot of outliers or noises may not be actual or trustworthy neighbors but a kind of pseudo neighbor, which will degrade process monitoring performance. This paper presents a new fault detection scheme using the mutual k-nearest neighbor (MkNN) method to solve this problem. The primary characteristic of our approach is that the calculation of the distance statistics for process monitoring uses MkNN rule instead of kNN. The advantage of the proposed approach is that the influence of outliers in the training data is eliminated, and the fault samples without MkNNs can be directly detected, which improves the performance of fault detection. In addition, the mutual protection phenomenon of outliers is explored. The numerical examples and Tenessee Eastman process illustrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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22 pages, 11218 KiB  
Article
Bearing Fault Diagnosis Based on a Novel Adaptive ADSD-gcForest Model
by Shuo Zhai, Zhenghua Wang and Dong Gao
Processes 2022, 10(2), 209; https://doi.org/10.3390/pr10020209 - 22 Jan 2022
Cited by 5 | Viewed by 2317
Abstract
With the continuous improvement of industrial production requirements, bearings work significantly under strong noise interference, which makes it difficult to extract fault features. Deep Learning-based approaches are promising for bearing diagnosis. They can extract fault information efficiently and conduct accurate diagnosis. However, the [...] Read more.
With the continuous improvement of industrial production requirements, bearings work significantly under strong noise interference, which makes it difficult to extract fault features. Deep Learning-based approaches are promising for bearing diagnosis. They can extract fault information efficiently and conduct accurate diagnosis. However, the structure of deep learning is often determined by trial and error, which is time-consuming and lacks theoretical support. To address the above problems, an adaptive (Adaptive Depthwise Separable Dilated Convolution and multi-grained cascade forest) ADSD-gcForest fault diagnosis model is proposed in this paper. Multiscale convolution combined with convolutional attention mechanism (CBAM) concentrates on effectively extracting fault information under strong noise, and the Meta-Activate or Not (Meta-ACON) activation function is integrated to adaptively optimize the model structure according to the characteristics of input samples, then gcForest outputs the final diagnosis result as the classifier. The experiment compares the effects of three bearings failure diagnoses under various noise and load conditions. The experimental results show the effectiveness and practicability of the proposed method. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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16 pages, 2572 KiB  
Article
A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling
by Yanxia Yang, Pu Wang and Xuejin Gao
Processes 2022, 10(1), 140; https://doi.org/10.3390/pr10010140 - 10 Jan 2022
Cited by 12 | Viewed by 4350
Abstract
A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it [...] Read more.
A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theoretically in the case of structure fixation and structure adjustment. Finally, the performance of the proposed RBFNN-GP is compared with some popular algorithms, using two numerical simulations and a practical application. The comparison results verified the effectiveness of RBFNN-GP. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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21 pages, 55136 KiB  
Article
Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network
by Ming-Li Zhang, Yi-Jie Zhang, Xiao-Long He and Zheng-Jie Gao
Processes 2021, 9(8), 1475; https://doi.org/10.3390/pr9081475 - 23 Aug 2021
Cited by 5 | Viewed by 2292
Abstract
In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters [...] Read more.
In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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9 pages, 2895 KiB  
Article
Group Acceptance Sampling Plan Using Marshall–Olkin Kumaraswamy Exponential (MOKw-E) Distribution
by Abdullah M. Almarashi, Khushnoor Khan, Christophe Chesneau and Farrukh Jamal
Processes 2021, 9(6), 1066; https://doi.org/10.3390/pr9061066 - 18 Jun 2021
Cited by 17 | Viewed by 1899
Abstract
The current research concerns the group acceptance sampling plan in the case where (i) the lifetime of the items follows the Marshall–Olkin Kumaraswamy exponential distribution (MOKw-E) and (ii) a large number of items, considered as a group, can be tested at [...] Read more.
The current research concerns the group acceptance sampling plan in the case where (i) the lifetime of the items follows the Marshall–Olkin Kumaraswamy exponential distribution (MOKw-E) and (ii) a large number of items, considered as a group, can be tested at the same time. When the consumer’s risk and the test terminsation period are defined, the key design parameters are extracted. The values of the operating characteristic function are determined for different quality levels. At the specified producer’s risk, the minimum ratios of the true average life to the specified average life are also calculated. The results of the present study will set the platform for future research on various nano quality level topics when the items follow different probability distributions under the Marshall–Olkin Kumaraswamy scheme. Real-world data are used to explain the technique. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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15 pages, 559 KiB  
Article
A TITO Control Strategy to Increase Productivity in Uncertain Exothermic Continuous Chemical Reactors
by Ricardo Aguilar-López, Juan Luis Mata-Machuca and Valeria Godinez-Cantillo
Processes 2021, 9(5), 873; https://doi.org/10.3390/pr9050873 - 16 May 2021
Cited by 8 | Viewed by 2840
Abstract
In this manuscript, a two-input two-output (TITO) control strategy for an exothermic continuous chemical reactor is presented. The control tasks of the continuous chemical reactor are related to temperature regulation by a standard proportional-integral (PI) controller. The selected set point increases reactor productivity [...] Read more.
In this manuscript, a two-input two-output (TITO) control strategy for an exothermic continuous chemical reactor is presented. The control tasks of the continuous chemical reactor are related to temperature regulation by a standard proportional-integral (PI) controller. The selected set point increases reactor productivity due to the temperature effect and prevents potential thermal runaway, and the temperature increases until it reaches isothermal operating conditions. Then, an optimal controller is activated to increase the mass reactor productivity. The optimal control strategy is based on a Euler-Lagrange framework, in which the corresponding Lagrangian is based on the model equations of the reactor, and the optimal controller is coupled with an uncertainty estimator to infer the unknown terms required by the proposed controller. As a benchmark, a continuous stirred tank reactor (CSTR) with a Van de Vusse chemical reaction is considered as an application case study. Notably, the proposed methodology is generally applicable to any continuous stirred tank reactor. The results of numerical experiments verify the satisfactory performance of the proposed control strategy. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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22 pages, 4873 KiB  
Article
Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques
by Zhihui Zhang, Hongyu Wei, Wei Zhang and Jianan Jiang
Processes 2021, 9(4), 710; https://doi.org/10.3390/pr9040710 - 16 Apr 2021
Cited by 12 | Viewed by 1976
Abstract
Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To [...] Read more.
Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To improve the steady-state performance of the IM drives, this paper proposes an improved FCS-MPTC strategy, based on a novel fuzzy adaptive speed controller and an adaptive weighting factor, tuning strategy to reduce the speed, torque and flux ripples caused by different factors. Firstly, a discrete predicting plant model (PPM) with a new flux observer is established, laying the ground for achieving an FCS-MPTC algorithm accurately. Secondly, after analyzing the essential factors in establishing a fuzzy adaptive PI controller, with high ripple suppression capacity, an improved three-dimensional controller is designed. Simultaneously, the implementation procedures of the fuzzy adaptive PI controller-based FCS-MPTC are presented. Considering that a weighting factor must be employed in the cost function of an FCS-MPTC method, system ripples increase if the value of the weighting factor is inappropriate. Then, on that basis, a novel fuzzy adaptive theory-based weighting factor tuning strategy is proposed, with the real-time torque and flux performance balanced. Finally, both simulation and hardware-in-loop (HIL) test are conducted on a 1.1 kW IM drive to verify the proposed ripple reduction algorithms. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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15 pages, 21447 KiB  
Article
A Quality Integrated Fuzzy Inference System for the Reliability Estimating of Fluorochemical Engineering Processes
by Feng Xue, Xintong Li, Kun Zhou, Xiaoxia Ge, Weiping Deng, Xu Chen and Kai Song
Processes 2021, 9(2), 292; https://doi.org/10.3390/pr9020292 - 3 Feb 2021
Cited by 2 | Viewed by 1904
Abstract
Hypertoxic materials make it critical to ensure the safety of the fluorochemical engineering processes. This mainly depends on the over maintenance or the manual operations due to the lack of precise models and mechanism knowledge. To quantify the deviations of the operating variables [...] Read more.
Hypertoxic materials make it critical to ensure the safety of the fluorochemical engineering processes. This mainly depends on the over maintenance or the manual operations due to the lack of precise models and mechanism knowledge. To quantify the deviations of the operating variables and the product quality from their target values at the same time and to overcome the measurement delay of the product quality, a novel quality integrated fuzzy inference system (QFIS) was proposed to estimate the reliability of the operation status as well as the product quality to enhance the performance of the safety monitoring system. To this end, a novel quality-weighted multivariate inverted normal loss function was proposed to quantify the deviation of the product quality from the target value to overcome the measurement delay. Vital safety process variables were identified according to the expert knowledge. Afterward, the quality loss and the vital variables were inputs to an elaborate fuzzy inference system to estimate the process reliability of the fluorochemical engineering processes. By integrating the abundant expert knowledge and a data-driven quality prediction model to design the fuzzy rules of QFIS, not only the operation reliability but also the product quality can be monitored on-line. Its superiority in estimating system reliability has been strongly proved by the application of a real fluorochemical engineering process located in East China. Moreover, the application of the Tennessee Eastman process also confirmed its generalization performance for other complicated black-box chemical processes. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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12 pages, 2546 KiB  
Article
DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO
by Jui-Chung Hung
Processes 2020, 8(11), 1429; https://doi.org/10.3390/pr8111429 - 9 Nov 2020
Cited by 6 | Viewed by 1854
Abstract
In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order [...] Read more.
In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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17 pages, 5363 KiB  
Article
A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System
by Yuan Liu, Song Xu, Seiji Hashimoto and Takahiro Kawaguchi
Processes 2020, 8(11), 1365; https://doi.org/10.3390/pr8111365 - 28 Oct 2020
Cited by 5 | Viewed by 2672
Abstract
Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to [...] Read more.
Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified. Full article
(This article belongs to the Special Issue Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control)
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