Special Issue "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 (30 September 2021).

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 papers will be 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 2000 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 (7 papers)

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Research

Article
Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network
Processes 2021, 9(8), 1475; https://doi.org/10.3390/pr9081475 - 23 Aug 2021
Viewed by 333
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
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Article
Group Acceptance Sampling Plan Using Marshall–Olkin Kumaraswamy Exponential (MOKw-E) Distribution
Processes 2021, 9(6), 1066; https://doi.org/10.3390/pr9061066 - 18 Jun 2021
Viewed by 340
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
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Article
A TITO Control Strategy to Increase Productivity in Uncertain Exothermic Continuous Chemical Reactors
Processes 2021, 9(5), 873; https://doi.org/10.3390/pr9050873 - 16 May 2021
Viewed by 527
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
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Article
Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques
Processes 2021, 9(4), 710; https://doi.org/10.3390/pr9040710 - 16 Apr 2021
Cited by 2 | Viewed by 524
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
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Article
A Quality Integrated Fuzzy Inference System for the Reliability Estimating of Fluorochemical Engineering Processes
Processes 2021, 9(2), 292; https://doi.org/10.3390/pr9020292 - 03 Feb 2021
Viewed by 543
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
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Article
DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO
Processes 2020, 8(11), 1429; https://doi.org/10.3390/pr8111429 - 09 Nov 2020
Cited by 1 | Viewed by 521
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
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Article
A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System
Processes 2020, 8(11), 1365; https://doi.org/10.3390/pr8111365 - 28 Oct 2020
Cited by 2 | Viewed by 702
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
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