Processes

Editorial

5 pages, 191 KiB

Open AccessEditorial

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 814

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

Open AccessArticle

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 9 | Viewed by 2932

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)

► Show Figures

Figure 1

10 pages, 985 KiB

Open AccessArticle

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 8 | Viewed by 4855

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)

► Show Figures

Figure 1

16 pages, 3964 KiB

Open AccessArticle

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 1290

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)

► Show Figures

Figure 1

13 pages, 1991 KiB

Open AccessArticle

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 14 | Viewed by 2072

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)

► Show Figures

Figure 1

20 pages, 6490 KiB

Open AccessArticle

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 4 | Viewed by 3373

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)

► Show Figures

Figure 1

18 pages, 457 KiB

Open AccessFeature PaperArticle

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 1676

Abstract

This paper focuses on the problem of event-triggered

H_{\infty}

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_{\infty}

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_{\infty}

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)

► Show Figures

Figure 1

18 pages, 2684 KiB

Open AccessArticle

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 9 | Viewed by 2020

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)

► Show Figures

Figure 1

17 pages, 2917 KiB

Open AccessArticle

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 15 | Viewed by 2737

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)

► Show Figures

Figure 1

22 pages, 11218 KiB

Open AccessFeature PaperArticle

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 7 | Viewed by 2649

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)

► Show Figures

Figure 1

16 pages, 2572 KiB

Open AccessFeature PaperArticle

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 22 | Viewed by 5450

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)

► Show Figures

Figure 1

21 pages, 55136 KiB

Open AccessArticle

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 8 | Viewed by 2883

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)

► Show Figures

Figure 1

9 pages, 2895 KiB

Open AccessArticle

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 21 | Viewed by 2342

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)

► Show Figures

Figure 1

15 pages, 559 KiB

Open AccessArticle

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 9 | Viewed by 3376

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)

► Show Figures

Figure 1

22 pages, 4873 KiB

Open AccessArticle

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 14 | Viewed by 2358

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)

► Show Figures

Figure 1

15 pages, 21447 KiB

Open AccessArticle

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 2226

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)

► Show Figures

Figure 1

12 pages, 2546 KiB

Open AccessArticle

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 2154

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)

► Show Figures

Figure 1

17 pages, 5363 KiB

Open AccessArticle

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 6 | Viewed by 3176

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)

► Show Figures

Figure 1

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (18 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI