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

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 33259

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Special Issue Editor

Dr. Zhiwei Gao

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Guest Editor

Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Interests: power conversion systems; machine learning; intelligent optimization; condition monitoring; tolerant control; wind turbine systems; offshore renewable energy; complex systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial automation systems, such as chemical processes, manufacturing processes, power networks, transportation systems, sustainable energy systems, wireless sensor networks, robotic systems, and biomedical systems, are becoming more complex, but more expensive, and have higher requirements for operation performance, quality of products, productiveness, and reliability. Stimulated by Industry 4.0, automation industries are keen to improve the reliability and operational performance of complex industrial processes by using advanced modelling, monitoring, optimization and control techniques. Recently, artificial intelligence, data-driven techniques, cyber–physical systems, digital-twin, and cloud computation have further stimulated research and applications of modelling, monitoring, optimization and control techniques.

The Special Issue on "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes " aims to provide a forum for researchers and engineers to report their recent results, exchange research ideas, and overlook emerging research and application directions in modelling, condition monitoring, optimization and advanced control for complex industrial processes.

Potential topics include, but are not limited to, the following:

Complex dynamic analysis and modelling of industrial processes
Data-driven modelling approaches for complex industrial processes
Multiscale dynamic simulations of industrial processes
Condition monitoring and fault diagnosis techniques
Prognosis and predictive maintenance for complex industrial systems
Advanced optimization methodologies
Resilient control techniques
Advanced approaches and techniques for cyber–physical systems
Validation and real-time applications

Dr. Zhiwei Gao
Guest Editor

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 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

data-driven modeling and simulations
advanced control
intelligent optimization
complex industrial processes
dynamic analysis
real-time validation and applications

Published Papers (23 papers)

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Editorial

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Open AccessEditorial

Special Issue on “Modelling, Monitoring, Control and Optimization for Complex Industrial Processes”

Zhiwei Gao

Processes 2023, 11(1), 207; https://doi.org/10.3390/pr11010207 - 09 Jan 2023

Viewed by 711

Abstract

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

Research

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Open AccessArticle

Dynamically Triggering Resilient Control for Networked Nonlinear Systems under Malicious Aperiodic DoS Attacks

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Processes 2022, 10(12), 2627; https://doi.org/10.3390/pr10122627 - 07 Dec 2022

Cited by 1 | Viewed by 558

Abstract

Networked nonlinear systems (NNSs) have great potential security threats because of malicious attacks. These attacks will destabilize the networked systems and disrupt the communication to the networked systems, which will affect the stability and performance of the networked control systems. Therefore, this paper aims to deal with the resilient control problem for NNSs with dynamically triggering mechanisms (DTMs) and malicious aperiodic denial-of-service (DoS) attacks. To mitigate the impact from DoS attacks and economize communication resources, a resilient dynamically triggering controller (RDTC) is designed with DTMs evolving an adaptive adjustment auxiliary variable. Thus, the resulting closed-loop system is exponentially stable by employing the piecewise Lyapunov function technique. In addition, according to the minimum inter-event time, the Zeno behavior can be excluded. Finally, the merits of the proposed controllers and theory are corroborated using the well-known nonlinear Chua circuit. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Identification and Analysis of Factors Influencing Green Growth of Manufacturing Enterprises Based on DEMATEL Method—Wooden Flooring Manufacturing Companies as a Case

Wei Li

and

Xia Wu

Processes 2022, 10(12), 2594; https://doi.org/10.3390/pr10122594 - 05 Dec 2022

Cited by 1 | Viewed by 684

Abstract

It is significant to scientifically identify what factors influence the green growth of manufacturing enterprises and analyze the relationship among these factors, thus promoting green growth. Firstly, the corresponding conceptual model is designed; then, the DEMATEL method and steps used to identify the influencing factors are introduced; finally, the DEMATEL method is adopted to empirically analyze wooden flooring manufacturing companies so as to identify influencing factors of their green growth. According to the results, there are six reason factors, namely environmental standard constraints, green market demand, market competition, green technology advancement, upstream and downstream synergy of green industrial chain, and policy support, which provide the most important external support to enterprises’ green growth and main driving power to wooden flooring manufacturing ones. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Music Generation System for Adversarial Training Based on Deep Learning

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Processes 2022, 10(12), 2515; https://doi.org/10.3390/pr10122515 - 27 Nov 2022

Cited by 1 | Viewed by 1290

Abstract

With the rapid development of artificial intelligence, the application of this new technology to music generation has attracted more attention and achieved gratifying results. This study proposes a method for combining the transformer deep-learning model with generative adversarial networks (GANs) to explore a more competitive music generation algorithm. The idea of text generation in natural language processing (NLP) was used for reference, and a unique loss function was designed for the model. The training process solves the problem of a nondifferentiable gradient in generating music. Compared with the problem that LSTM cannot deal with long sequence music, the model based on transformer and GANs can extract the relationship in the notes of long sequence music samples and learn the rules of music composition well. At the same time, the optimized transformer and GANs model has obvious advantages in the complexity of the system and the accuracy of generating notes. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Dynamic Cooperation of the O2O Supply Chain Based on Time Delays and Bidirectional Free-Riding

Jing Zheng

and

Qi Xu

Processes 2022, 10(11), 2424; https://doi.org/10.3390/pr10112424 - 16 Nov 2022

Cited by 2 | Viewed by 714

Abstract

Advertising and service investment can enhance brand goodwill to increase the sales of branded goods. However, the impact of advertising and services on brand goodwill is not immediate but delayed. At the same time, due to the different service characteristics provided by various channels, the phenomenon of bidirectional free-riding occurs. Therefore, this paper studies the dynamic cooperation between service and advertising in the O2O (online to offline) supply chain dominated by brand owners and explores the impacts of advertising, service delay and service free-riding among channels on the dynamic cooperation decisions of the O2O supply chain. A differential game model between brands and retailers is constructed by incorporating the delay effect and the bidirectional free-riding phenomenon. The optimal advertising and service strategies and performance problems of O2O supply chain enterprises under a centralized decision, brand cost-sharing decision and bilateral cost-sharing decision are compared and analyzed. The influence of delay time, showrooming and webrooming effects on the profit of each firm is investigated by example. The results show that the service strategy, advertising strategy and brand goodwill of the O2O supply chain members are optimal under a centralized decision. Still, the supply chain profit is not necessarily optimal under the delay time, showrooming and webrooming effect coefficients. Bilateral cost-sharing contracts can achieve Pareto improvement of supply chain performance. Appropriate setting of a bilateral cost-sharing ratio can adjust the adverse effects of delay and bidirectional free-riding. The long-term strategies to deal with the delay and bidirectional free-riding phenomena are as follows: the bilateral cost-sharing contract can improve corporate profits. Setting the wholesale price, online direct-selling price and service-sharing ratio by brand owners can effectively promote retailers’ investment in service, achieving a win-win situation. Retailers maintain high pricing and service levels to enhance the brand premium ability of physical stores and achieve long-term development. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Remote Wind Farm Path Planning for Patrol Robot Based on the Hybrid Optimization Algorithm

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Processes 2022, 10(10), 2101; https://doi.org/10.3390/pr10102101 - 17 Oct 2022

Cited by 1 | Viewed by 741

Abstract

Globally, wind power plays a leading role in the renewable energy industry. In order to ensure the normal operation of a wind farm, the staff will regularly check the equipment of the wind farm. However, manual inspection has some disadvantages, such as heavy workload, low efficiency and easy misjudgment. In order to realize automation, intelligence and high efficiency of inspection work, inspection robots are introduced into wind farms to replace manual inspections. Path planning is the prerequisite for an intelligent inspection robot to complete inspection tasks. In order to ensure that the robot can take the shortest path in the inspection process and avoid the detected obstacles at the same time, a new path-planning algorithm is proposed. The path-planning algorithm is based on the chaotic neural network and genetic algorithm. First, the chaotic neural network is used for the first step of path planning. The planning results are encoded into chromosomes to replace the individuals with the worst fitness in the genetic algorithm population. Then, according to the principle of survival of the fittest, the population is selected, hybridized, varied and guided to cyclic evolution to obtain the new path. The shortest path obtained by the algorithm can be used for the robot inspection of the wind farms in remote areas. The results show that the proposed new algorithm can generate a shorter inspection path than other algorithms. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Optimization of the Sustainable Distribution Supply Chain Using the Lean Value Stream Mapping 4.0 Tool: A Case Study of the Automotive Wiring Industry

Yousra El Kihel

Ali El Kihel

and

Soufiane Embarki

Processes 2022, 10(9), 1671; https://doi.org/10.3390/pr10091671 - 23 Aug 2022

Cited by 3 | Viewed by 1773

Abstract

The transformation to Supply Chain (SC) 4.0 promises new opportunities for companies to gain competitiveness. The Lean Value Stream Mapping (VSM) tool allows the supervision of all the processes of the entire SC, from which we can identify the different types of waste that hinder the competitiveness of the SC. Following the existing problems detected with the help of a diagnostic, we will propose a new process design by integrating 4.0 technologies to modernize the company. For our case study, we treat the multinational SC of Automotive Wiring Equipment Morocco, where we will focus on the downstream part of the SC composed of the warehouse and the different stages of road and sea transport until the final delivery in Austria. Then, we will consider the opportunities offered by each country in terms of logistics competitiveness. In this research work, we will show how Lean VSM4.0 will contribute to sustainable development by integrating the three pillars economic, environmental, and social. With the Lean VSM 4.0 tool, all logistic processes will be visualized, from which improvements could be obtained, especially the optimization of the lead-time, the cost, the energy consumed, and the follow-up of the products during the whole SC while reducing accidents. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

The Effect of Changes in Settings from Multiple Filling Points to a Single Filling Point of an Industry 4.0-Based Yogurt Filling Machine

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Processes 2022, 10(8), 1642; https://doi.org/10.3390/pr10081642 - 18 Aug 2022

Cited by 2 | Viewed by 916

Abstract

In process optimization, a process is adjusted so as to optimize a set of parameters while meeting constraints, with the objective to either minimize the total processing time or maximize the throughput. This article focused on the process optimization of a fully automated yogurt and flavor-filling machine developed based on the industrial revolution 4.0 concept. Mathematical models were developed for minimizing the total processing time or maximizing the throughput of an Industry 4.0-based yogurt filling system with two different machine settings called Case-I and Case-II. In Case-I, the yogurt and flavors are filled at two distinct points while Case-II considers the filling of yogurt and flavors at a single point. The models were tested with real data and the results revealed that Case-II is faster than Case-I in processing a set of customer orders. The results were used as inputs for the single-dimension rules to check which one results in more intended outputs. Additionally, different performance measures were considered and the one with most importance to the management was selected. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Tracking Control of a Hyperchaotic Complex System and Its Fractional-Order Generalization

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Processes 2022, 10(7), 1244; https://doi.org/10.3390/pr10071244 - 22 Jun 2022

Cited by 1 | Viewed by 746

Abstract

Hyperchaotic complex behaviors often occur in nature. Some chaotic behaviors are harmful, while others are beneficial. As for harmful behaviors, we hope to transform them into expected behaviors. For beneficial behaviors, we want to enhance their chaotic characteristics. Aiming at the harmful hyperchaotic complex system, a tracking controller was designed to produce the hyperchaotic complex system track common expectation system. We selected sine function, constant, and complex Lorenz chaotic system as target systems and verified the effectiveness by mathematical proof and simulation experiments. Aiming at the beneficial hyperchaotic complex phenomenon, this paper extended the hyperchaotic complex system to the fractional order because the fractional order has more complex dynamic characteristics. The influences order change and parameter change on the evolution process of the system were analyzed and observed by MATLAB simulation. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Performance Improvement of H8 Transformerless Grid-Tied Inverter Using Model Predictive Control Considering a Weak Grid

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Processes 2022, 10(7), 1243; https://doi.org/10.3390/pr10071243 - 22 Jun 2022

Cited by 5 | Viewed by 1014

Abstract

There is increasing utilization of photovoltaic (PV) grid-connected systems in modern power networks. Currently, PV grid-connected systems utilize transformerless inverters that have the advantages of being low cost, low weight, a small size, and highly efficient. Unfortunately, these inverters have an earth leakage current problem due to the absence of galvanic isolation. This phenomenon represents safety and electrical problems for those systems. Recently, the H8 transformerless inverter was introduced to eliminate the earth leakage current. The present study proposes improving the performance of an H8 transformerless inverter using model predictive control (MPC). The inverter was supplied by PV energy and attached to the grid through an LCL filter. During system modeling, the grid weakness was identified. The discrete model of the overall system, including the PV panel, the boost converter, the H8 transformerless inverter, and the controllers, was derived. Then, the introduced H8 transformerless inverter system was simulated and analyzed by the Matlab/Simulink program. The proposed system response using MPC was tested under step disturbances in the PV insolation level. Moreover, the effect of the weak and strong grid operations was considered. The simulation results indicate that the MPC controller has better performance and high-quality injected power. Despite the excellent performance of the strong grid, the nearly weak grid performance is acceptable. Moreover, the Hardware-in-the-Loop (HIL) of the proposed system was implemented using the DSP target LaunchPadXLTMS320F28379D kit to validate the simulation results. Finally, the system performance under the parameter variations showed good robustness. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Modelling Method and Application of Anti-Corrosion Pill Particles in Oil and Gas Field Wellbore Casing Annulus Based on the Discrete Element Method

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Processes 2022, 10(6), 1164; https://doi.org/10.3390/pr10061164 - 09 Jun 2022

Cited by 2 | Viewed by 884

Abstract

This study uses a self-developed anti-corrosion pill particle as the research object and develops the pill particle population modelling method in order to optimize the anti-corrosion process of oil and gas wellbore casing annuli. The shape of the pill particle is similar to a cylinder, according to the test and analysis of geometrical characteristics, and can be simplified into three types based on height, namely pill particles A (5.4 mm), B (5.8 mm), and C (6.2 mm). The multi-sphere approach is then used to create models of three different types of pill particles with varying degrees of precision. The feasibility and effectiveness of the modelling method for pill particle populations are proven by comparing the simulation results of the bulk density test and the angle of repose test. The results show that the 12-sphere models of pill particles A, B, and C are accurate representations of genuine pill particle morphologies and are adequate for simulating particle mechanics and flow processes. The applicability and practical use of the modelling method are then demonstrated using an example of a self-designed pill particle discharging mechanism. The results show that the modelling method can accurately simulate the pill discharging process and provide an accurate simulation model and theoretical basis for the optimization of the structural parameters, dimension parameters, and operating parameters of the discharging device. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

An Effective Temperature Control Method for Dividing-Wall Distillation Columns

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Processes 2022, 10(5), 1018; https://doi.org/10.3390/pr10051018 - 20 May 2022

Cited by 1 | Viewed by 1099

Abstract

Temperature control is widely perceived to be superior to direct composition control for the control of dividing-wall distillation columns (DWDCs) due to its advantages in dynamic characteristics. However, because of the limited estimation accuracy to the controlled product purities, the former cannot eliminate the steady-state errors in the maintained product purities as completely as the latter. In order to reduce the steady-state deviations in the maintained product purities, an effective temperature control method is proposed in the current article by means of a kind of simple but effective product quality estimator (PQE). For the proposed PQE, temperatures of three stages located in the controlled column section (T_I1, T_I2, and T_I3) are employed as inputs, and a linear sum of these three inputted stage temperatures (α × T_I1 + β × T_I2 + γ × T_I3) is given as output. A genetic algorithm with an elitist preservation strategy is used to optimize the locations of the three stage temperatures and the values of α, β, and γ to ensure the estimation accuracy of the PQE. Concerning the controls of two DWDCs, i.e., one Petlyuk DWDC separating an ethanol/propanol/butanol ternary mixture and one Kaibel DWDC separating a methanol/ethanol/propanol/butanol quaternary mixture, the effectiveness of the PQE is assessed through comparing the performance of the temperature inferential control scheme using the PQE and the double temperature difference control scheme. According to the dynamic simulation results obtained, the former control scheme displays not only smaller steady-state deviations in the maintained product purities, but also better dynamic characteristics as compared with the latter control scheme. This result fully demonstrates that the proposed PQE can be a useful tool for the temperature inferential control of the DWDC. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessFeature PaperArticle

Linear Golden Section Speed Adaptive Control of Permanent Magnet Synchronous Motor Based on Model Design

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Processes 2022, 10(5), 1010; https://doi.org/10.3390/pr10051010 - 19 May 2022

Cited by 2 | Viewed by 1194

Abstract

Permanent magnet synchronous motor (PMSM) is a multi-variable, strongly coupled, nonlinear complex system. It is usually difficult to establish an accurate mathematical model, and the introduction of new complex algorithms will increase the difficulty of embedded code development. In order to solve this problem, we establish the characteristic model of permanent magnet synchronous motor in this paper, and the speed control scheme of the linear golden-section adaptive control and integral compensation, which is adopted. Finally, using the model-based design (MBD) method, how to build the simulink embedded code automatic generation model is introduced in detail, and then we complete the PMSM speed control physical verification experiment. Simulation and experimental results show that compared with traditional proportional-integral-derivative (PID) control, the speed control accuracy of PMSM is improved about 3.8 times. Meanwhile, the development method based on the model design can increase the PMSM control system physical verification, and then improve the development efficiency. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessFeature PaperArticle

Material Removal Optimization Strategy of 3D Block Cutting Based on Geometric Computation Method

Hui Shao

Qimeng Liu

and

Zhiwei Gao

Processes 2022, 10(4), 695; https://doi.org/10.3390/pr10040695 - 02 Apr 2022

Cited by 1 | Viewed by 1172

Abstract

During the material removal stage in stone rough processing, milling type has been widely explored, which, however, may cause time and material consumption, as well as substantial stress for the environment. To improve the material removal rate and waste reuse rate in the rough processing stage for three-dimensional stone products with a special shape, in this paper, circular saw disc cutting is explored to cut a convex polyhedron out of a blank box, which approaches a target product. Unlike milling optimization, this problem cannot be well solved by mathematical methods, which have to be solved by geometrical methods instead. An automatic block cutting strategy is proposed intuitively by considering a series of geometrical optimization approaches for the first time. To obtain a big removal block, constructing cutting planes based on convex vertices is uniquely proposed. Specifically, the removal vertices (the maximum thickness of material removal) are searched based on the octree algorithm, and the cutting plane is constructed based on this thickness to guarantee a relatively big removal block. Moreover, to minimize the cutting time, the geometrical characteristics of the intersecting convex polygon of the cutting plane with the convex polyhedron are analyzed, accompanied by the constraints of the guillotine cutting mode. The optimization algorithm determining the cutting path is presented with a feed direction accompanied by the shortest cutting stroke, which confirms the shortest cutting time. From the big removal block and shortest cutting time, the suboptimal solution of the average material removal rate (the ratio of material removal volume to cutting time) is generated. Finally, the simulation is carried out on a blank box to approach a bounding sphere both on MATLAB and the Vericut platform. In this case study, for the removal of 85% of material with 19 cuts, the proposed cutting strategy achieves five times higher the average material removal rate than that of one higher milling capacity case. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

A Simple and Effective Modeling Method for 3D Porous Irregular Structures

Lijing Ren

and

Denghui Zhang

Processes 2022, 10(3), 464; https://doi.org/10.3390/pr10030464 - 25 Feb 2022

Cited by 1 | Viewed by 1404

Abstract

Porous structures are kinds of structures with excellent physical properties and mechanical characteristics through components and internal structure. However, the irregular internal morphology of porous structures poses new challenges to product modeling techniques. Traditional computer-aided design (CAD) modeling methods can only represent the external geometric and topological information of models, lacking the description of the internal structure and conformation, which limits the development of new porous products. In this paper, a new simple and effective modeling method for 3D irregular porous structures is proposed, which improves the controllability of pore shape and porosity, thus overcoming the limitations of existing methods in 3D and concave structures. The key idea is to solve isothermal for modeling the porosity of porous units. Experimental results show that the method can easily obtain smooth and approximate porous structures from arbitrary irregular 3D surfaces. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

Aihua Zhang

Danlu Yu

and

Zhiqiang Zhang

Processes 2022, 10(2), 362; https://doi.org/10.3390/pr10020362 - 14 Feb 2022

Cited by 4 | Viewed by 1521

Abstract

In fault-diagnosis classification, a pressing issue is the lack of target-fault samples. Obtaining fault data requires a great amount of time, energy and financial resources. These factors affect the accuracy of diagnosis. To address this problem, a novel fault-diagnosis-classification optimization method, namely TLSCA-SVM, which combines the sine cosine algorithm and support vector machine (SCA-SVM) with transfer learning, is proposed here. Considering the availability of fault data, this thesis uses the data generated by analog circuits from different faults for analysis. Firstly, the data signal is collected from different faults of the analog circuit, and then the characteristic data are extracted from the data signals by the wavelet packets. Secondly, to employ the principal component analysis (PCA) reduces the feature-value dimension. Lastly, as an auxiliary condition, the error-penalty item is added to the objective function of the SCA-SVM classifier to construct an innovative fault-diagnosis model namely TLSCA-SVM. Among them, the Sallen–Key bandpass filter circuit and the CSTV filter circuit are used to provide the data for horizontal- and vertical-contrast classification results. Comparing the SCA with the five optimization algorithms, it is concluded that the performance of SCA optimization parameters has certain advantages in the classification accuracy and speed. Additionally, to prove the superiority of the SCA-SVM classification algorithm, the five classification algorithms are compared with the SCA-SVM algorithm. Simulation results showed that the SCA-SVM classification has higher precision and a faster response time compared to the others. After adding the error penalty term to SCA-SVM, TLSCA-SVM requires fewer fault samples to process fault diagnosis. Ultimately, the method which is proposed could not only perform fault diagnosis effectively and quickly, but also could run effectively to achieve the effect of transfer learning in the case of less failure data. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Performance Optimization of a Steam Generator Level Control System via a Revised Simplex Search-Based Data-Driven Optimization Methodology

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Processes 2022, 10(2), 264; https://doi.org/10.3390/pr10020264 - 28 Jan 2022

Cited by 3 | Viewed by 1733

Abstract

A Steam generator is a crucial device of a nuclear power plant. Control performance of the steam generator level control system is key to its normal operation. To improve its performance, the control system parameters should be optimized by utilizing a proper optimization method. Furthermore, the method’s efficiency is critical for its operability in the actual plant. However, the steam generator level process is a complex process, with high nonlinearity and time-varying properties. Traditional parameters tuning methods are experience-based, cumbersome, and time-consuming. To address the challenge, a systemic data-driven optimization methodology based on the model-free optimization with a revised simplex search method was proposed. Rather than the traditional controller parameter tuning method, this method optimizes the control system directly by using control performance measurements. To strengthen its efficiency, two critical modifications were incorporated into the traditional simplex search method to form a knowledge-informed simplex search based on historical gradient approximations. Firstly, with the help of the historical gradient approximations, the revised method could sense the optimization direction more accurately and accomplish the iteration step size tuning adaptively, significantly reducing the optimization cost. Secondly, a revised iteration termination control strategy was developed and integrated to monitor the optimization progress, which can promptly terminate the progress to avoid unnecessary iteration costs. The effectiveness and the efficiency of the revised method were demonstrated through simulation experiments. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

T-S Fuzzy Model-Based Fault Detection for Continuous Stirring Tank Reactor

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Processes 2021, 9(12), 2127; https://doi.org/10.3390/pr9122127 - 25 Nov 2021

Cited by 4 | Viewed by 1031

Abstract

Continuous stirring tank reactors are widely used in the chemical production process, which is always accompanied by nonlinearity, time delay, and uncertainty. Considering the characteristic of the actual reaction of the continuous stirring tank reactors, the fault detection problem is studied in terms of the T-S fuzzy model. Through a fault detection filter performance analysis, the sufficient condition for the filtering error dynamics is obtained, which meets the exponential stability in the mean square sense and the given performance requirements. The design of the fault detection filter is transformed into one that settles the convex optimization issue of linear matrix inequality. Numerical analysis shows the effectiveness of this scheme. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessFeature PaperArticle

Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

Dapeng Zhang

Lifeng Du

and

Zhiwei Gao

Processes 2021, 9(10), 1848; https://doi.org/10.3390/pr9101848 - 18 Oct 2021

Cited by 3 | Viewed by 1108

Abstract

It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Data-Driven State Prediction and Sensor Fault Diagnosis for Multi-Agent Systems with Application to a Twin Rotational Inverted Pendulum

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Processes 2021, 9(9), 1505; https://doi.org/10.3390/pr9091505 - 26 Aug 2021

Cited by 4 | Viewed by 1272

Abstract

When a multi-agent system is subjected to faults, it is necessary to detect and classify the faults in time. This paper is motivated to propose a data-driven state prediction and sensor fault classification technique. Firstly, neural network-based state prediction model is trained through historical input and output data of the system. Then, the trained model is implemented to the real-time system to predict the system state and output in absence of fault. By comparing the predicted healthy output and the measured output, which can be abnormal in case of sensor faults, a residual signal can be generated. When a sensor fault occurs, the residual signal exceeds the threshold, a fault classification technique is triggered to distinguish fault types. Finally, the designed data-driven state prediction and fault classification algorithms are verified through a twin rotational inverted pendulum system with leader-follower mechanism. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessArticle

Analysis and Design of a Standalone Electric Vehicle Charging Station Supplied by Photovoltaic Energy

Ibrahem E. Atawi

Essam Hendawi

and

Sherif A. Zaid

Processes 2021, 9(7), 1246; https://doi.org/10.3390/pr9071246 - 19 Jul 2021

Cited by 23 | Viewed by 4976

Abstract

Nowadays, there is a great development in electric vehicle production and utilization. It has no pollution, high efficiency, low noise, and low maintenance. However, the charging stations, required to charge the electric vehicle batteries, impose high energy demand on the utility grid. One way to overcome the stress on the grid is the utilization of renewable energy sources such as photovoltaic energy. The utilization of standalone charging stations represents good support to the utility grid. Nevertheless, the electrical design of these systems has different techniques and is sometimes complex. This paper introduces a new simple analysis and design of a standalone charging station powered by photovoltaic energy. Simple closed-form design equations are derived, for all the system components. Case-study design calculations are presented for the proposed charging station. Then, the system is modeled and simulated using Matlab/Simulink platform. Furthermore, an experimental setup is built to verify the system physically. The experimental and simulation results of the proposed system are matched with the design calculations. The results show that the charging process of the electric vehicle battery is precisely steady for all the PV insolation disturbances. In addition, the charging/discharging of the energy storage battery responds perfectly to store and compensate for PV energy variations. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessFeature PaperEditor’s ChoiceArticle

A Real-Time Optimization Strategy for Small-Scale Facilities and Implementation in a Gas Processing Unit

Marcos Vinícius C. Gomes

and

Argimiro R. Secchi

Processes 2021, 9(7), 1179; https://doi.org/10.3390/pr9071179 - 07 Jul 2021

Cited by 2 | Viewed by 1684

Abstract

The rise of new digital technologies and their applications in several areas pushes the process industry to update its methodologies with more intensive use of mathematical models—commonly denoted as digital twins—and artificial intelligence (AI) approaches to continuously enhance operational efficiency. In this context, Real-time Optimization (RTO) is a strategy that is able to maximize an economic function while respecting the existing constraints, which enables keeping the operation at its optimum point even though the plant is subjected to nonlinear behavior and frequent disturbances. However, the investment related to the project of commercial RTOs may make its application infeasible for small-scale facilities. In this work, an in-house, small-scale RTO is presented and its successful application in a real industrial case—a Natural Gas Processing Unit—is shown. Besides that, a new method for enhancing the efficiency of using sequential-modular simulator inside an optimization framework and a new method to account for the economic return of optimization-based tools are proposed and described. The application of RTO in the industrial case showed an enhancement in the stability of the main variables and an increase in profit of 0.64% when compared to the operation of the regulatory control layer alone. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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Open AccessFeature PaperEditor’s ChoiceArticle

Successful Pass Schedule Design in Open-Die Forging Using Double Deep Q-Learning

and

Processes 2021, 9(7), 1084; https://doi.org/10.3390/pr9071084 - 22 Jun 2021

Cited by 5 | Viewed by 2391

Abstract

In order to not only produce an open-die forged part with the desired final geometry but to also maintain economic production, precise process planning is necessary. However, due to the incremental forming of the billet, often with several hundred strokes, the process design is arbitrarily complicated and, even today, often only based on experience or simple mathematical models describing the geometry development. Hence, in this paper, fast process models were merged with a double deep Q-learning algorithm to enable a pass schedule design including multi-objective optimization. The presented implementation of a double deep Q-learning algorithm was successfully trained on an industrial-scale forging process and converged stably against high reward values. The generated pass schedules reliably produced the desired final ingot geometry, utilized the available press force well without exceeding plant limits, and, at the same time, minimized the number of passes. Finally, a forging experiment was performed at the institute of metal forming to validate the generated results. Overall, a proof of concept for the pass schedule design in open-die forging via double deep Q-learning was achieved which opens various starting points for future work. Full article

(This article belongs to the Special Issue Modelling, Monitoring, Control and Optimization for Complex Industrial Processes)

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