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Applied Sciences
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Process Control and Optimization
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Process Control and Optimization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 4135

Special Issue Editors

Prof. Dr. Xiaojun Zhou

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: operations research; process control; machine learning; decision making
Dr. Yitao Yan

E-Mail Website
Guest Editor
School of Chemical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Interests: process control; data-based control; machine learning; distributed control
Dr. Zhaoke Huang

E-Mail Website
Guest Editor
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
Interests: intelligent optimization; deep learning; decision making

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute your research to our upcoming Special Issue titled “Process Control and Optimization”. Process control and optimization (PCO) is the discipline of adjusting a process to maintain or optimize a specified set of parameters without violating process constraints. The PCO market is being driven by rising demand for energy-efficient production processes, safety and security concerns, and the development of the industrial internet of things (IIoT), facilitating the seamless integration of diverse devices, sensors, and systems, thereby enhancing overall operational performance. Technological advancements empower industrial enterprises to control and optimize the overall processes to improve efficiency, product quality, and safety while reducing costs and risks. However, controlling and optimizing industrial processes has become a major challenge in an increasingly competitive environment focused on economic profitability. Therefore, this Special Issue will provide a good opportunity for researchers from all over the world to communicate and move forward.

This Special Issue will delve into the intricacies of process control and optimization, covering a wide range of topics and disciplines. We invite contributions that explore theoretical developments, novel methodologies, practical applications, and case studies related to process control and optimization.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following topics:

  • Process modeling and simulation;
  • Process control and instrumentation;
  • Process monitoring and fault detection;
  • Process optimization;
  • Production planning and scheduling;
  • Process management and decision support systems;
  • Advanced control strategies and algorithms;
  • Model predictive control (MPC);
  • Real-time optimization;
  • Industrial big data analytics and cyber–physical systems;
  • Artificial intelligence in process control;
  • Integration of process systems engineering (PSE) methodologies;
  • Digital twins and industrial internet of things;
  • Optimization of energy consumption and resource utilization;
  • Industrial case studies and applications.

Prof. Dr. Xiaojun Zhou
Dr. Yitao Yan
Dr. Zhaoke Huang
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

  • process control
  • process optimization
  • process systems engineering
  • artificial intelligence
  • digital twins
  • industrial internet of things
  • decision support systems

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

13 pages, 1603 KiB  
Article
Precise Pre-Close Wind Volume Calculation for Aluminum Electrolysis Based on Unscented Kalman and Average Filters
by Jiawei Zhao, Mengfan Wang, Xue Hu and Lixin Zhang
Appl. Sci. 2024, 14(24), 12046; https://doi.org/10.3390/app142412046 - 23 Dec 2024
Cited by 1 | Viewed by 516
Abstract
To improve the accuracy of calculating the aluminum electrolysis pre-close wind volume, this study focused on optimizing the two main factors that influence its magnitude: the aluminum output speed and the pre-close wind volume coefficient. First, the Unscented Kalman Filter (UKF) algorithm was [...] Read more.
To improve the accuracy of calculating the aluminum electrolysis pre-close wind volume, this study focused on optimizing the two main factors that influence its magnitude: the aluminum output speed and the pre-close wind volume coefficient. First, the Unscented Kalman Filter (UKF) algorithm was used to estimate the aluminum output speed, and its application in real production was verified through simulation experiments. The results demonstrate that UKF provides more accurate speed estimates when handling the non-linear dynamic system of aluminum electrolysis. When there was a sudden change in speed, the UKF achieved a relative error of only 0.0373%, significantly lower than the 2.52% error of the traditional Kalman Filter (KF). At the same time, the UKF exhibited a shorter runtime in the simulation. Additionally, this research introduces a self-correction mechanism for the pre-close wind volume coefficient for the first time. By dynamically adjusting the parameter based on aluminum output deviations and applying the Average Filter (AF) to improve the compensation accuracy, the pre-close wind volume coefficient can be precisely calculated. The combination of these methods significantly enhances the accuracy and robustness of pre-close wind volume calculations, providing solid theoretical foundations and the technical support needed to achieve high-precision aluminum output control. Full article
(This article belongs to the Special Issue Process Control and Optimization)
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25 pages, 623 KiB  
Article
Quantitative and Qualitative Analysis of Main Parameters and Their Interactions in Thermoacoustic Refrigerators Performance
by Humberto Peredo Fuentes and Carlos Amir Escalante Velázquez
Appl. Sci. 2024, 14(22), 10470; https://doi.org/10.3390/app142210470 - 14 Nov 2024
Viewed by 1083
Abstract
Efforts to optimize the design and enhance the efficiency of standing-wave thermoacoustic refrigerators (SWTARs), particularly those with parallel plate stacks, are crucial for achieving rapid and straightforward engineering estimates. This study primarily focused on optimizing the coefficient of performance (COP) by combining linear [...] Read more.
Efforts to optimize the design and enhance the efficiency of standing-wave thermoacoustic refrigerators (SWTARs), particularly those with parallel plate stacks, are crucial for achieving rapid and straightforward engineering estimates. This study primarily focused on optimizing the coefficient of performance (COP) by combining linear thermoacoustic theory (LTT) with the design of experiments (DOE) approach. The investigation centered around five key parameters affecting the COP once the working gas had been selected. Then, based on LTT, the COP was estimated numerically over defined intervals of those five parameters. Moreover, through quantitative and qualitative effect analyses, these five parameters and their interactions were determined. Utilizing a transfer function, the study aimed to delineate the best COP value (1.76) over a defined interval of the parameters as well as the contribution of the thermoacoustic main parameters (55.69%) and their interactions (two-way interactions = 33.30%, three-way interactions = 7.36%, and four-way interactions = 3.35%). Furthermore, a comparison between contour and surface responses and several statistical decision approaches applying the full factorial design verified the robustness of the study’s findings. Ultimately, the COP results obtained aligned with the existing literature, underscoring the validity and relevance of the study’s methodologies and conclusions. Full article
(This article belongs to the Special Issue Process Control and Optimization)
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25 pages, 1702 KiB  
Article
An Anomaly Detection Method for Oilfield Industrial Control Systems Fine-Tuned Using the Llama3 Model
by Jianming Zhao, Ziwen Jin, Peng Zeng, Chuan Sheng and Tianyu Wang
Appl. Sci. 2024, 14(20), 9169; https://doi.org/10.3390/app14209169 - 10 Oct 2024
Viewed by 1919
Abstract
The device anomaly detection in an industrial control system (ICS) is essential for identifying devices with abnormal operating states or unauthorized access, aiming to protect the ICS from unauthorized access, malware, operational errors, and hardware failures. This paper addresses the issues of numerous [...] Read more.
The device anomaly detection in an industrial control system (ICS) is essential for identifying devices with abnormal operating states or unauthorized access, aiming to protect the ICS from unauthorized access, malware, operational errors, and hardware failures. This paper addresses the issues of numerous manufacturers, complex models, and incomplete information by proposing a fingerprint extraction method based on ICS protocol communication models, applied to an anomaly detection model fine-tuned using the Llama3 model. By considering both hardware and software characteristics of ICS devices, the paper designs a fingerprint vector that can be extracted in both active and passive network communication environments. Experimental data include real ICS network traffic from an oilfield station and extensive ICS device traffic data obtained through network scanning tools. The results demonstrate that the proposed method outperforms existing methods in terms of accuracy and applicability, especially in differentiating devices from various manufacturers and models, significantly enhancing anomaly detection performance. The innovation lies in using large language models for feature extraction and the anomaly detection of device fingerprints, eliminating dependency on specific ICS scenarios and protocols while substantially improving detection accuracy and applicability. Full article
(This article belongs to the Special Issue Process Control and Optimization)
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