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Processes
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Process Optimization, Diagnosis, and Control for Complex Industrial Processes
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Process Optimization, Diagnosis, and Control for Complex Industrial Processes

A Special Issue of Processes (ISSN 2227-9717) belonging to the section "Process Control, Modeling and Optimization".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 5476

Editors

Dr. Dapeng Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
Interests: process control; reinforcement learning; machine learning; artificial intelligence; fault diagnosis; fault tolerant control
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanhong Liu

E-Mail Website
Guest Editor
School of Electrical Information Engineering, Northeast Petroleum University, Daqing 163318, China
Interests: fault diagnosis; intelligent control; manifold learning; deep learning
Dr. Shumei Zhang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Interests: process monitoring; fault detection; fault diagnosis; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pursuing safety, reliability and efficiency is a persistent project of industrial processes, including in the metallurgical, manufacturing, chemical and energy system industries, amongst others. On this note, a wide variety of technologies have been employed for control, monitoring and optimization in this context. These cover all stages of design, production, maintenance and other key processes in industry, with great progress having been made. Recently, emerging technologies such as data-driven methods, digital twins, machine learning, large models and artificial intelligence have shown great prospects in complex industrial processes. This Special Issue, “Process Optimization, Diagnosis, and Control for Complex Industrial Processes” aims to cover recent advances in the development and application of complex industrial process. Topics include, but are not limited to, methods and/or applications in the following areas:

  1. Advanced control technology and intelligent control.
  2. Process monitoring and fault diagnosis technology.
  3. Process optimization and system optimization.
  4. Emerging technologies (data-driven methods, digital twins, deep learning, etc.) in complex industrial systems.

Dr. Dapeng Zhang
Dr. Yuanhong Liu
Dr. Shumei Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 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

  • advanced control technology
  • process monitoring
  • fault diagnosis
  • optimization
  • emerging technologies
  • complex industrial systems

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

Published Papers (2 papers)

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Research

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26 pages, 2752 KB  
Article
Intelligent Impedance Strategy for Force–Motion Control of Robotic Manipulators in Unknown Environments via Expert-Guided Deep Reinforcement Learning
by Hui Shao, Weishi Hu, Li Yang, Wei Wang, Satoshi Suzuki and Zhiwei Gao
Processes 2025, 13(8), 2526; https://doi.org/10.3390/pr13082526 - 11 Aug 2025
Cited by 11 | Viewed by 3513
Abstract
In robotic force–motion interaction tasks, ensuring stable and accurate force tracking in environments with unknown impedance and time-varying contact dynamics remains a key challenge. Addressing this, the study presents an intelligent impedance control (IIC) strategy that integrates model-based insights with deep reinforcement learning [...] Read more.
In robotic force–motion interaction tasks, ensuring stable and accurate force tracking in environments with unknown impedance and time-varying contact dynamics remains a key challenge. Addressing this, the study presents an intelligent impedance control (IIC) strategy that integrates model-based insights with deep reinforcement learning (DRL) to improve adaptability and robustness in complex manipulation scenarios. The control problem is formulated as a Markov Decision Process (MDP), and the Deep Deterministic Policy Gradient (DDPG) algorithm is employed to learn continuous impedance policies. To accelerate training and improve convergence stability, an expert-guided initialization strategy is introduced based on iterative error feedback, providing a weak-model-based demonstration to guide early exploration. To rigorously assess the impact of contact uncertainties on system behavior, a comprehensive performance analysis is conducted by utilizing a time- and frequency-domain approach, offering deep insights into how impedance modulation shapes both transient dynamics and steady-state accuracy across varying environmental conditions. A high-fidelity simulation platform based on MATLAB (version 2021b) multi-toolbox co-simulation is developed to emulate realistic robotic contact conditions. Quantitative results show that the IIC framework significantly reduces settling time, overshoot, and undershoot under dynamic contact conditions, while maintaining stability and generalization across a broad range of environments. Full article
(This article belongs to the Special Issue Process Optimization, Diagnosis, and Control for Complex Industrial Processes)
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Review

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20 pages, 3325 KB  
Review
Intelligent Monitoring and Early Warning Diagnosis Technology for Ethylene Cracking Furnace Tubes: A Review of Current Status and Future Prospects
by Jia-Kuan Ren, Xiu-Qing Xu, Zhi-Hong Li, Peng Wang, Guang-Li Zhang, Li-Juan Zhu, Zhen-Quan Bai and Fang-Wei Luo
Processes 2026, 14(5), 811; https://doi.org/10.3390/pr14050811 - 2 Mar 2026
Cited by 1 | Viewed by 1071
Abstract
As the “flagship” unit of the petrochemical industry, the operational status of ethylene cracking furnaces directly impacts the stability and efficiency of the entire production chain. During long-term operation under extreme temperatures and complex reaction environments, cracking furnace tubes face core bottlenecks primarily [...] Read more.
As the “flagship” unit of the petrochemical industry, the operational status of ethylene cracking furnaces directly impacts the stability and efficiency of the entire production chain. During long-term operation under extreme temperatures and complex reaction environments, cracking furnace tubes face core bottlenecks primarily related to thermal and coking effects, such as coke deposition, tube metal overheating, and associated creep damage, which restrict the long-term, safe, and efficient operation of the unit. This paper systematically reviews the key technologies for condition monitoring of cracking furnace tubes, providing an in-depth analysis of various monitoring methods—from traditional infrared thermometry and acoustic emission to emerging optical fiber sensing—covering their working principles, application status, and inherent limitations. Furthermore, it elaborates on the evolution from mechanism-based “white-box” models to data-driven “black-box” models, and further to “gray-box” intelligent diagnostic models that integrate expert knowledge. Industrial application cases of integrated monitoring and diagnostic systems are also introduced. Finally, the paper critically addresses the current severe challenges in data fusion, model generalization, real-time performance, and cost-effectiveness, while outlining future development trends toward digital twins, cross-modal fusion, edge intelligence, and self-evolving systems. The aim is to provide valuable references for technological innovation and engineering applications in this field. Full article
(This article belongs to the Special Issue Process Optimization, Diagnosis, and Control for Complex Industrial Processes)
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