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Processes
Special Issues
AI-Driven Safe and High-Quality Development in Process Industries
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AI-Driven Safe and High-Quality Development in Process Industries

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1330

Special Issue Editors

Dr. Zhe Cui

E-Mail Website
Guest Editor
College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
Interests: dynamic simulation; wastewater treatment; chemical looping gasification; intelligent traceability; multi-scale modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wende Tian

E-Mail Website
Guest Editor
College of Chemical Engineering/Environment and Safety Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
Interests: process control; modeling analysis; system optimization; process integration; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Process systems engineering (PSE) integrates process engineering, systems engineering, intelligent engineering, control engineering, information technology, computer technology, management science and other disciplines of theory and technology. With the development of artificial intelligence, 5G technology, big data, blockchain and robots, PSE aims at energy saving, environmental protection, safety control, optimized operation and process strengthening of complex process production systems. In recent years, artificial intelligence technology has transformed process system engineering, comprehensively reshaping its core methods such as modeling, optimization, control and decision-making, and driving its advancement towards intelligent upgrading and deep integration.

This Special Issue on “AI-Driven Safe and High-Quality Development in Process Industries” seeks high-quality works focusing on the latest novel developments in process systems engineering. Topics include, but are not limited to, the following:

  • Intelligent methods and applications in process engineering;
  • Application and practice of large models in process industry;
  • Process simulation, analysis, optimization and monitoring;
  • Risk analysis, fundamental safety theory and practice;
  • Development and application of information technology integration.

Dr. Zhe Cui
Prof. Dr. Wende Tian
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-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 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 systems engineering
  • safety, high-quality development
  • artificial intelligence
  • process industry

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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

22 pages, 10589 KB  
Article
An Improved Fault Diagnosis Method for Diesel Engines Based on Optimized Variational Mode Decomposition and Transformer-SVM
by Xiaoxin Ma, Shuyao Tian, Xianbiao Zhan, Hao Yan and Kaibo Cui
Processes 2026, 14(7), 1131; https://doi.org/10.3390/pr14071131 - 31 Mar 2026
Viewed by 288
Abstract
Due to the non-stationary and nonlinear characteristics of diesel engine vibration signals, fault features cannot be fully extracted, which limits fault diagnosis performance. To address this issue, an improved fault diagnosis method combining optimized Variational Mode Decomposition with a Transformer and Support Vector [...] Read more.
Due to the non-stationary and nonlinear characteristics of diesel engine vibration signals, fault features cannot be fully extracted, which limits fault diagnosis performance. To address this issue, an improved fault diagnosis method combining optimized Variational Mode Decomposition with a Transformer and Support Vector Machine is proposed. An improved dung beetle optimization algorithm is employed to obtain optimal parameters for Variational Mode Decomposition. The envelope entropy minimization principle is applied to select the optimal intrinsic mode functions after Variational Mode Decomposition, achieving signal denoising. Analysis of variance is integrated for feature significance testing to screen critical features. The selected features are fed into a Transformer network for training. At the final classification stage, the traditional SoftMax classifier is replaced with a Support Vector Machine classifier. Full article
(This article belongs to the Special Issue AI-Driven Safe and High-Quality Development in Process Industries)
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16 pages, 4532 KB  
Article
Pattern Recognition of Hazardous Gas Leak Monitoring Data Based on Field Sensors
by Jian Xi, Lei Guan, Xiaoguang Zhu, Kai Zong and Wenrui Yan
Processes 2026, 14(1), 108; https://doi.org/10.3390/pr14010108 - 28 Dec 2025
Viewed by 701
Abstract
Hazardous gas leaks are a major trigger of chemical incidents. If not handled in time, they can easily lead to secondary disasters such as fires and explosions. In recent years, with the construction of hazardous chemical monitoring and early-warning systems in China, large [...] Read more.
Hazardous gas leaks are a major trigger of chemical incidents. If not handled in time, they can easily lead to secondary disasters such as fires and explosions. In recent years, with the construction of hazardous chemical monitoring and early-warning systems in China, large volumes of field operating data from flammable and toxic gas sensors have been accumulated, providing a data foundation for leak-pattern studies grounded in real-world scenarios. In this study, 56 leak samples verified by site feedback were selected. Time-aware interpolation and Z-score normalization were used for preprocessing, and time-series features—including standard deviation of first differences, autocorrelation coefficients, and frequency-domain energy—were extracted. Leak patterns were then identified using two unsupervised approaches: K-Means clustering and a 1D-CNN autoencoder. Results show that K-Means effectively distinguishes macro-patterns such as sustained leaks, instantaneous leaks, fluctuating leaks, and interrupted leaks, while the autoencoder demonstrates stronger capability in extracting temporal features, revealing leak evolution and transition characteristics. The two methods are complementary and together provide a viable route to developing an end-to-end model for leak scenario identification and risk discrimination. This work not only verifies the feasibility of conducting leak-pattern recognition using real GDS data but also offers technical guidance for the intelligent upgrading of hazardous chemical monitoring and early-warning systems. Full article
(This article belongs to the Special Issue AI-Driven Safe and High-Quality Development in Process Industries)
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