Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition)

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 69

Special Issue Editors


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Guest Editor
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Interests: robot control; multi-agent cooperative control; high-precision control of electromechanical systems; active disturbance rejection control; advanced robust control; control theory and application
Special Issues, Collections and Topics in MDPI journals
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: theory of active defense for information-physical systems; privacy-preserving system state estimation and control; robot intelligent control; telematics security and control

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume of “Application of Artificial Intelligence in Industrial Process Modelling and Optimization” (https://www.mdpi.com/journal/processes/special_issues/0OX7N1I66C).

The integration of artificial intelligence (AI) into industrial process modelling and optimization has proven to be revolutionary. AI can automatically learn the characteristics of industrial processes, improve modelling accuracy, and avoid relying on a large amount of prior knowledge. It can additionally optimize the control strategy of industrial processes and improve their stability and performance and automatically adapt to complex and ever-changing environments. Most importantly, by intelligently analysing industrial process data, AI can enable intelligent monitoring and diagnosis, rapidly detecting and solving problems and thereby improving production efficiency and safety. This Special Issue aims to explore the application of AI approaches in industrial process modelling and optimization. Its focus is on advancing research that harnesses the power of AI to enhance efficiency, safety, and sustainability across various industrial processes.

Scope and Objectives:

This Special Issue primarily aims to foster more research and progress in the application of AI for industrial process modelling and optimization. Its scope encompasses a wide range of industries, including, for example, manufacturing, process engineering, automation, and robotics.

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

  • AI-based modelling techniques;
  • Data-driven modelling techniques;
  • The modelling of complex industrial processes;
  • The integration of AI algorithms;
  • Adaptive control systems;
  • Human–machine collaboration systems;
  • Optimization strategies;
  • Intelligent optimization in industrial processes;
  • Data-driven decision support systems;
  • Applications of AI in cyber–physical systems;
  • AI-based process monitoring and fault diagnosis;
  • AI-driven cyber–physical systems.

Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Dr. Pan Yu
Dr. Hao Liu
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. 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 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

  • industrial process modeling
  • intelligent optimization
  • artificial intelligence
  • decision support systems
  • cyber–physical systems

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Published Papers (1 paper)

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Review

46 pages, 10548 KiB  
Review
A Review of Hybrid LSTM Models in Smart Cities
by Bum-Jun Kim and Il-Woo Nam
Processes 2025, 13(7), 2298; https://doi.org/10.3390/pr13072298 - 18 Jul 2025
Abstract
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM [...] Read more.
Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM models that integrate LSTM with complementary algorithms, including CNN, GRU, ARIMA, and SVM. These hybrid architectures aim to enhance prediction accuracy, integrate diverse data sources, and improve computational efficiency. This study systematically reviews principles, trends, and real-world applications, quantitatively evaluating hybrid LSTM models using performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2), while identifying key study limitations. The case studies considered include traffic management, environmental monitoring, energy forecasting, public health, infrastructure assessment, and urban waste management. For example, hybrid models have achieved substantial accuracy improvements in traffic congestion forecasting, reducing their mean absolute error by up to 29%. Despite the inherent challenges related to structural complexity, interpretability, and data requirements, ongoing research on attention mechanisms, model compression, and explainable AI has significantly mitigated these limitations. Thus, hybrid LSTM models have emerged as vital analytical tools capable of robust spatiotemporal prediction, effectively supporting sustainable urban development and data-driven decision-making in evolving smart city environments. Full article
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