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Processes
Special Issues
Machine Learning Optimization of Chemical Processes
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Machine Learning Optimization of Chemical Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: 10 January 2026 | Viewed by 2447

Special Issue Editor

Dr. An Su

E-Mail Website
Guest Editor
College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: artificial intelligence; machine learning; computer assisted synthesis planning; chemical reaction optimization; continuous flow synthesis (flow chemistry); automated chemical synthesis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of machine learning techniques into chemical process optimization represents a transformative approach in the field of chemical engineering. As industries strive for efficiency, sustainability, and innovation, the application of machine learning offers unprecedented opportunities to enhance process design, control, and optimization.

This Special Issue on "Machine Learning Optimization of Chemical Processes" aims to gather cutting-edge research that explores the intersection of machine learning and chemical engineering. We invite submissions that demonstrate the application of machine learning algorithms to optimize chemical processes, improve process safety, and enhance product quality.

Topics of interest include, but are not limited to, the following:

  • Machine learning models for process optimization;
  • Predictive maintenance and fault detection;
  • Data-driven process control strategies;
  • Process simulation and modeling using AI;
  • Sustainable process design through machine learning;
  • Real-time process monitoring and analytics;
  • Case studies on industrial applications of machine learning.

Dr. An Su
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 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 optimization
  • process control
  • machine learning
  • models
  • fault detection

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

Published Papers (4 papers)

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Research

19 pages, 2895 KiB  
Article
Accurate and Efficient Process Modeling and Inverse Optimization for Trench Metal Oxide Semiconductor Field Effect Transistors: A Machine Learning Proxy Approach
by Mingqiang Geng, Jianming Guo, Yuting Sun, Dawei Gao and Dong Ni
Processes 2025, 13(5), 1544; https://doi.org/10.3390/pr13051544 - 16 May 2025
Viewed by 82
Abstract
This study proposes a novel framework integrating long short-term memory (LSTM) networks with Bayesian optimization (BO) to address process–device co-optimization challenges in trench-gate metal–oxide–semiconductor field-effect transistor (MOSFET) manufacturing. Conventional TCAD simulations, while accurate, suffer from computational inefficiency in high-dimensional parameter spaces. To overcome [...] Read more.
This study proposes a novel framework integrating long short-term memory (LSTM) networks with Bayesian optimization (BO) to address process–device co-optimization challenges in trench-gate metal–oxide–semiconductor field-effect transistor (MOSFET) manufacturing. Conventional TCAD simulations, while accurate, suffer from computational inefficiency in high-dimensional parameter spaces. To overcome this, an LSTM-based TCAD proxy model is developed, leveraging hierarchical temporal dependencies to predict electrical parameters (e.g., breakdown voltage, threshold voltage) with deviations below 3.5% compared to physical simulations. The model, validated on both N-type and P-type 20 V trench MOS devices, outperforms conventional RNN and GRU architectures, reducing average relative errors by 1.78% through its gated memory mechanism. A BO-driven inverse optimization methodology is further introduced to navigate trade-offs between conflicting objectives (e.g., minimizing on-resistance while maximizing breakdown voltage), achieving recipe predictions with a maximum deviation of 8.3% from experimental data. Validation via TCAD-simulated extrapolation tests and SEM metrology confirms the framework’s robustness under extended operating ranges (e.g., 0–40 V drain voltage) and dimensional tolerances within industrial specifications. The proposed approach establishes a scalable, data-driven paradigm for semiconductor manufacturing, effectively bridging TCAD simulations with production realities while minimizing empirical trial-and-error iterations. Full article
(This article belongs to the Special Issue Machine Learning Optimization of Chemical Processes)
16 pages, 2497 KiB  
Article
Bayesian Deep Reinforcement Learning for Operational Optimization of a Fluid Catalytic Cracking Unit
by Jingsheng Qin, Lingjian Ye, Jiaqing Zheng and Jiangnan Jin
Processes 2025, 13(5), 1352; https://doi.org/10.3390/pr13051352 - 28 Apr 2025
Viewed by 252
Abstract
The emerging machine learning techniques provide great opportunities for optimal operation of chemical systems. This paper presents a Bayesian deep reinforcement learning method for the optimization of a fluid catalytic cracking (FCC) unit, which is a key process in the petroleum refining industry. [...] Read more.
The emerging machine learning techniques provide great opportunities for optimal operation of chemical systems. This paper presents a Bayesian deep reinforcement learning method for the optimization of a fluid catalytic cracking (FCC) unit, which is a key process in the petroleum refining industry. Unlike the traditional reinforcement learning (RL) methods that use deterministic network weights, Bayesian neural networks are incorporated to represent the RL agent. The Bayesian treatment is integrated with the primal-dual method to handle the process constraints. Simulated experiments for FCC determined that the proposed algorithm achieves more stable control performance and higher economic profits, especially under parameter fluctuations and external disturbances. Full article
(This article belongs to the Special Issue Machine Learning Optimization of Chemical Processes)
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18 pages, 6092 KiB  
Article
VideoMamba Enhanced with Attention and Learnable Fourier Transform for Superheat Identification
by Yezi Hu, Xiaofang Chen, Lihui Cen, Zeyang Yin and Ziqing Deng
Processes 2025, 13(5), 1310; https://doi.org/10.3390/pr13051310 - 25 Apr 2025
Viewed by 204
Abstract
Superheat degree (SD) is an important indicator for identifying the status of aluminum electrolytic cells. The fire hole video of the aluminum electrolytic cell captured by an industrial camera is an important basis for identifying SD. This article proposes a novel method that [...] Read more.
Superheat degree (SD) is an important indicator for identifying the status of aluminum electrolytic cells. The fire hole video of the aluminum electrolytic cell captured by an industrial camera is an important basis for identifying SD. This article proposes a novel method that VideoMamba enhances with attention and learnable Fourier transform (CFVM) for SD identification. With a lower computational complexity and feature extraction capabilities comparable to transformers, VideoMamba offers the CFVM model a stronger feature extraction basis. The channel attention mechanism (CAM) block can achieve information exchange between channels. Through matrix eigenvalue manipulation, the learnable nonlinear Fourier transform (LNFT) block may guarantee stable convergence of the model. Furthermore, the LNFT block can efficiently use mixed frequency domain channels to capture global dependency information. The model is trained using the aluminum electrolysis fire hole dataset. Compared with recent fire hole identification models that primarily rely on neural networks, the method proposed in this paper is based on the concept of state space modeling, offering lower model complexity and enhanced feature extraction capability. Experimental results demonstrate that the proposed model achieves competitive performance in fire hole video identification tasks, reaching an identification accuracy of 85.7% on the test set. Full article
(This article belongs to the Special Issue Machine Learning Optimization of Chemical Processes)
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16 pages, 754 KiB  
Article
Transfer Learning for Thickener Control
by Samuel Arce Munoz and John D. Hedengren
Processes 2025, 13(1), 223; https://doi.org/10.3390/pr13010223 - 14 Jan 2025
Viewed by 816
Abstract
Thickener control is a key area of focus in the minerals processing industry, particularly due to its crucial role in water recovery, which is essential for sustainable resource management. The highly nonlinear nature of thickener dynamics presents significant challenges in modeling and optimization, [...] Read more.
Thickener control is a key area of focus in the minerals processing industry, particularly due to its crucial role in water recovery, which is essential for sustainable resource management. The highly nonlinear nature of thickener dynamics presents significant challenges in modeling and optimization, making it a strong candidate for advanced surrogate modeling techniques. However, traditional data-driven approaches often require extensive datasets, which are frequently unavailable, especially in new plants or unexplored operational domains. Developing data-driven models without enough data representative of the dynamics of the system could result in incorrect predictions and consequently, unstable response of the controller. This paper proposes the application of a methodology that leverages transfer learning to address these data limitations to enhance surrogate modeling and model predictive control (MPC) of thickeners. The performance of three approaches—a base model, a transfer learning model, and a physics-informed neural network (PINN)—are compared to demonstrate the effectiveness of transfer learning in improving control strategies under limited data conditions. Full article
(This article belongs to the Special Issue Machine Learning Optimization of Chemical Processes)
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