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Processes
Special Issues
Process Modeling, Simulation, and Optimization in Chemical Engineering
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Process Modeling, Simulation, and Optimization in Chemical Engineering

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 2375

Special Issue Editors

Dr. Javier Silva

E-Mail Website
Guest Editor
Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, Chile
Interests: nanobubbles technology; modelling; simulation; process control
Prof. Dr. Wei Sun

E-Mail Website
Guest Editor
College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
Interests: process modeling and analysis; process monitoring and fault diagnosis; industrial data mining; process system engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Rodrigo Ortiz-Soto

E-Mail Website
Guest Editor Assistant
Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340000, Chile
Interests: electrokinetics; contaminated soil; adsorption; design of experiments

Special Issue Information

Dear Colleagues,

Chemical engineering is undergoing an important transformation as advances in process modeling, simulation and optimization address the complex challenges of sustainability, efficiency and industrial competitiveness. This Special Issue on “Process Modeling, Simulation, and Optimization in Chemical Engineering” aims to bring together recent research that uses these tools to improve process design, reduce environmental impact, and maximize operational efficiency.

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

  • Development of new models for chemical processes.
  • Advanced simulation techniques for process analysis and design.
  • Optimization of chemical processes to increase efficiency and reduce emissions.
  • Application of artificial intelligence and machine learning for process simulation and optimization.
  • Techno-economic analysis of emerging chemical engineering processes.
  • Case studies on modeling and optimization implementation in industry.
  • Integration of renewable energy sources in chemical processes.

This Special Issue seeks to provide a platform for sharing innovative approaches and case studies that drive process optimization in chemical engineering.

Dr. Javier Silva
Prof. Dr. Wei Sun
Guest Editors

Dr. Rodrigo Ortiz-Soto
Guest Editor Assistant

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 modeling
  • process simulation
  • process optimization
  • chemical engineering
  • techno-economic analysis
  • environmental impact
  • integration of renewable energies
  • artificial intelligence in chemical processes
  • sustainable processes
  • advanced process control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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 (2 papers)

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Research

22 pages, 3448 KiB  
Article
Modeling and Evaluation of Attention Mechanism Neural Network Based on Industrial Time Series Data
by Jianqiao Zhou, Zhu Wang, Jiaxuan Liu, Xionglin Luo and Maoyin Chen
Processes 2025, 13(1), 184; https://doi.org/10.3390/pr13010184 - 10 Jan 2025
Viewed by 708
Abstract
Chemical process control systems are complex, and modeling the controlled object is the first task in automatic control and optimal design. Most chemical process modeling experiments require test signals to be applied to the process, which may lead to production interruptions or cause [...] Read more.
Chemical process control systems are complex, and modeling the controlled object is the first task in automatic control and optimal design. Most chemical process modeling experiments require test signals to be applied to the process, which may lead to production interruptions or cause safety accidents. Therefore, this paper proposes an improved transformer model based on a self-attention mechanism for modeling industrial processes. Then, an evaluation mechanism based on root mean square error (RMSE) and Kullback–Leibler divergence (KLD) metrics is designed to obtain more appropriate model parameters. The Variational Auto-Encoder (VAE) network is used to compute the associated KLD. Finally, a real nonlinear dynamic process in the petrochemical industry is modeled and evaluated using the proposed methodology to predict the time series data of the process. This study demonstrates the validity of the proposed transformer model and illustrates the versatility of using an integrated modeling, evaluation, and prediction scheme for nonlinear dynamic processes in process industries. The scheme is of great importance for the field of industrial soft measurements as well as for deep learning-based time series prediction. In addition, the issue of a suitable time domain for the prediction is discussed. Full article
(This article belongs to the Special Issue Process Modeling, Simulation, and Optimization in Chemical Engineering)
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20 pages, 2445 KiB  
Article
MOLA: Enhancing Industrial Process Monitoring Using a Multi-Block Orthogonal Long Short-Term Memory Autoencoder
by Fangyuan Ma, Cheng Ji, Jingde Wang, Wei Sun, Xun Tang and Zheyu Jiang
Processes 2024, 12(12), 2824; https://doi.org/10.3390/pr12122824 - 9 Dec 2024
Viewed by 1061
Abstract
In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to constrain the latent space [...] Read more.
In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to constrain the latent space output. This helps eliminate the redundancy in the features identified, thereby improving the overall monitoring performance. On top of this, a multi-block monitoring structure is proposed, which categorizes the process variables into multiple blocks by leveraging expert process knowledge about their associations with the overall process. Each block is associated with its specific orthogonal long short-term memory autoencoder model, whose extracted dynamic orthogonal features are monitored by distance-based Hotelling’s T2 statistics and quantile-based cumulative sum (CUSUM) designed for multivariate data streams that are nonparametric and heterogeneous. Compared to having a single model accounting for all process variables, such a multi-block structure significantly improves overall process monitoring performance, especially for large-scale industrial processes. Finally, we propose an adaptive weight-based Bayesian fusion (W-BF) framework to aggregate all block-wise monitoring statistics into a global statistic that we monitor for faults. Fault detection speed and accuracy are improved by assigning and adjusting weights to blocks based on the sequential order in which alarms are raised. We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman process and comparing the performance with various benchmark methods. Full article
(This article belongs to the Special Issue Process Modeling, Simulation, and Optimization in Chemical Engineering)
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