Data-Driven Modeling, Optimization and Control for 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: closed (10 November 2024) | Viewed by 745
Special Issue Editors
Interests: optimal control; colloidal self-assembly; machine learning; synthetic biology; time series analysis
Special Issue Information
Dear Colleagues,
Advances in digitalization, big data generation, collection, and analytics, as well as advanced computing, have revolutionized the modeling, optimization, and control of modern chemical process systems. In this Special Issue, we showcase original research articles and review articles that focus on the latest advancements and real-world applications of data-driven methods for chemical process modeling, optimization, and process control.
The topics covered in this Special Issue include simple yet powerful linear regression modeling, cutting-edge artificial intelligence modeling approaches, model-based optimization and control, and model-free control strategies (such as reinforcement learning) applied to a wide range of chemical process systems (such as crystallization, flow reactors, self-assembly, separations, and so on).
Dr. Xun Tang
Dr. Zheyu Jiang
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
- data-driven modeling
- data-driven optimization
- data-driven control
- machine learning
- chemical process
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