Machine Learning for Process Systems Engineering, Classification, Estimation, Prediction, and Updating
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: 30 April 2024 | Viewed by 14051
Special Issue Editors
Interests: system engineering; fault diagnosis; failure prognosis; modeling; simulation; AI
Interests: fault diagnosis; failure prognosis; modeling; simulation; AI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Control, FDI and FTC problems are defined in several cases of application as classification or regression issues. For example, the detection and identification of faults in complex systems in the presence of multiple faults can be considered as a classification problem, and the estimation of unmeasurable state variables for the control of a system as a regression problem. Artificial intelligence tools such as Machine Learning, Deep Learning, and Reinforcement Learning are powerful methods increasingly used to provide robust solutions to these issues.
This Special Issue aims to gather and highlight works using artificial intelligence tools to solve classification, regression and model-updating problems on application cases in various fields, such as sensors, microelectronics, transport and energy.
Prof. Dr. Nazih Moubayed
Dr. Mohand Djeziri
Dr. Hiba Al-Sheikh
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
- process systems engineering
- machine learning
- deep learning
- reinforcement learning
- FDI-FTC
- failure prognosis
- classification
- regression
- estimation