Advanced Hybrid Modelling of Chemical and Biochemical Processes

Dear Colleagues,

Industry 4.0 relies on the application of machine learning (ML) and other artificial intelligence (AI) techniques to the operation of industrial processes. It is foreseen that digitalization based on ML/AI will profoundly mark process manufacturing efficiency in the future. Digitalization based on ML/AI naturally challenges our traditional mathematical modeling vision in the process industries. A lot of effort is now being put into the application of new generation ML methods, such as deep artificial neural networks and support vector machines, in the practice of process operation. However, such emerging ML approaches neglect the vast wealth of “human” engineering/scientific knowledge accumulated over decades for the understanding and operation of chemical and biochemical processes. First Principles modeling, mechanistic modeling, phenomenological modeling, or even semi-empirical modeling embody a form of human knowledge abstraction, which is hardly compatible with current ML approaches. This Special Issue, entitled “Advanced Hybrid Modeling of Chemical and Biochemical Processes”, is focused on the latest scientific developments aimed at the compatibilization of traditional process modeling with emerging ML methods. The vision is that emerging AI/ML should enhance traditional mathematical modeling capacity rather than replace it. The broad scope of this Special Issue is hybrid physical/ML modeling theory and practice. The specific topics are (i) hybrid model physical/ML structures, (ii) hybrid model physical/ML identification, (iii) practical applications for process simulation, (iv) practical applications for process optimization and design, and (v) practical applications for process control. Studies focusing on the self-learning capacity of hybrid physical/ML models based on experience are especially welcome.

Authors are encouraged to contact one of the editors to discuss the relevance of the selected topic before the submission deadline.

Prof. Dr. Rui Manuel Freitas Oliveira
Dr. Moritz von Stosch
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.

• Machine learning
• Physical modeling
• Hybrid machine learning/Physical modeling
• Hybrid model structures
• Hybrid model identification
• Semi-parametric systems
• Learning algorithms
• Process optimization
• Process control
• Process scale-up