Synergetic Applications of Machine Learning and Chemical Engineering

Dear Colleagues,

The integration of machine learning into chemical engineering is ushering in a new era of innovation, particularly in process design, optimization, and control. For instance, in crystallization processes, machine learning techniques are enhancing our ability to predict and manipulate crystal formation, leading to improved product quality and process efficiency. Beyond crystallization, these advanced computational methods are revolutionizing other chemical engineering units by enabling more robust modeling and simulation, advanced control strategies, and effective real-time decision making.

The chemical industry and related sectors face increasing demand for more efficient and sustainable solutions across all development and operation stages. The resurgence of machine learning is laying the foundation for more robust solutions to current and future complex challenges. Harnessing these technologies is essential to deliver a paradigm shift in chemical engineering practices and to achieving breakthroughs that were previously unattainable.

This Special Issue on “Synergetic Applications of Machine Learning and Chemical Engineering” seeks high-quality contributions focusing on the latest advances in applications of machine learning in chemical engineering. The topics within the scope of this issue include, but are not limited to, the following:

• Modeling and simulation of chemical processes using machine learning (e.g., reaction kinetics, mass and heat transfer, process dynamics, population balance modeling
• Machine learning in crystallization processes (e.g., nucleation prediction, crystal growth modeling, polymorph control)
• Optimal design and operation of chemical engineering units (e.g., catalytic reactors, separation units, energy systems)
• Real-time optimization and self-optimization using machine learning techniques
• Advanced control strategies incorporating machine learning (e.g., predictive control, adaptive control, process monitoring
• Machine learning in process safety and risk assessment (e.g., hazard identification, failure prediction, safety system design)
• Data-driven approaches for process intensification and sustainability (e.g., energy efficiency, waste minimization, resource optimization)
• Predictive maintenance and fault diagnosis in chemical engineering equipment (e.g., pumps, compressors, heat exchangers)
• Applications of deep learning and neural networks in chemical engineering (e.g., property prediction, material design, process optimization)
• Integration of machine learning with computational chemistry and molecular modeling (e.g., catalyst development, drug discovery)
• Machine learning in environmental and green engineering processes (e.g., pollution control, waste treatment, renewable energy)
• Artificial intelligence in chemical engineering education and training (e.g., virtual labs, interactive learning platforms)
• Case studies on industrial implementation of machine learning techniques in chemical engineering

Prof. Dr. Brahim Benyahia
Dr. Zhenguo Gao
Dr. Seyed Soheil Mansouri
Dr. Yiming Ma
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
• chemical engineering
• crystallization processes
• process modeling and simulation
• advanced control strategies
• predictive maintenance
• process optimization
• deep learning and neural networks
• process intensification
• sustainability