Multi-Criteria Decision Making in Chemical and Process Engineering

Dear Colleagues,

The complexity of modern chemical processes requires the consideration of multiple criteria in the selection, design, and operation of systems. While financial viability remains crucial, other critical aspects include technical viability, which encompasses process maturity, safety, availability, and scalability, and social dimensions, such as acceptance by the workforce and local populations, along with environmental viability, assessed through life cycle assessment frameworks, adding layers of complexity to decision making. These multi-dimensional assessments, essential for evaluating different production and supply chain pathways for specific products or services, are well-suited to multi-criteria decision making (MCDM). Despite its growing application and significance in the field, MCDM has not yet been the topic of a Special Issue in chemical and process engineering journals.

This Special Issue on “Multi-Criteria Decision Making in Chemical and Process Engineering” focuses on new developments and applications of MCDM to processes and systems of interest to chemical and process engineers. The topics include but are not limited to the following:

• New or improved MCDM methods;
• MCDM applications in chemical processes;
• Development of MCDM software or tools;
• Sensitivity analysis (including rank reversal) of MCDM methods;
• MCDM used within single-objective optimization (SOO) or multi-objective optimization (MOO);
• Selection and design of novel chemicals and materials;
• Process selection, design, and operation with MCDM methods;
• Integration of MCDM with artificial intelligence (AI) and machine learning (ML);
• MCDM for green and sustainable chemical processes.

To maximize impact, authors contributing to this Special Issue are invited to deposit their process models in an open access repository and/or provide them as Supplementary Materials. These may include contributions such as process simulation files, computer codes, spreadsheets, model files, optimization codes, or other relevant digital objects used for the studies reported in the articles, and they can be either commercial or open-source software.

Thank you for reading this proposal/invitation. We hope that you will consider contributing to this Special Issue.

Prof. Dr. Gade Pandu Rangaiah
Dr. Andrew Hoadley
Dr. Zhiyuan Wang
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.

• multi-criteria decision making
• multi-criteria analysis
• process selection
• process design
• multi-objective optimization
• chemical processes
• sustainable chemical engineering
• sensitivity analysis