Special Issue "Modeling and Simulation of Polymerization Processes"
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 27656
A printed edition of this Special Issue is available here.
Interests: polymer science and engineering; polymer reaction engineering; modeling of polymerization processes; synthesis of materials for novel applications; development of biorefining processes
Interests: macromolecular reaction engineering; molecular simulation; artificial intelligence; data mining; machine learning; process optimization; membrane science and technology; electrospinning and nanofiber technology
Polymer reaction engineering (PRE) is the branch of engineering that deals with the technology of large-scale polymer production and the manufacture of polymer products through polymerization processes. PRE is a broad and multidisciplinary area, relatively young and developing fast, which combines polymer science, chemistry, and technology with the principles of process engineering. The practical history of PRE started and evolved during the first half of the twentieth century. The 1930s were rich with theoretical findings in polymer science and engineering and with the commercial production of several new polymers. These investigations would transform our understanding of polymer manufacture and culminate in the development of several continuous polymerization processes and the establishment of PRE as a new area of research in the 1940s. The period from 1950 to 1990 saw the continued growth and evolution of process technologies, largely stimulated by the combination of PRE principles with the fundamental understanding of polymerization kinetics developed in the earlier years. These principles include the development of mathematical models for polymerization processes, and their solution using mathematical packages or specialized chemical engineering or polymerization software. The modeling and simulation of polymerization processes (MSPP) has been fundamental in the development of polymerization technologies since the early stages of PRE to date.
The importance of MSPP has already been recognized by MDPI Processes. A few related issues have been published in the last few years: “Computational Methods for Polymers”, Masoud Soroush, August 2019; “Modeling, Simulation and Control of Chemical Processes”, José Carlos Pinto, 2019; “Renewable Polymers: Processing and Chemical Modifications”, Marc A. Dubé and Tizazu Mekonnen, March 2019; “Process Modelling and Simulation: Cesar de Prada, Costas Pantelides and Jose Luis Pitarch, February 2019; and “Polymer Modeling, Control and Monitoring”, Masoud Soroush, February 2016.
The previous issues of Processes on related PRE topics have focused on recent specialized topics. This Special Issue on “Modeling and Simulation of Polymerization Processes” aims to address both new findings on basic topics as well as modeling of emerging aspects of product design and polymerization processes. Topics include but are not limited to:
- Development of new aspects/models and/or improving the existing models on established polymerization processes;
- Development of deterministic and stochastic mathematical methods for modeling of polymerization processes;
- Modeling and simulation of reversible deactivation radical polymerization (RDRP) processes;
- Modeling and simulation of dispersed-phase polymerization processes;
- Modeling and simulation of step-growth polymerization processes;
- Modeling and simulation of polymerization processes using bio-based monomers;
- Modeling and simulation of nonlinear polymerization processes;
- Modeling and simulation of catalytic and enzymatic polymerization processes;
- Modeling of depolymerization and synthesis of hybrid materials;
- Modeling and simulation of olefin polymerization processes
- Modeling of novel polymerization processes;
- Modeling and simulation of ultrasound-induced and radiation (light)-induced polymerization processes;
- Molecular simulations in polymerization processes;
- Data mining, artificial intelligence and machine learning in polymerization processes;
- Classical and heuristic optimization algorithms in polymerization processes;
- Modeling the recipe–microstructure–property interrelationships in polymerization processes.
Prof. Dr. Eduardo Vivaldo-Lima
Dr. Yousef Mohammadi
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.
- reversible deactivation radical polymerization (RDRP)
- dispersed-phase polymerization processes
- catalytic and enzymatic polymerization processes
- synthesis of hybrid materials
- nonlinear polymerization processes
- recipe–microstructure–property interrelationship
- artificial intelligence, machine learning
- data mining
- process optimization
- kinetic Monte Carlo
- molecular simulation
- light-induced polymerization