Special Issue "Process Systems Engineering à la Canada"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Computational Methods".

Deadline for manuscript submissions: 30 April 2019

Special Issue Editors

Guest Editor
Prof. William R. Cluett

Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada
Website | E-Mail
Interests: process identification, control and design, systems biology
Guest Editor
Prof. Michel Perrier

Department of Chemical Engineering, École Polytechnique Montréal, Montréal H3C 3A7, Canada
Website | E-Mail
Interests: chemical and biochemical reactors, control systems, petrochemistry, biotechnology, biopharmacology
Guest Editor
Prof. Ana Inés Torres

Department of Chemical Engineering, Universidad de la República, J. Herrera y Reissig 565, Montevideo, Uruguay
Website | E-Mail
Interests: Analysis, design and optimization of processes; Modeling and simulation of chemical processes; Data analysis and parameter estimation; Biomass conversion to fuels and chemicals (biorefineries); Energy systems
Guest Editor
Prof. Simant Upreti

Department of Chemical Engineering, Ryerson University, Toronto, Ontario, Canada
Website | E-Mail
Interests: optimal control and optimization, process modeling and simulation

Special Issue Information

Dear Colleagues, 

The Canadian chemical engineering community (CSChE) has a long history of excellence in process systems engineering research and practice involving applied statistics and process design, control, and optimization. Members of this community recognized as early leaders include Park Reilly (Waterloo), David Bacon (Queen’s), John MacGregor (McMaster), and Grant Fisher (Alberta), all of whom served as both supervisors and mentors of hundreds of students who have themselves gone on to make contributions to these fields in both academia and industry, in Canada and around the world.  

Each year, this community gathers at the Canadian Chemical Engineering Conference to hear presentations based on work being done in the process systems engineering field on topics of relevance and importance to the chemical engineering community. This Special Issue is being coordinated with the 68th such conference that is being held in conjunction with the XXIX Interamerican Congress of Chemical Engineering in Toronto, Canada on October 28–31, 2018.  

This Special Issue focuses on, but is not limited to, papers that align with the “Systems and Control” thematic sessions of this conference:

  • Data analytics;
  • Data science;
  • Design of sustainable processes;
  • Optimal control.

We look forward to receiving your contributions.

Prof. William R. Cluett

Prof. Michel Perrier
Prof. Ana Inés Torres
Prof. Simant Upreti
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 papers will be 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 1100 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2019 an APC of 1200 CHF applies. 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.

Published Papers (1 paper)

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Open AccessArticle Parallel Conical Area Community Detection Using Evolutionary Multi-Objective Optimization
Processes 2019, 7(2), 111; https://doi.org/10.3390/pr7020111
Received: 21 December 2018 / Revised: 10 February 2019 / Accepted: 16 February 2019 / Published: 20 February 2019
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Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP [...] Read more.
Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies. Full article
(This article belongs to the Special Issue Process Systems Engineering à la Canada)

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