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Processes
Special Issues
Process System Engineering 4.0
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Process System Engineering 4.0

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: closed (21 March 2023) | Viewed by 4083

Special Issue Editors

Dr. Idelfonso B. R. Nogueira

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Guest Editor
Department of Chemical Engineering, Norwegian University of Science and Technology, 7034 Trondheim, Norway
Interests: artificial intelligence; scientific machine learning; process system engineering; systems control & optimization; cyber-physical systems; digital twins
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alexandre F. P. Ferreira

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Guest Editor
Laboratory of Separation and Reaction Engineering (LSRE), Department of Chemical, Faculty of Engineering of the University of Porto (FEUP), Porto, Portugal
Interests: cyclic adsorption processes; material sciences; processes intensification; heat integration
Prof. Dr. Márcio A. F. Martins

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Guest Editor
Departamento de Engenharia Química, Escola Politécnica, Universidade Federal da Bahia, Salvador 40210-630, BA, Brazil
Interests: process system engineering; control theory; artificial intelligence; oil and gas; sustainable energy systems; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Process System Engineering (PSE) is a consolidated field that provides the backbone for large-scale industrial production. Nowadays, PSE is positioned in a globalized context of intense renovation. The processes’ performances are affected not only by plant operation and consumer demands but also by environmental, social, political, and worldwide financial situations. The present social dynamics require the industry to quickly respond to external changes with the flexibility to reconfigure itself while constantly optimizing and controlling its processes. Those demands are starting to exceed the human capacity to efficiently cope with and swiftly respond to these situations simultaneously.

In this scenario, several new technologies are emerging to address these issues. This is promoting the 4th Industrial Revolution, so-called Industry 4.0. The synergy between these new technologies and PSE fosters the emergence of a new field, Process System Engineering 4.0. It is renewing Process System Engineering to enable it to face modern challenges in society. In this context, this Special Issue aims to promote and discuss the new developments in PSE. Its goal is to address the most recent advancements in PSE 4.0 and future perspectives in this field. Hence, this Special Issue will promote the debate surrounding the multidisciplinary intersection between PSE 4.0 and topics such as: scientific machine learning; advanced control systems; optimization theory; high-performance computing; distributed systems; digital twins; cyber–physical systems.

Dr. Idelfonso B. R. Nogueira
Prof. Dr. Alexandre F. P. Ferreira
Prof. Dr. Márcio A.F. Martins
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.

Keywords

  • process system engineering
  • scientific machine learning
  • advanced control systems
  • optimization theory
  • high-performance computing
  • distributed systems
  • digital-twins
  • cyber–physical systems

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

22 pages, 1512 KiB  
Article
Influence of Estimators and Numerical Approaches on the Implementation of NMPCs
by Fernando Arrais Romero Dias Lima, Ruan de Rezende Faria, Rodrigo Curvelo, Matheus Calheiros Fernandes Cadorini, César Augusto García Echeverry, Maurício Bezerra de Souza, Jr. and Argimiro Resende Secchi
Processes 2023, 11(4), 1102; https://doi.org/10.3390/pr11041102 - 4 Apr 2023
Cited by 2 | Viewed by 1504
Abstract
Advanced control strategies, together with state-estimation methods, are frequently applied to nonlinear and complex systems. It is crucial to understand which of these are the most efficient methods for the best use of these approaches in a chemical process. In the current work, [...] Read more.
Advanced control strategies, together with state-estimation methods, are frequently applied to nonlinear and complex systems. It is crucial to understand which of these are the most efficient methods for the best use of these approaches in a chemical process. In the current work, nonlinear model predictive control (NMPC) approaches were developed that considered three numerical methods: single shooting (SS), multiple shooting (MS), and orthogonal collocation (OC). Their performance was compared against the Van de Vusse reactor benchmark while considering set-point changes, unreachable set-point, disturbances, and mismatches. The results showed that the NMPC based on OC presented less computational cost than the other approaches. The extended Kalman filter (EKF), constrained extended Kalman filter (CEKF), and the moving horizon estimator (MHE) were also developed. The estimators’ performance was compared for the same benchmark by considering the computational cost and the mean squared error (MSE) for the estimated variables, thereby verifying the CEKF as the best option. Finally, the performance of the nine combinations of estimators and control approaches was compared to consider the Van de Vusse reactor and the same scenarios, thereby verifying the best performance of the CEKF with the OC. The present work can help with choosing the numerical method and the estimator for controlling chemical processes. Full article
(This article belongs to the Special Issue Process System Engineering 4.0)
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13 pages, 1699 KiB  
Article
Intelligent Prediction Model (IPM) of Foundation Pit Displacement Based on Extreme Learning Machine (ELM) and Its Application
by Shangge Liu, Changzhong Sun, Hui Zhou and Yuanhai Wang
Processes 2022, 10(5), 896; https://doi.org/10.3390/pr10050896 - 2 May 2022
Cited by 1 | Viewed by 1852
Abstract
In order to effectively predict the dynamic displacement and disaster, according to the analysis of the influencing parameters affecting the deformation of a subway foundation pit supported by piles (walls), the rough set attribute reduction method (RSARM) and the average influence value algorithm [...] Read more.
In order to effectively predict the dynamic displacement and disaster, according to the analysis of the influencing parameters affecting the deformation of a subway foundation pit supported by piles (walls), the rough set attribute reduction method (RSARM) and the average influence value algorithm (AIVA) are used to simplify the influencing factors of foundation pit deformation. Those simplified factors are taken as the input of the ELM, with the output being the displacement of the foundation pit. Finally, the IPM of foundation pit displacement derived from the ELM is obtained, which is finally used for engineering practice. The results show that it is feasible to simplify the influencing factors of the deformation of the foundation pit by RSARM and AIVA. The proposed IPM of foundation pit displacement has high accuracy and good generalization ability, which can be used for the deformation prediction. Full article
(This article belongs to the Special Issue Process System Engineering 4.0)
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