Special Issue "Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent"

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

Deadline for manuscript submissions: closed (31 May 2019).

Special Issue Editors

Guest Editor
Prof. Dr. Rafiqul Gani Website E-Mail
PSE for SPEED Company Ltd. 294/65 RK Office Park, Romklao Rd., Ladkrabang, Bangkok, Thailand 10520
Interests: energy-efficient, sustainable process synthesis; design and intensification; chemical product synthesis and design; modelling of properties of chemicals and their mixtures; development of computer-aided, model-based tools for product–process synthesis
Guest Editor
Prof. Dr. Ian Cameron Website E-Mail
School of Chemical Engineering, Faculty of Engineering, Architecture and Information Technology, The University of Queensland, Australia
Interests: process systems engineering, granulation, risk management, intelligent systems and engineering education

Special Issue Information

Dear Colleagues,

In 2019, Processes will be publishing a Special Issue to commemorate the life, work, and impact of Professor Roger W.H. Sargent. Professor Sargent worked at Imperial College London for almost 60 years and is widely regarded as the ‘academic father’ of Process Systems Engineering (PSE).

Professor Sargent’s impacts have been global. He leaves an immense legacy of many Imperial College PhD graduates and subsequent generations of researchers who in turn have contributed to the expansion of PSE as an influential area within higher education programs, as well as driving industry innovations and performance.

This Special Issue seeks to honour Professor Sargent’s legacy through insightful technical contributions, as well as through personal reflections of the authors on his impact on their own perspectives, thinking, and practice.

The editors ask interested authors to confirm their intention to submit a manuscript by 1st November 2018. The deadline for submission of the manuscript is 31 May 2019.

The Special Issue will be edited by Professors Rafiqul Gani ([email protected]) and Ian Cameron ([email protected]).

Prof. Dr. Rafiqul Gani
Prof. Dr. Ian Cameron
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 1200 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2019 an APC of 1400 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 (6 papers)

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Research

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Open AccessFeature PaperArticle
Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks
Processes 2019, 7(8), 507; https://doi.org/10.3390/pr7080507 - 02 Aug 2019
Abstract
The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing [...] Read more.
The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance. Full article
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Open AccessFeature PaperArticle
Towards the Grand Unification of Process Design, Scheduling, and Control—Utopia or Reality?
Processes 2019, 7(7), 461; https://doi.org/10.3390/pr7070461 - 18 Jul 2019
Abstract
As a founder of the Process Systems Engineering (PSE) discipline, Professor Roger W.H. Sargent had set ambitious goals for a systematic new generation of a process design paradigm based on optimization techniques with the consideration of future uncertainties and operational decisions. In this [...] Read more.
As a founder of the Process Systems Engineering (PSE) discipline, Professor Roger W.H. Sargent had set ambitious goals for a systematic new generation of a process design paradigm based on optimization techniques with the consideration of future uncertainties and operational decisions. In this paper, we present a historical perspective on the milestones in model-based design optimization techniques and the developed tools to solve the resulting complex problems. We examine the progress spanning more than five decades, from the early flexibility analysis and optimal process design under uncertainty to more recent developments on the simultaneous consideration of process design, scheduling, and control. This formidable target towards the grand unification poses unique challenges due to multiple time scales and conflicting objectives. Here, we review the recent progress and propose future research directions. Full article
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Open AccessFeature PaperArticle
Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications
Processes 2019, 7(7), 438; https://doi.org/10.3390/pr7070438 - 10 Jul 2019
Abstract
Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. [...] Read more.
Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contributions that address real-life industrial case studies of process scheduling is illustrated. Finally, the major reasons that impede the application of optimization-based scheduling are critically analyzed and possible remedies are discussed. Full article
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Open AccessFeature PaperArticle
Statistical Process Monitoring of the Tennessee Eastman Process Using Parallel Autoassociative Neural Networks and a Large Dataset
Processes 2019, 7(7), 411; https://doi.org/10.3390/pr7070411 - 01 Jul 2019
Abstract
In this article, the statistical process monitoring problem of the Tennessee Eastman process is considered using deep learning techniques. This work is motivated by three limitations of the existing works for such problem. First, although deep learning has been used for process monitoring [...] Read more.
In this article, the statistical process monitoring problem of the Tennessee Eastman process is considered using deep learning techniques. This work is motivated by three limitations of the existing works for such problem. First, although deep learning has been used for process monitoring extensively, in the majority of the existing works, the neural networks were trained in a supervised manner assuming that the normal/fault labels were available. However, this is not always the case in real applications. Thus, in this work, autoassociative neural networks are used, which are trained in an unsupervised fashion. Another limitation is that the typical dataset used for the monitoring of the Tennessee Eastman process is comprised of just a small number of data samples, which can be highly limiting for deep learning. The dataset used in this work is 500-times larger than the typically-used dataset and is large enough for deep learning. Lastly, an alternative neural network architecture, which is called parallel autoassociative neural networks, is proposed to decouple the training of different principal components. The proposed architecture is expected to address the co-adaptation issue of the fully-connected autoassociative neural networks. An extensive case study is designed and performed to evaluate the effects of the following neural network settings: neural network size, type of regularization, training objective function, and training epoch. The results are compared with those obtained using linear principal component analysis, and the advantages and limitations of the parallel autoassociative neural networks are illustrated. Full article
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Open AccessFeature PaperArticle
A Hybrid Multi-Objective Optimization Framework for Preliminary Process Design Based on Health, Safety and Environmental Impact
Processes 2019, 7(4), 200; https://doi.org/10.3390/pr7040200 - 08 Apr 2019
Abstract
Due to increasingly stringent legal requirements and escalating environmental control costs, chemical industries have paid close attention to sustainable development without compromising their economic performance. Thus, chemical industries are in need of systematic tools to conduct sustainability assessments of their process/plant design. In [...] Read more.
Due to increasingly stringent legal requirements and escalating environmental control costs, chemical industries have paid close attention to sustainable development without compromising their economic performance. Thus, chemical industries are in need of systematic tools to conduct sustainability assessments of their process/plant design. In order to avoid making costly retrofits at later stages, assessments during the preliminary design stage should be performed. In this paper, a systematic framework is presented for chemical processes at the preliminary design stage. Gross profit, Health Quotient Index (HQI), Inherent Safety Index (ISI) and the Waste Reduction (WAR) algorithm are used to assess the economic performance, health, safety and environmental impact of the process, respectively. The fuzzy optimization approach is used to analyse the trade-off among the four aspects simultaneously, as they often conflict with each other. Deviation between the solution obtained from mathematical optimization model and process simulator is determined to ensure the validity of the model. To demonstrate the proposed framework, a case study on 1, 4-butanediol production is presented. Full article
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Review

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Open AccessFeature PaperReview
Advances in Energy Systems Engineering and Process Systems Engineering in China—A Review Starting from Sargent’s Pioneering Work
Processes 2019, 7(6), 350; https://doi.org/10.3390/pr7060350 - 07 Jun 2019
Abstract
Process systems engineering (PSE), after being proposed by Sargent and contemporary researchers, has been fast developing in various domains and research communities around the world in the last couple of decades, with energy systems engineering featuring a typical yet still fast propagating domain, [...] Read more.
Process systems engineering (PSE), after being proposed by Sargent and contemporary researchers, has been fast developing in various domains and research communities around the world in the last couple of decades, with energy systems engineering featuring a typical yet still fast propagating domain, and the Chinese PSE community featuring a typical community with its own unique challenges for applying PSE theory and methods. In this paper, development of energy systems engineering and process systems engineering in China is discussed, and Sargent’s impacts on these two fields are the main focus. Pioneering work conducted by Sargent is firstly discussed. Then, a venation on how his work and thoughts have motivated later researchers and led to progressive advances is reviewed and analyzed. It shows that Sargent’s idea of optimum design and his work on nonlinear programming and superstructure modelling have resulted in well-known methods that are widely adopted in energy systems engineering and PSE applications in tackling problems in China. Following Sargent’s pioneering ideas and conceptual design of the PSE mansion, future development directions of energy systems engineering are also discussed. Full article
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