Processes

Editorial

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3 pages, 372 KiB

Open AccessEditorial

In Memoriam of Professor Roger W.H. Sargent, the Founder of “Process Systems Engineering”

by Rafiqul Gani and Ian Cameron

Processes 2020, 8(4), 405; https://doi.org/10.3390/pr8040405 - 30 Mar 2020

Cited by 1 | Viewed by 2539

Abstract

In September 2018, the global chemical engineering community lost a true pioneer in the field [...] Full article

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

Research

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13 pages, 5169 KiB

Open AccessFeature PaperArticle

Journey Making: Applying PSE Principles to Complex Curriculum Designs

by Ian Cameron and Greg Birkett

Processes 2020, 8(3), 373; https://doi.org/10.3390/pr8030373 - 23 Mar 2020

Cited by 2 | Viewed by 2734

Abstract

Since the 1950s, Process Systems Engineering (PSE) concepts have traditionally been applied to the process industries, with great effect and with significant benefit. However, the same general approaches and principles in designing complex process designs can be applied to the design of higher [...] Read more.

Since the 1950s, Process Systems Engineering (PSE) concepts have traditionally been applied to the process industries, with great effect and with significant benefit. However, the same general approaches and principles in designing complex process designs can be applied to the design of higher education (HE) curricula. Curricula represent intended learning journeys, these being similar to the design of process flowsheets. In this paper, we set out the formal framework and concepts that underlie the challenges in design of curricula. The approaches use generic and fundamental concepts that can be applied by any discipline to curriculum design. We show how integration of discipline-specific concepts, across time and space, can be combined through design choices, to create learning journeys for students. These concepts are captured within a web-based design tool that permits wide choices for designers to build innovative curricula. The importance of visualization of curricula is discussed and illustrated, using a range of tools that permit insight into the nature of the designs. The framework and tool presented in this paper have been widely used across many disciplines, such as science, engineering, nursing, philosophy and pharmacy. As a special issue in memory of Professor Roger W.H. Sargent; we show these new developments in curriculum design are similar to the development of process flowsheets. Professor Sargent was not only an eminent research leader and pioneer, but an influential educator who gave rise to a new area in Chemical Engineering, influencing its many directions for more than 50 years. Full article

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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23 pages, 344 KiB

Open AccessFeature PaperArticle

Modern Modeling Paradigms Using Generalized Disjunctive Programming

by Qi Chen and Ignacio Grossmann

Processes 2019, 7(11), 839; https://doi.org/10.3390/pr7110839 - 10 Nov 2019

Cited by 16 | Viewed by 3182

Abstract

Models involving decision variables in both discrete and continuous domain spaces are prevalent in process design. Generalized Disjunctive Programming (GDP) has emerged as a modeling framework to explicitly represent the relationship between algebraic descriptions and the logical structure of a design problem. However, [...] Read more.

Models involving decision variables in both discrete and continuous domain spaces are prevalent in process design. Generalized Disjunctive Programming (GDP) has emerged as a modeling framework to explicitly represent the relationship between algebraic descriptions and the logical structure of a design problem. However, fewer formulation examples exist for GDP compared to the traditional Mixed-Integer Nonlinear Programming (MINLP) modeling approach. In this paper, we propose the use of GDP as a modeling tool to organize model variants that arise due to characterization of different sections of an end-to-end process at different detail levels. We present an illustrative case study to demonstrate GDP usage for the generation of model variants catered to process synthesis integrated with purchasing and sales decisions in a techno-economic analysis. We also show how this GDP model can be used as part of a hierarchical decomposition scheme. These examples demonstrate how GDP can serve as a useful model abstraction layer for simplifying model development and upkeep, in addition to its traditional usage as a platform for advanced solution strategies. Full article

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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12 pages, 471 KiB

Open AccessFeature PaperArticle

Symmetry Detection for Quadratic Optimization Using Binary Layered Graphs

by Georgia Kouyialis, Xiaoyu Wang and Ruth Misener

Processes 2019, 7(11), 838; https://doi.org/10.3390/pr7110838 - 09 Nov 2019

Cited by 2 | Viewed by 2916

Abstract

Symmetry in mathematical optimization may create multiple, equivalent solutions. In nonconvex optimization, symmetry can negatively affect algorithm performance, e.g., of branch-and-bound when symmetry induces many equivalent branches. This paper develops detection methods for symmetry groups in quadratically-constrained quadratic optimization problems. Representing the optimization [...] Read more.

Symmetry in mathematical optimization may create multiple, equivalent solutions. In nonconvex optimization, symmetry can negatively affect algorithm performance, e.g., of branch-and-bound when symmetry induces many equivalent branches. This paper develops detection methods for symmetry groups in quadratically-constrained quadratic optimization problems. Representing the optimization problem with adjacency matrices, we use graph theory to transform the adjacency matrices into binary layered graphs. We enter the binary layered graphs into the software package nauty that generates important symmetric properties of the original problem. Symmetry pattern knowledge motivates a discretization pattern that we use to reduce computation time for an approximation of the point packing problem. This paper highlights the importance of detecting and classifying symmetry and shows that knowledge of this symmetry enables quick approximation of a highly symmetric optimization problem. Full article

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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20 pages, 3596 KiB

Open AccessFeature PaperArticle

Modeling and Analysis of Coal-Based Lurgi Gasification for LNG and Methanol Coproduction Process

by Jingfang Gu, Siyu Yang and Antonis Kokossis

Processes 2019, 7(10), 688; https://doi.org/10.3390/pr7100688 - 02 Oct 2019

Cited by 15 | Viewed by 4869

Abstract

A coal-based coproduction process of liquefied natural gas (LNG) and methanol (CTLNG-M) is developed and key units are simulated in this paper. The goal is to find improvements of the low-earning coal to synthesis natural gas (CTSNG) process using the same raw material [...] Read more.

A coal-based coproduction process of liquefied natural gas (LNG) and methanol (CTLNG-M) is developed and key units are simulated in this paper. The goal is to find improvements of the low-earning coal to synthesis natural gas (CTSNG) process using the same raw material but producing a low-margin, single synthesis natural gas (SNG) product. In the CTLNG-M process, there are two innovative aspects. Firstly, the process can co-generate high value-added products of LNG and methanol, in which CH₄ is separated from the syngas to obtain liquefied natural gas (LNG) through a cryogenic separation unit, while the remaining lean-methane syngas is then used for methanol synthesis. Secondly, CO₂ separated from the acid gas removal unit is partially reused for methanol synthesis reaction, which consequently increases the carbon element utilization efficiency and reduces the CO₂ emission. In this paper, the process is designed with the output products of 642,000 tons/a LNG and 1,367,800 tons/a methanol. The simulation results show that the CTLNG-M process can obtain a carbon utilization efficiency of 39.6%, bringing about a reduction of CO₂ emission by 130,000 tons/a compared to the CTSNG process. However, the energy consumption of the new process is increased by 9.3% after detailed analysis of energy consumption. The results indicate that although electricity consumption is higher than that of the conventional CTSNG process, the new CTLNG-M process is still economically feasible. In terms of the economic benefits, the investment is remarkably decreased by 17.8% and an increase in internal rate of return (IRR) by 6% is also achieved, contrasting to the standalone CTSNG process. It is; therefore, considered as a feasible scheme for the efficient utilization of coal by Lurgi gasification technology and production planning for existing CTSNG plants. Full article

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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17 pages, 1779 KiB

Open AccessFeature PaperArticle

An Intensified Reactive Separation Process for Bio-Jet Diesel Production

by Miriam García-Sánchez, Mauricio Sales-Cruz, Teresa Lopez-Arenas, Tomás Viveros-García and Eduardo S. Pérez-Cisneros

Processes 2019, 7(10), 655; https://doi.org/10.3390/pr7100655 - 25 Sep 2019

Cited by 11 | Viewed by 3926

Abstract

An intensified three-step reaction-separation process for the production of bio-jet diesel from tryglycerides and petro-diesel mixtures is proposed. The intensified reaction-separation process considers three sequentially connected sections: (1) a triglyceride hydrolysis section with a catalytic heterogeneous reactor, which is used to convert the [...] Read more.

An intensified three-step reaction-separation process for the production of bio-jet diesel from tryglycerides and petro-diesel mixtures is proposed. The intensified reaction-separation process considers three sequentially connected sections: (1) a triglyceride hydrolysis section with a catalytic heterogeneous reactor, which is used to convert the triglycerides of the vegetable oils into the resultant fatty acids. The separation of the pure fatty acid from glycerol and water is performed by a three-phase flash drum and two conventional distillation columns; (2) a co-hydrotreating section with a reactive distillation column used to perform simultaneously the deep hydrodesulphurisation (HDS) of petro-diesel and the hydrodeoxigenation (HDO), decarbonylation and decarboxylation of the fatty acids; and (3) an isomerization-cracking section with a hydrogenation catalytic reactor coupled with a two-phase flash drum is used to produce bio-jet diesel with the suitable fuel features required by the international standards. Intensive simulations were carried out and the effect of several operating variables of the three sections (triglyceride-water feed ratio, oleic acid-petro-diesel feed ratio, hydrogen consumption) on the global intensified process was studied and the optimal operating conditions of the intensified process for the production of bio-jet diesel were achieved. Full article

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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31 pages, 3896 KiB

Open AccessFeature PaperArticle

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

by Camilo Lima, Susana Relvas, Ana Barbosa-Póvoa and Juan M. Morales

Processes 2019, 7(8), 507; https://doi.org/10.3390/pr7080507 - 02 Aug 2019

Cited by 7 | Viewed by 4125

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

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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20 pages, 317 KiB

Open AccessFeature PaperArticle

Towards the Grand Unification of Process Design, Scheduling, and Control—Utopia or Reality?

by Baris Burnak, Nikolaos A. Diangelakis and Efstratios N. Pistikopoulos

Processes 2019, 7(7), 461; https://doi.org/10.3390/pr7070461 - 18 Jul 2019

Cited by 38 | Viewed by 4118

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

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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35 pages, 1809 KiB

Open AccessFeature PaperArticle

Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications

by Georgios P. Georgiadis, Apostolos P. Elekidis and Michael C. Georgiadis

Processes 2019, 7(7), 438; https://doi.org/10.3390/pr7070438 - 10 Jul 2019

Cited by 50 | Viewed by 9581

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

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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21 pages, 1633 KiB

Open AccessFeature PaperArticle

Statistical Process Monitoring of the Tennessee Eastman Process Using Parallel Autoassociative Neural Networks and a Large Dataset

by Seongmin Heo and Jay H. Lee

Processes 2019, 7(7), 411; https://doi.org/10.3390/pr7070411 - 01 Jul 2019

Cited by 20 | Viewed by 5737

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

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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18 pages, 1189 KiB

Open AccessFeature PaperArticle

A Hybrid Multi-Objective Optimization Framework for Preliminary Process Design Based on Health, Safety and Environmental Impact

by Shin Yee Teh, Kian Boon Chua, Boon Hooi Hong, Alex J. W. Ling, Viknesh Andiappan, Dominic C. Y. Foo, Mimi H. Hassim and Denny K. S. Ng

Processes 2019, 7(4), 200; https://doi.org/10.3390/pr7040200 - 08 Apr 2019

Cited by 10 | Viewed by 6112

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

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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Review

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22 pages, 647 KiB

Open AccessFeature PaperReview

Advances in Energy Systems Engineering and Process Systems Engineering in China—A Review Starting from Sargent’s Pioneering Work

by Wenhan Qian, Pei Liu and Zheng Li

Processes 2019, 7(6), 350; https://doi.org/10.3390/pr7060350 - 07 Jun 2019

Cited by 1 | Viewed by 3384

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

(This article belongs to the Special Issue Commemorative Issue to Celebrate the Life and Work of Prof. Roger W.H. Sargent)

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