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Pistikopoulos EN, Tian Y. Advanced Modeling and Optimization Strategies for Process Synthesis. Annu Rev Chem Biomol Eng 2024; 15:81-103. [PMID: 38594946 DOI: 10.1146/annurev-chembioeng-100522-112139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation-based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning-and quantum computing-assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.
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Affiliation(s)
- Efstratios N Pistikopoulos
- Texas A&M Energy Institute and Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas, USA;
| | - Yuhe Tian
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, West Virginia, USA;
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2
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Ramírez-Márquez C, Al-Thubaiti MM, Martín M, El-Halwagi MM, Ponce-Ortega JM. Processes Intensification for Sustainability: Prospects and Opportunities. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- César Ramírez-Márquez
- Facultad de Ingeniería Química, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Mich58060, México
| | | | - Mariano Martín
- Departamento de Ingeniería Química, Universidad de Salamanca, Plza. Caídos 1-5, Salamanca37008, Spain
| | - Mahmoud M. El-Halwagi
- Chemical Engineering Department, Texas A&M University, College StationTexas77843, United States
| | - José María Ponce-Ortega
- Facultad de Ingeniería Química, Universidad Michoacana de San Nicolás de Hidalgo, Morelia, Mich58060, México
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Saad DM, Alnouri SY. The Need for Speed - Optimal CO 2 Hydrogenation Processes Selection via Mixed Integer Linear Programming. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Liu J, Gao L, Liu X, Ren J, Dong M, Sun L. Evolutional Design and Plant-Wide Control for Dimethyl Ether Production by Combining Dynamic Process Intensification and Pervaporation Membranes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jiyan Liu
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
| | - Linkun Gao
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
| | - Xinglong Liu
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
| | - Junyao Ren
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
| | - Mengru Dong
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
| | - Lanyi Sun
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
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Yan L, Deneke TL, Heljanko K, Harjunkoski I, Edgar TF, Baldea M. Dynamic Process Intensification via Data-Driven Dynamic Optimization: Concept and Application to Ternary Distillation. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lingqing Yan
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Tewodros L. Deneke
- Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland
| | - Keijo Heljanko
- Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland
| | - Iiro Harjunkoski
- Department of Chemical and Metallurgical Engineering, Aalto University, 00076 Aalto, Finland
| | - Thomas F. Edgar
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
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Yan L, Edgar TF, Baldea M. Dynamic process intensification: Fundamentals and implementation to ternary distillation. AIChE J 2021. [DOI: 10.1002/aic.17262] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Lingqing Yan
- McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA
| | - Thomas F. Edgar
- McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA
| | - Michael Baldea
- McKetta Department of Chemical Engineering The University of Texas at Austin Austin Texas USA
- Oden Institute for Computational Engineering and Sciences The University of Texas at Austin Austin Texas USA
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Pistikopoulos EN, Tian Y, Bindlish R. Operability and control in process intensification and modular design: Challenges and opportunities. AIChE J 2021. [DOI: 10.1002/aic.17204] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
| | - Yuhe Tian
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
- Texas A&M Energy Institute, Texas A&M University College Station Texas USA
| | - Rahul Bindlish
- Engineering Solutions Technology Center, The Dow Chemical Company Texas USA
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Yan L, Witt PM, Edgar TF, Baldea M. Static and Dynamic Intensification of Water–Ethylene Glycol Separation Using a Dividing Wall Column. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c06139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Lingqing Yan
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Paul M. Witt
- The Dow Chemical Company, Midland, Michigan 48674, United States
| | - Thomas F. Edgar
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Dept. of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
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Liu J, Gao L, Ren J, Liu W, Liu X, Sun L. Dynamic Process Intensification of Dimethyl Ether Reactive Distillation Based on Output Multiplicity. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03973] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jiyan Liu
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Linkun Gao
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Junyao Ren
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Wei Liu
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Xinglong Liu
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
| | - Lanyi Sun
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
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Rapid Multi-Objective Optimization of Periodically Operated Processes Based on the Computer-Aided Nonlinear Frequency Response Method. Processes (Basel) 2020. [DOI: 10.3390/pr8111357] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The dynamic optimization of promising forced periodic processes has always been limited by time-consuming and expensive numerical calculations. The Nonlinear Frequency Response (NFR) method removes these limitations by providing excellent estimates of any process performance criteria of interest. Recently, the NFR method evolved to the computer-aided NFR method (cNFR) through a user-friendly software application for the automatic derivation of the functions necessary to estimate process improvement. By combining the cNFR method with standard multi-objective optimization (MOO) techniques, we developed a unique cNFR–MOO methodology for the optimization of periodic operations in the frequency domain. Since the objective functions are defined with entirely algebraic expressions, the dynamic optimization of forced periodic operations is extraordinarily fast. All optimization parameters, i.e., the steady-state point and the forcing parameters (frequency, amplitudes, and phase difference), are determined rapidly in one step. This gives the ability to find an optimal periodic operation around a sub-optimal steady-state point. The cNFR–MOO methodology was applied to two examples and is shown as an efficient and powerful tool for finding the best forced periodic operation. In both examples, the cNFR–MOO methodology gave conditions that could greatly enhance a process that is normally operated in a steady state.
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Abstract
We review the impact of control systems and strategies on the energy efficiency of chemical processes. We show that, in many ways, good control performance is a necessary but not sufficient condition for energy efficiency. The direct effect of process control on energy efficiency is manyfold: Reducing output variability allows for operating chemical plants closer to their limits, where the energy/economic optima typically lie. Further, good control enables novel, transient operating strategies, such as conversion smoothing and demand response. Indirectly, control systems are key to the implementation and operation of more energy-efficient plant designs, as dictated by the process integration and intensification paradigms. These conclusions are supported with references to numerous examples from the literature.
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Affiliation(s)
- Jodie M Simkoff
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Fernando Lejarza
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Morgan T Kelley
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Calvin Tsay
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
| | - Michael Baldea
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA; , , , ,
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Leon-Pulido J, Fajardo-Moreno WS, Arias-Tapia MJ, Gonzalez-Delgado AD, Cárdenas-Concha VO, Nunhez JR. Estudo termoquímico assistido por computador para a produção de biodiesel. REVISTA ION 2019. [DOI: 10.18273/revion.v32n2-2019008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
A importância de melhorar os processos de transformação industrial para processos mais eficientes é parte dos desafios atuais. Específicamente, o desenvolvimento de processos mais eficientes na produção de biocombustíveis, onde os processos de reação e separação podem ser intensificados, é de grande interesse para reduzir o consumo de energia associado ao processo. No caso do Biodiesel, o processo é definido por uma reação química e pelos componentes associados ao processo, onde o estudo termoquímico busca desenvolver cálculos para o entendimento subsequente do processo de reação e purificação. Assim, a análise da mistura dos componentes usando o simulador de processo Aspen Plus V9® descreve o estudo termoquímico. O método termodinâmico UNIFAC-DMD foi utilizado para estimar os parâmetros binários de equilíbrio dos reagentes usando o simulador. Os aspectos analisados apresentam o comportamento dos componentes em diferentes condições de temperatura, o comportamento azeotrópico e as condições termoquímicas determinadas
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Tula AK, Eden MR, Gani R. Computer‐aided process intensification: Challenges, trends and opportunities. AIChE J 2019. [DOI: 10.1002/aic.16819] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Anjan K. Tula
- Department of Chemical Engineering Auburn University Auburn Alabama
- College of Control Science and Engineering Zhejiang University Hangzhou China
| | - Mario R. Eden
- Department of Chemical Engineering Auburn University Auburn Alabama
| | - Rafiqul Gani
- College of Control Science and Engineering Zhejiang University Hangzhou China
- PSE for SPEED Allerød Denmark
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Yan L, Edgar TF, Baldea M. Dynamic process intensification of binary distillation based on output multiplicity. AIChE J 2019. [DOI: 10.1002/aic.16506] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Lingqing Yan
- McKetta Dept. of Chemical Engineering; The University of Texas at Austin; Austin TX 78712
| | - Thomas F. Edgar
- McKetta Dept. of Chemical Engineering; The University of Texas at Austin; Austin TX 78712
- The University of Texas at Austin Energy Institute; Austin TX 78712
| | - Michael Baldea
- McKetta Dept. of Chemical Engineering; The University of Texas at Austin; Austin TX 78712
- Institute for Computational Engineering and Sciences; The University of Texas at Austin; Austin TX 78712
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Yan L, Edgar TF, Baldea M. Dynamic Process Intensification of Binary Distillation via Periodic Operation. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b04852] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Lingqing Yan
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Thomas F. Edgar
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Michael Baldea
- McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States
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