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Pappas I, Bindlish R, Ali M, Pistikopoulos EN. Optimal Operation of an Industrial Dividing Wall Column through Multiparametric Programming. Ind Eng Chem Res 2023; 62:15029-15035. [PMID: 38356904 PMCID: PMC10863063 DOI: 10.1021/acs.iecr.3c00836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/22/2023] [Accepted: 08/22/2023] [Indexed: 02/16/2024]
Abstract
In this contribution, we present a high-fidelity dynamic model of an industrial dividing wall column and the application of explicit model predictive control for its regulation. Our study involves the separation of methyl methacrylate from a quaternary mixture. The process includes a dividing wall column coupled with a decanter, which results in highly concentrated methyl methacrylate and water streams from the middle side draw of the column and the decanter, respectively. An equation-oriented mathematical model of the process is developed and presented in detail, where non-ideal thermodynamic calculations are adopted to describe the complex nature of the component interactions. The operability of the process is enhanced by the synthesis and application of an explicit model predictive controller, which is used to track the purity specifications of the product. Our results demonstrate that the proposed modeling and control approach can be utilized for the optimal online operation of the studied system.
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Affiliation(s)
- Iosif Pappas
- Artie
McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Texas
A&M Energy Institute, Texas A&M
University, College Station, Texas 77843, United States
| | - Rahul Bindlish
- Technical
Expertise and Support Technology Center, The Dow Chemical Company, Houston, Texas 77077, United States
| | - Moustafa Ali
- Texas
A&M Energy Institute, Texas A&M
University, College Station, Texas 77843, United States
| | - Efstratios N. Pistikopoulos
- Artie
McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States
- Texas
A&M Energy Institute, Texas A&M
University, College Station, Texas 77843, United States
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Hu Y, Sun H, Li C, Wang H. Design of Reaction Region of Reactive Dividing Wall Column Based on Cross-Wall Heat Transfer. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
- Yuqi Hu
- School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
- National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
| | - Hui Sun
- School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
- National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
| | - Chunli Li
- School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
- National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
| | - Honghai Wang
- School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
- National-Local Joint Engineering Laboratory for Energy Conservation in Chemical Process Integration and Resources Utilization, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China
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Saini RST, Pappas I, Avraamidou S, Ganesh HS. Noncooperative Distributed Model Predictive Control: A Multiparametric Programming Approach. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c03057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Radhe S. T. Saini
- Discipline of Chemical Engineering, Indian Institute of Technology Gandhinagar, Gujarat382055, India
| | - Iosif Pappas
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas77843, United States
| | - Styliani Avraamidou
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin53706, United States
| | - Hari S. Ganesh
- Discipline of Chemical Engineering, Indian Institute of Technology Gandhinagar, Gujarat382055, India
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Study on a novel reactive internally heat integrated distillation process for the synthesis of ethyl acetate and its column configuration. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.121755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Heat-pump-assisted reactive distillation for direct hydration of cyclohexene to cyclohexanol: a sustainable alternative. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2021.119808] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Advancements in Optimization and Control Techniques for Intensifying Processes. Processes (Basel) 2021. [DOI: 10.3390/pr9122150] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Process Intensification (PI) is a vast and growing area in Chemical Engineering, which deals with the enhancement of current technology to enable improved efficiency; energy, cost, and environmental impact reduction; small size; and better integration with the other equipment. Since process intensification results in novel, but complex, systems, it is necessary to rely on optimization and control techniques that can cope with such new processes. Therefore, this review presents some advancements in the field of process intensification that are worthy of exploring in detail in the coming years. At the end, several important open questions that can be taken into consideration in the coming years are listed.
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Xing Y, Masuku CM, Biegler LT. Modular
gas‐to‐liquids
process with membrane
steam‐methane
reformer and Fischer–Tropsch reactive distillation. AIChE J 2021. [DOI: 10.1002/aic.17467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Yu Xing
- Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania USA
| | - Cornelius M. Masuku
- Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania USA
- Davidson School of Chemical Engineering Purdue University West Lafayette Indiana USA
| | - Lorenz T. Biegler
- Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 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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Pappas I, Kenefake D, Burnak B, Avraamidou S, Ganesh HS, Katz J, Diangelakis NA, Pistikopoulos EN. Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward. FRONTIERS IN CHEMICAL ENGINEERING 2021. [DOI: 10.3389/fceng.2020.620168] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.
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