Dynamic risk-based process design and operational optimization via multi-parametric programming
We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508123000145 |
| _version_ | 1797822051136831488 |
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| author | Moustafa Ali Xiaoqing Cai Faisal I. Khan Efstratios N. Pistikopoulos Yuhe Tian |
| author_facet | Moustafa Ali Xiaoqing Cai Faisal I. Khan Efstratios N. Pistikopoulos Yuhe Tian |
| author_sort | Moustafa Ali |
| collection | DOAJ |
| description | We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety–critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety–critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety–critical control of a continuous stirred tank reactor at T2 Laboratories. |
| first_indexed | 2024-03-13T10:02:08Z |
| format | Article |
| id | doaj.art-5aef55274b374fbeb8dd8564ba123db2 |
| institution | Directory Open Access Journal |
| issn | 2772-5081 |
| language | English |
| last_indexed | 2024-03-13T10:02:08Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj.art-5aef55274b374fbeb8dd8564ba123db22023-05-23T04:22:25ZengElsevierDigital Chemical Engineering2772-50812023-06-017100096Dynamic risk-based process design and operational optimization via multi-parametric programmingMoustafa Ali0Xiaoqing Cai1Faisal I. Khan2Efstratios N. Pistikopoulos3Yuhe Tian4Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, United StatesTexas A&M Energy Institute, Texas A&M University, College Station, TX 77843, United States; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United StatesMary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United StatesTexas A&M Energy Institute, Texas A&M University, College Station, TX 77843, United States; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United StatesDepartment of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, United States; Corresponding author.We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety–critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety–critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety–critical control of a continuous stirred tank reactor at T2 Laboratories.http://www.sciencedirect.com/science/article/pii/S2772508123000145Dynamic risk assessmentFault prognosisExplicit model predictive controlMulti-parametric optimizationDynamic optimization |
| spellingShingle | Moustafa Ali Xiaoqing Cai Faisal I. Khan Efstratios N. Pistikopoulos Yuhe Tian Dynamic risk-based process design and operational optimization via multi-parametric programming Digital Chemical Engineering Dynamic risk assessment Fault prognosis Explicit model predictive control Multi-parametric optimization Dynamic optimization |
| title | Dynamic risk-based process design and operational optimization via multi-parametric programming |
| title_full | Dynamic risk-based process design and operational optimization via multi-parametric programming |
| title_fullStr | Dynamic risk-based process design and operational optimization via multi-parametric programming |
| title_full_unstemmed | Dynamic risk-based process design and operational optimization via multi-parametric programming |
| title_short | Dynamic risk-based process design and operational optimization via multi-parametric programming |
| title_sort | dynamic risk based process design and operational optimization via multi parametric programming |
| topic | Dynamic risk assessment Fault prognosis Explicit model predictive control Multi-parametric optimization Dynamic optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2772508123000145 |
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