LAPSE:2025.0323
Published Article
LAPSE:2025.0323
Integrating Dynamic Risk Assessment with Explicit Model Predictive Control via Chance-Constrained Programming
June 27, 2025
Abstract
Maintaining operational efficiency while ensuring safety is a longstanding challenge in industrial process control, particularly in high-risk environments. This paper presents a novel Dynamic Risk-Informed Explicit Model Predictive Control (R-eMPC) framework that integrates safety and operational objectives using probabilistic constraints and real-time risk assessments. Unlike traditional approaches, this framework dynamically adjusts safety thresholds based on Bayesian updates, ensuring a balanced trade-off between reliability and efficiency. The validation of this approach is illustrated through a case study on tank level control, a safety-critical process where maintaining the liquid level within predefined safety limits is paramount. The results demonstrate the frameworks capability to optimize performance while maintaining robust safety margins. By emphasizing adaptability and computational efficiency, this research provides a scalable solution for integrating safety into real-time control strategies for similar process systems.
Maintaining operational efficiency while ensuring safety is a longstanding challenge in industrial process control, particularly in high-risk environments. This paper presents a novel Dynamic Risk-Informed Explicit Model Predictive Control (R-eMPC) framework that integrates safety and operational objectives using probabilistic constraints and real-time risk assessments. Unlike traditional approaches, this framework dynamically adjusts safety thresholds based on Bayesian updates, ensuring a balanced trade-off between reliability and efficiency. The validation of this approach is illustrated through a case study on tank level control, a safety-critical process where maintaining the liquid level within predefined safety limits is paramount. The results demonstrate the frameworks capability to optimize performance while maintaining robust safety margins. By emphasizing adaptability and computational efficiency, this research provides a scalable solution for integrating safety into real-time control strategies for similar process systems.
Record ID
Keywords
Bayesian risk analysis, Chance-constrained programming, Dynamic risk assessment, Model Predictive Control, Multi-parametric programming, Safety-aware control
Subject
Suggested Citation
Akundi SS, Liu Y, Braniff A, Dantas B, Niknezhad SS, Khan F, Tian Y, Pistikopoulos EN. Integrating Dynamic Risk Assessment with Explicit Model Predictive Control via Chance-Constrained Programming. Systems and Control Transactions 4:1065-1070 (2025) https://doi.org/10.69997/sct.176554
Author Affiliations
Akundi SS: Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA; Mary Kay OConnor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX, USA; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College
Liu Y: Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA; Mary Kay OConnor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX, USA; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College
Braniff A: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
Dantas B: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
Niknezhad SS: Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA
Khan F: Mary Kay OConnor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX, USA; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
Tian Y: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
Pistikopoulos EN: Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
Liu Y: Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA; Mary Kay OConnor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX, USA; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College
Braniff A: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
Dantas B: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
Niknezhad SS: Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA
Khan F: Mary Kay OConnor Process Safety Center (MKOPSC), Texas A&M University, College Station, TX, USA; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
Tian Y: Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
Pistikopoulos EN: Texas A&M Energy Institute, Texas A&M University, College Station, TX, USA; Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
Journal Name
Systems and Control Transactions
Volume
4
First Page
1065
Last Page
1070
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 1065-1070-1301-SCT-4-2025, Publication Type: Journal Article
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Published Article
LAPSE:2025.0323
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https://doi.org/10.69997/sct.176554
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References Cited
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