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This content will become publicly available on June 26, 2025

Title: A Real-Time Risk-Based Optimization Framework for Safe and Smart Operations
We present a systematic framework for real-time risk-based optimization via multi-parametric programming. A dynamic risk indicator is utilized to monitor online process safety performance and provide model-based prediction of risk propagation, as a function of safety-critical process variables. Risk-based explicit/multi-parametric model predictive control is then developed to generate fit-for-purpose control strategies for proactive risk management. Given the probabilistic nature of risk, the controller design is extended to adapt a chance-constrained programming setting coupled with Bayesian inference for continuous risk updating along the rolling time horizon. A hierarchical dynamic optimization formulation is further developed to integrate risk control, operational optimization, and fault prognosis across multiple temporal scales in an integral but computationally efficient manner. If a potential fault is detected and cannot be prevented by adjusting operating actions, an alarm will be raised well ahead of time with the controller and optimizer continuously performing to attenuate the fault propagation speed and severity. The potential and efficacy of the proposed framework are demonstrated on three safety-critical case studies with increasing level of complexity: (i) Tank filling, (ii) Batch reactor at T2 Laboratories, and (iii) Cyber-physical hydrogen water electrolysis prototype.  more » « less
Award ID(s):
2312457
NSF-PAR ID:
10536611
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Computer Aided Chemical Engineering
Date Published:
Volume:
53
Page Range / eLocation ID:
1915-1920
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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