(457b) Retro: A Real-Time Risk-Based Optimization Framework for Safe and Smart Operations

The current trend toward industrial digitalization has led to significantly more complex, dynamic, and integrated chemical plants which pose new challenges to process safety management [1-2]. Advanced control and data analytics provide promising tools which may prescriptively reduce safety losses. However, the tasks of systems-based operation and safety-critical decision making are traditionally performed independent of each other without accounting for their interactions and trade-offs. To address this gap, several recent works adapted model predictive control and moving horizon estimation to detect fault at the early developing stage [3-5]. However, key research gaps remain on: (i) Lack of a mechanistic-based understanding and metric to quantify real-time process safety performance with considerations of nonlinear process variable interactions, dynamic control, and uncertainties, (ii) Lack of a systematic method to prognostically detect fault while automatedly determining the mitigation strategy to reduce failure probability, and (iii) Lack of a cyber-physical prototype to implement and demonstrate the methodologies toward safe and smart manufacturing systems.

In this work, we present a risk-based model predictive control and real-time optimization framework via multi-parametric programming. A dynamic risk index [6] is leveraged for online process safety monitoring, which is formulated as a function of safety-critical process variable deviation from nominal operating conditions. Risk-based multi-parametric model predictive control (mp-MPC) is then developed with Bayesian state estimation to generate fit-for-purpose control strategies for progonostic risk management [7]. Given the probabilistic nature of risk, the controller design adapts a chance-constrained programming setting coupled with Bayesian inference for continuous risk updating along the moving time horizon. Two complementary decision making strategies, respectively via dynamic optimization and hierarchical multi-parametric programming formulations, are further developed to integrate risk control, operational optimization, and fault prognosis across multiple temporal scales. The framework thus allows a flexible selection of fault prognosis horizon tailored to the need of process and human operators. 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 continuouslly performing to attenuate the fault propagation speed and severity. The efficacy of the proposed framework is demonstrated on two in silico case studies including the filling of a tank and the quality control of an exothermic batch reactor conceptualized from T2 Laboratories Inc. The ongoing development of a cyber-physical prototype for proton exchange membrane water electrolysis is also discussed to showcase the implementation of dynamic risk-based control for clean energy production.

References

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[3] Bhadriraju, B., Kwon, J. S.-I. & Khan, F. OASIS-P: Operable Adaptive Sparse Identification of Systems for fault Prognosis of chemical processes. Journal of Process Control 107, 114–126 (2021).

[4] Ahooyi, T. M., Soroush, M., Arbogast, J. E., Seider, W. D. & Oktem, U. G. Model‐predictive safety system for proactive detection of operation hazards. AIChE Journal 62, 2024–2042 (2016).

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[6] Bao, H., Khan, F., Iqbal, T. & Chang, Y. Risk‐based fault diagnosis and safety management for process systems. Process Safety Progress 30, 6–17 (2011).

[7] Ali, M., Cai, X., Khan, F. I., Pistikopoulos, E. N., & Tian, Y. Dynamic risk-based process design and operational optimization via multi-parametric programming. Digital Chemical Engineering, 7, 100096 (2023).