(345k) A Functional Safety System That Prescribes Optimal Time-Varying Proactive Actions

In 2016 [1] we introduced the concept of model-predictive safety (MPS), and in 2017-2020 [1-6] we proposed a computationally-efficient method of online implementation of MPS, which requires solving min-max optimization problems offline. Solutions of these problems are the time-invariant optimal actions that MPS prescribes when process operation constraints are violated at the present time or will be violated in the future. An MPS system [1, 4] generates alarm signals that are predictive and systematically account for process nonlinearities and interactions, while typical existing functional safety systems generate reactive, non-interacting alarm signal(s) when a process variable exceeds a threshold.

In this paper, we expand the concept of MPS further so that MPS can prescribe optimal, time-varying, safety actions (including overriding controllers) that prevent or mitigate imminent and potential (current and future) operation hazards. We present min-max optimization problems, the solutions of which are the optimal time-varying safety actions when measured and unmeasured disturbance inputs and model uncertainties take worst-case time-varying profiles. A nested particle swarm optimization (PSO) algorithm is implemented to solve the min-max optimization problems on computers with parallel processors. The application and performance of the min-max optimization formulations, the PSO algorithm, and MPS, are shown through numerical simulations of a complex chemical plant.

References

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

[2] Soroush, M., J.E. Arbogast, and W.D. Seider, “Model-Predictive Safety System for Predictive Detection of Operation Hazards: Off-Line Calculation of Most Aggressive Control Actions and Worst-Case Uncertainties,” CAST Division 10 Plenary Session at the 2017 AIChE Annual Meeting, Minneapolis, MN (2017).

[3] Soroush, M., A.A. Shamsabadi, W.D. Seider, and J.E. Arbogast, "Implementation of Model-Predictive Safety Systems to Detect Predictively Operation Hazards in Non-Minimum-Phase Processes," 2018 AIChE Annual Meeting, Pittsburgh, PA (2018).

[4] M. Soroush, L.S. Masooleh, W.D. Seider, U. Oktem, J.E. Arbogast, Model‐predictive safety optimal actions to detect and handle process operation hazards, AIChE Journal, (2020) e16932.

[5] Soroush, M., Samandari Masooleh, L., Oktem, U., Seider, W.D., Arbogast, J.E. “Model-Predictive Safety: Min-Max Optimization to Calculate the Most Aggressive Control Actions and the Worst-Case Uncertainties,” AIChE Annual Meeting, Orlando, FL, November (2019).

[6] Soroush, M., Samandari Masooleh, L., Oktem, U., Seider, W.D., Arbogast, J.E. "Optimal Actions Prescribed by Model-Predictive Safety," AIChE Annual Meeting, November (2020)