(504d) Model-Predictive Safety: Min-Max Optimization to Calculate the Most Aggressive Control Actions and the Worst-Case Uncertainties
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Topical Conference: Next-Gen Manufacturing
Big Data and Applications in Advanced Modeling and Manufacturing
Wednesday, November 13, 2019 - 1:29pm to 1:47pm
An MPS system [1] 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. The MPS system design method allows for a systematic utilization of dynamic process models to generate alarm signals (alerts) for the predictive detection and proactive prevention of operation hazards (OHs) in real time. An MPS system uses a process model to predict the process safety status over a moving prediction horizon and to generate alarm signal(s) indicating the presence of a present or future OH with reasonable accuracy; it generates alarm signals that alert the process personnel to imminent and potential future OHs before the actual OHs occur.
In this paper, we propose and implement a particle-swarm optimization (PSO) method to solve the min-max optimization problems described in Ref. [2] for two process examples, a classical chemical reactor with series reactions and a free-radical polymerization reactor. Results from these two examples show that the PSO method reliably finds offline (a) the most aggressive control action that minimizes the infinity norm of each safety-constraint index over a prediction horizon, and (b) the most aggressive control action that minimizes the uncertainty-maximized infinity norm of each safety-constraint index over a prediction horizon. The performances of the MPS systems implemented with different prediction horizons are shown using numerical simulations.
References
[1] Mohseni Ahooyi, T., J.E. Arbogast, W.D. Seider, U.G. Oktem, and M. Soroush, "Model-Predictive Safety System for Proactive Detection of Operation Hazards," AIChE J., 62, 2024-2042 (2016).
[2] Soroush, M., J.E. Arbogast, and W.D. Seider, âModel-Predictive Safety System for Predictive Detection of Operation Hazards: Off-Line Calculation of Most Aggressive Control Actions and Worst-Case Uncertainties,â 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)