(584i) Implementation of Model-Predictive Safety Systems to Detect Predictively Operation Hazards in Non-Minimum-Phase Processes

In 2016 [1] we introduced a method of model-predictive safety (MPS) system design, and in 2017 [2] min-max optimization methods that can be used offline to calculate systematically (a) the most aggressive control action that minimizes each process-constraint index when uncertain model parameters take their nominal values, and (b) the most aggressive control action that minimizes each process-constraint index when uncertain model parameters take their worst-case values.

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, For the first time, the MPS system design method proposed a systematic utilization of the dynamic process models to generate predictive alarm signals (alerts) for the detection of present and future operation hazards (OHs) in real time. An MPS system uses a process model to project in real-time the process operability status and to generate alarm signal(s) indicating the presence of a present or future OH with reasonable accuracy; it generates predictive alarm signals that alert the process personnel to imminent and potential future OHs before the actual OHs occur. No control system is (will be) able to assure safe process operation in the presence of a current (future) OH in a process.

In this paper, the min-max optimization methods presented in Ref. [2] are used for a non-minimum-phase (NMP) chemical reactor to calculate offline the two most aggressive control actions that minimize each operability-constraint index. Because this particular NMP reactor shows inverse temperature response, a successful implementation of the MPS system on this reactor requires the use of a large prediction horizon for the MPS system. The performance of the MPS system implemented with difference prediction horizons will be 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, “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, November (2017)