(710a) An Operability-Based Approach for Integrated Process Design, Operations, and Risk Management

Process safety management (PSM) plays an instrumental role in maintaining safe, reliable, and efficient operations of chemical process systems under uncertainties. Analyzing the impact of design and/or operational variables on process safety offers the potential to proactively circumvent abnormal conditions associated with these processes starting from the early design stage [1,2]. This features an innovation from current PSM practices relying on passive protection layers. To achieve the integration of process design and operational optimization, process operability has emerged as a viable tool to systematically identify the optimally feasible operating window with holistic analyses of operational uncertainties, disturbances, and constraint violations [3-5]. However, key research questions remain on: (i) the integration of safety metrics into traditional operability concepts; and (ii) the development of a generalizable framework by employing operability analysis to enhance overall process safety performance.

To address these challenges, this work presents an integrated approach for process design, operations, and risk optimization leveraging operability analysis. The proposed approach aims to enhance the overall process safety performance by quantifying the achievability of a safe and feasible region for process operations. Risk analysis developed by [6] is adapted as the metric to quantify process safety. Herein, risk is calculated as the product of two major factors, in which: (i) the first factor quantifies the fault probability based on the three-sigma rule assuming a normal distribution of the key safety-critical variable; and (ii) the second factor corresponds to the severity consequence, which is represented by the deviation of the key safety-critical variables. For the operability analysis, input-output system representation is employed based on the discretization of design and/or operational variables to evaluate and rank competing designs. The integrated approach is demonstrated via two safety-critical process case studies, namely: (i) an exothermic CSTR to produce gasoline additive based on a major process safety accident at T2 Laboratories Inc. [7]; and (ii) a Proton Exchange Membrane Water Electrolyzer (PEMWE) to produce high-purity hydrogen gas. In the case of PEMWE, operating temperature is the key safety-critical variable since its elevation causes hydrogen safety concerns in the cell and the storage systems and shortens the PEMWE durability by accelerating membrane and catalyst degradation [8,9]. As the main outcome, this work will provide guidance for the chemical process design and operating strategies by mapping the available design and/or operational inputs of the given process to their respective achievable outputs with process safety considerations.

References

[1] Pozo Arcos, B., Bakker, C., Flipsen, B. & Balkenende, R. Practices of fault diagnosis in household appliances: Insights for design. Journal of Cleaner Production 265, 121812 (2020).

[2] Park, S., Xu, S., Rogers, W., Pasman, H., & El-Halwagi, M. M. (2020). Incorporating inherent safety during the conceptual process design stage: A literature review. Journal of Loss Prevention in the Process Industries, 63, 104040.

[3] Gazzaneo, V., Carrasco, J. C., Vinson, D. R. & Lima, F. V. Process Operability Algorithms: Past, Present, and Future Developments. Industrial and Engineering Chemistry Research 59, 2457–2470 (2020).

[4] V. Alves, S. Dinh, J. R. Kitchin, V. Gazzaneo, J. C. Carrasco, and F. V. Lima. Opyrability: A Python package for process operability analysis, Journal of Open Source Software, vol. 9, no. 94, p. 5966, https://doi.org/10.21105/joss.05966, 2024.

[5] Dinh, S. & Lima, F. V. Dynamic Operability Analysis for Process Design and Control of Modular Natural Gas Utilization Systems. Ind. Eng. Chem. Res. 62, 2052–2066 (2023).

[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).

[8] Ogumerem, G. S. & Pistikopoulos, E. N. Parametric optimization and control for a smart Proton Exchange Membrane Water Electrolysis (PEMWE) system. Journal of Process Control 91, 37–49 (2020).

[9] Falcão, D. S. & Pinto, A. M. F. R. A review on PEM electrolyzer modelling: Guidelines for beginners. Journal of Cleaner Production 261, 121184 (2020).