(15a) Systematic Incorporation of Safety Assessment in Process Design, Intensification, and Control

The recent initiatives towards modular chemical process intensification (MCPI) have posed new challenges and opportunities to process safety evaluation and management [1-2]. The increasing plant complexity and interactivity with novel modular and task-integrated unit operations necessitate advances to accurately quantify the impact of new equipment designs on plant safety and to effectively control the safety performance during real-time operation [3-4]. In view of this, several systems-based methods have been proposed to incorporate inherent safety index as a primary design objective or to apply model predictive control for operational safety [5-8]. Despite these efforts, key research gaps remain on the lack of: (i) a fundamental understanding of intensification and modularization impacts on process safety, and (ii) a systematic approach to generate optimal MCPI design and operational strategies with guaranteed safety performance under disturbances.

To address these challenges, in this work we will first investigate a series of comparative case studies to rigorously analyze the process safety performance in modular and intensified designs. Representative safety metrics [9-12] are tested to quantify process safety at the following steady-state and dynamic operation scenarios: (i) evaluation of different methyl tertiary butyl ether (MTBE) reactive distillation designs against inherent safety principles, (ii) design optimization of a benzene nitration reaction system with safety and modularization considerations, and (iii) dynamic safety performance of an intensified reactive distillation process vs. a conventional reactor-distillation-recycle process with model predictive control. Based on this, we further propose a safety-aware explicit/multi-parametric model predictive control (mp-MPC) strategy [13-14]. The SWeHI index value [11] is selected according to the MCPI analyses and integrated in the parametric space to jointly determine the optimal control actions and to operate the process at a desired level of safety. Additional safety requirements, e.g. on process temperature, can be explicitly formulated as mp-MPC path constraints to identify the maximum set of disturbances and optimal set point selection to theoretically prevent any constraint violation through operation. The extension of this approach for safety-oriented simultaneous design and control optimization will also be discussed based on our recent work in intensified process systems [15]. The proposed approach will be demonstrated on a continuous stirred tank reactor case study for the processing of methylcyclopentadienyl manganese tricarbonyl at T2 Laboratories.

References

[1] Etchells, J. C. (2005). Process intensification: safety pros and cons. Process Safety and Environmental Protection, 83(2), 85-89.

[2] Yelvington, P., Gokhale, A., Grieco, W., Nara, L. (2019) The link between process safety and process intensification. Available from: https://www.aiche.org/academy/webinars/link-between-process-safety-and-process-intensification.

[3] 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.

[4] Pistikopoulos, E. N., Tian, Y., & Bindlish, R. (2021). Operability and control in process intensification and modular design: Challenges and opportunities. AIChE Journal, 67(5), e17204.

[5] Castillo-Landero, A., Ortiz-Espinoza, A. P., & Jiménez-Gutiérrez, A. (2018). A process intensification methodology including economic, sustainability, and safety considerations. Industrial & Engineering Chemistry Research, 58(15), 6080-6092.

[6] Leveson, N. G., & Stephanopoulos, G. (2014). A system-theoretic, control-inspired view and approach to process safety. AIChE Journal, 60(1), 2-14.

[7] Albalawi, F., Durand, H., Alanqar, A., & Christofides, P. D. (2018). Achieving operational process safety via model predictive control. Journal of Loss Prevention in the Process Industries, 53, 74-88.

[8] Venkidasalapathy, J. A., & Kravaris, C. (2020). Safety-centered process control design based on dynamic safe set. Journal of Loss Prevention in the Process Industries, 65, 104126.

[9] American Institute of Chemical Engineers. (2010). Dow’s Fire & Explosion Index Hazard Classification Guide, Wiley.

[10] Marshall, J. T. & Mundt, A. (1995). Dow’s chemical exposure index guide. Process Safety Progress, 14, 163-170.

[11] Khan, F. I., Husain, T. & Abbasi, S. A. (2001). Safety weighted hazard index (SWeHI): A new, user-friendly tool for swift yet comprehensive hazard identification and safety evaluation in chemical process industry. Process Safety and Environmental Protection, 79, 65-80.

[12] Stoffen, P. G. (2005). Guidelines for quantitative risk assessment, Ministerievan Volkshuisvesting Ruimtelijke Ordening en Milieu. CPR E 18.

[13] Pistikopoulos, E. N., Diangelakis, N. A., & Oberdieck, R. (2020). Multi-parametric Optimization and Control. John Wiley & Sons.

[14] Pistikopoulos, E. N., Diangelakis, N. A., Oberdieck, R., Papathanasiou, M. M., Nascu, I., & Sun, M. (2015). PAROC – An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 136, 115-138.

[15] Tian, Y., Pappas, I., Burnak, B., Katz, J., & Pistikopoulos, E. N. (2021). Simultaneous design & control of a reactive distillation system – A parametric optimization & control approach. Chemical Engineering Science, 230, 116232.