(664a) Maintenance Optimization-Based Survival Analysis for Optimal and Safer Operation: Cooling Tower Case Study

In a process plant system, safe and reliable operations are highly sensitive to utilities such as power or steam, cooling water or nitrogen or instrument air. Most of these utilities play an important role as safety barriers. This is because a disturbance in their supply is likely to impact process operations downstream, reduce the production efficiency, lead to a sudden shut down or an unsafe condition. The main focus of this paper is to propose and evaluate a model for survival of a process system under an upset condition/s that relies on data-driven and model-based optimization approach utilizing resilience metrics^[1]. These metrics integrate both technical (process parameters variations) and social (policy/regulations, human and organizational) factors ^[2,3].

In this work, we present a framework for survival evaluation using cooling tower operations as a case study. Poor or degraded performance of cooling towers is typically attributed to factors such as failure of gearbox, bearings, motor, driveshaft; blockage and fouling of piping; corrosion; or uneven water distribution etc^[4]. Therefore, maintenance optimization of cooling towers with consideration of barriers robustness and any known/unknown uncertainties could improve the efficiencies of the process and overall safety and economics of the cooling tower. A methodology to select the best maintenance strategyfor optimal and safer operation of the process system considering all three system components (plant/equipment, procedures and people) is developed.Three different types of maintenance alternatives considered as part of this analysis are: condition-based, predictive, and preventive ^[5,6] based on cost–benefit analysis which includes, the downtime cost, production loss, maintenance cost and safety costs. We describe a method to maximize the performance of safety barriers that will be utilized in case of a process-upset condition to prevent the escalation of the event into a catastrophic incident.

References

1. Jain, P., Pasman, H. J., Waldram, S. P., Rogers, W. J., & Mannan, M. S. (2016). Did we learn about risk control since Seveso? Yes, we surely did, but is it enough? An historical brief and problem analysis. Journal of Loss Prevention in the Process Industries.
2. Dinh, L. T., Pasman, H., Gao, X., & Mannan, M. S. (2012). Resilience engineering of industrial processes: principles and contributing factors. Journal of Loss Prevention in the Process Industries, 25(2), 233-241.
3. Jackson, S. (2009). Architecting resilient systems: Accident avoidance and survival and recovery from disruptions (Vol. 66). John Wiley & Sons.
4. Raghuvanshi, N. S., & Singh, A. (2014). Development of Maintenance Strategy to Improve Performance of Natural Draft Cooling Tower. International Journal of Scientific And Research Publications, 4(8), 1-7.
5. Vassiliadis, C. G., & Pistikopoulos, E. N. (2001). Maintenance scheduling and process optimization under uncertainty. Computers & Chemical Engineering, 25(2), 217-236.
6. Pistikopoulos, E. N., Vassiliadis, C. G., & Papageorgiou, L. G. (2000). Process design for maintainability: an optimization approach. Computers & Chemical Engineering, 24(2-7), 203-208.