Implementation of Process Safety Management System in the Power Plant Industry Using the Monte Carlo Probabilistic Method

Power plants represent critical infrastructure within national energy systems, accounting for the majority of electricity generation and serving as key components of industrial and economic resilience. Despite their significance, recurring process incidents and the aging nature of some facilities expose these operations to escalating safety and reliability challenges. To address these concerns, this study presents a smart process safety management (PSM) framework that integrates probabilistic modeling and intelligent decision-support tools for risk prediction and prioritization within the power plant industry. The research builds on the foundation of traditional PSM, which aims to identify, evaluate, and control the release of hazardous materials or process deviations that may cause harm to people, assets, and the environment. The proposed framework advances this concept by embedding Monte Carlo probabilistic simulation within PSM components to quantify uncertainty, simulate multiple operational scenarios, and support data-driven safety decisions. Using MATLAB and SPSS software environments, empirical data from thermal and combined-cycle plants were analyzed to model system variability and failure likelihood under dynamic conditions. Results derived from the Copeland prioritization method indicate that key elements such as management of change, competency training, and hot work permit systems possess the highest influence on overall process safety performance. Conversely, auditing activities and trade secret management showed lower relative significance within the probabilistic hierarchy. The integration of Monte Carlo simulation with PSM not only enhances predictive accuracy but also transforms traditional safety frameworks into intelligent, adaptive systems capable of learning from operational data and continuously improving over time. This chapter demonstrates how probabilistic modeling can serve as a cornerstone for developing smart safety systems in high-risk industrial environments. By combining structured process safety principles with digital analytics and uncertainty quantification, the proposed model provides an effective pathway toward resilient, evidence-based risk management in the power generation sector.