Artificial intelligence in process safety and risk management

including those for text and data mining, AI training, and similar technologies. 383 performance improvement. In a highly regulated sector, constant improvement in safety control is necessary to safeguard lives, property, and public confidence. AI has radically changed chemical engineering, substantially enhancing safety and risk management through predictive maintenance, real-time monitoring, and data-driven decision-making. AI-based predictive maintenance reduced equipment breakdowns and unplanned downtime by approximately 50%. Real-time monitoring systems employ AI-based algorithms to identify process deviations and reduce the rate of events by 30%. AI-based risk assessment models improve hazard detection and prioritization, making decisions 40% more effective. In addition, AI improves operating parameters to increase production efficiency by 25% and ensure strict enforcement of safety protocols [6]. In addition to process optimization, AI enhances simulation-based training, emergency preparedness, and automatic compliance monitoring for improved workplace safety. Implementing AI-based security measures in organizations has helped achieve 50% of business goals. AI will account for 20% of the global workforce by 2028 and contribute 40% of economic output [7]. Applying AI in chemical engineering increases productivity and promotes proactive security through compliance with upcoming regulations. Companies using AI-based safety initiatives have improved their business objectives by 50%. AI solutions are expected to account for 20% of the total global workforce by 2028, and their share of economic productivity will be 40% [7]. Applying AI to chemical engineering improves operational efficiency and reinforces an active approach to safety, ensuring compliance with ever-changing regulatory standards. With the development of PSM and integration of advanced risk assessment techniques, AI has emerged as a powerful tool for enhancing safety and operational efficiency in the chemical industry. As industries shift toward flexible, datadriven safety protocols, AI technologies are transforming risk detection, real-time monitoring, and emergency response systems. Unlike traditional AI studies that focus primarily on technological development, this chapter explores AI's practical applications in chemical engineering-such as supporting process hazard analysis, strengthening risk management, and enabling predictive maintenance through digital twin (DT) technology. Going beyond theoretical discussions, it critically evaluates current applications and identifies emerging trends, challenges, and the regulatory, interpretability, and data integrity concerns that must be addressed to fully leverage AI in process safety.