Earticle

초록

영어

This study aims to prevent and minimize industrial accidents and their recurrence in complex chemical pnts located near urban areas by addressing human errors through an AI-integrated risk management approach. Modern chemical plants have become increasingly large-scale and complex, handling hazardous materials with properties such as toxicity, flammability, and explosiveness. Despite technological advancement in design and construction, the risk of accidents continues to increase due to aging facilities, operational complexity, and insufficient safety management practices. A significant proportion of these accidents originates from human errors, including design flaws, operational mistakes, and inadequate management systems. Such incidents not only result in severe loss of life but also causes substantial environmental damage and societal disruption. To address these challenges, this research proposes a Knowledge-based Process Safety Total Management (PSTM) framework that integrates AI technologies with systematic safety management principles. The proposed approach focuses on identifying root causes of accidents through precise data analysis, enhancing decision-making processes, and establishing proactive prevention mechanisms. The study contributes to the development of a more resilient and intelligent safety management system by emphasizing the synergistic coexistence between human expertise and AI. Ultimately, this research provides a practical framework for minimizing risks and improving safety performance in complex chemical plants, particularly those located in urban environments.

저자

• YoungHo Kim [ Y.H. Kim Engineering and Manufacturing International Consultant; Engineering Development Research Center (EDRC), Seoul National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재난정보학회 학술발표대회

• 간기

  부정기

• 수록기간

  2005~2026

• 십진분류

  KDC 338 DDC 361