In an industrial context where process safety is paramount, artificial intelligence (AI) is emerging as a transformative tool. High-risk industries—petrochemicals, chemicals, energy—face complex challenges: preventing incidents, capitalizing on lessons learned, and optimizing safety processes. AI offers innovative solutions to meet these challenges.
Challenges of Traditional Process Safety
Traditional process safety approaches rely on manual analysis, standardized procedures, and human expertise. While these methods have proven themselves, they have limitations: difficulty processing large volumes of data, late identification of weak signals, and limited capitalization of lessons learned. HSE teams are overwhelmed by incident reports, risk analyses, and audits, making it difficult to proactively identify trends and emerging risks.
How AI Transforms Process Safety
Artificial intelligence brings a new dimension to process safety. Through machine learning and natural language processing (NLP), AI can analyze thousands of incident reports, identify patterns invisible to the human eye, and predict emerging risks. AI systems can correlate apparently unrelated events, detect subtle deviations in processes, and alert teams before an incident occurs. AI also enables capitalization on decades of expertise by analyzing lessons learned and making them instantly accessible to operators and engineers.
Concrete Applications of AI in Process Safety
Several concrete applications illustrate the potential of AI. Predictive incident analysis identifies combinations of factors that precede serious events. AI-based recommendation systems suggest prevention measures adapted to the specific context of a facility. Virtual HSE assistants instantly answer technical questions from operators based on comprehensive document databases. AI also facilitates automatic analysis of audit and inspection reports, identifying recurring non-conformities and risk areas.
RAG: The Revolution in HSE Knowledge Access
Retrieval-Augmented Generation (RAG) represents a major advance for process safety. This technology combines semantic search and text generation to create AI assistants capable of answering complex questions based on thousands of technical documents. An engineer can query the system in natural language ('What are the best practices for H2S risk management in desulfurization units?') and get a synthetic answer with references to source documents. RAG significantly accelerates decision-making and team skill development.
Implementation and Best Practices
Implementing AI in process safety requires a structured approach. It is essential to start with targeted, high-value use cases, such as analyzing recurring incidents or decision support for risk analyses. Data quality is paramount: AI systems require clean, structured, and representative data. Involvement of subject matter experts is crucial to validate results and refine models. Finally, transparency and explainability of AI decisions are essential to gain the trust of operational teams.
Conclusion
Artificial intelligence is not a miracle solution, but a powerful tool that, when used properly, can transform process safety. By combining human expertise and AI analytical capabilities, high-risk industries can achieve unprecedented levels of safety and performance. At Securas Technologies, we support industrial companies in this transformation, from initial audit to implementation of custom AI solutions.