August 2025
Special Focus: Plant Safety and Environment
HAZOP study exploiting the benefits of artificial intelligence
Hazard and operability (HAZOP) studies are a cornerstone of safety management in the petrochemicals industry and are designed to identify potential hazards and operational problems in complex processes. Petrochemical plants handling hazardous materials such as flammable liquids and toxic gases and operating under high pressures are particularly prone to risks like fires, explosions and toxic releases. These studies are crucial for preventing accidents that could lead to loss of life, property damage and environmental harm. Traditionally, HAZOP studies involve a multidisciplinary team using a systematic approach, breaking down the process into nodes and applying guide words (e.g., no flow, more flow, less flow) to each process parameter (e.g., flow, temperature, pressure) to identify deviations, their causes, consequences and mitigation actions.
The integration of artificial intelligence (AI) technologies, including machine-learning, neural networks and real-time monitoring systems, has emerged as a transformative approach to enhance the efficiency and effectiveness of HAZOP studies. AI can automate repetitive tasks, provide predictive insights and improve the accuracy of hazard identification, addressing some limitations of traditional methods like time consumption and human error. This article explores the role of AI in HAZOP studies for petrochemical plants, detailing its advantages, disadvantages and best practices for application, and provides a case study to illustrate practical implementation.
Traditional HAZOP study process. A traditional HAZOP study is a systematic and structured method used to identify potential hazards and operability problems in chemical and petrochemical processes. The process begins with the formation of a multidisciplinary team comprising process engineers, operators and safety professionals.1 Typically, the team includes at least five members, often representing both the client and contractor stakeholders in the case of newly designed systems.
Once the team is assembled, it undertakes a comprehensive review of all relevant process documentation, including process flow diagrams (PFDs), piping and instrumentation diagrams (P&IDs) and material safety data sheets (MSDSs). This enables the team to understand the design intent, normal operating parameters and potential deviations.
The process system is then divided into manageable sections known as “nodes.” Each node represents a functional part of the plant, such as a reactor, column or pipeline where changes in state, flow or energy occur. At each node, structured guide words (e.g., no, more, less, reverse) are applied to process parameters such as pressure, temperature and flow to systematically generate deviations from normal conditions. For example, the guide word “no” applied to flow could suggest a scenario where a pump fails or a line becomes blocked.
For each identified deviation, the team investigates potential causes, including equipment malfunction or human error, as well as the consequences, which might range from production loss to environmental release or safety-critical events. Mitigating actions, such as installing interlocks, enhancing operator training or modifying control strategies are then recommended. All findings are meticulously documented—often using digital tools—and projected during the session to facilitate discussion and review.
Despite its methodological strengths, the traditional HAZOP approach is time-consuming, often requiring weeks or even months for large plants. It is also highly dependent on team experience, which may introduce subjectivity and the potential for critical oversights, particularly in highly complex petrochemical environments.
The role of AI in HAZOP studies. AI is increasingly being recognized as a powerful tool to enhance and support HAZOP studies. AI technologies, including machine-learning, ontologies and real-time monitoring, can be applied across multiple stages of the HAZOP workflow.2 A typical process for training AI can be seen in FIG. 1.
FIG. 1. A typical approach on how to train an AI tool.
AI can assist in automated deviation generation by analyzing process configurations and intelligently applying guide words to process variables. This reduces the manual effort required from the HAZOP team and ensures more exhaustive deviation identification. For example, platforms like HAZOP AI prompt users with deviation scenarios based on learned patterns and operational history, minimizing the chance of overlooked risks. Predictive analytics is another key area where AI contributes significantly. By leveraging historical process data and incident records, AI systems can learn to identify initiators of hazardous events. Techniques such as one-class anomaly detection and shallow neural networks have been successfully used to classify fault conditions and predict likely outcomes.3
AI’s integration with plant monitoring systems enables real-time surveillance of operational data. Process variables such as pressure and temperature are continuously tracked, and AI algorithms can detect deviations indicative of abnormal conditions, triggering alerts long before human operators would typically recognize them. In petrochemical contexts, AI-based vision systems can even detect unauthorized personnel in hazardous zones, enhancing process and personnel safety.
Simulation and modeling capabilities allow AI systems to explore a range of operational scenarios, identify safe operating envelopes and evaluate the implications of proposed design changes. AI-enhanced HAZOP, therefore, extends traditional analysis beyond static design reviews into dynamic scenario planning.
Furthermore, AI aids in knowledge management by organizing and retrieving information from prior HAZOP reports, safety databases and incident logs. Tools such as HAZID from Loughborough University facilitate efficient report generation and promote consistency across assessments.
Advantages of using AI in HAZOP. The integration of AI into the HAZOP process yields multiple benefits, especially in high-risk environments like petrochemical manufacturing.4 Two of the most notable advantages are the reductions in time and cost.5 By automating routine tasks such as deviation generation and risk classification, AI systems can significantly reduce study durations.6 Some tools, such as HAZOP AI, claim time savings of up to 50% for complex systems.4
AI enhances the accuracy of hazard identification by analyzing extensive datasets that may be infeasible for human teams to process manually. Pattern recognition and predictive modeling allow for the identification of anomalies and fault conditions that might otherwise go undetected. This leads to more comprehensive safety evaluations.
Consistency is another important benefit. Unlike human teams that may vary in judgment or thoroughness, AI systems apply standardized logic across all nodes and parameters, ensuring uniformity in the assessment process.7,8
AI also improves decision-making by offering real-time insights and predictive forecasting based on historical data. This not only supports better design-phase evaluations but also aids operational teams in preemptively mitigating risks.
Finally, AI’s scalability makes it particularly suited for large-scale petrochemical plants. Sophisticated models can handle the complexity of hundreds of variables and thousands of interdependent components.
Disadvantages and challenges of using AI in HAZOP. Despite its benefits, several challenges must be addressed when incorporating AI into HAZOP studies. A primary concern is data quality and availability. AI models are only as effective as the data on which they are trained. Incomplete or inconsistent data, such as from malfunctioning sensors or poorly documented incidents, can compromise model performance and lead to false conclusions.9,10
Interpretability is another limitation, particularly with deep-learning models with internal logic that may be hard to understand or explain. In safety-critical applications like a HAZOP study, the ability to understand and justify decisions is crucial. This “black box” nature of AI poses trust and regulatory challenges.
Initial costs can also be a barrier. Implementing AI tools requires investment in infrastructure, software and skilled personnel. Smaller organizations may find these requirements prohibitive without external support or a phased adoption strategy.5
Furthermore, AI cannot entirely replace human expertise. While it can generate deviations and classify scenarios, the contextual understanding, ethical judgment and regulatory compliance insight offered by experienced professionals remain irreplaceable.
Finally, regulatory frameworks may not yet be fully adapted to incorporate AI in process safety reviews. Ensuring that AI-enhanced HAZOP outputs align with standards such as the U.S. Occupational Safety and Health Administration’s (OSHA’s) Process Safety Management (PSM) requirements is critical for legal and operational acceptance.
Best practices for applying AI in HAZOP studies. To maximize the effectiveness of AI in HAZOP, several best practices should be adopted. High-quality data management is foundational. AI systems must be trained on accurate, up-to-date and relevant datasets, including sensor data, incident records and safety performance metrics. Regular validation and cleaning of data are essential to maintain model integrity.
Collaborative development is also key. Cross-functional teams comprising AI experts and process safety professionals ensure that the tools are grounded in real-world applicability and domain knowledge.
Continuous validation of AI models, comparing predictions against actual outcomes or expert evaluations, is necessary to ensure accuracy and reliability. Transparent AI logic, where users can understand how conclusions are reached, fosters trust and facilitates integration into traditional workflows.
An iterative approach should be adopted, allowing for continuous improvement of AI models based on new data and feedback from HAZOP sessions. This adaptability ensures relevance in evolving plant conditions and supports long-term deployment. By implementing these practices, AI can serve as a transformative complement to traditional HAZOP studies, enabling more proactive, efficient and resilient approaches to process safety.11
Case study: AI in HAZOP for a petrochemicals plant. A study showed advanced methodology to enhance HAZOP analysis by integrating dynamic process simulations with machine-learning techniques. A comprehensive set of 37,170 deviations in a polystyrene plant covering single, double and triple failures with varying amplitude levels was simulated for a styrene polymerization process.12,13
Each scenario was evaluated and categorized into one of three severity levels, representing increasing levels of risk. To automate and generalize this classification, a probabilistic neural network (PNN) was trained to accurately predict the severity of any arbitrary deviation scenario, eliminating the need for further simulations once trained. Additionally, multilayer perceptron (MLP) networks were developed to predict detailed process behavior indexes for key variables such as reactor temperature and pressure.12,13
Sensitivity analysis highlighted reactor pressure, temperature and the ‘PV110’ valve as critical factors strongly influencing the severity of outcomes. Notably, scenarios involving multiple simultaneous failures were found to be significantly more hazardous than single-failure cases; however, while 45% of single-failure cases were classified as high severity, this rose to 93% for triple-failure scenarios. These findings emphasize the importance of evaluating compound deviation events in HAZOP studies. The proposed methodology offers a scalable and data-driven approach to prioritize high-risk scenarios, thereby supporting more efficient and informed decision-making during hazard identification and risk assessment processes.12,13
Takeaways. In summary, traditional HAZOP usually examines one failure at a time. This research shows that combinations of failures are far more dangerous and require more attention. With these AI models, HAZOP teams can predict consequences faster, focus on the most dangerous scenarios, save time and improve safety. The researchers achieved that by using these two models, training them on 80% of the ‘What if’ scenarios and testing them on the other 20%.12,13
The incorporation of AI into HAZOP studies for petrochemical plants represents a significant advancement in process safety management. It offers substantial benefits, including time and cost savings, improved accuracy and consistency, by automating tasks and providing predictive insights. However, challenges such as data quality issues, interpretability and the need for human oversight must be addressed.
By following best practices like ensuring high-quality data, collaborating with experts and continuously improving AI systems, petrochemical plants can leverage AI to enhance HAZOP studies, ultimately leading to safer and more efficient operations. The case study of the polystyrene plant illustrates the practical impact, highlighting AI's role in predicting and mitigating hazards—a promising direction for future safety management.
LITERATURE CITED
1 Sinnott, R. K. and G. Towler, “Chemical engineering design,” 6th Ed., 2020, online: https://www.elsevier.com/books/chemical-engineering-design/sinnott/978-0-08-102599-4
2 Salimi, F. F., Safavi, A, A., Urbas, L., & Salimi, F. (2023), “Artificial intelligence for HAZOP in artificial intelligence in process engineering,” pp. 95–126, 2023, online: https://www.sciencedirect.com/science/article/abs/pii/B9780323905626000050
3 PI Research, “AI-Enhanced HAZOP,” online: https://www.pi-research.org/project/ai_hazop/
4 HAZOP.AI, “HAZOP AI,” online: https://hazop.ai
5 Postindustria, “AI in chemical industry: Use cases, benefits, and challenges,” online: https://postindustria.com/ai-in-chemical-industry-use-cases-benefits-and-challenges/
6 BIC Magazine, “5 transformative applications of AI in petrochemicals,” June 12, 2023, online: https://www.bicmagazine.com/industry/refining-petrochem/5-transformative-applications-ai-petrochemicals/
7 Single, J. I., Schmidt, J. & Denecke, J (2023), “Computer-aided HAZOP: Ontologies and AI for hazard identification and propagation,” In Artificial Intelligence in Process Engineering (pp. 377–398), online: https://www.sciencedirect.com/science/article/abs/pii/B9780128233771502986
8 Jamieson, D., “AI’s journey to becoming the best process safety engineer in the room,” The Chemical Engineer, March 27, 2023, online: https://www.thechemicalengineer.com/features/ai-s-journey-to-becoming-the-best-process-safety-engineer-in-the-room/
9 Loughborough University, “Developing AI to enhance process plant safety,” online: https://www.lboro.ac.uk/departments/compsci/innovation/ai-to-enhance-process-plant-safety/
10 SafetyCulture, “What is HAZOP? Hazard and operability study,” online: https://safetyculture.com/topics/hazop/
11 Precog, “Predictive analytics and AI can prevent process plant accidents,” January 20, 2023, online: https://precog.co/blog/predictive-analytic-can-prevent-process-plants-accidents/
12 Salimi, F. F., Safavi, A, A., Urbas, L., & Salimi, F. (2023), “Case study 2: Managing the complexities of the HAZOP for styrene polymerization plant,” In Artificial Intelligence in Process Engineering, pp. 39–70, 2023, online: https://www.sciencedirect.com/science/article/pii/B9780323905626000025
13 Mokhtarname, R., Urbas, L., Safavi, A. A., Salimi, F., Zerafat, M. M., & Harasi, N. (2024). “An artificial neural network approach to enrich HAZOP analysis of complex processes”, Online: https://www.sciencedirect.com/science/article/pii/S0950423024001402.
The Author
EBRAHIM ALHADIYAH is a chemical engineer for Petrochemical Industries Co., a major petrochemicals producer in the Gulf Cooperation Council region and the Middle East. Alhadiyah earned an MS degree in advanced chemical engineering from the Imperial College of London and specialized in polymers. His focus is on PSM solutions in the oil and gas industry.
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