Towards phenomena-based HAZOP support

This thesis presents the development of a partially automated Hazard and Operability (HAZOP) analysis framework, leveraging advanced computational methods to enhance safety and efficiency in process industries. Traditional HAZOP methods, while thorough, are time-intensive and prone to human error. By integrating a phenomenological approach and utilizing material and energy balances, this work improves the accuracy of deviation analysis, focusing on real-time interactions and the physical properties of processes. The study introduces a graph-based model, utilizing the .graphml format, to visually represent the system's components and interactions. This approach allows for effective deviation tracking and simplifies the identification of critical paths. The core contribution of this research is the development of Python code designed to automate key aspects of the HAZOP process. The algorithm generates deviation propagation paths, populating a preHAZOP table to document potential hazards systematically. The automated analysis reduces manual effort while maintaining the rigor of traditional methods, providing a scalable and efficient tool for preliminary HAZOP studies. The case study included demonstrates the method’s applicability and effectiveness, showcasing its potential to streamline risk identification and improve process safety management.