Research subject: Chemical process design is one of the foundational pillars for the advancement of oil, gas, and petrochemical industries. The inherent complexity and multidimensional nature of chemical processes have transformed process engineering projects into highly interdisciplinary collaborations. Consequently, beyond the challenges of process design itself, the synchronization and facilitation of cooperation among diverse engineering disciplines are among the core difficulties of such projects. The involvement of process design engineers as experts in chemical engineering is essential to address these challenges through direct engagement, indirect contribution, or overarching supervision.
Research approach: This study analyzes the structural framework of chemical process engineering projects, from the ideation phase to the end of the design life cycle, focusing on the complexities of oil, gas, and petrochemical systems. The roles of process design engineers across different project phases are identified and categorized. Their decision-making frameworks are modeled using process systems engineering (PSE) methodologies. To improve interdisciplinary collaboration and data integration, an ontology-based approach is adopted to define foundational concepts and standardize relationships between project components. Additionally, to clarify decision-making mechanisms and better understand the challenges and design stages, visualization techniques are applied to map information structures and depict the decision flows and responsibilities of process design engineers.
Main results: The findings demonstrate that process design engineers play a pivotal role in the success of engineering projects at three levels: direct involvement, indirect influence, and supervisory oversight. The role of process design engineers and their decision-making in addressing challenges within oil, gas, and petrochemical projects, across all stages from concept to operation, has been analyzed through the examination of process system characteristics. The application of these analytical tools in enhancing decision-making quality and improving understanding of process systems has been presented. Emerging AI tools significantly enhance the effectiveness of engineering decisions and contribute to cost and time reduction through enabling modeling, simulation, data analysis, and optimized decision-making. Moreover, the application of these tools improves understanding of complex industrial systems and enhances performance under uncertainty.