A review of optimization technologies in petrochemical industries and oil-rich regions

This PRISMA-guided systematic review synthesizes ۴۲ peer-reviewed studies and authoritative reports published between January ۲۰۲۲ and November ۲۰۲۵ to evaluate advanced optimization technologies in the petrochemical sector across major oil-rich regions (Middle East, North America, Russia, and China). Employing mathematical programming, artificial intelligence, digital twins, and hybrid decarbonization frameworks, the analysis reveals that AI-driven real-time optimization reduced refinery energy intensity by ۸-۲۲ %, predictive maintenance decreased unplanned downtime by up to ۳۵ %, and MILP/MINLP models improved capacity utilization by ۵-۱۴ %. Digital oilfield technologies increased ultimate recovery by ۱۰-۱۸ %, while optimized CCUS delivered ۲۵-۳۲ % emission reductions at ۳۵-۴۵ €/t CO۲. Middle Eastern NOCS and North American shale operators achieved the highest ROI, whereas Chinese refineries led in logistics optimization. Persistent challenges include data silos and limited explainable AI, with future directions emphasizing physics-informed neural networks and quantum-assisted optimization for profitable net-zero transition by ۲۰۵۰.

کلیدواژه ها:

Petrochemical optimization ، Oil-rich regions ، Artificial intelligence ، Digital twins ، CCUS ، Supply chain resilience