The global refinery and petrochemical sectors are currently facing unprecedented challenges stemming from fluctuating feedstock prices, rapid energy transitions, market volatility, and increasingly stringent environmental regulations. These factors have forced industry stakeholders to rethink conventional operational strategies and adopt innovative digital solutions to ensure competitiveness and sustainability. Among these emerging solutions, digital twin technologies (DTTs) have gained significant attention as transformative tools for the sector. Digital twin models (DTMs) create dynamic virtual representations of physical assets and processes by integrating real-time plant data, advanced simulations, and machine learning algorithms. This integration enables enhanced monitoring, predictive maintenance, and optimization of refinery and petrochemical operations.
The present study investigates the practical applications of DTMs within integrated refinery–petrochemical complexes, emphasizing their role in key performance indicator (KPI) tracking, production accounting, supply chain optimization, and real-time process control. By enabling operators to anticipate disruptions, improve decision-making, and enhance safety, DTMs not only improve efficiency but also contribute to decarbonization and sustainability goals. A case study focusing on suspension polyvinyl chloride (S-PVC) polymerization highlights the tangible benefits of DTMs in improving product quality, output consistency, and operational safety. The findings demonstrate that the adoption of digital twins can significantly advance both economic performance and environmental responsibility, positioning them as essential enablers in the transition toward smarter, more resilient, and sustainable petrochemical industries.
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