In this study, the author improved and modified g-C₃N₄ by doping it with the metals Ag and Cu, which changed the photochemical properties of g-C₃N₄, narrowed the band gap, and improved the photocatalytic performance regarding quantum efficiency. Organic hydrocarbons such as 1,2-dichloroethane (DCE) are very stable prepared materials produced as intermediates to obtain polyvinyl chloride, and the prepared photo-catalyst is an innovative method for extreme decomposition of chlorinated hydrocarbons. However, some significant results were obtained using different analysis techniques. X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR) showed that the addition of Ag and Cu-NPS partially altered the structure of pure graphitic carbon nitride (g-C₃N₄-Pure). Scanning electron microscopy (TEM) analysis revealed that the morphological features of Ag-Cu/g-C₃N₄ contain quantum dots of Ag and Cu nanoparticles in addition to 2d-g-C₃N₄. The better separation of the photo-generated charge carriers is attributed to better photoactivity in the case of 0.3 g Ag-Cu/g-C₃N₄ with a reaction time of less than 30 min. Furthermore, the Ag-Cu/g-C₃N₄ recycling experiment showed that the catalyst remained stable after three stages of the pyrolysis experimental cycle. Another clear indicator of DCE degradation is the measurement using the titration of the Cl ions released by the decomposition.

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.