Advanced Thermal Performance Optimization of Refinery Heat Transfer Systems for Saudi Vision 2030

The manner in which refinery heat-transfer systems function has a direct bearing on how efficiently high-grade process heat can be recovered before additional fuel, steam, or electricity is consumed; since this affects both operating costs and the achievement of industrial decarbonisation, the performance of these systems is of vital importance. This review gathers the research conducted between 2020 and 2025 on the advanced optimisation of the thermal performance of refinery heat exchangers and heat exchanger networks, with a specific emphasis on the relevance of these findings to Saudi Arabia's Vision 2030 transition. The evidence is organised around five interrelated factors: heat-integration retrofit, fouling-aware operation, equipment-level improvements, data-driven condition monitoring, and low-carbon heat upgrading. Recent studies of refineries have demonstrated that even technically sound measures can be undermined if the network's topology, pressure-drop constraints, changes in crude oil properties, the availability of cleaning operations, and interactions with utility systems are taken into account separately. A more dependable approach is to optimise thermal recovery and hydraulic operability over the entire operating cycle rather than merely at a single point that corresponds to a clean design condition. The review therefore offers a decision-making framework tailored to Saudi Arabia, giving priority to high measurement quality, accurate fouling diagnosis, operational changes that require no capital or only low capital, targeted network retrofit, and finally the integration of electrified or renewable heat. The synthesis indicates that the most beneficial action in the short term is to combine pinch-guided retrofit with fouling prediction and dynamic control, while longer-term decarbonisation will need the use of heat pumps, renewable energy, and flexible utility systems. There are still research gaps relating to plant-scale validation, the management of uncertainty, water-aware optimisation, operation at high ambient temperatures, and explainable machine-learning models. These gaps define a practical research programme for reducing refinery energy intensity without having to compromise reliability, throughput, or product quality.