(136a) Synergistic Optimization of Crude Oil Distillation Units and Heat Exchanger Networks: A Simultaneous Approach for Enhanced Energy Efficiency

Crude oil distillation, a substantial energy-consuming process, necessitates innovative approaches to enhance efficiency. This work introduces a simultaneous optimization strategy for both distillation units and Heat Exchanger Networks (HEN), differentiating itself by leveraging synergies within the system. Traditional methods sequentially optimize Crude Distillation Units (CDUs) and Vacuum Distillation Units (VDUs) before addressing the HEN, missing potential synergistic benefits. This technology, employs Artificial Neural Networks (ANNs) for distillation units, offering robustness, simplicity, and faster convergence compared to traditional models.

The simultaneous optimization considers variables like pump-around and stripping steam flow rates, coil outlet temperature, product flow rates, and HEN stream bypasses. By collectively optimizing these variables, The tool achieves superior outcomes in reducing energy costs, increasing product yields, and overall profitability. Notably, the technology accommodates various objectives, including minimizing energy consumption, maximizing profits, and adhering to practical constraints such as equipment capacities and product quality specifications.

Key distinguishing features include the simultaneous optimization approach and the use of ANNs for distillation models. Additionally, this technology considers practical constraints in HEN, offering a comprehensive solution for the complex challenges of crude oil distillation. This approach, represents a significant step towards advancing energy efficiency and economic viability in the crude oil distillation process, making it a potent tool for refinery optimization.