Optimization of energy consumption of oil refinery reboiler and condenser using neural network

Abstract

The distillation tower is a crucial component of the refining process. Its energy efficiency has been a major area of research, especially following the oil crisis. This study focuses on optimizing energy consumption in the Shiraz refinery’s distillation unit. The unit is simulated using ASPEN-HYSYS software. Simulation results are validated against real data to ensure model accuracy. The operational data aligns well with model predictions. Following the creation of a data bank using HYSYS software, the tower’s operating conditions are optimized using neural networks and MATLAB software. In this study, a neural network model is developed for the distillation tower. This modeling approach is cost-effective, does not require complex theories, and does not rely on prior system knowledge. Additionally, real-time modeling is achievable through parallel distributed processing. The findings indicate that the optimal feed tray is 9 and the optimal feed temperature is 283.5°C. Furthermore, the optimized number of trays in the distillation tower is 47. Results show that in optimal conditions, cold and hot energy consumption are reduced by approximately 9.7% and 10.8%, respectively. Moreover, implementing optimal conditions results in a reduction of hot energy consumption in the reboiler by 60,000 MW and a reduction of cold energy consumption in the condenser by 30,000 MW.