Energy Consumption Optimization of High Sulfur Natural Gas Purification Plant Based on Back Propagation Neural Network and Genetic Algorithms
Limin Ma, Shi-Peng Hu, Min Qiu, Qi Li, Zhongli Ji · Energy Procedia · 2017
In order to effectively reduce the energy consumption of high sulfur natural gas purification process, in this paper, optimization model based on the genetic algorithm (GA) was developed. The natural gas purification process steady state model was established by using process simulation software ProMax. 8 key operating parameters of the purification system were determined by the process simulation model and energy consumption analysis. To reduce the calculation time and to solve the no convergence problems in the process simulation model, the BP (Back Propagation) neural network model was applied to train and test the simulated data. Then the BP model was incorporated into Genetic Algorithms to develop the energy consumption optimization model. A case study was performed in a high-sulfur natural gas purification plant with the capacity of 300×10 4 Nm 3 /d. And the results demonstrate that the energy consumption of the purification plant was reduced by 12.7%.