储气库地面脱水系统能耗优化方法比选

Comparison and selection of energy consumption optimization method of surface dehydration system of gas storage

为降低储气库地面脱水系统能耗、节约油气田企业及天然气管网公司的操作运行成本,对储气库地面脱水系统能耗优化方法进行了研究。建立了储气库三甘醇脱水工艺HYSYS模型;通过分析各运行参数对储气库地面脱水系统能耗的影响,确定选取三甘醇循环量、重沸器温度及汽提气量作为优化操作参数;建立了储气库地面脱水系统能耗最优化模型,采用HYSYS自带优化器优化及基于BP神经网络与GA遗传算法结合优化两种方法分别对储气库地面脱水系统能耗进行了优化计算。结果表明,经HYSYS自带优化器优化后,单位总能耗下降6.59%的同时,三甘醇循环量及汽提气用量减少。经BP神经网络与GA遗传算法结合优化法优化后,单位总能耗下降18.13%,3个优化操作参数均得到优化,且该方法具有通用性,可以用于其他系统的参数优化。

In order to reduce the energy consumption of surface dehydration system of gas storage and save the operation cost of oil and gas field enterprises and natural gas pipeline network companies, the energy consumption optimization method of surface dehydration system of gas storage was studied. The HYSYS model of triethylene glycol dehydration process in gas storage was established. By analyzing the influence of various operating parameters on the energy consumption of ground dehydration system of gas storage, triethylene glycol circulation volume, reboiler temperature and stripping gas volume were selected as the optimized operating parameters. The energy consumption optimization model of surface dehydration system of gas storage was established, and the energy consumption of surface dehydration system of gas storage was optimized by two methods: HYSYS built-in optimizer and the optimization method based on BP neural network and GA genetic algorithm. The results show that, after optimized by HYSYS built-in optimizer, the total energy consumption per unit is reduced by 6.59%, and the consumption of triethylene glycol circulation and stripping gas are reduced. After optimized by the method based on BP neural network and GA genetic algorithm, the total energy consumption per unit is reduced by 18.13%, and the three optimized operating parameters are optimized. This method is universal and can be used for parameter optimization of other systems.