摘要
针对天然气净化厂脱硫单元能耗较高的问题,对天然气净化厂脱硫单元能耗优化方法进行研究。基于ASPEN HYSYS软件,建立天然气净化厂脱硫单元数值模拟模型,利用现场数据验证模型的准确性。通过单因素分析确定了胺液循环量、贫胺液进料温度及原料气温度对能耗的影响最大,将其作为优化参数建立天然气净化厂脱硫单元能耗最优化模型,采用BP神经网络及GA遗传算法相结合的方法进行优化计算。结果表明,经BP神经网络及GA遗传算法相结合的算法进行优化后,胺液循环量、贫胺液进料温度及原料气温度3个运行参数均得到了优化,将优化后的运行参数运用于现场实际生产后,工艺总能耗由10614.99 kW下降至8297.59 kW,能耗降低了21.83%。
Aiming at the high energy consumption problem at desulfurization unit in natural gas purification plant,the corresponding optimization method is studied.Based on ASPEN HYSYS software,the numerical simulation model is established for the desulfurization unit in natural gas purification plant,and the accuracy of the model is verified according to field data.Through single factor analysis,it is determined that amine circulation volume,lean amine feed temperature and feed gas temperature have the greatest impact on energy consumption.Taking these three factors as optimization parameters,the optimization model is established for energy consumption of desulfurization unit in natural gas purification plant,and the method combining BP neural network with GA genetic algorithm is utilized for optimization calculation.Results show that after optimizing by the method,three operation parameters including lean amine circulation volume,lean amine feed temperature and feed gas temperature are all optimized.After the optimized operation parameters are applied to the actual operation,total energy consumption of the process is reduced from 10614.99 kW to 8297.59 kW,a reduction of 21.83%.It can be seen that the parameter optimization method based on BP and GA algorithm can effectively help to reduce the energy consumption of desulfurization unit.
作者
尹晓云
李佳亿
文明
岳添漆
游香杨
董栖君
黄征宇
李杨
YIN Xiao-yun;LI Jia-yi;WEN Ming;YUE Tian-qi;YOU Xiang-yang;DONG Qi-jun;HUANG ZHENG-yu;LI Yang(Safety,Environmental Protection and Technical Supervision Research Institute,PetroChina Southwest Oilfield&Gasfield Company,Chengdu 610041,China;Sichuan Provincial Key Laboratory of Shale Gas Evaluation and Exploitation,Chengdu 610041,China;Guizhou Pipeline Network Company,PipeChina Southwest Petroleum and Gas Pipeline Company Limited,Chengdu 610000,China;PetroChina Suining Natural Gas Purification Co.,Ltd.,Suining 610500,China;Central Sichuan Oil-Gas District,PetroChina Southwest Oilfield&Gasfield Company,Suining 629000,China)
出处
《现代化工》
CAS
CSCD
北大核心
2024年第S01期332-337,共6页
Modern Chemical Industry
基金
中国石油西南油气田公司科研项目“天然气净化厂能源管控技术研究”(20230307-07)
中国石油西南油气田公司科研项目“气田典型耗能设备优化级能源管控模型研究”(20220307-09)。
关键词
天然气脱硫
GA遗传算法
BP神经网络
能耗优化
HYSYS
数值模拟
natural gas desulfurization
GA genetic algorithm
BP neural network
energy consumption optimization
HYSYS
numerical simulation
