大型天然气压缩机组节能优化软件设计

摘要

As the key equipment in the conveying process, the natural gas compressor efficiency and power consumption are directly related to the maintenance costs,and its optimization difficulty is how to accurately predict the inlet pressure and shaft power.In order to solve the above problems,we used C language to compile the software and put forward a method to predict the inlet pressure and shaft power based on BP neural network.In Lab Windows/CVI, we developed the energy saving optimization software of natural gas compressor.The method predicted the compressor inlet pressure and shaft power by testing intake air temperature, output flow rate and output pressure, adjusted compressor operating conditions according to intake air pressure and adjusted compressor operating combinations according to shaft power to achieve energy saving of compressor units.We used the software development results of pressurized station large natural gas compressors for optimal operation of the test.The results showed that the software can be used to predict the inlet pressure,and the calculated error was less than 2.75%.By predicting the shaft power and adjusting the operating combination,it saved about 10%of the energy.The above results showed that the software could effectively improve the operating efficiency of compressors and reduced the operation and maintenance costs of booster stations.%天然气压缩机组作为天然气输送过程中的关键设备,其运行效率和功耗直接关系到增压站的运维成本,而天然气压缩机组节能优化运行中的难点在于如何准确地预测进气压力和轴功率.针对上述问题,采用C语言编译软件,提出基于BP神经网络的进气压力与轴功率预测方法,在Lab Windows/CVI中实现天然气压缩机组节能优化软件开发.该方法通过测试进气温度、输出流量、输出压力来预测压缩机的进气压力与轴功率,依据进气压力调整压缩机运行工况,依据轴功率调整压缩机运行组合,达到压缩机组节能降耗的目的.利用该软件的开发成果对某增压站大型天然气压缩机组进行优化运行实验.结果证明,采用该软件对进气压力进行预测,计算误差小于2.75%,通过预测轴功率,调整运行组合后,节约电能约10%.该软件可以有效地提高压缩机组的运行效率,降低增压站运维成本.