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增压站运行方案制定的难点在于如何根据下游耗气量的变化,在不超出压缩机最大功率参数的情况下精准快速地调整进站压力,并根据具体需求提前制定多机组联合运行方案。以大牛地气田塔榆增压站6RDSA-1型压缩机组为研究对象,采用BP神经网络算法建立了压缩机组运行优化模型。选择已有的压缩机进气温度、排气压力及排气流量这3个基本参数作为模型输入值,计算得到了合适的进气压力和机组的轴功率。通过不同工况多组数据对比,模型对进气压力的预测结果与现场实测值的相对误差小于2.75%,验证了基于BP神经网络算法的压缩机组运行优化模型的可靠性,有助于增压站提前制定多机组联机运行方案,提升机组的运行效率,降低能耗和运维成本。
The difficulty in formulating the operation plan of booster station lies in how to adjust the pitching pressure accurately and quickly without exceeding the maximum power parameter of the compressor according to the change of downstream air consumption and formulate the multi-unit joint operation plan according to the specific requirements in advance. Taking the 6RDSA-1 compressor unit of TaYu supercharger station in Daniudi gas field as the research object, the BP neural network algorithm was used to establish the operation optimization model of the compressor unit. Select the existing compressor inlet temperature, exhaust pressure and exhaust flow of these three basic parameters as a model input value, calculate the appropriate intake pressure and shaft power. Comparing the multi-group data of different working conditions, the relative error between the prediction result of the model and the measured value is less than 2.75%, which verifies the reliability of the compressor operation optimization model based on BP neural network algorithm, Station ahead of schedule to develop multi-unit on-line operation program to improve the unit’s operating efficiency and reduce energy consumption and operation and maintenance costs.