Study on Soft Sensing Technology of Gas Pipeline Compressor Flow Based on Random Forest
Compressor is the main energy consuming equipment of gas transportation pipeline.The accuracy of its flow measurement results is directly related to the management of the whole pipeline.On the basis of field test experiments,a data-driven soft sensing model based on random forest is constructed.The hyperparameters that affect the accuracy of the model are optimized by grid search and cross-validation.The optimal prediction model is constructed finally,and the prediction results are compared with those of SVM,NB,GS-SVM and other models.The results show that the correlation between fuel gas consumption and atmospheric pressure on compressor flow is small through Gini index,and its variables should be eliminated.When the number of decision tree n is 300,and the number of split features m is 5,the prediction effect is the best.The RMSE and MAPE of the random forest model are both the smallest,which indicates that the modelhas a good regression effect for complex nonlinear data sets such as compressor flow,and has certain advanced and scientific nature.
random forestcompressorGini indexsoft sensing modelgrid searchcross validation
万方数据
维普
