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| Prediction Method of Natural Gas Water Dew Point Based on the Fusion of eXtreme Gradient Boosting and Random Forest Regression |
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| DOI：10.7643/issn.1672-9242.2022.06.000 |
| KeyWord:triethylene glycol dehydration unit gas water dew point extreme gradient boosting (XGBOOST) feature extraction random forest (RF) |
| Author | Institution |
| XIONG Wei |
Chongqing Gas District, Southwest Oil and Gasfield Company, Chongqing , China |
| HE Yan-lin |
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing , China |
| SONG Wei |
Chongqing Gas District, Southwest Oil and Gasfield Company, Chongqing , China |
| ZHANG Hou-wang |
Chongqing Gas District, Southwest Oil and Gasfield Company, Chongqing , China |
| YIN Ai-jun |
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing , China |
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| Abstract: |
| Aiming at the problems that the current water dew point data are mostly manually measured with measuring instruments, the timeliness is low at the time with the high cost, this paper establishes a prediction method natural for gas water dew point based on extreme gradient boosting (XGBoost) and random forest (RF). This paper analyzes all the monitored process parameters by using the XGBoost method, and filtrates the pivotal process characteristic parameters that mainly affect the water dew point to eliminate the interference of irrelevant typical parameters to the prediction; the RF prediction mode is established, and the typical characteristic parameters are inputted to realize the real-time prediction of the water dew point. Taking the monitoring data and production data of a dewatering monitoring system in the Chongqing gas mine as an instance, this paper compares and analyzes the proposed prediction method. Compared with the other prediction methods, such as XGBoost and SVM, RF model has the best prediction performance, and after XGBoost feature selection, the MAE value and RMSE value of RF prediction results are reduced by 0.016 9 ℃ and 0.014 6 ℃ respectively. The results show that the water dew point prediction method based on the fusion of eXtreme Gradient Boosting and Random forest regression has better prediction accuracy and robustness. What's more, it has a positive effect on guiding dehydration on-site production. |
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