Modeling the flow rate of dry part in the wet gas mixture using decision tree/kernel/non-parametric regression-based soft-computing techniques
Dayev Z. Shopanova G. Toksanbaeva B. Yetilmezsoy K. Sultanov N. Sihag P. Bahramian M. Kıyan E.
August 2022Elsevier Ltd
Flow Measurement and Instrumentation
2022#86
Owing to its importance in extraction of natural gas from underground gas storage as well as its crucial role in determination of final gas mixture in the production facilities of gas/oil industry, the dry content of wet gas mixture needs to be calculated precisely. The present study explores the potential of different soft-computing techniques in estimation of the dry gas flow rate (kg/h) (output variable) of wet gas mixture based on two input variables of wet gas flow rate (kg/h) and absolute gas humidity (g/m3). Decision tree-based methods (M5P tree, random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), kernel function-based approaches (Gaussian process regression (GPR) and support vector machines (SVM)), and non-parametric regression-based technique (multivariate adaptive regression splines (MARS)) were implemented for the first time to estimate the dry gas flow rate, and their respective prediction performances were analyzed statistically. Coefficient of correlation (CC), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), Legates and McCabes index (LMI), and Willmotts Index (WI) were used as the statistical indicators for validating the performance of each soft-computing model. While M5P model (MAE = 122.2382 kg/h, RMSE = 580.5626 kg/h, CC = 0.9875 for the testing data set) was better than other tree-based models (MAE = 363.2802–542.6119 kg/h, RMSE = 871.9363–1025.3444 kg/h, CC = 0.9587–0.9706 for the testing data set) and MARS model (MAE = 128.0083 kg/h, RMSE = 622.9515 kg/h, CC = 0.9852 for the testing data set), the statistical indicators approved the superiority of the radial basis kernel function-based GPR model (GPR-RBKF) model (MAE = 163.3266 kg/h, RMSE = 483.1359 kg/h, CC = 0.9915 for the testing data set) over other implemented models in predicting the dry gas flow rate. The findings highlighted the potential of soft-computing methodologies in precise estimation of dry gas flow rate in wet gas mixture, particularly, in situations where the measurement of such parameters with traditional deterministic models is practically not possible.
Decision tree-based modeling , Dry gas flow rate , Gas flow rate measurement , Kernel function-based modeling , Multivariate adaptive regression splines , Soft computation
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Information-Communication and Engineering Department, School of Engineering, Baishev University, Kazakhstan
Department of Automation of Technological Processes and Production, Orenburg State University, Orenburg, Russian Federation
Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, Esenler, Istanbul, 34220, Turkey
Department of Civil Engineering, Shoolini University, Himachal Pradesh, Solan, 173229, India
School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Ireland
Information-Communication and Engineering Department
Department of Automation of Technological Processes and Production
Department of Environmental Engineering
Department of Civil Engineering
School of Chemical and Bioprocess Engineering
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