FLOW MEASUREMENT AND INSTRUMENTATION, cilt.86, ss.1-14, 2022 (SCI-Expanded)
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 McCabe's index
(LMI), and Willmott's 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.