Modeling of the mass flow rate of natural gas flow stream using genetic/decision tree/kernel-based data-intelligent approaches. (April 2023)

• Record Type: Journal Article
• Authors: Dayev, Zhanat; Yetilmezsoy, Kaan; Sihag, Parveen; Bahramian, Majid; Kıyan, Emel
• Is Part Of: Flow measurement and instrumentation. Volume 90(2023)
• Publication Date: 2023-04
• Subjects: Decision tree -- Differential pressure method -- Gas flow rate -- Random forest -- Soft-computing; Fluid dynamic measurements -- Periodicals; Flow meters -- Periodicals; Fluides, Dynamique des -- Mesure -- Périodiques; Débitmètres -- Périodiques; 681.2805
• DOI: 10.1016/j.flowmeasinst.2023.102331
• Languages: English
• ISSNs: 0955-5986

Abstract

Abstract: The large consumption of natural gas, one of the most important energy sources in the world, necessitates reliable, precise, and accurate calculation of gas flow rate and amount in order to use this resource in an efficient and sustainable way. The present computational study investigates the possibilities of several soft-computing strategies in estimating the mass flow rate of natural gas flow stream (kg/h) (output variable) based on four input variables of orifice plate diameter ratio, differential pressure of orifice plate (kPa), operating pressure of the natural gas (bar), and operating temperature of the natural gas (°C). A genotype/phenotype genetic algorithm (gene expression programming (GEP) technique), two decision tree-based methods (random forest (RF), random tree (RT) models), and two kernel-based approaches (Gaussian process regression (GPR) and support vector machines (SVM) methods) were applied for the first time to predict gas mass flow rate. Coefficient of correlation (CC), mean absolute error (MAE), root mean square error (RMSE), Scattering index (SI), Nash–Sutcliffe efficiency (NSE), and mean absolute relative error (MARE) were computed as the statistical performance evaluators to determine of the best-performing soft-computing approach. The performance assessment indices corroborated the superiority of the Pearson VII universal kernel function-based GPR model (GPR-PUKF) model (CC = 0.9997, MAE = 64.8091 kg/h, RMSE = 248.7584 kg/h, SI = 0.0237, and NSE = 0.9993 for the testing dataset) over other data-intelligent models in predicting the gas mass flow rate. In addition, statistical results revealed that the predictions of the RF method were better than those of the GEP- and RT-based models, but the GEP approach showed the lowest performance among all applied models. Although the CC values of all models were satisfactory (>0.993), the percentile deviation of GPR model (1.7325%) from the actual values showed competitive lower values, indicating its superior performance than other models (GEP = 15.1436%, RF = 6.5403%, RT = 9.5576%, and SVM = 3.2107%). This study highlighted the significance of employing advanced soft-computing approaches in determining the mass flow rate of natural gas, a vital source of energy, as well as its value to the gas sector. Graphical abstract: Image 1 Highlights: Soft-computing implementation for prediction of natural gas mass flow rate. Benchmarking of genetic/decision tree/kernel-based data-intelligent models. Lower deviations (1.73% and 0.36%) of GPR-PUKF over other methods. Superiority of RF (6.54%) over GEP (15.14%) and RT (9.56%) in terms of errors. Flexibility of soft computation in highly nonlinear real-world gas measurement.