Development of soft computing methods to predict moisture content of natural gases

作者：Ghiasi Mohammad M^*; Esmaeili Jaghdan Zohre; Halali Mohamad A; Lee Moonyong; Abbas Ali; Bahadori Alireza

来源：Journal of the Taiwan Institute of Chemical Engineers, 2015, 55: 36-41.

DOI：10.1016/j.jtice.2015.03.032

• Natural gas
• Water content
• Predictive tool
• Support vector machine

In this paper, several numerical models have been presented for predicting the water content of natural gases in equilibrium with liquid water. Machine learning approaches including multilayer perceptron (MLP) neural network, radial basis RBF) neural network, and least squares support vector machine (LSSVM) algorithm have been utilized for precise determination of water content of natural gases.
The presented models work for pressures up to 69 MPa and temperatures between 298.15 and 450.15 K as well as acid gas mole fractions up to 0.4. With accordance to the error analysis results it was found that the proposed LSSVM, RBF, and MLP models reproduce targets with the average absolute relative deviations (%AARD) being less than 2.8%, 4.1%, and 7.7%, respectively. Coefficients of determination values of the developed models are found to be greater than 0.99, illustrating good association of the predictions with corresponding reported data in the literature.

• 出版日期2015-10