Journal article
Development of soft computing methods to predict moisture content of natural gases
Journal of the Taiwan Institute of Chemical Engineers, Vol.55, pp.36-41
2015
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Source: InCites
Abstract
<p><p id="x-x-spara0006">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 function (RBF) neural network, and least squares support vector machine (LSSVM) algorithm have been utilized for precise determination of water content of natural gases. <p id="x-x-spara0007">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.</p>
Details
- Title
- Development of soft computing methods to predict moisture content of natural gases
- Creators
- Mohammad M Ghiasi - National Iranian Gas CompanyZohre Esmaeili-Jaghdan - Petroleum University of TechnologyMohamad A Halali - Petroleum University of TechnologyMoonyong Lee - Yeungnam UniversityAli Abbas - University of SydneyAlireza Bahadori - Southern Cross University
- Publication Details
- Journal of the Taiwan Institute of Chemical Engineers, Vol.55, pp.36-41
- Identifiers
- 3626; 991012820423702368
- Academic Unit
- School of Environment, Science and Engineering; Faculty of Science and Engineering
- Resource Type
- Journal article