000			01527nab a2200205 4500
005			20260520001831.0
008			260224s2010 xxu
245	0	0	_aPrediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components
260			_a _b _cabr. 2010
270			_a13/07/2010 ; 13/07/2010
300			_a8 p. ; 187-194
520			_aTranscripción del resumen del autor. Numerous investigations have shown that ANN can be successful for correlating experimental data sets for macroscopic single phase flow characteristics. The approach proved its worth when rigorous fluid mechanics treatment based on the solution of first principle equations is not tractable. Evaluation and prediction of the frictional pressure drop across different piping components such as orifices, gate and globe valves and elbows in 0.0127 m piping components for non-Newtonian liquid flow are manifested in this paper. The experimental data used for the prediction is taken from our earlier work (Bandyopadhyay and Das, 2007). The proposed approach towards the prediction is done using a multilayer perceptron (MLP), which is trained with backpropagation algorithm because the function approximation is achieved with very good accuracy using MLPs.
581			_a3-4
773	0		_tJournal of Petroleum Science & Engineering _g71
942			_cARTICULO
100	1		_aBar, N. _945633
100	1		_aBandyopadhyay, T.K. _945634
100	1		_aBiswas, M.N. _945635
999			_c174953 _d174953