Prediction of pulsation frequency of pulsing flow in trickle-bed reactors using artificial neural network

Abstract

Based on an extensive experimental database (946 measurements) set up from the literature published over past 30 years, a new correlation relying on artificial neural network (ANN) was proposed to predict the basic pulsation frequency of pulsing flow in the trickle-bed reactors. Seven dimensionless groups employed in the proposed correlation were liquid and gas Reynolds ( Re L , Re G ) , liquid Weber ( We L ) , gas Froude ( Fr G ) , gas Stokes ( St G ) and liquid Eötvös ( E o ¨ L ) numbers and a bed correction factor ( S b ) . The performance comparisons of literature and present correlations showed that ANN correlation is significantly an improvement in predicting pulsation frequency with an AARE of 10% and a standard deviation less than 18%. The effects of the variables including the properties of fluid and bed, and flow rate of liquid and gas on pulsing frequency were investigated by ANN parametric simulations and the trends were compared with exiting experimental results that confirmed the coherence of the proposed method with the previous experiments.