Performance prediction of pneumatic conveying of powders using artificial neural network method

This paper studies the ability of the ANN to predict the pneumatic conveying performance of powders. At high air velocity, the flow is dilute. But at low air velocities it is fluidized dense. Fluidized dense flow involves several complex interactions among gas, particle and wall. But in dilute flow, the particle-particle interaction is less. Implementation of these interactions in a model is difficult. Experimental data available for pneumatic conveying were used to train the network and then predict the pressure drop. Three different training methods Levenberg Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used. The BR method takes more time for analysis, but it gives better results than others when sample size is small and noisy. For larger sample size the LM method gives better results than the other two methods. The model is also predicts the pressure drop within +/- 10% error margin under length scale-up conditions. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study investigates the ability of Artificial Neural Networks to predict the pneumatic conveying performance of powders. Three different training methods were employed, with Bayesian Regularization performing better with small sample sizes and noisy data, while the Levenberg Marquardt method outperformed the other two with larger sample sizes. The model was able to predict pressure drop within +/- 10% error margin under length scale-up conditions.