J S Shijo

TL;DR: In this paper, the authors used ANN to predict the pneumatic conveying performance of powders in high and low air velocities, respectively, and used three different training methods: Levenberg Marquardt, Bayesian Regularization and Scaled Conjugate Gradient.

TL;DR: In this paper, the authors evaluate three parameters (Hurst exponent, Shannon entropy, and phase space attractor size) used in signal analysis to predict the change from dense to dilute mode and the location along the length of the pipeline.

TL;DR: In this paper, the authors presented solids friction factor correlations for the fluidized dense phase conveying of fine particles or powders and showed that the pressure drop for conveying can be predicted using these correlations.

TL;DR: In this paper, the authors focus on predicting the pressure drop in fluidized dense phase pneumatic conveying of fine particles through pipelines by modelling the solids friction factor in terms of non-dimensional parameters using experimental data of definite pipeline configuration.