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Prediction of degree of particle misplacement in liquid solid fluidization using artificial neural network

ABSTRACT A predictive model for “misplacement index” and “normalized misplacement index” for predicting particle misplacement has been developed using artificial neural network (ANN). The ANN is having three-layer MLP 7-4-2 architecture. The ANN was trained using Broyden–Fletcher–Goldfarb–Shanno algorithm. The performance of the model was judged by sum of squares error function. The correlation coefficient values of 0.9942 and 0.9657 are achieved for training, dataset for prediction of misplacement index and normalized misplacement index, respectively. It was found that the ANN model developed is highly sensitive to the MPSR and least sensitive to static bed height.