Evaluation of multilayer perceptron neural networks and adaptive neuro-fuzzy inference systems for the mass transfer modeling of Echium amoenum Fisch. & C. A. Mey.

BACKGROUND Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first order Takagi-Sugeno type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed drying process of Echium amoenum Fisch. & C. A. Mey. The moisture ratio evolution is calculated based on the drying temperature, airflow velocity and process time. Different ANN topologies are examined by evaluating the neuron number (3 to 20), the activation functions and the addition of a second hidden layer. Respectively, different number (2 to 5) and shapes of membership functions are examined for the ANFIS, using the grid partitioning method. The models with the best performance in terms of prediction accuracy as evaluated by the statistical indices, are compared with the best fit thin-layer model and the available data from the experimental cases of 40, 50 and 60°C temperatures at 0.5, 0.75 and 1 ms-1 airflow velocity. RESULTS The best performed ANFIS model, comprised by 5-2-2 of π-shaped and triangular membership functions for time, temperature and airflow velocity inputs respectively, was able to describe the moisture ratio evolution of Echium amoenum more precisely than the best ANN topology with R2 =0.9992, RMSE=0.0078 and SSE=1.06·10-2 . Best thin-layer model involving six adjustable parameters, managed to describe experimental data with R2 =0.9996, RMSE=0.0057 and SSE=7.3·10-4 . CONCLUSION The results of the comparative study indicate that empirical regression models with increased number of adjustable parameters, constitute a simpler and more accurate modeling approach for estimating the moisture ratio of Echium amoenum Fisch. & C. A. Mey under fluidized bed drying. This article is protected by copyright. All rights reserved.