Producing non-traditional flour from watermelon rind pomace: Artificial neural network (ANN) modeling of the drying process

FABANI, MARÍA PAULA; CAPOSSIO, JUAN PABLO; ROMAN, MARÍA CELIA; ZHU, WENLEI MICHAEL; RODRIGUEZ, ROSA; MAZZA, GERMAN

JOURNAL OF ENVIRONMENTAL MANAGEMENT

ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD

Lugar: Amsterdam; Año: 2021 vol. 281 p. 1 - 14

0301-4797

An artificial neural network(ANN) model was developed to simulate the convective drying process ofwatermelon rind pomace used in the fabrication of non-traditional flour. Also,the drying curves obtained experimentally were fitted with eleven differentempirical models to compare both modeling approaches. Lastly, to reduce therequired fossil fuel in the convective drying process, two types of solar airheaters (SAH) were presented and experimentally evaluated.The optimization of the ANNby a genetic algorithm (GA) resulted in an optimal number of neurons of nine(9) for the first hidden layer and ten (10) for the second hidden layer. Also,the ANN performed better than the best fitted empirical model. Simulations withthe trained ANN showed very promising generalization capabilities.The type II SAH showed the best performance and the highest airtemperature it reached was 45 °C. The specific energy consumption (SEC) neededto dry the watermelon rind at this temperature and the CO2 emissionswere 609 kWh.kg-1 and 318 kg CO2.kWh-1,respectively. Using the type II SAH, this energy amount would be saved withoutCO2 emissions. To reach higher drying temperatures the combinationof the SAH and the electrical convective dryer is possible.