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

An artificial neural network (ANN) model was developed to simulate the convective drying process of watermelon rind pomace used in the fabrication of non-traditional flour. Also, the drying curves obtained experimentally were fitted with eleven different empirical models to compare both modeling approaches. Lastly, to reduce the required fossil fuel in the convective drying process, two types of solar air heaters (SAH) were presented and experimentally evaluated. The optimization of the ANN by 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 with the trained ANN showed very promising generalization capabilities. The type II SAH showed the best performance and the highest air temperature it reached was 45 degrees C. The specific energy consumption (SEC) needed to dry the watermelon rind at this temperature and the CO2 emissions were 609 kWh.kg(-1) and 318 kg CO2 .kWh(-1), respectively. Using the type II SAH, this energy amount would be saved without CO2 emissions. To reach higher drying temperatures the combination of the SAH and the electrical convective dryer is possible.

Automated summary

An artificial neural network (ANN) model was developed to simulate the convective drying process of watermelon rind pomace used in the fabrication of non-traditional flour, outperforming empirical models. Experimentally evaluated solar air heaters (SAH) were found to reduce fossil fuel consumption in drying processes, with promising potential for future applications in combination with electrical convective dryers to reach higher temperatures.