Specific chemical bioexergy and microwave-assisted torrefaction optimization via statistical and artificial intelligence approaches

Abstract

The artificial neural network (ANN) in artificial intelligence (AI) is a computational model that portrays how nerve cells (neurons) work in the human brain. Meanwhile, specific chemical bioexergy (SCB) is a vital indicator to provide essential information on how high the actual energy may be contained within biomass fuel (biofuel). In this study, the Taguchi method, analysis of variance (ANOVA), and artificial neural network (AI-ANN) are utilized to predict the SCB of biofuels from the spent mushroom substrate (SMS) torrefaction via microwave-assisted heating (MAH). In Taguchi’s orthogonal array, washing, catalyst, and power operating parameters are considered. Acid and water washings lower the SMS’s ash content by 58.45 % and 36.29 %, respectively. The optimum conditions for combining with acid washing, a catalyst with higher Fe2O3 (35 %), and microwave power 540 W render the highest total SCB in biofuels (biochar + bio-oil) of 47.90 MJ·kg−1, which is close to the SCB of crude oil derivatives (41–49 MJ·kg−1). The enhancement of biomass’s SCB value from optimal torrefaction approximately 3 folds (256.30 %) from 12.84 (raw) to 47.90 MJ·kg−1. The ANN model with an architecture of 1 hidden layer (sigmoid activation function) with 3 neurons and the output layer (piecewise linear activation function) with a quick propagation algorithm for the training process of all layers poses excellent prediction with high accuracy R2 = 1. This result demonstrates that ANN with the designed scheme is suitable for predicting the SCB of SMS-derived biofuels.