Binary energy production from pineapple peel waste and optimized by statistical and machine learning approaches

Abstract

This study uses a two-stage process to treat pineapple peel waste (PPW), including alkaline ionized water (AIW) pretreatment and microwave-assisted heating (MAH) hydrolysis. It also contains two objective functions: hydrochar yield and hydrolysis ratio. The experimental design utilizes the Taguchi method to determine optimal conditions by calculating the signal-to-noise ratio. The designed experimental results are used to predict optimal conditions through artificial neural networks (ANN). In the first step, AIW pretreatment reduces the lignin content of PPW by 17.5 % and increases the cellulose content by 14.8 %. SEM analysis reveals a roughening of its surface, while BET analysis identifies a slightly porous structure. These characteristics provide an increased efficiency for subsequent hydrolysis. In the second step, thermogravimetric analysis indicates that a substantial hemicellulose loss is observed when the hydrolysis concentration or temperature increases. This finding aligns with the ranking of factor impacts obtained from the Taguchi method. This study also demonstrates the potential of utilizing PPW for energy production. The hydrochar, hydrolyzed under optimal conditions, increases its calorific value by 10.11 % to 18.29 MJ‧kg−1, making it suitable for use as solid fuel. The hydrolysate has 52.85 g‧L-1 reducing sugar, which can be fermented to produce ethanol and is much higher than those reported in the literature. Through the Taguchi method and ANN, the optimal experimental factor combination prediction reveals the significant impact of acid hydrolysis compared to AIW pretreatment. The optimal hydrochar yield and hydrolysis ratio in the Taguchi method are 38.75 % and 63.62 %, respectively. In ANN, they are 41.51 % and 58.38 %, respectively, with relative errors of 7.08 % and 8.23 % compared to the experimental results. This research helps turn solid waste into green fuels and approach a circular bioeconomy.