Artificial neural network-assisted thermokinetic modeling of sugarcane bagasse fast pyrolysis for enhanced bio-oil production.

This study explores the pyrolysis of sugarcane bagasse to produce valuable products such as bio-oil, char, and gas as part of a sustainable strategy for green energy generation and waste valorization. The sugarcane bagasse rotary screw pyro-oil reactor can process feedstocks with a moisture content of less than 20% and a feeding capacity of 50 kg/h. For rapid pyrolysis, the reactor temperature is kept between 500 and 600° C. During operation, pyro-oil vapors are rapidly extracted and quenched, yielding approximately 30-38 L of bio-oil per hour from 50 kg of biomass. The fast pyrolysis process efficiently converts biomass into bio-oil at around 500 °C and a low pressure of 0.05  bar. Results reveal that maintaining a temperature between 500 and 600 °C significantly improves bio-oil production. A maximum bio-oil yield of 75% was achieved using biomass particles sized between 1 and 3  mm with a residence time of 25 s in the reactor. In contrast, elevated heating rates and extended residence times resulted in enhanced biochar yield with increased carbon content, rendering it appropriate for uses such as soil amendment and carbon sequestration. The experimental findings indicated that optimizing parameters can yield bio-oil with enhanced energy density and biochar with increased fixed carbon, rendering the products advantageous for energy and agricultural purposes. The predicted artificial neural network (ANN) model demonstrated significant accuracy. The ANN model demonstrated strong predictive capability and achieved R2 values of 0.958 for bio-oil and 0.941 for biochar yield prediction, with RMSE values below 2 wt% across the evaluated operating range.