Production of bio-oil from lychee-based biomass through pyrolysis and maximization of bio-oil yield with statistical and machine learning techniques

Abstract

This study's goal is to maximize the yield of pyrolysis oil utilizing lychee-based biomass for a quick pyrolysis process. Response surface methodology (RSM) and artificial neural network with generalized neural network (GNN) technique were used to optimize the maximum bio-oil yield while considering temperature, heating rate, retention time, and argon gas flow rate as independent variables and bio-oil production as the response. Typical neural networks have constraints such as a long training time, a large number of neurons, and a large number of hidden layers. A generalized neural network (GNN) has been constructed to address these limitations and build a non-linear controller for acquiring access to bio-oil production output. To anticipate bio-oil yield, a new prediction model is proposed that combines the benefits of an auto-adaptive management technique with the rapid reaction of a GNN. Three measures (MSE, RMSE, and R2) are explored in depth to assess the performance efficacy of the models. Based on the finding, 350 °C temperature, 125 min of retention time, 120 °C/min heating rate and 110 mL/min argon flow rate was used to produce the highest bio-oil production (38.43%). Based on techniques, both models used in the study produce equally acceptable outcomes for projecting bio-oil yield. The greatest R2 value for the bio-oil yield ranges from 0.94 to 0.99, and all outcomes are classified as "good" regarding RMSE (all RMSE values are close to one). When all indicators are combined, GNN provides superior accuracy in predicting bio-oil yield in this investigation, followed by ANN.