Advancements in artificial neural networks and fast pyrolysis of biomass processing: A comprehensive review and a bibliometric analysis

The current literature review discusses recent advances of ANN applied to biomass fast pyrolysis under the convergence of ML with bioenergy production. Bibliometric analysis shows that the growth of research activity has accelerated from an average of 2.05 publications per year for the period 1994-2013-24.4 publications per year for the period 2014-2023. Key findings include that ANN models, such as FFNN and BP, are able to predict pyrolysis outcomes with a high degree of accuracy, with most studies reporting R2 values above 0.95. ANN-assisted optimization of feedstock selection and kinetic modeling has been shown to improve the yield of biooil, with efficiencies as high as 78 % reported in some fluidized bed reactors. This review highlights ANN's transformative role in enhancing pyrolysis efficiency, reducing experimental costs, and enabling innovative biofuel production technologies.