Ozbay, GunayKokten, Erkan Sami2024-09-292024-09-2920201302-09002147-9429https://hdl.handle.net/20.500.14619/8636This study is dedicated to developing a reliable artificial neural network (ANN) model to model the pyrolysis liquid product (bio-oil). Some related parameters with the bio-oil yield such as the pyrolysis temperature, duration, catalyst type, catalyst ratio, particle size, proximate, and ultimate analysis of the biomass were tested. Due to the different characteristics of different biomass types and pyrolysis methods, only slow and intermediate pyrolysis data from woody biomass were used in modeling. The correlation coefficients (R) were 0.992, 0.933, and 0.951 for training, validation, and testing, respectively. In order to evaluate the predictability of the ANN model, the predicted results were compared with the experimental results that were not introduced before. The simulated data were in good agreement with the experimental results indicating the reliability of the developed model. The relative impact results revealed that the most important parameter that affects the bio-oil yield was catalyst type (11.4%).eninfo:eu-repo/semantics/closedAccessArtificial neural networkbio-oilcatalystmodelingpyrolysisArticle12644125523WOS:000581901200033N/A