Akademik Veri Yönetim Sistemi
Biomass Conversion and Biorefinery, 2025 (SCI-Expanded)
The physical and chemical activation of biomass prior to pyrolysis significantly affects the properties of the activated carbon produced. In this study, raw tea waste (TW) and hazelnut shells (HS) were used to produce biochar and activated carbon samples by pyrolysis at different pyrolysis temperatures with and without chemical and physical activation. Subsequently, an artificial neural network (ANN) was developed based on the pyrolysis conditions, proximate and elemental analyses of the biomass feedstocks and the obtained biochar and activated carbon to predict the higher heating value (HHV) and specific surface area (SSA) of the biochar. For this purpose, machine learning algorithms such as ANN, Gaussian process regression (GPR), regression trees (RT), and support vector machines (SVM) were compared to find the best-performing algorithm for the prediction of HHV and SSA of biochar. Algorithms based on ANNs performed better than SVM, RT, and GPR models, with higher regressions and lower prediction errors. The resilient backpropagation (RProp) algorithm proved to be the most suitable training algorithm as it provided satisfactory results with a low percentage of mean squared error (MSE) and mean absolute error (MAE). The ANN models showed moderate to strong performance in the tests, with correlation coefficient (R) values of 0.82 and 0.95, coefficient of determination (R2) values of 0.67 and 0.90, and low MAE and MSE, indicating reasonable prediction accuracy for HHV and SSA of the biochar. The energy efficiency of biochar produced with conventional pyrolysis ranged from 9.84% to 21.13%, while the energy efficiency of activated carbon ranged from 45.26% to 67.21%, with the maximum reached at 300 °C. Based on the results of the thermodynamic analysis, it was found that the energy and exergy yields of the biochar and activated carbon produced depend on the activation conditions and temperature.