Abstract
Biomass has attracted significant interest as a renewable energy source recently. This study employs different artificial intelligence (AI) scenarios to determine biomass heat capacity (Cp). Cumulatively, 1025 experimental measurements for 25 different biomass types (block and powder forms) were utilized to design/identify the most accurate AI model. Validity checking approves that more than 98% of the data are valid. The Cp is estimated as a function of biomass source, appearance shape, and temperature. The cascade feedforward (CFF) neural network appears as the most precise tool for the concerned matter. This CFF predicts the biomass Cp by absolute average relative deviation (AARD%) and regression coefficient (R2) of 0.42% and 0.99347, respectively. Despite the empirical correlation that only considers the temperature effect on the biomass heat capacity, the CFF paradigm also incorporates the effect of biomass source and appearance shape. The powder form of biomasses has higher Cp than their block form.
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