Comparison of artificial neural network and response surface methodology for evaluation of the predictive capability of bio-oil yield from pyrolysis of Mangifera indica wood sawdust

Abstract

In this research article, application of artificial neural network (ANN) and response surface methodology (RSM) is employed to predict the extraction yield (EY) of bio-oil from the pyrolysis of Mangifera indica wood (MIW) sawdust, developing the two optimized models (i.e., ANN-FFBP and RSM-CCD). The effect of three input parameters with their ranges, viz. particle size (0.2–0.8 mm), temperature (430–630 °C), and N2-gas flow rate (60–160 mL/min) on EY of MIW bio-oil are investigated. The Levenberg–Marquardt (LM) algorithm is used to train the network and an ANN-FFBP based model is developed for predicting the EY. Sensitivity analysis confirmed the superiority of optimal ANN-FFBP [3–9–1] model over the optimized RSM-CCD model with the values (AARD%= 0.25945, MSE = 0.00001 and R2 = 0.99711). The maximum EY of bio-oil (45.50 mass%) was observed at optimum conditions of parameters (0.6 mm, 564 °C, and 120 mL/min). The analysis of bio-oil through GC–MS confirmed the presence of high-valued compounds, e.g., methylenecyclopropanecarboxylic acid (8.11 mass%) and p-Cresol (11.41 mass%). In addition to this, the commercial significance of the extracted pyrolysis products has been confirmed via FTIR, ignition point, elemental analyzer, 1H NMR, and FE-SEM-EDX. Moreover, the techno-economic assessment of bio-oil production at industrial scale of 10 TPD assured a profit margin of 109–128 % for per kg production of bio-oil.