LAUSR

Pyrolysis is one of the well-known technologies used for bio-oil production, where temperature, heating rate, and nitrogen flow rate are the crucial parameters governing its kinetics. Optimization of design is important for a pyrolyzer to achieve higher bio-oil yield, which necessities the importance of the information such as interactive effects of controlling parameters. Though quite a good number of studies were conducted to analyze the individual impact of these parameters on pyrolysis, their interactive effects have not been addressed well. Also, there are barely any studies investigating such interactive effects using a local grass species Saccharum ravannae, especially in slow pyrolysis. On this perspective, the present study comprises of experiments followed by analysis of data using artificial neural networks. Here, the data obtained from pyrolysis experiments was used to develop a model which can predict the bio-oil yield for a given temperature, nitrogen flow rate, and heating rate. Initially, an experimental program was conducted to study the yield of bio-oil at various temperature regimes, nitrogen flow rates, and heating rates using pyrolyzer. This experimental data was used to train the neural networks, followed by development of an in-house program in C language of trained networks, to predict the yield. Both response surface regression and artificial neural networks (ANN) were used to develop a model and interactive effects of parameters were investigated. An optimum condition of temperature 505 °C with a heating rate of 6.36 °C/min and a nitrogen flow rate of 311 ml/min to obtain maximum yield of 45.69% by wt of bio-oil from Saccharum ravannae was obtained by the optimization analysis of ANN. Regression model has performed better than ANN model. Results of univariate tests show the significant terms in the developed regression equation. From sensitivity analysis, temperature is noted as the highly significant factor contributing to yield followed by nitrogen flow rate.