Fast characterization of biomass pyrolysis oil via combination of ATR-FTIR and machine learning models

This study proposed a fast characterization method of bio-oil via the combination of attenuated total reflection flourier transformed infrared spectroscopy (ATR-FTIR) and machine learning models. The input to the model is high-dimensional infrared spectral data. Unsaturated concentration, effective hydrocarbon ratio, low calorific value, C content, H content, and O content are all relevant bio-oil indicators. The model parameters were optimized based on prediction accuracy and correlation coefficient. By comparing the sole support vector regression (SVR) model versus principal component analysis (PCA) preprocessed SVR model, the results showed that PCA preprocessing can significantly improve the overall performance of SVR model towards prediction of bio-oil characteristics. Under optimal parameters, the predicted accuracies for unsaturated concentration, effective hydrocarbon ratio, low calorific value, C content, H content, and O content reached 91.98%, 97.44%, 99.50%, 98.65%, 98.56%, and 97.88%, respectively. The correlation coefficient of sole SVR model was 0.3, and the correlation coefficient of PCA preprocessed SVR model was 0.9. Furthermore, the characteristic peaks of the infrared spectra at the optimal PC were analyzed, and PC6 and PC7 were found to have the most influence on the predicting performance. (c) 2022 Published by Elsevier Ltd.

Automated summary

This study proposes a fast characterization method of bio-oil using attenuated total reflection flourier transformed infrared spectroscopy (ATR-FTIR) and machine learning models. The results show that principal component analysis (PCA) preprocessing can significantly improve the overall performance of the support vector regression (SVR) model for bio-oil characteristic prediction.