Supervised and unsupervised machine learning for elemental changes evaluation of torrefied biochars

This study implements a comprehensive analysis of supervised and unsupervised learning to evaluate elemental changes and investigate the merits of the machine learning method for torrefied biochar property analysis. The focus is on data analysis using artificial neural networks (ANNs) and k-means algorithms for analyzing the carbonization index (CI) and deoxygenation index (DI) after biomass torrefaction. The predictive response surfaces for CI and DI are formulated and analyzed accordingly by preprocessing the raw data as either lignocellulosic or microalgal datatype. Based on ANNs, the relative importance weights of the factors of temperature, duration, and biomass type are 45.87 %, 23.19 %, and 30.94 %, respectively, for the CI response, while 35.27 %, 26.62 %, and 38.11 %, respectively for the DI response. For k-means analysis, the optimal number of clusters for lignocellulosic and microalgal datasets are two and three, respectively. The R2 value of ANNs is 0.9565. The distribution and percentage of the dataset within the clusters are influenced by the time for the lignocellulosic datatype, while they are influenced by temperature for the microalgal datatype. The obtained results are conducive to improving the cognition of the machine learning method on torrefaction performance analysis.