Applying ANFIS and LSSVM Models for the Estimation of Biochar Aromaticity

Abstract

The main aim of this work is the determination of aromaticity in biochar from easier accessible parameters (e.g., elemental composition). To this end, two machine learning models, including adaptive neurofuzzy inference system (ANFIS) and least-squares support vector machine (LSSVM), were used to predict this constant form 98 dataset gathered from earlier reported sources. The outputs of the statistical parameters showed that the LSSVM model has the ability to estimate the target parameter with R-squared values of 0.986 and a mean relative error of 3.821 for the overall dataset. Also, by analyzing the sensitivity on the input parameters, it was shown that the carbon percentage has the greatest effect on the target values, and a high focus should be placed on this parameter. Finally, by comparing the methods proposed in this paper with other models published in previous studies, our model has shown higher accuracy in predicting the target parameter.