Adaptive neuro fuzzy predictive models of agricultural biomass standard entropy and chemical exergy based on principal component analysis

Abstract

In order to effectively utilize energy of agricultural biomass, there is a need to evaluate energy potential. For such a purpose, chemical exergy and standard entropy of typical agricultural biomass were examined analytically. Element compositions of the exergy and entropy were acquired for further statistical evaluation. Adaptive neuro fuzzy inference system (ANFIS) was used as the statistical methodology for data analyzing. ANFIS is an efficient estimation model among machine learning techniques. The main weakness of the ANFIS is its dimensionality problem with large inputs. Therefore, the main goal in this study was to estimate the parameters’ influence on the chemical exergy and standard entropy prediction in order to reduce the number of inputs. Principal component analysis was used for presentation of the obtained ANFIS predictive models. Obtained results have shown the best predictive performances for standard entropy based on hydrogen as composite element of the agricultural biomass. Exergy prediction was the best for oxygen as composite element of the agricultural biomass. ANFIS coefficient of determination for standard entropy prediction based on hydrogen is 0.9832 and for chemical exergy prediction is 0.919. The results show the high predictive accuracy of ANFIS models.