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.

为了有效地利用农业生物质的能量，需要评估能量潜力。为此，对典型农业生物质的化学能级和标准熵进行了分析研究。获得了本能和熵的元素组成，以进行进一步的统计评估。自适应神经模糊推理系统（ANFIS）被用作数据分析的统计方法。ANFIS是机器学习技术中一种有效的估计模型。ANFIS的主要缺点是输入大的尺寸问题。因此，本研究的主要目的是估计参数对化学能级的影响和标准熵的预测，以减少投入的数量。主成分分析用于表示获得的ANFIS预测模型。所得结果表明，基于氢作为农业生物量的复合元素的标准熵的最佳预测性能。火用预测是最理想的氧作为农业生物质的复合元素。基于氢的标准熵预测的ANFIS确定系数为0.9832，化学能级预测的ANFIS确定系数为0.919。结果表明，ANFIS模型具有较高的预测准确性。