The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects. The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly. However, limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials. In this study, some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed. Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models. Dependent variables for multiple prediction models were selected as carbon, hydrogen, and oxygen. Using volatile matter, fixed carbon, moisture and ash contents as independent variables, three different prediction models were developed for each dependent parameter using ANFIS, ANN, and MLR. In addition, a routine for selecting the best predictive model was suggested in the study. The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory. Therefore, the developed models can be used to determine the elemental composition of coal and biomass for practical purposes.

煤炭和生物质的元素组成提供了可用于几乎所有能源转换系统和项目设计的重要参数。确定煤和生物质元素组成的实验室测试既费时又昂贵。但是，有限的研究表明，从这些材料的元素分析和邻近分析获得的参数之间存在相关性。在这项研究中，使用软计算和回归分析开发了一些煤和生物质元素组成的预测模型。在分析过程中使用了包括元素分析和最近分析参数在内的31个样本来建立多个预测模型。选择了多个预测模型的因变量，例如碳，氢和氧。使用挥发性物质，固定碳，水分和灰分含量作为自变量，使用ANFIS，ANN和MLR为每个因变量开发了三种不同的预测模型。此外，研究中建议了选择最佳预测模型的常规方法。通过使用各种预测性能指标测试了建立的模型的可靠性，发现模型令人满意。因此，开发的模型可用于确定实际用途的煤和生物质的元素组成。通过使用各种预测性能指标测试了建立的模型的可靠性，发现模型令人满意。因此，开发的模型可用于确定实际用途的煤和生物质的元素组成。通过使用各种预测性能指标测试了建立的模型的可靠性，发现模型令人满意。因此，开发的模型可用于确定实际用途的煤和生物质的元素组成。