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.

Rock properties are important for design of surface and underground mines as well as civil engineering projects. Among important rock properties is the characteristic impedance of rock. Characteristic impedance plays a crucial role in solving problems of shock waves in mining engineering. The characteristics impedance of rock has been related with other rock properties in literature. However, the regression models between characteristic impedance and other rock properties in literature do not consider the variabilities in rock properties and their characterizations. Therefore, this study proposed two soft computing models [i.e., artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)] for better predictions of characteristic impedance of igneous rocks. The performances of the proposed models were statistically evaluated, and they were found to satisfactorily predict characteristic impedance with very strong statistical indices. In addition, multiple linear regression (MLR) was developed and compared with the ANN and ANFIS models. ANN model has the best performance, followed by ANFIS model and lastly MLR model. The models have Pearson's correlation coefficients of close to 1, indicating that the proposed models can be used to predict characteristic impedance of igneous rocks.