Spontaneous combustion liability between coal seams: A thermogravimetric study

The spontaneous combustion liability of coal can be determined by using different experimental techniques.These techniques are well-known in their application,but no certain test method has become a standard to prove the reliability of all of them.A general characterisation which included proximate and ultimate analyses,petrographic properties and spontaneous combustion tests(thermogravimetric analysis(TGA)and the Wits-Ehac tests)were conducted on fourteen coal and four coal-shale samples.The spontaneous combustion liability of these samples collected between coal seams(above and below)were predicted using the TGA and the Wits-Ehac tests.Six different heating rates(3,6,9,15,20 and 25
C/min)were selected based on the deviation coefficient to obtain different derivative slopes and a liability index termed the TGspc index.This study found that coal and coal-shale undergo spontaneous combustion between coal seams when exposed to oxygen in the air.Their intrinsic properties and proneness towards spontaneous combustion differ considerably from one seam to the other.The Wits-Ehac test results agreed with the TGspc results to a certain extent and revealed the incidents of spontaneous combustion in the coal mines.

Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR

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.