Development of Multiple Regression Model for Assessment of Coal Calorific Value in Indian Opencast Mines

Abstract

Coal, the most important fossil fuel in India, accounts for 75% of India's total electricity requirement in Dec 2020 and 51.65% of energy requirement overall as on Dec 2021. Coal contains varying amounts of fixed carbon, volatile matter, ash, moisture, and sulphur. The available coal's specific properties and reduced emissions of impurities such as ash, slag, sulphur, and dioxins must be considered while designing coal-fired thermal power plants. Coal properties that affect combustion and environmental performance, namely gross calorific value, moisture, sulphur, and ash content, often form the basis of sales contracts between coal-producing companies and coal-fired power plants. Indian coal quality shows wide variation from coalfield to coalfield, from mine to mine and even from coal seam to seam within a coalfield, depending upon their depth of occurrence and composition. This paper attempts to develop a multiple linear regression model to assess the gross calorific value of coal. This model uses gross calorific value on Air-Dried Basis (ADB) as a dependent variable and moisture content, ash content and humidity percentage as independent variables, also on Air-Dried Basis. A scatterplot between the percentage of ash content in collected coal samples from Indian opencast mines and the gross calorific value of coal indicated a strong negative correlation between them. This model's performance was assessed and confirmed its applicability in evaluating the gross calorific value of coal. The quick assessment of gross calorific value using this model can help identify the area of a specific grade, which facilitates separate stockpiling of coal based on the sales contracts and a more accurate economic valuation of the entire coal reserve. © 2023, The Institution of Engineers (India).