Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The rising number of occurrences of coal grade slippage among coal suppliers and users is causing worry in the Indian coal industry.One of the most important metrics for determining coal quality is the Gross Calorific Value (GCV). As a result, good GCV prediction is one of the most important techniques to boost heating value and coal output. This system aims to estimate GCV of the coal samples from proximate and ultimate parameters of coal using machine learning regression algorithm. The Multiple Linear Regression (MLR) and Local Polynomial Regression (LPR). The performance of this system is evaluated using Coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) parameters. The results of the proposed system in terms of RMSE, MAE, and R2 of the MLR and LPR are observed as 0.124, 0.119, 0.414, and 0.654, 0.547, and 0.414 respectively.
"Prediction of Gross Calorific Valueof Coal using Machine Learning Algorithm ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 11, page no.547 - 554, November-2022, Available :http://www.ijrti.org/papers/IJRTI2211079.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator