Indirect Gross Calorific Value prediction using Random Forest

Waqas Ahmed; Khan Muhammad

Indirect Gross Calorific Value prediction using Random Forest

Vol. 7 Issue 01 PP. 58-61 January 2020

Vol. 7, Issue 01, PP. 58-61, January 2020

Keywords: Gross Calorific Value, Random Forest, Proximate Analyses, Ultimate Analyses, Neural Networks.

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During operation of coal-based power plants, frequent calorific Value measurement is necessary. Previously, Artificial Intelligence based models have been developed for instant calorific value calculation based on proximate analyses or ultimate analyses or combination of both. In this paper, random forest was used for comparison of all the three methods and computing relative analyses parameters importance. This study uses well known USGS coal qual dataset. In this work, 10-fold validation strategy and R-squared was used as validation strategy and performance metric respectively. Ultimate analyses (R-squared = 0.9984) performed slightly better than proximate analyses (R-squared = 0.9861) or combination of both (R-squared = 0.9982). Lastly, carbon was found to be the most important feature in all models.

Waqas Ahmed, , Msc Mining Enginnering Student at UET Peshawar, Pakistan.
Khan Muhammad, , Assistant Professor Department of Mining Engineering UET Peshawar, Pakistan.

Waqas Ahmed and Khan Muhammad Indirect Gross Calorific Value prediction using Random Forest International Journal of Engineering Works Vol. 7 Issue 01 PP. 58-61 January 2020 https://doi.org/10.34259/ijew.20.7015861

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