Predicting Calorific Value of Thar Lignite Deposit: A Comparison between Back-propagation Neural Networks (BPNN), Gradient Boosting Trees (GBT), and Multiple Linear Regression (MLR)
Calorific value provides a strong measure of useful energy during coal utilization. Previously, different AI techniques have been used for the prediction of calorific value; however, one model is not valid for all geographic locations. In this research, Lower Calorific Value (LCV) of the Thar coal r...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2020.1824091 |