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 |
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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 |
| Summary: | 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 region in Pakistan is predicted from proximate analysis of 693 drill holes extending to 9,000 sq. km. Researchers have applied different techniques to produce the best model for prediction of calorific value; however, Gradient Boosting Trees (GBT) has not been used for this purpose. A comparison of GBT, Back-propagation Neural Networks (BPNN), and Multiple Linear Regression (MLR) is presented to predict the calorific value from a total of 8,039 samples with 1 m support interval. The samples were split randomly into 70:15:15 for training, testing, and validation of GBT, BPNN, and MLR models, reporting correlations of 0.90, 0.89, and 0.80, respectively. The features’ importance was reported by the intuitive and best-performing GBT model in decreasing order of importance as: Volatile Matter, Fixed Carbon, Moisture, and Ash with corresponding feature importance values of 0.50, 0.30, 0.12, and 0.08. |
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| ISSN: | 0883-9514 1087-6545 |