Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload?
The gross calorific value (GCV) of coal is pivotal in shaping policies across various sectors of the Indian economy. It plays a crucial role in classification and valuation of coal and is a major factor in determining electricity tariffs charged by thermal power plants. With coal production escalati...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Energy Exploration & Exploitation |
| Online Access: | https://doi.org/10.1177/01445987241284111 |
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| author | Saroj K Sadangi Rudra P Pradhan |
| author_facet | Saroj K Sadangi Rudra P Pradhan |
| author_sort | Saroj K Sadangi |
| collection | DOAJ |
| description | The gross calorific value (GCV) of coal is pivotal in shaping policies across various sectors of the Indian economy. It plays a crucial role in classification and valuation of coal and is a major factor in determining electricity tariffs charged by thermal power plants. With coal production escalating year-on-year to meet India's increasing electricity demand, there is significant rise in coal testing activities along the pit-to-power supply chain at multiple points and by multiple testing agencies often driven by sector-specific policy requirements. While laboratory testing accurately determines GCV, it is costly and time-consuming due to the reliance on expensive equipment and skilled personnel. Global researchers have previously devised a plethora of empirical formulae predicting GCV based on its correlations with easy-to-measure properties like moisture and ash content. However, the applicability and utility of these formulae to the prevalent policy matrix of coal and power sector remain to be explored. The introduction of independent third-party assessment of coal quality by Coal India Limited in 2016 has generated a vast dataset of coal sample-test results, offering an opportunity to reassess existing empirical formulae, test their alignment with existing policies, and explore possibility of a unified, region-neutral formula for rapid GCV prediction with a special focus on alleviating the current overload in coal testing. |
| format | Article |
| id | doaj-art-c0aeb9f8b1ff46b68ed2cf7c02fa97b9 |
| institution | Kabale University |
| issn | 0144-5987 2048-4054 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Energy Exploration & Exploitation |
| spelling | doaj-art-c0aeb9f8b1ff46b68ed2cf7c02fa97b92025-01-15T11:04:41ZengSAGE PublishingEnergy Exploration & Exploitation0144-59872048-40542025-01-014310.1177/01445987241284111Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload?Saroj K SadangiRudra P PradhanThe gross calorific value (GCV) of coal is pivotal in shaping policies across various sectors of the Indian economy. It plays a crucial role in classification and valuation of coal and is a major factor in determining electricity tariffs charged by thermal power plants. With coal production escalating year-on-year to meet India's increasing electricity demand, there is significant rise in coal testing activities along the pit-to-power supply chain at multiple points and by multiple testing agencies often driven by sector-specific policy requirements. While laboratory testing accurately determines GCV, it is costly and time-consuming due to the reliance on expensive equipment and skilled personnel. Global researchers have previously devised a plethora of empirical formulae predicting GCV based on its correlations with easy-to-measure properties like moisture and ash content. However, the applicability and utility of these formulae to the prevalent policy matrix of coal and power sector remain to be explored. The introduction of independent third-party assessment of coal quality by Coal India Limited in 2016 has generated a vast dataset of coal sample-test results, offering an opportunity to reassess existing empirical formulae, test their alignment with existing policies, and explore possibility of a unified, region-neutral formula for rapid GCV prediction with a special focus on alleviating the current overload in coal testing.https://doi.org/10.1177/01445987241284111 |
| spellingShingle | Saroj K Sadangi Rudra P Pradhan Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload? Energy Exploration & Exploitation |
| title | Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload? |
| title_full | Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload? |
| title_fullStr | Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload? |
| title_full_unstemmed | Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload? |
| title_short | Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload? |
| title_sort | prediction and policy do empirical gross calorific value prediction help reduce coal testing overload |
| url | https://doi.org/10.1177/01445987241284111 |
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