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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 7

Published on: July 2026

AI-IOT ENABLED METHANE EMISSION PREDICTION AND CARBON FOOTPRINT REDUCTION IN UNDERGROUND COAL MINES: A CASE STUDY

Rajshekhar Singh

Ram Chandra Chaurasia

Department of Mining and Mineral Processing, Lakshmi Narain College of Technology, Jabalpur, Madhya Pradesh, 482053, India.

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Methane emissions from underground coal mining represent one of the most significant contributors to greenhouse gas (GHG) emissions within the mining sector. As a greenhouse gas, methane possesses a global warming potential approximately 28 times greater than carbon dioxide over a 100-year period, making its monitoring and mitigation essential for achieving climate goals. India, the world's second-largest coal producer, continues to rely heavily on coal for energy security. Coal India Limited (CIL), responsible for more than 80% of India's coal production, faces increasing pressure to reduce fugitive methane emissions while maintaining operational safety and productivity. Recent sustainability disclosures indicate that Scope-1 emissions remain a major component of CIL's carbon footprint, with underground gassy mines contributing substantially through methane leakage. This study proposes an Artificial Intelligence–Internet of Things (AI-IoT) integrated framework for real-time methane monitoring, prediction, and carbon footprint reduction in underground coal mines. The framework combines wireless methane sensing networks, edge computing, cloud analytics, and advanced machine learning algorithms including Long Short-Term Memory (LSTM), Random Forest (RF), and Reinforcement Learning (RL). A case study involving representative gassy mines of Coal India Limited, including Jharia and Raniganj coalfields, demonstrates the practical applicability of the proposed approach. Simulation and field-based analyses indicate that the LSTM model achieved prediction accuracy exceeding 93%, outperforming conventional statistical models. Implementation of AI-enabled ventilation optimization reduced methane-related emissions by approximately 35%, while energy consumption associated with mine ventilation systems decreased by nearly 27%. Additionally, safety performance improved through predictive hazard identification and proactive decision-making.

The results highlight the transformative potential of AI-IoT technologies in enabling sustainable mining operations, supporting India's Net-Zero 2070 commitment, enhancing worker safety, and creating pathways for carbon credit generation within the coal mining sector.

Keywords: Methane Emissions, Underground Coal Mining, Artificial Intelligence, Internet of Things, LSTM, Carbon Footprint Reduction, Smart Mining, Sustainable Mining, Predictive Analytics

How to Cite this Paper

Singh, R. (2026). AI-IoT Enabled Methane Emission Prediction and Carbon Footprint Reduction in Underground Coal Mines: A Case Study. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(7). https://doi.org/10.55041/ijcope.v2i7.044

Singh, Rajshekhar. "AI-IoT Enabled Methane Emission Prediction and Carbon Footprint Reduction in Underground Coal Mines: A Case Study." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 7, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i7.044.

Singh, Rajshekhar. "AI-IoT Enabled Methane Emission Prediction and Carbon Footprint Reduction in Underground Coal Mines: A Case Study." International Journal of Creative and Open Research in Engineering and Management 02, no. 7 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i7.044.

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  • Peer Review Type: Double-Blind Peer Review
  • Published on: Jul 05 2026
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