AI monitored precision tracking & coal quality prediction system

DOI

10.2991/978-94-6463-738-0_7How to use a DOI?

Keywords

Coal quality sampling; GCV and moisture prediction; Coal tracking; Quality and weight variance; Transition delay and quality variance; Coal special sampling

Abstract

The process of importing coal into industries faces critical challenges, compromising efficiency, accuracy, and quality control. Weight discrepancies often occur between challan-reported quantities and actual deliveries due to transit losses or moisture, making inventory management complicated. Even ensuring coal quality is difficult, as predicting key metrics such as Gross Calorific Value (GCV) and moisture content is limited. Sampling practices whereby only a fraction of the trucks is checked increase the likelihood of undetected quality differences in the batches that were not tested, hence leading to defective outputs. In addition, the absence of real-time analytical tools delays decisions and worsens inefficiencies. An automated tracking system can thus monitor the coal entry from gate entry to weighing and sampling stages. Its variance-detection mechanisms identify weight variance, which, in turn, can prompt further checks. Machine-learning-based predictive models can work in real time to generate coal quality metrics such as GCV and moisture levels. Automation of special sampling alerts provides for better quality checks of the consignment, minimizes the risk factors, and streamlines operations.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Omkar Deota
AU  - Sandeep Sahu
AU  - Srishti Verma
AU  - Yogesh Kumar Rathore
PY  - 2025
DA  - 2025/06/22
TI  - AI monitored precision tracking & coal quality prediction system
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 72
EP  - 89
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_7
DO  - 10.2991/978-94-6463-738-0_7
ID  - Deota2025
ER  -