• Shaikh Mohammed Raihan
• Ghugare Suhas B

In the current study, the coal’s classification utilized in thermal power plants is reported. The coal samples were collected from India, Pakistan, Turkey, Indonesia and New Zealand. These coals were categorized using the conventional unsupervised clustering approach K-Means Clustering and Auto-Model, which selects the best clustering model iteratively. To carry out the aforementioned classification, coal can be divided in to water content, ashes, volatile matter, combustible carbon, and gross calorific value (GCV). Based on the lower Davies Bouldin index, the Auto-model based classifier performed better then k-means classifier. As stated by the study's classification findings, Power coals from the aforementioned specific regions may be categorized most efficiently using conventional and artificial intelligence methods into seven groups.

K-Means, Auto-Model, Clustering, Coal data

"Classification of Regional Coals using Unsupervised Clustering Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g655-g665, April-2024, Available :http://www.jetir.org/papers/JETIR2404682.pdf

"Classification of Regional Coals using Unsupervised Clustering Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg655-g665, April-2024, Available at : http://www.jetir.org/papers/JETIR2404682.pdf