Open AccessIndramayu 3 x 330 MW CFPP Coal Yard Management Optimization with K-Means Clustering
Author(s) -
S Andria
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1096/1/012096
Subject(s) - yard , coal , environmental science , coal combustion products , index (typography) , waste management , electricity generation , cluster analysis , engineering , environmental engineering , computer science , power (physics) , physics , quantum mechanics , machine learning , world wide web
Coal yard management is part of the generation management to ensure that coal supply to the unit can continue to be met. The coal entering the boiler is regulated considering various coal characteristics that differ between suppliers with coal mixing strategies, to ensure the generating unit works following the performance target. One of the generating units operated by PT Pembangkit Jawa Bali (PJB) is the Indramayu 3 x 330 MW coal-fired power plant (CFPP). Using 1507 data shipments of PLTU Indramayu from January 2017 to June 2020, a more straightforward coal yard management method can be analyzed. Data were analyzed using k-means clustering by using important parameters from coal property data. Important parameters used are Gross Caloric Value (GCV), Hardgrove Grindability Index (HGI), theoretical air for combustion, and slagging index. From the modeling with k-means clustering, the six optimal clusters are obtained. These six clusters are much simpler than before, managed in 16 clusters in the coal yard. Simplified coal yard management will have a significant impact on operational efficiency.