Analysis of Boiler Operational Efficiency Based on Fly Ash and Bottom Ash Production Using the Data Envelopment Analysis Method at PT. XYZ
DOI:
https://doi.org/10.35718/jinseng.v3i`1.8481975Keywords:
Data Envelopment Analysis (DEA), Constant Returns to Scale (CRS), EfisiensiAbstract
XYZ is a coal-fired steam power plant. This study aims to analyze the efficiency of boiler operations based on fly ash and bottom ash production at PT XYZ using the Data Envelopment Analysis (DEA) method through an input-oriented approach with the Constant Returns to Scale (CRS) model. The input variables used include boiler load, operating hours, main steam pressure, main steam temperature, coal consumption, coal calorific value, coal ash content, coal moisture content, and electricity production, while the output variables consist of fly ash and bottom ash production. The analysis was conducted on six Decision Making Units (DMUs) representing the period from July to December 2025. The results indicate that several DMUs have not yet reached efficient conditions due to non-optimal operational scales. The variables that most influence inefficiency are boiler load, operating hours, main steam pressure, coal ash content, and coal moisture content, which have slack values. Based on the calculation results, in July it is necessary to reduce the boiler load input to approximately 229.184 tons and coal ash content to about 3.06%. In September, adjustments are required by reducing the boiler load to approximately 41.965 tons and setting the operating hours to 720 hours. In October, the main steam pressure needs to be reduced to around 46 bar and the coal ash content to about 30.14%. In November, the boiler load needs to be reduced to approximately 43.866 tons, with coal ash and moisture content approaching 0%. Meanwhile, in August and December no slack values were found, indicating that no input adjustments are required. These input adjustments are intended to optimize the use of resources in the boiler operation process.
Keywords: Data Envelopment Analysis (DEA), Constant Returns to Scale (CRS), Efficiency
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