Analysis Study of Performance and Reliability Impact in Boiler Through Differential Coal Calorific Value (Case Study: Pelabuhan Ratu Coal-Fired Power Plant)

Yudisaputro, Hendra; Yuniarto, Muhammad Nur; Yohanes, Yohanes; Wibawa, Agus

doi:10.1007/978-981-19-1581-9_12

Cited by 1 publication

(1 citation statement)

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“…Furthermore, CFPP operators should also implement biomass cofiring, although this policy remains controversial regarding reducing greenhouse gas emissions [2]. CFPP Pelabuhan Ratu has developed the NEMESYS application based on Artificial Intelligence to meet the optimization needs of blending [4]. This application utilizes Genetic Algorithms and provides the best blending composition output based on available barge and coal yard coal specifications.…”

Section: Introductionmentioning

confidence: 99%

Improve Coal Blending Optimization in CFPP by Cromosom and Fitness Function Redefinition of the Genetic Algorithm

Solihah,

Zuhdi,

Rochman

et al. 2024

JUITA

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Blending coal before it enters the power plant boiler unit is necessary to adjust the coal categories according to the boiler unit specifications. The power plant must also comply with the regulations regarding coal-biomass co-firing through blending. Applying a Genetic Algorithm that only considers the composition and fitness based on the blend's quality leads to accumulation issues, decreasing coal quality. This research proposes redefining chromosomes, fitness functions, mutation rules, population determination, and output as the best chromosome used in the Genetic Algorithm. Testing uses various compositions of coal inputs from the barge, coal yard, and biomass to simulate different conditions. The test results demonstrate that the developed algorithm can provide all possible alternative blends between the coal in the barge and at the coal yard. Under specific conditions, operators can choose a blend composition that involves coal stored in the coal yard for an extended period.

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Section: Introductionmentioning

confidence: 99%