OPTIMASI RELIABILITY-BASED PREVENTIVE MAINTENANCE GLAND PACKING SOOT BLOWER DENGAN MIXED INTEGER NONLINEAR PROGRAMMING (MINLP) (STUDI KASUS: PT PAITON OPERATION & MAINTENANCE INDONESIA UNIT 3)

Muhammad Naufal Daffa Satria, NIM.: 22106060068 (2026) OPTIMASI RELIABILITY-BASED PREVENTIVE MAINTENANCE GLAND PACKING SOOT BLOWER DENGAN MIXED INTEGER NONLINEAR PROGRAMMING (MINLP) (STUDI KASUS: PT PAITON OPERATION & MAINTENANCE INDONESIA UNIT 3). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

In the power generation industry, equipment reliability is the key to operational continuity. One of the critical components in the soot blower system is the gland packing. Currently, the maintenance policy for gland packing at PT Paiton Operation & Maintenance Indonesia Unit 3 is still based on fixed intervals and corrective actions. This approach ignores the stochastic degradation pattern of the components, leading to mismatched maintenance schedules, increased failure frequencies, and cost overruns. This study aims to design a reliability-based preventive maintenance policy to determine the optimal maintenance schedule using a probability distribution approach and Mixed Integer Nonlinear Programming (MINLP). Time to Failure (TTF) data analysis shows the Weibull distribution as the best model with an increasing failure rate or wear-out pattern. These parameters are integrated into the MINLP model to determine optimal decisions namely do nothing, maintenance, or replacement at each time interval. This optimization model is constrained by the absolute requirement that the component is prohibited from operating if its reliability falls below the 70% minimum threshold based on Standar Industri Indonesia (SII). The results indicate that this reliability-based preventive maintenance policy yields lower total maintenance costs by 4.80%, which equals a saving of Rp 441,431 compared to the company's current method. The proposed method successfully minimizes total maintenance costs and prevents over-maintenance and unexpected downtime by consistently maintaining equipment performance above the 70% reliability threshold.

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