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All Journal JATI UNIK : Jurnal Ilmiah Teknik dan Manajemen Industri Journal Research of Social Science, Economics, and Management
I Ketut Gunarta
Institut Teknologi Sepuluh Nopember
Engineering Environmental Science Industrial & Manufacturing Engineering

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Perencanaan Mitigasi Risiko Berbasis Event Tree Analysis untuk Meningkatkan Keandalan PLTGU Sastya Adzani Syahbania; I Ketut Gunarta
JATI UNIK : Jurnal Ilmiah Teknik dan Manajemen Industri Vol. 9 No. 2 (2026): April
Publisher : Industrial Engineering, Engineering of Faculty, Universitas Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/jatiunik.v9i2.6495

Abstract

Repeated failures of critical equipment at power generation facilities can significantly disrupt operations and reduce system availability. In 2023, PT X's Block 1 Unit A Combined Cycle Power Plant recorded its lowest Equivalent Availability Factor (EAF) of 84.95% due to repeated failures of several critical components. Previous studies have identified these equipment failures and analyzed their reliability levels. This study aims to develop mitigation scenarios to reduce operational risks using Event Tree Analysis (ETA). ETA uses failure probabilities from previous reliability assessments as initiating events to model potential event sequences and evaluate their consequences. The results show that each tool generates between 4 and 16 scenario pathways, depending on the number of mitigation programs. All proposed mitigation scenarios successfully reduced the overall risk level to “Low” across all seven critical pieces of equipment. This study concludes that ETA provides an effective way to systematically organize mitigation efforts based on failure probability and consequence analysis. The contribution of this study is the development of a scenario-based mitigation framework using ETA to support decision-making and enhance operational reliability at combined-cycle power plants.
Investment Analysis Upgrading Capacity of Produced Water Treatment System (PWTS) at a Gas Processing Facility I Made Bayu Sukma Firmanjaya; I Ketut Gunarta
Journal Research of Social Science, Economics, and Management Vol. 5 No. 5 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i5.1237

Abstract

The oil and gas industry remains a strategic pillar for national energy security, yet it faces significant operational challenges as production fields mature. A critical issue is the exponential increase in produced water, which often surpasses the design limits of existing Produced Water Treatment Systems (PWTS). This research evaluates the investment feasibility of upgrading the PWTS capacity at a major gas processing facility where current water production has reached 3,110 BWPD, significantly exceeding the 2,000 BWPD design capacity. To address this, a hybrid framework was developed by integrating Technical and Economic Risk Assessments. Technical risks were quantified using Failure Mode and Effect Analysis (FMEA), involving 28 failure modes across seven primary equipment units. To mitigate the subjectivity of expert judgment, a Monte Carlo Simulation with 10,000 iterations was implemented, followed by a Risk Matrix to translate technical failures into quantifiable "Expected Costs." The Economic Risk Assessment was conducted via Cost-Benefit Analysis (CBA), incorporating these expected costs as avoided-loss benefits. The results identify an optimal upgrade capacity of 6,000 BWPD, which yields a Net Present Value (NPV) of USD 84.94 million, an Internal Rate of Return (IRR) of 80.88%, a Benefit-Cost Ratio (BCR) of 16.59, and a Payback Period (PP) of 24 months. Furthermore, sensitivity analysis underscores the project’s robustness, demonstrating resilience against operational expenditure (OPEX) hikes of up to 50%, while establishing a critical production decline tolerance of -1.5% per day. This integrated methodology provides a robust, data-driven decision-making tool for managing high-risk energy infrastructure investments.
Co-Authors I Made Bayu Sukma Firmanjaya Sastya Adzani Syahbania