Implementasi Preventive Maintenance Untuk Meningkatkan Keandalan Pada Komponen Kritis Boiler Di Pembangkit Listrik Tenaga Uap

Pamungkas, Iing; Irawan, Heri; Pandria, T.M Azis

doi:10.38038/vocatech.v2i2.53

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…Over the years, multilevel preventive maintenance scheme has been relied upon for the management of the plant and its components. However, deployment of predictive maintenance that anchors on historical operational data is beginning to gain acceptance (Pamungkas et al, 2021). The data required for this study was obtained as the steam turbine was carefully examined and the relevant data picked to give the desired outcome for proper planning through parameters forecasting.…”

Section: Data Collection and Descriptionmentioning

confidence: 99%

Employment of Intelligent Predictive Maintenance on Thermal Power Plant Component Parts Taking Condenser Vacuum as a Case Study

Ajewole,

Onarinde,

Agboola

et al. 2023

TJET

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This work proposes deployment of machine learning in the maintenance of individual constituent parts of steam power plant assemblages. With the condenser vacuum of a steam turbine (in a six-turbine plant assemblage) taken as a case study, information on the past operating parameters of the selected plant component was used to forecast its future working condition. Based on Exponential Gaussian Process of Regression, a model was developed, trained using the diachronic operational data, and employed in determining the future. A quantitative evaluation was employed to provide the distribution of the test values of the data about the lines of regression, as well as to measure the prediction accuracy of the model. The results show MAE and RMSE values are 6.1602 and 7.9286 respectively during the training; while for the prediction, the values are 92.6544 and 92.7235 respectively. It is concluded that modern power plants with myriads of instrumentation and data acquisition mechanisms can leverage on the approach of this study to model and plan the maintenance scheme that best suits and fits individual component units of power plants, since understanding of the anticipatory values of operational parameters helps to determine the likelihood of components failures.

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Section: Data Collection and Descriptionmentioning

confidence: 99%

Employment of Intelligent Predictive Maintenance on Thermal Power Plant Component Parts Taking Condenser Vacuum as a Case Study

Ajewole,

Onarinde,

Agboola

et al. 2023

TJET

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“…Pembangkit listrik tenaga uap (PLTU) memiliki peranan yang sangat penting dalam sistem ketenagalistrikan di Indonesia. Sebagian besar pasokan tenaga listrik di Indonesia di suplai oleh PLTU karena dianggap lebih ekonomis dan dapat menghasilkan energi listrik dalam jumlah besar [1]. Salah satu komponen yang sangat penting pada PLTU adalah boiler.…”

Section: Pendahuluanunclassified

Pemodelan Sistem Pengendalian Level Deaerator Berbasis Self-Tuning Fuzzy PID Controller di PLTU Teluk Balikpapan

Raharjo,

Kasrani

2023

JTT (Jurnal Teknologi Terpadu)

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Deaerator merupakan peralatan yang sangat penting pada pembangkit listrik tenaga uap. Deaerator berfungsi untuk menghilangkan gas-gas terlarut yang terkandung dalam air pengisi (feed wate). Level deaerator adalah parameter yang sangat penting untuk dijaga saat proses deaerari (pembuangan gas-gas terlarut). Jika level deaerator terlalu rendah, dapat mengakibatkan rusaknya boiler feed pump dan menngganggu pasokan air ke boiler, namun jika level terlalu tinggi, kandungan gas-gas terlarut seperti karbondioksida (CO2) dan Oksigen (O2) akan tinggi. Pengaturan level deaerator pada PLTU Teluk Balikpapan harus memperhatikan condensate pump discharge pressure. Pengaturan level deaerator dan condensate pump discharge pressure pada PLTU Teluk Balikpapan dilakukan dengan mengatur bukaan deaerator level control valve dan condenser recycle control valve. Penelitian ini akan memodelkan sistem kontrol self-tuning fuzzy PID controller yang disimulasikan menggunakan software MATLA-Simulink. Berdasarkan 3 pengujian yang dilakukan menggunakan 3 karakteristik gangguan yang berbeda, self-tuning fuzzy PID controller memberikan respon yang memuaskan karena berhasil menjaga level deaerator dan condensate pump discharge pressure sesuai dengan batasan operasi kedua parameter tersebut. Dari hasil 3 pengujian yang telah dilakukan, didapatkan nilai error maksimum level sebesar 6,6288 % dan Root Mean Square Error (RMSE) sebesar 20 mm sedangkan untuk error maksimum tekanan sebesaar 21 % dan Root Mean Square Error (RMSE) sebesar 0,035 Mpa.

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“…Tahap pertama yaitu dengan membangkitkan bilangan acak dengan bantuan Microsoft excel dengan fungsi =RAND() sejumlah N. Bilangan acak tersebut berdistribusi uniform 0,1 (Pamungkas et. al., 2020).…”

Section: Simulasi Monte Carlounclassified

Penjadwalan Perawatan Mesin Milling Pipe Dengan Pendekatan Simulasi Monte Carlo Pada Pt WPP

Firmansyah,

Widiasih

2023

JISO

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PT WPP merupakan industri manufaktur yang memproduksi pipa stainlees. Karena keadaan daya saing bisnis saat ini, organisasi harus dapat mempertahankan ketergantungan peralatan manufaktur mereka untuk memastikan kelancaran proses produksi.. PT WPP mengaplikasikan metode preventive maintenance pada perawatan mesin produksinya hanya saja dalam penentuan jadwal belum optimal, hanya berlandaskan patokan dari kegagalan-kegagalan yang terjadi sebelumnya. Dalam mesin produksi pipa stainlees, PT WPP memiliki 3 mesin produksi yaitu mesin slitter, mesin milling pipe, dan mesin polishing. Mesin produksi yang memiliki breakdown tertinggi yaitu mesin milling pipe dengan frekuensi breakdown 444. Berdasarkan analisis pareto didapatkan 8 komponen kritis dengan downtime tertinggi yaitu abrasive, gearbox, sander, cutter, as cutter, welding, water pump, dan hower. Simulasi monte carlo dapat menguji jadwal perawatan yang optimal. Hasil dari Simulasi didapatkan interval waktu perawatan preventive untuk komponen abrasive 11 hari, komponen gearbox 15 hari, komponen sander 23 hari, komponen cutter 9 hari, komponen as cutter 170 hari, komponen welding 43 hari, komponen water pump 32 hari, dan komponen hower 25 hari. Dengan mengaplikasikan interval waktu optimum dapat meningkatkan keandalan menjadi 35,33% hingga 65,33%. ABSTRACT PT WPP is a manufacturing industry that produces stainless steel pipes. Due to the current state of business competitiveness, organizations must be able to maintain dependence on their manufacturing equipment to ensure the smooth running of the production process. PT WPP applies the preventive maintenance method to the maintenance of its production machines but the schedule is not optimal, only based on the benchmark of failures that occur previously. In terms of stainless steel pipe production machines, PT WPP has 3 production machines namely slitter machines, pipe milling machines and polishing machines. The production machine that has the highest breakdown is the pipe milling machine with a breakdown frequency of 444. Based on the Pareto analysis, 8 critical components with the highest downtime are obtained, namely abrasives, gearbox, sander, cutter, axle cutter, welding, water pump, and hower. Monte carlo simulation can test the optimal maintenance schedule. The results of the simulation obtained preventive maintenance time intervals for abrasive components 11 days, gearbox components 15 days, sander components 23 days, cutter components 9 days, axle components cutter 170 days, welding components 43 days, water pump components 32 days, and hower components 25 day. Applying the optimum time interval can increase the reliability to 35.33% to 65.33%.

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Implementasi Preventive Maintenance Untuk Meningkatkan Keandalan Pada Komponen Kritis Boiler Di Pembangkit Listrik Tenaga Uap

Cited by 7 publications

References 5 publications

Employment of Intelligent Predictive Maintenance on Thermal Power Plant Component Parts Taking Condenser Vacuum as a Case Study

Employment of Intelligent Predictive Maintenance on Thermal Power Plant Component Parts Taking Condenser Vacuum as a Case Study

Pemodelan Sistem Pengendalian Level Deaerator Berbasis Self-Tuning Fuzzy PID Controller di PLTU Teluk Balikpapan

Penjadwalan Perawatan Mesin Milling Pipe Dengan Pendekatan Simulasi Monte Carlo Pada Pt WPP

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