Kurnadi, Mohamad (2021) Optimasi Pencampuran Batubara Dengan Jaringan Syaraf Tiruan Dan Algoritma Genetika Pada In-Furnace Blending Boiler Tangensial. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dalam rangka mempercepat pengembangan infrastruktur ketenagalistrikan di Indonesia, pemerintah Republik Indonesia membuat program Fast Track Program tahap 1 (FTP-1) yang didominasi oleh pembangunan pembangkit listrik tenaga uap (PLTU) batubara. PLTU Banten 3 Lontar adalah salah satu pembangkit program FTP-1 yang dibangun PT. PLN dengan kapasitas 3x315 MW dan berbahan bakar utama batubara.
Batubara mempunyai peranan yang sangat penting dalam menentukan karakteristik pembakaran suatu pembangkit. Batubara dengan kualitas yang baik akan meningkatkan kualitas pembakaran dan kelancaran operasi suatu pembangkit. Namun, pada kenyataannya seringkali suatu pembangkit tidak mendapatkan batubara yang sesuai spesifikasi sehingga perlu dilakukan pencampuran batubara.
Pencampuran batubara (coal blending) adalah proses pencampuran batubara berkualitas baik dengan batubara berkualitas rendah sehingga didapatkan batubara dengan kualitas menengah. Salah satu metode coal blending adalah pencampuran didalam furnace dimana dengan hanya meletakkan satu jenis batubara yang ada disetiap coal burner yang dikenal dengan istilah in furnace blending.
Pencampuran batubara yang dilakukan adalah dengan mencampurkan batubara berkalori menengah (medium rank coal, MRC) dan batubara berkalori rendah (low rank coal, LRC) dengan komposisi 50%:50% yang diumpankan kedalam boiler melalui empat buat burner dengan elevasi berbeda. Batubara MRC dan LRC yang digunakan pada pembangkit listrik berasal dari berbagai macam sumber dan supplier sehingga memiliki kualitas yang berbeda juga. Pada penelitian ini dilakukan pencarian optimal pencampuran batubara MRC dan LRC serta komposisi pengumpanannya pada layer burner dengan model algoritma Jaringan Syaraf Tiruan dan Algoritma Genetika pada software Matlab.
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In order to accelerate the development of electricity infrastructure in Indonesia, the government of the Republic of Indonesia has created a Phase 1 Fast Track Program (FTP-1) which is dominated by the construction of coal-fired power plants (PLTU). PLTU Banten 3 Lontar is one of the FTP-1 program generators built by PT. PLN with a capacity of 3x315 MW and the main fuel is coal.
Coal has a very important role in determining the combustion characteristics of a generator. Coal with good quality will improve the quality of combustion and smooth operation of a power plant. However, in reality often a power plant does not get coal according to specifications so it is necessary to mix coal.
Coal blending is the process of mixing good quality coal with low quality coal to obtain medium quality coal. One method of coal blending is mixing in the furnace where by only placing one type of coal in each coal burner, it is known as in furnace blending.
The coal blending is done by mixing medium rank coal (MRC) and low rank coal (LRC) with a composition of 50%:50% which is fed into the boiler through four burners with different elevations. The MRC and LRC coal used in power plants come from various sources and suppliers so they have different qualities. In this research, the optimal search for mixing MRC and LRC coal and the composition of the feed on the burner layer is carried out with the Artificial Neural Network and Genetic Algorithm model in Matlab software.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Coal blending, burner, MRC, LRC, komposisi, Jaringan Syaraf Tiruan, Algoritma Genetika, Coal blending, burner, MRC, LRC, composition, Neural Networks, Genetic Algorithm |
| Subjects: | Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA402.5 Genetic algorithms. Interior-point methods. T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Mechanical Engineering > 21101-(S2) Master Thesis |
| Depositing User: | Mohamad Kurnadi |
| Date Deposited: | 27 Aug 2021 12:44 |
| Last Modified: | 27 Aug 2021 12:44 |
| URI: | http://repository.its.ac.id/id/eprint/89940 |
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