A Linear Regression Modeling Analysis of the Energy, Water, and Chemical Consumption in the Operating Configuration at 740 MW Priok Combined Cycle Power Plant

https://doi.org/10.22146/jmdt.97748

Raihan Muhammad(1*), Arief Rahman(2), Muhamad Fauzi Jamil(3)

(1) Indonesia Power Priok PGU Perusahaan Listrik Negara Jakarta, Indonesia
(2) Indonesia Power Priok PGU Perusahaan Listrik Negara Jakarta, Indonesia
(3) Indonesia Power Priok PGU Perusahaan Listrik Negara Jakarta, Indonesia
(*) Corresponding Author

Abstract


In realizing efficient energy use, the Government of Indonesia has issued a National Energy Policy in Government Regulation (Peraturan Pemerintah) No. 70 of 2009 concerning Energy Conservation, PT PLN Indonesia Power Priok Unit has carried out efficient operational activities. Therefore, to support the company's sustainability and operational performance, especially in terms of efficiency and operational activities, it is necessary to evaluate the process of energy use. The Combine Cycle Power Plant (CCPP) has several operating configurations according to the gas turbine, heat recovery steam generator (HRSG), and steam turbine amount. CCPP Priok Blok 3 operates full-block 2-2-1 or half-block 1-1-1, which means one gas turbine, HRSG, and steam turbine. This configuration of operation impacts the use of energy, water, and chemicals. For this reason, this project aims to model the use of energy, water, and chemicals using linear regression to determine which operating configurations are highly effective in using energy, water, and chemicals. The result of this linear regression modeling is that at the peak load, operation GT2 (gas turbine 2) is more energy efficient, 1.93% more efficient than GT1, than GT1 (gas turbine 1). At the minimum load, GT1 is 9.36% more energy efficient than GT2. At the same time, the water consumption of GT2 is 35.01% more efficient than that of GT1.

Keywords


modeling, energy, chemicals, water

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References

A. Kurniawan and K. Kokanda, 2021. Penerapan Simple linear regression untuk memprediksi jumlah kasus Covid di Indonesia. Journal of Digital Ecosystem for Natural Sustainability, Vol. 1, pp. 67-72.

A. Mehrpanahi, S. Nikbakht Naserabad, dan G. Ahmadi, 2019. Multi-objective linear regression based optimization of full repowering a single pressure steam power plant. Energy, Vol. 179, pp. 1017– 1035.

A. Ribeiro, A. Silva, dan A. R. da Silva, 2015. Data modeling and data analytics: A survey from a big data perspective. Journal of Software Engineering and Applications, Vol. 8, pp. 617–634.

C. Fan, M. Chen, X. Wang, J. Wang, dan B. Huang, 2021. A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in Energy Research, Vol. 9, 652801.

E. Erlangga, A. Sandra, F. Azharuddin, K. S. Gunawan, dan T. R. Biyanto, 2017. Adjustment of logic operational of chemical injection pump in PH control of water steam cycle to improve steam turbine 5.8 Reliability of power plant PT. PJB UP Muara Tawar. IOSR Journal of Electrical and Electronics Engineering, Vol. 12, pp. 4–9.

E. Grubert, 2020. Same-plant trends in capacity factor and heat rate for US power plants, 2001–2018. IOP SciNotes, Vol. 1, p. 024007.

G. A. Najla and D. Fitrianah, 2019. Penerapan metode regresi linear untuk prediksi penjualan properti pada PT XYZ. Jurnal Telematika, Vol. 14, pp. 79–85.

H. Henderi and R.L. Wanda, 2017. Preprocessing data untuk sistem peramalan tingkat kedisiplinan mahasiswa. Neliti, Innovative Creative and Information Technology, Vol. 3, pp. 296-308.

H. Pan and X. Xu, 2022. Research on Factors Affecting Boiler Feedwater Quality and Its Improvement. Open Journal of Applied Sciences, Vol. 12, pp. 901–911.

H. W. Herwanto, T. Widiyaningtyas, and P. Indriana, 2019. Penerapan algoritme linear regression untuk prediksi hasil panen tanaman padi. Jurnal Nasional Teknik Elektro and Teknologi Informasi, Vol. 8, pp. 364-370.

I. Veza, Irianto, H. Panchal, P.A. Paristiawan, M. Idris, I.M.R. Fattah, N.R. Putra, and R. Silambarasan, 2022. Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms. Results in Engineering, Vol. 16, p. 100688.

J. Cohen, Statistical power analysis for the behavioral sciences, Lawrence Erlbaum Associates Publishers, New York, 1988.

J. I. E. Hoffman, Variations based on linear regression. In: Biostatistics for Medical and Biomedical Practitioners, Elsevier, 2015.

L. J. Li, G. Y. Qiu, and C. H. Yan, 2022. Relationship between water use and energy generation from different power generation types in a megacity facing water shortages: A case study in Shenzhen. Water, Vol. 14, p. 14203226.

M. Karadas, H. M. Celik, U. Serpen, and M. Toksoy, 2015. Multiple regression analysis of performance parameters of a binary cycle geothermal power plant. Geothermics, Vol. 54, pp. 68–75.

M. R. Fahlevy, D. Mardiansah, and D. P. Jannus, 2019. Analisa performa PLTGU kapasitas 740 MW terhadap pola operasi and pembebanan menggunakan heat rate gap analysis. Proceedings Seminar Nasional Teknik Mesin Politeknik Negeri Jakarta, pp. 1199–1207.

M.S. Manzar, M. Benaafi, R. Costache, O. Alagha, N.D. Mu'azu, M. Zubair, J. Abdullahi, and S.I. Abba, 2022. New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia. Ecological Informatics, Vol. 70, p. 101696.

M. Syafruddin, L. Hakim, and D. Despa, 2014. Metode Regresi Linier untuk Prediksi KebutuhanEnergi Listrik Jangka Panjang (Studi Kasus Provinsi Lampung). Jurnal Informatika dan Teknik Elektro Terapan, Vol 2.

N. Set Foong, C. Yee Ming, C. Pei Eng, and N. Kok Shien, 2018. An insight of linear regression analysis. Scientific Research Journal, Vol . 15, pp. 1-16.

P. Breeze, Combined Cycle Power Plants. In: Gas-Turbine Power Generation, Elsevier, 2016.

R. F. Falk, 1992, A Primer for Soft Modeling, 1st ed. University of Akron Press, Ohio, 1992.

R. Muhammad and S. Yulianto, 2023. Penerapan pemrograman python dalam menentukan waktu overhoul kondensor turbin uap. Jurnal Konversi Energi dan Manufaktur, Vol. 8, pp. 49–57.

R. Melky, 2022. Pengelolaan Lingkungan Berkelanjutan PT Indonesia Power Priok POMU.

S. D. Permai and H. Tanty, Linear regression model using bayesian approach for energy performance of residential building. In: Procedia Computer Science, Elsevier, 2018.

S. W. Mohammed Ali, N. Vahedi, C. Romero, and A. Oztekin, 2020. An optimization for water requirement in natural gas combined cycle power plants equipped with once-through and hybrid cooling systems and carbon capture unit. Water- Energy Nexus, Vol. 3, pp. 117–134.

T. Indarwati, T. Irawati, and E. Rimawati, 2019. Penggunaan metode linear regression untuk prediksi penjualan smartphone. Jurnal Teknologi Informasi dan Komunikasi, Vol. 6, pp. 1-6.

Y.E.B. Mawartika and H.D. Kesuma, 2022. Implementasi simple linear regression untuk meramalkan perkembangan pelanggan PLN. Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya, Vol. 4, pp. 10-16.

Z. Wang, Water treatment equipment and system. In: Design of Solar Thermal Power Plants, Chemical Industry Press, 2019.



DOI: https://doi.org/10.22146/jmdt.97748

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