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All Journal Reaktor Journal of Engineering and Technological Sciences World Chemical Engineering Journal JURNAL INTEGRASI PROSES Teknika: Jurnal Sains dan Teknologi Prosiding Semnastek Jurnal Tirtayasa Ekonomika (Tirtayasa Economica Journal) Jurnal Teknika Jurnal Akta Journal Of Chemical Process Engineering (JCPE) Jurnal Chemurgy Jurnal Kartika Kimia ASEAN Journal of Chemical Engineering Narotama Jurnal Teknik Sipil Jurnal Teknik Kimia Indonesia Jurnal Ilmiah Wahana Pendidikan Automotive Experiences Jurnal Kajian Stratejik Ketahanan Nasional Journal Of Chemical Process Engineering ASEAN Journal for Science and Engineering in Materials
Anton Irawan
Universitas Sultan Ageng Tirtayasa, Indonesia
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Journal : ASEAN Journal for Science and Engineering in Materials

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Analysis of Bed Temperature on Circulated Fluidized Bed Boiler Using Simple Multivariable Regression Kurniawan, Asep; Kurniawan, Teguh; Alwan, Hafid; Bindar, Yazid; Irawan, Anton
ASEAN Journal for Science and Engineering in Materials Vol 3, No 2 (2024): AJSEM: Volume 3, Issue 2, September 2024
Publisher : Bumi Publikasi Nusantara

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Abstract

A Circulated Fluidized Bed (CFB) boiler is a type of steam boiler with more complex phenomena of fluidization and combustion occurring in the furnace. One of the operating problems is the temperature bed which is difficult to predict. Bed temperature prediction is important as a reference to know the combustion process and heat transfer along the furnace. The purpose of this study is multivariable data analysis to predict bed temperature based on historical data. The amount of historical data is then prepared for the dataset and passes through the stages of data cleansing, visualization, exploration, and engineering judgment. The parameters selected as control variables after going through the first principal analysis are 5 parameters, namely gross power, coal feed (X1), primary air (PA) flow (X2), secondary air (SA) flow (X3), and average bed temperature (y). The dataset is then divided based on the load into 2 groups a low load of 20.03-30.00 MW and a high load of 30.01-54.41 MW. Each parameter is converted to the natural logarithm (ln) then multivariable regression is performed. The result is a low load model equation  with Root Mean Square Error (RMSE) = 23.2813 and a high load model equation  with RMSE = 4.8416. This model can be used to predict the average bed temperature at certain input conditions of coal feed, PA flow, and SA flow according to operating load. Prospects for bed temperature prediction with this multivariable can be developed using data-based machine learning so that the operating patterns obtained are more accurate and real-time forecast prediction.
Co-Authors . Rahmayetty, . ACHMAD RIFAI Alwan, Hafid Alwan, Hafid Ari Susandy Sanjaya, Ari Susandy Asep Bayu Dani Nandiyanto Asep Kurniawan Bakar, Muhammad Saifullah Abu Bambang Prasetya Carolus Borromeus Rasrendra D. R. Barleany Darisman Darisman Darisman, Darisman Dermawan, Rizki Kurnia Dina Pujianti Endang Suhendi, Endang Fakhrul Wildan Fathoni, Rif'an Fitri Febriyanti Hafid Alwan Hafid Alwan Hanny Hafiar Hesti Prihastuti Hidayatullah, Andra Indar Kustingsih Jenny Rizkiana Kawiarso Kawiarso Kustiningsih, Indar Levina Mandalagiri Mandalagiri, Levina Misri Gozan Muhammad Saifullah Abu Bakar Nadia Kartika Dewi, Nadia Kartika Nadia Kartika Sari Naela Fadila Nisa Aina Fauziah Novan Prihasa Nurindah, Erlin Nurmalisa Nurmalisa Nuryoto Oki Muraza Pramudita, Marta Pujianti, Dina Rahmayetty Rahmayetty Rizki Kurnia Dermawan Rosid Rosid Runturambi, Arthur Josias Simon Santika Santika Septhian Marno Septian Arief Nur Rahman Setyawati Setyawati Setyawati Yani Sjaifuddin Sjaifuddin Sri Utami Sri Utami Sukirno Sukirno Suwandi, Ahmad Teguh Kurniawan Teguh Kurniawan Tubagus Riadz Yana Meliana Yani, Setyawati Yazid Bindar Yazid Bindar Yazid Bindar Yazid Bindar Yazid Bindar Yazid Bindar Yazid Bindar Yogi W. Budhi