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All Journal JOURNAL OF MECHANICAL ENGINEERING, MANUFACTURES, MATERIALS AND ENERGY ASEAN Journal for Science and Engineering in Materials
Alwan, Hafid
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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The Influence of Operational Conditions on the Cold Test of CFB Boiler for Fluidization Process Using Air Cap Bell-Type Mauk, Syifa Ainun; Pajri, Oppy Oktaviani; Ashfani, Khansa; Alwan, Hafid
JOURNAL OF MECHANICAL ENGINEERING MANUFACTURES MATERIALS AND ENERGY Vol. 9 No. 1 (2025): June 2025 Edition
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jmemme.v9i1.10626

Abstract

Fluidization is a method of contacting solid particles with a fluid so that they exhibit fluid-like properties. One industrial application of fluidization is the fluidization process in power plants using Circulating Fluidized Bed (CFB) boilers where air is blown from the bottom as coal is burned to keep it suspended above the furnace. The variations used were air flow rates of 32,09; 62,72; 91,89; 123,98 and 154,61 m3/hour, bed material heights of 20; 30 and 40 cm, and types of bed material of coal and silica sand. This research aims to investigate the effects of changes in primary air flow, bed height, and type of bed material in the bed on fluidization behavior in CFB using an air cap bell-type through cold testing for easier observation. The results of this research show that at an air flow rate of 154,61 m3/hour and a bed material height of 40 cm, the highest pressure drop was obtained. For silica sand and coal, the pressure drops were 3,51 and 1,11 kPa, respectively. For the silica sand bed material, various fluidization phenomena were observed, while for coal, only the fixed bed phenomenon was observed.
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 Anton Irawan Asep Bayu Dani Nandiyanto Asep Kurniawan Ashfani, Khansa Bakar, Muhammad Saifullah Abu Darisman, Darisman Mauk, Syifa Ainun Pajri, Oppy Oktaviani Pujianti, Dina Teguh Kurniawan Yazid Bindar