Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions

作     者：Grochowalski, Jaroslaw Jachymek, Piotr Andrzejczyk, Marek Klajny, Marcin Widuch, Agata Morkisz, Pawel Hernik, Bartlomiej Zdeb, Janusz Adamczyk, Wojciech 

作者机构：Tauron Wytwarzanie SA Promienna 51 PL-43603 Jaworzno Poland Silesian Tech Univ Fac Energy & Environm Engn Dept Thermal Engn Konarskiego 22 PL-44100 Gliwice Poland Silesian Tech Univ Fac Energy & Environm Engn Dept Power Engn & Turbomachinery Konarskiego 18 PL-44100 Gliwice Poland AGH Univ Sci & Technol Fac Appl Math Al Mickiewicza 30 PL-30059 Krakow Poland Sumitomo SHI FW Ul Konstantynow PL-41200 Sosnowiec Poland 

出 版 物：《ENERGY》 (能)

年 卷 期：2021年第237卷

页      面：121538-121538页

核心收录：

学科分类：0820[工学-石油与天然气工程] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：National Center for Research and Development [POIR.01.01.01-00-1253/19-0 0] National Science Center within the OPUS scheme [2018/31/B/ST8/02201] 

主　　题：Prediction Machine learning CFB Erosion Boiler malfunction Deep learning algorithms 

摘      要：The availability of the power unit for electricity production is one of the most important issues of the power plant operator. Considering the power unit, boiler is the main source of its malfunction. Depending of the boiler construction, source of problem can be different. In case of usage of the circulating fluidized bed boiler the potential issue that can lead to interruption of the energy production is its failure caused by leakages of heating surfaces. In order to mitigate this risk different treatments or procedures are introduced. Nowadays, thanks to continuous development of mathematical tools its is possible to introduce a new solution to reduce the risk of heating surface erosion cause by the friction of solid material used in fluidization. One of possible option, that can help to resolve such a problem is application of machine learning technique. Based on real observation of the boiler operation and data analysis, it is believed that the uniform temperature distribution at the lower part of the combustion chamber should has positive impact on erosion reduction at the kick-out level where tapered walls changed to vertical one. This can be attain by careful manipulation of selected boiler operating parameters. Due to the reason that in order to find requires setup dozen of input data configuration need to be considered appropriate toll need to be developed. That is the main reason way the machine learning technique need to be applied for such purpose. Indeed, of this work is to develop artificial models that can help in adjustment boiler setup. In order to check models functionality, they were on-site tested by boiler operator. Developed model shows tremendous potential and confirm that it is worth to investigated this topic farther. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).