Advanced combustion balance analysis in circulating fluidized bed boilers: A machine learning approach with integrated key performance indicators

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Journal Title

Journal ISSN

Volume Title

Kemian tekniikan korkeakoulu | Master's thesis

Authors

Date

2024-06-10

Department

Major/Subject

Industrial Energy Solutions and Sustainability

Mcode

CHEM3044

Degree programme

Master’s Programme in Advanced Energy solutions (AAE)

Language

en

Pages

71+4

Series

Abstract

This thesis investigates the use of machine learning (ML) techniques for analyzing Circulating Fluidized Bed (CFB) boiler data using Key Performance Indicators (KPIs). To gain insights into the operational dynamics and combustion characteristics of CFB boilers, various methodologies are used, including Cluster Analysis, Self-Organizing Map (SOM) Analysis, Random Forest Method, and Support Vector Machine (SVM) Method. Significant findings about data distribution, relationships between KPIs and underlying data structures, and classification algorithm performance are discovered through analysis of selected KPIs. The Random Forest Method performed exceptionally well, as evidenced by the results, which show 100% accuracy in classifying instances in all cases. The SVM Method with the Radial Basis Function (RBF) kernel, on the other hand, stands out as the best option because of its ability to reliably capture intricate patterns and adapt well to new data. A useful tool for evaluating the combustion balance inside the boiler system is the Combustion Index (CI) metric, was developed. Moreover, validation using new data confirms the reliability of the SVM model, showcasing its ability to accurately predict previously unseen industrial CFB boiler data. Overall, this thesis advances ML methods for the analysis of CFB boiler data, which has implications for improving sustainability and operational effectiveness in industrial settings.

Description

Supervisor

Järvinen, Mika

Thesis advisor

Liukkonen, Mika

Keywords

machine learning, circulating fluidized bed boiler, classification, cluster analysis, combustion balance, key performance indicators

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202406234857

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[dipl] Kemian tekniikan korkeakoulu / CHEM