Abstract

Dynamic operation of dispatchable energy sources is crucial for enabling the efficient integration of intermittent renewable energy into the electricity grid. Coal-fired boilers have been required to increase transient operation as renewable energy expands in order to avoid excess renewable energy going to waste. The frequent transient operation required to meet residual energy demand has created a challenge for coal-fired units to operate efficiently. This work utilizes an Advanced Sensor Network (ASN) to calculate Net Unit Heat Rate (NUHR) of a coal-fired boiler in real time through combustion calculations and statistical correlations to provide the tools for optimizing dynamic operation. Real-time heating values that were necessary to determine fuel input energy to calculate accurate NUHR were found using both fundamental and data-driven methods. Real-time NUHR shows distinct shifts that reflect changes in process conditions that improve the ability to optimize transient operation. Data-driven heating value correlations had 24% lower Root Mean Square Error (RMSE) than the fundamental combustion calculation approach when compared to daily retrospective proximate analysis. The data-driven method RMSE improved by 7% with the inclusion of ASN data. The performance of the boiler was statistically compared before and after the inclusion of real-time NUHR. Models were fit to the NUHR results as a function of generation level and confidence bands for the model were used to determine statistical significance in the change in boiler performance. Student's t-tests were also used to compare data at common generation levels. Improvements in NUHR ranging between 0.4% and 1.3% were observed over the typical range of generation levels experienced by the plant. These improvements are estimated to result in yearly savings of about $200,000. The most significant increase in NUHR was at high loads where the plant spends less time at steady operation and was often transient. Overall, the real-time NUHR has enabled dynamic optimization to better control transient operation of the coal-fired boiler.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Chemical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2023-12-19

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd13471

Keywords

Coal-fired Boiler, Dynamic Optimization, Data-driven Models, Renewable Integration

Language

english

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Engineering Commons

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