Abstract
The energy efficiency of recovery boiler power plants is largely influenced by the heat transfer to the superheaters. In the design process of such very large-scale applications, one of the key challenges is the a priori geometry optimization by robust numerical approaches. The main objectives of this work are to demonstrate a numerical optimization framework and to optimize the geometry of the superheater region to enhance the heat transfer. The framework is implemented as a surrogate-based optimization method, which combines simulated annealing, local polynomial regression, and computational fluid dynamics. The novelty of this work consists of the following: 1) The optimization framework is designed and introduced. 2) The connection between the geometry and heat transfer is quantified by formulating the optimal design curve. 3) The optimal design for a typical, existing recovery boiler is identified. The results indicate that the uniformity of the flow field is improved, and the heat transfer rate is increased by 5%. 4) The results indicate the importance of minimizing the separation vortices through the geometrical design with a strong linkage to the overall heat transfer rate. 5) The potential of optimization methods in this very large-scale energy production application is demonstrated for the first time.
Original language | English |
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Pages (from-to) | 361-377 |
Number of pages | 17 |
Journal | Energy |
Volume | 160 |
DOIs | |
Publication status | Published - 1 Oct 2018 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Computational fluid dynamics
- Energy efficiency
- Heat transfer
- Optimization
- Recovery boiler
- Surrogate modeling