Data-driven multi-objective optimisation of coal-fired boiler combustion systems
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1821
- Type
- D - Journal article
- DOI
-
10.1016/j.apenergy.2018.07.101
- Title of journal
- Applied Energy
- Article number
- -
- First page
- 446
- Volume
- 299
- Issue
- -
- ISSN
- 0306-2619
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Emissions of coal-fire stations need to be reduced whilst green technology alternatives are developed. This work performs multi-objective optimisation under uncertainty, exploiting a Gaussian process surrogate model, to find optimal nitrogen oxides and unburned-coal content trade-off. It uses a novel solution selection method based on maximum probability of dominance. Methods are demonstrated on practical data collected from Jianbi power plant, China. As part of our work on expensive data-driven multi-objective optimisation, this has led on to grant awards such as the Innovate UK/EPSRC KTP with Hydro International Ltd, KTP11477 (£180,584), on CFD-based product design optimisation (Daniel Jarman, djarman@hydro-int.com).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -