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It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.<\/jats:p>","DOI":"10.1017\/s026357472200145x","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T06:31:25Z","timestamp":1666593085000},"page":"1087-1097","source":"Crossref","is-referenced-by-count":13,"title":["An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics"],"prefix":"10.1017","volume":"41","author":[{"given":"Jing","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1724-117X","authenticated-orcid":false,"given":"Hao","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunjie","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caitong","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"key":"S026357472200145X_ref25","doi-asserted-by":"crossref","unstructured":"[25] Liang, J. , Guo, H. , Yu, K. 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