A new online optimization method for boiler combustion system based on the data-driven technique and the case-based reasoning principle
To adapt to the time-variability of boiler combustion systems, a new online combustion optimization method for boiler is proposed in this paper. The massive historical combustion data are preprocessed, and then an improved constrained fuzzy weighted rule is employed to extract combustion rules from...
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| Published in: | Energy (Oxford) Vol. 263; p. 125508 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
15.01.2023
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| ISSN: | 0360-5442 |
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| Abstract | To adapt to the time-variability of boiler combustion systems, a new online combustion optimization method for boiler is proposed in this paper. The massive historical combustion data are preprocessed, and then an improved constrained fuzzy weighted rule is employed to extract combustion rules from historical combustion data. After that, an improved particle swarm optimization-based least square support vector machine is adopted to construct the dynamic mathematic model for boiler efficiency and NOx emission, respectively, and an improved multi-objective particle swarm optimization algorithm based on the well-construction dynamic mathematical model is proposed and applied to excavate deeply the combustion rules of boiler, and the optimization case library is constructed by integrating all combustion rules. At last similarity measure-based case-based reasoning method is employed to rapidly identify the well-performance similar cases from the optimization case library, which is helpful to complete the online combustion optimization. The effectiveness of proposed online optimization method for boiler is proved by applying it to an actual combustion process. The results showed that proposed online optimization method could take less time to gain a set of excellent operating solution, the NOx emission reduced by 9.236% on average and the boiler efficiency increased by 0.046% on average. Therefore, the proposed online combustion optimization method for boiler has the ability to realize the online combustion optimization of boiler.
•Bullet Points:•ICFWRE method is proposed to extract combustion rules.•IPSO-LSSVM is adopted to construct the dynamic mathematic model for boiler.•IMOPSO is applied to further extract combustion rules.•Clustering algorithm-based CBR is applied to realize online combustion optimization of boiler. |
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| AbstractList | To adapt to the time-variability of boiler combustion systems, a new online combustion optimization method for boiler is proposed in this paper. The massive historical combustion data are preprocessed, and then an improved constrained fuzzy weighted rule is employed to extract combustion rules from historical combustion data. After that, an improved particle swarm optimization-based least square support vector machine is adopted to construct the dynamic mathematic model for boiler efficiency and NOx emission, respectively, and an improved multi-objective particle swarm optimization algorithm based on the well-construction dynamic mathematical model is proposed and applied to excavate deeply the combustion rules of boiler, and the optimization case library is constructed by integrating all combustion rules. At last similarity measure-based case-based reasoning method is employed to rapidly identify the well-performance similar cases from the optimization case library, which is helpful to complete the online combustion optimization. The effectiveness of proposed online optimization method for boiler is proved by applying it to an actual combustion process. The results showed that proposed online optimization method could take less time to gain a set of excellent operating solution, the NOx emission reduced by 9.236% on average and the boiler efficiency increased by 0.046% on average. Therefore, the proposed online combustion optimization method for boiler has the ability to realize the online combustion optimization of boiler. To adapt to the time-variability of boiler combustion systems, a new online combustion optimization method for boiler is proposed in this paper. The massive historical combustion data are preprocessed, and then an improved constrained fuzzy weighted rule is employed to extract combustion rules from historical combustion data. After that, an improved particle swarm optimization-based least square support vector machine is adopted to construct the dynamic mathematic model for boiler efficiency and NOx emission, respectively, and an improved multi-objective particle swarm optimization algorithm based on the well-construction dynamic mathematical model is proposed and applied to excavate deeply the combustion rules of boiler, and the optimization case library is constructed by integrating all combustion rules. At last similarity measure-based case-based reasoning method is employed to rapidly identify the well-performance similar cases from the optimization case library, which is helpful to complete the online combustion optimization. The effectiveness of proposed online optimization method for boiler is proved by applying it to an actual combustion process. The results showed that proposed online optimization method could take less time to gain a set of excellent operating solution, the NOx emission reduced by 9.236% on average and the boiler efficiency increased by 0.046% on average. Therefore, the proposed online combustion optimization method for boiler has the ability to realize the online combustion optimization of boiler. •Bullet Points:•ICFWRE method is proposed to extract combustion rules.•IPSO-LSSVM is adopted to construct the dynamic mathematic model for boiler.•IMOPSO is applied to further extract combustion rules.•Clustering algorithm-based CBR is applied to realize online combustion optimization of boiler. |
| ArticleNumber | 125508 |
| Author | Liu, Yuqing Chen, Yuzhu Yu, Mengzhu Zou, Yiran Xu, Wentao Song, Siheng Cao, Gehan Chen, Bo Huang, Yaji Zhang, Rongchu |
| Author_xml | – sequence: 1 givenname: Wentao surname: Xu fullname: Xu, Wentao organization: Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education, Southeast University, Nanjing, 210096, China – sequence: 2 givenname: Yaji surname: Huang fullname: Huang, Yaji email: heyyj@seu.edu.cn organization: Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education, Southeast University, Nanjing, 210096, China – sequence: 3 givenname: Siheng surname: Song fullname: Song, Siheng organization: State Grid Liaoning Electric Power Co., Ltd. Dalian Power Supply Company, Dalian, 116001, China – sequence: 4 givenname: Yuzhu surname: Chen fullname: Chen, Yuzhu organization: Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education, Southeast University, Nanjing, 210096, China – sequence: 5 givenname: Gehan surname: Cao fullname: Cao, Gehan organization: Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education, Southeast University, Nanjing, 210096, China – sequence: 6 givenname: Mengzhu surname: Yu fullname: Yu, Mengzhu organization: Key Laboratory of Energy Thermal Conversion and Process Measurement and Control of Ministry of Education, Southeast University, Nanjing, 210096, China – sequence: 7 givenname: Bo surname: Chen fullname: Chen, Bo organization: JiangSu Frontier Electric Technology Co., Nanjing 211102, China – sequence: 8 givenname: Rongchu surname: Zhang fullname: Zhang, Rongchu organization: Nanjing Changrong Acoustics Co., Ltd, Nanjing, 210008, China – sequence: 9 givenname: Yuqing surname: Liu fullname: Liu, Yuqing organization: Nanjing Changrong Acoustics Co., Ltd, Nanjing, 210008, China – sequence: 10 givenname: Yiran surname: Zou fullname: Zou, Yiran organization: Nanjing Changrong Acoustics Co., Ltd, Nanjing, 210008, China |
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| Cites_doi | 10.3233/AIC-1994-7104 10.1109/TII.2006.873598 10.1016/j.energy.2013.02.062 10.1016/j.combustflame.2012.07.013 10.1016/j.fuel.2021.122352 10.1016/j.mlwa.2021.100082 10.1016/j.fuel.2009.04.023 10.1016/j.energy.2021.119859 10.1109/TEC.2007.914183 10.1002/er.1070 10.1109/TEVC.2004.826067 10.1016/j.knosys.2011.08.002 10.1016/j.chemolab.2015.04.006 10.1016/j.ijepes.2014.04.036 10.1016/j.isatra.2020.03.024 10.1016/j.fuel.2015.12.065 10.1016/j.jss.2005.03.005 10.1109/TII.2006.890530 10.1007/BF00155578 10.1016/j.jprocont.2011.06.001 |
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| Keywords | Improved multi-objective particle swarm optimization algorithm Online combustion optimization of boiler Improved constrained fuzzy weighted rule Similarity measure -based case-based reasoning |
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| SubjectTerms | combustion energy Improved constrained fuzzy weighted rule Improved multi-objective particle swarm optimization algorithm mathematical models Online combustion optimization of boiler Similarity measure -based case-based reasoning support vector machines system optimization |
| Title | A new online optimization method for boiler combustion system based on the data-driven technique and the case-based reasoning principle |
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