Multi-objective combustion optimization based on data-driven hybrid strategy

Wei Zheng, Chao Wang, Yajun Yang and Yongfei Zhang

Energy, 2020, vol. 191, issue C

Abstract: In order to reduce pollutant discharge and improve boiler efficiency, data-driven hybrid strategy is proposed to solve the problem of multi-objective combustion optimization. First, massive historical operation data of a coal-fired power station are preprocessed (including resampling, steady-state detection, data cleaning and cluster analysis), so as to divide the whole boiler working condition into different partitions. Next, combustion association rules based multi-objective optimal strategy is applied to extract a combustion optimal rule from every partition, and a combustion optimal rule-base is built up by merging all the rules, so that the preliminary combustion optimization can be quickly finished based on the combustion optimal rule-base. Meanwhile, combustion mathematical model based multi-objective optimal strategy is applied to develop the LSSVR (least square support vector regression) model of boiler combustion process for every partition, and a combustion optimal model-base, which contains all the LSSVR models, is built up. After that, an improved multi-objective particle swarm optimization algorithm is presented to calculate Pareto optimal solutions depend on the corresponding LSSVR model with the constraint of real-time boiler working condition. To achieve further combustion optimization, the method of multiple attribute decision making is used to determine the unique solution from all the Pareto optimal solutions. Data-driven hybrid strategy is to combine the above two strategies. Simulation experiments verified the validity and feasibility of data-driven hybrid strategy on multi-objective test function ZDT1. Taking advantage of data-driven hybrid strategy, the comprehensive average of NOx emissions dropped by 29.63% and the comprehensive average of boiler efficiency increased by 0.69% in the application experiments with historical operation data under some boiler working conditions. Data-driven hybrid strategy based multi-objective combustion optimization makes the integration of instantaneity and effectiveness, so that it is suitable for online application.

Keywords: Data-driven; Multi-objective combustion optimization; Association rules; Least square support vector regression; Particle swarm optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321735

DOI: 10.1016/j.energy.2019.116478

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