化工学报2019，Vol.70Issue(z2)：301-310,10.DOI:10.11949/0438-1157.20190037
基于多模型智能组合算法的锅炉炉膛温度建模
Furnace temperature modeling based on multi-model intelligent combination algorithm
摘要
Abstract
Furnace temperature is an important parameter which can reflect boiler combustion status. However, furnace temperature is affected by many parameters and the mechanism is complicated. Therefore, it is difficult to establish accurate prediction model. To solve this problem, a multi-model intelligent combination algorithm (MICA) is proposed to construct an accurate prediction model. First, the actual production data is pre-processed by wavelet denoising algorithm, and the model input variables were selected based on classification and regression trees algorithm and mechanism analysis. Then, several furnace temperature prediction models are constructed by many data-driven algorithms. Finally, a C4.5 algorithm is applied to combine these models into a multi-model intelligent combination model. The experimental results illustrate that the proposed algorithm can construct an accurate furnace temperature prediction model through actual operating data.关键词
数据驱动/炉膛温度/小波/智能组合/算法/模型/预测Key words
data-driven/ furnace temperature/ wavelet/ intelligent combination/ algorithm/ model/ prediction分类
能源科技引用本文复制引用
唐振浩,张宝凯,曹生现,王恭,赵波..基于多模型智能组合算法的锅炉炉膛温度建模[J].化工学报,2019,70(z2):301-310,10.基金项目
国家自然科学基金项目(61503072,51606035) （61503072,51606035）
国家重点研发计划项目(2018YFB1500803) （2018YFB1500803）
吉林省科技厅自然科学基金项目(20190201095JC,20190201098JC) （20190201095JC,20190201098JC）
