[1]黄山,蒋鹭,王天才,等.神经网络与遗传算法结合的球团竖炉燃烧优化[J].东南大学学报(自然科学版),2012,42(1):88-93.[doi:10.3969/j.issn.1001-0505.2012.01.017]
 Huang Shan,Jiang Lu,Wang Tiancai,et al.Optimization of combustion for pellet shaft furnace based on artificial neural network and genetic algorithm[J].Journal of Southeast University (Natural Science Edition),2012,42(1):88-93.[doi:10.3969/j.issn.1001-0505.2012.01.017]
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神经网络与遗传算法结合的球团竖炉燃烧优化()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
42
期数:
2012年第1期
页码:
88-93
栏目:
能源与动力工程
出版日期:
2012-01-18

文章信息/Info

Title:
Optimization of combustion for pellet shaft furnace based on artificial neural network and genetic algorithm
作者:
黄山1蒋鹭1王天才2刘飞2钟文琪1金保昇1张智2冯上进2
(1东南大学能源与环境学院,南京 210096)(2南京南钢产业发展有限公司,南京 210035)
Author(s):
Huang Shan1Jiang Lu1Wang Tiancai2Liu Fei2Zhong Wenqi1Jin Baosheng1Zhang Zhi2Feng Shangjin2
(1 School of Energy and Environment,Southeast University, Nanjing 210096, China)
(2Nanjing Nangang Industrial Development Co. Ltd, Nanjing 210035, China)
关键词:
竖炉神经网络能耗NOx污染物排放遗传算法
Keywords:
shaft furnace neural network gas consumption NOx emission genetic algorithm
分类号:
TK16
DOI:
10.3969/j.issn.1001-0505.2012.01.017
摘要:
对神经网络与遗传算法结合的球团竖炉燃烧优化方法进行了研究.首先构建了以矿料成分及含水率、相关操作参数以及燃烧室和炉膛温度等16个参数作为输入量,球团竖炉煤气吨耗和NOx污染物排放浓度作为输出量的人工神经网络模型.采用700组现场运行数据作为样本对神经网络进行训练,训练后的模型具有良好的泛化能力和预测精度,煤气吨耗预测误差低于3%且NOx排放浓度的相对误差在5%以内.此外,结合所建模型,采用实数编码的遗传算法,对球团竖炉燃烧进行优化计算,在寻优过程中对煤气吨耗及NOx排放这2个优化分量采用线性加权和的方法转化为单一数值的目标函数.通过选择不同的权重比例得出不同侧重条件下的优化目标函数,并给出该优化函数下寻优所得的操作参量优化控制方案.由所选优化方案数值解可以看出在煤气吨耗上升1.7%的情况下,NOx的排放浓度下降了20.37%.
Abstract:
Combined the neural network with genetic algorithms, a model for a shaft furnace which has tons of gas consumption and NOx emission is built. There are sixteen input parameters in this model, containing mineral aggregate components, moisture content, furnace temperature and so on. Output parameters are the gas consumption and the concentration of NOx emission. Based on the 700 groups of field data, the neural network has been trained. The results show that the prediction error of the gas consumption is less than 3% and the prediction error of NOx emission is less than 5%. Base on this model, real-coded genetic algorithm is applied to linear weight low gas consumption and low NOx emission and switch the model into a function with single variable parameter. Multiple objective functions and operating parameters focusing on different conditions can be discovered under different wight ratios. According to the optimization, the result shows that NOx emission decreases by 20. 37% while gas consumption increases by 1. 7%.

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备注/Memo

备注/Memo:
作者简介:黄山(1986—),男,硕士生;钟文琪(联系人),男,博士,教授,博士生导师,wqzhong@seu.edu.cn.
引文格式: 黄山,蒋鹭,王天才,等.神经网络与遗传算法结合的球团竖炉燃烧优化[J].东南大学学报:自然科学版,2012,42(1):88-93.[doi:10.3969/j.issn.1001-0505.2012.01.017]
更新日期/Last Update: 2012-01-20