东南大学学报（自然科学版）2012，Vol.42Issue(1)：88-93,6.DOI:10.3969/j.issn.1001-0505.2012.01.017
神经网络与遗传算法结合的球团竖炉燃烧优化
Optimization of combustion for pellet shaft furnace based on artificial neural network and genetic algorithm
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
作者信息
- 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
- 折叠
摘要
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%.关键词
竖炉/神经网络/能耗/NOx污染物排放/遗传算法Key words
shaft furnace/ neural network/ gas consumption/ NOx emission/ genetic algorithm分类
能源科技引用本文复制引用
黄山,蒋鹭,王天才,刘飞,钟文琪,金保昇,张智,冯上进..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):88-93,6.
