| 基于改进差分极端学习机的燃煤锅炉NOx预测 |
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| 引用本文： | 李庆伟,申志文. 基于改进差分极端学习机的燃煤锅炉NOx预测[J]. 热科学与技术, 2022, 21(1): 98-104 |
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| 作者姓名： | 李庆伟 申志文 |
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| 作者单位： | 上海电力大学能源与机械工程学院,上海201306 |
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| 基金项目： | 国家重点基础研究发展计划（973计划）;上海市青年科技英才扬帆计划资助 |
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| 摘 要： | 低NOx燃烧优化是一种简单、高效、廉价的燃煤电站NOx减排方法.建立NOx预测模型是该方法的重要组成部分.极端学习机(extreme learning machine,ELM)是一种简单有效的建模方法,但随机生成的输入权值和隐层阈值会影响ELM的泛化性能和逼近能力.针对该问题,利用一种改进的差分进化算法(improve...
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| 关 键 词： | 差分进化算法 极端学习机 NOx排放 预测模型 |
| 收稿时间： | 2020-01-28 |
| 修稿时间： | 2020-11-07 |
Prediction of NOx emission of coal-fired boilers based on improved differential evolution algorithm and extreme Llearning machine |
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| LI Qing-wei and. Prediction of NOx emission of coal-fired boilers based on improved differential evolution algorithm and extreme Llearning machine[J]. Journal of Thermal Science and Technology, 2022, 21(1): 98-104 |
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| Authors: | LI Qing-wei and |
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| Affiliation: | (College of Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 201306,China) |
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| Abstract: | Low NOx combustion optimization is a simple, efficient and inexpensive NOx emission reduction technology for coal-fired power plants. Establishing NOx Emission prediction model is an important part of low NOx combustion optimization. Extreme learning machine (ELM) is a simple and effective modeling method. However, randomly generated input weights and hidden layer thresholds would affect the performance of ELM. To solve this problem, this paper employed an improved differential evolution (IDE) to optimize the input weights and hidden layer thresholds. The proposed model was applied to some 330MW boiler and compared with the standard ELM and DE-ELM. Each model was repeated 101 runs independently. Results show that IDE-ELM improves the generalization ability and stability of ELM. |
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| Keywords: | differential evolution extreme learning machine NOx Emission prediction model |
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