一种混合软传感器模型,用于测量炉烟气中的氧含量
Yonggang Wang1, Zhida Li1, Nannan Zhang1
1College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
在燃煤发电厂中精确的烟气氧气测量对于效率和环境保护至关重要. 结合鱼优化算法 (WOA) 和长短期记忆 (LSTM) 网络的新混合模型显著提高了预测准确性.
科学领域:
- 燃烧工程 燃烧工程
- 人工智能在能源中的作用
- 环境监测 环境监测
背景情况:
- 燃煤发电厂的炉对于蒸汽产生至关重要,烟气中的氧含量表明燃烧效率.
- 不准确的氧气测量会影响热效率,能源利用和环境排放.
- 精确的烟气氧气监测对于优化发电厂运营和减少污染至关重要.
研究的目的:
- 开发一个准确的预测模型,用于燃煤发电厂的烟气氧含量.
- 通过改进氧气监测来提高能源利用效率和减少排放.
- 用现实世界发电厂数据验证拟议模型的实用性和有效性.
主要方法:
- 提出了一个混合预测模型,将鱼优化算法 (WOA) 与长期短期记忆 (LSTM) 网络集成在一起.
- 利用WOA优化关键的LSTM参数:学习率,隐藏层数量和规则化系数.
- 通过使用350兆瓦燃煤发电厂单元的数据验证了WOA-LSTM模型.
主要成果:
- 该WOA-LSTM模型实现了高精度,MAE为0.16493,RMSE为0.12712,MAPE为2.2254%,R2为0.98664. 该模型实现了高精度,MAE为0.16493,RMSE为0.12712,MAPE为2.2254%,R2为0.98664.
- 与LSSVM,LSTM,PSO-LSSVM和PSO-LSTM模型相比,表现出卓越的性能.
- 与这些基准模型相比,分别实现了4.93%,4.03%,1.35%和0.49%的精度改进.
结论:
- 拟议的WOA-LSTM软传感器模型为烟气氧气预测提供了显著提高的准确性.
- 该模型的性能符合燃煤发电厂的实际要求,以优化运营.
- 这种方法为提高能源效率和减少发电对环境的影响提供了可行的解决方案.
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