首页|城市固废焚烧过程炉温与烟气含氧量多目标鲁棒预测模型

城市固废焚烧过程炉温与烟气含氧量多目标鲁棒预测模型

扫码查看
原文链接
  • 万方数据
  • 维普
为实现城市固废焚烧(Municipal solid waste incineration,MSWI)过程炉温与烟气含氧量的准确预测,提出一种基于改进随机配置网络的多目标鲁棒建模方法(Multi-target robust modeling method based on improved stochastic configuration network,MRI-SCN).首先,设计了一种并行方式增量构建SCN隐含层,通过信息叠加与跨越连接来增强隐含层映射多样性,并利用参数自适应变化的监督不等式分配隐含层参数;其次,使用F范数与L2,1范数正则项建立矩阵弹性网对模型参数进行稀疏约束,以建模炉温与烟气含氧量间的相关性;接着,采用混合拉普拉斯分布作为每个目标建模误差的先验分布,通过最大后验估计重新评估SCN模型的输出权值,以增强其鲁棒性;最后,利用城市固废焚烧过程的历史数据对所提建模方法的性能进行测试.实验结果表明,所提建模方法在预测精度与鲁棒性方面具有优势.
Multi-target Robust Prediction Model for Furnace Temperature and Flue Gas Oxygen Content in Municipal Solid Waste Incineration Process
To achieve accurate prediction of furnace temperature and flue gas oxygen content in municipal solid waste incineration(MSWI)process,a multi-target robust modeling method based on improved stochastic configura-tion network(MRI-SCN)is proposed.First,a parallel method is designed to incrementally build SCN hidden layers,which enhances the diversity of hidden layer mapping through information superposition and spanning connection,and assign hidden layer parameters using the supervised inequality with adaptive parameter changes.Second,a matrix elastic net is established by using F-norm and L2,1-norm regularization terms to sparsely constrain the model parameters to model the correlation between furnace temperature and flue gas oxygen content.Then,the mixture Laplace distribution is used as the prior distribution of each target modeling error,and the output weights of the SCN model are re-evaluated by maximum a posteriori estimation to enhance its robustness.Finally,the per-formance of the proposed modeling method is tested on the historical data of municipal solid waste incineration pro-cess.The experimental results show that the proposed modeling method has advantages in prediction accuracy and robustness.

Municipal solid waste incineration(MSWI)furnace temperatureflue gas oxygen contentstochastic configuration network(SCN)hidden layer parallel constructionmulti-target robust modeling

胡开成、严爱军、汤健

展开 >

北京工业大学信息学部 北京 100124

数字社区教育部工程研究中心 北京 100124

城市轨道交通北京实验室 北京 100124

城市固废焚烧 炉温 烟气含氧量 随机配置网络 隐含层并行构造 多目标鲁棒建模

国家自然科学基金国家自然科学基金北京市自然科学基金

62373017620730064212032

2024

10.16383/j.aas.c230430

自动化学报
中国自动化学会 中国科学院自动化研究所

自动化学报

CSTPCD北大核心
影响因子：1.762
ISSN：0254-4156
年,卷(期)：2024.50(5)
  • 4