SIA OpenIR  > 广州中国科学院沈阳自动化研究所分所
Alternative TitleA Multi-output adaptive soft-sensor modeling based on deep neural network
邱禹; 刘乙奇; 吴菁; 黄道平
Source Publication化工学报
Indexed ByCSCD
Contribution Rank2
Funding Organization国家自然科学基金资助项目(61673181,61533002) ; 广东省自然科学基金资助项目(2015A030313225) ; 广东省科技计划项目(2016A020221007)
Keyword污水 软测量 神经网络 多输出 预测 时差建模 Vip变量选择


Other Abstract

In the wastewater treatment process (WWTP), the existence of several important but hard-to-measure process variables hinders not only the monitoring of productive processes, but also the adjustment or optimization of process control strategies. Even though the soft-sensor models are reasonably constructed, which also suffer the degradation problem, resulting in high maintenance cost. Additionally, the selection of proper secondary variables affects the subsequent modeling directly. Therefore, a multi-output adaptive soft sensor model based on deep neural network is proposed, which used for simultaneous online prediction of multiple target variables in wastewater treatment. The deep neural network is constructed on the basis of a stacked auto-encoder, displaying satisfactory online prediction performance under extremely complex scenarios. In order to deal with the degradation problem and select proper secondary variables, a time difference modeling method and VIP (Variable importance in projection) method are assimilated in modeling. Finally, the proposed model is validated through a real WWTP case. Results show that the proposed soft-sensor model not only has the better multi-output prediction performance, but also has satisfactory results on single-target prediction.

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Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Corresponding Author邱禹
Recommended Citation
GB/T 7714
邱禹,刘乙奇,吴菁,等. 基于深层神经网络的多输出自适应软测量建模[J]. 化工学报,2018,69(7):3101-3113.
APA 邱禹,刘乙奇,吴菁,&黄道平.(2018).基于深层神经网络的多输出自适应软测量建模.化工学报,69(7),3101-3113.
MLA 邱禹,et al."基于深层神经网络的多输出自适应软测量建模".化工学报 69.7(2018):3101-3113.
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