Institutional Repository of Coll Elect & Informat Engn
Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN | |
Osman, Yousuf Babiker M.1,2; Li, Wei1,2 | |
2020-05-29 | |
发表期刊 | Journal of Control Science and Engineering |
ISSN | 16875249 |
卷号 | 2020 |
摘要 | Real-time measurements of key effluent parameters play a highly crucial role in wastewater treatment. In this research work, we propose a soft sensor model based on deep learning which combines stacked autoencoders with neural network (SAE-NN). Firstly, based on experimental data, the secondary variables (easy-to-measure) which have a strong correlation with the biochemical oxygen demand (BOD5) are chosen as model inputs. Moreover, stochastic gradient descent (SGD) is used to train each layer of SAE to optimize weight parameters, while a strategy of genetic algorithms to identify the number of neurons in each hidden layer is developed. A soft sensor model is studied to predict the BOD5 in a wastewater treatment plant to evaluate the proposed approach. Interestingly, the experimental results show that the proposed SAE-NN-based soft sensor has a better performance in prediction than the current common methods. © 2020 Yousuf Babiker M. Osman and Wei Li. |
关键词 | Biochemical oxygen demand Deep learning Effluent treatment Effluents Genetic algorithms Gradient methods Learning systems Parameter estimation Reclamation Sewage treatment plants Stochastic systems Real time measurements Secondary variables Soft sensor models Stochastic gradient descent Strong correlation Wastewater treatment plants Wastewater treatment process Weight parameters |
DOI | 10.1155/2020/6347625 |
收录类别 | EI ; ESCI |
语种 | 英语 |
WOS研究方向 | Automation & Control Systems |
WOS类目 | Automation & Control Systems |
WOS记录号 | WOS:000540596400001 |
出版者 | Hindawi Limited, 410 Park Avenue, 15th Floor, 287 pmb, New York, NY 10022, United States |
EI入藏号 | 20202608870253 |
EI主题词 | Wastewater treatment |
EI分类号 | 452 Municipal and Industrial Wastes ; Waste Treatment and Disposal - 921.6 Numerical Methods - 961 Systems Science |
来源库 | Compendex |
分类代码 | 452 Municipal and Industrial Wastes; Waste Treatment and Disposal - 921.6 Numerical Methods - 961 Systems Science |
引用统计 | 无
|
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/115495 |
专题 | 电气工程与信息工程学院 |
通讯作者 | Li, Wei |
作者单位 | 1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China |
第一作者单位 | 电气工程与信息工程学院 |
通讯作者单位 | 电气工程与信息工程学院 |
第一作者的第一单位 | 电气工程与信息工程学院 |
推荐引用方式 GB/T 7714 | Osman, Yousuf Babiker M.,Li, Wei. Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN[J]. Journal of Control Science and Engineering,2020,2020. |
APA | Osman, Yousuf Babiker M.,&Li, Wei.(2020).Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN.Journal of Control Science and Engineering,2020. |
MLA | Osman, Yousuf Babiker M.,et al."Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN".Journal of Control Science and Engineering 2020(2020). |
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