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Interval multiple-output soft sensors development with capacity control for wastewater treatment applications: A comparative study
Xiao HJ(肖红军); Ba, Bingxin; Li, Xianxiang; Liu, Jian; Liu YQ(刘乙奇); Huang DP(黄道平)
Department广州中国科学院沈阳自动化研究所分所
Source PublicationCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
ISSN0169-7439
2019
Volume184Pages:82-93
Indexed BySCI
WOS IDWOS:000456903800008
Contribution Rank2
Funding OrganizationNational Natural Science Foundation of China ; Science and Technology Planning Project of Guangdong Province, China ; Technology Innovation Special Fund of Foshan, China ; Science and Technology Program of Guangzhou, China ; Fundamental Research Funds for the central Universities, SCUT
KeywordSoft-sensors Multi-output Capacity control Wastewater Uncertainty
AbstractSoft-sensor is the most common strategy to predict hard-to-measure variables in the wastewater treatment processes. However, existence of a large number of hard-to-measure variables always renders a generic single-output soft-sensor inadequate. This study developed multi-output soft-sensors using Multivariate Linear Regression model (MLR), Multivariate Relevant Vector Machine (MRVM) and Multivariate Gaussian Processes Regression (MGPR) models aiming to predict multiple hard-to-measure variables simultaneously and to capture the joint distribution of the response variables. This, in turn, ensures that the proposed soft-sensors are not just able to obtain prediction values, but also to indicate the credibility of information for hard-to-measure quantities. To further compromise the computational overhead of multi-output soft-sensors, improved Variable Importance in Projection (VIP) and Least Absolute Shrinkage and Selection Operator (Lasso) are proposed to reduce the dimensions of data, thereby alleviating the complexity of predicted models. The proposed methodologies were firstly demonstrated by applying the design algorithm to a wastewater plant (WWTP) simulated with the wellestablished model, BSM1, then extended to a full-scale WWTP with data collecting from the field. Results showed that the proposed strategy significantly improved the prediction performance.
Language英语
WOS SubjectAutomation & Control Systems ; Chemistry, Analytical ; Computer Science, Artificial Intelligence ; Instruments & Instrumentation ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS KeywordVARIABLE SELECTION ; PLS ; REGRESSION ; MODEL
WOS Research AreaAutomation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics
Funding ProjectNational Natural Science Foundation of China[61873096] ; National Natural Science Foundation of China[61673181] ; National Natural Science Foundation of China[61533002] ; National Natural Science Foundation of China[61803086] ; Science and Technology Planning Project of Guangdong Province, China[2016A020221007] ; Technology Innovation Special Fund of Foshan, China[2014AG10018] ; Science and Technology Program of Guangzhou, China[201804010256] ; Fundamental Research Funds for the central Universities, SCUT[2017MS053] ; National Natural Science Foundation of China[61873096] ; National Natural Science Foundation of China[61673181] ; National Natural Science Foundation of China[61533002] ; National Natural Science Foundation of China[61803086] ; Science and Technology Planning Project of Guangdong Province, China[2016A020221007] ; Technology Innovation Special Fund of Foshan, China[2014AG10018] ; Science and Technology Program of Guangzhou, China[201804010256] ; Fundamental Research Funds for the central Universities, SCUT[2017MS053]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24136
Collection广州中国科学院沈阳自动化研究所分所
Corresponding AuthorLiu YQ(刘乙奇)
Affiliation1.School of Automtion, Foshan University, Jiangwan Road, Fo Shan, 528000, China
2.ShenYang Institute of of Automation, GuangZhou, Chinese Academy of Sciences, Hai Bin Road, Guang Zhou, 511458, China
3.School of Automation Science & Engineering, South China University of Technology, Wushan Road, Guang Zhou, 510640, Chinales R China
Recommended Citation
GB/T 7714
Xiao HJ,Ba, Bingxin,Li, Xianxiang,et al. Interval multiple-output soft sensors development with capacity control for wastewater treatment applications: A comparative study[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,2019,184:82-93.
APA Xiao HJ,Ba, Bingxin,Li, Xianxiang,Liu, Jian,Liu YQ,&Huang DP.(2019).Interval multiple-output soft sensors development with capacity control for wastewater treatment applications: A comparative study.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,184,82-93.
MLA Xiao HJ,et al."Interval multiple-output soft sensors development with capacity control for wastewater treatment applications: A comparative study".CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 184(2019):82-93.
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