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Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater

IOP conference series. Materials Science and Engineering, 2019-04, Vol.490 (6), p.62027 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1757-8981 ;ISSN: 1757-899X ;EISSN: 1757-899X ;DOI: 10.1088/1757-899X/490/6/062027

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  • Title:
    Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater
  • Author: Xing, Yajuan ; Cheng, Zhong ; Shan, Shengdao
  • Subjects: Anaerobic digestion ; Chemical oxygen demand ; Deviation ; Dynamic stability ; Effluents ; Industrial development ; Iterative methods ; Model testing ; Optimal control ; Optimization ; Paper mills ; Papermaking ; Parameters ; Pollutants ; Principal components analysis ; Process variables ; Regression models ; Regularization ; Resource utilization ; Support vector machines ; Swarm intelligence ; Wastewater
  • Is Part Of: IOP conference series. Materials Science and Engineering, 2019-04, Vol.490 (6), p.62027
  • Description: With the rapid development of paper industry, the pressure of environmental pollution is going more and more serious. Recently, resource utilization of wastewater by anaerobic digestion has become a feasible way to solve this problem. In order to maintain the safe and efficient production of the process, a novel adaptive soft sensor model was developed to infer the chemical oxygen demand (COD) of paper mill effluent in this paper. First, the principal component analysis technique was performed in this model so as to eliminate the col-linearity between the process variables and accordingly obtain the low-dimensional feature principal component. Then, the least square support vector machine method was used to construct a quantitative regression model between principal component and the effluent COD. Along with it, particle swarm optimization was implemented to search for the best value of the LSSVM model parameters, namely the kernel parameters and the regularization factor. Finally, an online calibration strategy was designed to adapt to the process dynamic changes in an adaptive iterative manner. When the constructed model tested for performances in a full-scale factory, the average relative deviation and maximum deviation are 1.80% and 6.26%, respectively. The experimental results show that this proposed soft sensor model is featured with high accuracy and strong dynamic stability, and it can provide good guidance for COD prediction and optimal control of paper mill wastewate treatment.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1757-8981
    ISSN: 1757-899X
    EISSN: 1757-899X
    DOI: 10.1088/1757-899X/490/6/062027
  • Source: IOP Publishing Free Content
    IOPscience (Open Access)
    GFMER Free Medical Journals
    ProQuest Central
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