SAMSE 2018 IOP Conf. Series: Materials Science and Engineering 490 (2019) 062027
IOP Publishing doi:10.1088/1757-899X/490/6/062027
Dynamic soft sensing of organic pollutants in effluent from UMIC anaerobic reactor for industrial papermaking wastewater Yajuan Xing 1 , Zhong Cheng 2,* , Shengdao Shan 3 1 Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province, School of Environment and Resources, Zhejiang University of Science and Technology, Hangzhou 310023 2 School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023 3 Key Laboratory of Recycling and Eco-treatment of Waste Biomass of Zhejiang Province, School of Environment and Resources, Zhejiang University of Science and Technology, Hangzhou 310023 *Corresponding author e-mail: chengzhong@zust.edu.cn Abstract. 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.
1. Introduction The paper-making industry is a major water consumer and also a major wastewater discharger. According to the statistics of the Ministry of Ecology and Environment, In 2015, the total water consumption of the paper-making industry and the paper product industry (4,180 enterprises involved in the statistics) was 11.835 billion tons, and the wastewater discharge was 2.367 billion tons,
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