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

2nd International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Xing, Yajuan^1 ; Cheng, Zhong^2 ; Shan, Shengdao^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, China^1

School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou

310023, China^2

关键词: Adaptive soft-sensor;    Dynamic soft sensing;    Environmental pollutions;    Industrial papermaking;    Least square support vector machines;    Paper mill effluents;    Quantitative regression model;    Resource utilizations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/490/6/062027/pdf
DOI  :  10.1088/1757-899X/490/6/062027

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

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.