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Soft-sensing modeling of chemical oxygen demand in photo-electro-catalytic oxidation treatment of papermaking wastewater

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Journal of Bioresources and Bioproducts 2018年第2期3卷 71-77页

作者： Xuewen Zhang Yuefei Zhu Xiaoquan Chen Wenhao Shen ryan lutes State Key Laboratory of Pulp and Paper Engineering South China University of TechnologyGuangzhou510640China Chemical Engineering University of New BrunswickFrederictonCanadaE3B 5A3

Photo-electro-catalytic(PEC)oxidation has been widely recognized as an effective technology for advanced treatment of papermaking *** optimize the oxidation process,it is important of monitor continuously the chemical oxygen demand(COD)of inflow and outflow ***,online COD sensors are expensive difficult to maintain,and therefore COD is usually analyzed off-line in laboratories in most *** objective of this study is to develop an inexpensive method for on-line COD *** oxidation-reduction potential(ORP),pH,and dissolved oxygen(DO)of wastewater were selected as the key parameters,which consists of four different types of artificial neural network(ANNs)methods:multi-layer perceptron neural network(MLP),back propagation neural network(BPNN),radial basis neural network(RBNN)and generalized regression neural network(GRNN).These parameters were applied in the development of COD soft-sensing *** batches of papermaking wastewater with different pollution loads were treated with PEC technology over a period of 90 minutes,and a total of 546 data points was collected,including the on-line measurements of ORP,pH and DO,as well as off-line COD *** 546 data points were divided into training set(410 data,75%of total)and validation set(136 data,25%of total).Four statistical criteria,namely,root mean square error(RMSE),mean absolute error(MAE),mean absolute relative error(MARE),and determination coefficient(R2)were used to assess the performance of the models developed with the training set of *** comparison of results for the four ANN models for COD soft-sensing indicated that the RBNN model behaved most favorably,which possessed precise and predictable results with R2=0.913 for the validation ***,the proposed RBNN model was applied to a new batch of PEC oxidation of papermaking wastewater,and the results indicated that the model could be applied successfully for COD soft-sensing for the wastewater.

关键词： COD PEC oxidation papermaking wastewater soft-sensing

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