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

Photo-electro-catalytic (PEC) oxidation has been widely recognized as an effective technology for advanced treatment of papermaking wastewater. To optimize the oxidation process, it is important of monitor continuously the chemical oxygen demand (COD) of inflow and outflow wastewater. However, online COD sensors are expensive difficult to maintain, and therefore COD is usually analyzed off-line in laboratories in most cases. The objective of this study is to develop an inexpensive method for on-line COD measurement. The 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 models. Six 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 data. The 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 data. The 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 set. Lastly, 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.