Catalytic ozonation treatment of papermaking wastewater by Ag-doped NiFe2O4 : Performance and mechanism

The catalytic ozonation treatment of secondary biochemical effluent for papermaking wastewater by Ag-doped nickel ferrite was investigated.Ag-doped catalysts prepared by sol-gel method were characterized,illustrating that Ag entirely entered the crystalline of Ni Fe2O4 and changed the surface properties.The addition of catalyst enhanced the removal efficiency of chemical oxygen demand and total organic carbon.The results of gas chromatography-mass spectrometer,ultraviolet light absorbance at 254 nm and threedimensional fluorescence excitation-emission matrix suggested that aromatic compounds were efficiently degraded and toxic substances,such as dibutyl phthalate.In addition,the radical scavenging experiments confirmed the hydroxyl radicals acted as the main reactive oxygen species and the surface properties of catalysts played an important role in the reaction.Overall,this work validated potential applications of Ag-doped Ni Fe2O4 catalyzed ozonation process of biologically recalcitrant wastewater.

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.

Quality-related monitoring of papermaking wastewater treatment processes using dynamic multiblock partial least squares

Environmental problems have attracted much attention in recent years,especially for papermak-ing wastewater discharge.To reduce the loss of effluence discharge violation,quality-related multivariate statistical methods have been successfully applied to achieve a robust wastewater treatment system.In this work,a new dynamic multiblock partial least squares(DMBPLS)is pro-posed to extract the time-varying information in a large-scale papermaking wastewater treatment process.By introducing augmented matrices to input and output data,the proposed method not only handles the dynamic characteristic of data and reduces the time delay of fault detection,but enhances the interpretability of model.In addition,the DMBPLS provides a capability of fault location,which has certain guiding significance for fault recovery.In comparison with other mod-els,the DMBPLS has a superior fault detection result.Specifically,the maximum fault detection rate of the DMBPLS is improved by 35.93%and 12.5%for bias and drifting faults,respectively,in comparison with partial least squares(PLS).

Micro-electrolysis technology for industrial wastewater treatment

Experiments were conducted to study the role of micro-electrolysis in removing chromaticity and COD and improving the biodegradability of wastewater from pharmaceutical, dye-printing and papermaking plants. Results showed that the use of micro-electrolysis technology could remove more than 90% of chromaticity and more than 50% of COD and greatly improved the biodegradability of pharmaceutical wastewater. Lower initial pH could be advantageous to the removal of chromaticity. A retention time of 30 minutes was recommended for the process design of micro-electrolysis. For the use of micro-electrolysis in treatment of dye-printing wastewater, the removal rates of both chromaticity and COD were increased from neutral condition to acid condition for disperse blue wastewater; more than 90% of chromaticity and more than 50% of COD could be removed in neutral condition for vital red wastewater.