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 th...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.展开更多
It is hard to achieve efficient photoelectrochemical(PEC)water splitting with BiVO_(4) due to the severe electron/hole recombination and slow carrier migration.In this work,BiVO_(4)/BNQDs/CoBi photoanode was rationall...It is hard to achieve efficient photoelectrochemical(PEC)water splitting with BiVO_(4) due to the severe electron/hole recombination and slow carrier migration.In this work,BiVO_(4)/BNQDs/CoBi photoanode was rationally designed and prepared for efficient PEC water splitting,utilizing boron nitride quantum dots(BNQDs)as hole extractors and cobalt borate(CoBi)as a cocatalyst.The BiVO_(4)/BNQDs/CoBi exhibits an excellent photocurrent density of 5.1 m A/cm^(2) at 1.23 V vs.RHE,which is 3.4 times that of the pure BiVO_(4).Systematic studies show that BNQDs and CoBi can simultaneously promote charge separation and migration,with a charge injection and separation efficiency of 82% and 93%at 1.23 V vs.RHE,respectively.The enhanced dynamic behavior at the BiVO_(4)/BNQDs/CoBi interface was systematically and quantitatively evaluated by intensity modulated photocurrent spectroscopy(IMPS)and transient surface photovoltage(TPV)spectroscopy.It is found that BNQDs and CoBi play a similar role for inhibiting charge recombination while BNQDs play significant role for improving the charge transfer rate than CoBi.展开更多
基金supported by the Research Funds of the National Science Foundation of Guangdong,China(No.2016A030313478)State Key Laboratory of Pulp and Paper Engineering(No.2017ZD03).
文摘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.
基金financially supported by the National Natural Science Foundation of China(No.52173277)the Young Teachers’Research Ability Improvement Project of Northwest Normal University(No.NWNU-LKQN2020–01)。
文摘It is hard to achieve efficient photoelectrochemical(PEC)water splitting with BiVO_(4) due to the severe electron/hole recombination and slow carrier migration.In this work,BiVO_(4)/BNQDs/CoBi photoanode was rationally designed and prepared for efficient PEC water splitting,utilizing boron nitride quantum dots(BNQDs)as hole extractors and cobalt borate(CoBi)as a cocatalyst.The BiVO_(4)/BNQDs/CoBi exhibits an excellent photocurrent density of 5.1 m A/cm^(2) at 1.23 V vs.RHE,which is 3.4 times that of the pure BiVO_(4).Systematic studies show that BNQDs and CoBi can simultaneously promote charge separation and migration,with a charge injection and separation efficiency of 82% and 93%at 1.23 V vs.RHE,respectively.The enhanced dynamic behavior at the BiVO_(4)/BNQDs/CoBi interface was systematically and quantitatively evaluated by intensity modulated photocurrent spectroscopy(IMPS)and transient surface photovoltage(TPV)spectroscopy.It is found that BNQDs and CoBi play a similar role for inhibiting charge recombination while BNQDs play significant role for improving the charge transfer rate than CoBi.
