Volatile organic pollutants such as benzene and formaldehyde are commonly detected in the ambient air of paper mills.To remove these pollutants from the air,a photo-catalytic reactor was developed in this study.The re...Volatile organic pollutants such as benzene and formaldehyde are commonly detected in the ambient air of paper mills.To remove these pollutants from the air,a photo-catalytic reactor was developed in this study.The reactor had a series of honeycomb aluminum meshes coated with nanosized titanium dioxide as the catalyst for the degradation reactions of gaseous pollutants.Both formaldehyde and benzene could be completely degraded in the reactor.However,the degrading time for benzene was much longer than that for formaldehyde,and the degradation rate of benzene decreased with increasing initial benzene concentration.It was found that the reaction pathway for formaldehyde in the mixture was different from that in its single component form,and it took about two times longer time to be degraded than that for its single component form.The reaction pathway of benzene was similar in either case although the degradation time for benzene was about 50%shorter in the mixture form.展开更多
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
基金supported by the Research Funds of State Key Laboratory of Pulp and Paper Engineering(No.2015C05)Science and Technology Planning Project of Guangdong,(No.2015A020215012)+1 种基金National Science Foundation of Guangdong(No.2016A030313478)Science and Technology Program of Guangzhou(No.201607010050).
文摘Volatile organic pollutants such as benzene and formaldehyde are commonly detected in the ambient air of paper mills.To remove these pollutants from the air,a photo-catalytic reactor was developed in this study.The reactor had a series of honeycomb aluminum meshes coated with nanosized titanium dioxide as the catalyst for the degradation reactions of gaseous pollutants.Both formaldehyde and benzene could be completely degraded in the reactor.However,the degrading time for benzene was much longer than that for formaldehyde,and the degradation rate of benzene decreased with increasing initial benzene concentration.It was found that the reaction pathway for formaldehyde in the mixture was different from that in its single component form,and it took about two times longer time to be degraded than that for its single component form.The reaction pathway of benzene was similar in either case although the degradation time for benzene was about 50%shorter in the mixture form.
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.
