Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of t...Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.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the National Natural Science Foundation of China(Nos.61374110 and 61074060)the Specialized Research Fund for the Doctoral Program of Higher Education of China(No.20120073110017)
文摘Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.
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.
