To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and ...To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.展开更多
Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and sof...Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.展开更多
Soft sensor technology has been widely applied in key areas of industrial process monitoring.To address challenges such as strong nonlinearity,complex temporal dependencies,and dynamic system behavior commonly encount...Soft sensor technology has been widely applied in key areas of industrial process monitoring.To address challenges such as strong nonlinearity,complex temporal dependencies,and dynamic system behavior commonly encountered in industrial soft sensor data modeling,we propose a hybrid dynamic modeling method that integrates gated recurrent unit(GRU)with temporal convolutional network-Transformer(TCN-Transformer)architecture.TCN-Transformer module is employed to extract multi-scale temporal patterns and capture long-range dependencies among auxiliary variables,while GRU network processes the historical information of target variables through its gated memory mechanism.The complementary feature representations from both components are summed before being passed into a fully connected layer for prediction.To validate the effectiveness of GRU-TCN-Transformer framework,comprehensive case studies were conducted on two typical industrial processes:the prediction of butane(C4)concentration in a debutanizer column and the estimation of hydrogen sulfide(H_(2)S)and sulfur dioxide(SO_(2))concentrations in a sulfur recovery unit(SRU).Experimental results demonstrate that the proposed hybrid dynamic modeling method significantly outperforms traditional dynamic modeling methods—convolutional neural network(CNN),long short-term memory(LSTM),and TCN—across multiple evaluation metrics.Specifically,for C4 concentration estimation,the proposed method reduced root mean squared error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE)by 55.0%,51.0%and 50.1%,respectively,and improved R^(2)by 2.3%compared to the best-performing TCN-Transformer model.For H_(2)S estimation,it achieved reductions of 30%,30.61%and 29.23%in RMSE,MAE,and MAPE,respectively,while increasing R^(2)by 11.09%over the best LSTM-TCNTransformer model.For SO_(2)estimation,the proposed model reduced RMSE,MAE,and MAPE by 7.91%,9.09%and 9.64%,respectively,with a 0.87%increase in R^(2).These comparative results further confirm the improvements in prediction accuracy,indicating that the proposed model is capable of meeting the stringent requirements of industrial applications.展开更多
The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on ...The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.展开更多
The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A(BPA).For online estimation of the catalytic activity,a catalytic deactivation ...The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A(BPA).For online estimation of the catalytic activity,a catalytic deactivation model is studied for a production plant of BPA,state equation and observation equation are proposed based on the axial temperature distribution of the reactor and the acetone concentration at reactor entrance.A hybrid model of state equation is constructed for improving estimation precision.The unknown parameters in observation equation are calculated with sample data.The unscented Kalman filtering algorithm is then used for on-line estimation of the catalytic activity.The simulation results show that this hybrid model has higher estimation accuracy than the mechanism model and the model is effective for production process of BPA.展开更多
基金supported by National Natural Science Foundation of China(No.52266012)Gansu Province College Industry Support Plan Project(No.2022CYZC-34)+1 种基金Gansu Province Major Science and Technology Special Project(Nos.20ZD7GF011,22ZD6GA063)Jiuquan City Science and Technology Programme Project(No.2023CA3058).
文摘To address the stochasticity and nonlinearity of solar collector power systems,a soft sensor prediction model with a hybrid convolutional neural network(CNN)and long short-term memory network(LSTM)was constructed,and the hyperparameter optimization of the hybrid neural network(CNN-LSTM)was carried out by using the sparrow search algorithm(SSA).The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables.The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model,and the random discard technique was used to prevent the soft-sensing model from overfitting.Finally,the mean absolute error(MAE)was used to assess the accuracy of the soft sensor model predictions.This study compared the proposed model with soft sensor prediction models like Bp,Elman,CNN,LSTM,and CNN-LSTM,using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant.The deep learning-based soft sensor model outperformed the other models according to the experimental data.Its coefficients of determination(namely R^(2))are higher by 6.35%,8.42%,5.69%,6.90%,and 3.67%,respectively.The accuracy and robustness have been significantly improved.
基金Supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2016RCJJ046)the National Basic Research Program of China(2012CB720500)
文摘Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.
基金financially supported by National Natural Science Foundation of China(No.52467008)Key Project of Natural Science Foundation of Gansu Province(No.25JRRA150)+1 种基金Key Research and Development Planning Project of Gansu Province(No.23YFWA0007)Lanzhou Science and Technology Plan Project(No.2023-1-16)。
文摘Soft sensor technology has been widely applied in key areas of industrial process monitoring.To address challenges such as strong nonlinearity,complex temporal dependencies,and dynamic system behavior commonly encountered in industrial soft sensor data modeling,we propose a hybrid dynamic modeling method that integrates gated recurrent unit(GRU)with temporal convolutional network-Transformer(TCN-Transformer)architecture.TCN-Transformer module is employed to extract multi-scale temporal patterns and capture long-range dependencies among auxiliary variables,while GRU network processes the historical information of target variables through its gated memory mechanism.The complementary feature representations from both components are summed before being passed into a fully connected layer for prediction.To validate the effectiveness of GRU-TCN-Transformer framework,comprehensive case studies were conducted on two typical industrial processes:the prediction of butane(C4)concentration in a debutanizer column and the estimation of hydrogen sulfide(H_(2)S)and sulfur dioxide(SO_(2))concentrations in a sulfur recovery unit(SRU).Experimental results demonstrate that the proposed hybrid dynamic modeling method significantly outperforms traditional dynamic modeling methods—convolutional neural network(CNN),long short-term memory(LSTM),and TCN—across multiple evaluation metrics.Specifically,for C4 concentration estimation,the proposed method reduced root mean squared error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE)by 55.0%,51.0%and 50.1%,respectively,and improved R^(2)by 2.3%compared to the best-performing TCN-Transformer model.For H_(2)S estimation,it achieved reductions of 30%,30.61%and 29.23%in RMSE,MAE,and MAPE,respectively,while increasing R^(2)by 11.09%over the best LSTM-TCNTransformer model.For SO_(2)estimation,the proposed model reduced RMSE,MAE,and MAPE by 7.91%,9.09%and 9.64%,respectively,with a 0.87%increase in R^(2).These comparative results further confirm the improvements in prediction accuracy,indicating that the proposed model is capable of meeting the stringent requirements of industrial applications.
基金Item Sponsored by National Natural Science Foundation of China (60374003)
文摘The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.
基金Supported by the National Natural Science Foundation of China(60674092)
文摘The catalytic activity of cation exchange resins will be continuously reduced with its use time in a condensation reaction for bisphenol A(BPA).For online estimation of the catalytic activity,a catalytic deactivation model is studied for a production plant of BPA,state equation and observation equation are proposed based on the axial temperature distribution of the reactor and the acetone concentration at reactor entrance.A hybrid model of state equation is constructed for improving estimation precision.The unknown parameters in observation equation are calculated with sample data.The unscented Kalman filtering algorithm is then used for on-line estimation of the catalytic activity.The simulation results show that this hybrid model has higher estimation accuracy than the mechanism model and the model is effective for production process of BPA.
