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
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
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
The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow conve...The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.展开更多
In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth co...In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth component content. The hybrid models were composed of the extraction equilibrium calculation model and the Radial Basis Function (RBF) Neural Network (NN) error compensation model; the parameters of compensation model were optimized by the hierarchical genetic algorithms (HGA). In addition, application experiment research of this proposed method was carried out in the rare earth separation production process of a corporation. The result shows that this method is effective and can realize online measurement for the component content of rare earth in the countercurrent extraction.展开更多
Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to g...Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.展开更多
Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good metho...Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.展开更多
Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new s...Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.展开更多
The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rar...The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth (extraction) production. Simulation experiments with industrial operation data prove the effectiveness of the hybrid soft-(sensor).展开更多
Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the err...Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.展开更多
It has long been thought that a reheating furnace, with its inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers in steel plants. A novel software sensor is propos...It has long been thought that a reheating furnace, with its inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers in steel plants. A novel software sensor is proposed to make more effective use of those measurements that are already available, which has great importance both to slab quality and energy saving. The proposed method is based on the mixtures of Gaussian processes (GP) with the expectation maximization (EM) algorithm employed for parameter esti- mation of the mixture of models. The mixture model can alleviate the computational complexity of GP and also accords with the changes of operating condition in practical processes. It is demonstrated by on-line estimation of the furnace gas temperature in 1580 reheating furnace in Baosteel Corporation (Group).展开更多
Managers of cement plants are gradually becoming aware of the need for soft sensors in product quality assessment. Cement clinker quality parameters are mostly measured by offline laboratory analysis or by the use of ...Managers of cement plants are gradually becoming aware of the need for soft sensors in product quality assessment. Cement clinker quality parameters are mostly measured by offline laboratory analysis or by the use of online analyzers. The measurement delay and cost, associated with these methods, are a concern in the cement industry. In this study, a regression-based model was developed to predict the clinker quality parameters as a function of the raw meal quality and the kiln operating variables. This model has mean squared error, coefficient of determination, worst case relative error and variance account for (in external data) given as 8.96 × 10<sup>–7</sup>, 0.9999, 2.17% and above 97%, respectively. Thus, it is concluded that the developed model can provide real time estimates of the clinker quality parameters and capture wider ranges of real plant operating conditions from first principle-based cement rotary kiln models. Also, the model developed can be utilized online as soft sensor since they contain only variables that are easily measured online.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National Natural Science Foundation of China(No.20676013)
文摘The soft-sensor modeling for fermentation process based on standard support vector regression(SVR) needs to solve the quadratic programming problem(QPP) which will often lead to large computational burdens, slow convergence rate, low solving efficiency, and etc. In order to overcome these problems, a method of soft-sensor modeling for fermentation process based on geometric SVR is presented. In the method, the problem of solving the SVR soft-sensor model is converted into the problem of finding the nearest points between two convex hulls (CHs) or reduced convex hulls (RCHs) in geometry. Then a geometric algorithm is adopted to generate soft-sensor models of fermentation process efficiently. Furthermore, a swarm energy conservation particle swarm optimization (SEC-PSO) algorithm is proposed to seek the optimal parameters of the augmented training sample sets, the RCH size, and the kernel function which are involved in geometric SVR modeling. The method is applied to the soft-sensor modeling for a penicillin fermentation process. The experimental results show that, compared with the method based on the standard SVR, the proposed method of soft-sensor modeling based on geometric SVR for fermentation process can generate accurate soft-sensor models and has much less amount of computation, faster convergence rate, and higher efficiency.
文摘In consideration of the online measurement of the component content in rare earth countercurrent extraction separation process, the soft sensor method based on hybrid modeling was proposed to measure the rare earth component content. The hybrid models were composed of the extraction equilibrium calculation model and the Radial Basis Function (RBF) Neural Network (NN) error compensation model; the parameters of compensation model were optimized by the hierarchical genetic algorithms (HGA). In addition, application experiment research of this proposed method was carried out in the rare earth separation production process of a corporation. The result shows that this method is effective and can realize online measurement for the component content of rare earth in the countercurrent extraction.
文摘Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.
基金Supported by the National Natural Science Foundation of China (20476007)
文摘Biomass is a key parameter in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Hybrid soft-sensor modeling is a good method for on-line estimation of biomass. Structure of hybrid soft-sensor model is a key to improve the estimating accuracy. In this paper, a forward heuristic breadth-first reasoning approach based on rule match is proposed for constructing structure of hybrid model. First, strategy of forward heuristic reasoning about facts is introduced, which can reason complex hybrid model structure in the event of few known facts. Second, rule match degree is defined to obtain higher esti- mating accuracy. The experiment results of Nosiheptide fermentation process show that the hybrid modeling process can estimate biomass with higher accuracy by adding transcendental knowledge and partial mechanism to the process.
基金This project is supported by Special Foundation for Major State Basic Research of China (No.G1998030415).
文摘Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.
基金ProjectsupportedbytheNationalTenthFive Year PlanofKeyTechnology (2 0 0 2BA3 15A)
文摘The equilibrium model for multicomponent rare earth extraction is developed using neural networks, which combined with the material balance model could give online prediction of component content in countercurrent rare earth (extraction) production. Simulation experiments with industrial operation data prove the effectiveness of the hybrid soft-(sensor).
基金Supported by National Natural Science Foundation of P.R.China(50474020,60534010,60504006)
文摘Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.
基金This work was financially supported by the National High-Tech Research and Development Program of China(No.2002AA4120I0).
文摘It has long been thought that a reheating furnace, with its inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers in steel plants. A novel software sensor is proposed to make more effective use of those measurements that are already available, which has great importance both to slab quality and energy saving. The proposed method is based on the mixtures of Gaussian processes (GP) with the expectation maximization (EM) algorithm employed for parameter esti- mation of the mixture of models. The mixture model can alleviate the computational complexity of GP and also accords with the changes of operating condition in practical processes. It is demonstrated by on-line estimation of the furnace gas temperature in 1580 reheating furnace in Baosteel Corporation (Group).
文摘Managers of cement plants are gradually becoming aware of the need for soft sensors in product quality assessment. Cement clinker quality parameters are mostly measured by offline laboratory analysis or by the use of online analyzers. The measurement delay and cost, associated with these methods, are a concern in the cement industry. In this study, a regression-based model was developed to predict the clinker quality parameters as a function of the raw meal quality and the kiln operating variables. This model has mean squared error, coefficient of determination, worst case relative error and variance account for (in external data) given as 8.96 × 10<sup>–7</sup>, 0.9999, 2.17% and above 97%, respectively. Thus, it is concluded that the developed model can provide real time estimates of the clinker quality parameters and capture wider ranges of real plant operating conditions from first principle-based cement rotary kiln models. Also, the model developed can be utilized online as soft sensor since they contain only variables that are easily measured online.
