This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is intro...This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.展开更多
The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard...The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.展开更多
The grey system theory, with the characteristics of fewer modeling data and higher accuracy, was employed to model the batch dyeing process for the purpose of accurate online control. The GM（1, 1） and GM （0, N） mo...The grey system theory, with the characteristics of fewer modeling data and higher accuracy, was employed to model the batch dyeing process for the purpose of accurate online control. The GM（1, 1） and GM （0, N） models of the grey system theory were discussed for their feasibilities of modding for batch dyeing process. The combination of direct dyestuff Fast Red F3B on cotton was chosen as a representative of the common dyeing method for describing the modeling process. Firstly, the GM（ 1, 1 ） model and the GM（1, 1） combined with GM（0, N） model were employed to model the equilibrium percentage of dyeing uptake rate. Secondly, an integrated dyeing uptake rate model with three factors （ temperature, salt concentration, and pH） was established based on the adsorption rate equation. Experimental results show that this model has higher accuracy and beetler generalization ability, which can predict the results of batch dyeing process. Due to the application of grey system theory, the model has a lot of advantages, such as being easy to determine the parameter value and small amount of calculation. So it can also be suitable for the same type of combination of dyestuff-fahric by changing the parameters value only.展开更多
基金Supported by the Shandong Natural Science Foundation(ZR2013BL008)
文摘This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.
基金Project(61673399)supported by the National Natural Science Foundation of ChinaProject(2017JJ2329)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2018zzts550)supported by the Fundamental Research Funds for Central Universities,China
文摘The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.
基金National Natural Science Foundation of China(No.61074154)
文摘The grey system theory, with the characteristics of fewer modeling data and higher accuracy, was employed to model the batch dyeing process for the purpose of accurate online control. The GM（1, 1） and GM （0, N） models of the grey system theory were discussed for their feasibilities of modding for batch dyeing process. The combination of direct dyestuff Fast Red F3B on cotton was chosen as a representative of the common dyeing method for describing the modeling process. Firstly, the GM（ 1, 1 ） model and the GM（1, 1） combined with GM（0, N） model were employed to model the equilibrium percentage of dyeing uptake rate. Secondly, an integrated dyeing uptake rate model with three factors （ temperature, salt concentration, and pH） was established based on the adsorption rate equation. Experimental results show that this model has higher accuracy and beetler generalization ability, which can predict the results of batch dyeing process. Due to the application of grey system theory, the model has a lot of advantages, such as being easy to determine the parameter value and small amount of calculation. So it can also be suitable for the same type of combination of dyestuff-fahric by changing the parameters value only.
