The stable control of product quality when abnormal working conditions occur in the industrial production process is essential to improve product quality and economic efficiency. However, the process industry suffers ...
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The stable control of product quality when abnormal working conditions occur in the industrial production process is essential to improve product quality and economic efficiency. However, the process industry suffers from multivariate, nonlinear, uncertainty, long time delays and frequent failures, making its modeling and control difficult. In this paper, a novel method of neural network model predictive control integrated process monitoring (PM-NNMPC) is proposed. First, the combination of gated recurrent unit and convolutional neuralnetwork (GRU-CNN) is used to extract the features from the time and the space separately to build prediction models for different working conditions of the process. Then, a process monitoring and fault diagnosis method of principal component analysis combined with deep neuralnetwork and XGBoost (PCA-DNN-XGBoost) is designed to monitor the working conditions in real-time and diagnose accurately when faults occur. Finally, a new operation control framework integrating process monitoring and fault diagnosis into the model prediction control is designed to solve the problem that equipment operation failure, which makes the product quality fluctuation because of the model mismatch and the system runaway. The experimental results of hot rolling process show that PM-NNMPC can effectively control the system under normal working conditions and in case of the system failure to ensure the stable product quality.
pH neutralization is a difficult process to be controlled due to the nonlinear and time-varying process characteristics. modelpredictivecontrol appeared in industry as an effective means to deal with multivariable c...
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ISBN:
(纸本)9781467350907;9781467350891
pH neutralization is a difficult process to be controlled due to the nonlinear and time-varying process characteristics. modelpredictivecontrol appeared in industry as an effective means to deal with multivariable constrained control problems. This paper does the study of the pH neutralization process of a weak acid - strong base system using a neural network model predictive control technique. The simulation results are analyzed for step and random acid disturbances, which shows that the controller controls the pH within the required limits with less mean square error.
pH neutralization is a difficult process to be controlled due to the nonlinear and time-varying process characteristics. modelpredictivecontrol appeared in industry as an effective means to deal with multivariable c...
详细信息
ISBN:
(纸本)9781467350891
pH neutralization is a difficult process to be controlled due to the nonlinear and time-varying process characteristics. modelpredictivecontrol appeared in industry as an effective means to deal with multivariable constrained control problems. This paper does the study of the pH neutralization process of a weak acid - strong base system using a neural network model predictive control technique. The simulation results are analyzed for step and random acid disturbances, which shows that the controller controls the pH within the required limits with less mean square error.
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