Existing control systems for coiling temperature struggle with significant time lags and multi-objective synchronous control during cooling,limiting their temperature control accuracy.To overcome these drawbacks,an on...Existing control systems for coiling temperature struggle with significant time lags and multi-objective synchronous control during cooling,limiting their temperature control accuracy.To overcome these drawbacks,an online cooling system featuring multi-objective collaborative control is proposed.The proposed system achieves the synchronous control of the ultra-fast cooling temperature,middle temperature,and coiling temperature.First,the run-out table cooling zone is divided into multiple logical control zones,and traditional mechanism models are improved by introducing multiple heat flux adaptive coefficients.Then,a dynamic feedforward control method is developed to correct potential deviations in the calculation process.Finally,to enhance the proposed control system’s accuracy and self-learning capability,a multi-objective real-time adaptation strategy is introduced for dynamic heat flux adaptive coefficients adjustment.Analysis and application results show that the proposed multi-objective collaborative control system significantly improves the temperature control accuracy while ensuring the consistency of mechanical properties.Comparison results indicate that,under the proposed control system,the coiling temperature control accuracy within ±20℃ for segments located at 50 m from the strip head is improved by 26%,compared with the original control system.In addition,using the proposed system,the standard deviation of the yield strength is decreased by 38%,compared with the original control system.展开更多
During the hot rolling process,the performance of most control systems gradually degrades due to equipment aging and changing process conditions.However,existing gauge-looper-tension control method remain restricted b...During the hot rolling process,the performance of most control systems gradually degrades due to equipment aging and changing process conditions.However,existing gauge-looper-tension control method remain restricted by a lack of intelligent parameter maintenance strategies.To address this challenge and enhance the smart manufacturing capabilities of strip hot rolling,based on the digital twin method,this paper proposes a data-driven optimized control method for the gauge-looper-tension system of strip hot rolling.First,a hot rolling process model is constructed based on a digital twin method to serve as an evaluation and optimization platform.Subsequently,relevant data are collected to calculate the Hurst index for identifying the performance of the controller during the rolling process.Additionally,for controllers with poor Hurst index values,the crayfish optimization algorithm is employed for adjusting controller parameters to maximize the Hurst index.Experimental results demonstrate that the evaluation method effectively recognized the control state of gauge-looper-tension system and the optimization method successfully enhances the performance of the control system.Therefore,based on the digital twin platform,the proposed method can effectively maintain performance-degraded control systems.展开更多
基金financially supported by the National Key Research and Development Program of China(2022YFB3304800)the National Natural Science Foundation of China(Nos.52074085 and U21A20117).
文摘Existing control systems for coiling temperature struggle with significant time lags and multi-objective synchronous control during cooling,limiting their temperature control accuracy.To overcome these drawbacks,an online cooling system featuring multi-objective collaborative control is proposed.The proposed system achieves the synchronous control of the ultra-fast cooling temperature,middle temperature,and coiling temperature.First,the run-out table cooling zone is divided into multiple logical control zones,and traditional mechanism models are improved by introducing multiple heat flux adaptive coefficients.Then,a dynamic feedforward control method is developed to correct potential deviations in the calculation process.Finally,to enhance the proposed control system’s accuracy and self-learning capability,a multi-objective real-time adaptation strategy is introduced for dynamic heat flux adaptive coefficients adjustment.Analysis and application results show that the proposed multi-objective collaborative control system significantly improves the temperature control accuracy while ensuring the consistency of mechanical properties.Comparison results indicate that,under the proposed control system,the coiling temperature control accuracy within ±20℃ for segments located at 50 m from the strip head is improved by 26%,compared with the original control system.In addition,using the proposed system,the standard deviation of the yield strength is decreased by 38%,compared with the original control system.
基金supported by the National Natural Science Foundation of China[grant numbers U21A20117,52074085]the Fundamental Research Funds for the Central Universities[grant number N2004010]the LiaoNing Applied Basic Research Program Project[grant numbers 2022JH24/10200013].
文摘During the hot rolling process,the performance of most control systems gradually degrades due to equipment aging and changing process conditions.However,existing gauge-looper-tension control method remain restricted by a lack of intelligent parameter maintenance strategies.To address this challenge and enhance the smart manufacturing capabilities of strip hot rolling,based on the digital twin method,this paper proposes a data-driven optimized control method for the gauge-looper-tension system of strip hot rolling.First,a hot rolling process model is constructed based on a digital twin method to serve as an evaluation and optimization platform.Subsequently,relevant data are collected to calculate the Hurst index for identifying the performance of the controller during the rolling process.Additionally,for controllers with poor Hurst index values,the crayfish optimization algorithm is employed for adjusting controller parameters to maximize the Hurst index.Experimental results demonstrate that the evaluation method effectively recognized the control state of gauge-looper-tension system and the optimization method successfully enhances the performance of the control system.Therefore,based on the digital twin platform,the proposed method can effectively maintain performance-degraded control systems.
