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Research on Cold Rolling Force Modeling Method Based on AEGRU Network |
SUN Hao1,2,YE Guo-liang1,2,ZHAI Bo-hao1,2,HU Zi-yu1,2,ZHAO Zhi-wei3 |
1.Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, Hebei 066004, China
2. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004,China
3. Department of Computer Science and Technology, Tangshan University, Tangshan, Hebei 063000, China |
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Abstract In the process of cold continuous rolling of steel, the prediction results of rolling force directly affect the rolling precision and product quality of strip. A rolling force model based on AEGRU (autotncoder and gate recurrent unit) network is proposed to improve the accuracy of rolling force prediction, update the model online and avoid the drift problem. First of all, the processed input data is extracted through the AEGRU network. In order to speed up the network training, the mini-batch training method is added. Then the extracted features is fitted by the Gaussian process regression model. The simulation results exhibit that the prediction accuracy of the model can be up to 3%, and the rolling force can be predicted online with high precision.
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Received: 10 March 2021
Published: 19 September 2022
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[1]魏立新, 王恒, 孙浩, 等. 基于改进深度信念网络训练的冷轧轧制力预报[J]. 计量学报, 2021, 42(7):906-912.
Wei L X, Wang H, Sun H, et al. Prediction of cold rolling force based on improved deep belief network training[J].Acta Metrologica Sinica, 2021, 42(7):906-912.
[2]魏立新, 张宇, 孙浩, 等. 基于改进OS-ELM的冷连轧在线轧制力预报[J]. 计量学报, 2019, 40(1):111-116.
Wei L X, Zhang Y, Sun H, et al. On-line rolling force prediction of tandem cold rolling based on improved OS-ELM[J]. Acta Metrologica Sinica, 2019, 40(1):111-116.
[3]Hu Z Y, Wei Z H, Sun H, et al. Optimization of Metal Rolling Control Using Soft Computing Approaches: A Review[J]. Archives of Computational Methods in Engineering, 2021, 28(2):405-421.
[4]杨景明, 陈伟明, 车海军, 等. 基于粒子群算法优化支持向量机的铝热连轧机轧制力预报[J]. 计量学报, 2016, 37(1):71-74.
Yang J M, Chen W M, Che H J, et al. Prediction of rolling force of aluminum hot tandem Mill based on particle swarm optimization and support vector machine [J]. Acta Metrologica Sinica, 2016, 37(1):71-74.
[5]白振华, 宋和川, 侯彬, 等. 冷轧机组升降速过程轧制压力变化模型及其影响因素研究[J]. 塑性工程学报, 2017, 24(3):135-141.
Bai Z H, Song H C, Hou B, et al. Influencing factors of rolling pressure and its change model in the speed up and down process of cold rolling mill[J]. Journal of Plastic Engineering, 2017, 24(3):135-141.
[6]朱宝, 乔俊飞. 基于自编码神经网络特征提取的回声状态网络研究及过程建模应用[J]. 化工学报, 2019, 70(12):258-264.
Zhu B, Qiao J F. Research and application of echo state network based on self-coding neural network feature extraction[J]. CIESC Journal, 2019, 70(12):258-264.
[7]Jin H P, Chen X G, Yang J W, et al. Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes[J]. Chemical Engineering Science, 2015, 131: 282-303.
[8]Yin X H, Niu Z W, He Z, et al. Ensemble deep learning based semi-supervised soft sensor modeling method and its application on quality prediction for coal preparation process[J]. Advanced Engineering Informatics, 2020, 46: 101136-101150.
[9]喻胜华, 邓娟. 基于主成分分析和贝叶斯正则化BP神经网络的GDP预测[J]. 湖南大学学报(社会科学版), 2011, 25(6):42-45.
Yu S H, Deng J. GDP prediction based on principal component analysis and Bayesian regularization BP neural network[J]. Journal of Hunan University (Social Sciences), 2011, 25(6):42-45.
[10]曹卫华, 李熙, 吴敏, 等. 基于极限学习机的热轧薄板轧制力预测模型[J]. 信息与控制, 2014, 43(3):270-275.
Cao W H, Li X,Wu M, et al. Prediction model of hot rolled sheet rolling force based on limit learning machine[J]. Information and Control, 2014, 43(3):270-275.
[11]杨景明, 郭秋辰, 孙浩, 等. 基于改进果蝇算法与最小二乘支持向量机的轧制力预测算法研究[J]. 计量学报, 2016, 37(5):505-508.
Yang J M, Guo Q C, Sun H, et al. Research on Rolling Force Prediction Algorithm Based on Improved Fruit Fly Algorithm and Least Squares Support Vector Machine[J]. Acta Metrologica Sinica, 2016, 37(5):505-508.
[12]Mahmoodkhani Y, Wells M A, Song G. Prediction of roll force in skin pass rolling using numerical and artificial neural network methods[J]. Ironmaking & Steelmaking, 2017,44(4):280-286.
[13]金秀章, 李京. 基于互信息PSO-LSSVM的SO2浓度预测[J]. 计量学报, 2021, 42(5):675-680.
Jin X Z, Li J. Prediction of SO2 concentration based on mutual information PSO-LSSVM[J]. Acta Metrologica Sinica, 2021,42(5):675-680.
[14]Zhang X Y, Ge Z Q. Automatic Deep Extraction of Robust Dynamic Features for Industrial Big Data Modeling and Soft Sensor Application[J]. IEEE Transactions on Industrial Informatics, 2019,16(7): 4456-4467.
[15]Ma Y, Li H G. GRU-Auto-Encoder neural network based methods for diagnosing abnormal operating conditions of steam drums in coal gasification plants[J]. Computers & Chemical Engineering, 2020, 143(8):107097-107118.
[16]张研, 苏国韶, 燕柳斌. 基于高斯过程机器学习方法的隧道围岩分类模型[J]. 现代隧道技术, 2011, 48(6):32-37.
Zhang Y, Su G S,Yan L B. Classification of surrounding rocks in tunnel based on Gaussian process machine learning[J]. Modern Tunneling Technology, 2011, 48(6):32-37.
[17]Ding Z Y, Zhang J, Liu Y. Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction[J]. ISIJ Internationa, 2017, 57(11): 2022-2027.
[18]魏立新, 魏新宇, 孙浩, 等. 基于深度网络训练的铝热轧轧制力预报[J]. 中国有色金属学报, 2018, 28(10):128-134.
Wei L X, Wei X Y, Sun H, et al. Prediction of aluminum hot rolling Force based on deep network training[J]. The Chinese Journal of Nonferrous Metals, 2018, 28(10):128-134. |
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