时延系统中T-LSSVR动态软测量建模方法研究
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摘要
针对工业过程中由于系统存在延时导致软测量模型难以建立、模型精度偏低等问题,提出将系统延时(T)与最小二乘支持向量回归机(LSSVR)相结合,构建一种基于T-LSSVR的动态软测量建模方法;该方法在建模过程中利用互相关函数与一阶广义差分算法辨识得到"静态响应延时"和"动态响应延时",通过软测量手段对变量进行预测以实现辅助变量对主导变量的最佳估计。对某化工企业具有此类双延时性质的系统进行实验,实验结果表明该建模方法在动态和稳态数据预测方面都有良好的预测效果。
For the delay problem in industrial process system,which lead to not soft sensor modeling in real-time or the lower accuracy of measurement,a modeling method for dynamic soft measurement based on a new algorithm( TLSSVR) which combines the system delay( T) and least squares support vector regression( LSSVR) was presented. In order to achieve the best estimate of the auxiliary variables,the"static response delay"and"dynamic response delay"can be identified by cross-correlation function and the first order difference algorithm during modeling to the leading variables by the means of soft measurement to predict variables in the method. The soft measurement model was applied to a system with such double-delayed nature of a chemical company and the results show that its prediction achieved good results in terms of both dynamic and steady-state data.
For the delay problem in industrial process system,which lead to not soft sensor modeling in real-time or the lower accuracy of measurement,a modeling method for dynamic soft measurement based on a new algorithm( TLSSVR) which combines the system delay( T) and least squares support vector regression( LSSVR) was presented. In order to achieve the best estimate of the auxiliary variables,the"static response delay"and"dynamic response delay"can be identified by cross-correlation function and the first order difference algorithm during modeling to the leading variables by the means of soft measurement to predict variables in the method. The soft measurement model was applied to a system with such double-delayed nature of a chemical company and the results show that its prediction achieved good results in terms of both dynamic and steady-state data.
引文
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[6]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 the predictive algorithm of rolling force based on the improved fruit flies optimization algorithm with LSSVM[J]. Acta Metrologica Sinica,2016,37(5):505-508.
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[10] Wu Yao,Luo Xionglin,Yuan Zhihong. Soft sensor modeling with dynamic interpolation neutral network for multirate system[J]. Chemical Industry and Engineering Progress,2009,28(8):1323-1327.
[11] Wang Junyan,Huang Dexian. Time delay estimation of soft-sensor model based on hybrid differential evolution algorithm[J]. CIESC Journal,2008,59(8):2058-2064.
[12]李成志,邵富群,阚哲.基于广义倒谱的相关算法在声学温度检测系统中的应用[J].计量学报,2013,34(1):22-26.Li C Z,Shao F Q,Kan Z. Application in acoustic pyrometer system based on generalized cepstrum correlation algorithm[J]. Acta Metrologica Sinica,2013,34(1):22-26.
[13] Ionescu C M,Hodrea R,De Keyser R. Variable timedelay estimation for anesthesia control during intensive care[J]. Biomedical Engineering,IEEE Transactions on,2011,58(2):363-369.
[14]张靖悉,胡勇,李玥. QPSK调制多载波信号时延在线测量技术[J].计量学报,2016,37(6):649-652.Zhang J X,Hu Y,Li Y. A technology of on-line measurement of QPSK multicarrier signal delay[J].Acta Metrologica Sinica,2016,37(6):649-652.
[15] Gedalyahu K,Eldar Y C. Time-delay estimation from low-rate samples:A union of subspaces approach[J].Signal Processing, IEEE Transactions on, 2010, 58(6):3017-3031.
[2] Yu J J,Zhou C H. Soft-sensing techniques in process control[J]. Control Theory and Applications,1996,13(2):137-144.
[3] Sheridan T B. Space teleoperation through time delay:review and prognosis[J]. Robotics and Automation,IEEE Transactions on,1993,9(5):592-606.
[4]曹丽芳,赵朋程,陈颖,等.改进LMD和LS-SVM在小电流接地故障选线中的应用[J].计量学报,2016,37(6):632-637.Cao L F,Zhao P C,Chen Y,et al. Improved local mean decomposition and least squares support vector machines applied in fault selection for small current grounding system[J]. Acta Metrologica Sinica,2016,37(6):632-637.
[5] Shu Yunxing. Research on fault diagnosis and quality prediction using support vector machines in cement burning system[D]. Wuhan:Wuhan University of Technology,2008.
[6]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 the predictive algorithm of rolling force based on the improved fruit flies optimization algorithm with LSSVM[J]. Acta Metrologica Sinica,2016,37(5):505-508.
[7] David M H. Accounts of experiences in the application of artificial neural networks in chemical engineering[J].Industrial&Engineering Chemistry Research,2008,47(16):5782-5796.
[8] Song Yunxia, Zhu Xuefeng. Control Methods and Application for the Process with Large Time Delay[J].Control and Instruments in Chemical Industry,2001,28(4):9-15.
[9] Galicia H J,He Q P,Wang J. A reduced order soft sensor approach and its application to a continuous digester[J]. Journal of Process Control,2011,21(4):489-500.
[10] Wu Yao,Luo Xionglin,Yuan Zhihong. Soft sensor modeling with dynamic interpolation neutral network for multirate system[J]. Chemical Industry and Engineering Progress,2009,28(8):1323-1327.
[11] Wang Junyan,Huang Dexian. Time delay estimation of soft-sensor model based on hybrid differential evolution algorithm[J]. CIESC Journal,2008,59(8):2058-2064.
[12]李成志,邵富群,阚哲.基于广义倒谱的相关算法在声学温度检测系统中的应用[J].计量学报,2013,34(1):22-26.Li C Z,Shao F Q,Kan Z. Application in acoustic pyrometer system based on generalized cepstrum correlation algorithm[J]. Acta Metrologica Sinica,2013,34(1):22-26.
[13] Ionescu C M,Hodrea R,De Keyser R. Variable timedelay estimation for anesthesia control during intensive care[J]. Biomedical Engineering,IEEE Transactions on,2011,58(2):363-369.
[14]张靖悉,胡勇,李玥. QPSK调制多载波信号时延在线测量技术[J].计量学报,2016,37(6):649-652.Zhang J X,Hu Y,Li Y. A technology of on-line measurement of QPSK multicarrier signal delay[J].Acta Metrologica Sinica,2016,37(6):649-652.
[15] Gedalyahu K,Eldar Y C. Time-delay estimation from low-rate samples:A union of subspaces approach[J].Signal Processing, IEEE Transactions on, 2010, 58(6):3017-3031.