Multi-delays identification for alumina evaporation process based on time-correlation analysis

doi:10.11949/j.issn.0438-1157.20161604

Abstract

Abstract:

The multi-delays of the whole equipment unit in the process industry are indicated in the form of a sequence of integers. A restructured data matrix is generated based on the sequence and the original data. The time-correlation analysis is defined to describe the correlation between the columns in the data matrix and H_∞ norm is used to quantify the correlation of the matrix so that the multi-delays identification problem can be converted to calculating the biggest H_∞ norm. At last, the discrete state transition algorithm is adopted to quickly search the biggest H_∞ norm and the multi-delays are identified. The proposed method is used for multi-delays identification of alumina evaporation process and the result of multi-delays identification is applied to the data preprocessing of a prediction model. The accuracy of the prediction model is improved by 34.4% and the proposed identification method is effective.

Key words: alumina, evaporation, time-correlation analysis, multi-delays identification, discrete state transition algorithm, H_∞ norm, prediction

摘要：

将流程工业中物料在每个单元的滞留时间以整数序列的形式表示，根据时滞序列将原始数据重新匹配生成新的数据矩阵。定义时效关联分析来描述数据矩阵中各个时间序列之间的相关性并使用H_∞范数进行量化，将多重时滞辨识问题转化为求取最大H_∞范数的问题。最后采用离散状态转移算法进行快速求解，得到最优时基序列即为辨识得到的多重时滞。将所提方法用于氧化铝蒸发过程的多重时滞辨识，将辨识出的多重时滞用于预测模型数据的预处理，预测模型的准确性提高了34.4%，验证了辨识方法的有效性。

关键词: 氧化铝, 蒸发, 时效关联分析, 多重时滞辨识, 离散状态转移算法, H_∞范数, 预测

CLC Number:

TF821

WANG Feng, WANG Xiaoli, XIE Yongfang, XIE Sen, YANG Chunhua. Multi-delays identification for alumina evaporation process based on time-correlation analysis[J]. CIESC Journal, DOI: 10.11949/j.issn.0438-1157.20161604.

王峰, 王晓丽, 谢永芳, 谢森, 阳春华. 基于时效关联分析的氧化铝蒸发过程多重时滞辨识[J]. 化工学报, DOI: 10.11949/j.issn.0438-1157.20161604.

References 23

[1]	RAD A B, LO W L, TSANG K M. Simultaneous online identification of rational dynamics and time delay:a correlation-based approach[J]. IEEE Transactions on Control Systems Technology, 2003, 11(6):957-959.
[2]	ZHENG W X, FENG C B. Identification of stochastic time lag systems in the presence of colored noise[J]. Automatica, 1990, 26(4):769-779.
[3]	RAJAPANDIYAN C, CHIDAMBARAM M. Closed-loop identification of second-order plus time delay model of multivariable systems by optimization method[J]. Industrial & Engineering Chemistry Research, 2012, 51(28):9620-9633.
[4]	JIN Q B, CHENG Z J, DOU J, et al. A novel closed loop identification method and its application of multivariable system[J]. Journal of Process Control, 2012, 22(1):132-144.
[5]	LIU T, GAO F R. A frequency domain step response identification method for continuous-time processes with time delay[J]. Journal of Process Control, 2010, 20(7):800-809.
[6]	赵仕俊, 李逊, 左光远. 灰色预估神经网络在时滞系统控制中的应用[J]. 微电子学与计算机, 2008, 25(2):25-27. ZHAO S J, LI X, ZUO G Y. Research on neural network controller with grey-model prediction applied in controlling systems with uncertain time delay[J]. Microelectronics & Computer, 2008, 25(2):25-27.
[7]	CHEN W S, LI J M. Adaptive output-feedback regulation for nonlinear delayed systems using neural network[J]. International Journal of Automation and Computing, 2008, 5(1):103-108.
[8]	陈龙祥, 蔡国平. 基于粒子群算法的时滞动力学系统时滞辨识[J]. 应用力学学报, 2010, 27(3):433-437. CHEN L X, CAI G P. Delay identification in time-delay dynamic system using particle swarm optimizer[J]. Chinese Journal of Applied Mechanics, 2010, 27(3):433-437.
[9]	范剑超, 韩敏. 基于高斯微粒群优化的动态神经网络延迟系统辨识[J]. 控制与决策, 2010, 25(11):1703-1706. FAN J C, HAN M. Dynamic neural network on Gaussian particle swarm optimization for delay system identification[J]. Control & Decision, 2010, 25(11):1703-1706.
[10]	王谦, 孙京诰. 基于改进粒子群优化算法的闭环时滞系统辨识[J]. 华东理工大学学报(自然科学版), 2015, 41(2):159-165, 209. WANG Q, SUN J G. Closed-loop identification with time-delay based on improved particle swarm optimization algorithm[J]. Journal of East China University of Science and Technology (Natural Science Edition), 2015, 41(2):159-165, 209.
[11]	TANG Y G, GUAN X P. Parameter estimation for time-delay chaotic system by particle swarm optimization[J]. Chaos, Solitons & Fractals, 2009, 40(3):1391-1398.
[12]	AHMED S, HUANG B, SHAH S L. Parameter and delay estimation of continuous-time models using a linear filter[J]. Journal of Process Control, 2006, 16(4):323-331.
[13]	REN X M, RAD A B, CHAN P T, et al. Online identification of continuous-time systems with unknown time delay[J]. IEEE Transactions on Automatic Control, 2005, 50(9):1418-1422.
[14]	BEDOUI S, LTAIEF M, ABDERRAHIM K. New results on discrete-time delay systems identification[J]. International Journal of Automation and Computing, 2012, 9(6):570-577.
[15]	TANG Y G, GUAN X P. Parameter estimation of chaotic system with time-delay:a differential evolution approach[J]. Chaos, Solitons & Fractals, 2009, 42(5):3132-3139.
[16]	ORLOV Y, BELKOURA L, RICHARD J P, et al. Adaptive identification of linear time-delay systems[J]. International Journal of Robust and Nonlinear Control, 2003, 13(9):857-872.
[17]	DE I S M. Robust adaptive control of linear time-delay systems with point time-varying delays via multiestimation[J]. Applied Mathematical Modelling, 2009, 33(2):959-977.
[18]	WANG Z D, HUANG B, UNBEHAUEN H. Robust H∞ observer design of linear state delayed systems with parametric uncertainty:the discrete-time case[J]. Automatica, 1999, 35(6):1161-1167.
[19]	DRAKUNOV S V, PERRUQUETTI W, RICHARD J P, et al. Delay identification in time-delay systems using variable structure observers[J]. Annual Reviews in Control, 2006, 30(2):143-158.
[20]	黄灿, 桂卫华, 谢永芳, 等. 基于改进互相关函数的氧化铝碳分过程多重时滞辨识[J]. 中国有色金属学报, 2011, 21(5):1186-1191. HUANG C, GUI W H, XIE Y F, et al. Multi-delays identification for alumina carbonation decomposition process based on improved cross-correlation function[J]. The Chinese Journal of Nonferrous Metals, 2011, 21(5):1186-1191.
[21]	谢永芳, 张静, 黄灿, 等. 基于改进GA的多重时滞辨识方法研究[C]//Proceedings of the 2011 Chinese Control and Decision Conference. 2011:3263-3266. XIE Y F, ZHANG J, HUANG C, et al. An improved genetic algorithm for multi-delay times identification[C]//Proceedings of the 2011 Chinese Control and Decision Conference, 2011:3263-3266.
[22]	ZHOU X J, GAO D Y, SIMPSON A R. Optimal design of water distribution networks by a discrete state transition algorithm[J]. Engineering Optimization, 2015, 48(4):603-628.
[23]	ZHOU X J, GAO D Y, YANG C H, et al. Discrete state transition algorithm for unconstrained integer optimization problems[J]. Neurocomputing, 2016, 173(3):864-874.