Predictive PDF Control of Output Fiber Length Stochastic Distribution in Refining Process
-
摘要: 磨浆过程作为制浆和造纸工业最为重要的生产环节之一，其输出纤维长度随机分布（Fiber length stochastic distribution，FLSD）形状直接决定着后续造纸过程的能耗和纸品质量.针对传统的均值和方差难以描述输出FLSD特征，即具有非高斯分布特性，本文提出一种磨浆过程输出FLSD的预测概率密度函数（Probability density function，PDF）控制方法.首先，采用径向基函数（Radical basis function，RBF）神经网络逼近输出FLSD的PDF，在采用迭代学习方法完成基函数参数整定基础上对相应权值向量进行估计.其次，针对权值之间存在强耦合特点，利用随机权神经网络（Random vector functional-networks，RVFLNs）建立表征输入变量和权值之间关系的预测模型.最后，基于建立的输出FLSD模型设计预测PDF控制器，最终实现对期望输出PDF的跟踪控制.基于工业数据实验验证了所提方法的有效性，为磨浆过程优化运行和控制提供理论依据.Abstract: As one of the most important production links in the pulp and papermaking industry, the output fiber length stochastic distribution (FLSD) shaping of the refining process directly determines the energy consumption and paper quality of the subsequent papermaking processes. The traditional mean and variance are insufficient to describe the characteristics of the output FLSD, which displays non-Gaussian distributional properties. This paper proposes a predictive probability density function (PDF) control method for the output FLSD in the refining process. Firstly, use the radical basis function (RBF) neural network to approximate PDF of the output FLSD, the iterative learning technique is utilized to tune the parameters of basis functions, and the corresponding weights vector can be estimated. Secondly, in view of the strong coupling between these weighting vectors, the random vector functional link networks (RVFLNs) are employed to characterize the prediction model between the input variables and the weights. Finally, we design the predictive PDF controller based on the established output FLSD model, thus realizing the tracking control of the desired output PDF. Furthermore, the industrial data based experiment verifies the effectiveness of the proposed method, which provides theoretical basis for optimal operation and control of the refining process.1) 本文责任编委 吴立刚
-
图 2 输出纤维长度随机分布预测PDF控制策略图
Fig. 2 Strategy diagram of the predictive PDF control for the output FLSD
图 10 预测PDF控制器下输出PDF3D响应
Fig. 10 3D responses of the output PDF with the predictive PDF
-
[1] Smook G A. Handbook for Pulp and Paper Technologists. Vancouver:Angus Wilde Publications, 1992. [2] Sikter D, Karlström A, Sandberg C, Engstrand P. Economic perspectives on quality control in TMP refining processes. Nordic Pulp & Paper Research Journal, 2008, 23(3):305-314 http://cn.bing.com/academic/profile?id=56c4903ed68eeb5396ace5ee2e8162ef&encoded=0&v=paper_preview&mkt=zh-cn [3] Lama I, Perrier M, Stuart P. Controllability analysis of a TMP-newsprint refining process. Pulp & Paper Canada, 2006, 107(10):44-48 http://cn.bing.com/academic/profile?id=4bd9d47cd5d2cadb4d3ae04bfd1de79b&encoded=0&v=paper_preview&mkt=zh-cn [4] Karlström A, Hill J. Refiner optimization and control Part Ⅰ:fiber residence time and major dynamic fluctuations in TMP refining processes. Nordic Pulp & Paper Research Journal, 2014, 29(4):635-652 [5] Harinath E, Biegler L T, Dumont G A. Control and optimization strategies for thermo-mechanical pulping processes:nonlinear model predictive control. Journal of Process Control, 2011, 21(4):519-528 doi: 10.1016/j.jprocont.2011.01.009 [6] Harinath E, Biegler L T, Dumont G A. Predictive optimal control for thermo-mechanical pulping processes with multi-stage low consistency refining. Journal of Process Control, 2013, 23(7):1001-1011 doi: 10.1016/j.jprocont.2013.05.005 [7] Gharehkhani S, Sadeghinezhad E, Kazi S N, Yarmanda H, Badarudina A, Safaei M R, Zubira M N M. Basic effects of pulp refining on fiber properties-a review. Carbohydrate Polymers, 2015, 115:785-803 doi: 10.1016/j.carbpol.2014.08.047 [8] Lacerda T M, Zambon M D, Frollini E. Effect of acid concentration and pulp properties on hydrolysis reactions of mercerized sisal. Carbohydrate Polymers, 2013, 93(1):347-356 doi: 10.1016/j.carbpol.2012.10.039 [9] Savastano Jr H, Warden P G, Coutts R S P. Mechanically pulped sisal as reinforcement in cementitious matrices. Cement and Concrete Composites, 2003, 25(3):311-319 doi: 10.1016/S0958-9465(02)00055-0 [10] Wang H. Bounded Dynamic Stochastic Distributions:Modelling and Control. London:Springer-Verlag, 2000. [11] 王宏, 岳红.随机系统输出分布的建模、控制与应用.控制工程, 2003, 10(3):193-197 doi: 10.3969/j.issn.1671-7848.2003.03.001Wang Hong, Yue Hong. Output PDF control of stochastic distribution systems:modelling, control and applications. Control Engineering of China, 2003, 10(3):193-197 doi: 10.3969/j.issn.1671-7848.2003.03.001 [12] Wang H. Robust control of the output probability density functions for multivariable stochastic systems with guaranteed stability. IEEE Transactions on Automatic Control, 1999, 41(11):2103-2107 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=52cd550278df6c8daa22810b321b98de [13] Wang H, Zhang J H. Bounded stochastic distributions control for pseudo-ARMAX stochastic systems. IEEE Transactions on Automatic Control, 2001, 46(3):486-490 doi: 10.1109/9.911429 [14] Wang H. Minimum entropy control of non-Gaussian dynamic stochastic systems. IEEE Transactions on Automatic Control, 2002, 47(2):398-403 doi: 10.1109/9.983388 [15] Wang H, Afshar P. ILC-based fixed-structure controller design for output PDF shaping in stochastic systems using LMI techniques. IEEE Transactions on Automatic Control, 2009, 54(4):760-773 doi: 10.1109/TAC.2009.2014934 [16] Guo L, Wang H. PID controller design for output PDFs of stochastic systems using linear matrix inequalities. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2005, 35(1):65-71 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=388caf48dc43547ca83bb8c05cc5e252 [17] Yue H, Zhou J L, Wang H. Minimum entropy of B-spline PDF systems with mean constraint. Automatica, 2006, 42(6):989-994 doi: 10.1016/j.automatica.2006.02.004 [18] Zhou J L, Yue H, Zhang J F, Wang H. Iterative learning double closed-loop structure for modeling and controller design of output stochastic distribution control systems. IEEE Transactions on Control Systems Technology, 2014, 22(6):2261-2276 doi: 10.1109/TCST.2014.2306452 [19] Wang A P, Afshar P, Wang H. Complex stochastic systems modelling and control via iterative machine learning. Neurocomputing, 2008, 71(13-15):2685-2692 doi: 10.1016/j.neucom.2007.06.018 [20] Zhang J F, Yue H, Zhou J L. Predictive PDF control in shaping of molecular weight distribution based on a new modeling algorithm. Journal of Process Control, 2015, 30:80-89 doi: 10.1016/j.jprocont.2014.12.009 [21] Zhu J Y, Gui W H, Yang C H, Xu H L, Wang X L. Probability density function of bubble size based reagent dosage predictive control for copper roughing flotation. Control Engineering Practice, 2014, 29:1-12 doi: 10.1016/j.conengprac.2014.02.021 [22] Igelnik B, Pao Y H. Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Transactions on Neural Networks, 1995, 6(6):1320-1329 doi: 10.1109/72.471375 [23] Stosic D, Stosic D, Zanchettin C, Ludermir T, Stosic B. QRNN:q generalized random neural network. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(2):383-390 doi: 10.1109/TNNLS.2015.2513365 [24] Zhou P, Lv Y B, Wang H, Chai T Y. Data-driven robust RVFLNs modeling of a blast furnace iron-making process using Cauchy distribution weighted M-estimation. IEEE Transactions on Industrial Electronics, 2017, 64(9):7141-7151 doi: 10.1109/TIE.2017.2686369 [25] Lu X J, Zou W, Huang M H. A novel spatiotemporal LS-SVM method for complex distributed parameter systems with applications to curing thermal process. IEEE Transactions on Industrial Informatics, 2016, 12(3):1156-1165 doi: 10.1109/TII.2016.2557805 [26] Zhou P, Chai T Y, Sun J. Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system. IEEE Transactions on Control Systems Technology, 2013, 21(1):162-175 doi: 10.1109/TCST.2012.2182996 [27] Zhou P, Lu S W, Chai T Y. Data-driven soft-sensor modeling for product quality estimation using case-based reasoning and fuzzy-similarity rough sets. IEEE Transactions on Automation Science and Engineering, 2014, 11(4):992-1003 doi: 10.1109/TASE.2013.2288279 -
下载:
图(11)
计量
- 文章访问数: 1183
- HTML全文浏览量: 216
- PDF下载量: 83
- 被引次数: 0
