Building Predicting Model of Paper Tensile Strength Based on Partial Least-Squares Approach

doi:10.3969/j.issn.0438-1157.2014.00.000

Abstract

Abstract: The problems existing in the mechanism models include hard to obtaining the parameters and most of them had little relationship with production which reduced its practicability. To solve it, partial least-squares method (PLS) was taken to establish model to predict paper strength based on a production line of a corrugated paper mill. Selecting parameters and its data aided with mechanism analysis, this model was built up and the key parameters were obtained. The results showed that this model had a good precision that its Person's value was 0.732, Root Mean Square Error (RMSE) was 276 N·m^-1, and Mean Relative Error (MRE) was 5.17%. Besides that, the model had a good analytical ability that the key elements could be interpreted very well from mechanism angle.

Key words: chemical processes, paper sheet tensile strength, product engineering, partial least-squares, prediction

摘要： 为了解决抗张强度机理模型中参数难以测量，偏离工厂生产变量，实际指导关联性差的问题。以一瓦楞原纸生产线为研究对象，先通过经验分析、筛选影响纸张抗张强度的生产过程变量，再收集对应变量的生产数据后，应用偏最小二乘法建立抗张强度预测模型并对重要影响变量进行分析。结果表明该模型具有较好精度，其皮尔逊相关系数r=0.732，均方根误差RMSE值为276 N·m^-1，平均相对误差MRE值为5.17%。同时得到了影响纸张抗张强度的6个重要生产过程变量，通过机理分析和现场验证，发现结果具有较好的现实吻合度。

关键词: 化学过程, 纸张抗张强度, 产品工程, 偏最小二乘法, 预测

CLC Number:

TS75

LI Yuan-hua, TAO Jing-song, LI Ji-geng, LIU Huan-bin. Building Predicting Model of Paper Tensile Strength Based on Partial Least-Squares Approach[J]. CIESC Journal, DOI: 10.3969/j.issn.0438-1157.2014.00.000.

李远华, 陶劲松, 李继庚, 刘焕彬. 基于偏最小二乘法的纸张抗张强度预测模型[J]. 化工学报, DOI: 10.3969/j.issn.0438-1157.2014.00.000.

References

[1] Chen Kefu(陈克复). Pulp and paper machinery and equipment [M]. Beijing: Chia Light Industry Press, 2003

[2] Page D H. A theory for the tensile strength of paper[J]. TAPPI JOURNAL, 1969, 52(4): 674-679

[3] Garg P, Scott W. Potential Application of Predictive Tensile Strength Models in Paper Manufacture: Part Ⅰ-Development of a Predictive Tensile Strength Model from the Page Equation, TAPPI Papermakers Conference Proceedings, 2001

[4] Garg P, Scott W. Potential Application of Predictive Tensile Strength Models in Paper Manufacture: Part Ⅰ-Development of a Predictive Tensile Strength Model from the Page Equation, TAPPI Papermakers Conference Proceedings, 2001

[5] Anson S J I', Karademir A, Sampson W W. Specific contact area and the tensile strength of paper[J].Appita Journal,2006,59(4): 297-301

[6] Westerlind Bo S, Arnesson P, Sandstorm P, et al. Engineering approach to page and shear-lag theory for predicting tensile strength of paper, 61st Appita Annual Conference and Exhibition, Gold Coast, 2007

[7] Axelsson A. Fibre based models for predicting tensile strength of paper[D]. Department of Skellefteå Campus: Luleå University of Technology, Finland, 2009: 32-65

[8] Wang Baoyu(王宝玉). Study on the Relationship between the Chemical Characteristics of Wood Fiber and Paper Sheet Strength[D].South China University of Technology, 2011

[9] Navita S, Kumar Ra. Articficial Neural Network Modeling For Tensile Strength of Paper in Paper Manufacturing Process International[J]. Information Technology and Knowledge Management, 2011, 4(2): 409-412

[10] Wang Huiwen(王惠文). Partial least-squares regression method and application [M]. Beijing: National Defense Industry Press, 2000: 123-202

[11] Hu Yi(胡益), Wang Li(王丽), Ma Hehe(马贺贺). Online nonlinear process monitoring using kernel partial least squares[J]. CIESC Journal(化工学报), 2011, 62(9): 2555-2562

[12] Jia Runda(贾润达), Mao Zhizhong(毛志忠), Wang Fuli(王福利). KPLS model based product quality control for batch processes[J]. CIESC Journal(化工学报), 2013, 64(4): 1332-1341

[13] Wang Xiaoyong(汪小勇). Based on multivariate statistical projection method of soft measurement technology research industrial process[D]. Zhejiang University, 2004

[14] Chu Fei(褚菲), Wang Fuli(王福利), Wang Xiaogang(王小刚). Hybrid modeling method for performance prediction of multi-stage centrifugal compressor[J]. Chinese Journal of Scientific Instrument, 2011, 32(12): 2821-2827

[15] Noraini Ibrahim, Antoni Wibowo. Partial Least Squares Regression Based Variables Selection for Water Level Predictions [J]. American Journal of Applied Sciences, 2013.10(4): 322-330

[16] Mihaleva V V, van Schalkwijk D B, de Graaf A A. A Systematic Approach to Obtain Validated Partial Least Square Models for Predicting Lipoprotein Subclasses from Serum NMR Spectra [J]. Analytical Chemistry, 2014. 86(1): 543-550

[17] Pearson coefficient [DB/OL].

http://wiki.mbalib.com/wiki/Pearson coefficient

[18] Liu Guixiong(刘桂雄), Lin Zhuhong(林绪虹). Partial Least-Square Regressive Analysis and Modeling of Ultraweak Photon Emission of Fish [J]. Journal of South China University of Technology, 2006.34(11): 29-32

[19] Wang Xueming(王学民). Statistical analysis methods and applications. Shanghai: Shanghai University of Finance and Economics Press, 2010

[20] Lu Qianhe(卢谦和). Paper making theory and engineering[M]. Beijing: China Light Industry Press, 2007: 225-349