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基于RPLS的造纸废水处理过程软测量建模
Soft Sensor Modeling of Papermaking Effluent Treatment Processes Using RPLS
收稿日期：  
DOI：10.11980/j.issn.0254-508X.2016.10.007
关键词:  递归偏最小二乘  偏最小二乘  软测量建模  造纸废水处理
Key Words:recursive partial least squares  partial least squares  soft sensor modeling  papermaking effluent treatment
基金项目:南京林业大学高层次人才科研启动基金(No.16310-5996)；江苏省制浆造纸科学与技术重点实验室开放基金项目(201010)；江苏高校优势学科建设工程资助项目(PAPD)。
作者单位
杨　浩1 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 
莫卫林1 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 
熊智新1 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 
黄明智2 2.中山大学水资源与环境系,广东广州，510275 
刘鸿斌1,* 1.南京林业大学江苏省制浆造纸科学与技术重点实验室,江苏南京,210037 
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摘要:偏最小二乘(PLS)软测量预测模型在预测造纸废水处理过程中的出水化学需氧量(CODCr)和固体悬浮物(SS)时,易受过程非线性特性和系统外部干扰等因素的影响而失效。针对以上问题,研究了递归偏最小二乘(RPLS)算法的造纸废水处理过程软测量建模。计算结果表明,采用PLS模型预测出水CODCr时,平均绝对百分比误差(MAPE)、均方根误差(RMSE)和相关系数(R2)分别为5.3832%、4.6878和0.5892；采用RPLS模型预测时，MAPE、RMSE、R2分别为1.3861%、1.8792和0.9221。采用PLS模型预测SS时,MAPE、RMSE和R2分别为2.5962%、0.7412和0.6651；采用RPLS模型时MAPE、RMSE、R2分别为0.6795%、0.2198和0.9627。以上结果表明,RPLS预测模型比PLS预测模型具有更好的预测性能和更高的精度。
Abstract:Soft sensor modeling methods based on partial least squares (PLS) and recursive PLS （RPLS）were used to predict effluent chemical oxygen demand(CODCr) and effluent suspended solids(SS) in a papermaking wastewater treatment process. PLS is unsuitable for the systems with non-linear characteristics and external disturbances. The results showed that the mean absolute percentage error(MAPE),root mean square error(RMSE), and squared correlation coefficient(R2) for CODCr using PLS were 5.3832%, 4.6878, and 0.5892, respectively, and they were 1.3861%, 1.8792, and 0.9221, respectively using RPLS. In terms of SS, the MAPE, RMSE, and R2 were 2.5962%,0.7412, and 0.6651, respectively when using PLS, and the three indices using RPLS were 0.6795%, 0.2198, and 0.9627, respectively. These results indicated that the RPLS model had better prediction performance and higher accuracy compared to the PLS model.
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