Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 133-142.doi: 10.6040/j.issn.1672-3961.0.2019.009

• Others • Previous Articles    

Application of variable selection in soft sensor modeling of wastewater treatment processes

Hongbin LIU1,2(),Qiyue WU1,Liu SONG1   

  1. 1. Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
    2. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
  • Received:2019-01-07 Online:2020-06-20 Published:2020-06-16
  • Supported by:
    制浆造纸工程国家重点实验室开放基金资助项目(201813);南京林业大学高层次人才科研启动基金(GXL029)

Abstract:

Chemical oxygen demand and suspended solid were important monitoring indices of effluent discharge in paper-making industry. An effective model of effluent quality of wastewater treatment processes was of key importance to monitoring and controlling pollution emission. Concerning the strong correlations among the input variables and the complicated characteristics of wastewater treatment processes in paper-making industry, partial least squares (PLS) method was applied to extract information of variables importance in projection (VIP) for variable selection (VS). Then the optimal variables were chosen as new input variables for soft sensor models to predict the effluent qualities of a papermaking wastewater treatment process. Compared to the LSSVM model, the root mean square error (RMSE) of VS-based LSSVM model was reduced by 15.2%, and the correlation coefficient (r) was increased by 14.4%. For the effluent SS, the value of RMSE was decreased by 20.5%, and the value of r was increased by 16.1%. The results showed that the proposed method not only reduced the model complexity, but also enhanced the model generalization capacity.

Key words: wastewater treatment, effluent quality, variable selection, variables importance in projection, partial least squares

CLC Number: 

  • X703

Fig.1

VIP scores of auxiliary variables"

Fig.2

Coefficient of stepwise regression"

Fig.3

Prediction results of effluent SS"

Table 1

Prediction results of models"

模型出水COD出水SS
RMSEMAPE/%rRMSEMAPE/%r
BPNN训练集5.196 14.370 50.807 90.698 32.415 80.832 3
测试集5.523 66.373 70.660 40.714 82.610 80.715 7
VS-BPNN训练集4.396 44.553 10.860 40.632 62.259 30.864 7
测试集4.404 15.324 30.797 30.649 72.270 30.801 6
LSSVM训练集4.324 84.529 50.883 60.723 42.580 60.829 1
测试集4.948 15.740 40.697 60.758 42.680 70.701 6
VS-LSSVM训练集4.003 94.169 70.890 50.596 62.134 50.889 5
测试集4.194 64.800 00.798 40.603 32.167 60.814 3

Fig.4

Prediction results of effluent SS"

Fig.5

Prediction results of effluent COD"

Fig.6

Prediction results of effluent COD"

1 OLSSON G , NEWELL B . Wastewater treatment systems, modeling, diagnosis and control[M]. Britain, London: IWA Publishing, 1999.
2 SHEN Wenhao , CHEN Xiaoquan , CORRIOU J P . Application of model predictive control to the BSM1 benchmark of wastewater treatment process[J]. Computers & Chemical Engineering, 2008, 32 (12): 2849- 2856.
3 黄银蓉, 张绍德. MIMO最小二乘支持向量机污水处理在线软测量研究[J]. 自动化与仪器仪表, 2010, (4): 15- 17.
doi: 10.3969/j.issn.1001-9227.2010.04.006
HUANG Yinrong , ZHANG Shaode . Online soft measurement for wastewater treatment based on MIMO least squares support vector machine[J]. Automation & Instrumentation, 2010, (4): 15- 17.
doi: 10.3969/j.issn.1001-9227.2010.04.006
4 王欣, 宋翼颉, 秦斌, 等. 基于LSSVM的污水处理过程建模[J]. 湖南工业大学学报, 2016, 30 (1): 59- 63.
doi: 10.3969/j.issn.1673-9833.2016.01.011
WANG Xin , SONG Yijie , QIN Bin , et al. Modeling of sewage treatment process based on MIMO-LSSVM[J]. Journal of Hunan University of Technology, 2016, 30 (1): 59- 63.
doi: 10.3969/j.issn.1673-9833.2016.01.011
5 汪瑶, 徐亮, 殷文志, 等. 基于ANN和LSSVR的造纸废水处理过程软测量建模[J]. 中国造纸学报, 2017, 32 (1): 50- 54.
WANG Yao , XU Liang , YIN Wenzhi , et al. Soft sensor modeling of papermaking treatment processes based on ANN and LSSVR[J]. Transactions of China Pulp and Paper, 2017, 32 (1): 50- 54.
6 宋留, 杨冲, 张辉, 等. 造纸废水处理过程的高斯过程回归软测量建模[J]. 中国环境科学, 2018, 38 (7): 2564- 2571.
doi: 10.3969/j.issn.1000-6923.2018.07.023
SONG Liu , YANG Chong , ZHANG Hui , et al. Soft-sensor modeling of papermaking wastewater treatment process based on Gaussian process[J]. China Environmental Science, 2018, 38 (7): 2564- 2571.
doi: 10.3969/j.issn.1000-6923.2018.07.023
7 KOHAVI R , JOHN G H . Wrappers for feature subset selection[J]. Artificial Intelligence, 1997, 97 (1-2): 273- 324.
doi: 10.1016/S0004-3702(97)00043-X
8 MALDONADO S , WEBER R , FAMILI F . Feature selection for high-dimensional class-imbalanced data sets using support vector machines[J]. Information Sciences, 2014, 286, 228- 246.
doi: 10.1016/j.ins.2014.07.015
9 MALDONADO S , LÓPEZ J . Dealing with high-dimensional class-imbalanced datasets: embedded feature sele-ction for SVM classification[J]. Applied Soft Com-puting, 2018, 67, 94- 105.
doi: 10.1016/j.asoc.2018.02.051
10 ANDERSEN C M , BRO R . Variable selection in regression: a tutorial[J]. Journal of Chemometrics, 2010, 24 (11/12): 728- 737.
11 SHAHRIARI S , FARIA S , GONÇCALVES A M . Variable selection methods in high-dimensional regre-ssion: a simulation study[J]. Communications in Statistics-Simulation and Computation, 2015, 44 (10): 2548- 2561.
doi: 10.1080/03610918.2013.833231
12 DUPUIS D J , VICTORIA F M . Robust VIF regression with application to variable selection in large data sets[J]. The Annals of Applied Statistics, 2013, 7 (1): 319- 341.
doi: 10.1214/12-AOAS584
13 王树云, 宋云胜. 线性模型下基于AIC准则的Bayes变量选择[J]. 山东大学学报(理学版), 2010, 45 (6): 43- 45.
WANG Shuyun , SONG Yunsheng . Bayesian variable selection based on AIC criteria in linear models[J]. Journal of Shandong University (Natural Science), 2010, 45 (6): 43- 45.
14 杨慧中, 章军, 陶洪峰. 基于互信息的软测量变量选择[J]. 控制工程, 2012, 19 (4): 562- 565.
doi: 10.3969/j.issn.1671-7848.2012.04.004
YANG Huizhong , ZHANG Jun , TAO Hongfeng . A variable selection for soft sensor based on mutual information[J]. Control Engineering of China, 2012, 19 (4): 562- 565.
doi: 10.3969/j.issn.1671-7848.2012.04.004
15 朱群雄, 郎娜. 工业软测量模型结构与输入变量选择的研究[J]. 控制工程, 2011, 18 (3): 388- 392.
doi: 10.3969/j.issn.1671-7848.2011.03.017
ZHU Qunxiong , LANG Na . Research on model structure and input variable selection for industry soft sensing[J]. Control Engineering of China, 2011, 18 (3): 388- 392.
doi: 10.3969/j.issn.1671-7848.2011.03.017
16 WAN J , HUANG M , MA Y , et al. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system[J]. Applied Soft Computing, 2011, 11 (3): 3238- 3246.
doi: 10.1016/j.asoc.2010.12.026
17 CHONG I-G , JUN C-H . Performance of some variable selection methods when multicollinearity is present[J]. Chemometrics and Intelligent Laboratory Systems, 2005, 78 (1/2): 103- 112.
18 FARRÉ M , PLATIKANOV S , TSAKOVSKI S , et al. Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation[J]. Journal of Chemometrics, 2015, 29 (10): 528- 536.
19 韩放, 吴晶辰, 徐江峰, 等. 利用PLS-VIP方法筛选差异表达基因[J]. 北京大学学报(自然科学版), 2009, 45 (1): 1- 5.
doi: 10.3321/j.issn:0479-8023.2009.01.001
HAN Fang , WU Jingchen , XU Jiangfeng , et al. Searching for differentially expressed genes by PLS-VIP method[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2009, 45 (1): 1- 5.
doi: 10.3321/j.issn:0479-8023.2009.01.001
20 刘国海, 程锦翔, 丁煜函, 等. 青霉素发酵过程中基于PLS-VIP的神经网络逆软测量方法[J]. 计算机与应用化学, 2013, 30 (11): 1294- 1298.
doi: 10.3969/j.issn.1001-4160.2013.11.012
LIU Guohai , CHEN Jinxiang , DING Yuhan , et al. Neural network inverse soft-sensing method based on PLS-VIP in penicillin fermentation process[J]. Computers and Applied Chemistry, 2013, 30 (11): 1294- 1298.
doi: 10.3969/j.issn.1001-4160.2013.11.012
21 阎威武, 邵惠鹤. 支持向量机和最小二乘支持向量机的比较及应用研究[J]. 控制与决策, 2003, 18 (3): 358- 360.
doi: 10.3321/j.issn:1001-0920.2003.03.025
YAN Weiwu , SHAO Huihe . Application of support vector machines and least squares support vector machines to heart disease diagnoses[J]. Control and Decision, 2003, 18 (3): 358- 360.
doi: 10.3321/j.issn:1001-0920.2003.03.025
22 丁兰, 张文阳, 张良均, 等. 基于人工神经网络的居民生活垃圾可燃成分热值预测[J]. 环境工程学报, 2016, 10 (2): 899- 905.
DING Lan , ZHANG Wenyang , ZHANG Liangjun , et al. Prediction of household waste combustible component calorific value based on artificial neural network[J]. Chinese Journal of Environmental Engineering, 2016, 10 (2): 899- 905.
23 严文峰, 李晓东, 高智花, 等. 污泥厌氧消化的人工神经网络模型[J]. 环境工程学报, 2015, 9 (5): 2425- 2429.
YAN Wenfeng , LI Xiaodong , GAO Zhihua , et al. Artificial neural network model of sludge anaerobic digestion[J]. Chinese Journal of Environmental Engineering, 2015, 9 (5): 2425- 2429.
24 张恒德, 张庭玉, 李涛, 等. 基于BP神经网络的污染物浓度多模式集成预报[J]. 中国环境科学, 2018, 38 (4): 1243- 1256.
ZHANG Hengde , ZHANG Tingyu , LI Tao , et al. Forecast of air quality pollutants' concentrations based on BP neural network multi-model ensemble method[J]. China Environmental Science, 2018, 38 (4): 1243- 1256.
25 薛美盛, 冀若阳, 王旭. 基于多元线性回归与滚动窗的NOx排放量软测量[J]. 化工自动化及仪表, 2017, 44 (8): 721- 724.
doi: 10.3969/j.issn.1000-3932.2017.08.002
XUE Meisheng , JI Ruoyang , WANG Xu . Soft sensor for NOx emissions based on multiple linear regression and sliding window[J]. Control and Instruments in Chemical Industry, 2017, 44 (8): 721- 724.
doi: 10.3969/j.issn.1000-3932.2017.08.002
26 HWANG J S , HU T H . A stepwise regression algorithm for high-dimensional variable selection[J]. Journal of Statistical Computation & Simulation, 2015, 85 (9): 1793- 1806.
27 MA Mingda , KO J W , WANG S J , et al. Development of adaptive soft sensor based on statistical identification of key variables[J]. Control Engineering Practice, 2008, 17 (9): 1026- 1034.
[1] Hongbin LIU,Liu SONG. Study on modeling methods of wastewater treatment processes with canonical correlation analysis [J]. Journal of Shandong University(Engineering Science), 2020, 50(1): 101-108.
[2] Run XIANG,Sufen CHEN,Xueqiang ZENG. Facial age estimation based on multivariate multiple regression [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 54-60.
[3] YE Xiaofeng, WANG Peiliang, YANG Zeyu. Quality prediction method based on hybrid MPLS for multiphases process [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 246-253.
[4] SUN Cuiping, ZHOU Weizhi, ZHAO Haixia. Efficiency and mechanism of ferric salt enhanced biological phosphorus removal [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(2): 82-88.
[5] FANG Li-ying, LI Shuang, WANG Pu, CHEN Pei-yu. The application of varying coefficient model in the study of medical longitudinal data [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(6): 21-26.
[6] RAO Min1, LI Yan-hong1, CHEN Wei-hua1, SONG Jian-bo2, CHEN Guang-da1, LIU Xiao-zuo1. Study on the treatment of diazodinitrophenol industrial wastewater [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2012, 42(6): 100-106.
[7] YU Feng-jun, SHI Lai-shun*. Manganese  dioxide  catalytic  oxidation with  chlorine  dioxide  as  an  oxidant  of  simulated  wastewater  containing  acid chrome blue K [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(2): 88-94.
[8] DONG Zeng-Shou, LIU Ming-Jun. The application improvement of PLS in  boiler NOx  emissions testing [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(1): 126-128.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] ZHANG Yong-hua,WANG An-ling,LIU Fu-ping . The reflected phase angle of low frequent inhomogeneous[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 22 -25 .
[2] LI Liang, LUO Qiming, CHEN Enhong. Graph-based ranking model for object-level search
[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 15 -21 .
[3] WANG Bo,WANG Ning-sheng . Automatic generation and combinatory optimization of disassembly sequence for mechanical-electric assembly[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 52 -57 .
[4] QIN Tong, SUN Fengrong*, WANG Limei, WANG Qinghao, LI Xincai. 3D surface reconstruction using the shape based interpolation guided by maximal discs[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(3): 1 -5 .
[5] LIU Wen-liang, ZHU Wei-hong, CHEN Di, ZHANG Hong-quan. Detection and tracking of moving targets using the morphology match in radar images[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(3): 31 -36 .
[6] YUE Yuan-Zheng. Relaxation in glasses far from equilibrium[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(5): 1 -20 .
[7] WANG Pei,ZHANG Yanning,SHEN Jiazhen,LIU Juncheng, . Application of information measure and support vector machine in image edge detection[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(3): 95 -99 .
[8] LI Fangjia, GAO Shangce, TANG Zheng*, Ishii Masahiro, Yamashita Kazuya. 3D similar pattern generation of snow crystals with cellular automata[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 102 -105 .
[9] CHENG Daizhan, LI Zhiqiang. A survey on linearization of nonlinear systems[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(2): 26 -36 .
[10] QU Yan-peng,CHEN Song-ying,LI Chun-feng,WANG Xiao-peng,TENG Shu-ge . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(4): 16 -20 .