基于宽度学习的浓密机底流浓度软测量

doi:10.12068/j.issn.1005-3026.2021.09.003

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (9): 1231-1237.DOI: 10.12068/j.issn.1005-3026.2021.09.003

基于宽度学习的浓密机底流浓度软测量

贾润达，胡慧明，张树磊

(东北大学信息科学与工程学院，辽宁沈阳110819)

修回日期:2020-12-30 接受日期:2020-12-30 发布日期:2021-09-16
通讯作者: 贾润达
作者简介:贾润达(1981-)，男，辽宁沈阳人，东北大学副教授，博士生导师.
基金资助:
国家自然科学基金资助项目(61873049)；中央高校基本科研业务费专项资金资助项目(N180704013).

Soft Sensor of Underflow Concentration for Thickener Based on Broad Learning System

JIA Run-da， HU Hui-ming， ZHANG Shu-lei

School of Information Science & Engineering， Northeastern University， Shenyang 110819， China.

Revised:2020-12-30 Accepted:2020-12-30 Published:2021-09-16
Contact: JIA Run-da
About author:-
Supported by:
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摘要/Abstract

摘要： 由于浓密脱水过程中浓密机的底流浓度难以在线检测，本文提出了一种基于宽度学习的软测量建模方法，用以解决底流浓度的在线检测问题.该方法精度高，泛化能力强.首先，在浓密机内部安装压力传感器，建立正常工况下的历史数据集;然后，利用宽度学习系统对软测量模型进行训练，从而实现浓密机底流浓度的在线预测;最后，通过仿真实验验证了该方法的有效性.与传统的机器学习方法相比，宽度学习方法具有更高的预测精度.

关键词: 浓密机;宽度学习;底流浓度;软测量;深度学习

Abstract: Since it is difficult to online measure the underflow concentration of the thickener in the thickening-dehydration process， a broad learning system(BLS) based soft sensor modeling method is proposed in this paper. The method has high precision and strong generalization capability. First， several pressure sensors are installed inside the thickener， and the historical dataset under normal operating conditions is established. Then， the soft sensor model is trained by employing the BLS method to online predict the underflow concentration of the thickener. Finally， the efficiency of the proposed method is verified by simulation experiments. Compared with other traditional machine learning methods， the BLS method has higher prediction accuracy.

Key words: thickener; broad learning system (BLS); underflow concentration; soft sensor; deep learning

中图分类号:

TP273

贾润达，胡慧明，张树磊. 基于宽度学习的浓密机底流浓度软测量[J]. 东北大学学报（自然科学版）, 2021, 42(9): 1231-1237.

JIA Run-da， HU Hui-ming， ZHANG Shu-lei. Soft Sensor of Underflow Concentration for Thickener Based on Broad Learning System[J]. Journal of Northeastern University(Natural Science), 2021, 42(9): 1231-1237.

参考文献

[1]范继涛，朱勃霖.矿产资源节约与综合利用先进技术推广的思考［J］.中国矿业，2013，22(11):23-26.(Fan Ji-tao，Zhu Bo-lin.Thinking for mineral resource saving and comprehensive utilization advanced technology popularization［J］.China Mining Industry，2013，22(11):23-26.)
[2]Nunez F，Langarica S，Diaz P，et al.Neural network-based model predictive control of a paste thickener over an industrial internet platform［J］.IEEE Transactions on Industrial Informatics，2019，16(4):2859-2867.
[3]Zhang H，Wang F，Zhang I，et al.Soft sensing model of underflow concentration for thickener process based on data reconciliation［C］//2020 Chinese Control and Decision Conference(CCDC).Hefei，2020:3337-3341.
[4]Xiao D，Xie H，Jiang L，et al.Research on a method for predicting the underflow concentration of a thickener based on the hybrid model［J］.Engineering Applications of Computational Fluid Mechanics，2020，14(1):13-26.
[5]Yuan J，Wang S，Wang F，et al.A time-difference PLS based soft sensor method of underflow concentration for thickener in hydrometallurgy［C］//2017 29th Chinese Control and Decision Conference(CCDC).Chongqing，2017:248-253.
[6]Jia R D，Mao Z Z，Chang Y Q，et al.Kernel partial robust M-regression as a flexible robust nonlinear modeling technique［J］.Chemometrics and Intelligent Laboratory Systems，2010，100(2):91-98.
[7]Feng L，Zhao C，Chen C L P，et al.BNGBS:an efficient network boosting system with triple incremental learning capabilities for more nodes，samples，and classes［J］.Neurocomputing，2020，412:486-501.
[8]Chai Z，Zhao C.Multiclass oblique random forests with dual-incremental learning capacity［J］.IEEE Transactions on Neural Networks and Learning Systems， 2020，31(12):5192-5203.
[9]桂卫华，阳春华，陈晓方，等.有色冶金过程建模与优化的若干问题及挑战［J］.自动化学报，2013，39(3):197-207.(Gui Wei-hua，Yang Chun-hua，Chen Xiao-fang，et al.Problems and challenges for modeling and optimizing nonferrous metallurgy processes［J］.Journal of Automatica Sinica，2013，39(3):197-207.)
[10]马荣骏.湿法冶金新发展［J］.湿法冶金，2007，26(1):1-12.(Ma Rong-jun.Novel development of hydrometallurgy［J］.Hydrometallurgy，2007，26(1):1-12.)
[11]Chen C L P，Liu Z.Broad learning system:an effective and efficient incremental learning system without the need for deep architecture［J］.IEEE Transactions on Neural Networks & Learning Systems，2018，29(1):10-24.
[12]Shuang F，Philip C C L.Fuzzy broad learning system:a novel neuro-fuzzy model for regression and classification［J］.IEEE Transactions on Cybernetics，2018，50(2):414-424.
[13]Chen C L P，Liu Z，Feng S.Universal approximation capability of broad learning system and its structural variations［J］. IEEE Transactions on Neural Networks and Learning Systems，2019，30(4):1191-1204.
[14]Feng S，Ren W，Han M，et al.Robust manifold broad learning system for large-scale noisy chaotic time series prediction:a perturbation perspective［J］.Neural Networks，2019，117:179-190
[15]Zhu L，Lian C，Zeng Z，et al.A broad learning system with ensemble and classification methods for multi-step-ahead wind speed prediction［J］.Cognitive Computation，2020，12(4):654-666.
[16]Senave M，Reynders G，Bacher P，et al.Towards the characterization of the heat loss coefficient via on-board monitoring:physical interpretation of ARX model coefficients［J］.Energy and Buildings，2019，195:180-194.
[17]Baraldi P，Manginelli A A，Maieron M，et al.An ARX model-based approach to trial by trial identification of fMRI-BOLD responses［J］.Neuroimage，2007，37(1):189-201.
[18]Mcroberts R E，Tomppo E O，Finley A O，et al.Estimating areal means and variances of forest attributes using the k-nearest neighbors technique and satellite imagery［J］.Remote Sensing of Environment，2015，111(4):466-480.

基于宽度学习的浓密机底流浓度软测量

Soft Sensor of Underflow Concentration for Thickener Based on Broad Learning System

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