Soft Sensor for the Compaction Density of Powders in the Elongated Metal Tube Based on Gaussian Process Regression

被引：0

作者：

Lin Jingdong ^{[1
]}

Xu Dafa ^{[1
]}

You Jiachuan ^{[1
]}

机构：

[1] Chongqing Univ, Coll Automat, Chongqing, Peoples R China

来源：

2015 CHINESE AUTOMATION CONGRESS (CAC) | 2015年

关键词：

soft senosr; Gaussian process regression; compaction density of powders; PREDICTION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Online measurement of compaction density of powders in the elongated metal tube is typically unavailable due to the limited conditions. To solve this problem, a soft sensor model based on Gaussian process regression method is applied, analyzing the factors that influence the powder density in the compaction process. Compared with Bayesian linear regression and SVM methods, the predicted results show that the proposed soft sensor based on Gaussian process regression model has advantage in predicting the compaction density of powders in the elongated metal tube. With this model, the real-time monitoring and control of compaction density of powders could be satisfied, which could guarantee the final explosive quality of powders in the metal tube.

引用

页码：622 / 626

页数：5

共 19 条

[1] INFERENTIAL CONTROL OF PROCESSES .2. STRUCTURE AND DYNAMICS OF INFERENTIAL CONTROL-SYSTEMS [J].

BROSILOW, C ;

TONG, M .

AICHE JOURNAL, 1978, 24 (03) :492-500

[2]

Chen T, 2007, CHEMOMETR INTELL LAB, V87, P59, DOI 10.1016/j.chemolab.2006.09.004

[3] Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression [J].

di Sciascio, Fernando ;

Amicarelli, Adriana N. .

COMPUTERS & CHEMICAL ENGINEERING, 2008, 32 (12) :3264-3273

[4] Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models [J].

Grbic, Ratko ;

Sliskovic, Drazen ;

Kadlec, Petr .

COMPUTERS & CHEMICAL ENGINEERING, 2013, 58 :84-97

[5]

He Zhi-Kun, 2013, CONTROL DECISION

[6] Review of adaptation mechanisms for data-driven soft sensors [J].

Kadlec, Petr ;

Grbic, Ratko ;

Gabrys, Bogdan .

COMPUTERS & CHEMICAL ENGINEERING, 2011, 35 (01) :1-24

[7] Data-driven Soft Sensors in the process industry [J].

Kadlec, Petr ;

Gabrys, Bogdan ;

Strandt, Sibylle .

COMPUTERS & CHEMICAL ENGINEERING, 2009, 33 (04) :795-814

[8] Adaptive soft sensor model using online support vector regression with time variable and discussion of appropriate hyperparameter settings and window size [J].

Kaneko, Hiromasa ;

Funatsu, Kimito .

COMPUTERS & CHEMICAL ENGINEERING, 2013, 58 :288-297

[9] A multipoint optical fibre sensor system for use in process water systems based on artificial neural network pattern recognition techniques [J].

King, D ;

Lyons, WB ;

Flanagan, C ;

Lewis, E .

SENSORS AND ACTUATORS A-PHYSICAL, 2004, 115 (2-3) :293-302

[10] A systematic approach for soft sensor development [J].

Lin, Bao ;

Recke, Bodil ;

Knudsen, Jorgen K. H. ;

Jorgensen, Sten Bay .

COMPUTERS & CHEMICAL ENGINEERING, 2007, 31 (5-6) :419-425

← 1 2 →