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Vol. 157, Issue 10, October 2013, pp. 434-441

 

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Research on Reduction Section Temperature Soft Sensor Model of Hematite-Ore-Beneficiation Shaft Furnace Roasting Process with Compressed Sensing Sampling
 
Huayi LI, Wenjie GAI

School of Management, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
Tel.:+86 137 8090 0866
E-mail: cobehuayili@gmail.com

 

Received: 11 July 2013   /Accepted: 25 September 2013   /Published: 31 October 2013

Digital Sensors and Sensor Sysstems

 

Abstract: The roasting process of shaft furnace is one of the most important processes in the Minerals Processing Factory, the primary task of it is to provide the roasting ore of hematite with higher magnetism by high temperature deoxidization, in order to fit request of the integrated production indexes. Traditional math models can't describe the dynamics of many mineral industry processes due to their integrated complexities, and complex association of much technical equipment is in production process. This paper establishes a soft sensor model for predicting the reduction section temperature of shaft furnace roasting, and uses CS (Compressed Sensing) theory for data sampling, combining PCA (principal component analysis), case-based reasoning and RBF. The proposed approach can keep the stability of temperature away from some harmful effects, such as big time delay, nonlinearity, etc. Even it can avoid the coming faults during roasting. With production data of the hematite ore beneficiation process of a plant, the simulation experiments prove that the CS is effectiveness for low rate sampling, and industrial results prove the soft sensor model effectively.

 

Keywords: Beneficiation, Soft Sensor, RBF, Compressed sensing, PCA.

 

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