Soft Sensor Model for Coal Slurry Ash Content Based on Image Gray Characteristics

Abstract

Coal slurry ash content is an important variable in the control of a flotation process. It can be concluded that coal slurry image gray feature has a certain correlation with ash content by analyzing coal slurry images at different ash values in this article. Based on image gray features, coal slurry ash content soft sensor models are developed by using BP Neural Network and simple eigenvalue-based Least Square Regression Method (LS), respectively. The simulation results of the two models indicate that the soft sensor model of coal slurry ash content based on the BP Neural Network is more optimal than that based on LS. The model based on BP Neural Network has a high accuracy when coal slurry ash content is higher than 35%. Through this investigation, coal slurry ash can be detected rapidly online.

Keywords:

• BP neutral network
• Coal slurry ash content
• Image gray characteristics
• Soft sensor

Notes

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