Simultaneous multiple parameter optimization of multi-stage variable-inclination equal-thickness screening of coal

ABSTRACT

Screening is a key unit in the coal cleaning process. One of the most widely used screening technologies for solving the technical problems associated with coal accumulation is the multi-stage variable-inclination equal-thickness screening (MSVIETS) method. In this work, the Box-Behnken response surface methodology (BBRSM) was used to explore the synergistic mechanism of multiple operating parameters in the screening process. The simultaneous optimization of multiple parameters was conducted to obtain the optimal operating conditions and the significance order of the parameters. The significance order of the parameters affecting the screening efficiency η was β > f> Δθ. The parameter conditions for the model optimization experiments were set as β = 59.8°, f = 11.54 Hz, and Δθ = 4.16°. The screening experiments with different percentages of coarse and fine particles were carried out. The screening efficiency was higher than 86%, the separation accuracy was less than 1.1 mm, and the difference between the experimental results and the model prediction results was small. This verified the accuracy and the reliability of the prediction results of the response surface model.

KEYWORDS:

• Box-Behnken response surface methodology (BBRSM)
• simultaneous optimization
• multiple parameter
• multi-stage variable inclination equal thickness screening (MSVIETS)

Disclosure statement

There were no conflicts of interest in this article.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This study is financially supported by the Natural Science Foundation of Jiangsu Province (BK20180650), the State Key Laboratory of Process Automation in Mining & Metallurgy and Beijing Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2021-06), the National Natural Science Foundation of China (51904301, U1903132, 52125403) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX21_2409), the China Postdoctoral Science Foundation (2020M671652), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China.