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Soft Sensing of Overflow Particle Size Distributions in Hydrocyclones Using a Combined Method 被引量:2

Soft Sensing of Overflow Particle Size Distributions in Hydrocyclones Using a Combined Method
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摘要 Precise, real-time measurements of overflow particle size distributions in hydrocyclones are necessary for accurate control of the comminution circuits. Soft sensing measurements provide real-time, flexible, and low-cost measurements appropriate for the overflow particle size distributions in hydrocyclones. Three soft sensing methods were investigated for measuring the overflow particle size distributions in hydrocyclones. Simulations show that these methods have various advantages and disadvantages. Optimal Bayesian estimation fusion was then used to combine three methods with the fusion parameters determined according to the performance of each method with validation samples. The combined method compensates for the disadvantages of each method for more precise measurements. Simulations using real operating data show that the absolute root mean square measurement error of the combined method was always about 2% and the method provides the necessary accuracy for beneficiation plants. Precise, real-time measurements of overflow particle size distributions in hydrocyclones are necessary for accurate control of the comminution circuits. Soft sensing measurements provide real-time, flexible, and low-cost measurements appropriate for the overflow particle size distributions in hydrocyclones. Three soft sensing methods were investigated for measuring the overflow particle size distributions in hydrocyclones. Simulations show that these methods have various advantages and disadvantages. Optimal Bayesian estimation fusion was then used to combine three methods with the fusion parameters determined according to the performance of each method with validation samples. The combined method compensates for the disadvantages of each method for more precise measurements. Simulations using real operating data show that the absolute root mean square measurement error of the combined method was always about 2% and the method provides the necessary accuracy for beneficiation plants.
出处 《Tsinghua Science and Technology》 SCIE EI CAS 2008年第1期47-53,共7页 清华大学学报(自然科学版(英文版)
基金 the Key Technologies Research and Development Program of the Eleventh Five-Year Plan of China (No. 2006AA060206)
关键词 soft sensing overflow particle size distribution machine learning IDENTIFICATION soft sensing overflow particle size distribution machine learning identification
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参考文献15

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