Nonlinear optimization of gravity solids classification based on the feed and deck angles: A law of mass action approach

Research output: Contribution to journalArticle

Researchers

Research units

  • University of Waterloo
  • Lappeenranta University of Technology

Abstract

Deck screen design parameters e.g. material of construction, deck angle of inclination, the feed throughputs, and physicochemical properties of the particles, are critical factors to consider in solids classification. Two significant and easily manipulated parameters that greatly affect screen performance are the feed rate and design geometry configuration. In this work we apply statistical analysis of variance (ANOVA) and nonlinear least squares optimization with parameter estimation concepts, first, to assess the significance of the two factors and, second to formulate flow prediction models that optimize the feed rate and classification efficiency. Experiments were conducted on a prototype screen of 556.28cm2 effective area, (1380cm2 total area). For glass beads of sizes 0.75, 1, 2, and 3mm, with 16 feed batches of 10g to 160g, and six inclination angles 5, 10, 12.5, 15, 17.5, and 20°, a maximum efficiency of 66.7% was achieved with a screen loading of 86.5g, and an inclination angle of 17.5°. These results were then subjected to nonlinear least squares optimization, which showed that a maximum efficiency of 93.2% can be achieved at batch loading as low as 36g. There was a favorable performance at the range of angles 12.5≤θ≤17.5°, but poor performance outside this range. The screening efficiency did not respond significantly to changes in screen loading, although loading had a significant effect on the screening capacity. Confirmation tests conducted at selected optimum parameters achieved a maximum efficiency of 72% (at 12.5° with 49.6g batch load), and a maximum rate of 27g/s at 17.5° with 104g.

Details

Original languageEnglish
Pages (from-to)140-146
Number of pages7
JournalPowder Technology
Volume291
Publication statusPublished - 1 Apr 2016
MoE publication typeA1 Journal article-refereed

    Research areas

  • Gravity-classification, Mineral particles, Modeling

ID: 10770375