Deconvolution of inclined channel elutriation data to infer particle size distribution

TL;DR

This paper explores the use of regularisation techniques to deconvolve mineral fractionation data from a fluidised bed model, enabling accurate inference of particle size distribution despite the ill-posed nature of the problem.

Contribution

It introduces a novel application of optimisation and regularisation methods to deconvolve particle size distribution from fluidised bed data, improving accuracy with combined datasets.

Findings

Deconvolution accurately recovered particle size distribution from synthetic data.

Lower fluidisation rate acceleration improved deconvolution accuracy.

Combining data from different liquids enhanced the results.

Abstract

In this paper we investigate the application of optimisation techniques in the deconvolution of mineral fractionation data obtained from a mathematical model for the operation of a fluidised bed with a set of inclined parallel channels mounted above. The model involved the transport equation with a stochastic source function and a linearly increasing fluidisation rate, with the overflow solids being collected in a finite number of increments (bags). Deconvolution of this data is an ill-posed problem and regularisation is required to provide feasible solutions. Deconvolution with regularisation is applied to a synthetic feed consisting of particles of constant density that vary in size only. It was found that the feed size distribution could be successfully deconvolved from the bag weights, with an accuracy that improved as the rate acceleration of the fluidisation rate was decreased. The deconvolution error only grew linearly with error in the measured bag masses. It was also shown that combining data from two different liquids can improve the accuracy.