Fusion of laser diffraction and chord length distribution data for estimation of particle size distribution using multi-objective optimisation

TL;DR

This paper introduces a multi-objective optimization algorithm that fuses laser diffraction and chord length data to accurately distinguish true particle size distribution peaks from artificial ones in in situ measurements.

Contribution

The novel algorithm effectively discriminates between artificial and true PSD peaks by combining multiple data streams through multi-objective optimization.

Findings

Successfully eliminates artificial peaks when sufficiently separated from true peaks.

Partial suppression of artificial peaks when they are close to true peaks.

Validated against offline measurements showing improved PSD estimation accuracy.

Abstract

The in situ measurement of the particle size distribution (PSD) of a suspension of particles presents huge challenges. Various effects from the process could introduce noise to the data from which the PSD is estimated. This in turn could lead to the occurrence of artificial peaks in the estimated PSD. Limitations in the models used in the PSD estimation could also lead to the occurrence of these artificial peaks. This could pose a significant challenge to in situ monitoring of particulate processes, as there will be no independent estimate of the PSD to allow a discrimination of the artificial peaks to be carried out. Here, we present an algorithm which is capable of discriminating between artificial and true peaks in PSD estimates based on fusion of multiple data streams. In this case, chord length distribution and laser diffraction data have been used. The data fusion is done by means of multi-objective optimisation using the weighted sum approach. The algorithm is applied to two different particle suspensions. The estimated PSDs from the algorithm are compared with offline estimates of PSD from the Malvern Mastersizer and Morphologi G3. The results show that the algorithm is capable of eliminating an artificial peak in a PSD estimate when this artificial peak is sufficiently displaced from the true peak. However, when the artificial peak is too close to the true peak, it is only suppressed but not completely eliminated.