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The in situ measurement of the particle size distribution (PSD) of asuspension of particles presents huge challenges. Various effects from theprocess could introduce noise to the data from which the PSD is estimated. Thisin 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 theoccurrence of these artificial peaks. This could pose a significant challengeto in situ monitoring of particulate processes, as there will be no independentestimate of the PSD to allow a discrimination of the artificial peaks to becarried out.  Here, we present an algorithm which is capable of discriminating betweenartificial and true peaks in PSD estimates based on fusion of multiple datastreams. In this case, chord length distribution and laser diffraction datahave been used. The data fusion is done by means of multi-objectiveoptimisation using the weighted sum approach. The algorithm is applied to twodifferent particle suspensions. The estimated PSDs from the algorithm arecompared with offline estimates of PSD from the Malvern Mastersizer andMorphologi G3. The results show that the algorithm is capable of eliminating anartificial peak in a PSD estimate when this artificial peak is sufficientlydisplaced from the true peak. However, when the artificial peak is too close tothe true peak, it is only suppressed but not completely eliminated.

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