  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.
