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Multidimensional particle characterisation from in-situ sensors


Webinar on

Multi-dimensional particle characterisation from in-situ sensors

1st June 2023 at 3pm (UK time)


In-line PAT are useful for measurement of particle size distributions (PSDs) in a non-destructive manner and with high time resolution inaccessible off-line. These measurements can be useful for process monitoring and control, and population balance modelling. Different in-line sensors have different preferred operating ranges in terms of particle size. For example, imaging is limited by resolution when sizing small particles; FBRM can suffer from chord splitting.


This work aims to quantify operating ranges to inform choice of sensor and analysis. PSDs via in-line image and chord length distribution (CLD) analysis are evaluated against off-line measurements (imaging and laser diffraction) and manufacturer specification (for standard particles) using representative metrics. We employ two image analyses: a non-machine learning (non-ML) method, and Mask-RCNN (ML). We also employ two CLD analyses: a non-ML method and a ResNet (ML) based approach.


We obtain error maps which give estimates of error values for a given combination of nominal particle size and concentration. These maps can be used for choosing between different analyses or different sensors. We found image analysis to be affected mostly by particle size for both ML and non-ML methods. Deep learning image analysis is seen to be insensitive to concentration (within the studied range). Classical image analysis slightly preferred higher concentrations owing to few particle detections at low concentrations. Chord length distribution results show a slight trend down in error with larger particles, but don’t have any clear trends in error with concentration


This webinar will be presented by Christopher Boyle, University of Strathclyde.


The webinar will last no longer than one hour.


To register please contact Christine.stevenson@strath.ac.uk

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