Particle Size Analysis for Process Optimization - METTLER TOLEDO
White Paper

Particle Size Analysis for Process Optimization

White Paper

Particle Characterization From Small Scale Lab Reactors to Full Scale Production Pipelines

White Paper: Particle Size Analysis for Process Optimization
White Paper: Particle Size Analysis for Process Optimization

Particles, crystals and droplets cause issues for scientists. However, carefully designed particles will have the correct product quality attributes and simplify downstream operations.

With a complete particle engineering toolbox, including inline and offline analytical techniques, scientists can:

  • Design particles with the desired physical properties
  • Obtain evidence to identify the root causes of failures
  • Document process understanding and demonstrate process control
  • Use data to support confident transfer from lab to plant

This white paper, Particle Size Analysis for Process Optimization, will introduce some of the most common in process particle measurement approaches and how they can be deployed for the effective delivery of high quality particle products.

Download the paper, Particle Size Analysis for Process Optimization, for techniques to solve key issues with particles early in development.

Particles: Problem or Opportunity?

Particles, crystals and droplets occur in many chemical processes, across a range of industries, and often pose challenges for scientists and engineers who are tasked with optimizing product quality and process efficiency. Characterizing particle properties effectively, in particular particle size and count, allows processing problems to be solved and product quality to be improved. Historically, scientists have relied on off-line particle size analyzers, such as laser diffraction or sieving, to perform this type of characterization. But in recent years, newer technologies have emerged that describe particle size and count in real time, as particles naturally exist in process. In process measurement of particles can reduce the error associated with offline sampling, and can provide continuous information about how particles behave under changing process conditions, allowing scientists to understand and optimize difficult processes using evidence-based methods. Crystallization in particular is a challenging particle formation process where the particle size after isolation can have a dramatic impact on all downstream processing operations. 



 

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