Only a few percent of process optimization and improvement equals higher yield, energy savings and lower production costs

Mass production of high-quality crystals with a competitive production cost relies on automation and optimization of the crystallization. This can be achieved by switching to advanced automatic technology providing process-optimizing data.

The ParticleTech Solution

Continuous monitoring

Pharmaceutical, food and biotech industries aim towards implementing continuous monitoring of crystallization processes for optimization purposes. The montage below represents continuous monitoring of a crystallization process of lactose that visually shows crystal growth over time. The images were captured using the repetition feature that is capable of automatically scanning a crystallization process over time. This can also provide a video of the whole process. In combination with advanced algorithms, this application allows operators to monitor and control the processes by continuously receiving valuable information about size- and shape distributions of the crystals.

The graph below represents the D10, D50 and D90 feret mean of the lactose crystals over time during the process.

Crystallization and temperature control

Many crystallization processes are temperature dependent. ParticleTech has developed a temperature control system that keeps the sample at a constant temperature to match the condition of the crystallization tank.

Case study - From 6 hours to 3 hours production time

ParticleTech was addressed by a company who wanted to monitor their crystallization process for optimization purposes. They informed ParticleTech that:

  1. The size of the crystals was crucial
  2. The duration of the crystallization process was 6 hours
  3. They suspected that their stirring process broke the crystals somewhere in the process.

With ParticleTech solution the company measured the feret mean diameter of the crystals once per minute throughout the whole process. Furthermore, they analysed the waste liquid from the process. The results showed that:

  1. The growth of the crystals stagnated between two and three hours
  2. After three hours the crystal size started to decrease significantly.
  3. The waste liquid contained large quantities of crystals

Subsequently they decided to shorten their crystallization process with three hours. This operation resulted in:

  1. Improved quality of crystals
  2. Minimization of filter clogging
  3. Reduction of time and cost
  4. A less resource-intensive process

The classification algorithm as an efficient tool for analysing their results. This algorithm made it possible to distinguish between crystal fines and agglomerates. Read more (link to classification).

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