Process Scale Up and Transfer
If you’re ready to scale up your process from the lab or pilot plant to full scale, or transfer production from one site to another, having the right tools is important. Lack of fundamental models, differing process designs (e.g. batch vs continous, recent vs legacy), different operational procedures, different measured variables and other factors between the plants can make it very difficult to achieve the same product quality at each of them.
Have confidence in your ability to manufacture equivalent products at multiple scales and sites. Using a statistical model-based approach, we’ll help you:
- efficiently move production from lab-scale to pilot-scale to full-scale
- transfer production from one site to another
- ensure product characteristics are equivalent at multiple scales and sites
Our Clients Get Results
Our clients achieve:
- significantly reduced plant experimentation over traditional approaches
- significantly reduced time of development
- upon completion, a process model for both sites/scales that can be used for process monitoring and optimization
- a greater depth of understanding of the process relationships within each plant and of the important factors in the scale-up or transfer
Put ProSensus on your team.
We use systematic, statistical model-based optimization approaches that use all of your existing data and knowledge on each of the plants. Our methods also allow us to incorporate first principles models plus traditional engineering calulations and techniques for characterizing processes at different scales.
If data already exist on products made at all of the plants at the different scales or sites, then joint multivariate models allow us to run optimizations that provide the closest possible solution for the scale-up or transfer that respects all operational constraints. If very little data exists at the target plant, our approaches will provide an optimal sequence of designed trials and updated models that will enable you to iterate into the best solution in the minimum time.