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Knowledge management

Sharing and managing knowledge by means of model equations

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Mechanistic models enable profound knowledge management

Mechanistic models base on the laws of mathematics, physics and physical chemistry and inhere all elementary information of the system dynamics. The model parameters have an actual physical meaning, which provides fundamental benefits for the scientific interpretation and understanding. The model equations thereby present a clear distinct language, optimal for sharing growing process knowledge with a team.

Sharing and managing process knowledge

Typically, a first draft process is scratched in the early development phase. This process is then refined throughout the development life cycle. Using models, it is easy to manage and share this growing process knowledge within a team. Same as the actual process, also your model evolves in time. Starting with a draft model in early phase, the model evolves in time, by adding more and more available information to it. The improvement is thereby clearly documented in terms of the model equations and parameters. So instead of sharing complex process specifications, lab- and pilot-scale columns within your team, you simply hand over model equations and parameters.


generate a mechanistic process understanding and share with the team


model evolution during development life cycle from early to late stage DSP


maximize company knowledge and fulfil regulatory demands on QbD

Knowledge increase: DoE vs one-factor-at-a-time

Integrated design of experiments (DoE) is a common approach to reduce the experimental load, while increasing the amount of information. The key idea is to plan an experimental series, such that the most information is gained from a minimum number of experiments.

However, a real experiment takes up to two days and costs on average EUR 20,000. Liquid chromatography simulation is a high-potential innovation and a gamechanger. It allows to replace these lab experiments by computer simulations, that achieve the same results within seconds and at the price of computing power only.

In comparison to the traditional one-factor-at-a-time approach, the level of process knowledge increases quickly already with few experiments. After several hundreds of experiments, the maximum level is reached and no more additional information is generated by adding more experiments.

At current time, the required level for regulatory acceptance is however reached with a few hundred experiments – a lot faster and cheaper than in the fully experimental approach using one-factor-at-a-time.

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