Simultion for API Production

Boost Your API Production with Numerical Simulation – From Batch to Continous Processing

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Virtual Prototyping – From Laboratory to Production Processes

Traditional manufacturing processes are based on the “design-build-test” principle in which the effects of design changes are quantified by experimental tests on physical prototypes. There are currently very few suppliers who are developing integrated systems for continuous manufacturing and, as a result, physical prototyping is anticipated to be very costly.

Numerical simulations enable the engineer to build a virtual laboratory, providing insight into the performance of a product before tests are carried out. This means that the uncertainty resulting from major process and equipment changes can be evaluated up front, leading to a significant risk reduction and cost savings.

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Which Tools to Use for Simulation of Pharma–Processes

Multi-physics CFD and state-of-the-art visualization tools also offer a wealth of detailed information, not always readily available from laboratory or experimental tests. This not only results in an increased level of insight into the details of what is going on inside the processes, it also enables innovation. For example, multi-physics CFD can help explore new reactions and molecules for drugs manufactured with a continuous process.

Combing CFD Responses with Numerical and Statistical Data

In recent years, the phenomenal increase in computing power and the maturing of robust simulation tools have paved the way for using numerical design optimization in production environments. Parameter studies and optimization will be vitally important for designing and tuning of the new (often smaller) equipment required for continuous manufacturing while ensuring that the operation can efficiently handle fast reactions and remains flexible.

In addition, the CFD-generated responses — obtained through design of experiments over a range of operating conditions and equipment design parameters — can be combined with statistical models to identify risk and implement robust real-time process control. This will ultimately result in reduced variability and consistent, repeatable processes.

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