Using Evolutionary Operation to Improve Yield in Biotechnological Processes

In the biotechnological industry, production is often characterized by relatively few larger batches. In the design stages of a new process, the use of statistical methods for experimentation can provide invaluable information about the process. However, it is frequently found that optimum conditions in the laboratory or pilot plant give lower yields when transferred to full‐scale production. This fact is due to scale‐up effects and to the large inherent variations when dealing with biological material and processes such as fermentation. In full‐scale production there is not the same freedom to experimentation as at the laboratory scale. Simple one factor at a time trials are predominant when attempting to improve the yield at the production scale. In this way it is possible to control the outcome such that it still meets the requirements. However, this is a very inefficient way to perform experiments. The requirements for an alternative experimental procedure is that it should be robust to non‐controllable variations, it should contain automatic safeguards to ensure that unsatisfactory material is not manufactured and it should be possible to make decisions during the trial. Furthermore, it is important that the planning stage is short and the interpretation straightforward. The method of evolutionary operation (EVOP) suggested by George Box fulfilled these requirements. We present the implementation of EVOP in a major Danish biotechnological company. An example is presented where the method was used in the fermentation process of an industrial enzyme. In the particular example, process yield was improved by 45%. Copyright © 2005 John Wiley & Sons, Ltd.