Thesis subject

Iterative Learning Control for batch to batch improvement

Succeeding batches may expose different outcomes, such as variation in quality, and batch time. Part of the variation is caused by gradual changes in the characteristics of the used materials. For example changes in starter activity which is result of variations within the GMP procedures during upstream processing.

Make never the same mistake is an important motive to learn, and this principle can also be applied to realise batch to batch improvement in the production of biopharmaceuticals. The concept is of Iterative Learning Control is easy, but we try to make the concept more effective for bioreactor systems.