Bioprocess engineering has a major disadvantage compared to oil, chemical, and
similar industries. This is the complexity and highly nonlinear dynamics of the
catalytic processes that take place inside the reactor (the
microorganisms). While other areas can rely on fairly accurate models and
reproducible systems, biotechnology needs to deal with living organisms that
are constantly mutating and changing their behaviour. This is the main reason
why bioengineering processes cannot fully exploit model based and computer
aided methods for design, optimization, and control. To tackle this, methods
for adaptive modelling (recursive re-estimation of the parameter estimates) are
required. Combined with robotic facilities that can perfom many dynamic
experiments in parallel, the efforts and costs of fitting models to data can be
drastically reduced. But what is the optimal experiment for this purpose in a
problem with approx. 500 input variables per hour?

The challenge is to create optimization frameworks that are able to find the
optimal setup (combination of input variables as are, initial concentrations,
feeding strategies, pulses, etc.) so that the experiment can be re-designed as
the model estimates are recursively estimated. This requires a fast and robust
solution of: the dynamic model and up to second order mixed derivatives, the
typically ill-posed parameter estimation problem, the optimal experimental
design problem (optimal control), and the optimal scheduling problem (mixed
integer nonlinear problem). Fullfilment of nonlinear path constraints while
dealing with the uncertainties of the model predictions is essential to obtain
reliable strategies that can be carried out by the robots on time.