Workflow to set up substantial target-oriented mechanistic process models in bioprocess engineering

Abstract

Abstract A multitude of new applications in bioprocess technology strongly depend on model-based methods as they feature prediction and control capabilities. The critical path is usually the availability of suitable models. In this work a workflow for the generation of substantial target-oriented mechanistic process models is presented. This workflow is based on backpropagation starting from a material balance for a certain target variable. Iteratively, necessary states as well as mechanistic links are included in the model using a model library reducing the computational effort. The parameters of these links are estimated using a simplex algorithm whose objective function depends on the target variable only. Practical identifiability analysis is used for the assessment of the need of further iterations and for validating the mechanistic model. To demonstrate the workflow, a model describing a mammalian cell culture process aiming at modeling viable cell count is used as an example. The generated model satisfies the predefined requirements and is very simple, consisting of three states and seven model parameters only. The presented workflow is simple, generic, transparent, so that also applications in a regulatory environment should be possible. It also provides additional process knowledge that can be used in bioprocess development and optimization.