Developing Methodologies for Determining Operating Strategies for

This thesis examines techniques for analysing bioprocess flowsheet simulations so as to determine operating strategies. Currently approaches cited in the literature for analysing bioprocesses employ visualisation of two-dimensional subsets of the feasible region. However this approach is restricted to two control variables and relies heavily on the engineer’s judgement to estimate the potential impact of uncertainties in both the model and the process operation. The objective of this research was to generate methods capable of locating robust operating points for multivariate bioprocesses. Increasingly the biopharmaceutical firms are under economic pressure to speed up process development. This had lead to an increased interested in computer simulation as a tool to develop robust bioprocess. Whilst simulation has been applied extensively in the process industries it has not often been applied to bioprocesses as these tend to be more complex to model and frequently only a partial understanding of behaviour exists. Recent work has led to a capacity to simulate complete bioprocess sequences using models that capture the interactions between the unit operations. However, a major limitation is the interpretation of results from such simulations. In conventional process engineering studies optimisation routines have been used to identify the best operating conditions for a given set of objectives. Such techniques have not been applied effectively to bioprocesses due to limitations in the reliability of the models. These limitations mean that results obtained via such an approach are unlikely be useful as, in practice, the optimal points found are unlikely to be robust. The work in this thesis also looks at defining methodologies that are able to analyse multivariable bioprocesses. It looks at the application of techniques developed in the chemical process industry that can be used to account for the variability in the control variables and process parameters and at the application of statistical techniques for analysing bioprocess robustness. Overall work highlights the nature of the bioprocess insights that can be obtained through simulation and explores the utility of the application of the developed methods of analysis.