Gaussian process modelling and expected improvement with application to a pharmaceutical process Seminar

Design of experiments is often used in process optimisation to save on resources, cost and time. Standard response surface methodology fits polynomial response models to data from a sequence of experiments to locate the optimum. However, in situations where the response is more complex and mechanistic understanding is limited, for example as arises in many pharmaceutical processes, a more flexible modelling approach is required. We demonstrate how Gaussian process models and efficient global optimisation (EGO) algorithms, commonly used in computer experiments, can be employed in such situations. The EGO method uses expected improvement to choose the next point at which to run an experiment. In the pharmaceutical industry, sequential experimentation is typically carried out in batches, due to experimental restrictions. Therefore, we present a batch-sequential extension to EGO.