Iterative Data-based Modelling and Optimization for Rapid Design of Dynamic Processes

We consider an off-line process design problem where the response variable is affected by several factors. We present a data-based modelling approach that iteratively allocates new experimental points, update the model, and search for the optimal process factors. A flexible non-linear modelling technique, the kriging (also known as Gaussian processes), forms the cornerstone of this approach. Kriging model is capable of providing accurate predictive mean and variance, the latter being a quantification of its prediction uncertainty. Therefore, the iterative algorithm is devised by jointly considering two objectives: (i) to search for the best predicted response, and (ii) to adequately explore the factor's space so that the predictive uncertainty is small. This method is further extended to consider dynamic processes, i.e. the process factors are time-varying and thus the problem becomes to design a time-dependent trajectory of these factors. The proposed approach has been demonstrated by its application to a simulated chemical process with promising results being achieved.