Surrogatebased modeling techniques with application to catalytic reforming and isomerization processes Abstract   In this paper, we first briefly survey the main surrogate model building approaches discussed in the literature considering also design of experiments strategies and dimensionality reduction procedures: we mainly focus on subset approaches and sampling strategies for constrained regression problems. We delineate a systematic methodology for surrogate modelling in presence of model constraints, such as nonnegativity of the model responses. The main contribution of this paper is twofold: from one side we extend the principal component analysis framework to the case of constrained regression problem, from the other we propose a novel methodology which integrates the subset selection and the previous principal component regression procedure. Finally, we apply the two novel algorithms to two fundamental chemical processes in petroleum refinery, namely catalytic reforming and light naphtha isomerization. The numerical results show the comparisons between the two algorithms in terms of computational and accuracy tradeoffs.

Optimization Of Pharmaceutical Reactions Using The Dynamic Response Surface Methodology Abstract   Design of Dynamic Experiments (DoDE, Georgakis, 2013) and Dynamic Response Surface Methodology (DRSM, Klebanov and Georgakis, 2016; Wang and Georgakis, 2017) are two recent extensions to the Design of Experiments (DoE) Methodology for datadriven modeling. The DRSM models are used here for the optimization of the operating conditions of organic reactions in pharmaceutical process development. In particular, we investigate the problem of maximizing the operating time window, during which the concentrations of different species satisfy their preset constraints. This ensures the existence of a time window that is wide enough for a laboratory confirmation of acceptable compositional ranges in the final reaction mixture. We also consider the robustness of these optimization results with respect to deviations of the operating conditions and the inherent uncertainties in the DRSM model. Two case studies are examined using either simulated data from a complex reaction network or experimental data from an industrial example. In the latter case, we note the possibility of an optimization pitfall that a fractional factorial design can lead us to, when all the factors are significant and additional experimental data to improve the designu0027s resolution are not available.