Data-driven Process Systems Engineering Lab
Publications
Feasibility analysis of black-box processes using an adaptive sampling kriging-based method
Computers & Chemical Engineering 2012
This paper presents a new approach for performing feasibility analysis over a multivariate factor space when the explicit form of a process model is lacking or when its evaluation is expensive. Specifically, two issues are addressed: feasibility evaluation of black-box…
Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 2 – Population Balance and Data-Based Methods
Macromolecular Materials and Engineering 2012
The application of computationally inexpensive modeling methods for a predictive study of powder mixing is discussed. A multidimensional population balance model is formulated to track the evolution of the distribution of a mixture of particle populations with respect to position…
Dynamic Data-Driven Modeling of Pharmaceutical Processes
Industrial Engineering & Chemistry Research 2011
In many pharmaceutical process operations, first-principle models describing the behavior of the processed powders are difficult to derive or computationally very expensive and thus can be considered as “black-box” processes. Kriging is an efficient data-driven methodology that has been shown…
Design Space of pharmaceutical processes using data-driven based methods
Journal of Pharmaceutical Innovation 2010
Introduction The identification and graphical representation of process design space are critical in locating not only feasible but also optimum operating variable ranges and design configurations. In this work, the mapping of the design space of pharmaceutical processes is achieved…
