The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-
driven models that would enable the shrinking horizon nonlinear model predictive control of …

This paper addresses the problem of synergizing first-principles models with data-driven
models. This is achieved by building a hybrid model where the subspace model …

Industrial production of valuable chemical products often involves the manipulation of
phenomena evolving at different temporal and spatial scales. Product properties can be …

High-purity H 2 production from the water-gas shift (WGS) reaction was assessed. Since the
WGS is limited by the equilibrium, different reactor types that allow to extract one or more …

This work addresses the problem of implementing a model predictive control (MPC) scheme
that embeds a parallel hybrid subspace model as the predictive component of the control …

This manuscript illustrates the use of big data for modeling and control of batch processes. A
modeling and control framework is presented that utilizes data variety (temperature or …

The purpose of this study was to employ Artificial Neural Networks (ANNs) to develop data-
driven models that would enable optimal control of a stochastic multiscale system subject to …

This paper addresses the problem of enabling the use of complex first principles model
information as part of a linear Model Predictive Control implementation for improved control …

The focus of this study is to present the adherent transients that accompany the combustion
of waste derived fuels. This is accomplished, in large, by developing a dynamic model of the …

In this study, an operator training simulator was developed for an isopropyl-alcohol
producing plant. The main product of this plant is isopropyl alcohol—water azeotrope, by …