Data-driven Model Predictive Control of Nanoparticle Production in Modular Reactors

Microreactors are an essential part of modular chemical systems involved in the on-demand production of chemicals such as nanomaterials, pharmaceuticals, specialty chemicals, etc. Model-based nonlinear predictive control of microreactors is a challenging task due to the high online computational cost associated with developing and maintaining high-order first-principles nonlinear models. In this work, we propose a nonlinear data-driven model predictive control (NMPC) scheme for nanoparticle production in microreactors. In this paper, a non-linear Auto Regressive Exogenous Neural Network model (NARX-NN) is developed with the flow rates of the reactants as inputs and the peak value of the absorbance spectra (an indirect measure of the average size of nanoparticles) as output by performing a set of experiments in Corning Advanced-Flow
^TM
Reactors (AFR). Typically, producing a new desired average size nanoparticle on-demand is done by manual changes in the flow rates of reactants. In this work, a nonlinear model predictive controller using the identified NARX-NN model is formulated to track a change in the set point, the peak value of the spectra. The formulated controller with the identified NARX-NN model is demonstrated via the simulation studies. It is shown that the proposed NMPC with the NARX-NN model performs well in different scenarios of silver nanoparticle production.