Model Predictive Control framework for slug flow microfluidics processes

Miniaturizing devices and working in real-time in a non-invasive manner are prerequisites for studying microfluidic processes in Lab-on-a-chip. In this study, we present a novel system which uses integrated optical technology for the real-time control of a two-phase microfluidic process as a proof of concept for system-on-chip development. The integrated system is composed of a micro-optofluidic device specifically designed to have direct optical access to flow detection, avoiding discrete opto-mechanical components, and a microcontroller to manage actuation and sensing devices. A two-phase process, i.e. a sequence of two immiscible fluids, flows inside the microchannel and represents the presented application example. The objective is to control the fluids’ intermittency by imposing proper input flow rates. A linearized model of the process has been determined based on a data-driven identification approach and subsequently validated. Constrained Model Predictive Control (MPC) has been selected over Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) to regulate the input flows. Numerical simulations proved MPC’s better capabilities of balancing high accuracy and variations in the input commands throughout the control process. The combination of two extensive experimental campaigns show the presented approach’s validity and the integration of the entire framework into a simple and portable system suitable for various chemical and biomedical applications.