Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography. (2nd February 2019) - Rizkin, Benjamin A.; Popovich, Karina; Hartman, Ryan L.
Highlights: Artificial Neural Networks can be self-trained for pulsed-width modulation control. IR thermography delivered high-speed and high-accuracy data. Computer vision was used to segment an IR image into regions of interest. The system demonstrated resiliency to set point changes and disturbances. Improved temperature control and calculation speed was observed compared to PID. Abstract: High-speed and high-accuracy thermal control of reactors has always been of interest to chemical engineers. In this paper we present a new methodology for thermal control of a continuous-flow chemical reactor using non-contact IR thermography combined with computer vision and a predictive Artificial Neural Network. The system exhibits several key advantages over thermocouples and PID control including the ability to quantify and account for thermal diffusion in the system, to collect and process data very quickly and with high accuracy, to analyze the entire surface of the reactor, and to update its training based not only on the current thermal response, but also on external factors. We have constructed and validated such a system as well as shown improvements in its accuracy, rise time, settling time, set point tracking, and overshoot as compared to more traditional forms of thermal control, validating this as a possible approach for experimental and process control. Graphical abstract: Image, graphical abstract