Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography

Benjamin A. Rizkin, Karina Popovich, Ryan L. Hartman

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)584-593
Number of pages10
JournalComputers and Chemical Engineering
Volume121
DOIs
StatePublished - Feb 2 2019

Keywords

  • Autonomous microfluidics
  • Machine learning
  • Neural networks
  • Process control
  • Process resiliency
  • Thermal control

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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