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

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.

[1]  Yuvraj V. Parkale Comparison of ANN Controller and PID Controller for Industrial Water Bath Temperature Control System using MATLAB Environment , 2012 .

[2]  A. Sideris,et al.  A multilayered neural network controller , 1988, IEEE Control Systems Magazine.

[3]  John R. Thome,et al.  Infrared imaging of temperature profiles in microreactors for fast and exothermic reactions , 2013 .

[4]  Chin Pan,et al.  Infrared thermography measurement of two-phase boiling flow heat transfer in a microchannel. , 2016 .

[5]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[6]  Fan-Gang Tseng,et al.  Real-time monitoring of a micro reformer integrated with a microchannel heat exchanger by infrared thermography and high-speed flow images , 2016 .

[7]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[8]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[9]  P. A. Taylor,et al.  The design of experiments, training and implementation of nonlinear controllers based on neural networks , 1994 .

[10]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[11]  Richard J Ingham,et al.  A Systems Approach towards an Intelligent and Self-Controlling Platform for Integrated Continuous Reaction Sequences** , 2014, Angewandte Chemie.

[12]  Jean-Christophe Batsale,et al.  Processing of temperature field in chemical microreactors with infrared thermography , 2006 .

[13]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[14]  D. Himmelblau Applications of artificial neural networks in chemical engineering , 2000 .

[15]  Jean-Christophe Batsale,et al.  Enthalpy, kinetics and mixing characterization in droplet-flow millifluidic device by infrared thermography , 2015 .

[16]  Christine W. Chan,et al.  Artificial intelligence for monitoring and supervisory control of process systems , 2007, Eng. Appl. Artif. Intell..

[17]  Frank L. Lewis,et al.  Neural Networks in Feedback Control Systems , 2015 .

[18]  Martin T. Hagan,et al.  An introduction to the use of neural networks in control systems , 2002 .

[19]  Lalita Udpa,et al.  Artificial intelligence methods for selection of an optimized sensor array for identification of volatile organic compounds , 2001 .

[20]  M. J. D. Powell,et al.  Restart procedures for the conjugate gradient method , 1977, Math. Program..

[21]  Vinícius Gonçalves Maltarollo,et al.  Applications of Artificial Neural Networks in Chemical Problems , 2013 .

[22]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[23]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[25]  C. Storey,et al.  Generalized Polak-Ribière algorithm , 1992 .

[26]  Zhenhong Yuan,et al.  Artificial neural network-genetic algorithm based optimization for the immobilization of cellulase on the smart polymer Eudragit L-100. , 2010, Bioresource technology.

[27]  Guohe Huang,et al.  Artificial intelligence for management and control of pollution minimization and mitigation processes , 2003 .

[28]  P. G. Lee Process control and artificial intelligence software for aquaculture , 2000 .

[29]  Sebastian Dudzik,et al.  Investigations of a heat exchanger using infrared thermography and artificial neural networks , 2011 .

[30]  Milos Manic,et al.  Computational Intelligence as a Tool for Small Modular Reactors , 2011 .

[31]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[32]  J. Stoner,et al.  Performance of endovenous foam sclerotherapy in the USA , 2012, Phlebology.

[33]  Yacine Rezgui,et al.  Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .

[34]  S. Omatu,et al.  A neural network controller for a temperature control system , 1992 .

[35]  Jisong Zhang,et al.  Measuring enthalpy of fast exothermal reaction with infrared thermography in a microreactor , 2016 .

[36]  Mostafa Ahmadi,et al.  New Approach in Modeling of Metallocene‐Catalyzed Olefin Polymerization Using Artificial Neural Networks , 2009 .