Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography
Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography
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DOI：
10.1016/j.compchemeng.2018.11.016
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发表时间：
2019-02-02
影响因子：
4.3
通讯作者：
Hartman, Ryan L.
中科院分区：
文献类型：
--
作者：
Rizkin, Benjamin A.;Popovich, Karina;Hartman, Ryan L.
反应器的高速、高精度热控制一直是化学工程师们感兴趣的问题。在本文中，我们提出了一种新的方法，用于连续流化学反应器的热控制，使用非接触式红外热成像结合计算机视觉和预测人工神经网络。与热电偶和PID控制相比，该系统具有几个关键优势，包括能够量化和解释系统中的热扩散，非常快速和高精度地收集和处理数据，分析反应器的整个表面，以及不仅基于当前热响应而且基于外部因素更新其训练。我们已经构建并验证了这样一个系统，以及显示其精度，上升时间，稳定时间，设定点跟踪和过冲相比，更传统的形式的热控制，验证这是一个可能的方法，实验和过程控制的改进。(C)2018爱思唯尔有限公司版权所有
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. (C) 2018 Elsevier Ltd. All rights reserved.