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在不均的温度场中估计二氧化碳度,基于单通调节二极管激光吸收光谱

Junggon Choi¹, Cheolwoo Bong¹, Jihyung Yoo²

¹School of Mechanical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Korea.

Applied spectroscopy

|August 7, 2023

概括

这项研究引入了一种新的机器学习方法,用于精确测量二氧化碳 (CO2) 度,即使温度变化. 该技术使用单一的激光吸收光谱,在精度上优于传统方法.

科学领域:

频谱学是一种光谱学.
机器学习机器学习
化学传感器化学传感器

背景情况:

传统的可调节二极管激光吸收光谱 (TDLAS) 用于测量二氧化碳度,依赖于具有不同温度依赖性的光谱线的比率.
流场中的温度梯度可能会导致二氧化碳度测量中的显著偏差,分析补偿具有挑战性.

研究的目的:

开发一种使用单一激光吸收频谱和机器学习在温度梯度存在时准确预测二氧化碳度的新技术.
为了解决传统的TDLAS方法在不均的温度环境中的局限性.

主要方法:

利用整个吸收特性,考虑到形状的变化和峰值强度与温度和度的变化.
开发基于模拟数据 (数字双胞胎概念) 训练的数据驱动预测模型,以识别和补偿温度场效应.
使用实验数据验证模型的性能.

主要成果:

开发的机器学习模型准确地预测了温度梯度的流场中的二氧化碳度.
该模型在所有实验测试案例中,与传统的两行方法相比,在所有实验测试案例中都显示出优异的预测性能.
梯度加权回归激活映射证实了模型使用峰值强度和光谱形状变化进行预测.

结论:

拟议的机器学习技术为在具有挑战性的温度梯度条件下测量二氧化碳度提供了强大而准确的解决方案.
这种方法通过利用光谱特征分析和数据驱动建模来克服传统TDLAS的局限性.

关键词:

二氧化碳是二氧化碳中的一种.在TDLAS中.碳排放排放的碳排放量数字双胞胎数字双胞胎是什么意思机器学习是机器学习.可调节二极管激光吸收光谱学

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