便携式高精度二氧化碳检测仪的研制及湿度干扰消除的研究

周磊; 庞小兵; 吴振涛

doi:10.13205/j.hjgc.202310006

便携式高精度二氧化碳检测仪的研制及湿度干扰消除的研究

doi: 10.13205/j.hjgc.202310006

浙江工业大学环境学院, 杭州 310014

基金项目:

浙江省"领雁"研发攻关计划（2022C03073）；浙江省自然科学基金（LZ20D050002）；绍兴市科技计划项目（2022B41006）

详细信息

作者简介:
周磊(1996-),男,研究生。774149518@qq.com

通讯作者:
庞小兵(1978-),男,教授,博士,主要从事大气环境监测和环境仪器研制研究。pangxb@zjut.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2023-07-21
- 网络出版日期: 2023-12-26

DEVELOPMENT OF A PORTABLE HIGH PRECISION CARBON DIOXIDE DETECTOR AND ELIMINATION OF HUMIDITY INTERFERENCE

College of Environment, Zhejiang University of Technology, Hangzhou 310014, China

摘要

摘要: 二氧化碳（CO₂）的持续增加会加剧温室效应，从而影响人们的正常生活。目前，常见的CO₂监测设备体积大且价格高。因此，基于非分散红外（non-dispersive infrared，NDIR）光学吸收原理的传感器，设计了一种便携式CO₂监测设备。考虑到现场监测时，传感器容易受到相对湿度（relative humidity，RH）的影响。因此，研究了RH对传感器响应的影响，了解传感器在不同RH下的响应特征；并通过浓度和湿度变化的回归方程拟合，得到R²>0.94的二次函数校准公式。通过连续的室外监测与标准参比仪器进行对比发现，校准后的数据与标准参比仪器之间有更好的相关性，R²从校准前的0.62~0.73提高至0.83~0.97。为进一步提高数据的准确性，将传感器进行集群分析，这种方法能够极大程度上削弱因传感器个体差异造成的误差。通过多个传感器数据组合分析发现，平均相对偏差随传感器数量增加而减小，最小平均相对偏差仅为1.4%。
- 二氧化碳(CO₂) /
- 相对湿度干扰 /
- 干扰校准 /
- 传感器集群
Abstract: Increasing carbon dioxide (CO₂) exacerbated the greenhouse effect, which threatened human normal lives. Currently, the equipment used for CO₂ monitoring is heavy and expensive. Therefore, the study designed a portable CO₂ detector based on non-dispersive infrared (NDIR) optical absorption principles. Considering on-site monitoring, the sensor was susceptible to the interference of relative humidity (RH). Therefore, the effect of RH on the response of the sensor was studied, and the response characteristics of the sensor at different RH levels were understood. Through the combination of concentration and humidity changes, the correlation (R²) of the secondary function of the correlation was above 0.94. Through continuous outdoor monitoring and comparison with the standard reference instruments, it was found that there was a better correlation between the calibrated data and the standard reference instrument, with R² increasing from 0.62 to 0.73 before calibration, to 0.83 to 0.97. The cluster analysis of sensors can greatly reduce the error caused by individual differences in sensors, thus improving the accuracy of data. It was found that through analysis of multiple sensor data combinations, the average relative deviation decreased with the increase in the number of sensors, and the minimum average relative deviation was only 1.4%.
- carbon dioxide (CO₂) /
- relative humidity interference /
- interference calibration /
- sensor cluster

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