doi:10.16597/j.cnki.issn.1002-154x.2026.01.011
基于多传感器融合的油库油气回收现场VOCs浓度在线检测
臧卓超
（中石化湖北孝感石油分公司，孝感 432100）
摘 要传统的单传感器检测方法往往存在灵敏度低､选择性差等问题,难以满足复杂多变的油库油气回收现场挥发性有机物(VOCs)浓度检测需求,作者提出基于多传感器融合的油库油气回收现场VOCs浓度在线检测｡选择多个气体传感器采集油库油气回收现场的VOCs不同组分信号,并进行最优加权融合处理｡通过构建一个GRU-CNN模型,输入多传感器融合信号,输出VOCs组分的定性识别结果与各组分浓度的定量检测结果｡实验结果表明,该设计方法在油库油气回收现场VOCs浓度在线检测中具有较高的准确性和稳定性,满足检测需求｡
关键词多传感器融合；油库油气；油气回收；回收现场；挥发性有机物浓度；在线检测
Online Detection of VOCs Concentration at Oil Depot Oil and Gas Recovery Site Based on Multi-sensor Fusion
ZANG Zhuochao
(Sinopec Hubei Xiaogan Petroleum Branch, Xiaogan 432100, China)
Abstract Traditional single sensor detection methods have the problems of low sensitivity and poor selectivity, which are difficult to meet the requirements of volatile organic compounds (VOCs) concentration detection in complex and changeable oil depot oil and gas recovery sites. In this paper, an online detection method of VOCs concentration based on multi-sensor fusion is proposed. Multiple gas sensors are used to collect signals of different VOC components at the oil and gas recovery site, and optimal weighted fusion is carried out. A GRU-CNN model is constructed to input the fused multi-sensor signals and output the qualitative identification results of VOC components and quantitative detection results of each component concentration. The experimental results show that this method has high accuracy and stability in the online detection of VOCs concentration at oil depot oil and gas recovery sites, and can meet the detection requirements.
Keywords multi-sensor fusion; oil depot oil and gas; oil and gas recovery; recovery site; volatile organic compounds concentration; online detection