{"title":"在存在干扰气体和测量延迟的情况下利用人工智能估算乙醇浓度的方法","authors":"","doi":"10.1016/j.snb.2024.136502","DOIUrl":null,"url":null,"abstract":"<div><p>Gas concentration detection is a vital aspect of environmental monitoring, industrial safety, and various other applications. Metal Oxide (MOX) sensors have gained considerable attention in this context due to their low cost and ability to detect several gases, although their selectivity is a potential drawback. In this paper, a data-driven approach for the detection and estimation of ethanol concentration using MOX sensors in the presence of interfering gases is presented, with a dual emphasis on the impact of delay between gas transmission and sensor detection and the incorporation of heater current and voltage, alongside sensor current readings. A delay in sensor response can lead to erroneous readings and potentially compromise safety and environmental assessments. To tackle this issue, an incremental method for the identification of the delay length is proposed, and its effectiveness in improving the estimations of gas concentration is demonstrated by implementing three distinct regression techniques: Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF). The analysis is then extended to incorporate the utilization of heater current and voltage, alongside sensor current readings. The experimental results demonstrate the effectiveness of the proposed method in handling measurement delay processing and its robustness to the presence of interfering gases.</p></div>","PeriodicalId":425,"journal":{"name":"Sensors and Actuators B: Chemical","volume":null,"pages":null},"PeriodicalIF":8.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Methodology for estimating ethanol concentration with artificial intelligence in the presence of interfering gases and measurement delay\",\"authors\":\"\",\"doi\":\"10.1016/j.snb.2024.136502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Gas concentration detection is a vital aspect of environmental monitoring, industrial safety, and various other applications. Metal Oxide (MOX) sensors have gained considerable attention in this context due to their low cost and ability to detect several gases, although their selectivity is a potential drawback. In this paper, a data-driven approach for the detection and estimation of ethanol concentration using MOX sensors in the presence of interfering gases is presented, with a dual emphasis on the impact of delay between gas transmission and sensor detection and the incorporation of heater current and voltage, alongside sensor current readings. A delay in sensor response can lead to erroneous readings and potentially compromise safety and environmental assessments. To tackle this issue, an incremental method for the identification of the delay length is proposed, and its effectiveness in improving the estimations of gas concentration is demonstrated by implementing three distinct regression techniques: Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF). The analysis is then extended to incorporate the utilization of heater current and voltage, alongside sensor current readings. The experimental results demonstrate the effectiveness of the proposed method in handling measurement delay processing and its robustness to the presence of interfering gases.</p></div>\",\"PeriodicalId\":425,\"journal\":{\"name\":\"Sensors and Actuators B: Chemical\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sensors and Actuators B: Chemical\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925400524012322\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sensors and Actuators B: Chemical","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925400524012322","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
摘要
气体浓度检测是环境监测、工业安全和其他各种应用的一个重要方面。金属氧化物(MOX)传感器因其低成本和能够检测多种气体而在这方面获得了广泛关注,尽管其选择性是一个潜在的缺点。本文介绍了一种在存在干扰气体的情况下使用 MOX 传感器检测和估算乙醇浓度的数据驱动方法,重点关注气体传输和传感器检测之间延迟的影响,以及在读取传感器电流读数的同时读取加热器电流和电压。传感器响应延迟会导致读数错误,并可能影响安全和环境评估。为解决这一问题,我们提出了一种用于识别延迟长度的增量方法,并通过采用三种不同的回归技术证明了该方法在改进气体浓度估算方面的有效性:线性回归 (LR)、支持向量回归 (SVR) 和随机森林 (RF)。然后将分析扩展到利用加热器电流和电压以及传感器电流读数。实验结果表明,所提出的方法在处理测量延迟处理方面非常有效,而且在存在干扰气体的情况下也很稳健。
Methodology for estimating ethanol concentration with artificial intelligence in the presence of interfering gases and measurement delay
Gas concentration detection is a vital aspect of environmental monitoring, industrial safety, and various other applications. Metal Oxide (MOX) sensors have gained considerable attention in this context due to their low cost and ability to detect several gases, although their selectivity is a potential drawback. In this paper, a data-driven approach for the detection and estimation of ethanol concentration using MOX sensors in the presence of interfering gases is presented, with a dual emphasis on the impact of delay between gas transmission and sensor detection and the incorporation of heater current and voltage, alongside sensor current readings. A delay in sensor response can lead to erroneous readings and potentially compromise safety and environmental assessments. To tackle this issue, an incremental method for the identification of the delay length is proposed, and its effectiveness in improving the estimations of gas concentration is demonstrated by implementing three distinct regression techniques: Linear Regression (LR), Support Vector Regression (SVR), and Random Forest (RF). The analysis is then extended to incorporate the utilization of heater current and voltage, alongside sensor current readings. The experimental results demonstrate the effectiveness of the proposed method in handling measurement delay processing and its robustness to the presence of interfering gases.
期刊介绍:
Sensors & Actuators, B: Chemical is an international journal focused on the research and development of chemical transducers. It covers chemical sensors and biosensors, chemical actuators, and analytical microsystems. The journal is interdisciplinary, aiming to publish original works showcasing substantial advancements beyond the current state of the art in these fields, with practical applicability to solving meaningful analytical problems. Review articles are accepted by invitation from an Editor of the journal.