Noah Metzger, Ali Lashgari*, Umair Esmail, David Ball and Nathan Eichenlaub,
{"title":"一种优化连续甲烷监测系统配置的框架,以实现最短的盲时间:来自100多个油气设施的应用和见解","authors":"Noah Metzger, Ali Lashgari*, Umair Esmail, David Ball and Nathan Eichenlaub, ","doi":"10.1021/acsestair.4c00280","DOIUrl":null,"url":null,"abstract":"<p >Continuous monitoring systems (CMS) that utilize fixed-point sensors provide high temporal resolution point-in-space measurements of ambient methane concentration. This study introduces a modular framework for optimizing CMS configurations, encompassing sensor density (number of sensors) and near-optimal placement. By introducing a metric called ‘blind time’, this study attempts to capture periods where the network fails to make detections that could satisfy the regulatory requirement of quantifying emissions every 12 h. This framework is then applied to 124 operational oil and gas production facilities with a wide variety of site characteristics and meteorological conditions. This study determines a representative blind time for near-optimum CMS configurations for operational facilities and then investigates the impact of different sensor network densities on the performance of the CMS. The results demonstrate that 3-sensor networks, when placed in near-optimum arrangements, can achieve blind time of less than 10% and a mean time to detection of approximately 82 min.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 8","pages":"1439–1453"},"PeriodicalIF":0.0000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Framework for Optimizing Continuous Methane Monitoring System Configuration for Minimal Blind Time: Application and Insights from over 100 Operational Oil and Gas Facilities\",\"authors\":\"Noah Metzger, Ali Lashgari*, Umair Esmail, David Ball and Nathan Eichenlaub, \",\"doi\":\"10.1021/acsestair.4c00280\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Continuous monitoring systems (CMS) that utilize fixed-point sensors provide high temporal resolution point-in-space measurements of ambient methane concentration. This study introduces a modular framework for optimizing CMS configurations, encompassing sensor density (number of sensors) and near-optimal placement. By introducing a metric called ‘blind time’, this study attempts to capture periods where the network fails to make detections that could satisfy the regulatory requirement of quantifying emissions every 12 h. This framework is then applied to 124 operational oil and gas production facilities with a wide variety of site characteristics and meteorological conditions. This study determines a representative blind time for near-optimum CMS configurations for operational facilities and then investigates the impact of different sensor network densities on the performance of the CMS. The results demonstrate that 3-sensor networks, when placed in near-optimum arrangements, can achieve blind time of less than 10% and a mean time to detection of approximately 82 min.</p>\",\"PeriodicalId\":100014,\"journal\":{\"name\":\"ACS ES&T Air\",\"volume\":\"2 8\",\"pages\":\"1439–1453\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS ES&T Air\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsestair.4c00280\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS ES&T Air","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsestair.4c00280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Framework for Optimizing Continuous Methane Monitoring System Configuration for Minimal Blind Time: Application and Insights from over 100 Operational Oil and Gas Facilities
Continuous monitoring systems (CMS) that utilize fixed-point sensors provide high temporal resolution point-in-space measurements of ambient methane concentration. This study introduces a modular framework for optimizing CMS configurations, encompassing sensor density (number of sensors) and near-optimal placement. By introducing a metric called ‘blind time’, this study attempts to capture periods where the network fails to make detections that could satisfy the regulatory requirement of quantifying emissions every 12 h. This framework is then applied to 124 operational oil and gas production facilities with a wide variety of site characteristics and meteorological conditions. This study determines a representative blind time for near-optimum CMS configurations for operational facilities and then investigates the impact of different sensor network densities on the performance of the CMS. The results demonstrate that 3-sensor networks, when placed in near-optimum arrangements, can achieve blind time of less than 10% and a mean time to detection of approximately 82 min.