Optical Gas Imaging (OGI) as a Moderator for Interdisciplinary Cooperation, Reduced Emissions and Increased Safety

Log, Torgrim; Pedersen, Wegar Bjerkeli; Moumets, Heike

doi:10.3390/en12081454

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…One can see exactly which point the leak originates as well as getting an estimate of the leak rate by the gas plume character. In a recent OGI study, several methane leaks of about 1 cm 3 /s (0.0008 g/s) were identified in an LNG plant [22].…”

Section: Detecting Minor Gas Leaks and Seepagesmentioning

confidence: 99%

A Common Risk Classification Concept for Safety Related Gas Leaks and Fugitive Emissions?

Log

Pedersen

2019

Energies

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Gas leaks in the oil and gas industry represent a safety risk as they, if ignited, may result in severe fires and/or explosions. Unignited, they have environmental impacts. This is particularly the case for methane leaks due to a significant Global Warming Potential (GWP). Since gas leak rates may span several orders of magnitude, that is, from leaks associated with potential major accidents to fugitive emissions on the order of 10−6 kg/s, it has been difficult to organize the leaks in an all-inclusive leak categorization model. The motivation for the present study was to develop a simple logarithmic table based on an existing consequence matrix for safety related incidents extended to include non-safety related fugitive emissions. An evaluation sheet was also developed as a guide for immediate risk evaluations when new leaks are identified. The leak rate table and evaluation guide were tested in the field at five land-based oil and gas facilities during Optical Gas Inspection (OGI) campaigns. It is demonstrated how the suggested concept can be used for presenting and analysing detected leaks to assist in Leak Detection and Repair (LDAR) programs. The novel categorization table was proven valuable in prioritizing repair of “super-emitter” components rather than the numerous minor fugitive emissions detected by OGI cameras, which contribute little to the accumulated emissions. The study was limited to five land based oil and gas facilities in Norway. However, as the results regarding leak rate distribution and “super-emitter” contributions mirror studies from other regions, the methodology should be generally applicable. To emphasize environmental impact, it is suggested to include leaking gas GWP in future research on the categorization model, that is, not base prioritization solely on leak rates. Research on OGI campaign frequency is recommended since frequent coarse campaigns may give an improved cost benefit ratio.

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Section: Detecting Minor Gas Leaks and Seepagesmentioning

confidence: 99%

A Common Risk Classification Concept for Safety Related Gas Leaks and Fugitive Emissions?

Log

Pedersen

2019

Energies

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“…One of the main environmental impacts of refinery process units is represented by atmospheric emissions, which are typically constituent of CO, CO 2 , SO 2 , NO X , CH 4 , particulates, and volatile organic compounds (VOCs) [1][2][3]. VOCs are emitted from a variety of sources [4][5][6][7][8][9][10][11] and, in recent years, the attention toward the emission of VOCs has been increasing due to the consistent amount of VOCs emitted from different industrial processes and their environmental and health impact [12][13][14]. In addition, VOC emissions can produce odor annoyance [15][16][17], which can have a negative impact on life quality [18,19].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Emission Factors for Hazardous Air Pollutants from Petroleum Refineries

et al. 2021

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The hazardous air pollutants (HAPs) group is composed of 187 chemicals that are known to be potentially carcinogenic and dangerous for human health. Due to their toxicological impact, HAPs are an increasingly studied class of compounds. Of the different HAPs sources, refineries are one of the major sources. In order to obtain a preliminary assessment of the impact of a refinery in terms of emissions, a useful instrument is the determination of the emission factor (EF). For this reason, this work, focusing on the USA refining scenario, aims to provide evidence for a generic trend in refinery emissions to evaluate a correlation between the plant size and the amount of its emissions, in particular the HAPs emissions. Based on the analysis of the data collected from the U.S. Environmental Protection Agency (US EPA), a general trend in the emissions from refinery plants was established, showing a positive correlation between the HAPs emissions and the refinery size, represented by a value of the Pearson correlation coefficient r close to 1. Once this correlation was highlighted, a purpose of this work became the estimation of an organic HAPs emission factor (EF): from a whole refining plant, the EF of the total organic HAPs is equal to 10 g emitted for each ton of crude oil processed. Moreover, it was also possible to undertake the same evaluation for two specific HAP molecules: benzene and formaldehyde. The benzene and formaldehyde EFs are equal to, respectively, 0.8 g and 0.2 g for each ton of processed crude oil. This work provides a simple rule of thumb for the estimation of hazardous substances emitted from petroleum refineries in their mean operating conditions.

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“…This is also the case for industry processing hydrocarbons. Initiatives are therefore taken to detect the fire risk early, that is, before leaks can develop into dangerous situations [3]. Homes may also be involved in fires, and in several areas homes are traditionally made of wood based materials.…”

Section: Introductionmentioning

confidence: 99%

Modeling Indoor Relative Humidity and Wood Moisture Content as a Proxy for Wooden Home Fire Risk

Log¹

2019

Sensors

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Severe wooden home conflagrations have previously been linked to the combination of very dry indoor climate in inhabited buildings during winter time, resulting in rapid fire development and strong winds spreading the fire to neighboring structures. Knowledge about how ambient conditions increase the fire risk associated with dry indoor conditions is, however, lacking. In the present work, the moisture content of indoor wooden home wall panels was modeled based on ambient temperature and relative humidity recorded at meteorological stations as the climatic boundary conditions. The model comprises an air change rate based on ambient and indoor (22 °C) temperatures, indoor moisture sources and wood panel moisture sorption processes; it was tested on four selected homes in Norway during the winter of 2015/2016. The results were compared to values recorded by indoor relative humidity sensors in the homes, which ranged from naturally ventilated early 1900s homes to a modern home with balanced ventilation. The modeled indoor relative humidity levels during cold weather agreed well with recorded values to within 3% relative humidity (RH) root mean square deviation, and thus provided reliable information about expected wood panel moisture content. This information was used to assess historic single home fire risk represented by an estimated time to flashover during the studied period. Based on the modelling, it can be concluded that three days in Haugesund, Norway, in January 2016 were associated with very high conflagration risk due to dry indoor wooden materials and strong winds. In the future, the presented methodology may possibly be based on weather forecasts to predict increased conflagration risk a few days ahead. This could then enable proactive emergency responses for improved fire disaster risk management.

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Optical Gas Imaging (OGI) as a Moderator for Interdisciplinary Cooperation, Reduced Emissions and Increased Safety

Cited by 7 publications

References 23 publications

A Common Risk Classification Concept for Safety Related Gas Leaks and Fugitive Emissions?

A Common Risk Classification Concept for Safety Related Gas Leaks and Fugitive Emissions?

Estimation of Emission Factors for Hazardous Air Pollutants from Petroleum Refineries

Modeling Indoor Relative Humidity and Wood Moisture Content as a Proxy for Wooden Home Fire Risk

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