Using Quantum Gas Lidar for Continuous Methane Emissions Quantification of Gas to Grid Plants in Water Sewage Treatment Works.
Notice bibliographique
Résumé
The recent global focus on anthropogenic methane emissions mitigation has accelerated the development and deployment of novel technologies to detect, quantify, locate and prioritize mitigation of fugitive and process emissions of methane, particularly within the oil and gas supply chain. The majority of emissions within this infrastructure result from large and intermittent sources. One study1 showed that the largest 5% of emissions (i.e. super emitters) typically contribute over 50% of the total emission volumeand another study2 found that most sources (66%) are intermittent, and account for most (48%) of the emissions. The largest impact on emissions mitigation therefore can be realized by deploying detection methods and technologies that are matched to how these two classes of emissions manifest in the infrastructure. Continuous monitoring solutions that can image and pinpoint emission sources are especially well-suited for use at sites that are expected to have intermittent process emissions.We present recent work utilizing novel Quantum Gas Lidar for continuous methane emissions monitoring in sludge treatment works where methane-rich biogas is produced from the anaerobic digestion of sewage sludge. Some of the biogas is consumed on site and the rest is cleaned, upgraded and fed into the natural gas distribution network. Monitoring campaigns are ongoing at three such plants in the UK, owned and operated by Severn Trent, two of which have full gas to grid infrastructure. The third site uses combined heat and power engines to convert the biogas into electricity for use on site. Specific elements of the infrastructure are targeted for continuous, automated measurement by the lidar system (especially the digesters, gas storage, combined heat and power engines and gas to grid plants) and any detected methane emission plumes are imaged, their origins are pinpointed, and their emission rates are quantified. This results in a methane emission rate dataset having both high spatial and temporal resolution which can be used for both component and site-level emissions reporting within, for example, the OGMP 2.0 level 4 and level 5 framework, and IPCC Tier 3 (facility level) reporting. 3 The individual emission sources are intermittent and can have emission rates that vary in time depending on various process variables (i.e. varying pressures within the equipment) so that an accurate accounting of the overall emissions over time is reliant on the high-accuracy quantification and high temporal resolution that the lidar system provides. The continuous measurements have so far identified some previously unknown emission sources and have allowed the actual emissions of those sources to be accurately quantified for the first time, offering a high-confidence, measurement-based accounting of the methane emissions at these sites. [1] https://pubs.acs.org/doi/10.1021/acs.est.6b04303 [2] https://pubs.acs.org/doi/10.1021/acs.estlett.1c00173 [3] https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».