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Record W4392617278 · doi:10.5194/egusphere-egu24-2391

Using Quantum Gas Lidar for Continuous Methane Emissions Quantification of Gas to Grid Plants in Water Sewage Treatment Works.

2024· preprint· en· W4392617278 on OpenAlexaff
Bharanitharan Srinamasivayam, Alex G. Harrison, Doug Millington-Smith, Gavin Lindsay

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsBiogasEnvironmental scienceMethaneGreenhouse gasRenewable natural gasNatural gasAnaerobic digestionFugitive emissionsMethane emissionsWaste managementMethane gasEnvironmental engineeringSewage sludgeElectricitySewage treatmentFuel gasEngineeringChemistry

Abstract

fetched live from OpenAlex

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/

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.284
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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