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Record W4380886747 · doi:10.3389/fenvs.2023.1194994

Automated CO2 and CH4 monitoring system for continuous estimation of degassing related to hydropower

2023· article· en· W4380886747 on OpenAlexafffund
Charles P. Deblois, M. Demarty, F. Bilodeau, A. Tremblay

Bibliographic record

VenueFrontiers in Environmental Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsHydro-Québec
FundersHydro-Québec
KeywordsHydropowerEnvironmental scienceGreenhouse gasProcess engineeringModular designCarbon footprintCloggingTurbineDownstream (manufacturing)Continuous monitoringReliability (semiconductor)Computer scienceEnvironmental engineeringPower (physics)EngineeringMechanical engineeringOperations management

Abstract

fetched live from OpenAlex

Reliable measurement of greenhouse gas emissions from reservoirs is essential for estimating the carbon footprint of the hydropower industry. Among the different emission pathways, degassing downstream of the turbines and spillway is poorly documented mainly because of the safety stakes related to sampling up and downstream the power plants. The alternative being to sample the water from the turbine inside the station, this study aimed to assemble a custom automated CO 2 and CH 4 monitoring system (SAGES), especially designed for long-term surveys in hydropower facilities, with a special focus on low maintenance requirements. The SAGES combines infrared and laser technologies with a modular programming approach and run with a specifically designed plexiglass equilibration system (PES) that maintain a permanent headspace and avoid clogging by suspended solids. Although the SAGES is based on commercially available devices, it is the first time they are combined and used with the gas equilibrator. To ensure the reliability of the mounting and to control the quality of the readings, the system was tested in laboratory prior to its installation in generating stations. SAGES and PES performances were compared with those of generic devices available on the market although less adapted to the specific deployments targeted. The SAGES gas partial pressure measurements were accurate and linear in the entire range tested: 0 to 5,000 ppm for p CO 2 and 0 to 600 and 10,000 ppm for p CH 4 . Gas PP measurements were comparable to the reference CO 2 /CH 4 sensor and there was no drift during long term deployment. The SAGES/PES installed in 2021 in cascading generating stations of the Romaine complex collected more than 28,000 data points over a 10-month period and required only two maintenances. Results show that the SAGES is a reliable tool that provide long-term CO 2 and CH 4 dataset in generating stations while requiring minimal energy, care and maintenance. The data collected in turbine water and the recent use of the SAGES in peat land by a collaborative team demonstrate how the SAGES systems can efficiently contribute to the understanding of reservoir carbon cycles.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.216
Teacher spread0.212 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2023
Admission routes2
Has abstractyes

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