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Record W7132531558

Feasibility of low-cost CO₂ sensors for demand-controlled ventilation-laboratory chamber testing

2020· article· en· W7132531558 on OpenAlexvenueno aff
Justin Berquist, Carsen Banister

Bibliographic record

VenueNPARC · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon dioxide sensorVentilation (architecture)RepeatabilitySensitivity (control systems)Energy consumptionWork (physics)Line (geometry)Current sensor
DOInot available

Abstract

fetched live from OpenAlex

Demand-controlled ventilation (DCV) systems have some advantages over other building ventilation strategies, as they can maintain acceptable indoor air quality (IAQ) of a space while reducing the overall energy consumption of building ventilation. When selecting sensors for DCV, it is important to consider their performance and cost. Over the past several years, there has been an increase in the availability of low-cost sensors. However, the feasibility of using the currently available low-cost sensors within DCV is an area that requires further investigation. The focus of the work presented in this paper is to evaluate the feasibility of current low-cost carbon dioxide (CO₂) sensors for use in DCV using a controlled environment. The performance of three low-cost CO₂ sensor models was verified to determine their suitability in the control ofDCV. Preliminarytesting revealedunacceptable inaccuracy in one of the three sensors. This sensor uses micro-hotplate technology for gas sensing and was excluded from detailed testing. The two sensors tested in detail use nondispersive infrared (NDIR) technology. The accuracy of the first NDIR sensor (model A) was satisfactory; some of the sensor measurements deviated from the dosed concentration by more than 100 ppm, but remained within 150 ppm. The nonlinearity of sensor model A was greater than model B but was acceptable—the maximum deviation from the linear line of best fit ranged between 55 and 92 ppm. The repeatability of sensor model A was acceptable; sensor measurements during the three days of testing were always within 100 ppm when measuring the same CO₂ concentration. In fact, the maximum recorded nonrepeatability was 73 ppm. The hysteresis of sensor model A was satisfactory; most sensor measurements were within 100 ppm and all were within 150 ppm when measuring the same CO₂ concentration when approached from varying directions. However, sensor model A had a tendency to underreport CO₂ concentrations, which could reduce IAQ if the sensors were not calibrated or if the tendency to underreport was not considered in the control algorithm. Sensor model A consistently underreported decreasing CO₂ at a larger magnitude, which would likely cause the DCV system to turn off sooner than desired, potentially negatively impacting IAQ. The accuracy of the second NDIR sensor (model B) was found to be better than model A and was deemed acceptable. The sensor measurements were always within 100 ppm of the dosed concentration. Sensor model B did have a tendency to overreport CO₂ concentrations, which could result in more energy consumption than ideal; however, the impact is expected to be low due to the better accuracy ofthe sensor. The nonlinearity ofsensor model B was satisfactory; the maximum deviation from the linear line of best fitrangedbetween 30and55 ppm. The repeatability ofsensor model B was satisfactory; all but one of the sensor measurements during the three days of testing were within 100 ppm when measuring the same CO₂ concentration. The hysteresis of sensor model B was exceptional; sensor measurements were typically within 20 ppm when measuring the same CO₂ concentration when approached from either high or low concentrations and were always within 60 ppm. Sensor model B was found to be suitable for use in DCV. This work shows that ultra low-cost CO₂ sensors in the area of $27 CAD ($20 USD) each may not be suitable for DCV, but that low-cost CO₂ sensors in the area of $80 CAD ($60 USD) could be suitable for developing a low-cost controller and sensor package for managing indoor CO₂ concentrations.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.066
GPT teacher head0.289
Teacher spread0.223 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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