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Low Cost Water Disinfectant System Using Solar Energy

2012· article· en· W2318287323 on OpenAlexvenueno aff
Anees Fatima, Atif Shahzad, Sara Umair Siddiqui

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceWater disinfectionSolar energyDisinfectantEnvironmental engineeringWaste managementPulp and paper industryWater treatmentChemistryEngineering

Abstract

fetched live from OpenAlex

Solar disinfection unit is unsophisticated, efficient and reasonably priced water treatment process appropriate for use in developing countries. Water was filtered through cloth, net and coconut husk to remove any suspended particles in water which would directly increase the efficiency of solar disinfection. The filtered water is then transferred in solar disinfection unit. Water with Escherichia coli as indicator organism was filled in the solar disinfection unit comprising of four polyethylene terepthalate (PET) plastic bottles joined together with PVC pipes. These bottles were kept in direct sunlight for 12-48 hours. Weather conditions and solar radiation were obtained using different programs. Solar radiations and elevated temperature destroyed the indicator organism efficiently. Health peril of chemical released in water was well thought-out and was determined to be safe with respect to human consumption. The efficiency of solar disinfection was augmented by use of semi-conductor titanium dioxide (TiO2) which reduced the time for exposure up to 90%. The temperature increase and dissolved oxygen decrease in the disinfection process which was overcome by retaining this water in traditional earthenware water storage vessels (mutka). Allowing accessibility to better life through improved quality of water.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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 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
Published2012
Admission routes1
Has abstractyes

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