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Enregistrement W2966654119

Drinking Water Softening/Scale Prevention Technology Assessment and Performance of Template Assisted Crystallization

2019· dissertation· en· W2966654119 sur OpenAlexfundaboutno aff
Lin Shen

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2019
Typedissertation
Langueen
DomaineMaterials Science
ThématiqueCalcium Carbonate Crystallization and Inhibition
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
Mots-clésCrystallizationSofteningScale (ratio)Water softeningMaterials scienceEngineeringChemical engineeringGeographyComposite materialCartography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Hard water typically has a hardness concentration over 120 mg/L CaCO3. Hardness is not a
\nregulated drinking water parameter and does not have severe health effects. However, hard water
\ncauses more soap and detergent consumption and can cause scaling problems on household
\nheating appliances, distribution pipes, and industrial cooling equipment.
\nThere are various approaches to soften hard water or prevent scale formation at the
\ncentralized and the household scale. In order to choose the best treatment for a specific set of
\nconditions, an appropriate technology evaluation is necessary. Prior research have tested single
\ntechnology or compared two or three technologies with respect to their performance. Not many
\npapers have compared all available technologies using the same assessment criteria.
\nAt the household scale, point-of-entry (POE) devices are commonly used. Among these, ion
\nexchange is the most widely applied POE device in Canada, though it has two major
\ndisadvantages: high sodium concentration in softened water and high chloride content in the
\nbrine which is often discharged into the sewer. Hence, there is an increasing interest in adopting
\nsalt-free treatment technology. Template assisted crystallization (TAC) is a relatively new
\nhousehold scale prevention technology. TAC media transforms free calcium (Ca2+) and
\nmagnesium (Mg2+) ions into insoluble microcrystals. TAC technology has the potential to be an
\nalternative to ion exchange, but there is very little published journals about this technology.
\nTherefore, this study had two objectives: 1) to assess and rank currently available softening
\nand scale prevention technologies at both the centralized and household level, and 2) test the
\nperformance of the TAC technology using two source waters.
\nThe multi-criteria assessment (MCA) method was utilized to evaluate centralized
\ntechnologies (lime softening, pellet softening, nanofiltration, and ion exchange) as well as
\nhousehold technologies (TAC, ion exchange, nanofiltration, electrically induced precipitation,
\nmagnetic water treatment, and capacitive deionization). Criteria that were chosen in this
\nassessment were: waste disposal, energy requirement, life-cycle cost, efficiency, subsequent
\ntreatment needed for finished water, chemical addition, easy to use, and validated technology.
\nThe initial assessment assigned a higher weight to the first four criteria listed. A sensitivity
\nanalysis (SA) was done by changing the weight assignment of different criteria. Three cases
\nwere selected: more focus on waste disposal and energy requirement; more emphasis on cost;
\niv
\neach criterion shared equal importance. For the centralized technologies assessment, pellet
\nsoftening had the highest score, followed by ion exchange, lime softening, and nanofiltration. SA
\nresults showed that although the total score of each technology varied, the final rank did not
\nchange. For the household technologies assessment, TAC had the highest score, followed by ion
\nexchange, magnetic water treatment, electrically induced precipitation, nanofiltration, and
\ncapacitive deionization. SA results showed that the total score varied, but the final rank did not
\nchange.
\nThe performance of TAC technology was assessed using four tests which compared
\nuntreated and treated water samples of two selected source waters. The first test measured the
\nreduction of free Ca2+ by a Ca2+ selective electrode after being treated by TAC in two source
\nwaters and, test results did not show a lot of reduction with percentage reductions ranging from
\n4.0% to 5.0% for both locations. The reductions were statistically significant but not large
\nenough to be of much practical value. The second test was to measure the change in total Ca2+
\nand Mg2+ concentration after TAC. The changes were relatively small in both source waters,
\nwith percentage reductions ranging from 2.7% to 4.4% for Ca2+ and 4.0% to 6.9% for Mg2+.
\nAgain, the reductions were statistically significant but not large enough to be of much practical
\nvalue. The third test was a sequential ultrafiltration test utilizing membranes (3000 Da, 1000 Da,
\nand 500 Da) to identify the microcrystal size. This test was not able to isolate substantial
\namounts of microcrystal, nor did it identify the approximate microcrystal size. The last test was
\ndeveloped as a simplified scale test. Results showed that treated water forms somewhat less scale
\nthan untreated water for both source waters. Scale formation potential indices: Langelier
\nSaturation Index (LSI) and Calcium Carbonate Precipitation Potential (CCPP) were also
\ncalculated, and results showed that there was essentially no change in both indices after the TAC
\ntreatment.
\nOverall, assessment results showed that using this study’s criteria, pellet softening and TAC
\nwere the two most suitable technologies to be applied at the centralized and household level,
\nrespectively. The TAC performance tests did not establish a substantial reduction in free calcium
\nions, nor were any crystals isolated. The scale test only showed relatively small differences
\nbetween untreated and treated water. Future research could construct a flow-through system to
\ntest the performance of TAC technology and should also conduct some tests on new and used
\nmedia.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,085
Score d'incertitude au seuil0,863

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,227
Écart entre enseignants0,216 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2019
Routes d'admission2
Résumé présentoui

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