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Record W1949062687 · doi:10.51415/10321/1366

Development of a small scale water treatment system for fluoride removal for rural areas

2015· dissertation· en· W1949062687 on OpenAlexaboutno aff
Thulani Dlamini

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicFluoride Effects and Removal
Canadian institutionsnot available
FundersDurban University of Technology
KeywordsTurbidityFluorideEnvironmental engineeringWater treatmentEnvironmental scienceReuseWaste managementEngineeringGeographyChemistryGeology

Abstract

fetched live from OpenAlex

Several areas in the world such as the United States of America, Sri Lanka, China, Argentina, Canada, Tanzania, Kenya, South Africa and many others have a problem of high fluoride content in drinking water. Generally fluoride levels above 1.5 ppm in water may result in dental and skeletal fluorosis in humans depending on quantity consumed (Fan et al., 2003; Meenakshi, 2004). Remote rural areas where there are no water treatment facilities are more vulnerable to this problem. Adsorbents such as activated alumina and FR-10 resin seem to have a potential for successful application in rural areas. These methods however require pre-treatment if the feed has high turbidity. A membrane based system called woven fabric microfiltration gravity filter (WFMFGF) developed by Durban University of Technology proved to be suitable for turbidity removal. The main objective of this research was to develop a small water treatment system for fluoride removal. The small water treatment system developed in this study consists of WFMFGF for pre-treatment and an adsorption column. The WFMFGF is made up of a 40 L container packed with 15 immersed flat sheet membrane elements. The operation of the WFMFGF is in batch mode, driven by varying static head. The static head variation results in flow rate variation through the system. This in turn result in variation of contact time, velocity as well as pressure drop in the fluoride removal unit. Specific objectives of the study were: (1) to establish the maximum and minimum flow rates through the WFMFGF system, the total run time before cleaning is required and the best cleaning method for this particular membrane system. (2) to evaluate and compare the performance of activated alumina and FR-10 resin on varying contact time, velocity and pressure drop on the fluoride removal unit. The adsorbents were also compared on adsorption capacity, cost and ease of operation. The minimum and maximum flow rates through the WFMFGF were found to be 5 l/hr and 100 l/hr respectively. It was found that the system can be run for more than a month before requiring cleaning. The suitable cleaning method was found to be soaking the membranes in 0.0225 percent sodium hypochlorite solution overnight and brushing them using a plastic brush. The comparison of the performance of FR-10 resin to activated alumina found that the adsorbents gave equal performance based on the given criteria. FR-10 resin had higher adsorption capacity, gave good quality treated water even with shorter contact time and operated at wider velocity range. Activated alumina on the other hand had an advantage of lower costs, lower pressure drop and ease of use. According to Pontius (1990), the performance of activated alumina can be improved by intermittent operation. Point of use (POU) systems are generally operated intermittently. This improves the fluoride removal efficiency of activated alumina giving it more advantage over FR-10 resin. Based on this activated alumina was selected as the best adsorbent for the system. After the adsorbent was selected, the adsorption column was designed. The column operation regime was 3.5 minutes minimum contact time and 1.17 to 7.8 m/hr velocity range. The activated alumina adsorption capacity was 1.53mg/g. The column had an inside diameter of 70 mm. It was packed with activated alumina to a bed height of 400 mm. The column inlet and outlet pipes were made of PVC with a standard pipe size of 20 mm outside diameter. A valve at the column inlet pipe allowed water to flow through the system.

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)0.000
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: none
Teacher disagreement score0.654
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.246
Teacher spread0.230 · 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
Published2015
Admission routes1
Has abstractyes

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