Integrating the Local Material of Adobe With Solar Distillation to Produce Affordable Drinking Water
Bibliographic record
Abstract
It is estimated that nearly three billion people are living in water scarce conditions. This research uses modeling and field studies to assess the quantity, quality, and economics of distillate produced for drinking water from a brackish water source using two single-sloped, single-basin (SSSB) distillation reactors. The reactors were constructed from adobe and concrete in San Luis Potosí, Mexico and tested from August to October of 2011. The cost of one adobe reactor with an evaporative area of 0.72 m2 is 535 pesos, whereas the same size reactor made from concrete costs 770 pesos. Results show that desalination reactors made from adobe produce 848 (L m-2d-1) and reactors made from concrete produce 979 (L m-2d-1) of distillate, while similar reactors made from other materials are estimated to produce over 2100 (L m-2d-1) under similar meteorological conditions. These volumes represent approximately 10 percent of drinking water needs of a local family with typical water use habits, however, after five years of operation the unit cost of potable water would be reduced by 50%. Results also showed that the concentrations of total dissolved solids in the source water decreased from 1102 (mg L-1) to 40 (mg L-1) over the study’s duration for a removal of 96% which is comparable to current desalination systems (97%). Finally, the results were modeled using a regression analysis to estimate the distillate yield based upon ambient temperature and solar radiation. The model was then applied using historical global climate data estimate the appropriateness of the adobe SSSB globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".