A Conceptual Model for Clothes Drying Using Composite Energy Sources
Bibliographic record
Abstract
Laundering clothes by consumers is paramount for maintaining health and hygiene.Drying of clothes is a crucial part of laundry, and in most developing countries this means spreading clothes on lines to be dried by the sun, or passive outdoor drying (POD).However, due to urbanisation and the proliferation of condos there is little room for sun drying clothes.Additionally, electric washing machines and clothes dryers have become commonplace in many modern homes, however, they consume enormous amounts of energy when drying clothes.Moreover, most families in under-served countries often cannot afford exorbitant electricity bills making these methods to domestic laundry not sustainable.With the continued drive for sustainable living, there is a need for energy conservation alternatives.In other related research work solar energy has been applied in agriculture for drying and preserving food, in electricity and lighting using Photovoltaic (PV) cells, and for heating via radiation.This article explores an alternative for domestic laundry with solar energy, harnessed heat and related technologies.This work does not rely solely on PV cells to generate electrical energy to power heaters, rather we borrow from the food drying process used in agriculture.This entails combining solar concentrators, which focus the sun's energy into a chamber, with a system that channels household heat sources into the same chamber.Within this chamber clothes are hung using smart clothes pegs, which hold the clothes in place.The pegs also measure the moisture level of each garment, then use the telemetry data to control the heat within the chamber.Using these combined systems, enormous amounts of grid electricity and carbon-dioxide emissions can be saved.Actor Network Theory (ANT) and relevant adoption models and theories will investigate consumer adoption and how this technological design is shaped by encompassing socio-cultural factors and physical realities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".