Passive solar evaporation and emissions reduction of process-affected and produced water using buoyant photothermal beads
Bibliographic record
Abstract
Process-affected water (PAW), including flowback and produced water, presents considerable challenges for oil and gas industries and wastewater treatment providers. The complex chemistry and large volume production of PAW means that innovative solutions are required to treat and dispose of PAW while reducing environmental burden. Buoyant photothermal beads (BPBs) can operate in conjunction with existing evaporation ponds (EPs) by forming a connective layer and using sunlight to enhance the rate of water evaporation while also reducing the total amount of volatile organic compounds (VOCs) emitted from a water body. In this work, BPBs are shown to enhance the evaporation rate of a simulated seawater brine by 80%, as well as 5 different real PAW samples by 25% to 73%. Further analysis suggests that BPB performance is affected by the absorbance and apparent vapor pressure of the water matrix. Solid phase microextraction (SPME) is used to show that BPBs can significantly reduce VOC emissions compared to a bare surface when treating a volatile water matrix. Techno-economic analysis of the technology estimates a levelized cost of water of 0.44 USD/m3, demonstrating that BPBs have the potential to be a competitive technology and highly economical retro-fit for EPs. Graphical abstract. • Buoyant photothermal beads passively accelerate evaporation using sunlight. • Beads dewater process-affected waters and block VOC emissions. • Composite is easily manufactured and deployed at scale in evaporation ponds. • Competitive LCOW compared to existing wastewater disposal technologies.
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 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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".