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Record W4385363111 · doi:10.1680/jenes.23.00045

Enviroeconomic analysis of a hybrid active solar desalination system using nanoparticles

2023· article· en· W4385363111 on OpenAlexvenueno aff
Dharamveer Singh, Satyaveer Singh, Aakersh Chauhan, Anil Kumar

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsnot available
Fundersnot available
KeywordsDesalinationSolar energyEnvironmental scienceSolar stillPhotovoltaic systemLow-temperature thermal desalinationEnvironmental engineeringSolar desalinationEngineeringChemistry

Abstract

fetched live from OpenAlex

The water crisis is the focus of this work, and there is a dire need to develop eco-friendly and self-sustainable water supply units. This study performed an economic and enviroeconomic analysis of N identical photovoltaic thermal compound parabolic concentrator collectors with double-slope solar desalination units with a heat exchanger using water-based aluminium oxide (Al2O3) nanoparticles. In this analytical study, a program was fed into the Matlab environment, and the analysis was monitored on an annual basis in New Delhi, India. The Indian Metrological Department in Pune, India, provided the input data necessary for the mathematical procedure. From the year-round solar energy, yield and energy production were calculated. The economic, environmental and energy-related performance of the system was assessed, and it was compared with those of previous systems. Additionally, based on annual and lifespans of 15, 20 and 30 years, it was discovered that there is an 8.5% greater yield, 7.31% greater annual energy, 3.9 and 2.85% less carbon dioxide (CO2) mitigation/t energy and 5.17% greater annual productivity, carbon dioxide credit respectively. Based on energy, environmental and economic factors, it was determined that the suggested system was superior to alternative systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.239
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2023
Admission routes1
Has abstractyes

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