MétaCan
Menu
Back to cohort
Record W4393254831 · doi:10.2118/218960-ms

Use of the Life Cycle Approach for the Evaluation of Industrial Water Management Alternatives

2024· article· en· W4393254831 on OpenAlexaff
J. S. Boeira, Anne‐Marie Boulay, Matthieu Jacob, D. Dardor, Pierre Pedenaud, Manuele Margni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsProduct life-cycle managementComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract In a context of more and more stress on the water resource, the industries are pushed to improve their water efficiency. Water management must reconcile legal requirements with technical and environmental performances to ensure that one does not compromise the other. Therefore, a fundamental question arises: What are the environmental impacts associated with different industrial water management alternatives? To address this inquiry, this research conducts a case study, analyzing different water management alternatives using a Life Cycle Analysis approach. A Combined Cycle Power Plant was chosen due to its simplicity and significance in terms of water use. The scenarios compared are based on the functional unit "managing water necessary to produce 1 MWh of electricity". Only water treatment associated structure, energy and chemicals to fulfill the defined functional unit were considered. Three distinct water recovery systems were analyzed and subsequently combined with different water supply and release options. Zero-recovery scenario, representing base case; partial recovery scenario through reverse osmosis, and total recovery scenario under Zero Liquid Discharge, in which thermo-distillation is applied. Furthermore, all scenarios were virtually reassigned to another water-scarce context for a more comprehensive geographical sensitivity analysis. In this research a Life Cycle Analysis was performed. Results are presented as carbon footprint (in CO2-eq) and water footprint (in m3 world-eq using AWARE) as mid-point indicators. A damage assessment has also been conducted to evaluate the relative contribution of global warming potential and water scarcity relative on Human Health and Ecosystem Quality Areas of Protection, among the contribution of all other midpoint impact categories. Withdrawn and released water volumes decrease with higher recovery rates while water consumption remains unaltered. Thus, the water footprint, based on freshwater consumption, substantially changes with different recovery rates only if non-freshwater resource is involved. CO2-equivalent emissions are caused mainly due to natural gas burned to produce the required electricity. Human health impacts are primarily dominated by global warming potential in non-water-scarce or highly developed countries. In this aspect, lower energy intensive water treatment routes should be prioritized over freshwater savings. However, the water scarcity footprint impacts dominate human health impacts for scarce and less developed countries. Thus, freshwater savings become important in those cases. Ecosystem quality exhibits lower geographical variation compared to human health impacts, and the differences between scenarios are dominated by global warming potential variation. Recycling does not necessarily lead to lower water scarcity footprints and can result in higher greenhouse gas emissions. It is crucial to consider the water scarcity context and trade-offs before making decisions about water management. Legislation based solely on water withdrawal and release volumes may lead to undesirable environmental impacts, beyond not ensuring water savings. Nevertheless, when debating water management options, the present work aims to facilitate informed decision-making regarding potential environmental impacts.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.125
GPT teacher head0.286
Teacher spread0.161 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicWastewater Treatment and ReuseFrench-language works237,207