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Record W4296306927 · doi:10.3390/en15186828

Cheese Whey as a Potential Feedstock for Producing Renewable Biofuels: A Review

2022· review· en· W4296306927 on OpenAlexaff
Carlos S. Osorio‐González, Natali Gómez-Falcon, Satinder Kaur Brar, Antonio Avalos Ramírez

Bibliographic record

VenueEnergies · 2022
Typereview
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsCentre National en Électrochimie et en Technologies EnvironnementalesCollège ShawiniganYork University
Fundersnot available
KeywordsRaw materialBiofuelBagasseEnvironmental sciencePomaceVinasseBiotechnologyBioenergyBiohydrogenWaste managementPulp and paper industryBusinessFood scienceEngineeringChemistryBiology

Abstract

fetched live from OpenAlex

Agro-industrial residues such as bagasse, pomace, municipal residues, vinasse and cheese whey are an environmental problem around the world, mainly due to the huge volumes that are generated because of the food production to satisfy the nutritional needs of the growing world population. Among the above residues, cheese whey has gained special attention because of its high production with a worldwide production of 160 million tons per year. Most of it is discarded in water bodies and land causing damage to the environment due to the high biological oxygen demand caused by its organic matter load. The environmental regulations in developing countries have motivated the development of new processes to treat transform cheese whey into added-value products such as food supplements, cattle feed and food additives. In addition, during the last decade, several processes and technologies have been developed to produce bioenergy through the biotechnological process using cheese whey as a potential feedstock. This review discusses the production of bioethanol, biohydrogen, biomethane and microbial lipid-biodiesel production using cheese whey as a potential substrate.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.037
GPT teacher head0.289
Teacher spread0.252 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations41
Published2022
Admission routes1
Has abstractyes

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