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Record W2014117944 · doi:10.3390/en20200445

Biological Hydrogen Production from Corn-Syrup Waste Using a Novel System

2009· article· en· W2014117944 on OpenAlexaffabout
Hisham Hafez, George Nakhla, M. Hesham El Naggar

Bibliographic record

VenueEnergies · 2009
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsWestern University
Fundersnot available
KeywordsBiohydrogenChemistryHydrogen productionPulp and paper industryFood wasteWastewaterHydrogenBiofuelFood scienceAnimal scienceWaste managementEnvironmental scienceBiotechnologyEnvironmental engineeringBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

The reported patent-pending system comprises a novel biohydrogen reactor with a gravity settler for decoupling of SRT from HRT. The biohydrogenator was operated for 100 days at 37 °C, hydraulic retention time 8 h and solids retention time ranging from 2.2–2.5 days. The feed was a corn-syrup waste generated as a byproduct from an industrial facility for bioethanol production located in southwestern Ontario, Canada. The system was initially started up with a synthetic feed containing glucose at concentration of 8 g/L and other essential inorganics. Anaerobicaly-digested sludge from the St. Mary’s wastewater treatment plant (St. Mary, Ontario, Canada) was used as the seed, and was heat treated at 70 °C for 30 min to inhibit methanogens. After 10 days, when the hydrogen production was steady, the corn-syrup waste was introduced to the system. Glucose was the main constituent in the corn-syrup; its concentration was varied over a period of 90 days from 8 to 25 g/L. The change in glucose concentration was used to study the impact of variable organic loading on the stability of hydrogen production in the biohydrogenator. Hydrogen production rate increased from 10 L H2/L·d to 34 L H2/L·d with the increase of organic loading rate (OLR) from 26 to 81 gCOD/L·d, while a maximum hydrogen yield of 430 mL H2/gCOD was achieved in the system with an overall average of 385 mL H2/gCOD.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.416

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 head0.025
GPT teacher head0.208
Teacher spread0.183 · 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 designBench or experimental
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

Citations34
Published2009
Admission routes2
Has abstractyes

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