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Record W2904018527 · doi:10.31472/ihe.4.2018.07

WORLD AND DOMESTIC EXPERIENCE OF BIOETHANOL PRODUCTION

2018· article· en· W2904018527 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIndustrial Heat Engineering · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsBiofuelRaw materialHemicelluloseEthanol fuelPulp and paper industryCelluloseBiotechnologyStarchFermentationSugarWaste managementEnvironmental scienceChemistryFood scienceEngineeringBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The paper presents an overview of bioetanol production technologies. It is noted that world fuel ethanol production in 2017 amounted to more than 27,000 million gallons (80 million tons). Eight countries, namely the USA, Brazil, the EU, China, Canada, Thailand, Argentina, India, together produce about 98% of bioethanol. In Ukraine, the volume of bioethanol production by alcoholic factories in recent years has been gradually increasing and amounted to 2,992.8 ths. dal in 2017. The production of ethanol as an additive to gasoline, with regard to the raw materials used, as well as the corresponding technologies, is historically divided into three generations. The first generation of biofuels produced from food crops rich in sugar or starch is currently dominant.
 Production of advanced biofuels from non-food crop feedstocks is limited. Output is anticipated to remain modest in the short term, as progress is needed to improve technology readiness. The main stages of bioethanol production from lignocellulosic raw materials are pre-treatment, enzymatic hydrolysis and fermentation. The pre-treatment process aims to reduce of sizes of raw material particles, provision of the components exposure (hemicellulose, cellulose, starch), provision of better access for the enzymes (in fermentative hydrolysis) to the surface of raw materials, and reduction of crystallinity degree of the cellulose matrix. The pre-treatment process is a major cost component of the overall process. The pre-treatment process is highly recommended as it gives subsequent or direct yield of the fermentable sugars, prevents premature degradation of the yielded sugars, prevents inhibitors formation prior hydrolysis and fermentation, lowers the processing cost, and lowers the demand of conventional energy in general. From the perspective of efficiency, promising methods of pre-treatment of lignocellulosic raw materials to hydrolysis are combined methods combining mechanical, chemical and physical mechanisms of influence on raw materials. One method that combines several physical effects on a treated substance is the discrete-pulsed energy input (DPIE) method. The DPIE method can be applied in the pre- treatment of lignocellulosic raw material in the technology bioethanol production for intensifying the process and reducing energy consumption. Ref. 15, Fig. 2.

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.

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 score0.259
Threshold uncertainty score0.148

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.230
Teacher spread0.202 · 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