MétaCan
Menu
Back to cohort
Record W2951654281 · doi:10.1115/1.4044056

Renewable Energy Based Dimethyl-Ether Production System Linked With Industrial Waste Heat

2019· article· en· W2951654281 on OpenAlexaff
Magd N. DinAli, İbrahim Dinçer

Bibliographic record

VenueJournal of Energy Resources Technology · 2019
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDimethyl etherExergyRenewable energyWaste heatMethanolProcess engineeringWaste heat recovery unitThermal energyWaste managementPhotovoltaic systemExergy efficiencyProton exchange membrane fuel cellEnvironmental scienceChemistryEngineeringHeat exchangerThermodynamicsMechanical engineeringOrganic chemistryElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

A new renewable energy based dimethyl-ether (DME) production system is proposed in this paper. The DME is then produced through the indirect synthesis method where methanol is produced first through carbon hydrogenation process, then methanol derived to a process called methanol dehydration to produce the DME. The proposed integrated system consists of four main subsystems named as carbon capturing and heat recovery system, proton exchange membrane (PEM) hydrogen production system, methanol synthesis system, and the DME synthesis system. The main inputs are electrical energy from photovoltaic (PV) solar panels and thermal energy from flue gas waste heat. The system is modeled and simulated using both aspen plus process simulation software and engineering equation solver (EES) and assessed based on energy and exergy approaches. The energy and exergy efficiencies are determined to be 40.46% and 52.81%, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Energy Resources TechnologySame topicHybrid Renewable Energy SystemsFrench-language works237,207