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Record W2749456883 · doi:10.3390/su9081474

Conceptual Design and Energy Analysis of Integrated Combined Cycle Gasification System

2017· article· en· W2749456883 on OpenAlexaff
Hossam A. Gabbar, Mohamed Aboughaly, Stefano Lo Russo

Bibliographic record

VenueSustainability · 2017
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsIntegrated gasification combined cycleAir separationSyngasWood gas generatorWaste managementPressure swing adsorptionEnvironmental scienceProcess engineeringMethaneTonneProcess integrationGas compressorElectricity generationEnvironmental engineeringEngineeringCoalChemistryHydrogenMechanical engineeringThermodynamicsOxygen

Abstract

fetched live from OpenAlex

In this paper, an integrated gasification combined cycle conceptual design that achieves optimum energy efficiency and 82.9% heat integration between hot and cold utilities is illustrated. The integrated combined gasification cycle (IGCC) is also modeled and evaluated for the co-production of electricity, ammonia and methane for 543.13 kilo tonne per annum (KTA) of municipal solid waste (MSW). The final products are 1284.89 MW, 8731.07 kg/h of liquid ammonia at 8 °C and 32,468 kg/h of methane gas at 271 °C. The conceptual design includes advanced heat integration between syngas and hot and cold streams in all process units. The water gas shift (WGS) unit includes integration between equilibrium reactors and cold streams. The air separation unit (ASU) includes four air compressors followed by a pressure swing adsorber (PSA), which separates oxygen and nitrogen gases into separate streams. Both O2 and N2 gases are compressed and sent to gasifier and syngas cleaning unit, respectively. The overall design shows reliability and solved steady state equations for all process units with improvements in thermal efficiency in comparison with single cycle gasification plants. The environmental emissions for GHGs such CO2 and SO2 are lower due to higher overall energy efficiency.

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)0.001
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 score0.694
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.222
Teacher spread0.213 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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