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Record W7128732387 · doi:10.26180/4679455.v1

Towards flexible operation of post-combustion CO₂ capture from brown coal derived flue gas

2017· dissertation· W7128732387 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

VenueMonash University · 2017
Typedissertation
Language
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFlue gasProcess (computing)Power stationSoftware deploymentRange (aeronautics)Pilot plantElectricityCoal

Abstract

fetched live from OpenAlex

The world’s first commercial-scale CO₂ capture plant in Boundary Dam power station uses amine-based post-combustion CO₂ capture (PCC) technology (Boundary Dam, Canada). The issues of large energy requirement and high cost have hindered worldwide deployment of PCC. Flexible operation has been proposed as a way to improve the economic and technical feasibility of PCC. Flexible operation coordinates reductions in CO₂ emissions with electricity demand by: (i) ramping up CO₂ capture during periods of low energy demand, and (ii) turning down or switching off CO₂ capture during high energy demand. The immediate and long term impact of process disturbances from flexible operation is unclear. This thesis investigates the technical influence of flexible operation during amine-based CO₂ capture from brown coal derived flue gas. Dynamic pilot plant studies have provided practical experience in flexible operation of PCC plants. The pilot plant study demonstrates the successful implementation of flexible operation in the form of parameter step-changes to a PCC process. The PCC pilot plant is run by CSIRO and located at AGL Loy Yang in Australia. The operation of the PCC plant under a broad range of transient conditions has captured the dynamics of the process and provides suitable data dynamic model validation. Additionally, the density meters provide the advantage for online monitoring of liquid CO₂ concentration. Statistical analysis revealed that data variance may occur due to changes in: (i) ambient temperature, (ii) MEA concentration, or (iii) amine degradation. Although PCC plants of different scales and configurations have different response times, it is likely the dynamic trends to parameter changes would be similar. Thus, the dynamic behaviour observed in this thesis is of greater significance compared to the absolute values. Based on this study, changing the flue gas flow rate would produce the most rapid response. The greatest CO₂ removal percentage was achieved at the lowest flue gas flow rate or at the highest absorbent flow rate. However, the latter provides high CO₂ removal percentage with significantly lower reboiler heat duty in terms of MJ/kg CO₂. The steam pressure parameter provides the ability to adjust the temperature of all the columns simultaneously. This effect may be used to compensate for effects from ambient conditions or heat losses. Flexible operation of PCC is modelled using Aspen Plus Dynamics®. Dynamic modelling of flexible PCC operation in the pilot plant uses the following stand-alone models: (i) Absorber Column 2 (ABS2), (ii) Absorber column 1 (ABS1), and (iii) Stripper Column. Stand-alone models provide the advantage of greater flexibility compared to integrated models; also they carry-through of successive errors is avoided. Additionally, disturbances can be introduced to intermediate streams without convergence issues. Each stand-alone model simulates the following flexible operation scenarios: (i) step-changes in flue gas flow rate, (ii) step-changes in absorbent flow rate, and (iii) step-changes in steam pressure. Modelling of property changes individually, elucidates which properties generate the dynamic responses observed in the pilot plant. The overall effect observed in the step-changes scenarios was the result of a combination of property changes that occur in tandem. The combination of these property changes accurately describes the dynamic behaviour observed in the pilot plant results. Although the dynamic modelling could not replicate the absolute values obtained in the pilot plant, the models demonstrated the same trends observed in pilot plant results. The modelled behaviour and pilot plant observations are in agreement for comparisons of the following results: (i) column temperature, (ii) CO₂ composition of the liquid product, (iii) CO₂ composition of the vapour product, and (iv) CO₂ removal/capture percentage. Modelled dynamic response is in agreement with pilot plant trends, despite the influence of non-ideal conditions (e.g. amine degradation, ambient temperature effects, heat loss). Some key novel outcomes of the dynamic modelling include: (i) Model specifications in Aspen Plus® and Aspen Plus Dynamics® are based on data from the CSIRO PCC pilot plant at AGL Loy Yang; (ii) Modelling of a PCC process with a double-absorber configuration; (iii) The process of converting equilibrium reactions into forward/reverse kinetic reactions is documented in detail; (iv) The number of equilibrium stages for this PCC study is greater compared to previous Aspen Plus Dynamics® studies by Lin et al. (2011), Lin et al. (2012) and Léonard et al. (2013); (v) Demonstrated the necessity of adjustment factors to achieve model agreement with experimental data (particular when pilot plant data is affected by non-ideal conditions); (vi) Compared the precision of different mass transfer coefficient correlations for rate-based PCC modelling.

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 categoriesMeta-epidemiology (narrow), Research integrity
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 score0.128
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.001
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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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