MétaCan
Menu
Back to cohort
Record W2466174921 · doi:10.1177/0954407016650782

Disturbance rejection in DOC-out temperature control for DPF regeneration

2016· article· en· W2466174921 on OpenAlexaff
Jinbiao Ning, Fengjun Yan

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFeed forwardControl theory (sociology)Robustness (evolution)Controller (irrigation)Environmental scienceComputer scienceEngineeringControl engineeringControl (management)Chemistry

Abstract

fetched live from OpenAlex

Control of diesel oxidation catalyst (DOC) outlet temperature is critical for downstream diesel particulate filter regeneration, but is challenging to control due to the non-minimum phase behavior and varying time delay. To effectively address this issue, a novel and time-efficient composite controller based on modified active disturbance rejection control (mADRC) is proposed for DOC-out temperature control in this paper. The proposed mADRC-based composite controller is a new combination of a model-based feedforward controller and a mADRC with time delay compensation through the mass flow rate of exhaust gas. The model-based feedforward controller is designed to partially compensate the variations of DOC inlet temperature and mass flow rate, while the mADRC is proposed to address the remain disturbances and model uncertainties including time delay uncertainties. Simulation and test results through a high-fidelity Gamma Technologies-Power model demonstrate the effectiveness and robustness of the proposed composite controller in the DOC-out temperature control under steady state and a highly transient new European dynamic cycle (NEDC).

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)0.002
metaresearch head score (Gemma)0.002
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 score0.086
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part D Journal of Automobile EngineeringSame topicCatalytic Processes in Materials ScienceFrench-language works237,207