Feature selection and uncertainty analysis for bubbling fluidized bed oxy-fuel combustion data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F21%3A00352430" target="_blank" >RIV/68407700:21220/21:00352430 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/pr9101757" target="_blank" >https://doi.org/10.3390/pr9101757</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr9101757" target="_blank" >10.3390/pr9101757</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Feature selection and uncertainty analysis for bubbling fluidized bed oxy-fuel combustion data

Popis výsledku v původním jazyce

This paper presents a novel feature extraction and validation technique for data-driven prediction of oxy-fuel combustion emissions in a bubbling fluidized bed experimental facility. The experimental data were analyzed and preprocessed to minimize the size of the data set while preserving patterns and variance and to find an optimal configuration of the feature vector. The Boruta Feature Selection Algorithm (BFSA) finds feature vector’s configuration and the Multiscale False Neighbours Analysis (MSFNA) is newly extended and proposed to validate the BFSA’s design for emission prediction to assure minimal uncertainty in mapping between feature vectors and corresponding outputs. The finding is that the standalone BFSA does not reflect various sampling period setups that appeared significantly influencing the false neighborhood in the design of feature vectors for possible emission prediction, and MSFNA resolves that.

Název v anglickém jazyce

Feature selection and uncertainty analysis for bubbling fluidized bed oxy-fuel combustion data

Popis výsledku anglicky

This paper presents a novel feature extraction and validation technique for data-driven prediction of oxy-fuel combustion emissions in a bubbling fluidized bed experimental facility. The experimental data were analyzed and preprocessed to minimize the size of the data set while preserving patterns and variance and to find an optimal configuration of the feature vector. The Boruta Feature Selection Algorithm (BFSA) finds feature vector’s configuration and the Multiscale False Neighbours Analysis (MSFNA) is newly extended and proposed to validate the BFSA’s design for emission prediction to assure minimal uncertainty in mapping between feature vectors and corresponding outputs. The finding is that the standalone BFSA does not reflect various sampling period setups that appeared significantly influencing the false neighborhood in the design of feature vectors for possible emission prediction, and MSFNA resolves that.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

<a href="/cs/project/EF16_019%2F0000753" target="_blank" >EF16_019/0000753: Centrum výzkumu nízkouhlíkových energetických technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Processes

ISSN

2227-9717

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

15

Strana od-do

—

Kód UT WoS článku

000717091500001

EID výsledku v databázi Scopus

2-s2.0-85116587363