Neural Model of Biomass Fired Boiler Emission Changes Caused by Grate Motion

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F09%3A00157218" target="_blank" >RIV/68407700:21220/09:00157218 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Neural Model of Biomass Fired Boiler Emission Changes Caused by Grate Motion

Popis výsledku v původním jazyce

The problem of reducing harmful emissions produced in small-scale biomass fired boilers shortly after periodically performed grate movement is a part of project that is in general aimed at biomass combustion process investigations. Significant aim is toobtain such a boiler model quantifying dynamic impacts on the flue gas composition from those quantities which can be manipulated, or at least measured. After reducing these impacts, increase of the boiler efficiency will be the next goal in the search for an optimal control of small-scale biomass fired boilers. This paper shows ability of a neural network to extract mathematical dependencies from measured data even for very complex systems. Different types of neural network architecture are described in this paper and some results of modeling of CO, CO2 and NOx emission by the neural model of the biomass boiler are presented.

Název v anglickém jazyce

Neural Model of Biomass Fired Boiler Emission Changes Caused by Grate Motion

Popis výsledku anglicky

The problem of reducing harmful emissions produced in small-scale biomass fired boilers shortly after periodically performed grate movement is a part of project that is in general aimed at biomass combustion process investigations. Significant aim is toobtain such a boiler model quantifying dynamic impacts on the flue gas composition from those quantities which can be manipulated, or at least measured. After reducing these impacts, increase of the boiler efficiency will be the next goal in the search for an optimal control of small-scale biomass fired boilers. This paper shows ability of a neural network to extract mathematical dependencies from measured data even for very complex systems. Different types of neural network architecture are described in this paper and some results of modeling of CO, CO2 and NOx emission by the neural model of the biomass boiler are presented.

Druh

D - Stať ve sborníku

CEP obor

JB - Senzory, čidla, měření a regulace

OECD FORD obor

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Projekt

<a href="/cs/project/GA101%2F07%2F1667" target="_blank" >GA101/07/1667: Provozně zajištěná pokročilá regulace tepelně-energetických zařízení</a><br>

Návaznosti

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

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název statě ve sborníku

Automatizácia a riadenie v teórii a praxi 2009

ISBN

978-80-553-0146-4

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

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Název nakladatele

Technická Univerzita v Košiciach

Místo vydání

Košice

Místo konání akce

Stará Lesná

Datum konání akce

4. 3. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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