Air gasification of high-ash sewage sludge for hydrogen production: Experimental, sensitivity and predictive analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10248554" target="_blank" >RIV/61989100:27240/22:10248554 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85121370150&origin=resultslist&sort=plf-f" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85121370150&origin=resultslist&sort=plf-f</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijhydene.2021.11.192" target="_blank" >10.1016/j.ijhydene.2021.11.192</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Air gasification of high-ash sewage sludge for hydrogen production: Experimental, sensitivity and predictive analysis

Popis výsledku v původním jazyce

In this work, air gasification of sewage sludge was conducted in a lab-scale bubbling fluidized bed gasifier. Further, the gasification process was modeled using artificial neural networks for the product gas composition with varying temperatures and equivalence ratios. Neural network-based prediction will help to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The gasification efficiency and lower heating values were also established as a function of temperatures and equivalence ratios. The maximum H2 and CO was recorded as 16.26 vol% and 33.55 vol%. Intraileally at ER 0.2 gas composition H2, CO, and CH4 show high concentrations of 20.56 vol%, 45.91 vol%, and 13.32 vol%, respectively. At the same time, CO2 was lower as 20.20 vol% at ER 0.2. Therefore, optimum values are suggested for maximum H2 and CO yield and lower concentration of CO2 at ER 0.25 and temperature of 850 oC. A predictive model based on an Artificial Neural network is also developed to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The network has been trained with different topologies to find the optimal structure for temperature and equivalence ratio. The obtained results showed that the regression coefficients for training, validation, and testing are 0.99999, 0.99998, and 0.99992, respectively, which clearly identifies the training efficiency of the trained model. (C) 2021 Hydrogen Energy Publications LLC

Název v anglickém jazyce

Air gasification of high-ash sewage sludge for hydrogen production: Experimental, sensitivity and predictive analysis

Popis výsledku anglicky

In this work, air gasification of sewage sludge was conducted in a lab-scale bubbling fluidized bed gasifier. Further, the gasification process was modeled using artificial neural networks for the product gas composition with varying temperatures and equivalence ratios. Neural network-based prediction will help to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The gasification efficiency and lower heating values were also established as a function of temperatures and equivalence ratios. The maximum H2 and CO was recorded as 16.26 vol% and 33.55 vol%. Intraileally at ER 0.2 gas composition H2, CO, and CH4 show high concentrations of 20.56 vol%, 45.91 vol%, and 13.32 vol%, respectively. At the same time, CO2 was lower as 20.20 vol% at ER 0.2. Therefore, optimum values are suggested for maximum H2 and CO yield and lower concentration of CO2 at ER 0.25 and temperature of 850 oC. A predictive model based on an Artificial Neural network is also developed to predict the hydrogen production from product gas composition at various temperatures and equivalence ratios. The network has been trained with different topologies to find the optimal structure for temperature and equivalence ratio. The obtained results showed that the regression coefficients for training, validation, and testing are 0.99999, 0.99998, and 0.99992, respectively, which clearly identifies the training efficiency of the trained model. (C) 2021 Hydrogen Energy Publications LLC

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/LTI19002" target="_blank" >LTI19002: Zapojení českých výzkumných organizací do Evropské aliance pro energetický výzkum EERA (EERA-CZ 2)</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

International Journal of Hydrogen Energy

ISSN

0360-3199

e-ISSN

—

Svazek periodika

2021

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

nestrankovano

Kód UT WoS článku

000889311300016

EID výsledku v databázi Scopus

2-s2.0-85121370150