A SZTE Mérnöki Kar Folyamatmérnöki Intézetében folyó mikrohullámú szennyvíziszap-kezelés eredményei

At the Department of Process Engineering of the University of Szeged Faculty of Engineering have been investigated the wastewater sludge treatment technologies since over ten years. The main area of the researches is the examination of the effects of thermal pre-treatments on the structural and bioc...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Beszédes Sándor
Keszthelyi-Szabó Gábor
Dokumentumtípus: Cikk
Megjelent: 2013
Sorozat:Review of faculty of engineering : analecta technica Szegedinensia
Kulcsszavak:Természettudomány, Mérnöki tudományok
Online Access:http://acta.bibl.u-szeged.hu/30827
Leíró adatok
Tartalmi kivonat:At the Department of Process Engineering of the University of Szeged Faculty of Engineering have been investigated the wastewater sludge treatment technologies since over ten years. The main area of the researches is the examination of the effects of thermal pre-treatments on the structural and biochemical changes of different type of sludge. We focused mainly on the deeper analysis of the effects of microwave irradiation (MW) on the change of biodegradability of food industry sludge. Our experimental results verified that MW pre-treatments have significant effect on the solubility of organic matter, the aerobic and anaerobic degradability of sludge. MW sludge conditioning process needs significantly shorter process time and has stronger disintegration effect than conventional thermal pre-treatments, which resulted in higher biodegradability. Advantages of MW treatment over the conventional sludge conditioning methods prior to anaerobic digestion process has also been manifested in higher biogas yield and reduced lagphase of anaerobic decomposition. Results of modeling and optimization of MW process show that the irradiated energy and the specific microwave power intensity has also effect on the biodegradability, biogas yield and the energy efficiency, as well.
Terjedelem/Fizikai jellemzők:33-47
ISSN:1788-6392