Modelling gas-liquid mass transfer in wastewater treatment: when current knowledge needs to encounter engineering practice and vice versaShow others and affiliations
2019 (English)In: Water Science and Technology, ISSN 0273-1223, E-ISSN 1996-9732, Vol. 80, no 4, p. 607-619Article in journal (Refereed) Published
Abstract [en]
Gas-liquid mass transfer in wastewater treatment processes has received considerable attention over the last decades from both academia and industry. Indeed, improvements in modelling gas-liquid mass transfer can bring huge benefits in terms of reaction rates, plant energy expenditure, acid-base equilibria and greenhouse gas emissions. Despite these efforts, there is still no universally valid correlation between the design and operating parameters of a wastewater treatment plant and the gas-liquid mass transfer coefficients. That is why the current practice for oxygen mass transfer modelling is to apply overly simplified models, which come with multiple assumptions that are not valid for most applications. To deal with these complexities, correction factors were introduced over time. The most uncertain of them is the α-factor. To build fundamental gas-liquid mass transfer knowledge more advanced modelling paradigms have been applied more recently. Yet these come with a high level of complexity making them impractical for rapid process design and optimisation in an industrial setting. However, the knowledge gained from these more advanced models can help in improving the way the α-factor and thus gas-liquid mass transfer coefficient should be applied. That is why the presented work aims at clarifying the current state-of-the-art in gas-liquid mass transfer modelling of oxygen and other gases, but also to direct academic research efforts towards the needs of the industrial practitioners.
Place, publisher, year, edition, pages
NLM (Medline) , 2019. Vol. 80, no 4, p. 607-619
Keywords [en]
oxygen, gas, theoretical model, uncertainty, waste water, Gases, Models, Theoretical
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-40889DOI: 10.2166/wst.2019.253Scopus ID: 2-s2.0-85074272029OAI: oai:DiVA.org:ri-40889DiVA, id: diva2:1373296
2019-11-262019-11-262023-05-25Bibliographically approved