TY - JOUR
T1 - Modelling gas-liquid mass transfer in wastewater treatment
T2 - when current knowledge needs to encounter engineering practice and vice versa
AU - Amaral, Andreia
AU - Gillot, Sylvie
AU - Garrido-Baserba, Manel
AU - Filali, Ahlem
AU - Karpinska, Anna M.
AU - Plósz, Benedek G.
AU - De Groot, Christopher
AU - Bellandi, Giacomo
AU - Nopens, Ingmar
AU - Takács, Imre
AU - Lizarralde, Izaro
AU - Jimenez, Jose A.
AU - Fiat, Justine
AU - Rieger, Leiv
AU - Arnell, Magnus
AU - Andersen, Mikkel
AU - Jeppsson, Ulf
AU - Rehman, Usman
AU - Fayolle, Yannick
AU - Amerlinck, Youri
AU - Rosso, Diego
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85074272029&partnerID=8YFLogxK
U2 - 10.2166/wst.2019.253
DO - 10.2166/wst.2019.253
M3 - Article
C2 - 31661440
AN - SCOPUS:85074272029
SN - 0273-1223
VL - 80
SP - 607
EP - 619
JO - Water science and technology : a journal of the International Association on Water Pollution Research
JF - Water science and technology : a journal of the International Association on Water Pollution Research
IS - 4
ER -