Modelling gas–liquid mass transfer in wastewater treatment: when current knowledge needs to encounter engineering practice and vice versa

Author:

Amaral Andreia12,Gillot Sylvie3,Garrido-Baserba Manel4,Filali Ahlem5,Karpinska Anna M.6,Plósz Benedek G.7,De Groot Christopher8,Bellandi Giacomo19,Nopens Ingmar1,Takács Imre10,Lizarralde Izaro11,Jimenez Jose A.12,Fiat Justine5,Rieger Leiv13,Arnell Magnus1415,Andersen Mikkel16,Jeppsson Ulf14,Rehman Usman117,Fayolle Yannick5,Amerlinck Youri1,Rosso Diego4

Affiliation:

1. BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium

2. MARETEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal

3. Irstea, UR REVERSAAL, centre de Lyon-Villeurbanne, 5 rue de la Doua, Villeurbanne cedex F-69926, France

4. Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697-2175, USA and Water-Energy Nexus Center, University of California, Irvine, CA 92697-2175, USA

5. Irstea, UR PROSE, 1 Rue Pierre-Gilles de Gennes – CS 10030, F-92761, Antony Cedex, France

6. Southern Water, Wastewater Wholesale Services, Asset Performance-Asset Optimisation, Southern House-Falmer, Lewes Road, Falmer, Brighton BN1 9PY, UK

7. Department of Chemical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK

8. Department of Mechanical and Materials Engineering, Western University, 1151 Richmond St. N., London N6A 5B9, Canada

9. Department of Civil and Environmental Engineering, University of Florence, via di S. Marta, 3, Florence 50139, Italy

10. Dynamita, 7 Eoupe, 26110 Nyons, France

11. Ceit, Manuel Lardizabal 15, 20018 Donostia/San Sebastián, Spain and Universidad de Navarra, Tecnun Escuela de Ingenieros, Manuel Lardizabal 13, 20018 Donostia/San Sebastián, Spain

12. Brown and Caldwell, 2301 Lucien Way, Suite 250, Maitland, Florida 32751, USA

13. inCTRL Solutions Inc., 7 Innovation Drive Suite 107 Dundas ON L9H 7H9, Canada

14. Department of Biomedical Engineering (BME), Division of Industrial Electrical Engineering and Automation (IEA), Lund University, P.O. Box 118, SE-221 00 Lund, Sweden

15. RISE Research Institutes of Sweden, Gjuterigatan 1D, SE-582 73 Linköping, Sweden

16. DHI, Aarhus DK-8200, Denmark

17. AM-TEAM, Advanced modelling for process optimization, Okrooiplein 1 - box 601, 9000 Ghent, Belgium

Abstract

Abstract 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.

Publisher

IWA Publishing

Subject

Water Science and Technology,Environmental Engineering

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