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Insight into microalgal-bacterial consortia for sustainable wastewater treatment. Investigations at lab-scale with real wastewater

High costs for aeration, greenhouse-gas emissions and excess sludge disposal have entailed a paradigm shift in the wastewater treatment.
Microalgal-bacterial-based wastewater treatments have gained increasing attention because of their potential in energy demand reduction and biomass resource recovery. In particular, photosynthetic oxygenation is combined with bacterial activity to treat wastewater avoiding external artificial aeration. To optimize the technology in order to become more competitive than activated sludge, an in-depth investigation about the treatment performance and the microbiology interactions under real operational condition is needed.
This work focused on the study of wastewater-borne microalgal-bacterial consortia treating real municipal wastewater. The main objectives were to: (i) Understand the removal mechanisms and the influence of operational conditions to optimize the process; (ii) Analyze the microbial community. At first, a photo-sequencing batch reactor (PSBR), called Pilot, was started up and continuously monitored for two years to analyze the evolution of the treatment performance and of the biomass composition. At the same time, other two lab-scale PSBRs were installed to evaluate if microalgal inoculation is essential to start up a consortium. Samples of these consortia were collected over a period of one year and analyzed through microscopic observations, flow cytometry and metagenomics, to investigate the microbial structure and diversity.A second part of the research focused on the optimization of the Pilot to explore its limit in view of the scale-up of the system. In addition, respirometry was adapted to test microalgal-bacterial consortia to estimate the removal kinetic parameters for future modelling.
To conclude, the research project addressed many aspects and lay the foundation to apply a methodological research approach to scale-up this promising technology.

Identiferoai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/264967
Date28 May 2020
CreatorsPetrini, Serena
ContributorsPetrini, Serena, Foladori, Paola, Andreottola, Gianni
PublisherUniversità degli studi di Trento, place:Trento
Source SetsUniversità di Trento
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/doctoralThesis
Rightsinfo:eu-repo/semantics/openAccess
Relationfirstpage:1, lastpage:359, numberofpages:359

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