Balancing Microalgae and Nitrifiers for Wastewater Treatment: Can Inorganic Carbon Limitation Cause an Environmental Threat?

Casagli, Francesca; Rossi, S.; Steyer, Jean-Philippe; Bernard, Olivier; Ficara, Elena

doi:10.1021/acs.est.0c05264

Cited by 55 publications

(24 citation statements)

References 73 publications

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“…This adaptation is necessary to match the limitations imposed by the national normative standards concerning nutrient discharge in water bodies. More details about the experimental campaign and the influent characteristics can be found in Casagli et al [ 12 ], in Pizzera et al [ 17 ], and in Supporting Information (SI.8).…”

Section: Methodsmentioning

confidence: 99%

“…Hereafter, we recall the main structure of the ALBA model, but we refer the reader to the work of Casagli et al [ 10 , 12 ] for more details.…”

Section: Modelling Approachmentioning

confidence: 99%

“…It was then successfully validated [ 12 ] on the case study presented in Section 2 , where measured temperature was taken as input. Here, the objective is to predict temperature and to assess the prediction accuracy of the model coupling thermal and biological representations.…”

Section: Modelling Approachmentioning

confidence: 99%

“…Several models were developed to represent the dynamics of algal-bacterial processes, in particular in the field of wastewater treatment using high-rate algal-bacterial ponds (HRABP) [ 6 , 7 , 8 , 9 , 10 , 11 ]. Among these models, the originality of the ALBA model [ 10 , 12 ] is that it relies on an unprecedented validation of more than 623 days, carried out under outdoor conditions at two different locations and for the four seasons. However, until now, none of these HRABP models were fully predictive, as they require knowledge of the water temperature (or of the temperature below the raceway).…”

Section: Introductionmentioning

confidence: 99%

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How Heat Transfer Indirectly Affects Performance of Algae-Bacteria Raceways

Casagli

Bernard

2022

Microorganisms

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Oxygenation in wastewater treatment leads to a high energy demand. High-rate algal-bacterial ponds (HRABP) have often been considered an interesting solution to reduce this energy cost, as the oxygen is provided by microalgae during photosynthesis. These complex dynamic processes are subject to solar fluxes and consequently permanent fluctuations in light and temperature. The process efficiency therefore highly depends on the location and the period of the year. In addition, the temperature response can be strongly affected by the process configuration (set-up, water depth). Raised pilot-scale raceways are typically used in experimental campaigns, while raceways lying on the ground are the standard reactor configuration for industrial-scale applications. It is therefore important to assess what the consequences are for the temperature patterns of the different reactor configurations and the water levels. The long-term validated algae-bacteria (ALBA) model was used to represent algae-bacteria dynamics in HRABPs. The model was previously validated over 600 days of outdoor measurements, at two different locations and for the four seasons. However, the first version of the model, like all the existing algae-bacteria models, was not fully predictive, since, to be run, it required the measurement of water temperature. The ALBA model was therefore updated, coupling it with a physical model that predicts the temperature evolution in the HRABP. A heat transfer model was developed, and it was able to accurately predict the temperature during the year (with a standard error of 1.5 ∘C). The full predictive model, using the temperature predictions, degraded the model’s predictive performances by less than 3%. N2O predictions were affected by ±7%, highlighting the sensitivity of nitrification to temperature The temperature response for two different process configurations were then compared. The biological process can be subjected to different temperature dynamics, with more extreme temperature events when the raceway does not lie on the ground and for thinner depths. Such a situation is more likely to lead to culture crashes.

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Section: Methodsmentioning

confidence: 99%

“…Hereafter, we recall the main structure of the ALBA model, but we refer the reader to the work of Casagli et al [ 10 , 12 ] for more details.…”

Section: Modelling Approachmentioning

confidence: 99%

Section: Modelling Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Heat Transfer Indirectly Affects Performance of Algae-Bacteria Raceways

Casagli

Bernard

2022

Microorganisms

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“…According to Lacroux et al (2020), pH fluctuations when algae consume VFA can greatly impact growth. Associating a pH model that accounts for the various chemical species and their speciation, as proposed by Casagli et al (2021a), would allow the calculation of the pH and the concentration of the undissociated form of the acids, which is the one actually taken up by the microalgae.…”

Section: Model Perspectivesmentioning

confidence: 99%

A universal dynamical metabolic model representing mixotrophic growth of Chlorella sp

Pessi

Baroukh

Bacquet³

et al. 2022

Preprint

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An emerging idea is to couple wastewater treatment and biofuel production using microalgae to achieve higher productivities and lower costs. This paper proposes a metabolic modelling of Chlorella sp. growing on wastes in mixotrophic conditions, accounting also for the possible inhibitory substrates. A metabolic model considering several possible carbon substrates was developed and run. The addition of several organic carbon substrates such as acetate, butyrate or glucose were tested, along with glycerol, a more realistic substrate from an economical point of view. The metabolic model was built using DRUM framework and consists of 188 reactions and 176 metabolites. After a calibration phase, the model was successfully challenged with data from 122 experiments collected from scientific literature in autotrophic, heterotrophic and mixotrophic conditions. The optimal feeding strategy estimated with the model reduces the time to consume the volatile fatty acids from 16 days to 2 days. The high prediction capability of this model opens new routes for enhancing design and operation in waste valorisation using microalgae.

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