A mechanistic model for oil recovery in a region of high oil droplet concentration from multiphasic fermentations

Basto, Rita M. Da Costa; Casals, Maria P.; Mudde, Robert F.; Wielen, Luuk A.M. van der; Cuellar, Maria C.

doi:10.1016/j.cesx.2019.100033

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…Mathematically, these models contain time-based derivative terms on all variables of interest. In general, these models are employed to identify optimal operational conditions [73][74][75] and for scalingup design and process control, e.g., for the identification of operational strategies. It is also important to note that dynamic process models can be purely data-driven, such as the one used for the identification of the end time of a fermentation process [76,77].…”

Section: Step 1: Steady-state Modelmentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

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The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

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Section: Step 1: Steady-state Modelmentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

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“…For the 6 fermentations, 150 mL of fermentation broth was added to the glass column. To the top of the mixture, 2.2 cm of uncolored oil was added (Da Costa Basto et al, 2019). After 2 h sparging, the gas flow was stopped, the mixture was left to phase-separate for 1 h and the volume of clear oil recovered was determined.…”

Section: Oil Recovery Experimentsmentioning

confidence: 99%

“…Where n is column or tube and i can be oil or cream. The oil fraction ( oil ) for cream in fermentation broth is assumed to be 0.5 while in synthetic emulsion was measured to be 0.7 (Da Costa Basto et al, 2019).…”

Section: Creaming Rate and Oil Recovery Measurementsmentioning

confidence: 99%

“…In case of fermentation broth, the fraction of antifoam (AF) added during fermentation was subtracted to obtain the percentage of oil recovery (Eq. ( 5)) (Da Costa Basto et al, 2019).…”

Section: Creaming Rate and Oil Recovery Measurementsmentioning

confidence: 99%

“…It was observed that when an emulsion had the capacity to cream, oil recovery by GEOR could be achieved. However, the oil recovery achieved during the fermentation process is generally low (10-30% oil recovery) due to small oil droplet size, back-mixing and SACs (Da Costa Basto et al, 2019;Pedraza-de la Cuesta et al, 2017). In contrast, mechanical methods combined with de-emulsifiers and temperature swings, such as centrifugation, have been reported to recover 90% of the oil in fermentation broth emulsion (Tabur and Dorin, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Impact of flocculant addition in oil recovery from multiphasic fermentations

Basto

Jiménez

Mudde

et al. 2020

Food and Bioproducts Processing

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Emulsion formation is a major concern when dealing with multiphasic fermentations. Flocculants can be used together with other demulsification techniques to improve oil recovery in multiphasic fermentations. In this paper, the impact of adding flocculants during a multiphasic fermentation with 10 wt% dodecane, to destabilize the broth emulsion, improve creaming formation and enhance oil recovery is studied. Flocculants, CaCl 2 and (NH 4 ) 2 SO 4 were shown to be the most promising flocculants. Flocculant addition, their time of addition, and its impact on multiphasic fermentations has been evaluated by comparing fermentation performance against reference fermentations and three oil recovery methods: gravity settling, gas enhanced oil recovery and centrifugation. When adding 75 mM of (NH 4 ) 2 SO 4 during fermentation, the creaming rate during gravity settling increased 3-fold and the oil recovery by gas enhanced oil recovery was 35%, without altering fermentation performance.Addition of CaCl 2 during fermentation resulted in 88% and 67% oil recovery for early and late addition, which is a 4 and 3-fold increase in comparison with the reference. Yet, CaCl 2 deviated from standard fermentation performance when added immediately after second phase addition. In conclusion, flocculant addition during multiphasic fermentation can be used to destabilize microbial emulsions and potentially improve in situ oil recovery.

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