Dynamics of cells attachment, aggregation, growth and detachment in trickle-bed bioreactors

Iliuta, Ion; Larachi, Faı ̈çal

doi:10.1016/j.ces.2006.03.042

Cited by 13 publications

(9 citation statements)

References 66 publications

(88 reference statements)

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“…Otherwise, too short a hydraulic retention time fails to achieve the targeted discharge levels (sub‐optimal design) whereas too long a hydraulic retention time leads to over‐designed units. Bioreactor hydraulic retention time is a key packed‐bed bioreactor dependent indicator that tells how long residence time biodegradable compounds must sojourn in the bioreactor to achieve their bacterial degradation . Unlike biological reaction time, which is obtained through standard bench‐scale biokinetic activity tests for a specified wastewater, hydraulic retention time is difficult to predict a priori due to the magnitude of dead regions, stream bypassing, backmixing driven by turbulence and in situ recirculation in packed‐bed bioreactors which depend on bioreactor geometry (porous medium, internal baffles, bioreactor aspect ratio, and feed/exit location), fluids physical characteristics (density and viscosity) and process variables (flow rates, temperature, pressure).…”

Section: Introductionmentioning

confidence: 99%

“…Bioreactor hydraulic retention time is a key packed-bed bioreactor dependent indicator that tells how long residence time biodegradable compounds must sojourn in the bioreactor to achieve their bacterial degradation. [6,7] Unlike biological reaction time, which is obtained through standard bench-scale biokinetic activity tests for a specified wastewater, hydraulic retention time is difficult to predict a priori due to the magnitude of dead regions, stream bypassing, backmixing driven by turbulence and in situ recirculation in packed-bed bioreactors which depend on bioreactor geometry (porous medium, internal baffles, bioreactor aspect ratio, and feed/ exit location), fluids physical characteristics (density and viscosity) and process variables (flow rates, temperature, pressure). One way to diagnose packed-bed bioreactor flow pattern is by conducting tracer studiesby injecting a tracer upstream and detecting its evolution downstreamwhereby liquid residence time distribution (RTD) is measured.…”

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confidence: 99%

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Liquid residence time distribution in a two‐compartment wastewater treatment bioreactor

Iliuta

Larachi

Déry³

et al. 2015

Can J Chem Eng

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Residence time distribution (RTD) of liquid phase in a two‐compartment (packed‐bed/packed‐bed or packed‐bed/packing‐free) bioreactor for wastewater treatment was evaluated via a complex liquid flow structure. RTD in packed‐bed compartments was modelled using a modified axial dispersion‐exchange model with a liquid stream which bypasses the dynamic liquid region. The liquid in the dynamic region is dispersed in axial and transverse directions, whereas the stagnant region exchanges mass with the dynamic region, the two regions being in direct contact. RTD in packing‐free compartment was modelled as a continuous stirred‐tank reactor with ideal or non‐ideal mixing zone. Time‐domain analysis coupled with the phenomenological RTD model was used to identify the model parameters. The proposed RTD model offers the opportunity to quantify parameters as dynamic liquid fraction, dynamic liquid flow rate fraction, number of transfer units between dynamic and stagnant liquid regions, stagnant liquid holdup, which otherwise would have been difficult to estimate using other methods and which can affect the bioreactor performance. The proposed RTD model describes adequately the hydrodynamics of the two‐compartment packed‐bed bioreactor while the simulations unveil likely tendency of the RTD curves subject to different sets of model parameters.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Liquid residence time distribution in a two‐compartment wastewater treatment bioreactor

Iliuta

Larachi

Déry³

et al. 2015

Can J Chem Eng

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“…(biodegradation of toluene) in order to verify the model developed by Iliuta and Larachi . In the next two studies Iliuta and Larachi expanded their model by introducing into it the growth dynamics of the biomass, which consists of the phenomena of attachment, aggregation, growth and detachment. The model developed is very complicated and demands many special parameters, considerably impeding its application.…”

Section: Introductionmentioning

confidence: 99%

“…The review of literature presenting mathematical models of the biochemical reaction process taking place in a biotrickling filter (BTF) allows us to separate two groups which apply different formulation methods to the model. They are: the group of averaged macroscopic models (Dicks and Ottengraf, 25,26 Hekmat and Vortmeyer, 27 Mpanias and Baltzis 28 and Baltzis et al, 29 Iliuta and Larachi 30,32,33 and Iliuta et al, 31 San-Valero et al, 20 Rene et al 34,35 ) and the group of microscopic models (Alonso et al 36,37 and Liao et al, 38,39 Lu et al 40 ); detailed discussion of the microscopic models will be presented in a future paper.…”

Section: Introductionmentioning

confidence: 99%

Experiments and modelling of a biotrickling filter (BTF) for removal of styrene from airstreams

Gąszczak

Bartelmus

Burghardt

et al. 2018

J of Chemical Tech & Biotech

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BACKGROUND Studies on the removal of styrene from airstreams carried out in pilot‐scale biotrickling filters (BTF) are still scarce thus making its industrial application difficult. Moreover, it seems necessary to develop a comprehensive description of the phenomena occurring in the bioreactor that could be used as a tool for BTF design and performance prediction. RESULTS The removal of styrene from airstreams in a pilot‐scale biotrickling filter (BTF) inoculated with Pseudomonas sp. E‐93486 bacteria was investigated (operational parameters: Vg*= 100–150 m3 h−1, VL*= 8 m3 h−1, Cs0= 0.2–1 g−3, EBRTs = 41–62 s, T = 303 K). The experiments performed during a period of more than 120 days confirmed high effectiveness of the examined process; RE = 78–94.2% was obtained for all the sets of operational parameters. The experimental database was exploited to validate the three mathematical models of the process. All the tested models approximate very well the experimental data; the mean percentage error of the RE value prediction did not exceed 3%. CONCLUSION The formulated approximate simple one‐substrate model (SOSM) described the investigated process with excellent accuracy (eY = 2.72%) and was numerically relatively simple. Therefore, this model was recommended as a useful tool for modelling the biodegradation processes of moderately hydrophobic compounds carried out in a BTF. © 2018 Society of Chemical Industry

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“…On the other hand, new immobilization technology also was developed to further improve the biodegradation rate of the airlift reactor (Edwards, Bownes, Leukes, Jacobs, Sanderson, Rose, and Burton, 1999;. Some literature (Iliuta and Larachi, 2006;Sokol and Korpal, 2004;Teliou et al, 2005) revealed that the packing-bed reactor effectively removed phenolic compounds in wastewater. The high packing density of the bioreactor provided superior degradation efficiency compared with the active sludge method or the fixed-biofilm reactor.…”

Section: Introductionmentioning

confidence: 99%

Microbial Degradation of Phenol in a Modified Three‐Stage Airlift Packing‐Bed Reactor

Huang

Liou

Chen

et al. 2010

Water Environment Research

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Phenol degradation was carried out by using a modified three‐stage airlift packing‐bed bioreactor. A laboratory‐scale airlift packing‐bed reactor, with hydrodynamic flexible packing material in the three‐stage bioreactor, was constructed and operated for phenol removal from synthetic wastewater. The airlift packing‐bed reactor successfully degraded phenol and lowered the chemical oxygen demand (COD) of wastewater. High COD removal was observed, and much lower sludge effluent was obtained in this investigation. This airlift bioreactor showed a superior hydrodynamics performance and broad operating conditions for phenolic material removal. Different operating modes were discussed to obtain the optimal condition for phenol degradation (i.e., hydraulic retention time [HRT] and gas flowrate of airlift). The HRT and feed phenol concentration of wastewater dominated the removal efficiency of phenol and COD. In this bioreactor, surface loading up to 2.84 g phenol/m2·d, almost 100% phenol removal, and over 90% COD removal was achieved. The lower operating cost combined with higher phenol‐removal efficiency and a low sludge effluent concentration can be achieved by using this reactor for phenol wastewater treatment.

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Dynamics of cells attachment, aggregation, growth and detachment in trickle-bed bioreactors

Cited by 13 publications

References 66 publications

Liquid residence time distribution in a two‐compartment wastewater treatment bioreactor

Liquid residence time distribution in a two‐compartment wastewater treatment bioreactor

Experiments and modelling of a biotrickling filter (BTF) for removal of styrene from airstreams

Microbial Degradation of Phenol in a Modified Three‐Stage Airlift Packing‐Bed Reactor

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