Predictive model for treatment of phenolic wastes by activated sludge

Rozich, Alan F.; Gaudy, A. F.; D’Adamo, Paola

doi:10.1016/0043-1354(83)90277-4

Cited by 31 publications

(11 citation statements)

References 8 publications

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“…These kinetic models were based on the Haldane equation and made use of an inhibition constant, Ki, to relate substrate concentrations to specific growth rates. The model was successfully used to predict biomass production during the treatment of phenolic wastes by activated sludge (16,17). A modification of the basic model was proposed by Neufeld et al (14):…”

Section: Methodsmentioning

confidence: 99%

“…We chose to study the effect of immobilization on the activity of a phenol-degrading methanogenic consortium. Phenol and phenolic compounds are toxic pollutants (9) and inhibitors of biodegradation (6,7,15) and have been used as models to test the effect of inhibitory substrates on cellular metabolism and growth kinetics (16,17). The phenoldegrading methanogenic consortium is postulated to contain three interacting physiological groups of bacteria: a phenolmetabolizer, an H2-utilizing methanogen, and an acetotrophic methanogen, all of which are required to complete the mineralization of phenol to CH4 and CO2 (2,4).…”

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confidence: 99%

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Kinetics of Phenol Biodegradation by an Immobilized Methanogenic Consortium

Dwyer

Krumme

Boyd

et al. 1986

Appl Environ Microbiol

109

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A phenol-degrading methanogenic enrichment was successfully immobilzed in agar as shown by the stoichiometric conversion of phenol to CH4 and CO2. The enrichment contained members of three physiological groups necessary for the syntrophic mineralization of phenol: a phenol-oxidizing bacterium, a Methanothrixlike bacterium, and an H2-utilizing methanogen. The immobilization technique resulted in the cells being embedded in a long, thin agar strand (1 mm in diameter by 2 to 50 cm in length) that resembled spaghetti. Immobilization had three effects as shown by a comparative kinetic analysis of phenol degradation by free versus immobilized cells. (i) The maximum rate of degradation was reduced from 14.8 to 10.0 ,ug of phenol per h; (ii) the apparent Km for the overall reaction was reduced from 90 to 46 ,ug of phenol per ml, probably because of the retention of acetate, H2 and CO2 in the proximity of immobilized methanogens; and (iii) the cells were protected from substrate inhibition caused by high concentrations of phenol, which increased the apparent Ki value from 900 to 1,725 ,ug of phenol per ml. Estimates for the kinetic parameters Ki, Ki, and Vmax were used in a modified substrate inhibition model that simulated rates of phenol degradation for given phenol concentrations. The simulated rates were in close agreement with experimentally derived rates for both stimulatory and inhibitory concentrations of phenol.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Kinetics of Phenol Biodegradation by an Immobilized Methanogenic Consortium

Dwyer

Krumme

Boyd

et al. 1986

Appl Environ Microbiol

109

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“…These processes often are difficult to control due to problems arising from large changes in phenolic loads (occasional upstream disturbances). Many attempts previously have been made to improve our understanding of phenolics degradation dynamics, taking into account the inhibitory effect of these compounds on microbial growth (Pawloski and Howell, 1973;Rozich et al, 1983Rozich et al, , 1985.…”

Section: Introductionmentioning

confidence: 99%

Double inhibition model for degradation of phenol byPseudomonas putida Q5

Sánchez

Kamp

Onysko

et al. 1998

Biotechnol. Bioeng.

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A semiempirical model, based on the presence of an inhibitory intermediate metabolite excreted to the broth, was developed to better predict the dynamic responses to shock loadings of Pseudomonas putida Q5 degrading phenol. Compared to the Haldane equation, the new model exhibited better prediction capabilities for a broad range of inlet concentration and dilution rate step changes. The experiments were performed at 10° and 25°C and ranged from stable responses to washouts. The time delays observed experimentally were successfully predicted with the dual‐inhibition model and a very good agreement with the observed phenol profile also was found in a pulse experiment. A possible intermediate metabolite was detected by HPLC analyses based on the high correlation shown with the predicted inhibitory intermediate metabolite in the model. © 1998 John Wiley & Sons, Inc. Biotechnol Bioeng 60: 560–567, 1998.

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“…the four parameters identified as most important to the Dosa [1994] simulation model results: Ks, I•ma•, Ko, and %. The parameter ranges given inTable 1were obtained from several studies of phenol degradation under various conditions[Rozich et al, 1983[Rozich et al, , 1985Chang and Rittmann, 1987;Speitel et al, 1987;Hobson and Mills, 1990;Lin, 1992]. The sensitivity analysis of the optimization model was conducted on those four parameters only.…”

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confidence: 99%

Quantifying the effects of uncertainty on optimal groundwater bioremediation policies

Minsker¹,

Shoemaker²

1998

Water Resources Research

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This paper describes a method for quantifying the economic and environmental effects of uncertainty in biological parameter values on optimal in situ bioremediation design. The range of uncertainty in model results associated with a range of input parameter values is quantified for both individual parameter errors and errors in combinations of parameters. Three measures of sensitivity are presented that quantify different aspects of the effects of model error on an implemented optimal policy. Numerical results are presented for an example site contaminated with phenol, with parameter ranges derived from values reported in the literature. For the example site, Ks (the substrate half‐velocity coefficient in the Monod kinetic equation for biodegradation) was found to be the most sensitive biological parameter and this sensitivity was asymmetric; i.e., reductions in the value of Ks have a much greater effect than increases in the value of Ks. The methodology applied in this paper could also be applied to other water resource management problems, allowing the user to quantify the effects of wide ranges of possible parameter values on model results. The method is particularly useful for computationally intensive optimization models, as it requires a manageable number of model runs, and for the many situations where insufficient data are available to permit accurate estimation of probability distributions.

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Predictive model for treatment of phenolic wastes by activated sludge

Cited by 31 publications

References 8 publications

Kinetics of Phenol Biodegradation by an Immobilized Methanogenic Consortium

Kinetics of Phenol Biodegradation by an Immobilized Methanogenic Consortium

Double inhibition model for degradation of phenol byPseudomonas putida Q5

Quantifying the effects of uncertainty on optimal groundwater bioremediation policies

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