Kinetics of Propionate Conversion in Anaerobic Continuously Stirred Tank Reactors

Nielsen, Henrik Aalborg; Mladenovska, Zuzana; Ahring, Birgitte Kiær

doi:10.1080/09593330802028535

Cited by 9 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the degradation rate is in the order HBu>HAc>HPr, the rate of gas production will be HAc>HBu>HPr taking full degradation to the nal products CH 4 and CO 2 into account. The apparent order is consistent throughout the reviewed literature, however the value of determined parameters can be very dierent for a single VFA as outlined by Nielsen et al (2008), who emphasizes that the parameters are depending on biomass composition, reactor type, substrate, temperature and HRT.…”

Section: Resultsmentioning

confidence: 55%

“…It stands to reason that at a higher OLR the substrate concentrations are higher and therefore a faster methane production was observed in this experiment. Other factors inuencing the level of estimated parameters in kinetic studies outlined by Nielsen et al (2008) are a more adapted microorganism (MO) population, reactor type, substrate, temperature and HRT.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Demand-driven biogas production in anaerobic filters

Lemmer

Krümpel

2017

Applied Energy

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 55%

Section: Resultsmentioning

confidence: 99%

Demand-driven biogas production in anaerobic filters

Lemmer

Krümpel

2017

Applied Energy

View full text Add to dashboard Cite

“…However, not all biological systems can be described by the Michaelis–Menten equation and one class of exceptions includes systems such as sediments, sludge from anaerobic digestors, and rumen fluid, among others, where the substrate being consumed is also endogenously produced (Nielsen et al, 2008; Robinson and Characklis, 1984; Robinson and Tiedje, 1982). Using the Michaelis–Menten equation to describe such systems could result in >100% error in K m estimates when the rate of endogenous substrate production was 10% of V m (Robinson and Characklis, 1984).…”

Section: Error In S Values Computed From Equation (2) Along With the mentioning

confidence: 99%

Accurate kinetic parameter estimation during progress curve analysis of systems with endogenous substrate production

Goudar¹

2011

Biotech & Bioengineering

View full text Add to dashboard Cite

ABSTRACT:We have identified an error in the published integral form of the modified Michaelis-Menten equation that accounts for endogenous substrate production. The correct solution is presented and the error in both the substrate concentration, S, and the kinetic parameters V m , K m , and R resulting from the incorrect solution was characterized. The incorrect integral form resulted in substrate concentration errors as high as 50% resulting in 7-50% error in kinetic parameter estimates. To better reflect experimental scenarios, noise containing substrate depletion data were analyzed by both the incorrect and correct integral equations. While both equations resulted in identical fits to substrate depletion data, the final estimates of V m , K m , and R were different and K m and R estimates from the incorrect integral equation deviated substantially from the actual values. Another observation was that at R ¼ 0, the incorrect integral equation reduced to the correct form of the Michaelis-Menten equation. We believe this combination of excellent fits to experimental data, albeit with incorrect kinetic parameter estimates, and the reduction to the Michaelis-Menten equation at R ¼ 0 is primarily responsible for the incorrectness to go unnoticed. However, the resulting error in kinetic parameter estimates will lead to incorrect biological interpretation and we urge the use of the correct integral form presented in this study. Biotechnol. Bioeng. 2011;108: 2499-2503. ß 2011 Wiley Periodicals, Inc. KEYWORDS: Michaelis-Menten equation; nonlinear parameter estimation; endogenous substrate production; progress curve analysis; enzyme kinetics Progress curve analysis is an attractive method of analyzing biological systems as kinetic parameter determination from a single substrate/product concentration versus time data set is possible (Duggleby, 1991(Duggleby, , 2001Robinson, 1998;Robinson and Tiedje, 1983). The Michaelis-Menten equation continues to be the most widely used equation to describe a variety of biological systems and substantial advances have been made for robust analysis of progress curve data using the Michaelis-Menten equation. For instance, parameter correlation, which can impact final kinetic parameter estimates has been well characterized (Ellis et al., 1996;Robinson, 1985Robinson, , 1998 and recommendations for experimental design have been made to enable robust kinetic parameter estimation (Duggleby, 1995(Duggleby, , 2001Holmberg, 1982;Liu and Zachara, 2001;Robinson, 1985Robinson, , 1998Robinson and Tiedje, 1983). Furthermore, kinetic parameter estimation using an explicit closed form solution of the Michaelis-Menten equation has been demonstrated (Goudar et al., 1999(Goudar et al., , 2004) which simplifies analysis since iterative computation of the substrate/product concentration is eliminated.However, not all biological systems can be described by the Michaelis-Menten equation and one class of exceptions includes systems such as sediments, sludge from anaerobic digestors, and rumen fluid, among others...

show abstract

“…Among the limited studies on POB, almost all of them have focused on either granular sludge systems (Harmsen et al, 1996;Sekiguchi et al, 1999;Heppner et al, 1992) or single-stage anaerobic digesters (Ariesyady et al, 2007;McMahon et al, 2004;Nielsen et al, 2008) with very long residence times (SRT >20-d).…”

Section: Introductionmentioning

confidence: 98%

Characterization of Propionate Utilizing Acetogens in Phased Anaerobic Sludge Digestion Systems

Zamanzadeh¹,

Parker²

2011

proc water environ fed

View full text Add to dashboard Cite

The impact of phase separation and operational conditions on the bio-kinetic characteristics of propionate oxidizing bacteria (POB) developed in each phase of phased anaerobic sludge digesters with either identical or different temperatures was investigated. The results of this study revealed that the POB which developed in the phased anaerobic digestion systems differed between the phases, indicating dominance of fast-growing species in the heavily loaded 1 st -phase and slow-growing species within the 2 nd -phase digesters. It was found that the maximum specific growth rate in the mesophilic and thermophilic 1 st -phase was 11.6 d -1 and 24.5 d -1 , respectively; while those of the mesophilic and thermophilic 2 nd -phase digesters were, on average, 5.9 d -1 and 18.5 d -1 , respectively. It is recommended that different species of POB be employed in the modeling of phased anaerobic digesters to reflect population shifts on the basis of propionate concentration (and/or organic loading rates). Modeling of acetogenesis in anaerobic sludge digestion would be improved through application of at least two sets of bio-kinetic parameters, allowing the prevalence of one species in each condition. The relationship between POB biokinetic parameters and temperature was found to be inconsistent and the application of Arrhenius-like equations for temperature correction of kinetic coefficients should be applied with caution. On the basis of the bio-kinetic values estimated for the various digesters, achieving low levels of propionate with either thermophilic or short HRT digesters is challenging due to a low affinity of the POB developed in these digesters. Therefore, a mesophilic digester should be employed following either a thermophilic or mesophilic 1 st -phase digester to accomplish low concentrations of propionate in the effluent.

show abstract

Kinetics of Propionate Conversion in Anaerobic Continuously Stirred Tank Reactors

Cited by 9 publications

References 34 publications

Demand-driven biogas production in anaerobic filters

Demand-driven biogas production in anaerobic filters

Accurate kinetic parameter estimation during progress curve analysis of systems with endogenous substrate production

Characterization of Propionate Utilizing Acetogens in Phased Anaerobic Sludge Digestion Systems

Contact Info

Product

Resources

About