Bayesian estimation and sensitivity analysis of toluene and trichloroethylene biodegradation kinetic parameters

Abstract

Parameter estimation is needed for process management, design, and reactor scaling when values from the literature vary tremendously or are unavailable. A Bayesian approach, implemented via Markov chain Monte Carlo (MCMC) simulations using SAS software, was used to estimate the kinetic parameters of toluene and trichloroethylene (TCE) biodegradation by the microorganism Pseudomonas putida F1 in batch cultures. The prediction capabilities of Bayesian estimation were illustrated by comparing predicted and observed data and reported in goodnessof-fit statistics. The sensitivity analysis showed that the parameters obtained using this approach were consistent under the designated toluene and TCE concentration range. Moreover, the impact of TCE on toluene degradation kinetics was numerically exhibited, verifying the fact that TCE was able to stimulate toluene degradation; hence, TCE's presence increased the apparent maximum toluene-specific rate. Various kinetic models were explored at different degrees of complexity. At a low TCE concentration range (e.g., <2 mg L −1 ), a simplified Michaelis-Menten model (i.e., substrate half-saturation parameters approximated the inhibition parameters) was adequate to describe the reaction kinetics. However, at a higher TCE range (e.g., 5 mg L −1 ), a full-scale Michaelis-Menten model was needed to discriminate among the inhibition parameters in the model. The results demonstrated that a Bayesian estimation method is particularly useful for determining complex bioreaction kinetic parameters in the presence of a small volume of experimental data.Abbreviations: DIC, deviance information criterion; MAE, mean absolute error; MCMC, Markov chain Monte Carlo; MSE, mean squared error; ODE, ordinary differential equation; TCE, trichloroethylene.

Continuous trichloroethylene biodegradation by Pseudomonas putida F1 in a biofilm reactor and determination of an optimal feeding path via a response surface model

“…This phenomenon was in contrast to TCE's toxic effect. Shingleton et al 38 discovered that TCE could induce dioxygenase to enhance toluene degradation, which was later numerically verified by Yu et al 26 This may explain why, at this level of TCE concentration, its effect of enzyme inducement might be greater than its product toxic effect on biomass, which thereby lifted up the removal efficiency. Another possibility is the random variation of the data that might affect the model.…”

“…of about 1.0 at 600 nm was first prepared via batch incubation. 26 These active growing cells were then used for bioreactor inoculation at the top of the column. All parts of the bioreactor system (Figure 1) were sterilized before use.…”

“…To sustain TCE treatment, simultaneous toluene and TCE feeding was chosen for TCE degradation in the biofilm reactor for toluene being the primary substrate to regenerate the active biomass. Yet excessive toluene supply would inhibit TCE degradation because both substances use the same nonspecific enzyme toluene dioxygenase, 26,30,31 and toluene has more affinity than TCE to this enzyme. The impact of influent toluene concentration on TCE degradation was investigated in the biofilm reactor by varying the influent toluene concentration daily for an average influent TCE concentration at 1.86 mg/L.…”

“…These critical toluene concentrations have been substantially extended to a much larger level than those that would cause observable inhibition in a batch experiment starting around 5 mg/L. 26 This is due to the recycled stream that diluted the toluene inside the bioreactor, which led to the significant reduction of toluene inhibition on TCE degradation. Note that almost no toluene was detected in the effluent, so the recycle stream separated from the effluent contained nearly no toluene.…”