Model-based sensitivity analysis of a fluidised-bed bioreactor for mercury uptake by immobilised Pseudomonas putida cells

Abstract

A model-based sensitivity analysis was performed in order to evaluate the importance of the individual operating parameters of a three-phase fluidised-bed biological reactor used for removing mercury ions from wastewater. The parameters analysed involve the immobilised biomass load (bacteria P. putida) on alginate beads, particle size, inlet flow-rate, mercury ion loads in the fed wastewater, and the solid fraction in the reactor. Predictions were generated by using pseudo-first-order, Michaelis-Menten, or pseudo-Haldane kinetic models. The results highlight the major influence of the biomass/solid load and of the liquid residence time on the reactor efficiency. Also, the resultant significant differences in the model predictions underline the importance of using a more accurate kinetic model for process design and control purposes.

Model‐based optimization of the feeding policy of a fluidized bed bioreactor for mercury uptake by immobilized Pseudomonas putida cells

“…Predictions are generated by using a Michaelis-Menten kinetic model with first-order substrate inhibition. This nonlinear kinetic model was proved [19,23,24] to be enough accurate for process design and control.…”

“…[32] The resulted cross-and self-control of the mer operon expression is aiming at eventually "shrinking" the import of large amounts of mercury, which might lead to the blockage of RSH cell species, exhausting the cell resources, and eventually compromising the whole cell metabolism. [23] An overall Michaelis-Menten type kinetics has been proposed by Deckwer et al [19] for P. putida sp. cells.…”

“…Mercury is also used in numerous industrial and medical applications (fungicides, disinfectants, dental products, catalysts, igniters, dyes production, etc.). [19,23] The current treatment procedures to remove mercury from wastewater register in the following categories [19] : (i) precipitation with hydrogen sulfide, with the disadvantage of handling large amounts of toxic H 2 S and resulting large volumes of mercury contaminated sludge necessary to be deposited in special places, with no possibility of recycling the precipitated mercury [24] ; (ii) retention on the ion exchange columns, with the disadvantage of requiring costly adsorbents: it can be applied only for removing low loads of mercury from wastewater; besides, most of ion-exchange resins cannot be regenerated thus raising disposal problems, while the renewable resins are expensive [25] ; (iii) retention on various cheap sorbents, such as activated carbon, char from coal, volcanic tuff [20] , modified cellulose or agro-based waste materials [26,27] ; (iv) renewable polymeric membranes also display promising sorption and filtration capabilities [28] ; (v) bioaccumulation into genetically modified bacteria cells by sequestering mercury via binding to metallothionein molecules or cytosolic chelating agents [29] : this method is limited by the cell metabolic resources, for example, by using cyanobacteria, Bacillus sp. or Escherichia coli.…”

“…Its reduced quasi-first-order form, applicable for [Hg 2+ ] < 1 mg L À1 , and presented in Table 5, has been proved to be ineffective and very inaccurate. [23] Based on experiments using modified E. coli cell cultures, Philippidis et al [32] and Deckwer et al [19] completed the bioprocess kinetic model, by including a second-order inhibition by the substrate (Haldane [19] ; Table 5) and proved that mercury membrane permeation is the slowest process step, by using separate experiments conducted with cultures of intact cells or 'permeabilized' cells to ionic species. [32] Eventually, a reduced Michaelis-Menten model for mercury uptake by E. coli…”

“…The engineering problem to be solved in this paper is related to the model-based determination of the best operating alternative of a continuous TPFB reactor ensuring cost minimization and performance maximization, thus extending the engineering studies on the TPFB biological reactor performances of Deckwer et al [19] and those of Maria et al [23] on the bioreactor sensitivity to various operating parameters. Predictions are generated by using a Michaelis-Menten kinetic model with first-order substrate inhibition.…”