This study evaluated the kinetics of water vapour adsorption on the surface of starch molecules derived from wheat. The aim of the study was to determine an equation that would allow estimation of water content in tested material in any time-point of the adsorption process aimed at settling a balance with the environment. An adsorption isotherm of water vapour on starch granules was drawn. The parameters of the Guggenheim, Anderson, and De Boer equation were determined by characterizing the tested product and adsorption process. The equation of kinetics of water vapour adsorption on the surface of starch was determined based on the Guggenheim, Anderson, and De Boer model describing the state of equilibrium and on the model of a first-order linear inert element describing the changes in water content over time.
The bacterial degradation of phenol by Alcaligenes xylosoxidans and Pseudomonas putida was studied in a laboratory-scale stirred tank bioreactor. Several kinetic experiments were employed to obtain values of kinetic parameters by applying nonlinear regression methods to differential kinetic models and integrated experimental data. The extra sum of squares for statistical tests of reliability of proposed kinetic models was attached. The values of kinetic parameters based on the Haldane substrate inhibition model for both strains were: μmax=0.347 h-1, Ks=21.5 mg/1 and K1=707 mg/1 for Alcaligenes and μmax=1.988 h-1, Ks=15.92 mg/1 and K1=197.7 mg/1 for Pseudomonas. The measured yield coefficients YX/s were in agreement with values commonly published in literature (0.3-0.8). Degradation rates up to 0.5 g/(l- h) were obtained.
The values of kinetic parameters Ea (activation energy) and A (preexponential factor) were evaluated for different kinetic models using data from thermogravimetric (TGA) curves obtained in isothermal and polythermal conditions. It was found that diffuse kinetic models D3 and D4 aproximated the experimental data better than first order reaction model F1. The values of kinetic parameters for D3 and D4 models evaluated from both types of TG curves were comparable.
Ionic channels form pores in biomembranes. These pores are large macromolecular structures. Due to thermal fluctuations of countless degrees-of-freedom of the biomembrane material, the actual form of the pores is permanently subject to modification. Furthermore, the arrival of an ion at the binding site can change this form by repolarizing the surrounding aminoacids. In any case the variations of the pore structure are stochastic. In this paper, we discuss the effect of such modifications on the channel conductivity. Applying a simple kinetic description, we show that stochastic variations in channel properties can significantly alter the ionic current, even leading to its substantial increase or decrease for the specific matching of some time-scales of the system.
Celem pracy było modelowanie procesu pobierania wody przez nasiona wybranych roślin uprawnych. Opis matematyczny funkcją logistyczną oraz równaniem Chapmana przeprowadzono dla pierwszej i drugiej fazy procesu poprzedzających kiełkowanie. Wyznaczono parametry charakteryzujące proces kinetyki pobierania wody wynikające z właściwości tych funkcji, związanych z ważnymi parametrami charakteryzującymi badane nasiona. Współczynnik MWR (maksymalnej szybkości pobierania wody w punkcie przegięcia krzywej logistycznej) osiągnął największą wartość dla rzodkiewki (39,61⋅10–3 kg wody⋅kg–1s. m⋅h–1) i buraka (69,16⋅10–3 kg wody⋅kg–1 s.m⋅h–1), najniższą zaś dla bobiku (5,35⋅10–3 kg wody⋅kg–1 s.m⋅h–1) i fasoli (7,07⋅10–3 kg wody⋅kg–1 s.m⋅h–1.
An intermittently aerated completely mixing lab-scale bioreactor was applied in the modelling of simultaneous nitrification and denitrification processes carried out in the Carrousel system. Two kinetic models based on the IAWPRC task group kinetic and stoichiometric concept were developed and verified. The model parameters number is reduced and the influence of waste water composition on oxygen transport rate is implicitly incorporated by the evaluation of oxic and anoxic "switching functions" based on oxygen profile data monitored in the bioreactor. The advantages of this approach in comparison to the tank-in-series model are discussed.
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