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Published in Chemical Papers. Full-text access:https://rdcu.be/cZGaK

Several theoretical and empirical models are available to correlate experimental breakthrough curves of water pollutants, one of which is the two-parameter Belter model. Although not as well known as the century-old Bohart–Adams model, the Belter model is being used with sufficient frequency to merit wider awareness of its strengths and weaknesses. Through a systematic analysis, it is shown that the two adjustable parameters of the Belter model are analogous to the equilibrium capacity and rate parameters of the Bohart–Adams model. Breakthrough curves predicted by the Belter model are perfectly symmetric because their inflection points are invariant and always correspond to the midpoint of the curves. As a consequence, the Belter model provides poor fits to asymmetric breakthrough curves. In this work, an improved version of the Belter model is introduced. The new model with a floating inflection point manifests excellent conformity with mildly and, more importantly, severely asymmetric breakthrough curves.

For two-phase flows of immiscible displacement processes in porous media, we proposed a simplified model to capture the interfacial fronts, which is given by explicit expressions and satisfies the continuity conditions of pressure and normal velocity across the interface. A new similarity solution for the interfacial evolution in the rectangular coordinate system was derived by postulating a first-order approximation of the velocity distribution in the region that the two-phase fluids co-exist. The interfacial evolution equation can be explicitly expressed as a linear function, where the slope of the interfacial equation is simply related to the mobility ratio of two-phase fluids in porous media. The application of the proposed solutions to predictions of interfacial evolutions in carbon dioxide injected into saline aquifers was illustrated under different mobility ratios and operational parameters. For the purpose of comparison, the numerical solutions obtained by level set method and the similarity solutions based on the Dupuit assumptions were presented. The results show that the proposed solution can give a better approximation of interfacial evolution than the currently available similarity solutions, especially in the situation that the mobility ratio is large. The proposed approximate solutions can provide physical insight into the interfacial phenomenon and be readily used for rapidly screening carbon dioxide storage capacity in subsurface formations and monitoring the migration of carbon dioxide plume.

The ability of live cells (LC), freeze dried cells (FDC) and oven dried cells (ODC) of the yeast Rhodotorula glutinis to remove lead from aqueous solution has been studied. Discernible differences were found between the biosorption properties of LC and the other two types of cell preparation. The LC preparation exhibited an uptake level of about 12 mg g−1 in a batch contactor with a biomass dosage of 2 g l−1 and an initial lead concentration of 100 mg l−1. This compared with, respectively, about 26 and 30 mg g−1 for the FDC and ODC biosorbents under the same experimental conditions. It is seen that the level of lead uptake by the two latter biosorbents was increased to, respectively, 2.2- and 2.5-fold of the level observed for the LC preparation. The superior performance of the FDC and ODC biosorbents in the lead binding process was attributed to the presence of additional binding sites on their cell wall surfaces as indicated by potentiometric titration data. These binding sites were ascribed to carboxylic and phosphoric groups, which are the primary sites of divalent metal complexation. Modeling of the titration data revealed that subjecting R. glutinis biomass to freeze drying or oven drying increased its proton binding site concentration by a factor of 3. It appears that the two simple physical treatments were able to compromise the R. glutinis cell wall structure in such a way as to make sites normally inaccessible to become active in proton and lead binding.

Carotenoid production by Rhodotorula aurantiaca K-505 was optimized in shake-flask cultures using a central composite design. Quadratic polynomial models were used to correlate the relationships between six fermentation factors (pH, temperature, and the concentrations of glucose, yeast extract, peptone extract, and ammonium sulfate) and three response variables (cell mass yield, carotenoid content, and carotenoid production). Different optimum culture conditions were predicted by the models for maximizing the three response variables, indicating that there is no direct correlation between cell growth and carotenoid production. The maximum carotenoid production of 4.9 mg/L predicted by the relevant model under optimum culture conditions agreed well with the experimentally measured value of 5.3 mg/L under the same culture conditions.

In this study, we report the effects of pH and divalent cations on the adsorption of arsenate (As(V)) by titanium dioxide (TiO2) nanoparticles. The extent of As(V) adsorption on TiO2 decreased with increasing pH due to the decrease of positively charged binding sites on the TiO2 surface. The Langmuir maximum uptake capacity at pH 4 is about three times higher than that at pH 7. Here we show that the relatively low As(V) uptake at circumneutral pH could be substantially enhanced by the addition of common divalent cations such as magnesium and calcium. At a concentration of approximately 7 mM, magnesium and calcium increased the extent of As(V) adsorption from 2.1 to 6.5 and 7.7 mg As(V)/g TiO2, respectively.

Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.