The increasing use of petroleum plastics has caused environmental damage due to the degradation time of these materials. An alternative to petroleum plastics could be thermoplastic starch (TPS). However, thermoplastic starch does not exhibit satisfactory tensile properties. The mechanical properties of thermoplastic starch can be improved by adding sisal microfibers. Thus, the objective of this study was to evaluate the influence of different levels of glycerol and sisal microfibers on the thermal and tensile properties of thermoplastic corn starch composites. The microfibers were obtained via mechanical treatment followed by chemical treatment (alkaline treatment and bleaching). The films were obtained by the casting method using commercial corn starch and glycerol as a plasticizing agent, reinforced with sisal microfibers. Fourier transform infrared spectroscopy (FTIR) results revealed that the addition of microfibers did not change the chemical structure of the TPS matrix. The films from the samples with 18% glycerol and 10% microfibers had the highest value for the maximum tension, equal to 4.78 MPa. The thermal decomposition profile of TPS was not altered by the addition of microfibers. Our findings demonstrated the profound influence of glycerol and microfiber contents on the tensile properties of thermoplastic starch composites.

This research is focused on investigating the utilization of Bayesian methodologies, specifically the Markov Chain Monte Carlo method, as well as filter sampling by importance and sequential resampling. The objective is to estimate kinetic parameters and state variables associated with the uranium bioleaching process by Acidithiobacillus ferrooxidans. Experimental data of cell concentration, uranium concentration, and concentrations of ferrous and ferric ions, obtained from literature, were employed. These measurements were evaluated using a mathematical model expressed by a system of ordinary differential equations. Three different mathematical models were evaluated, considering different uncertainties in experimental measurements and mathematical models (1% and 5%). The estimation results presented a good fit to the experimental data, with coefficients of determination in the range of 0.95 to 0.99. The validation of the mathematical models was obtained by reproducing the experimental measurements and the Bayesian techniques proved to be suitable for application in the bioleaching process.

The treatment of industrial effluents has great environmental and human health importance. The purification of water from polluting components, such as metals and organic compounds, can be considered one of the main applications in this field, with adsorption being one of the main treatment methods. Therefore, with the objective of describing the dynamics of the process in an adsorption column and estimating the parameters involved, in this work, an algorithm for the Method of Lines (MOL) was used in order to numerically solve the model formed by the mass balance in liquid phase, the linear driving force equation (LDF), and the Langmuir isotherm for equilibrium. In addition, a sensitivity analysis of the phenomenon was carried out in relation to the parameters and a subsequent estimation of these was made through the Monte Carlo technique via the Markov chain (MCMC). The validation algorithm was created using data from actual breakthrough curves found in the literature. The experimental data were obtained from the literature for the adsorption of Cadmium (Cd), Copper (Cu), Nickel (Ni), Zinc (Zn), and Chrome (Cr) ions. Among all the estimates, the one that had the lowest adjustment to the data was that related to zinc metal, which had an R² equal to 0.8984. For the other metals, the correlation coefficient had a value closer to unity. This demonstrates that, in general, the estimates were good enough to represent the dynamics of adsorption.

The present study modeled the adsorption process of the drug diclofenac sodium on activated charcoal. For this purpose, a mass balance-based model was used considering a fixed bed column. The mass transfer rate in the solid phase was represented by a driving force model proposed in this study, and a gamma exponent with a range of $0>\gamma \le 2$ was assigned to the model. Different isotherms were adopted to represent the equilibrium at the solid/liquid interface: the Langmuir, Freundlich, Sips and Redlich-Peterson isotherms. The modeling was approached from the perspective of Bayesian statistics, and the Markov chain Monte Carlo method was used for parameter estimation. Model validation was performed with experimental data obtained under different operating conditions of initial concentration ($C_{0

The use of the waste of the Bayer process, red mud, is due to its chemical and mineralogical composition that shows a material rich in oxides of iron, titanium and aluminum. Some studies conducted show that this waste can be applied as a source of alternative raw material for concentration and subsequent recovery of titanium compounds from an iron leaching process, which is present in higher amounts, about 30% by weight. To obtain a greater understanding about the leaching kinetics, the information of the kinetic data of this process is very important. In this context, the main objective of this work is the development of a mathematical model that is able to fit the experimental data (conversion / extraction iron, titanium and aluminum) of the leaching process by which is possible to obtain the main kinetic parameters such as the activation energy and the velocity of chemical reactions as well as the controlling step of the process. The development of the mathematical model was based on the model of core decreasing. The obtained model system of ordinary differential equations was able to fit the experimental data obtained from the leaching process, enabling the determination of the controlling step, the rate constants and the activation energies of the leaching process.