The global attention has been focused on degradation of the environmental organic pollutants through green methods such as advanced oxidation processes (AOPs) under sunlight. However, AOPs have not yet been efficient in function of the photocatalyst that has been used. In this work, firstly, CaCu3Ti4O12 nanocomposite was simultaneously synthesized and decorated in different amounts of graphene oxide to enhance photodegradation of the organics. The result of the photocatalyst characterization showed that the sample with 8% graphene presented optimum photo-electrical properties such as low band gap energy and a great surface area. Secondly, the photocatalyst was applied for photodegradation of an organic model in a batch photoreactor. Thirdly, to scale up the process and optimize the efficiency, the photodegradation was modeled by multivariate semi-empirical methods. As the optimized condition showed, 45 mg/L of the methyl-orange has been removed at pH 5.8 by 0.96 g/L of the photocatalyst during 288 min of the light irradiation. Moreover, the photodegradation has been scaled up for industrial applications by determining the importance of the input effective variables according to the following organics order > photocatalyst > pH > irradiation time.
Hyperbranched polyisoprene was prepared by anionic copolymerization under high vacuum condition. Size exclusion chromatography was used to characterize the molecular weight and branching nature of these polymers. The characterization by differential scanning calorimetry and melt rheology indicated lower Tg and complex viscosity in the branched polymers as compared with the linear polymer. Degradation kinetics of these polymers was explored using thermogravimetric analysis via non-isothermal techniques. The polymers were heated under nitrogen from ambient temperature to 600°C using heating rates from 2 to 15°C min-1. Three kinetics methods namely Friedman, Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose were used to evaluate the dependence of activation energy (Ea ) on conversion (α). The hyperbranched polyisoprene decomposed via multistep mechanism as manifested by the nonlinear relationship between α and Ea while the linear polymer exhibited a decline in Ea at higher conversions. The average Ea values range from 258 to 330 kJ mol-1 for the linear, and from 260 to 320 kJ mol-1 for the branched polymers. The thermal degradation of the polymers studied involved one-dimensional diffusion mechanism as determined by Coats-Redfern method. This study may help in understanding the effect of branching on the rheological and decomposition kinetics of polyisoprene.
The artificial neural network (ANN) modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software's option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.
While the oil palm industry has been recognized for its contribution towards economic growth and rapid development, it has also contributed to environmental pollution due to the production of huge quantities of by-products from the oil extraction process. A phytoremediation technique (floating Vetiver system) was used to treat Palm Oil Mill Secondary Effluent (POMSE). A batch study using 40 L treatment tanks was carried out under different conditions and Response Surface Methodology (RSM) was applied to optimize the treatment process. A three factor central composite design (CCD) was used to predict the experimental variables (POMSE concentration, Vetiver plant density and time). An extraordinary decrease in organic matter as measured by BOD and COD (96% and 94% respectively) was recorded during the experimental duration of 4 weeks using a density of 30 Vetiver plants. The best and lowest final BOD of 2 mg/L was obtained when using 15 Vetiver plants after 13 days for low concentration POMSE (initial BOD = 50 mg/L). The next best result of BOD at 32 mg/L was obtained when using 30 Vetiver plants after 24 days for medium concentration POMSE (initial BOD = 175 mg/L). These results confirmed the validity of the model, and the experimental value was determined to be quite close to the predicted value, implying that the empirical model derived from RSM experimental design can be used to adequately describe the relationship between the independent variables and response. The study showed that the Vetiver system is an effective method of treating POMSE.
This study focuses on the modification of natural hydroxyapatite (HAp) derived from clamshell by impregnation with palladium (Pd) at different pH in wet precipitation method to produce photoactive green materials for the degradation of synthetic dyes. It was found that, at pH 10, Pd has been successfully impregnated into HAp lattice with Ca/P ratio of 1.77 and particle distribution size range of 40-470 nm. The impregnation has resulted in the band gap of Pd/HAp at 3.19 eV, as calculated using Tauc's plot from the UV-Vis spectroscopy data of the Pd/HAp. Next, the photocatalytic activities of Pd/HAp were carried out with methyl oranges (MO) as models of water pollutants under UV irradiation. The photodegradation efficiency of the catalyst reached the optimum value of 41.63%, 48.17%, and 43.64% after 120 min of continuous UV irradiation for pH values 8, 10, and 11.5 of Pd/HAp samples, respectively. This study opens a new paradigm in using naturally derived materials as photocatalysts in the reduction of persistent water pollutants at a low cost and green sustainable approach.
It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up.