International audienceThis work presents the possibility of optimising 3D organised mesoporous silica (OMS) coated with both iron and aluminium oxides for the optimal removal of As(III) and As(V) from synthetic contaminated water. The materials developed were fully characterised and were tested for removing arsenic in batch experiments. The effect of total Al to Fe oxides coating on the selective removal of As(III) and As(V) was studied. It was shown that 8% metal coating was the optimal configuration for the coated OMS materials in.removing arsenic. The effect of arsenic initial concentration and pH, kinetics and diffusion mechanisms was studied, modelled and discussed. It was shown that the advantage of an organised material over an un-structured sorbent was very limited in terms of kinetic and diffusion under the experimental conditions. It was shown that physisorption was the main adsorption process involved in As removal by the coated OMS. Maximum adsorption capacity of 55 mg As(V) g(-1) was noticed at pH 5 for material coated with 8% Al oxides while 35 mg As(V) g(-1) was removed at pH 4 for equivalent material coated with Fe oxides. (C) 2014 Elsevier Inc. All rights reserved
In this paper gas engine model was developed in Aspen HYSYS V7.3 and validated with Waukesha 16V275GL+ gas engine. Fuel flexibility, fuel types and part load performance of the gas engine were investigated. The design variability revealed that the gas engine can operate on poor fuel with low lower heating value (LHV) such as landfill gas, sewage gas and biogas with biogas offering potential integration with bottoming cycles when compared to natural gas. The result of the gas engine simulation gave an efficiency 40.7% and power output of 3592kW.
Abstract. The adsorption of chromium(VI) metal ion in aqueous solutions by activated carbon resorcinol formaldehyde xerogels (ACRF) was investigated. The results showed that pore structure, surface area and the adsorbent surface chemistry are important factors in the control of the adsorption of chromium(VI) metal ions. The isotherm parameters were obtained from plots of the isotherms and from the application of Langmuir and Freundlich Isotherms. Based on regression analysis, the Langmuir isotherm model was the best fit. The maximum adsorption capacity of ACRF for chromium (VI) was 241.9 mg/g. The pseudo-second-order kinetic model was the best fit to the experimental data for the adsorption of chromium metal ions by activated carbon resorcinol formaldehyde xerogels. The thermodynamics of Cr(VI) ions adsorption onto ACRF was a spontaneous and endothermic process.
Bioactive compounds in the fruits of Tetrapleura tetraptera is widely used in food as a flavouring agent and for spices. In this study, bioactive compounds were extracted by solid-liquid extraction process and the yield was optimized by response surface methodology (RSM) and artificial neural network (ANN). The process parameters optimized were the extraction temperature, particle size and extraction time. Box-Behnken Design was used to study the effect of the process parameters on the extract yield. A quadratic model was obtained by RSM which was used topredict the extract yield. While for ANN, Bayesian Regularization learning algorithm with hyperbolic function (Tanh) for both hidden and output layers was the best model for predicting the extract yield. The performance of both models was established based on their R2 and RMSE values. (R2 and RMSE values were 0.9391 and 3.10 for RSM and 0.9637 and 0.8193 for ANN respectively). ANN gave the maximum extract yield of 29.15 % higher than that of RSM which evaluated a yield of 27.70 % with optimum conditions at extraction temperature of 90℃, particle size of 3.26 mm and extraction time of 50 mins. It was therefore concluded that ANN is better than RSM in the modeling and optimization of the extraction process parameters.
Keywords: Tetrapleura tetraptera, bioactive compounds, process parameters, optimization
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