Process modeling and optimization of simultaneous direct conversion of CO2 and CH4 greenhouse gas mixture over TiO2/webnet photocatalyst

Modeling and optimization of CO2 mass transfer flux into Pz-KOH-CO2 system using RSM and ANN

In this research, artificial neural networks (ANN) and response surface methodology (RSM) were applied for modeling and optimization of carbon dioxide (CO₂) absorption using KOH-Pz-CO₂ system. In the RSM approach, the central composite design (CCD) describes the performance condition in accordance with the model using the least-squares technique. The experimental data was placed in second-order equations applying multivariate regressions and appraised applying analysis of variance (ANOVA). The p-value for all dependent variables was obtained to be less than 0.0001, indicating that all models were significant. Furthermore, the experimental values obtained for the mass transfer flux satisfactorily matched the model values. The R² and Adj-R² models are 0.9822 and 0.9795, respectively, which, it means that 98.22% of the variations for the N_CO2 is explained by the independent variables. Since the RSM does not create any details about the quality of the solution acquired, the ANN method was applied as the global substitute model in optimization problems. The ANNs are versatile utensils that can be utilized to model and anticipate different non-linear and involved processes. This article addresses the validation and improvement of an ANN model and describes the most frequently applied experimental plans, about their restrictions and generic usages. Under different process conditions, the developed ANN weight matrix could successfully forecast the behavior of the CO₂ absorption process. In addition, this study provides methods to specify the accuracy and importance of model fitting for both methodologies explained herein. The MSE values for the best integrated MLP and RBF models for the mass transfer flux were 0.00019 and 0.00048 in 100 epochs, respectively.

Removal of dibutyl phthalate from aqueous environments using a nanophotocatalytic Fe, Ag-ZnO/VIS-LED system: modeling and optimization

An (Fe, Ag) co-doped ZnO nanostructure was synthesized by a simple chemical co-precipitation method and used for the degradation of dibutyl phthalate (DBP) in aqueous solution under visible light-emitting diode (LED) irradiation. (Fe, Ag) co-doped ZnO nanorods were characterized by powder X-ray diffraction, Fourier transform infrared spectroscopy, UV-VIS diffuse reflectance spectroscopy, elemental mapping, Field emission scanning electron microscopy, transmission electron microscope and Brunauer-Emmett-Teller surface area analysis. A Central Composite Design was used to optimize the reaction parameters for the removal of DBP by the (Fe, Ag) co-doped ZnO nanorods. The four main reaction parameters optimized in this study were the following: pH, time of radiation, concentration of the nanorods and initial DBP concentration. The interaction between the four parameters was studied and modeled using the Design Expert 10 software. A maximum reduction of 95% of DBP was achieved at a pH of 3, a photocatalyst concentration of 150 mg L^-1 and a DBP initial DBP concentration of 15 mg L^-1. The results showed that the (Fe, Ag) co-doped ZnO nanorods under low power LED irradiation can be used as an effective photocatalyst for the removal of DBP from aqueous solutions.