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Background: The mitigation of global warming effect requires intensified research efforts to reduce greenhouse gas emissions. This study was aimed at investigating the valorization of two principal greenhouse gases, namely carbon dioxide (CO2) and methane (CH4), over CeO2‐doped Co–Ni/GO catalytic materials. The CeO2‐doped Co–Ni/GO catalysts were synthesized using a sequential wet impregnation method and employed for CO2 reforming of CH4. The catalytic materials were characterized using various instrumental techniques. Response surface methodology (RSM) was employed to investigate the impact of process factors, namely reaction temperature (ranging from 700 to 800 °C), CeO2 loading (ranging from 5% to 15%) and feed flowrate (ranging from 10n to 50 mL min−1), on the CH4 conversions. Results: The three factors were observed to have significant influence on the CH4 conversion based on analysis of variance. The analysis of the RSM quadratic model revealed that the optimum conditions of 800 °C, 14.22% and 10.00 mL min−1 were obtained for the reaction temperature, CeO2 loading and feed flowrate resulting in maximum CH4 conversion of 98.24%. The desirability function for these results was calculated to be 0.934. The predicted process parameters aligned with the results of the actual experimental analysis. Conclusion: This study has demonstrated that the conversion of CH4 to value‐added products such as syngas can be optimized using RSM. The optimum conditions obtained could be used to improve the process performance. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

One interesting method for environmental remediation is the use of ZnO/ZrO2 composites in the photocatalytic degradation of lead (Pb) in wastewater. Several studies have investigated different types of composites for the removal of heavy metals from wastewater. However, the efficiency of these composites in removing the heavy metals remains debatable. Hence, this study investigated the potential of using a ZnO/ZrO2 composite for the removal of Pb from wastewater. Response surface methodology (RSM) was utilized in this work to maximize the Pb photocatalytic removal over ZnO/ZrO2 in simulated wastewater. Based on a central composite design (CCD), the experimental design included adjusting critical process parameters such as catalyst dosage, initial Pb concentration, and pH. The ZnO/ZrO2 composite was synthesized using a physical mixing technique, and its physicochemical properties were studied by field emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), Fourier transform infra-red (FTIR), and X-ray diffraction (XRD). Under visible light irradiation, photocatalytic Pb removal tests were carried out in a batch reactor. The findings showed that a ZnO/ZrO2 dose of 100 mg/L, a pH of 10, and an initial Pb content of 15 ppm were the optimal conditions for maximal Pb removal (above 91.2%). The actual Pb removal obtained from the experimental runs was highly correlated with that predicted using the RSM quadratic model. The usefulness of ZnO/ZrO2 composites for photocatalytic Pb removal is demonstrated in this work, which also emphasizes the significance of RSM in process parameter optimization for improved pollutant degradation. The models that have been proposed offer significant perspectives for the development and scalability of effective photocatalytic systems intended to remove heavy metals from wastewater. [ABSTRACT FROM AUTHOR]

BACKGROUND The advanced oxidation process using photocatalysts has been proven to be an efficient technique used for the degradation of organic pollutants in wastewater. However, there exists a nonlinear relationship between the process parameters of the photodegradation reaction, which needs to be well understood for the design of an efficient photoreactor. This study employed a backpropagation artificial neural network (BPANN) for the modelling of photocatalytic degradation of indole, anthraquinone dye and methyl blue using undoped and Ag+‐doped TiO2 catalysts. RESULTS: A Levenberg–Marquardt algorithm was employed to train the BPANN by varying the hidden neurons to obtained an optimized architecture. Optimized architectures with 3‐14‐1, 4‐12‐1 and 3‐16‐1 consist of the input layers, hidden layer and the output layer, were obtained using the datasets from photodegradation of indole, anthraquinone dye and methyl blue, respectively. The optimized BPANN accurately predicts the indole, anthraquinone dye and methyl blue degradation as a function of colour removal from the wastewater. High coefficients of determination (R2) of 0.999, 0.961 and 0.993 were obtained for the prediction of the photodegradation of indole, anthraquinone dye and methyl blue, respectively, with over 95% confidence level. The study revealed that dye concentration, catalyst dosage and reaction time have the highest level of importance for the photodegradation of indole, anthraquinone dye and methyl blue, respectively. CONCLUSION: This study has demonstrated the robustness of BPANN for predictive modelling of photodegradation of organic pollutants such as indole, anthraquinone dye and methyl blue. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

This study investigates the applicability of the Leven–Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H2 production by methane dry reforming over a Co/Pr2O3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH4 partial pressure, CO2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven–Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H2 production were strongly correlated with the observed values. [ABSTRACT FROM AUTHOR]

In this study, activated carbon -based adsorbent was prepared from eggshells and coconut shells. The effects of contact time, initial H2S concentration, and the calcium impregnated coconut shell activated carbon (Ca-CSAC) adsorption dosage on the hydrogen sulphide (H2S) removal efficiency and adsorption capacity were investigated. The batch adsorption data obtained from the experimental runs were employed to fit an artificial neural network (ANN) model. An initial optimization was performed to obtain the most suitable number of hidden neurons for training and validation of the ANN. The optimization results show that 16 hidden neurons was the most appropriate choice. The trained ANN was adequately validated and tested with coefficients of determination (R²) of 0.99 and 0.95, respectively. The ANN was found to be a robust tool for modeling of H2S removal efficiency by and adsorption capacity on Ca-CSAC under different process conditions. [ABSTRACT FROM AUTHOR]