Enhancement of Ozonation Reaction for Efficient Removal of Phenol from Wastewater Using a Packed Bubble Column Reactor

In the ozonation process, the phenol degradation in wastewater undergoes a low mass transfer mechanism. In this study, ozonized packed bubble column reactor was designed and constructed to remove phenol. The reactor’s inner diameter and height were 150 and 8 cm, respectively. The packing height was kept constant at 1 m in accordance with the reactor hydrodynamics. The gas distributor was designed with 55 holes of 0.5 mm. The phenol removal efficiency was evaluated at ozone concentrations of 10, 15, and 20 mg/L, contact times of 15, 30, 45, 60, 75, 90, 105, and 120 min, and phenol concentrations of 3, 6, 9, 12, and 15 mg/L. The results indicated that the highest phenol removal efficiency of 100% was achieved at 30 min in presence of packing. Moreover, the use of packing improved the contact between the gas and liquid, which significantly enhanced the phenol degradation. Actually, a thin film over a packing surface enhances the mass transfer. Also, it was found that the phenol is degraded into CO2 and H2O through a series of reaction steps. Additionally, a kinetic study of a first-order reaction provided an efficient estimation of reaction parameters with a correlation factor of 0.997

Catalytic performance of bimetallic cobalt–nickel/graphene oxide for carbon dioxide reforming of methane

The design of economical and robust catalysts is a substantial challenge for the dry reforming of methane (DRM). Monometallic nickel-based catalysts used for DRM reactions had comparable activity to noble metals. However, they turned out to be less stable during the reactions. As a continuation of the interest in synthesizing catalysts for DRM, this paper evaluates the catalytic performance of bimetallic Co–Ni catalysts regarding their synergy effect, with graphene oxide (GO) as support for the first time. The synthesized bimetallic catalysts prepared via the wet-impregnation method were characterized using N2 physisorption analysis, scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray diffraction (XRD). The catalytic test was performed in a stainless-steel tubular reactor in atmospheric conditions with a reaction temperature of 800 °C, time-on-stream (TOS) of 300 min and CH4: CO2 being fed with a ratio of 1:1. The bimetallic 10 wt%Co–10 wt%Ni/GO and 20 wt%Co–10 wt%Ni/GO catalysts had a similar BET specific surface area in N2 physisorption analysis. The XRD pattern displayed a homogeneous distribution of the Co and Ni on the GO support, which was further validated through SEM–EDX. The conversion of CO2, CH4, and H2 yield decreased with reaction time due to the massive occurrence of side reactions. High conversions for CO2 and CH4 were 94.26% and 95.24%, respectively, attained by the bimetallic 20 wt%Co–10 wt%Ni/GO catalyst after 300 min TOS, meaning it displayed the best performance in terms of activity among all the tested catalysts

Artificial intelligence modelling approach for the prediction of CO-rich hydrogen production rate from methane dry reforming

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

Response surface optimization of multilayer graphene growth on alumina-supported bimetallic cobalt–nickel substrate

This study investigates the optimization of multilayer graphene (MLG) growth on Co–Ni/Al2O3 substrate. The MLG synthesized by chemical vapor deposition technique (CVD) was characterized using various instrument techniques. The surface area and pore volume of the MLG were estimated as ~ 642 m2/g and ~ 2.7 cm3/g, respectively. The Raman spectrometric analysis showed evidence of MLG. The effects of parameters such as temperature, Co–Ni composition and ethanol flow rate were investigated using response surface methodology (RSM) and central composite design. The maximum MLG yield of 77% was attained at optimum conditions of 800 °C, Co–Ni composition of 0.3/0.7 and ethanol flow rate of 11 ml/min. The analysis of variance (ANOVA) results showed that the RSM quadratic model is significant with a p value < 0.0001. The coefficient of determination (R2) values of 0.9694 revealed the reliability of the RSM model. The potential of CVD as a technique to synthesize MLG growth of a highly ordered crystallinity structure has been demonstrated in this study. The resulting MLG films are promising materials for the use in improving graphene-based electronics, sensing and energy devices