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.

Conventional treatment methods such as chlorination and ozonation have been proven not to be effective in eliminating and degrading contaminants such as Bisphenol A (BPA) from wastewater. Hence, the degradation of BPA using a photocatalytic reactor has received a lot of attention recently. In this study, a model-based approach using a multilayer perceptron neural network (MLPNN) coupled with back-propagation, as well as support vector machine regression coupled with cubic kernel function (CSVMR) and Gaussian process regression (EQGPR) coupled with exponential quadratic kernel function, were employed to model the relationship between the textural properties such as pore volume (V_p), pore diameter (V_d), crystallite size, and specific surface area (S_BET) of erbium- and iron-modified TiO₂ photocatalysts in degrading BPA. Parametric analysis revealed that effective degradation of the Bisphenol up to 90% could be achieved using photocatalysts having textural properties of 150 m²/g, 8 nm, 7 nm, and 0.36 cm³/g for S_BET, crystallite size, particle diameter, and pore volume, respectively. Fifteen architectures of the MPLNN models were tested to determine the best in terms of predictability of BPA degradation. The performance of each of the MLPNN models was measured using the coefficient of determination (R²) and root mean squared errors (RMSE). The MLPNN architecture comprised of 4 input layers, 14 hidden neurons, and 3 output layers displayed the best performance with R² of 0.902 and 0.996 for training and testing. The 4-14-3 MLPNN robustly predicted the BPA degradation with an R² of 0.921 and RMSE of 4.02, which is an indication that a nonlinear relationship exists between the textural properties of the modified TiO₂ and the degradation of the BPA. The CSVRM did not show impressive performance as indicated by the R² of 0.397. Therefore, appropriately modifying the textural properties of the TiO₂ will significantly influence the BPA degradability.