Examining rheological behavior of CeO(2)-GO-SA/10W40 ternary hybrid nanofluid based on experiments and COMBI/ANN/RSM modeling

Application of response surface methodology for optimization of the test condition of oxygen evolution reaction over La0.8Ba0.2CoO3 perovskite-active carbon composite

The Experimental Design was applied to optimize the electrocatalytic activity of La0.8Ba0.2CoO3 perovskite oxide/Active Carbon composite material in the alkaline solution for the Oxygen Evolution Reaction. After the preparation of La0.8Ba0.2CoO3, and structural characterizations, the experimental design was utilized to determine the optimal amount of the composite material and testing conditions. The overpotential was defined as the response variable, and the mass ratio of perovskite/active carbon, Potassium hydroxide (KOH) concentration, and Poly(vinylidene fluoride) (PVDF) amount were considered effective parameters. The significance of model terms is demonstrated by P-values less than 0.0500. The proposed prediction model determined the optimal amounts of 0.665 mg of PVDF, a KOH concentration of 0.609 M, and A perovskite/Active Carbon mass ratio of 2.81 with 308.22 mV overpotential (2.27% greater than the actual overpotential). The stability test of the optimized electrode material over 24 h suggests that it could be a good candidate electrocatalyst for OER with reusability potential.

Laboratory investigation of GO-SA-MWCNTs ternary hybrid nanoparticles efficacy on dynamic viscosity and wear properties of oil (5W30) and modeling based on machine learning

In the present study, the properties of ternary hybrid nanofluid (THNF) of oil (5W30) - Graphene Oxide (GO)-Silica Aerogel (SA)-multi-walled carbon nanotubes (MWCNTs) in volume fractions ([Formula: see text] of 0.3%, 0.6%, 0.9%, 1.2%, and 1.5% and at temperatures 5 to 65 °C has been measured. This THNF is made in a two-step method and a viscometer device made in USA is used for viscosity measurements. The wear test was performed via a pin-on-disk tool according to the ASTM G99 standard. The outcomes show that the viscosity increases with the increase in the [Formula: see text], and the reduction in temperature. By enhancing the temperature by 60 °C, at [Formula: see text] = 1.2% and a shear rate (SR) of 50 rpm, a viscosity reduction of approximately 92% has been observed. Also, the results showed that with the rise in SR, the shear stress increased and the viscosity decreased. The estimated values of THNF viscosity at various SRs and temperatures show that its behavior is non-Newtonian. The efficacy of adding nanopowders (NPs) on the stability of the friction and wear behavior of the base oil has been studied. The findings of the test display that the wear rate and friction coefficient increased about 68% and 4.5% for [Formula: see text] = 1.5% compared to [Formula: see text] = 0. Neural network (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gaussian process regression (GPR) based on machine learning (ML) have been used to model viscosity. Each model predicted the viscosity of the THNF well, and Rsquare > 0.99.

Optimization of reaction temperature and Ni-W-Mo catalyst soaking time in oil upgrading: application to kinetic modeling of in-situ upgrading

Decreasing the conventional sources of oil reservoirs attracts researchers' attention to the tertiary recovery of oil reservoirs, such as in-situ catalytic upgrading. In this contribution, the response surface methodology (RSM) approach and multi-objective optimization were utilized to investigate the effect of reaction temperature and catalysts soaking time on the concentration distribution of upgraded oil samples. To this end, 22 sets of experimental oil upgrading over Ni-W-Mo catalyst were utilized for the statistical modeling. Then, optimization based on the minimum reaction temperature, catalysts soaking time, gas, and residue wt.% was performed. Also, correlations for the prediction of concentration of different fractions (residue, vacuum gas oil (VGO), distillate, naphtha, and gases) as a function of independent factors were developed. Statistical results revealed that RSM model is in good agreement with experimental data and high coefficients of determination (R² = 0.96, 0.945, 0.97, 0.996, 0.89) are the witness for this claim. Finally, based on multi-objective optimization, 378.81 °C and 17.31 h were obtained as the optimum upgrading condition. In this condition, the composition of residue, VGO, distillate, naphtha, and gases are 6.798%, 39.23%, 32.93%, 16.865%, and 2.896%, respectively, and the optimum condition is worthwhile for the pilot and industrial application of catalyst injection during in-situ oil upgrading.