Selective Homogeneous Oxidation System for Producing Hydroperoxides Concentrate: Kinetic Simulation of Catalytic Oxidation of Gas Oils

Abstract

Homogeneous production of hydroperoxides concentrate has been simulated via multivariate calibration of yield data, obtained during an earlier study [Naqvi and Khan Ind. Eng. Chem. Res. 2009, 48, 5642] made on the air oxidation of diluted gas oils in the presence of chemically generated redox couple Co(III)/Co(II). Principal component analysis has been applied to abstract dynamic hydroperoxides yield data that provides the basis for the simulation. A novel chemometric technique, inverse nonlinear principal component regression, has been introduced to simulate experimental yield profiles with exceptional accuracy (R 2 ) 0.9841). Simulated yield profiles have then been subjected to Levenberg-Marquardt method in order to estimate the rate constants for formation and decomposition of hydroperoxides. These estimations have permitted the development of two reasonably accurate multivariate global models that relate the specific rates, for formation (R 2 ) 0.8873) and decomposition (R 2 ) 0.9504) of hydroperoxides, to process and composition variables. Construction of such models allows the specific rates to be optimized so that the reactor could be operated at an oil conversion (up to ≈5%) that is almost proportional to the yield of hydroperoxides ensuring selectivity ≈88%.

Optimized Liquid-Liquid Extractive Rerefining of Spent Lubricants

“…In the selected spectral region saturates and olefins do not interfere as they usually have absorption maxima below 200 nm. Maxima in the spectra lie around 230 nm a common wavelength for diaromatics in oils whereas higher PAH may be seen around 254 nm [17, 18, 20]. The progressive decrease in PAH as a result of extractive rerefining may clearly be noticed at 254 nm.…”

“…As (2) is linear in all the coefficients β i , the coefficients can be estimated by the procedure of MLR, which results in the following equations for the estimates of β i [9–12]: $\begin{array}{c}{\widehat{\beta }}_{\mathbf{A}}={\left({\mathbf{X}}^{\mathbf{T}}\mathbf{X}\right)}^{-\mathbf{1}}{\mathbf{X}}^{\mathbf{T}}{\mathbf{y}}_{\mathbf{A}},\\ {\widehat{\beta }}_{\mathbf{K}}={\left({\mathbf{X}}^{\mathbf{T}}\mathbf{X}\right)}^{-\mathbf{1}}{\mathbf{X}}^{\mathbf{T}}{\mathbf{y}}_{\mathbf{K}},\end{array}$ where ${\hat{\beta }}_{\mathrm{boldA}}false(N\times \mathrm{normal1}false)$ or ${\hat{\beta }}_{\mathrm{boldK}}false(N\times \mathrm{normal1}false)$ are the vectors of the estimates of the coefficients β i .…”

“…The indicators for the goodness of fit, during MLR, were the coefficients of determination R 2 or R Adjusted 2 defined as [12, 13] $\begin{array}{c}{R}^{\mathrm{2}}=\frac{\text{S}{\text{S}}_{\text{Regression}}}{\text{S}{\text{S}}_{\text{Total}}},\hspace{1em}\hspace{1em}{R}_{\text{Adjusted}}^{\mathrm{2}}=\mathrm{1}-\frac{\text{S}{\text{S}}_{\text{Residual}}/\text{D}{\text{F}}_{\text{Residual}}}{\text{S}{\text{S}}_{\text{Total}}/\text{D}{\text{F}}_{\text{Total}}}.\end{array}$ …”

“…The backward elimination variable selection technique has been employed in the present study to develop the multivariate process models for the sludge yield during liquid-liquid extraction of spent lubricant. The indicators for the goodness of fit, during MLR, were the coefficients of determination R 2 or R Adjusted 2 defined as [ 12 , 13 ] …”

Liquid-Phase Homogeneous Catalytic Oxidation of Gas Oil: Elucidation of Complex Chemical Transformation Network

“…In addition to that there are numerous examples of processes where complex hydrocarbon mixtures are used as substrates. For example coal [2][3][4][5], bitumen/asphalt/tar [6][7][8][9][10], wax [11], and middle distillates [12][13][14][15][16][17][18][19] have been shown to be catalytically processed either to upgrade or to desulfurize these complex mixtures.…”