A.G.Gasanov, R.P.Jafarov, S.T.Aliyeva, I.G.Ayyubov, G.D.Gasanova, I.M.Mamedova, M.M.Qurbanova, F.S.Qurbanova(Institute of Petrochemical Processes of NAS of Azerbaijan) Е-mail: email@example.com
Оптимизация процесса каталитического декарбоксилирования нефтяных кислот
Keywords: petroleum acids, mathematical modeling, mixture of naphthenic hydrocarbons, input and output parameters.
Over the past few years, studies have been carried out in the field of catalytic decarboxylation of petroleum acids isolated from crude oil. Natural and synthetic aluminosilicates, as well as nanosized oxides of magnesium and titanium, were used as catalysts for this purpose. The dependence of the yield of the decarboxylation product, which is a mixture of naphthenic hydrocarbons (mono-, bi- and polycyclic hydrocarbons), on various factors, including temperature, was studied. In addition, the influence of the nature of the catalyst on the process was described, the likely mechanism of the process was presented, and the presence of a high calorific value of the decarboxylation product was established, and therefore a recommendation was given for the possible use of this product as an additive to diesel and rocket fuels. In the present work, on the basis of experimental data, a regression mathematical model of the process of producing components of jet and diesel fuels is developed — a mixture of naphthenic hydrocarbons obtained by catalytic decarboxylation of petroleum acids, reflecting the influence of the main technological factors (temperature, amount of catalyst) on acid conversion. A statistical analysis of the obtained model is carried out, the adequacy of the developed model to experimental data is proved. The optimal values of the input parameters are found at which the maximum value of the conversion of petroleum acids is achieved.
This paper presents the results of statistical processing of laboratory studies of the reaction of catalytic decarboxylation of petroleum acids. The main goal of the work is to compile a mathematical model of the process with the subsequent solution of the optimization problem, as well as forecasting the results of the process and making recommendations on possible impacts on its course.
To establish quantitative ratios reflecting the influence of the main factors of the technological regime, which include: a catalyst sample — Z1 (mol) and a test temperature — Z2 (0С) for acid conversion — Y1 (%), we used the method of active design of the 22-type experiment subsequent mathematical and statistical processing of experimental data. The main conditions of the experiment were: change in the weight of the catalyst in the range from 0.02 mol to 0.06 mol, temperature in the range of 250-3500C. The mathematical expression of the dependence of the optimization parameter on the input independent variables is presented in the form of a regression equation:
where Yk is the value of the optimization parameter; Zi Zj — coded designations of model factors; n is the number of factors; ao is the free term in the regression equation; ai aij aii — coefficients of the linear effect and pairwise interaction of factors, respectively; i — serial number of the factor (i = 1,2,3); k is the number of output parameters.
To determine the coefficients of equation (1), we used the S-plus 2000 Professional program developed by Mathwork for automated mathematical processing and statistical analysis of the data for calculating linear regression coefficients, pair correlation, and quadratic effects for these samples.
In the case of a process using a synthetic aluminosilicate catalyst, with a change in the amount of a sample from 0.01 to 0.04 mol, the conversion in all cases increases to a maximum: 7% for a temperature of 2500C, 12.5% for a temperature of 3000C, 65% for a temperature of 3500C. A further increase in the amount of the sample to 0.06 mol leads to a decrease in acid conversion.
In the case of using nano-sized magnesium oxide with a change in the amount of the sample from 0.01 to 0.04 mol, the conversion in all cases increases to a maximum: 70% for a temperature of 2500C, 89% for a temperature of 3000C, 98.5% for a temperature of 3500C. A further increase in the amount of the sample to 0.06 mol leads to a decrease in acid conversion.
In the case of using nano-sized titanium oxide catalyst with a change in the amount of a sample from 0.01 to 0.04 mol, the conversion in all cases increases to a maximum: 65% for a temperature of 2500C, 88% for a temperature of 3000C, 98.7% for a temperature of 3500C. A further increase in the amount of the sample to 0.06 mol leads to a decrease in acid conversion.
Using the developed regression model on a personal computer, calculations were performed to study the effect of each input factor on the output parameters.
Thus, after analyzing the results of calculations and graphs, it was concluded that to determine the optimal values of the input variables, it is necessary to choose an optimization criterion. The maximum of the functional ƒ (X1, X2) was taken as this. To solve the optimization problem, the MATLAB-6.5 program was used, which contains modern algorithms for solving the linear programming problem. As a result of optimization, it was found that with a sample quantity of 0.035 mol and a temperature of 3500С, the acid conversion is 99.4% for the case of Y2 and 98.9% for the case of Y3.
With the calculated optimal values of the input variables found, a control experiment was carried out, which allowed us to determine the yield value of 99%, which indicates the acceptability of the developed regression model.
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