УДК 66.095: 547.562:547.425
Haydarli G.Z., Jafarov R.P., Rasulov Ch.K., A.R. Manafova (Institute of Petrochemical Processes named after Yu.G. Mammadaliyev of the National Academy of Sciences of Azerbaijan)
Оптимизация процесса ацилирования пара-(1-метилцик-логексил) фенола уксусной кислотой в присутствии нано-каталитической системы
Keywords: para-(1-methylcyclohexyl) phenol, acetic acid, catalyst, acylation, acetophenone, optimization, regression model, adequacy.
Abstract. Determining the theoretical optimal conditions for the acylation of para-(1-methylcyclohexyl) phenol with acetic acid creates the basis for evaluating the prospects of this process. For the carring out the acylation process, para-(1-methylcyclohexyl) phenol (AP) and acetic acid (AcOH) were used as a feedstock. To determine the optimal conditions for the acylation reaction of AP with the help of AcOH in a pilot plant, the effects of temperature, molar ratio of initial compounds, reaction time on the yield and selectivity of the target product were studied. The study of the acylation reaction was carried out in the temperature range of 120-1600C, the reaction time was 20-50 minutes, the molar ratio of AP:AcOH was within 1:0.5÷3.
To develop a regression model of the process, it is necessary to identify the functional relationship between the process parameters and use it for further process prediction. Considering that the number of experiments is m=12, and the input variables are n=3, the functional connection can be represented as a non-linear polynomial.
To determine the coefficients of the equation, the S-plus 2000 Professional program was used, which allows us to automatically calculate statistical analysis data: quadratic effect coefficients, regression model coefficients and pair correlation coefficients. Applying Student’s criterion, significant and insignificant coefficients of the equation were found. To test the adequacy of the model, the Fisher criterion was used, which makes it possible to prove the adequacy of the description of the response surface by regression equations.