Study on the selection of optimal reaction conditions for ethanol coupling to C4 olefins based on analytic hierarchy process and Bayesian network

Abstract

In this paper, based on the real experiments and experimental data of ethanol coupling reaction to produce C4 olefins, a model which can be used to determine the best combination of catalyst and temperature is constructed by using regression, interpolation, Bayesian neural network and genetic algorithm. First of all, the laws between the temperature corresponding to different catalyst combinations and the conversion of ethanol or the selectivity of C4 olefins are mathematically described and analyzed. Finally, 42 equations with higher goodness of fit were constructed according to different temperatures and ethanol conversion or C4 olefin selectivity to effectively describe the problem, and the analytic hierarchy process was used to evaluate all the quantities that meet the conditions. Then control variables are used to analyze the influence of individual variables, and correlation is used to evaluate the degree of interaction among variables. finally, the Bayesian neural network model is used to fit the functional relationship model between the dependent variable ethanol conversion, the selectivity of C4 olefins and the temperature of independent variables and the combination of catalysts (three variables). Then it mainly discusses how to find the optimal catalyst combination and temperature value to maximize the olefin yield.