Study on the process of ethanol coupling to prepare C4 olefins based on SPSS fitting

Ruiyang Wang

doi:10.1117/12.2625435

6 December 2021 Study on the process of ethanol coupling to prepare C4 olefins based on SPSS fitting

Ruiyang Wang

Proceedings Volume 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021); 1208514 (2021) https://doi.org/10.1117/12.2625435
Event: 2021 International Conference on Green Communication, Network, and Internet of Things, 2021, Kunming, China

Abstract

In this paper, the relationship between ethanol conversion, C4 olefin selectivity and temperature is studied. Firstly, the initial three-dimensional surface is drawn with catalyst combination and temperature as independent variables and ethanol conversion and C4 olefin selectivity as dependent variables. Then, in order to more accurately obtain the change rate of dependent variables, take the catalyst combination as independent variables, perform linear, logarithmic and exponential fitting on the other variables respectively, obtain the fitting optimal curve and total parameter table, and draw the final three-dimensional surface diagram to further optimize the fitting results, The differential equation is established to consider the influence of the change of product concentration and reactant concentration on the dependent variable. Then, this paper analyzes the changes of substances at different time points in an experiment, in which the independent variable is time, and carries out linear, logarithmic and exponential fitting with ethanol conversion and product selectivity respectively. The fitting parameters are determined when the degree of fitting is the highest. Thus, the relationship between reactants and products with time in the reaction process can be analyzed.

Citation Download Citation

Ruiyang Wang "Study on the process of ethanol coupling to prepare C4 olefins based on SPSS fitting", Proc. SPIE 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021), 1208514 (6 December 2021); https://doi.org/10.1117/12.2625435

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