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Research on multiple regression -PSO algorithm for C4 olefin preparation

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DOI: 10.23977/jmpd.2021.050113 | Downloads: 5 | Views: 901

Author(s)

Huifang Chen 1, Qian Zhang 1

Affiliation(s)

1 Jiangnan University, Wuxi, Jiangsu, 214122

Corresponding Author

Huifang Chen

ABSTRACT

In this paper, based on the experimental data of C4 olefin yield under different catalyst combinations and temperatures, the optimal experimental conditions for the preparation of olefin by ethanol catalytic coupling[1] were explored by establishing multiple regression model[2] and solving by particle swarm optimization algorithm. Firstly, Spearman correlation coefficients were calculated for each catalyst combination based on standardized data processing. It was found that ethanol conversion was positively correlated with C4 olefins selectivity and temperature under most preparation conditions. Then, multiple linear regression model was used to set experimental parameters and temperature range with C4 olefin yield as the objective function. Finally, particle swarm optimization algorithm was introduced to search for optimal solution.

KEYWORDS

PSO algorithm, Spearman correlation coefficient, multiple regression

CITE THIS PAPER

Huifang Chen, Qian Zhang. Research on multiple regression -PSO algorithm for C4 olefin preparation. Journal of Materials, Processing and Design (2021) 5: 63-66. DOI: http://dx.doi.org/10.23977/jmpd.2021.050113.

REFERENCES

[1] LU SHAopei.Synthesis of butanol and C_4 olefin by ethanol coupling [D]. Dalian University of Technology, 2018.
[2] Chen Xiaojun, Ji Fuxing. Customer relationship management, internal control and Comprehensive evaluation of m&a Performance Measurement based on Multiple Linear Regression Model [J]. Management Review, 2021.
[3] Chen Xi. Using control variable method to solve complex mass spectrometer problems [J]. Mathematical and Physicochemical Problem Solving Research, 2021.
[4] Wei Wei, YAN Xinghua. Research on investment efficiency of Chinese tourism listed companies based on multiple regression Analysis [J]. Journal of Chongqing Normal University (Natural Science Edition), 2013.

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