Open AccessMULTIPLE REGRESSION ANALYSIS OF THE INFLUENCE OF CATALYST CHARACTERS SUPPORTED ON γ-Al<sub>2</sub>O<sub>3</sub> TOWARDS THEIR HYDROCRACKING CONVERSION OF ASPHALTENE
Author(s) -
Wega Trisunaryanti,
Triyono Triyono,
Mudasir Mudasir,
Akhmad Syoufian
Publication year - 2010
Publication title -
indonesian journal of chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.273
H-Index - 14
eISSN - 2460-1578
pISSN - 1411-9420
DOI - 10.22146/ijc.21868
Subject(s) - catalysis , chemistry , regression analysis , linear regression , multivariable calculus , correlation coefficient , regression , radius , asphaltene , cracking , volume (thermodynamics) , analytical chemistry (journal) , chromatography , statistics , thermodynamics , organic chemistry , mathematics , physics , computer security , control engineering , computer science , engineering
Multiple regression study of the influence of catalyst's characters with γ-Al2O3 as a support, including acidity, specific area, average pore volume, average pore radius, Ni content, and Mo content the hydrocracking conversion of asphaltene has been conducted. A multivariable regression analysis method, including regression analysis and correlation analysis, was applied on this study. Using multivariable regression, the characters of catalyst was correlated together with the data of the asphaltene conversions. Furthermore, using this method, the characters of catalyst, which have the greatest influence on conversion, may be evaluated. The results showed that there was a high correlation between catalyst characters and hydrocracking conversion of asphalten (r = 0.983). It means that the conversion was 98.3% correlated with the catalyst characters. The value of the multivariable determination coefficient was 0.966, indicating that at least 96.6% variation on the conversions was determined by combination of catalyst characters on this research. From the parameter value of regression equation, it could also be known that average pore radius and specific surface area were the two characters that have the greatest influence on the hydrocracking conversion of asphalten. Keywords: multivariable regression, catalyst's characters, high correlation degree, determination coefficient