Screening of new additives of active-carbon-supported heteropoly acid catalyst for Friedel-Crafts reaction by Gaussian process regression

Industrial and Engineering Chemistry Research

Volumn 50, Issue 19, 2011, Pages 10948-10954

  Omata, Kohji ^a  

^a TOHOKU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE CARBON; EXPECTED IMPROVEMENTS; GAUSSIAN PROCESS REGRESSION; HETEROPOLY ACIDS; PHYSICOCHEMICAL PROPERTY; PRINCIPAL COMPONENTS; REGRESSION MODEL; SUPPORT VECTOR MACHINE (SVM);

CHEMICAL REACTIONS; EXPERIMENTS; FRIEDEL-CRAFTS REACTION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); PLATINUM; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84860155473     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie102477y     Document Type: Article

References (15)

1. Kito, S.; Hattori, T.; Murakami, Y. Determination of synergistically generated acid strength by neural network combined with experiment. Anal. Sci. 1991, 7, 761-764.
2. Kito, S.; Hattori, T.; Murakami, Y. Estimation of the acid strength of mixed oxides by a neural network. Ind. Eng. Chem. Res. 1992, 31, 979-981.
3. Kito, S.; Hattori, T.; Murakami, Y. Estimation of catalytic performance by neural network: product distribution in oxidative dehydrogenation of ethylbenzene. Appl. Catal., A 1994, 114, 173-178.
4. Omata, K.; Yamada, M. Prediction of effective additives to a Ni/active carbon catalyst for vapor-phase carbonylation of methanol by an artificial neural network. Ind. Eng. Chem. Res. 2004, 43, 6622-6625.
5. Omata, K.; Yamazaki, Y.; Watanabe, Y.; Kodama, K.; Yamada, M. Artificial Neural Network (ANN)-Aided Optimization of ZSM-5 Catalyst for the Dimethyl Ether to Olefin (DTO) Reaction from Neat Dimethyl Ether (DME). Ind. Eng. Chem. Res. 2009, 48, 6256-6261.
6. 2. Ind. Eng. Chem. Res. 2010, 49, 1541-1549.
7. Omata, K. Screening of Supports and Additives for Heteropoly Acid Catalyst for Friedel-Crafts Reaction by Means of High-Throughput Screening and Data Mining. J. Jpn. Pet. Inst. 2011, 54, 114-118.
8. R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2009 (ISBN 3-900051-07-0).
9. Gramacy, R. tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models. J. Stat. Software 2007, 19, 46 pp.
10. Gramacy, R.; Taddy, M. A. Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models. J. Stat. Software 2010, 33, 48 pp.
11. SynergyCreations. Periodic Table X is downloadable from the site of Synergy Creations.
12. Barin, I.; Sauert, F.; Schultze-Rhonhof, S.; Sheng, W. Thermochemical Data of Pure Substances, 2nd ed.; VCH: Weinheim, Germany, 1993.
13. Chi, G.; Yan, W.; Chen, T. Iterative Data-Based Modelling and Optimization for Rapid Design of Dynamic Processes. In 9th International Symposium on Dynamics and Control of Process Systems (DYCOPS 2010), 2010.
14. 2+-NaX catalysts. Chem. Eng. J. 2010, 156, 423-431.
15. Jones, D. A taxonomy of global optimization methods based on response surfaces. J. Global Opt. 2001, 21, 345-383.