This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Multiple Polynomial Regression Approach to Design Optimization of an Exhaust Emission Reduction Device
Annotation ability available
Sector:
Language:
English
Abstract
The application of a multiple polynomial regression procedure for optimization of design and performance parameters has been presented. To illustrate the statistical method, data from a laboratory test of an exhaust emission reduction device was used. There were four factor variables for which the response was constructed. Among the factor variables two were design parameters; the remaining two were the operating parameters. The selected response variables were CO, NO, and HC's.
The assumptions for the analysis procedure are presented. Problems which could affect the validity of the statistical procedures for the design optimization are discussed. Experimental verification of the optimization results was performed. Overall agreement between the predicted level of pollutants reduction and measured values did not differ by more than ten percent.
Authors
Citation
Ziejewski, M. and Gill, D., "Multiple Polynomial Regression Approach to Design Optimization of an Exhaust Emission Reduction Device," SAE Technical Paper 891893, 1989, https://doi.org/10.4271/891893.Also In
References
- Bartle, R. Elements of Real Analysis Wiley New York 1976
- SAS User's Guide (1985) SAS Institute Inc. Box 8000, Cary, MC
- Box, G. E. P. Hunter J. S. “Multifaotor Experimental Designs for Exploring Response Surfaces” Annuals of Mathematical Statistics 28 195 242 1957
- Box, G. E. P. Wilson K. J. “On the Experimental Attainment of Optimum Conditions” Journal of the Royal Statistical Society 13 1 15 1951
- Cochran, W. G. Cox G. M. Experimental Designs 2nd John Wiley and sons New York 1957
- John, P. W. M. Statistical Design and Analysis of Experiments Macmillan New York 1971
- Myers, Raymond H. Response Surface Methodology Virginia Polytechnic Institute and State University Blacksburg, VA 1976