Response surfaces, desirabilities and rational functions in optimizing sugar production

Response surface methods have long been use in various chemical applications, most typically in optimization problems. Very often the problem, in addition to several design variables, has also several response variables to be optimized. A plethora of different approaches to multi‐response optimizati...

Full description

Saved in:
Bibliographic Details
Published in:Journal of chemometrics Vol. 24; no. 7-8; pp. 505 - 513
Main Authors: Taavitsainen, Veli-Matti, Lehtovaara, Anssi, Lähteenmäki, Mira
Format: Journal Article Conference Proceeding
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01.07.2010
Wiley
Wiley Subscription Services, Inc
Subjects:
Chemistry
Chemometrics
desirability functions
Exact sciences and technology
General and physical chemistry
General. Nomenclature, chemical documentation, computer chemistry
Mathematical analysis
Mathematical models
Matlab
Measurement
multi-response optimization
Optimization
Polynomials
Rational functions
Regression
Regression analysis
Response surfaces
ridge regression
Sugar
Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
multi-response optimization
response surfaces
ridge regression
Modeling
desirability functions
Optimization
Design
R
rational functions
Computer program
Efficiency
Chemometrics
Sugar
ISSN:0886-9383, 1099-128X, 1099-128X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Response surface methods have long been use in various chemical applications, most typically in optimization problems. Very often the problem, in addition to several design variables, has also several response variables to be optimized. A plethora of different approaches to multi‐response optimization exists but, in terms of simplicity, it is difficult to beat the desirability function technique. In spite of its simplicity and efficiency, the desirability function technique seems not to be familiar for all chemometricians. This case study of optimizing full‐scale sugar production will show how desirability functions were successfully used in response surface modeling (RSM)‐based optimization. The optimization was based on ordinary least‐squares regression polynomials. In addition to ordinary polynomial modeling, a new method of using rational functions, a linearizing transform and ridge regression is also introduced and discussed. In this study, a freeware computer program, R, for implementing response surface and desirability function methods is used. R plays the same de‐facto standard role among statisticians as perhaps does Matlab among chemometricians. R is already used by some chemometricians and contains some chemometrics or chemometrics‐related packages (R‐packages are analogous to Matlab toolboxes). This study illustrates the use of some common RSM tools and desirability functions. Copyright © 2010 John Wiley & Sons, Ltd. This study shows a solution to an optimization problem of a sugar raffination process with respect to two response variables. The solution is based on response surface methodology, desirability functions and R. The study exemplifies the power of response surface methodology and desirability functions. In addition, and a new promising method of using rational functions, a linearizing transform and ridge regression is also introduced and discussed. The R‐code for solving the problem is available upon request.
Bibliography:istex:A2E631353F347459123C1A15831258F3921DC561
ark:/67375/WNG-73SMLFC9-6
ArticleID:CEM1299
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0886-9383
1099-128X
1099-128X
DOI:10.1002/cem.1299