Adaptive sequential experimentation technique for 3³ factorial designs based on revised simplex search

This paper is an extension to the work carried out in the field of sequential experimentation strategy by Siddiqui and Yang (2009). The methodology presented in this paper deals with expensive industrial experiments under the constraint of limited testing budget. This research focuses on involving three factors, each being at three levels. Another constraint appropriately assumed for these experiments is that of inadequate prior knowledge of the system, i.e., the behaviour of the system is not very well known to the experimenter. The aim of this research is to explore high quality parameter space in a minimum number of experimental runs in such situations. The explained experimentation strategy uses adaptive one factor at a time method, simplex downhill method, and response surface method.

[1]  Daniel D. Frey,et al.  A role for "one-factor-at-a-time" experimentation in parameter design , 2003 .

[2]  P. Dhulster,et al.  Application of the method of the experimental design to the study of a processing of unshrinkableness of wool fibers , 2003 .

[3]  R. Karthikeyan,et al.  Mathematical modelling for electric discharge machining of aluminium–silicon carbide particulate composites , 1999 .

[4]  A. Pasamontes,et al.  Fractional factorial design and simplex algorithm for optimizing sequential injection analysis (SIA) and second order calibration , 2006 .

[5]  Michael Sjöström,et al.  Multivariate Methods in the Development of a New Tablet Formulation , 2003, Drug development and industrial pharmacy.

[6]  Jiju Antony,et al.  Design of experiments for engineers and scientists , 2003 .

[7]  J. Tyson,et al.  Determination of methylmercury and inorganic mercury in water samples by slurry sampling cold vapor atomic absorption spectrometry in a flow injection system after preconcentration on silica C(18) modified. , 2007, Talanta.

[8]  G. Mori,et al.  In-flow speciation of copper, zinc, lead and cadmium in fresh waters by square wave anodic stripping voltammetry Part II. Optimization of measurement step , 1995 .

[9]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[10]  V. L. Anderson.  Evolutionary Operation : A Method for Increasing Industrial Productivity , 1970 .

[11]  J. Vaquero,et al.  Optimized Synthesis of Di, Tri and Tetrafused Pyridazinium Cations , 2000 .

[12]  E. Carrilho,et al.  Optimization of solid-phase microextraction procedures for the determination of tricyclic antidepressants and anticonvulsants in plasma samples by liquid chromatography , 2006, Analytical and bioanalytical chemistry.

[13]  George E. P. Box,et al.  Evolutionary Operation: A Statistical Method for Process Improvement , 1969 .

[14]  G. R. Hext,et al.  Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .

[15]  Lucy Camacho,et al.  Statistical analysis of process and composition factors affecting the leaching of heavy metals from fly ash solidified/stabilized wastes , 2005 .

[16]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[17]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[18]  Kai Yang,et al.  Adaptive sequential experimentation based on revised simplex search , 2009 .

[19]  C. Gustafsson,et al.  Multivariate methods in tablet formulation suitable for early drug development: Predictive models from a screening design of several linked responses , 2007 .

[20]  J. Dahlén,et al.  Development of a capillary zone electrophoresis method including a factorial design and simplex optimisation for analysis of amphetamine, amphetamine analogues, cocaine, and heroin. , 2006, Forensic science international.

[21]  S. M. Sultan,et al.  Chemiluminescence method for the assay of perphenazine in drug formulation using permanganate in sulphuric acid with flow injection technique and a chemometrical optimization approach. , 1999, Talanta.

[22]  Daniel D. Frey,et al.  The Mechanisms by Which Adaptive One-factor-at-a-time Experimentation Leads to , 2006 .