Box-Wilson strategy

Initial screens can be distinguished between methods that are used to determine what factors are most important, and follow-up screens that allow optimization and improvement of crystal quality (Table 14.1). In experimental design, this is known as the Box-Wilson strategy (Box et al., 1978). The first group of screens is generally based on a so-called factorial plan which determines the polynomial coefficients of a function with k variables (factors) fitted to the response surface. It can be shown that the number of necessary experiments n increases with 2 if all interactions are taken into account. Instead of running an unrealistic, large number of initial experiments, the full factorial matrix can... 

This section introduces the two most common empirical optimization strategies, the simplex method and the Box-Wilson strategy. The emphasis is on the latter, as it has a wider scope of applications. This section presents the basic idea the techniques needed at different steps in following the given strategy are given in the subsequent sections. 

In this section we try to give an overall picture of the Box-Wilson strategy, and the different types of designs used within the strategy will be explained in subsequent sections the focus is on the strategy itself. 

Simulation models like the one used in this example are very useful in practising Box-Wilson strategy. It can be obtained upon request from the author in the form of a Matlab, R or Excel VBA. For maximal learning, the user is advised to start the procedure at different locations. 

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