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Chapter 9 Optimisation

Elsevier Science & Technology
DOI: 10.1016/s0922-3487(05)80013-9


Publisher Summary This chapter focuses on the problem of optimizing a synthetic reaction. The investigation usually starts under conditions that are far from the optimum conditions and the problem is to reach the optimum with a minimum of effort. There are three straightforward methods to establish optimum conditions by experimental studies. The method of steepest ascent starts by determining a linear model to approximate the response surface. From the model, the direction that has the steepest slope upwards along the response surface is determined. This direction points towards the optimum conditions. A series of experiments can then be run along this steepest ascent vector. The sequential simplex search method does the same job as the method of steepest ascent, but with this method, the experiments are run one at a time. Which experiment should be performed next is determined from the previous experiments. Thus, an iterative progression along the path of the steepest ascent is accomplished. These two methods are useful for locating a near-optimum region, but they are not well suited for a more precise location of the specific optimum. Response surface modeling is used to locate the detailed optimum conditions. The principle is to establish a response surface model that maps the optimum region. The map can then be used for navigation in the optimum region.

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