Time to harvest …

In Chapter 9, the tools allowing us to optimize and to rationally design chromatographic processes have been presented. Rules of thumb like “continuous processes must be preferred at large scale” are dangerous, and only a rigorous approach based on a detailed definition of tuning parameters, constrained parameters, objective function and algorithms guarantees the best option among those considered. The rigorous approach makes extensive use of numerical simulations and of the more or less detailed models presented in the first nine chapters of this book. We are, however, aware that a complete numerical design approach does not say a lot about the key drivers for performance. This chapter is therefore a series of situations designed as pedagogical examples, to be solved using the framework presented in the previous chapters. Our objective is not to make the reader an expert in specific processes, some of which have benefited from 40 years of continuous improvement, nor to give recipes for ready-to-cook meals, but to illustrate a methodology, to give decent orders of magnitude and, it is hoped, ideas to help the reader solve his own problems. Instead of proposing detailed process design, we will therefore favor shortcuts to better highlight features that we consider essential. We will keep the mathematics and detailed modeling to a minimum, in order to focus on a few messages that we consider key. With the essence of the problem well in mind, the previous chapters give the reader the methodologies and techniques for addressing specific topics requiring further detail and precision.

For confidentiality reasons, while the problems are real, the laboratory results and design parameters are not exact industrial data; they are, however, reasonable and precise enough for our conclusions to be realistic. Having orders of magnitude and references in mind is very important for performing an engineer's work; that is why typical characteristics of chromatographic processes are given in Appendix J.