Application of Recovery and Separation Optimization

The analysis in Secs. 3.1.2 and 3.1.3 is important whenever product specs are not well defined, or whenever there may be an incentive to generate a product better than the specs. The analysis applies both to new and existing columns. In existing columns, this optimization is often incorporated in a computer control strategy and performed online. When computer control is unavailable, the optimization can be performed off-line and translated into operating procedures. 

In the above analysis, it was assumed that product prices and the value of products as a function of their impurity content are well defined. Often, this is not so, for example, when one of the products is recycled to a reactor. In this case, the product value needs to be calculated as a function of its impurity content from data on the expected effect of impurity and recycle on the reactor performance. This analysis can become complicated. 

One variable not considered in the analysis is fluctuations in feed composition. Such fluctuations may have an effect on product values and on the separation, and need to be considered in the optimization. Where significant fluctuations are expected, it may be worthwhile to work with component recoveries (-D lk/ zlk) rather than D/F. 

The analysis also did not take into account the thermal state of the feed. In most situations, the thermal state can be optimized separately before the product recovery and separation are optimized. If the thermal state optimization leads to the conclusion that preheating or precooling is unjustified, and the thermal state is likely to vary (e.g., feed coming in from a reactor), fluctuations in this thermal state need to be taken into account in the product specs and recovery optimization. 

Finally, it should be noted that the optimization described above is of a shortcut nature. The author is yet to encounter a practical situation where the recovery and separation optimization is performed more rigorously. In the majority of new designs experienced by the author, the optimization did not even go that far. Procedures for more rigorous optimization are available (e.g., Ref. 3), but uncertainties in product values, project life, and costs often makes further fine-tuning difficult to justify. 

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