MechChem Africa

By David Ming et al

On 12 October, a new book for chemical and plant engineers on attainable regions was launched, a theory that principal author, David Ming, outlines below.

Suppose you (an engineer) and a colleague (a pastry chef) are supplied eggs, sugar, flour, vanilla and jam, and asked to each bake a Swiss roll. Although it is almost certain that your friend, with his training and experience, will produce a better tasting result than yours, how does one know with certainty that it is truly the best?

An introduction to choosing an optimal reactorDavid Ming

Although we don’t think of it in this way, process design faces a similar problem: given set of raw materials and design constraints, how do we produce desired products with the least waste, lowest cost, highest purity, etc? Often, training and experience is crucial in separating a good design from a bad one, but given one good design, how do we know that there are no other superior designs?

As engineers we might not even ask this question, yet understanding where you stand in terms of global performance may often affect future decisions. For instance, would you try to optimise your design if you knew it was 50% of the absolute best or 99.9% of the absolute best? Asking the question ‘how do we know that we are the best’ is ultimately the idea behind performance targeting.

For chemical reactors, formulating even a single design might be a challenge in itself. Why select a single reactor when multiple reactors could be arranged to work together as a reactor network? The optimal design of a network of chemical reactors for a given duty is called reactor network synthesis (or RNS). A common approach to the RNS problem is to create a very large, generalised, reactor network, called a reactor superstructure, where the optimal answer is a subset of the superstructure. But even the most sophisticated superstructures are subject to performance targeting, because there exist infinite numbers of superstructure designs.

Attainable Region (AR) theory seeks to help understand both the performance targeting and RNS problems. The AR is the collection of all possible outputs for all possible reactor designs, even those that we have not yet imagined. This is achieved by interpreting chemical processes as geometric objects that define a region of achievability without having to explicitly enumerate all possible design combinations.

By disassociating the physical equipment from what is achievable, one can target an achievable state and then work backwards to find the physical equipment required to achieve that state. Consequently, the resulting design from this approach is then more appropriate, as we know how well it performs in terms of the global pool of designs.
Ultimately, it is important to appreciate that you cannot fix what you don’t know. And although we may never know who makes the best Swiss roll, it is possible to know what the best chemical reactor network looks like. Design is understandably difficult, but great designs first come from understanding where you stand.

To learn more about AR theory, see the AR theory textbook (‘Attainable Region Theory: An Introduction to Choosing an Optimal Reactor’), and visit the AR website at www.attainableregions.com

Click here to download the full article in PDF format.

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