Virtual ExperienceApril 7, 2022

Digital Obtainium

“Obtainium” is slang which describes found materials used to create works of…
Avatar David Nicolaides

“Obtainium” is slang which describes found materials used to create works of art or other made objects.  It is just what the artist needs, and is ideally free.  The same concept exists in the digital twin, or virtual twin of our world, and in this post we describe a simple example which, ideally, helps us understand why we make things (digital or otherwise).

“Digital Obtainium” is already widely available to coders.  If I’m looking for how to use a particular Python method then a web search of the method name plus “examples” turns up what I need.  Similarly with JavaScript, Java, or any other language you want to use.  The coding community deserves a large amount of thanks for setting up this sharing ecosystem.

However, the further you climb up the slopes of “Mount Science”, the scarcer the digital obtainium becomes (which is probably why many of us choose to live at those elevations).  There is also the issue of assembling the found bits into the artwork, and here language-neutral developer tools are a must.

In what follows we describe our assembly of a digital artefact which allows a formulation or process scientist to understand and optimize both the mixture and process parts of their product, in the most effective way possible.

KCV Models

The first chunk of obtainium is the work by Kowalski, Cornell and Vining [1], who found a new set of mixture-process models which are much simpler than those used previously.  This is worthy of an entire blog on its own, but the essence is that many of the model cross-terms are left out, and the value is that you can fit the model from far fewer experiments (often less than half the number).  Saving experimentation is at the core of the value of the digital twin.

Now most of the commercial DOE software providers have already included these designs and models in their software, but a key property of obtainium, that the material be free, is thankfully met by the R “mixexp” package [2].

The package also has model-fitting routines for the KCV forms, and allows you to make mixture-specific response-surface plots such as the one below:

.  It fits well with data science and machine learning tasks; indeed, a model is simply another component which is added to the system library available for re-use.

This is captured at a high level in the following screenshot, where the simplicity of the component model for data science becomes clear.  Inside the selected “Learner” component there is a lot of clever R-script, most of it coming from [2].  However, components “encapsulate” these details, and can be used without having to worry about them.

Pipeline Pilot

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