I realized after 20 or so batches on the machine that while the controls are intuitive (heat, fan, and drum speeds), the physics can be unintuitive. I wanted to use my historical roast data to create and tune a model that I could use to do roast planning, control, and to help me build my own intuition for roasting. This website lets you interact with my roaster in a virtual, risk-free setting!
The models are custom Machine Learning modules that honor roaster physics and bean physics (this is not GPT/transformer-based). Buncha math.
The models are trained on about a dozen real roasts. The default bean model is an Ethiopian Guji bean.
My next steps are to add other roasters and the ability to practice control/reference tracking.
nxobject•3mo ago
If you ever did a writeup on how your ML modelling worked and what real-life data you needed, I'd learn so much point of view of someone who's applied a little bit of control theory to robotics and aquarium controllers, but with traditional models. (Hell, I'd even pay $CUP_OF_COFFEE_PRICE for it, since I'd get that much learning time out of it.)
Also: you advertise custom models for roasters. But can you make a digital twin of my toaster?
skylurk•3mo ago