EvoPhase will use AI algorithms, coupled with simulations of particulates in systems such as industrial mixers, to ‘evolve’ an optimised design for the mixing blade, and the shape or size of the blending vessel.
This approach is applicable to a range of process equipment, including mills, dryers, roasters, coaters, fluidised beds, stirred tanks and is expected to produce cost and energy savings for the industry.
EvoPhase has been set up using a model of commercialisation known as an Operating Division, which allows industry access to flexible services from Birmingham’s academic innovators.
Founder chief executive officer Dominik Werner said: “Up to 50% of the world’s products are created by processes that use granular materials, but granules are difficult to characterise or understand. If you consider coffee, its granules are solid when they are contained, like a liquid-like when poured out of the container, and become gas-like and dispersed if you blow on them. This type of variability means granules are the most complex form of matter to process.”
The team will use an AI technology called highly-autonomous rapid prototyping for particulate processes, which tests out designs it has evolved to come up with to find the best one. It allows the user to set multiple parameters for optimisation, allowing evolution of a design that will meet, for instance, targets on power draw, throughput and mixing rate, rather than trading these parameters off against each other.
EvoPhase will also use a numerical method called DEM (Discrete Element Method) which predicts the behaviours of granular materials by computing the movement of all particles. These computations can be validated using Positron Emission Particle Tracking another technique invented at Birmingham, which is a variant of the medical imaging technique positron emission tomography.
Chief technology officer Leonard Nicusan said: “Our technologies enable us to undertake assignments in material characterisation, digital model development, experimental imaging and validation, optimisation of process conditions, geometric design optimisation and scale-up, and predictive model development. Our approach is suitable for designing powder, granule and fluid processing equipment across all industries, where it will deliver cost savings by increasing energy efficiency, mixing effectiveness and throughput.”