Whatever your product – plastics, pharmaceuticals, paints, inks, cosmetics, foodstuffs, personal care products – formulation is difficult. You must bring together the right combination of ingredients and processing conditions to deliver a quality product, repeatably, while controlling cost and minimising environmental impact.
Alchemite applies powerful machine learning to guide your testing program and identify formulation improvements that deliver better product performance, reduce costs, and meet regulatory constraints. It gets more value from your existing data, even where that data is sparse and noisy, and helps you to find the most efficient routes to improve the quality of this data and your understanding of it.
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Case studies
Food formulation development at Yili
Leading global dairy producer, Yili, gained valuable insights into the behaviour of UHT cream formulations over time, including understanding that enabled them to accelerate their development of improved recipes.
“We relatively quickly could drop out a number of the ingredients we had been testing… This wasn’t obvious if you just looked at them one-by-one, because you always have some cross-interactions. This was a big learning and helped speed up the development.”
– Matthias Eisner, Innovation Manager, Yili
Ink formulation design at Domino Printing Sciences
Domino Printing Sciences applied Alchemite to help guide testing and find optimal formulations for their inks. This case study shows how to reduce time-to-market, identify new candidate formulations, and enable reformulation in response to market, environmental, or regulatory drivers.
“We were impressed with the ability of Alchemite™ to identify novel formulations quickly and accurately. This enabled us to make the most of limited lab resources and continue innovating during the COVID-19 lockdown.”
– Dr Andrew Clifton, Director of Marking Materials and Test Engineering Team at Domino
Blog articles
Formulating our way towards net zero with machine learning
Intellegens help organisations that make stuff – chemicals, materials, manufactured goods. And this stuff is a big part of the climate change problem. The chemicals industry accounts for at least 7% of global emissions. Production of cement alone generates at least 4% of emissions. How can machine learning help?
Surface appearances – machine learning for paints and coatings
How we are helping developers of paints, coatings, dyes, inks, and related chemicals to optimise their formulation products and processes.
Publications
White paper: Machine learning for adaptive experimental design
Identifying the optimal composition and processing parameters to achieve commercial performance goals as quickly as possible is the key objective of formulation design projects. Machine learning identifies improved formulations with 50-80% fewer experiments than using traditional DOE approaches, by focusing experimental effort directly on formulations that will lead to successful products in as few experimental cycles as possible.
Alchemite™ for Formulations
Alchemite™ is ideal for quick response to formulation or reformulation challenges. Key features are:
- Gap-fill and validate sparse, noisy data from suppliers, experiment, simulation, and production
- Auto-generate models that identify key ingredient-process-property relationships
- Quantify uncertainty to support a rational business case for key decisions
- Design experimental programs, reducing the number of experiments by up to 90%
- Identify new or improved formulations and optimise process parameters
- Capture knowledge of formulation recipes as models for sharing and re-use
