Back to ProjectsMulti-Component Oil Formulation Parameter Prediction Algorithm
We developed an ML prototype for predicting parameters of multi-component oil formulations based on component data — simplifying formulation work and reducing manual calculations.
Client
NDA
Period
2 weeks
Format
ML prototype
About the project
We developed an ML prototype for predicting parameters of multi-component oil formulations based on component data. The project aimed to simplify formulation work and reduce manual calculations. Instead of iterating through combinations empirically, the goal was an algorithmic foundation for forecasting final mixture properties from component characteristics.
The Challenge
We needed an initial algorithm for predicting multi-component oil formulation parameters — verifying whether the task is ML-solvable, which features prove significant, and whether a working foundation can be built for further development into a practical technologist tool.
Our Solution
- Prepared ML prototype for predicting multi-component oil formulation parameters
- Developed input data structure for mixture components and characteristics
- Built data cleaning, normalization, and training preparation pipeline
- Selected and tested multiple predictive modeling approaches
- Identified significant features influencing final formulation parameters
- Built foundation for further development toward practical formulation tools
Results
- Initial ML prototype for multi-component oil formulation prediction delivered in 2 weeks
- ML approach applicability to mixture property analysis and prediction confirmed
- Basic technological foundation created for further solution development
- Project enabled transition from manual selection to formalized algorithmic approach
- Case shows expertise in rapid ML prototyping for chemistry and materials science
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