FinlandEnergy and utilities2024 – ongoingNeste
Digifuels machine-learning tools for fuel-quality prediction
Neste added machine-learning fuel predictions alongside traditional testing
Problem
Testing fuel properties relied heavily on engine experiments. Those experiments were expensive and time-consuming.
Approach
Neste and Aalto University developed digital tools through their Digifuels research collaboration. Neste put machine-learning tools for predicting fuel quality into use alongside traditional testing. Its research executive reported better accuracy than the previous tools.
Results
No published figures
Takeaway
Digital prediction supplemented experimental testing. Neste's estimate of annual financial benefits was prospective, with no realized savings total disclosed.
Categories
- Industry
- Energy and utilities
- Business function
- IT and R&DProduction and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Partner co-development
- Evidence
- Company's own claim
Sources
Source summaries
Aalto reported that Neste was using machine-learning fuel-quality tools developed through their collaboration. A Neste executive described improved accuracy and potential annual financial benefits.
Full details are in the original sources.
