Alkanalyze
Process Intelligence Layer
Alkanalyze is our process intelligence layer, built to analyze feedstock quality, predict output properties, monitor reactor conditions, and optimize the system for consistent production. It combines machine learning models with inline sensor data to close the quality loop that makes waste-to-fuel systems unreliable.
Intelligence Capabilities
Alkanalyze closes the quality loop that makes waste-to-fuel systems unreliable. Every parameter is monitored, predicted, and optimized.
- Feedstock quality analysis and characterization
- Real-time reactor condition monitoring via inline sensors
- Output property prediction using trained ML models
- Process optimization for consistent fuel/feedstock quality
- Traceability and data logging for compliance readiness
How Alkanalyze creates consistency from chaos.
A four-stage intelligence loop that runs continuously alongside Oleum V1.
Feedstock Characterization
Analyze incoming waste composition, contamination levels, and polymer mix before conversion begins.
Reactor Monitoring
Inline sensors track temperature, pressure, flow rates, and reaction parameters in real-time.
Output Prediction
ML models predict output fuel properties during conversion, enabling proactive adjustments.
Process Optimization
Automated parameter tuning maintains target output specifications across variable feedstock.
Technical Overview
Key parameters for the Alkanalyze intelligence platform.
Specifications reflect current development status. Updates will be published as the platform matures.
Want to integrate Alkanalyze intelligence into your operations?
We're looking for technology partners, research collaborators, and pilot sites.