Core Technologies
An algal strain library tuned to the water and environment of each region, paired with a high-precision cultivation control platform built on IoT sensing and AI inference. ABC runs on this in-house technology base.
Request technical documentationThree pillars of the technology base
Algal strain library
A library of microalgal strains matched to the water quality, salinity, temperature and light of each region, built in-house. Genomic analysis with bioinformatics tools lets us assess CO₂ uptake capacity, nutrient uptake efficiency and cultivation stability, and propose a suitable strain or blend ratio. For general knowledge on CO₂ uptake and nutrient recovery by microalgae, see the references below[1][2].
- ✓Integrated with regional water-quality databases
- ✓Genetic stability assessment of strains
- ✓Custom blend design
IoT sensing
Real-time measurement of the state of the culture. Edge computing handles on-site processing while a unified cloud dashboard manages data across multiple sites. Approaches to online monitoring of large microalgal cultivations are reviewed in Havlik et al.[3], among others.
- ✓Real-time multivariate sensing
- ✓Low-latency control through edge processing
- ✓Unified cloud dashboard
AI cultivation control
AI models optimise cultivation parameters automatically from sensor data, using our own algorithms. Control adapts to weather, seasonal and load variation, with the aim of keeping algal density and quality steady through the year. The performance of early anomaly detection is being evaluated in demonstration.
- ✓Autonomous parameter optimisation
- ✓Anticipatory, forecast-based control
- ✓Anomaly detection and alerting
How the three layers fit together
Selection × Library × Physical AI
Three in-house layers combine to run Algal Bloom Capture as one system
Non-GMO Strain Selection
Without genetic modification, we screen and select naturally isolated strains for those that grow reliably in the water on site.
Strain Library × Env. Matching
A strain library mapped across salinity, temperature, pH and influent composition. The optimal strain is supplied as "Strain as a Service" from customer water-quality data.
Physical AI for Bloom Control
A control model trained on operating data predicts the state of the culture and adjusts control in advance. It aims to lower the risk of contamination and collapse; the effect is being evaluated in demonstration.
The evidence behind it
The three pillars of Core Technologies rest on the academic literature of microalgal cultivation, sensing and control, on top of which we have built our own library, algorithms and implementation. Simultaneous measurement of more than ten parameters is our own implementation. Stable cultivation through the year is a target, and how far it is achieved depends on the deployment environment.
References
- Mohsenpour S.F., Hennige S., Willoughby N., Adeloye A., Gutierrez T. Integrating micro-algae into wastewater treatment: A review. Science of the Total Environment, 2021, 752, 142168. DOI →
- Cheah W.Y., Show P.L., Chang J.-S., Ling T.C., Juan J.C. Biosequestration of atmospheric CO₂ and flue gas-containing CO₂ by microalgae. Bioresource Technology, 2015, 184, 190–201. DOI →
- Havlik I., Lindner P., Scheper T., Reardon K.F. On-line monitoring of large cultivations of microalgae and cyanobacteria. Trends in Biotechnology, 2013, 31(7), 406–414. DOI →
Technical detail and joint research
Technical materials on Core Technologies are available under a non-disclosure agreement. We are also happy to discuss joint research or a demonstration deployment.