A team of Northeastern University researchers is using artificial intelligence to tackle a persistent problem in clean energy production: algae cells that appear identical but behave differently.
If the effort succeeds, it could help make algae a more reliable source of biofuel while reducing waste and lowering costs for companies trying to scale greener manufacturing.
Here's what to know
As Northeastern Global News reported, Miguel Fuentes-Cabrera, a professor in the Khoury College of Computer Sciences, and his colleagues are building a system that tracks algae at the level of individual cells. By identifying underperforming cells, they hope to prevent those differences from quietly dragging down output in large biomanufacturing tanks.
A major reason algae are considered for biofuel is that they naturally produce lipids, or oils, that can be converted into fuel. Because they grow through photosynthesis using sunlight and carbon dioxide, researchers have found that stressful conditions such as limited nitrogen can push them to make more of those oils.
"You assume that all of them behave in the same manner, but they actually don't," Fuentes-Cabrera explained.
That mismatch becomes hard to manage at industrial scale, where algae that perform well in a small lab flask can be far less predictable once production is moved into tanks holding hundreds or even thousands of gallons of water.
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Even small drops in productivity can raise costs, slow progress, and make it harder for cleaner fuels to compete with conventional energy sources.
More background
Standard monitoring usually measures tank conditions — including temperature, acidity, and oxygen — rather than what is happening inside individual cells, so early performance differences can go unnoticed.
To get a closer look, the team is using the Autonomous Real-Time Microbial Scope, or ARTiMiS. It is an imaging platform that continually photographs cells, while AI analyzes traits such as size, shape, and texture and turns each image into a mathematical profile so thousands of cells can be compared and unusual ones identified.
Researchers think the method might later be applied beyond algae, including in biomanufacturing processes that use natural building blocks to create other materials.
What's being done?
Next, the researchers plan to test the idea directly by inducing lipid buildup in several algae species and continuously photographing the cells to determine whether the AI can detect meaningful differences.
Once they better understand how variables such as light, temperature, and nutrients affect cell behavior — factors Fuentes-Cabrera likened to "knobs" scientists can turn — the team plans to compare tanks guided by ARTiMiS and AI with tanks that rely only on standard monitoring.
The project has already been recognized by the Genesis Mission, a Department of Energy program focused on accelerating scientific discovery with AI.
"With substantial improvements in biomanufacturing science over the past decades, many more and new types of materials can be made," Paul Hill, principal and founder of Berkeley BioProcess, told Northeastern Global News, adding that scientists will be able "to develop new and potentially better products from these natural building blocks."
Where can I learn more?
This algae research is part of a wider push to use AI in energy and environmental systems that are tough to track in real time. Similar tools are being used to balance renewable power grids, cut ship fuel use, and clean up pollution.
• At MIT, engineers are working to optimize renewable power grids as clean electricity grows less predictable.
• At sea, startups are using AI to read ocean currents and cut fuel use.
• Developers are testing AI-powered ocean cleanup robots to remove plastic before it spreads.
What connects these stories is the need to spot patterns early in messy, complex systems. From algae tanks to power grids, AI that catches hidden inefficiencies could help clean industries scale faster while wasting less.
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