Most biogas plants already use automation through SCADA and PLC systems. These systems monitor and control equipment based on predefined rules. They are reliable but limited. They react to inputs. They don’t adapt or learn. They don’t make independent decisions.
Agentic AI brings a new layer of intelligence. Instead of simply following instructions, it understands goals and takes initiative. It monitors data in real time, makes decisions based on changing conditions, and improves its responses over time — all without waiting for human input.
In the biogas sector, where variability is the norm, this matters. Feedstock quality changes daily. Equipment behaviour shifts with use. Weather and environmental factors introduce constant fluctuation. Traditional control systems aren’t built to handle this complexity. Agentic AI is.
From Automation to Adaptation: Real-World Impact
Agentic AI is already being used in biogas operations to improve performance, prevent failures, and reduce manual oversight. Here are three areas where it is making a measurable difference:
Digester Load Balancing
AI continuously tracks input volumes, temperature, and chemical conditions. If a digester begins to operate outside optimal parameters, the system automatically adjusts feed rates, suggests chemical corrections, or redistributes loads to maintain stability.
Predictive Maintenance
By analysing historical SCADA and PLC data, AI can detect subtle patterns that indicate early signs of mechanical wear or failure. It responds by scheduling preventative maintenance, optimizing workflows to minimize downtime, and ensuring critical parts are available before issues arise.
Performance Optimization
AI reviews system-wide performance indicators such as gas yield, energy consumption, and throughput. It tests small adjustments, monitors the results, and fine-tunes operations continuously to maximize output and efficiency. This process happens automatically and improves over time.
Zebra EM: Turning SCADA Data into Intelligent Action
Agent Layer
Our AI agents are trained to pursue clear operational goals such as maximizing gas yield, reducing downtime, or maintaining process stability. They analyse incoming data and make autonomous adjustments based on what they observe.
Feedback Loop
These agents are not static. They evaluate the results of each action, learn from performance outcomes, and improve over time. This closed feedback loop leads to increasingly efficient operation without manual intervention.
This system is already in use. It is not experimental. It is running in real plants, adapting to real-world conditions, and outperforming traditional rule-based automation.
The Future of Biogas Is Self-Improving
Biogas facilities do not need more dashboards or static reports. They need systems that understand what is happening and know how to respond.
That is the role of agentic AI — not just smarter software, but a system that acts.
At Zebra EM, we are building that system today. At Zebra EM, we are actively deploying agentic AI within operational biogas environments. Our platform connects directly to SCADA and PLC systems, feeding high-frequency data into a centralized database. From there, agent-based logic transforms that data into real-time decisions.
Here’s how our system works:
Data Ingestion
Live data is continuously collected from all plant systems, including sensors, controllers, pumps, and digesters.