[Madiha]

Running a biogas plant means juggling feedstock quality, microbial health, gas production rates, and safety regulations, all while ensuring profitability.

A minor fluctuation in feedstock composition or ammonia levels can cause costly shutdowns.

AI-powered monitoring can predict potential failures before they happen. Anessa AI provides real-time insights and automated optimization for smoother, safer, and more profitable operations.

Intrigued? Read our blog: https://www.anessa.com/blog/biogas-monitoring-101-feedstock-monitoring-in-biogas-operations

anessa.com

Biogas Monitoring 101: Feedstock Monitoring in Biogas Operations | Anessa | Biogas Software

Real-time monitoring in biogas operations involves the continuous measurement and tracking of a range of parameters to ensure optimal process performance and maximize efficiency. By closely monitoring these key parameters, operators can gain valuable insights into the health and stability … Continue reading

[Natalia Bourenane]

Thank you for sharing this article. I wonder how fast AI is developing in biogas and RNG industry. Do you see a quick uptake? @anessa.inc

[Madiha]

Thank you for the question, Natalia. Top energy companies such as GRDF (Gaz Réseau Distribution France) and seasoned developers are already leveraging Anessa AI to drive innovation in biogas. Integrating AI in biogas projects is no longer an option; it has become a necessity. We are supporting clients in 14 + countries, and the momentum is picking up faster than anticipated.

[Natalia Bourenane]

@AzuraAssociates.Deidre Let me know if your team wants to share some of their experience?

[Deidre]

Thanks Natalia. One of the common questions our team is asked is “Can I put this in my digester?” Trisha created a great video that highlights the major categories of feedstocks and highlights potential opportunity and risks – https://youtu.be/M8BhrS1eeok?si=BZ0q4tHPVxscE5v0

[Peter]

Many digester owners select feedstocks based on two main factors: fuel value (how much methane or energy the material can produce) and economics (hauling costs, contract length, tipping fees, etc.). However, as we see it, making decisions solely on these criteria can be short-sighted.

We encourage clients to take a more holistic approach by balancing fuel value with other key characteristics such as degradability, nutritional content, contamination levels, and how each of these factors interacts with the specific design and biology of their digester system. At Azura, we’ve seen firsthand how a poor match between feedstock and system can lead to operational disruptions, reduced gas yields, and dead bugs.

To assess fuel potential, the Biomethane Potential (BMP) test is the industry standard. However, BMP tests are time-consuming and costly, which may not make sense for early-stage projects or when resources are limited.

In many cases you can accurately estimate the methane production potential of manure and agricultural residues without a lengthy and expensive BMP test, by analyzing key feedstock characteristics. Most of the characteristics are similar to forage characteristics that are analyzed for animal nutrition – such as:

• Total and volatile solids (dry matter and organic matter)
• Protein, fat, and carbohydrate composition
• Recalcitrant organic matter (e.g., cellulose, lignin, woody stems, and other hard-to-degrade components)

At Azura we apply correction factors based our experience with manure and food waste feedstocks collected across North America to account for recalcitrant materials that are unlikely to break down and produce biogas. This ensures a realistic estimate that avoids overestimation. We call this refined metric Total Digestible Energy (TDE), a fast and practical estimate of biogas production compared to long BMP tests.

Building an in house database of TDE can help predict biogas production for digesters receiving feedstocks from multiple sources. It can be a useful tool for plant operators to understand the nutritional value of what they feed their digesters, identify nutritional gaps, and closely monitor if they are efficiently converting feedstock to gas.

[Trisha]

For guidance with your specific project

If you need help with evaluating different feedstocks for your project, please email me at trisha.aldovino@AzuraAssociates.com or peter.quosai@AzuraAssociates.com and I can set you up with our bioprocess experts who can help you understand the opportunities and costs for each feedstock.

[Gurkeerat]

Thanks for sharing the video by Trisha on Feedstock introduction. I am interested to know more about Agri resudue as feed stock. Will explore.

[Ryan]

Ag source materials generally have less gas production potential and therefore less financial return potential, but on digesters built on farm do offer advantages for the management of digestate, as farms will likely have storage availability, land available for digestate application, and hauling equipment to get the digestate to the fields for beneficial reuse as fertilizer and soil amendment.

[Peter]

Thanks Gurkeerat, there is a lot cover on agri residues. Some quick things to keep in mind are:

-What mechanical treatments might be needed to make residues more digestible?

-Do the residues contain high concentrations of lignin or sulfur?

-How will the addition of dry residuals to the digester affect viscosity and mixing?

[Hatem]

I think that Artificial Intelligence (AI) holds transformative potential for advancing anaerobic digestion by pinpointing the optimal mix of process variables to maximize biogas production. But, its true power lies not in generic modeling but in capturing the unique biochemical dynamics of each plant. Anaerobic digestion is more than a statistical system—it’s a living, adaptive process driven by complex interactions among substrates, microbial communities, and operational parameters. As such, models must be meticulously crafted, grounded in a deep understanding of biochemical mechanisms and powered by high-quality, plant-specific historical data.
At GazEoL, we recognize and embrace the inherent complexities of anaerobic digestion, which has guided the development of a dynamic AI framework that thoughtfully combines advanced machine learning with deep scientific insight.
Informed by our contributions to peer-reviewed research, our approach enables us to generate nuanced, reliable predictions—carefully adapted to the specific conditions and character of each biogas facility.