[Hatem]

Thank you Natalia for the excellent question.

I think that Microbiology and Artificial Intelligence (AI) can work synergistically to optimize and revolutionize anaerobic digestion (AD) processes in several impactful ways. However, leveraging AI effectively requires a deep understanding of the microbiological and kinetic aspects of the system:

1- Deep Understanding of AD Compartment Interactions

An AI engineer must first gain a solid grasp of the biological and chemical interactions between the compartments of the AD process — hydrolysis, acidogenesis, acetogenesis, and methanogenesis. Each phase involves distinct microbial communities and specific metabolic pathways. AI models built without this understanding risk oversimplification, which can lead to misleading predictions or poor control strategies.

2- Awareness of Kinetic Dynamics Across Phases

Each phase of AD operates at different kinetic rates. For instance, hydrolysis (especially of complex lignocellulosic substrates) is often the slowest step, while acidogenesis can proceed relatively quickly once soluble compounds are available. Recognizing these differences helps AI systems account for temporal lags and bottlenecks in biogas production, enhancing both prediction and control algorithms.

3- Experimental Validation with Substrate Characteristics

Before building robust AI models, it is essential to conduct lab-scale experiments (or have historical data) to understand how varying substrate characteristics — such as C/N ratio, particle size, or biodegradability — affect microbial activity and gas yield. These empirical insights feed valuable input data to train and validate AI models.

4- Microbial Growth Rate Modeling

Effective AI systems should integrate established microbiological kinetics, such as Monod, Haldane, or Contois models. These equations differ in how they represent substrate inhibition or nutrient limitation, and their selection affects the accuracy of growth rate predictions under dynamic loading conditions.

5- AI-Driven Optimization and Control

Once trained with high-quality biological, kinetic, and operational data, AI algorithms — such as machine learning or reinforcement learning models — can be deployed to:

— Optimize feedstock mixtures for maximum biogas yield

— Predict system failures or process upsets before they occur

— Provide real-time operational recommendations (e.g., adjusting organic loading rates)

— Reduce downtime and improve reactor stability through adaptive control

Thanks you

[Madiha]

Thank you for the question, Natalia! We can definitely talk about how AI plays an important role when it comes to biogas and RNG projects.

Before investing in or starting a biogas project, it is essential to assess whether it is both technically and financially viable. Traditional studies can take months or even years, requiring manual calculations and assumptions that may not even be entirely accurate. This is where our software suite comes into play.

• Anessa AD A quickly analyzes inputs, financial considerations, and outputs needed to confidently predict biogas project feasibility.

• The software helps you account for what-if scenarios and risks by letting you explore multiple scenarios so that you can plan ahead for variables such as tipping fees, transportation costs, quantity and quality of feedstock, and energy prices.

• Anessa AI identifies potential risks and opportunities to maximize return on investment.

Once the roadmap is established, the next challenge is to design an efficient system that will maximize biogas yield and reduce operational costs.

• AI-driven simulations like Anessa AD•O (https://www.anessa.com/anessa-ado) allow us to test different scenarios, feedstock combinations, and optimize plant layout and process parameters.

• The predictive nature of these products helps forecast the best feedstock mix for sustained biogas production.

• Anessa’s digital twin technology allows you to test and refine your biogas project in a virtual environment before breaking ground, saving you from costly mistakes. In an industry where a single miscalculation can lead to massive financial setbacks, delays, or even operational failures, having the ability to predict outcomes and optimize designs ahead of time and before spending resources is a game-changer. By identifying potential issues before they become real-world problems, Anessa helps you build smarter, faster, and with confidence.

Once the design of the efficient biogas system is identified, the next challenge is operating a biogas plant efficiently, which requires real-time and continuous monitoring, as well as quick decision-making. Equipment failures, feedstock inconsistencies or environmental fluctuations can impact performance and profitability in expensive ways that can be easily mitigated with reliable biogas monitoring

• Anessa AD•M (https://www.anessa.com/anessa-adm) continuously monitors plant performance and provides AI-generated recommendations for any adjustments.

• It detects anomalies in gas production, temperature, pH and pressure, preventing breakdowns before they even happen.

• The platform also receives alerts and provides suggested corrective actions to help operators maintain the safety of their plant operations.

As the demand for clean and renewable energies increases and biogas projects scale, compliance with government regulations and sustainability goals becomes critical. Tracking emissions, reporting and lifecycle assessments require precise data management.

How Anessa AI helps:

• AI automates compliance tracking, reducing the burden of manual documentation.

• Data analytics help organizations meet carbon credit requirements and maximize sustainability.

• AI tools simplify regulatory submissions, making it easy to adhere to environmental laws.

Please feel free to reach out to us if there are any questions related to AI and our suite of products.

[Madiha]

Adding more to the above conversation (to keep track of it here):

Anessa’s AI-powered biological and digestate modelling offers a comprehensive, data-driven approach to understanding the core biological processes within biogas production. By simulating the conditions inside the digester, this functionality allows operators to model microbial activity, nutrient flows, and the composition of digestate under various scenarios.

The platform helps predict how factors like feedstock type, temperature, and pH can affect the microbial community and overall biogas yield. By accurately forecasting how changes in feedstock or digester conditions might impact both gas production and digestate quality, Anessa ensures plant operations are continuously optimized for maximum efficiency and sustainability.

Additionally, the digestate modeling component aids in maintaining the nutrient balance by simulating the nutrient content of digestate, helping operators avoid common issues like nutrient imbalances that could affect plant performance or the potential reuse of digestate in agriculture. This AI-driven functionality empowers operators to anticipate potential disruptions, adjust processes proactively, and ensure long-term operational stability in biogas production.

[Hatem]

Passive monitoring or active parametrisation?

We often use sensitivity analysis to understand how key input variables—like feedstock variability or temperature shifts—impact model outputs and system performance. We’ve built tools and methods, such as the Sobol method, to help identify the most influential parameters in different operational scenarios.

Thanks!