[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