[Peter]

Thank you for the detailed response @gazeol-renouvelable_hatem.

There are a lot of things we agree on.

1. Whole heartedly agree that “a structured, historical data repository is essential for consistent operational knowledge transfer.” Data discipline is critical and when AI tools help operators get there that is a good thing.

2. Agree that “a historical database supports standardization and benchmarking. With structured records, operators can compare performance across years, seasons, or feedstock shifts, and refine their strategy accordingly.”

Could even add that this data makes the facility more valuable and scalable because it grants a greater understanding of how process changes can affect digester performance.

This data discipline and gathering a well contextualized multiparameter data set over years would be valuable to many operating digesters and is valuable to the full-scale digesters and fleets that we work with to help implement robust monitoring protocols.

In cases of aviation, finance, and weather forecasting there are many well instrumented parameters monitoring a very large number of analysed processes. For existing biogas plants with largely manual data sets frequently only tracking a few parameters like pH, temperature, and tonnes fed per day, there may not be a signal to train a predictive model on.

Once a plant has been collecting a robust data set for several years, I think the potential for using AI tool to learn insights, potentially optimize, and assess future possibilities is exciting.

I remain skeptical about the necessity of AI tools for digester operation. Many full-scale digesters run well with just the detailed process monitoring that you describe, and without AI tools. Think that owners should carefully consider the cost and value of such tools after they have implemented the process monitoring and data handling practices needed to reduce their risk.