[Peter]

Would like to push back on the need for AI tools. We work with several the food waste digesters and manure/food waste co-digestion facilities that receive a complex feedstock. These plants are able to predict daily gas production to within 5% with only basic lab testing and excel based tools used by the operators on site.

AI tools still need to get feedstock characteristics and digester health indicators from somewhere and conventional chemical tests currently produce more reliable results than inline spectroscopy-based tools or industry averages pulled from a database.

Additionally, a facility’s knowledge of the local feedstock landscape, how to handle them, and how to best operate their digester is part of the facility owners IP that can help them hold a competitive advantage. Plant owners might not want their operational lessons and data to be used to train the competition.

Finally, the currently available inline sensing technologies cannot screen for all possible toxins and contaminants in feedstock. Without a perfect sensor, plants still need well trained and knowledgeable operators and regular lab testing to make informed decisions about receiving new feedstocks. Since you need a skilled operator checking the AI’s work – I am not sure how investing in such a tool is expected to justify the considerable cost.

Would be happy to be educated about cases where AI tools have generated additional revenue for digesters that are performing well.

Thanks