[Hatem]

Thank you for sharing your perspective. I respectfully disagree with your assessment, as it underestimates both the current capabilities and the future potential of AI tools in anaerobic digestion (AD) operations. Your comments reflect a conventional, lab-centered view of process management, which does not scale effectively to the operational complexities of commercial facilities.

First, scaling up from lab tools and Excel-based forecasting to full-scale plant operations is not a linear process—they are fundamentally different worlds. A commercial digester processing tons of heterogeneous feedstock per day, with fluctuating input composition, operational disturbances, and dynamic energy markets, demands a level of system-wide integration and predictive capacity that simply cannot be met with spreadsheets and manual inputs alone. What works in a stable academic or pilot setting does not generalize to the volatile conditions of real-world operations.

Second, the AD business does not operate like traditional chemical processing plants where one-size-fits-all lab routines suffice. We’re dealing with living systems that vary daily in microbial dynamics, temperature responses, substrate synergy, and inhibition potential. AI excels at learning patterns in such complex, non-linear systems—especially when combined with continuous or semi-continuous data acquisition.

Regarding your valid point that AI needs data from conventional tests—yes, absolutely. But the value of AI is in how it connects, interprets, and predicts using these data streams. It doesn’t replace chemistry or expertise—it augments them.

On the concern that plant owners might not want to share their data for training purposes: that is a business model and data governance issue, not a limitation of the technology. Models can be customized per facility and trained solely on their own data to protect IP while still yielding substantial insights. AI is not just for optimizing yield; it’s about risk mitigation, early fault detection, and predictive maintenance—key to uptime and revenue protection.

Also, the argument about sensing technologies not being perfect is fair but misses the point: AI doesn’t depend on a single sensor. Its strength is in integrating multiple imperfect signals to yield a robust forecast or recommendation. It’s the same principle we trust in aviation, finance, and weather forecasting.

Finally, and critically:

1. Building a structured, historical data repository is essential for consistent operational knowledge transfer. This ensures that insight doesn’t walk out the door when a staff member leaves. AI or not, data discipline adds resilience to your operation.

2. 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. It’s not only about AI; it’s about professionalizing operations and making informed decisions with traceability.

In short, AI tools are not a silver bullet, but to dismiss them based on lab-scale practices and assumptions about operator skill levels is to ignore the operational reality and business potential of modern digesters. We’re not replacing people—we’re giving them better tools to compete in a changing energy and waste landscape.

Would be glad to continue the conversation and share some concrete examples where data-driven models have made a measurable difference in well-performing plants.

Note:
I’ve attached a quick example from one of our applications, LimusAI, that shows how AI can precisely identify which features have the greatest impact on biogas production at a given site. This level of insight empowers operators with targeted, data-backed decisions that go far beyond what’s possible with manual tools alone.