Federal Grant Award Summary

Takachar LIMITED received a $305,000 Phase I Small Business Innovation Research (SBIR) award from the National Science Foundation's Technology, Innovation, and Partnerships (TIP) Directorate (CFDA 47.084), effective July 15, 2026, with completion targeted for December 31, 2027. The award supports the development of physics-informed neural networks (PINNs) to optimize monitoring and operational control of small-scale thermochemical reactor arrays. Takachar will establish the feasibility of physics-constrained machine learning methods that can infer unmeasured internal reactor states and support operational decision-making in secondary organic materials processing systems by extracting actionable insights from sparse, noisy, and potentially corrupted sensor data collected from an actual fleet of deployed chemical reactors.

The technology addresses a critical operational challenge in distributed energy and manufacturing systems by enabling more efficient conversion of secondary organic materials into value-added products while reducing manual intervention and operational costs. The intellectual contribution includes development of learning frameworks that embed physical constraints directly into machine learning models, allowing them to generalize across varying operating conditions while remaining robust to imperfect data—a capability where conventional data-driven approaches typically fail. Beyond commercial applications, the project supports national priorities in economic development, energy independence, and resource management, with potential secondary benefits for wildfire risk mitigation.