Haniwis™
Designed for deployment on NVIDIA® Jetson Orin™ edge modules. Engineered by Vantimond for demanding industrial fluid systems.
Driven by constrained Deep Reinforcement Learning and physics-informed models, Haniwis is designed to move chemical dosing from reactive control toward predictive, multi-variable optimization. Edge processing keeps high-frequency telemetry close to the process while deterministic controls retain final authority.
Core Technology
Each layer is designed for local operation, traceable decisions, and integration with existing process hardware.
Designed around NVIDIA Jetson Orin modules and JetPack SDK. The reference target supports configurations up to 100 TOPS, subject to the selected module, power mode, model, and validated deployment profile.
Local inference reduces continuous cloud dependence. It does not by itself guarantee zero latency or absolute security.Selected fluid-dynamics and reaction-kinetics relationships can be introduced as training constraints, helping models remain consistent with the defined engineering domain.
Online bounds, confidence checks, and deterministic fallback remain mandatory.Connects dosing pumps, lined control valves, digital actuators, and process sensors through a versioned equipment model for real-time state and condition monitoring.
Predictive warning horizons are a validation target and depend on field data and labeled failure history.Control Architecture
The supervisory policy operates inside approved process limits. PLC logic, actuator interlocks, operator authority, and safe fallback modes remain part of the final control path.
Normalize telemetry and verify input quality.
Estimate current process state at the edge.
Propose bounded pump and valve set-points.
Apply deterministic limits and fallback logic.
Vertical Markets and Roadmap
Transfer learning can reduce development time, but every process requires its own operating envelope, baseline comparison, and safety review.
PAC and PAM dosing under variable influent conditions. Program target: assess a 15%-25% chemical-cost reduction against a tuned baseline. Results remain subject to pilot validation.
Adaptive closed-loop control for retention chemistry and white-water variability.
Robust supervisory control within approved strong-acid and strong-alkali operating boundaries.
Adaptive dosing and flow control for high-viscosity, non-Newtonian slurry conditions.
Technical Whitepaper
Review the concept architecture, validation gates, algorithmic framework, and field-integration boundary.
NVIDIA, Jetson, Jetson Orin, JetPack, CUDA, and TensorRT are trademarks or registered trademarks of NVIDIA Corporation. Reference to NVIDIA technology does not imply endorsement, certification, or partnership. Published performance targets require project-specific validation.