PHYSICAL AI · AUTONOMOUS INDUSTRIAL INTELLIGENCE
· First-principles Physical AI that builds Equipment Agents and Process Agents for mineral processing, metallurgy, and biopharmaceutical industries, enabling closed-loop optimal control.
// WHY PHYSICAL AI
Cannot reliably solve governing equations. High compute demands and high latency do not meet industrial closed-loop control requirements.
CFD/FEM is offline, open-loop, single-shot. Relies on manual data collection → offline solving → manual PLC setpoints, lacking online autonomy.
First-principles constraints + online learning + safety barriers. Each equipment unit / production line carries an independent closed-loop cognitive engine with millisecond-level self-tuning.
Physical conservation laws provide prior constraints, compressing the feasible solution space by 10²–³×. New equipment requires as few as single-digit operating conditions for parameter identification and controller initialization.
Physical equations provide structural invariants across operating domains. The agent needs only online identification of a few boundary parameters to transfer to unseen operating regimes without retraining.
Every decision traces back to physical laws rather than statistical correlations. Control Barrier Functions (CBF) enforce hard constraints guaranteeing safety boundaries are never violated under any operating condition.
9 proprietary dataset categories · 3 industries · 6,000+ batch-level data · PHORCE SDK 26 architectures
Explore our established technical depth// PRODUCT ARCHITECTURE
From autonomous cognition for a single equipment unit to system-level optimization across entire production lines.
Equipment Agent (EA)
The autonomous cognitive brain for a single equipment unit. Embeds thermodynamics, fluid mechanics and mass transfer kinetics into edge computing nodes, enabling equipment-level closed-loop autonomous perception, reasoning and control.
Physics-Constrained Perception — PINN multi-modal soft sensing, online inference of unmeasurable state variables
Online Parameter Identification — Dynamic inverse estimation of physical coefficients under conservation law constraints
Safe Closed-Loop Control — MPC optimal trajectory planning within CBF safety envelopes
Process Agent (PA)
The full-process autonomous optimization brain for complete industrial processes. Embeds Physical AI into penicillin fermentation, anode furnace refining, slag-grinding-flotation and other multi-unit continuous processes, enabling process-level closed-loop optimal scheduling.
Full-Process Coordinated Scheduling — Cross-stage multi-agent consensus, eliminating inter-stage bottlenecks
Coupled Material–Energy Balance — Online tracking of coupled mass conservation and energy conservation constraints
Multi-Objective Pareto Optimization — Dynamic Pareto frontier approach for yield–energy–quality trade-offs
// COGNITIVE ARCHITECTURE
Fusing the determinism of physical laws with the learning capability of neural networks, continuously self-iterating within safety boundaries.
Fuses temperature, vibration, flow and other multi-source sensor signals to infer in real time the critical process variables that cannot be directly measured — such as slurry fineness, fermentation respiratory quotient, and melt temperature distribution in the furnace.
INPUT
Temp · Vibration · Flow · Pressure
OUTPUT
Unmeasurable Core State Variables
// PERFORMANCE VALIDATION
+700%
vs. classical deep learning baselines
-52%
Phase trajectory convergence rate
<1ms
Edge compute direct-to-PLC
// AGENT INSTANTIATION LIBRARY
Modular encapsulation of physical equations, building a rapidly deployable equipment agent matrix.
// INDUSTRIAL APPLICATIONS
Equipment Agents and Process Agents autonomously operate across four industrial scenarios, upgrading offline analysis to real-time closed-loop control.
// CO-DEVELOPMENT PROGRAM
We invite partners in metallurgy, chemical processing, mineral processing and heavy industry to jointly validate the on-site control capabilities of Physical AI Agents.