Overview
ARBOK Digital Twin (ADT) is a virtual modeling system designed to create a real-time, data-driven replica of physical ARBOK installations (evaporative, chemical, or mechanical). The model simulates operational parameters, environmental conditions, system responses, and process efficiencies — all updated live from sensors and operational logs. Unlike generic digital twins, ADT is designed specifically for non-linear, multi-phase systems such as vacuum evaporation with variable humidity, phase separation under thermal drift, and fluid-solid interactions in crystallization chambers. The system is critical for scaling and monitoring ARBOK technologies deployed in remote or sensitive zones (deserts, offshore, disaster areas).
Applications
Predictive analytics for maintenance and failure prevention; process optimization through scenario simulations and parameter sweeps; remote diagnostics and operational control; training environments for operators without physical access. Applied to ARBOK installations in remote or sensitive zones — deserts, offshore, disaster areas.
Operating Principle
ADT operates using a multi-layer neural network with embedded physical constraints (physics-informed ML), integrating real-time field data with dynamic simulations. Sensor and operational-log data are streamed into a hybrid engine that couples machine learning with a parametric physics model, producing a live replica of the installation. Built-in anomaly detection compares current behaviour against baseline and historical traces and raises deviation alerts.
Key Parameters
| Parameter | Value |
|—|—|
| Update frequency | Every 0.2–2 seconds (sensor-dependent) |
| Latency | < 1.2 s for full-model refresh |
| Input data types | Flow rate, pressure, temperature, salinity, turbidity, voltage, current, TDS, VOCs |
| Modeling engine | Hybrid — ML + parametric physics model |
| User interface | Modular dashboard (browser or AR headset) |
| Anomaly detection | Built-in deviation alerts vs baseline and historical traces |
| Simulation modes | Real-time / predictive / diagnostic / design |
| Security | Encrypted edge-node sync + cloud replication |
Architecture and Components
Multi-layer neural network with embedded physical constraints; parametric physics model; sensor data acquisition layer; modular dashboard interface (browser or AR headset); anomaly detection module; encrypted edge-node synchronization with cloud replication.
Advantages
Purpose-built for non-linear, multi-phase systems (vacuum evaporation with variable humidity, phase separation under thermal drift, fluid-solid interactions in crystallization chambers) rather than generic industrial assets. Enables predictive maintenance, process optimization, remote diagnostics, and operator training without physical access to the installation.
Integrations
ARBOK-OASIS · ARBOK-CRYSTALLIZER · Waste Heat Capture · Mobile Evaporator Units · Hybrid Sensors · Water Forecast AI
Deployment & Operation
Partial deployment in pilot ARBOK modules (OASIS, Crystallizer) under review. Integration with predictive control loops and failure prediction underway. Full deployment expected in 2026.
TRL
TRL 5–6 — Digital twin validated in testbeds with real-world sensor data.
Market Potential
[требует уточнения из базы]
Typical Project Economics
CAPEX и OPEX по проекту не ведём — считаются под конкретную площадку.
Risk Factors
[требует уточнения из базы]
Related Technologies
ARBOK-OASIS · ARBOK-CRYSTALLIZER · Waste Heat Capture · Mobile Evaporator Units · Hybrid Sensors · Water Forecast AI