Introduction:

Static digital twins are no longer enough to meet today’s operational challenges. Aventus AI’s Dynamic, Closed-Loop RTDT integrates AI-driven predictive analytics, IoT data, and multi-physics simulations into a hyper-scalable, real-time solution tailored to any industry vertical.

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Industry Application

Sector: Cross-industry (Energy, Manufacturing, Infrastructure, Healthcare, Oil & Gas, Smart Cities)

Use Case: Dynamic and customisable closed-loop digital twin for real-time optimisation.

The Challenge

• Existing digital twin solutions are limited by static models, scalability issues, and lack of adaptability.
• Industries require tailored, scalable solutions to handle dynamic, nonlinear processes and real-time feedback loops.

Global Market Opportunity

The global digital twin market is expected to grow from $16.75 billion in 2023 to over $150 billion by 2030, driven by demand for predictive, adaptable, and scalable solutions.

Solution

Aventus AI’s Dynamic, Closed-Loop RTDT provides:

1. Hyper-Scalability: Capable of handling any asset scale, from single facilities to interconnected systems across regions.
2. Customisable Cores: Tailored digital twin frameworks for any target vertical.
3. Real-Time Adaptability: Dynamic feedback loops powered by AI-driven predictive analytics.
4. Advanced Simulations: Multi-physics simulations for operational and risk optimisation.
5. Full Enterprise Integration: Seamless compatibility with SCADA, ERP, and BMS systems.

Key Features:

• Real-Time Data Collection: IoT-enabled sensors feed data streams into the twin.
• Multi-Physics Simulation: Simulates and predicts system behaviours under various conditions.
• Autonomous Adjustments: AI dynamically optimises parameters like temperature, flow rates, and energy usage.
• Scalable Frameworks: Adaptable to evolving industry requirements and asset expansions.

Execution Plan

Phase 1: Assessment and Design

• Workshops to identify specific operational challenges and integration needs.

Phase 2: Data Integration and Model Development

• Deploy IoT systems and train AI models on historical and real-time data.

Phase 3: Deployment and Validation

• Implement and validate closed-loop systems in live environments.

Phase 4: Continuous Improvement and Scaling

• Real-time optimisation using AI-driven feedback and scenario testing.

Return on Investment (ROI)

• Efficiency: Up to 30% improvement in operational efficiency.
• Downtime Reduction: Predictive maintenance reduces unplanned outages by 40%.
• Sustainability: Decreases emissions and energy consumption by 20-30%.

Regulatory Standards

• ISO 55001: Asset management standards.
• ISO 27001: Data security standards.
• Industry-Specific Compliance: Adapted to meet regulations for targeted verticals.

Outcome

• Dynamic, scalable, and adaptive digital twin ecosystems.
• Improved asset reliability, operational efficiency, and sustainability.

Optimise your operations with Aventus AI’s RTDT—Contact us today.