Advancing Autonomous Operations with AVEVA Dynamic Simulation and NVIDIA Raptor

Advancing Autonomous Operations with AI-Driven Solutions

Summary

The energy industry is witnessing a significant shift towards autonomous operations, driven by advancements in artificial intelligence (AI) and simulation technologies. AVEVA and NVIDIA have partnered to create an AI-driven solution that provides autonomous plant control through steady-state and complex transient conditions. This article explores how this collaboration is advancing autonomous operations and what benefits it brings to the energy sector.

The Challenge of Autonomous Operations

Autonomous operations in the energy industry involve managing complex systems with minimal human intervention. This requires sophisticated AI solutions that can handle various scenarios, including startups, unplanned disruptions, and changing feed levels. Traditional automation systems often fall short in these situations, necessitating a more advanced approach.

AVEVA and NVIDIA Partnership

AVEVA and NVIDIA have joined forces to develop an AI-driven solution for autonomous plant control. This partnership leverages AVEVA’s Dynamic Simulation, optimized to run on NVIDIA GPUs, along with the Raptor reinforcement learning engine and other AVEVA software technologies. The result is a cutting-edge solution that can handle both steady-state and complex transient conditions.

Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is a key technology in this partnership. DRL agents are trained using accurate digital process twins, allowing them to learn from simulations and apply this knowledge in real-world scenarios. This approach enables the transition from Level 3 to beyond Level 4 control autonomy, where systems can operate with minimal human intervention.

Benefits of Autonomous Operations

The adoption of autonomous operations brings several benefits to the energy industry:

• Enhanced Safety: Reduces human exposure to hazardous environments.
• Increased Operational Efficiency: Optimizes production and equipment use.
• Cost Reduction: Lowers labor and maintenance costs.
• Improved Decision-Making: Provides AI-driven insights for better accuracy and performance.
• Environmental Sustainability: Optimizes resource usage and reduces emissions.
• Scalability: Enables efficient management of large or remote operations.

Case Study: Autonomous Plant Control

The AVEVA and NVIDIA solution has been applied in various scenarios, including startups and unplanned disruptions. By using DRL agents trained on accurate digital twins, the system can stabilize product quality in shorter times than human operators, minimizing the production impact of plant upsets.

Challenges and Future Directions

While autonomous operations offer significant benefits, there are challenges to be addressed. For instance, the nature of human actions on autonomous systems differs from those in existing systems, requiring new approaches to human reliability analysis. Ongoing research aims to better evaluate tasks in autonomous systems and provide more realistic simulation results.

Table: Comparison of Autonomous and Existing Systems

Table: Benefits of Autonomous Operations

Conclusion

The partnership between AVEVA and NVIDIA marks a significant step forward in advancing autonomous operations in the energy industry. By leveraging AI-driven solutions and deep reinforcement learning, this collaboration offers a robust platform for managing complex systems with minimal human intervention. As the industry continues to evolve, the benefits of autonomous operations, including enhanced safety, increased efficiency, and environmental sustainability, will become increasingly important.