Environment & Safety Gas Processing/LNG Maintenance & Reliability Petrochemicals Process Control Process Optimization Project Management Refining

AWS launches process optimization solution for downstream and midstream operations

Amazon Web Services announced today at the ADIPEC Exhibition and Conference the introduction of the Process Optimization solution for downstream and midstream operations. The Process Optimization solution uses artificial intelligence and machine learning  to provide timely and actional insights for engineers and operators.

Process optimization activities for downstream and midstream operations are often cumbersome and onerous due to scale and complexity, while existing workflows are heavily reliant upon legacy technologies, and disparate and disconnected tools. The Process Optimization solution can help overcome these challenges and drive operational enhancements by improving unit throughput, product quality and product yields, in addition to improving energy consumption.

The AWS Process Optimization solution is a cloud-native solution that uses innovative services like Amazon SageMaker to build, train, and deploy ML models, and AWS IoT TwinMaker to easily create digital twins of real-world assets. The AI-powered offering is built on a foundational data architecture for open-loop insights, predictions, and recommendations. Leveraging ML, the solution provides models and supporting infrastructure to infer suggested process changes. Artificial intelligence is used for higher level goal-oriented inference providing computer vision, conversational interfaces, and chatbots for improved information accessibility and insight detection. The Process Optimization solution’s digital twin simulation capabilities help users to gain a virtual representation of assets for process and visualization oversight. This allows operators to simulate proposed facility changes, streamline remote job planning, and re-optimize following unplanned upsets and events.

Related News

From the Archive



{{ error }}
{{ comment.name }} • {{ comment.dateCreated | date:'short' }}
{{ comment.text }}