Process Optimization
Using Artificial Intelligence and Machine Learning to provide timely and actionable insights for engineers and operators.
Process optimization activities are often cumbersome and onerous because of the scale and complexity of sites. Existing workflows are heavily reliant on legacy technologies, disparate and disconnected tools, and there is an extreme drive to find fit-for-purpose solutions to drive operational excellence.
The Process Optimization solution can drive operational improvements in the following ways:
- Improving unit throughput, product quality, product yields
- Improving energy consumption and GHG emissions
- Re-optimizing after unplanned upsets and events
The AWS Process Optimization solution is a cloud-native, AI-powered offering built on a foundational data architecture for open-loop insights, predictions, and recommendations.
Machine Learning (ML)
Models and supporting infrastructure to infer suggested process changes.
- Improving unit throughput
- Providing predictable product qualities
- Maximizing product yields
Artificial Intelligence (AI)
Higher level goal-oriented inference providing computer vision, conversational interfaces, and chatbots for improved information accessibility and insight detection.
- Improving energy consumption and GHG emissions
- Reducing risk for the field workforce
Digital Twin Simulations
Virtual representation of assets for process visualization and oversight.
- Simulating proposed facility changes
- Streamlining remote job planning
- Reoptimizing after unplanned upsets and events
Customer References
Cepsa used AWS in its chemical plant in Huelva, Spain, so it could better monitor and optimize the production process for phenol, a raw material used to manufacture different types of polycarbonates, nylon, medicines or insulating materials. The system now provides Cepsa engineers with recommendations on how to balance raw materials, energy use, and output, resulting in increased phenol production, while carbon dioxide emissions decreased by 1,500 metric tons per year.
Technical Machine Learning Blogpost: Read how Cepsa industrialized their ML projects to operate their models at scale >>
How to get started
Discover
- Define use case
- Qualify business value
- Readiness and maturity assessment
- Align on data strategy and requirements
Align
- Define architecture and models
- Define risks and mitigations
- Design technical interfaces
- Define effort, resources, and timeline
- Build, deploy, and test solutions
Launch
- Implement solution
- Test and optimize
- Measure results and business value
- Build roadmap for scaling
Visit the AWS Solutions Library so you can learn how to get started with Process Optimization and other solutions for the energy industry.
Solution Partners
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