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Guidance for Downstream Process Optimization on AWS

Overview

This Guidance is a cloud-native, fully customizable, artificial intelligence (AI) offering that provides insights, predictions, and recommendations for hydrocarbon manufacturers. Downstream refineries and petrochemical facilities are complex in infrastructure, constantly working under tightened margins subject to volatile market prices. By providing timely, transparent, AI-driven insights for engineers and operators, this Guidance helps to improve efficiencies for cross-unit processes and systems, so energy manufacturers can maintain safe, reliable, and profitable performance.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This Guidance helps you to understand the health of your workloads and operations through the use of Amazon SNS, a fully managed application-to-application (A2A) and application-to-person (A2P) service. Amazon SNS notifications can be sent directly to operators, allowing for near real-time visibility into events requiring attention. 

Read the Operational Excellence whitepaper 

In this Guidance, data is protected both in-transit and at rest. Data in-transit is encrypted and data at rest backed by the Amazon S3 Service Level Agreement which is a policy that includes features like S3 Object Lock and Replication. With S3 Object Lock, you can store objects and prevent them from being deleted or overwritten. Replication allows you to replicate objects across Amazon S3 buckets and fail over to a bucket in another Region, ensuring that when the data is written to the S3 bucket that traffic fails over to, the data is replicated back to the source.  

Read the Security whitepaper 

SageMaker, AWS IoT TwinMaker, and Amazon Rekognition Custom Labels are services and features that are implemented in this Guidance to support a reliable, application-level architecture. SageMaker helps you build and deploy machine learning models, whereas AWS IoT TwinMaker allows you to create digital twins of your industrial equipment to improve equipment performance. And with Amazon Rekognition Custom Labels, you can easily spot objects in images specific to your business needs, such as machine parts in an assembly line. 

Read the Reliability whitepaper 

The foundational AI/ML models in Amazon SageMaker can leverage Provisioned Concurrency for high scalability and predictive performance to meet high throughput demands. With provisioned concurrency, your serverless endpoints can instantaneously respond to bursts in traffic up to the pre-defined amount of provisioned concurrency. 

Read the Performance Efficiency whitepaper 

The Guidance prioritizes serverless AWS services including AWS IoT Core, AWS IoT TwinMaker, and Amazon Rekognition to provide automated scalability within a consumption-based pricing model. AWS IoT Core helps you securely connect and manage devices without you having to provision servers. AWS IoT TwinMaker creates digital twins of your existing Internet of Things (IoT) data without needing to move or reingest your data to another location. And with Amazon Rekognition, you can quickly add pre-trained APIs to your applications without having to build machine learning models from the beginning.  

Read the Cost Optimization whitepaper 

Amazon S3 is one of the technologies that support data access and storage patterns. Industrial data is stored in Amazon S3 with storage class analysis providing insights into the frequency of the accessed objects. This allows you to transition objects to the appropriate Amazon S3 object storage class for optimized performance and sustainability.

Read the Sustainability whitepaper 

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.