AWS empowers energy companies to accelerate sustainable biofuel supply chains – Amazon Web Services (AWS)

Report on Leveraging Cloud Technology for Sustainable Fuel Supply Chains

Introduction

Cloud technology plays a pivotal role in enabling sustainable energy pioneers to accelerate their transition to sustainable fuels, while maintaining operational excellence and competitive advantage in the evolving energy landscape. This report explores how energy companies utilize Amazon Web Services (AWS) to revolutionize their supply chains for sustainable fuels, with a focus on hydrotreated vegetable oil (HVO).

Alignment with Sustainable Development Goals (SDGs)

• SDG 7: Affordable and Clean Energy – Promoting sustainable biofuels as alternatives to fossil fuels.
• SDG 9: Industry, Innovation, and Infrastructure – Leveraging cloud and AI technologies to optimize supply chains.
• SDG 12: Responsible Consumption and Production – Ensuring compliance with sustainability certifications and traceability.
• SDG 13: Climate Action – Reducing greenhouse gas emissions through biofuel adoption.

Objectives for Energy Companies

1. Ensure compliance with industry certifications and regulations such as REDCert, Roundtable on Sustainable Biomaterials (RSB), International Sustainability and Carbon Certification (ISCC), and California Low Carbon Fuel Standard (LCFS).
2. Optimize renewable fuel supply chains to enhance efficiency and sustainability.

Technologies Employed

• Geospatial Artificial Intelligence and Machine Learning (AI/ML)
• Internet of Things (IoT) Solutions
• Advanced Data Governance
• Data Analytics

Biofuel Overview

Biofuel is derived from biomass such as plants and biodegradable waste from agriculture, households, or industry. It generally produces fewer greenhouse gas (GHG) emissions compared to fossil fuels. HVO, a second-generation biofuel made from renewable waste materials, is functionally similar to fossil diesel, allowing direct replacement without infrastructure changes.

Environmental Benefits of HVO

• 2%–25% reduction in nitrogen oxides (NOx) emissions
• 50%–80% reduction in particulate matter emissions
• 60%–95% reduction in greenhouse gas emissions across the value chain

Biofuel Supply Chain

The biofuel supply chain can be simplified into key stages:

1. Farmers grow crops and ship to aggregators.
2. Aggregators supply feedstock to pre-treatment facilities.
3. Pre-treatment plants produce intermediate products (e.g., vegetable oil for HVO).
4. Biofuel refineries produce the final biofuel product.
5. Distribution to fueling stations.

Specialized collectors and processors also handle waste oils and animal fats as feedstock. Visibility and tracking across this supply chain are essential for regulatory compliance and operational efficiency.

Challenges in the Biofuel Supply Chain

• Manual Land Assessment: Time-consuming, costly, and unscalable processes for assessing land suitability, especially in remote regions.
• Track and Trace Limitations: Reliance on paper-based and disjointed data systems causes visibility gaps and compliance risks.
• Data Silos: Operational data trapped in isolated systems limits remote access and analytics.
• Inefficient Supply Chain: Lack of integration and visibility leads to quality degradation and delivery delays.

AWS Architecture for Sustainable Fuel Supply Chains

The AWS architecture addresses these challenges by integrating advanced technologies:

Key AWS Services and Technologies

• Amazon SageMaker AI: Builds, trains, and deploys AI/ML models using geospatial data and satellite imagery to automate land assessment and sustainability compliance.
• AI/ML Use Cases:
  • Abandoned land detection
  • Low sodium, carbon, and erosion land detection
  • Polygon and crop classification
  • Vegetation indices analysis
• Amazon Textract: Extracts data from reports such as feedstock quality and soil analysis.
• Amazon Bedrock: Provides large language model access and agentic workflows to assist knowledge workers.
• AWS IoT SiteWise: Collects and analyzes live and historical operational data from industrial equipment to optimize production and maintenance.
• Amazon Managed Blockchain: Enables immutable, decentralized traceability of products throughout the supply chain with smart contracts and real-time tracking.
• Data Lakehouse: Centralized repository managing industrial data, earth observation data, feedstock quality, and enterprise application data to enable comprehensive analytics.
• Amazon SageMaker Unified Studio: Unified development environment for data analytics and AI model building.
• AWS Supply Chain: Provides end-to-end visibility, AI-powered insights, and risk mitigation recommendations.

Benefits of AWS Adoption in Sustainable Fuel Supply Chains

• Accelerated Land Assessment: Reduces assessment time from months to weeks by automating identification of suitable lands.
• Cost Reduction: Minimizes soil sample analyses and optimizes use of agronomists, lowering operational expenses.
• Enhanced Compliance: Automates sustainability KPI computation, reducing compliance time from months to days with improved accuracy.
• Production Optimization: Uses AI/ML to maximize output of intermediate products and biofuels.
• Improved Traceability: Enables self-auditing and transparent reporting, reducing traceability reporting time from months to days.

Conclusion

Energy companies engaged in sustainable fuel production are encouraged to evaluate and optimize their supply chain processes by leveraging AWS cloud technologies. Integrating data across systems facilitates real-time decision-making and regulatory compliance, advancing the transition to sustainable energy in alignment with the Sustainable Development Goals.

Whether managing the entire supply chain or specific segments, AWS provides scalable solutions to transform sustainable fuel supply chains effectively.

For further information, contact an AWS Representative.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 7: Affordable and Clean Energy
   • The article focuses on sustainable fuels, particularly biofuels like hydrotreated vegetable oil (HVO), which contribute to cleaner energy sources.
2. SDG 9: Industry, Innovation and Infrastructure
   • Use of advanced technologies such as AI/ML, IoT, blockchain, and cloud computing to optimize the biofuel supply chain and industrial processes.
3. SDG 12: Responsible Consumption and Production
   • Emphasis on sustainable biofuel production, supply chain transparency, and compliance with certifications and regulations.
4. SDG 13: Climate Action
   • Reduction of greenhouse gas emissions through the use of biofuels and decarbonization efforts.
5. SDG 15: Life on Land
   • Land assessment using geospatial AI/ML to avoid using degraded or unsuitable lands and to promote sustainable land use.

2. Specific Targets Under Those SDGs Identified

1. SDG 7: Affordable and Clean Energy
   • Target 7.2: Increase substantially the share of renewable energy in the global energy mix.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.
3. SDG 12: Responsible Consumption and Production
   • Target 12.2: Achieve the sustainable management and efficient use of natural resources.
   • Target 12.6: Encourage companies to adopt sustainable practices and to integrate sustainability information into their reporting cycle.
4. SDG 13: Climate Action
   • Target 13.2: Integrate climate change measures into national policies, strategies, and planning.
5. SDG 15: Life on Land
   • Target 15.3: Combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world.

3. Indicators Mentioned or Implied to Measure Progress

1. Greenhouse Gas Emissions Reduction
   • Reduction percentages in GHG emissions (60%–95%) across the biofuel value chain as a measure of decarbonization effectiveness.
2. Air Pollutant Emissions
   • Reduction in NOx emissions (2%–25%) and particulate matter emissions (50%–80%) as environmental benefit indicators.
3. Land Assessment Metrics
   • Use of geospatial AI/ML indexes such as NDVI (Normalized Difference Vegetation Index) and SIPI (Structure Insensitive Pigment Index) for vegetation and land quality assessment.
   • Detection of abandoned land, erosion levels (using models like RUSLE, SIMWE, USPED).
4. Supply Chain Traceability and Compliance
   • Tracking and traceability metrics enabled by blockchain and IoT technologies to ensure compliance with certifications (REDCert, RSB, ISCC, LCFS).
   • Compliance KPIs automated by AI/ML for sustainability reporting and auditing.
5. Operational Efficiency
   • Production optimization metrics from AI/ML models monitoring industrial processes and asset conditions.
   • Reduction in time and cost for land assessment, compliance reporting, and traceability reporting.

4. Table: SDGs, Targets and Indicators

Source: aws.amazon.com