Renewable Natural Gas (RNG), also known as biomethane, transforms organic waste into a grid-ready, low-carbon fuel. But turning food scraps, manure, and municipal waste into pipeline-grade gas is a complex and sensitive process.
AI is now critical for unlocking RNG’s full potential – boosting methane yields, optimizing digestion, and automating carbon intensity tracking from feedstock to fuel tank.
🎯 How AI Can Make This Product or Solution Much Better
🧪 Feedstock Quality & Methane Yield Prediction
AI uses spectral and sensor data to estimate the biochemical methane potential (BMP) of organic inputs—manure, MSW, crop waste, food scraps.
This enables smart feedstock blending and pre-treatment to avoid microbial imbalance and ensure optimal biogas production.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist🔁 Anaerobic Digestion (AD) Optimization
AI continuously controls digester temperature, C:N ratio, retention time, and pH, adjusting based on real-time microbial and gas production data.
Reduces risks like foaming and acidification while maximizing methane yield—automating a highly sensitive process.
🧼 Biogas Upgrading to RNG
AI enhances CO₂, H₂S, and moisture removal using real-time monitoring of membrane fouling and pressure levels.
Ensures pipeline-grade biomethane purity (≥ 96% CH₄) with minimal energy consumption and downtime.
📉 Carbon Intensity (CI) & Lifecycle GHG Tracking
AI quantifies methane leakage, parasitic energy use, and digestate emissions, automating MRV (Monitoring, Reporting, Verification) for LCFS, RFS, and carbon credit schemes.
Supports traceable, auditable RNG with verified net-negative emissions.
🚚 Smart Feedstock Supply & Logistics
AI predicts biomass supply by location and season, optimizes collection and routing, and synchronizes delivery to maximize gas output and minimize costs.
Especially useful for decentralized plants aggregating from farms, cities, or food processing sites.
🛠️ How AI Can Overcome Challenges
| Challenge | AI Solution |
|---|---|
| Feedstock variability | Predicts BMP and recommends ideal feedstock mix per batch |
| Anaerobic digester instability | Detects early warning signs and auto-corrects digestion environment |
| Fouling in upgrading units | Predicts membrane clogging, controls flows, and schedules proactive maintenance |
| CI reporting complexity | Tracks emissions, energy use, and methane slip across entire RNG value chain |
🤖 Main AI Tools and Concepts Used
- Predictive analytics for gas yield and CI scores
- Reinforcement learning for multi-variable process optimization
- Digital twins of digesters and upgrading systems
- Spectral ML models for feedstock classification
- GIS-integrated logistics optimization for feedstock supply
📊 Case Studies
- Nature Energy (Denmark):
Runs over 12 commercial RNG plants using AI for feedstock optimization, digester stability, and CI reporting. - Bright Biomethane (Netherlands):
Uses AI to control gas upgrading units and reduce membrane fouling. - Waga Energy (France):
Landfill RNG operator using AI to maximize methane recovery and optimize upgrading.
🚀 Relevant Startups & Providers
| Company | TRL | Focus |
|---|---|---|
| Nature Energy | TRL 9 | AI-optimized digester ops, feedstock blending, and CI scoring |
| Bright Biomethane | TRL 9 | Membrane-based upgrading systems with AI flow and purity control |
| StormFisher Hydrogen | TRL 8 | RNG + green hydrogen integration with AI for dispatch and emissions |
| Waga Energy | TRL 9 | Landfill gas-to-RNG specialist with predictive AI modeling |
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