Green Fuel Intelligence System
Unlocking India's Biogas Potential
A Strategic Roadmap for AI-Driven Transformation and Commercialization
Pioneering AI-driven digital infrastructure to revolutionize India's biogas sector, optimizing the entire anaerobic digestion value chain from feedstock to lucrative carbon credits.
Chatake Innoworks Pvt. Ltd.
Project Team:
  • Ms. Tanishka Deshpande
  • Ms. Anushka Hitanalli
  • Ms. Aditi Gangji
  • Ms. Shruti Hiremath
© 2026 Chatake Innoworks Pvt. Ltd. All rights reserved.
Executive Vision
From Prototype to Critical Infrastructure
The global transition toward renewable energy has identified biogas as a pivotal component in decarbonization, particularly within agrarian economies like India. GFIS is conceptualized not merely as a monitoring dashboard but as a holistic, multi-layered digital infrastructure designed to optimize the anaerobic digestion value chain.
The system addresses critical inefficiencies plaguing the sector—process instability, feedstock variability, and logistical fragmentation—by integrating IoT, AI, and cloud computing into a cohesive three-tiered ecosystem.
Market Opportunity
₹24.8 billion annual revenue potential
5,000 plants targeted under SATAT initiative
15 MMT biogas production by 2030
The Three-Tiered GFIS Ecosystem
Layer 3: Plant Monitoring
Real-time telemetry from ATEX-certified industrial sensors tracking digester performance, gas quality, and process stability at individual facilities.
Layer 2: Regional Hubs
Logistics optimization managing complex waste supply chains, coordinating feedstock delivery, and aggregating data across plant clusters.
Layer 1: National Intelligence
Unified data fabric supporting algorithmic verification for carbon credits, policy formulation, and integration with government portals like GOBARdhan.
Bridging the Operational Technology Gap
Current biogas infrastructure operates in silos with limited data interoperability between digester control systems and business intelligence tools. GFIS creates a unified data fabric connecting all three layers.
1
Siloed Operations
Disconnected systems, manual reporting, no predictive capabilities
2
GFIS Middleware
Unified data layer, automated compliance, AI-driven optimization
3
Value Creation
Carbon credit verification, efficiency gains, regulatory compliance
Market Architecture
Regulatory Landscape and Policy Alignment
GFIS operates within a complex regulatory environment defined by evolving bio-energy policies and stringent data governance laws. Understanding this landscape is crucial for ensuring the platform meets both technical specifications and legal frameworks governing India's energy sector.
SATAT Initiative Integration
National Targets
  • 5,000 Compressed Biogas (CBG) plants by 2030
  • 15 Million Metric Tonnes production target
  • Significant market opportunity for monitoring systems
  • Mandatory efficiency and compliance requirements
GFIS Value Proposition
New plants require sophisticated monitoring systems to ensure operational efficiency and regulatory compliance. GFIS provides the digital backbone for this massive infrastructure buildout.
5000
Target Plants
CBG facilities planned under SATAT by 2030
15M
Production Goal
Metric tonnes of biogas annually
₹24.8B
Market Size
Annual revenue potential for GFIS platform
GOBARdhan Portal Integration
The GOBARdhan Unified Registration Portal acts as a centralized repository for biogas plant registration, facilitating investment assessment and sector participation. A critical feature of GFIS Layer 1 will be seamless API integration with this portal.
01
Automated Reporting
Real-time updates on plant status—functional, completed, or under construction—eliminating manual data entry.
02
Production Tracking
Continuous monitoring of production volumes and feedstock utilization for government databases.
03
Compliance Verification
Transparent data for subsidy disbursement and policy formulation, reducing operator burden.
04
Waste Valorization
Quantifying organic waste diverted from landfills and converted to energy, supporting "Waste to Wealth" objectives.
Regulatory Technology Asset
Integration with national portals transforms GFIS from a standalone product into a regulatory technology (RegTech) asset, making it indispensable for plant operators seeking central financial assistance (CFA) and other fiscal benefits.
"The system's ability to track waste valorization provides the empirical evidence needed to justify government incentives and unlock funding mechanisms."
Carbon Markets
Carbon Credit Trading and Compliance
A pivotal element of the GFIS business model is its capacity to monetize environmental attributes through carbon credits. The emerging Carbon Credit Trading Scheme (CCTS) in India establishes a compliance mechanism where obligated entities must meet GHG emission intensity targets.
Methane Avoidance
Capturing methane that would otherwise be released from decomposing organic waste
MRV Systems
Rigorous Monitoring, Reporting, and Verification of emission reductions
Credit Generation
Tamper-proof audit trails enabling participation in voluntary and compliance markets
CI Scoring
Automated Carbon Intensity calculations determining fuel value in low-carbon markets
Algorithmic Verification for Carbon Credits
GFIS implements calculation methodologies directly into its analytics engine, continuously logging parameters such as biogas flow rate, methane concentration, and flare efficiency to generate tamper-proof audit trails.
The credibility of carbon credits in voluntary markets depends heavily on the accuracy of emission reduction calculations and assurance of permanence. GFIS automates these complex calculations based on real-time operational data.
Gate-to-Gate Methodology
  • Direct process emissions tracking
  • Indirect energy consumption accounting
  • Continuous parameter logging
  • Immutable data records
Carbon Intensity Calculation
The GFIS platform incorporates specific algorithms to calculate the Carbon Intensity (CI) score of produced biogas, expressed in gCO2e/MJ. This score determines the fuel's value in low-carbon fuel markets.
28x
Methane GWP
Global Warming Potential compared to CO2
100%
Automation
Real-time CI calculation with zero manual input
10-20%
Revenue Share
GFIS commission on carbon credit value
By automating CI calculation, GFIS enables plant operators to participate in high-value carbon trading with minimal administrative overhead, aligning with international standards such as the Clean Development Mechanism (CDM) of the UNFCCC.
Data Governance
DPDP Act 2023 Compliance
The deployment of GFIS involves collecting granular data from thousands of distributed assets, many located on private farmlands or owned by smallholder farmers. This brings the system under the purview of India's Digital Personal Data Protection (DPDP) Act 2023.
Data Fiduciary Responsibilities
Consent Management
Clear, vernacular-language consent forms detailing data collection (slurry output, gas production) and sharing mechanisms (GOBARdhan portal, carbon registries).
Purpose Limitation
Data used strictly for defined purposes—optimization of plant operations and credit verification—as mandated by the Act.
Security Measures
End-to-end encryption and strict access controls to protect sensitive operational data, with significant penalties for breaches.
Data Principal Rights
Features allowing farmers to withdraw consent or request data erasure, baked into the user management module.
Technical Architecture
Industrial IoT Ecosystem
The transformation from simulation-based academic project to robust industrial solution requires comprehensive overhaul of hardware and networking layers. The pilot utilized simulated data generators; commercial deployment must survive the harsh, hazardous environment of anaerobic digesters.
ATEX Compliance and Hazardous Environments
Environmental Challenges
  • Methane (CH4) creates explosion risk
  • Hydrogen sulfide (H2S) causes corrosion
  • High moisture and temperature extremes
  • Standard electronics unsuitable
Certification Requirements
All sensors and enclosures must meet ATEX (Atmosphères Explosibles) and IECEx certifications for Zone 1 and Zone 2 hazardous areas.
Consumer-grade electronics are completely unsuitable for this environment.
Zone 1
Explosive atmosphere likely during normal operation
Zone 2
Explosive atmosphere not likely or only for short periods
Methane and Gas Composition Analysis
Accurate measurement of methane concentration is critical for determining energy value and carbon credit quantification. The pilot simulation assumed simple percentage values, but in practice, sensor selection is complex.
NDIR Technology
Non-Dispersive Infrared sensors strongly recommended over catalytic bead sensors. Immune to poisoning by siloxanes and H2S, offering long-term stability for revenue-grade calculations.
Specifications
Measure CH4 (0-100% volume, ±2% accuracy), CO2 (0-100%), and O2 (0-25%) for complete gas quality profile.
Hydrogen Sulfide Monitoring
H2S is a critical contaminant causing corrosion in Combined Heat and Power (CHP) engines. Electrochemical sensors are the standard for H2S detection, but they have limited lifespan and require frequent calibration.
The GFIS maintenance module must track the "health" of these sensors and trigger replacement alerts before they fail, ensuring continuous protection of expensive equipment.
Maintenance Schedule
  • Monthly bump testing
  • Bi-annual full calibration
  • 3-6 month replacement cycle
  • Automated health tracking
Liquid Analytical Sensors: pH and ORP
The stability of anaerobic digestion is heavily dependent on pH and Oxidation-Reduction Potential (ORP). Acidosis, caused by accumulation of Volatile Fatty Acids (VFAs), is a leading cause of digester failure.
1
Industrial Probes
Retractable assemblies allow probe withdrawal for cleaning and calibration without interrupting the process. Teflon or varying junctions resist clogging by high-solids digestate.
2
Calibration Protocol
Real-world pH sensors drift. System includes "Calibration Mode" where operators input buffer solution values for slope and offset correction.
3
Alert Thresholds
Deviation of >0.5 pH units triggers "Maintenance Required" alert, preventing process failures before they occur.
Flow Measurement Technology
Critical Importance
For carbon credit verification, the volume of gas produced is the single most important metric. Accuracy is paramount for revenue-grade measurement.
Technology Choice
Thermal Mass Flow Meters are the industry standard for biogas. They provide direct mass flow measurement (correcting for temperature and pressure) and have no moving parts, reducing maintenance needs.
Installation Requirements
10x pipe diameter upstream, 5x downstream for laminar flow and accurate measurement
Moisture Management
Condensate traps upstream prevent damage to sensitive measurement components
Connectivity
Edge Computing Architecture
Reliable data transmission is a significant hurdle in rural India, where many SATAT and GOBARdhan projects are located. A hybrid connectivity architecture is proposed to ensure data continuity even in challenging network conditions.
Edge Gateway Strategy
The "Edge Gateway" serves as the local brain of the Layer 3 system, aggregating data from all sensors via industrial protocols (Modbus RS-485 or 4-20mA loops) and performing local processing.
Industrial Hardware
Ruggedized Raspberry Pi Compute Module 4 or proprietary IoT gateways like Teltonika, housed in IP67-rated enclosures for outdoor conditions.
Local Buffering
"Store and Forward" capability buffers 30+ days of high-frequency data on SD card or eMMC storage during connectivity outages.
Asynchronous Upload
When connectivity restores, buffered data uploads to cloud, ensuring no gaps in carbon credit audit trail.
LoRaWAN vs. NB-IoT: Communication Protocols
For connecting distributed sensors across large feedstock storage yards or multiple digesters in a cluster, wireless protocols are necessary. The choice between LoRaWAN and NB-IoT depends on specific deployment requirements.
LoRaWAN (Local Network)
  • Preferred for sensor networks
  • 10-15 km range in rural areas
  • Low power consumption
  • Battery-operated sensors run for years
  • Private network avoids SIM costs
NB-IoT/LTE-M (Backhaul)
  • Recommended for Edge Gateway to Cloud
  • Better signal penetration than GSM
  • Supported by major Indian telecom operators
  • Satellite IoT fallback for zero-coverage sites
Functional Safety and Control Systems
While GFIS provides advanced analytics, direct control of pumps and heaters involves safety risks. If software crashes or issues erroneous commands, it could lead to tank overflow or pressure buildup.
1
Safety Interlocks
Critical safety functions (high-pressure flare ignition, emergency pump stop) handled by local, hard-wired Safety Instrumented System (SIS) independent of cloud.
2
GFIS Optimization
GFIS serves as optimization overlay, writing "setpoints" (optimal temperature target) rather than direct actuator commands.
3
Local Authority
Local PLC retains final authority over machinery, adhering to IEC 61511 functional safety standards.
"The architecture strictly separates 'Optimization Control' from 'Safety Control' to ensure plant safety is never compromised by software failures."
Proposed Sensor and Hardware Specifications
Cloud Infrastructure
Cloud-Native Architecture
The transition from local Python script to national-scale platform necessitates cloud-native architecture capable of handling high-velocity time-series data, complex relational structures for user management, and scalable machine learning pipelines.
AWS Platform Selection and Cost Optimization
AWS (Amazon Web Services) is selected as the primary cloud provider due to its mature IoT ecosystem and granular pricing model, essential for maintaining healthy SaaS margins.
AWS IoT Core handles secure connection (MQTT over TLS) of thousands of devices, supporting mutual authentication (X.509 certificates) to ensure only authorized gateways transmit data.
Cost Analysis (1,000 Devices)
Connectivity: $0.08 per million minutes
Messaging: ~$1.00 per million messages
Total Monthly: $200-$300
Per Device: $0.25/month
This low infrastructure cost structure is critical for offering affordable subscription tiers to small rural operators, enabling widespread adoption across the SATAT network.
Data Pipeline and Storage Strategy
The data architecture distinguishes between "Hot," "Warm," and "Cold" data paths to optimize for both real-time monitoring and long-term analytics.
01
Hot Path (Real-Time)
Data routed via Rules Engine to Amazon Timestream or DynamoDB. High-speed writes and low-latency reads power live dashboards showing current tank pressure and temperature.
02
Cold Path (Analytics)
Data streamed via Kinesis Firehose to S3 Data Lake. Raw, immutable data serves as Single Source of Truth for carbon credit audits and ML model retraining.
03
Digital Twins
AWS IoT TwinMaker creates 3D virtual representations of biogas plants. Live sensor data mapped to spatial models enables remote visual inspection.
Software Stack Migration
The pilot's use of Streamlit is excellent for rapid prototyping but faces scalability bottlenecks regarding multi-user concurrency and custom state management. The commercial platform requires a complete architectural evolution.
Frontend Evolution
Migration to Single Page Application (SPA) framework like React.js or Vue.js. Decouples frontend from backend, supporting thousands of concurrent users and responsive mobile experience crucial for field operators.
Backend Services
Migration to Django or FastAPI (Python). Python retained for backend to leverage rich ecosystem of data science libraries (Pandas, Scikit-learn) for on-the-fly analytics.
Containerization
All microservices (Auth, Ingestion, ML Inference, API) Dockerized and orchestrated using Amazon ECS or Kubernetes. Ensures consistent environments and automated scaling.
Artificial Intelligence
Predictive Analytics and Machine Learning
GFIS differentiates itself from standard SCADA systems through its "Intelligence"—the application of machine learning to the biological complexities of anaerobic digestion. Implementation requires rigorous operationalization strategy (MLOps).
Biological Process Optimization
Anaerobic digestion involves hydrolysis, acidogenesis, acetogenesis, and methanogenesis. Imbalances between these stages lead to failure modes like acidosis (pH drop) or foaming.
Hydrolysis
Complex organics broken into simple sugars
Acidogenesis
Sugars converted to volatile fatty acids
Acetogenesis
VFAs converted to acetate and hydrogen
Methanogenesis
Methane production from acetate and H2
Foaming Detection and Prevention
The Problem
Foaming is a severe operational issue that can block gas pipes and rupture tanks. Often caused by organic overloading or inconsistent feedstock. Rapid increase in VFA to Alkalinity ratio is a precursor.
GFIS Solution
Since online VFA sensors are expensive, GFIS employs "Soft Sensors"—virtual sensors inferring VFA levels from surrogate variables like pH, temperature, gas production rate, and conductivity.
Data Collection
Continuous monitoring of pH, temperature, conductivity, gas production rate
Anomaly Detection
Isolation Forest or One-Class SVM trained on historical data detects multivariate signatures
Early Warning
Alert triggered hours before physical foam is visible, enabling preventive action
Feedstock Quality Classification
Feedstock variability (varying moisture content in press mud vs. dry straw) drastically affects gas yield. The FeedstockQualityClassifier addresses this critical challenge using computer vision.
1
Image Capture
Ruggedized IP cameras at feedstock intake hopper capture images of incoming loads
2
CNN Classification
Convolutional Neural Network trained on local biomass types (Napier grass, cattle dung, poultry litter, municipal waste)
3
Yield Adjustment
System classifies feedstock type, estimates dry matter content, adjusts expected gas output, detects contaminants
Predictive Yield Modeling and MLOps
The BioGasYieldPredictor (using XGBoost/LSTM) provides forecasts necessary for supply chain planning (Layer 2) and energy grid integration (Layer 1). Biogas plants are dynamic; a model trained in summer may fail in winter.
1
Feature Engineering
Model inputs expanded to include Hydraulic Retention Time (HRT), Organic Loading Rate (OLR), ambient weather conditions
2
Performance Monitoring
Automated MLOps pipeline continuously monitors model performance against actual yield
3
Model Drift Detection
When error rate exceeds 10%, system detects drift and triggers retraining
4
Automated Retraining
System retrains using recent data from S3 Data Lake and redeploys updated model
Operations
Operational Implementation and Maintenance
The success of GFIS depends not just on software but on the reliability of the physical assets it monitors. A structured maintenance regime is essential to ensure data integrity and system uptime.
Standard Operating Procedures for Sensors
Data quality is only as good as sensor health. The harsh biogas environment leads to sensor fouling and drift, requiring rigorous maintenance protocols.
pH/ORP Probes
Weekly: Removal and cleaning with soft brush and water
Monthly: Calibration using standard buffer solutions (pH 4.0 and 7.0)
Quarterly: Replacement of reference electrolyte
H2S Scrubbers & Sensors
Weekly: Inspection of iron sponge or activated carbon media
Monthly: Bump testing with known gas concentration
Bi-annual: Full calibration
Flow Meters
Quarterly: Inspection of condensate drains upstream
Annual: Verification of calibration via certified lab or master meter comparison
Preventive Maintenance for Mechanical Systems
GFIS Layer 3 includes a "Maintenance Module" that tracks run-hours of critical equipment and auto-generates work orders, preventing failures before they occur.
Pumps and Agitators
System tracks amperage draw of feed pumps and mixers. Gradual increase in current draw indicates ragging (debris wrapping around impeller) or bearing wear.
Alerts operators to inspect pump before it trips on overload, preventing costly downtime.
Digester Cleanout
Inorganic solids (sand, grit) accumulate at tank bottom, reducing active volume.
GFIS tracks total feedstock volume processed and estimates grit accumulation, scheduling cleanout every 1-2 years to maintain hydraulic efficiency.
Business Strategy
Monetization and Financial Modeling
To ensure economic sustainability of GFIS, a robust monetization strategy is required that creates value for all stakeholders—farmers, operators, and the GFIS enterprise itself.
SaaS Pricing Structure
A tiered Subscription-as-a-Service (SaaS) model is proposed to lower the barrier to entry while capturing value from large-scale users.
Carbon Credit Monetization: The Revenue Multiplier
The most significant value unlock for GFIS is enabling access to carbon finance. Methane has a Global Warming Potential (GWP) 28 times that of CO2. By capturing and using it, plants generate carbon credits.
The Problem
Administrative cost of verification makes carbon credits unviable for small plants
GFIS Aggregation
Acts as aggregator and Digital MRV platform, providing high-frequency, tamper-proof data
Cost Reduction
Reduces verification cost and time, making carbon credits accessible to all plant sizes
Shared Success
10-20% revenue share on carbon credits generated, aligning incentives with plant success
Financial Projections
Market Opportunity
With SATAT target of 5,000 plants, the Total Addressable Market (TAM) is substantial. Capturing just 10% (500 plants) represents significant revenue potential.
Revenue Streams
Subscription ARR: ₹60 Million (500 plants × ₹10,000/month)
Carbon Revenue: ₹200 Million ($2.5M at $1/tonne × 2.5M tonnes)
500
Target Plants
10% market capture of SATAT initiative
₹60M
Subscription ARR
Annual recurring revenue from SaaS fees
₹200M
Carbon Revenue
Annual revenue from carbon credit facilitation
Carbon revenue dwarfs subscription income, highlighting the strategic importance of the carbon module as the primary value driver.
Implementation
Phased Execution Roadmap
The project execution follows a phased approach to mitigate risk and validate technology before scaling. Each phase builds on the previous, ensuring technical and commercial viability at every step.
Four-Phase Implementation Strategy
1
Phase 1: Pilot Validation (Months 1-3)
Location: Solapur Municipal Corporation waste-to-energy plant
Objective: Deploy industrial hardware stack and validate ML models against ground truth
Deliverables: ATEX sensor kit installation on one digester, BioGasYieldPredictor calibration with real-world data
2
Phase 2: Cloud Transformation (Months 4-6)
Objective: Migrate from local Streamlit scripts to AWS microservices architecture
Deliverables: Functional AWS IoT Core pipeline, React-based dashboard beta launch, user authentication and RBAC system implementation
3
Phase 3: Regional Hub Deployment (Months 7-12)
Objective: Activate Layer 2 capabilities and onboard first cluster of plants
Deliverables: Logistics Optimization module deployment for waste collection, GOBARdhan Unified Portal API integration
4
Phase 4: National Expansion (Year 2)
Objective: Scale to Layer 1 and monetize data
Deliverables: Digital MRV methodology accreditation by carbon registry (Verra/Gold Standard), National Intelligence Platform launch for federal stakeholders
Risk Management
Comprehensive Risk Mitigation Strategy
Successful deployment requires proactive identification and mitigation of technical, operational, financial, regulatory, and cybersecurity risks.
Security Architecture
Network Segmentation
Air-gapping operational technology (OT) from information technology (IT) networks prevents lateral movement of threats
Mutual TLS Authentication
X.509 certificates ensure only authorized devices can connect to the cloud platform
Penetration Testing
Regular security audits identify vulnerabilities before they can be exploited
Transforming India's Renewable Energy Future
The Green Fuel Intelligence System represents a transformative leap in the management of renewable energy assets. By moving beyond passive monitoring to active, AI-driven optimization, GFIS directly addresses the efficiency challenges that have historically constrained the biogas sector.
Strategic Impact
  • Digital backbone for SATAT initiative
  • Carbon credit verification infrastructure
  • Waste-to-energy revolution enabler
  • Net-zero target acceleration
40,000
CO2 Reduction
Tonnes annually
₹24.8B
Market Value
Revenue potential
"As India accelerates towards its net-zero targets, GFIS stands poised to become the critical digital infrastructure underpinning the country's waste-to-energy revolution, converting operational data into financial assets and building a sustainable, high-revenue enterprise."
Team & Partners
Driving Innovation Through Strategic Partnership & Expertise
Strategic Partnership: Chatake Innoworks Pvt. Ltd.
GreenWorks Division – Sustainable Energy Innovation Unit
Neharu Industrial Estate, Solapur, Maharashtra, India
Chatake Innoworks is the powerhouse behind the GreenFuel Intelligence System (GFIS). Their game-changing industry insights, meticulous technical validation, and unwavering commitment are actively transforming our bold vision into tangible, sustainable real-world impact. This isn't just collaboration; it's co-creation, building the future of green energy, together.
The Dedicated Project Execution Team
Ms. Tanishka Deshpande
Ms. Anushka Hitanalli
Ms. Aditi Gangji
Ms. Shruti Hiremath
This talented student team is at the forefront of GFIS development, executing the project under Chatake Innoworks' expert technical guidance. Their collective proficiency in AI/Cloud architecture and rigorous model validation is key to bringing this innovative system to life.