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• To develop a real-time methane detection system for landfills, mining, and other methane-emitting industries.

• To improve detection accuracy and range using advanced sensor technologies.

• To enable wireless remote monitoring and early warning alerts for safety and compliance.

• To reduce maintenance through auto-calibration and rugged design.

• To support environmental monitoring, worker safety, and regulatory reporting.

Methane & other obnoxious gases are highly flammable and potent greenhouse gas commonly emitted from landfills, coal mines, and industrial sites. Its accumulation poses significant threats, including explosions, fire hazards, and environmental degradation. Current methane detection systems in use suffer from limitations such as short detection range, dependence on manual calibration, and lack of real-time alert capabilities. These limitations hinder timely response and increase the risk to worker safety and environmental health. This project proposes the design and development of a real-time, accurate, and low-maintenance gas detection system for deployment in landfills, mining operations, biogas plants, and other related industries. The system will integrate advanced gas sensors (such as NDIR or TDLAS), microcontrollers, and wireless communication modules to ensure continuous monitoring, rapid leak detection, and automated alert mechanisms. The proposed solution aims to increase detection accuracy beyond 95%, enable remote data access via IoT-based platforms, and reduce maintenance needs through auto-calibration features. It will also include user-friendly dashboards for data visualization and compliance reporting, supporting proactive decision-making and regulatory adherence. By focusing on modular design and scalability, the system can be customized to suit diverse industrial setups. This initiative not only addresses urgent industrial safety needs but also contributes to environmental sustainability by mitigating methane emissions. The successful implementation of this project will lead to safer work environments, improved operational efficiency, and better environmental stewardship across high-risk sectors.

This proposal builds upon existing know-how in sensor-based environmental monitoring and introduces a novel, handheld IoT-based device specifically designed for the real-time measurement, classification, and analysis of gases such as hydrogen sulfide (H₂S) and ammonia (NH₃). The current development lies between Technology Readiness Level (TRL) TRL 3 and TRL 4, indicating that the technology has been validated in a laboratory environment and is ready for testing in relevant field conditions. Key innovations include:

• High Selectivity and Low Interference: Enhanced electrochemical sensors ensure precise detection (0–100 ppm, 0.1 ppm resolution), reducing cross-sensitivity to other gases.

• Compact, Portable Design: Smartphone-sized device (<500 gm) supports mobile field deployment for on-site air quality assessments.

• IoT-Based Connectivity: Real-time data transmission and remote access through integration with cloud databases or local servers.

• Machine Learning for Odour Classification: Enables intelligent categorization and prediction of gas sources based on collected data patterns.

• Advanced Data Handling: Incorporates algorithms for missing data imputation and gas source prediction, improving data quality and reliability. While IoT-enabled gas sensors and e-nose systems are well-documented in research and development, challenges remain in ensuring robust calibration, reliability, and performance under varying environmental conditions. This proposal advances the TRL by integrating these elements into a fully functional prototype with real-world applicability, moving the technology toward field deployment (TRL 7).

• Portable Detection Device: A handheld, IoT-enabled device for real-time methane, H₂S, and NH₃ detection with advanced electrochemical sensors.

• IoT Integration: Wireless communication for remote monitoring and cloud-based data storage.

• ML Analytics Platform: Machine learning-driven tools for gas classification, source prediction, and data imputation.

• Field Testing Report: A detailed report from real-world testing, assessing device performance and accuracy.

• Calibration Methodology: Self-calibration system to reduce maintenance and improve long-term reliability.

• Technical Documentation: Full documentation and user manual for device setup, calibration, and maintenance.

• Scalability Guidelines: Recommendations for scalable deployment in different industries (mining, landfills, etc.).

• Final Report: A comprehensive project report with knowledge transfer for stakeholders.

• Improved Worker Safety: Real-time methane and gas detection systems will help prevent accidents, fires, and explosions in hazardous industrial environments like mines and landfills, ensuring the safety of workers.

• Environmental Protection: By reducing methane emissions, a potent greenhouse gas, the project contributes to climate change mitigation and supports sustainable waste management and energy practices.

• Enhanced Operational Efficiency: The IoT-enabled, real-time monitoring system will allow for proactive maintenance, minimizing operational disruptions and energy waste due to undetected gas leaks.

• Regulatory Compliance: The system will assist industries in meeting environmental regulations by providing accurate, real-time data and supporting compliance with air quality standards.

• Technological Innovation: The development of a portable, cost-effective gas detection device pushes the boundaries of industrial monitoring technologies, enabling scalable applications across various sectors, including mining, biogas, and waste management.

• Contribution to SDGs: The project directly contributes to key UN Sustainable Development Goals, including Climate Action (SDG 13), Sustainable Cities (SDG 11), and Good Health and Well-being (SDG 3), fostering a safer and more sustainable industrial future.

Self-Implemented (In-house Development)

• Design and Development: The core components of the device (hardware and software) will be developed by an in-house team with expertise in sensor technology, IoT, and machine learning.

• Field Testing and Calibration: The project team will handle the real-world testing of the device in mining, landfill, and industrial environments.

• Data Analytics Platform: The development of the machine learning models for gas classification and source prediction will be done in-house, ensuring full control over the algorithm design. Outsourced Partnerships

• Sensor Procurement: Partnering with sensor manufacturers (e.g., electrochemical sensors, gas sensors) to source high-quality, specialized components for the device.

• Cloud and IoT Integration: Collaborating with cloud service providers for remote data storage, IoT integration, and real-time data management.

• Regulatory Compliance and Certification: Partnering with industry experts for certifications and compliance assessments (e.g., safety certifications, environmental standards).

Collaboration with Industry Stakeholders

• Pilot Sites and Field Testing: Working closely with mining companies, landfill operators, and other industrial partners for field validation, deployment, and feedback loops to optimize the technology for real-world use.