AIMNET: An IoT-Empowered Digital Twin for Continuous Gas Emission Monitoring and Early Hazard Detection

TL;DR

AIMNET proposes an IoT-powered Digital Twin framework to enable continuous, high-resolution methane and carbon gas monitoring over large basins. It fuses a custom low-power NDIR IoT sensor network with a physics-based, multiscale gas transport model (WRF-GHG and LES) within a DTW for real-time inference, anomaly detection, and hazard forecasting. Key contributions include a self-calibrating CH4 sensor, a robust MQTT-based bidirectional network, AI-assisted calibration, and a closed-loop analytics platform with visualization for decision support. Field validation at oil/gas and wastewater sites demonstrates improved plume resolution and source discrimination, highlighting AIMNET’s potential for scalable, intelligent environmental monitoring and risk mitigation.

Abstract

A Digital Twin (DT) framework to enhance carbon-based gas plume monitoring is critical for supporting timely and effective mitigation responses to environmental hazards such as industrial gas leaks, or wildfire outbreaks carrying large carbon emissions. We present AIMNET, a one-of-a-kind DT framework that integrates a built-in-house Internet of Things (IoT)-based continuous sensing network with a physics-based multi-scale weather-gas transport model, that enables high-resolution and real-time simulation and detection of carbon gas emissions. AIMNET features a three-layer system architecture: (i) physical world: custom-built devices for continuous monitoring; (ii) bidirectional information feedback links: intelligent data transmission and reverse control; and (iii) digital twin world: AI-driven analytics for prediction, anomaly detection, and dynamic weather-gas coupled molecule transport modeling. Designed for scalable, energy-efficient deployment in remote environments, AIMNET architecture is realized through a small-scale distributed sensing network over an oil and gas production basin. To demonstrate the high-resolution, fast-responding concept, an equivalent mobile-based emission monitoring network was deployed around a wastewater treatment plant that constantly emits methane plumes. Our preliminary results through which, have successfully captured the methane emission events whose dynamics have been further resolved by the tiered model simulations. This work supports our position that AIMNET provides a promising DT framework for reliable, real-time monitoring and predictive risk assessment. In the end, we also discuss key implementation challenges and outline future directions for advancing such a new DT framework for translation deployment.

Figures (6)

• Figure 1: The large-scale Digital Twin gas monitoring framework integrated with a distributed IoT gas sensing network, advanced gas transport modeling and data visualization technologies.
• Figure 2: Prototypes of IoT gas sensing instrument: left one features a completed system with battery and power management modules; middle one is the revised version isolating the core sensing control from the battery module for enhanced field operating robustness; right one shows the total power consumption of our device.
• Figure 3: Real-time and historical visualization.
• Figure 4: Experimental deployment (Map from Google Maps).
• Figure 5: Mobile $\text{CH}_4$ measurement.