AIMNET: An IoT-Empowered Digital Twin for Continuous Gas Emission Monitoring and Early Hazard Detection
🤖 AI Summary
To address the challenge of real-time monitoring and early warning for sudden environmental hazards—such as industrial gas leaks and wildfires—this paper proposes an IoT-driven three-tier digital twin (DT) framework. The framework integrates a custom-designed low-power multi-parameter gas sensor network, a multi-scale meteorological–gas coupled physical model, and a lightweight AI-based anomaly detection algorithm, enabling edge–cloud collaborative data acquisition, bidirectional feedback modeling, and dynamic risk inference. Its key innovation lies in establishing a deep closed-loop integration of physical sensing, mechanistic simulation, and data-driven intelligence within the DT system, significantly enhancing spatiotemporal resolution and early-warning timeliness for carbon-based gas leaks (e.g., methane). Field deployments at oil–gas fields and wastewater treatment plants demonstrate stable detection of minor emission events, accurate reconstruction of dispersion pathways, and an average alert latency of <90 seconds—validating the system’s high reliability and engineering applicability.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Monitors carbon gas emissions continuously using IoT and digital twin technology.
Detects environmental hazards like industrial leaks and wildfires in real-time.
Simulates gas transport with AI for predictive risk assessment and anomaly detection.
Innovation
Methods, ideas, or system contributions that make the work stand out.
IoT sensing network integrates with physics-based gas transport model
Three-layer architecture enables real-time simulation and anomaly detection
Scalable deployment uses distributed sensors for remote emission monitoring
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