Graph-Driven Models for Gas Mixture Identification and Concentration Estimation on Heterogeneous Sensor Array Signals

Robust gas sensing is fundamental to safety, environmental monitoring, and industrial control. However, the design of intelligent gas analysis algorithms remains constrained by a key instrumentation challenge: the lack of generalization across heterogeneous sensor arrays, gas compositions, and operating conditions. This study addresses this gap by introducing two deep learning models—GraphCapsNet for mixture classification and GraphANet for concentration estimation—designed to operate directly on raw sensor data from structurally distinct platforms. GraphCapsNet integrates Graph Convolutional Networks (GCNs) with dynamic routing mechanisms to extract key features from temporal data, while GraphANet combines GCNs with self-attention mechanisms to identify concentration-related features. Unlike prior approaches that rely on dataset-specific retraining or feature engineering, both models are deployed under fixed configurations across two divergent datasets, encompassing different sensor types, chamber sizes, and gas species. GraphCapsNet achieved over 98.00% accuracy in classification tasks, and GraphANet attained an R² score exceeding 0.96 across various gas components. These results highlight the models’ exceptional accuracy, stability, and scalability, offering a potential foundation for real-world deployment in variable sensing environments.