Accurately identifying gas mixtures and estimating their concentrations are crucial across various industrial applications using gas sensor arrays. However, existing models face challenges in generalizing across heterogeneous datasets, which limits their scalability and practical applicability. To address this problem, this study develops two novel deep-learning models that integrate temporal graph structures for enhanced performance: a Graph-Enhanced Capsule Network (GraphCapsNet) employing dynamic routing for gas mixture classification and a Graph-Enhanced Attention Network (GraphANet) leveraging self-attention for concentration estimation. Both models were validated on datasets from the University of California, Irvine (UCI) Machine Learning Repository and a custom dataset, demonstrating superior performance in gas mixture identification and concentration estimation compared to recent models. In classification tasks, GraphCapsNet achieved over 98.00% accuracy across multiple datasets, while in concentration estimation, GraphANet attained an R2 score exceeding 0.96 across various gas components. Both GraphCapsNet and GraphANet exhibited significantly higher accuracy and stability, positioning them as promising solutions for scalable gas analysis in industrial settings.

翻译：利用气体传感器阵列精确识别气体混合物并估计其浓度，在各类工业应用中至关重要。然而，现有模型在处理异质数据集时面临泛化挑战，这限制了其可扩展性和实际适用性。为解决此问题，本研究开发了两种集成时序图结构以提升性能的新型深度学习模型：采用动态路由机制进行气体混合物分类的图增强胶囊网络（GraphCapsNet），以及利用自注意力机制进行浓度估计的图增强注意力网络（GraphANet）。两种模型均在加州大学欧文分校（UCI）机器学习库数据集及自定义数据集上进行了验证，相较于现有模型，在气体混合物识别与浓度估计任务中均展现出更优性能。在分类任务中，GraphCapsNet 在多个数据集上取得了超过 98.00% 的准确率；在浓度估计任务中，GraphANet 对不同气体成分的 R2 分数均超过 0.96。GraphCapsNet 与 GraphANet 均表现出显著更高的准确性与稳定性，使其成为工业场景中可扩展气体分析的有力解决方案。