文献类型：期刊文献

英文题名：Dynamic prediction of multisensor gas concentration in semi-closed spaces: A unified spatiotemporal inter-dependencies approach

作者：Chen, Shikuan[1];Du, Wenli[1,2];Wang, Bing[1];Cao, Chenxi[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：94

外文期刊名：JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES

收录：;EI(收录号：20250617828082);WOS:【SCI-EXPANDED(收录号:WOS:001424493300001)】；

基金：This work was supported by National Key Research and Development Program of China (2022YFB3305900) , National Natural Science Foundation of China (62394343, 62394345) , the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Gas dispersion; FourierGNN; Multivariate time series forecasting; Gas turbine enclosure

摘要：Flammable gas leakage in industrial environments poses significant risks to human health and environmental safety. Developing accurate and efficient spatiotemporal models for gas dispersion is essential for mitigating these dangers. The gas diffusion is inherently a spatiotemporal process, yet most research has focused on modeling spatial or temporal correlations separately, failing to capture the dynamic relationships between various variables. To overcome this limitation, we propose a novel approach based on FourierGNN, a multivariate time series forecasting (MTS) method, which treats concentration values from multiple sensors as multivariates and predicts their future trends. By utilizing a fully-connected hypervariate graph structure, the model adaptively learns high-resolution representations across different timestamps and variates simultaneously. Experimental data are generated by simulating a methane leak scenario in a semi-closed gas turbine enclosure using computational fluid dynamics (CFD) software. The method is evaluated on the dataset with three distinct prediction horizons and compared with FC-LSTM and StemGNN. Results indicate that the approach outperforms others in terms of MAPE, MAE and RMSE across different prediction horizons while reducing parameter counts by 61.26% and 82.83%, respectively. Furthermore, the method demonstrates robustness under varying noise levels, confirming its reliability.