Work

A novel sensor layout method and deep learning model: leakage source term estimation for natural gas station sites

Year: 2026

Type: article

Abstract: Accurate and efficient source term estimation (STE) methods are critical to ensure the safe and smooth operation of natural gas station sites when hazardous gases are accidentally released. A well-designed sensor deployment, as the foundation of STE, directly affects the accuracy of source term estimation methods. Most existing sensor deployment methods are tailored to specific operating conditions and require prior knowledge of the leak source, which is impractical in the context of STE. In addition, the poor real-time performance of existing STE methods makes it difficult to respond effectively to dynamic scenarios. This paper proposes a novel sensor optimization deployment method based on information entropy theory, designed to be applicable under multi-objective operating conditions. The proposed method employs a hybrid algorithm combining genetic algorithm (GA) and particle swarm optimization (PSO) to minimize the objective function, enabling optimal sensor placement under multiple wind direction conditions. Building on this foundation, the CEEMDAN algorithm is employed to denoise and reconstruct the sensor’s noisy data. A fused model combining a bidirectional temporal convolutional network and a bidirectional gated recurrent unit (BiTCN-BiGRU) is then constructed to achieve high-precision estimation of the leak source term. Compared with traditional sensor deployment strategies, the optimized configuration improves the accuracy of source location estimation in STE methods by 24.5%, while reducing the estimation error of leak intensity by 87.3%. In terms of the STE model, the proposed CEEMDAN-BiTCN-BiGRU model achieves a leak rate estimation accuracy of 99.90%, representing a 6.5% improvement in source location accuracy over the best-performing conventional model. These results demonstrate that the proposed sensor optimization approach and CEEMDAN-BiTCN-BiGRU model hold significant promise for applications in gas leak source term estimation.

Source: Journal of Cleaner Production

Authors: Houjia Xu, Yuntao Li, Bingcai Sun, Qi Jing, Ke Shan

Institutions: China University of Petroleum, Beijing, Ministry of Emergency Management of the People's Republic of China, China National Petroleum Corporation (China), Shenzhen Gas Corporation (China)

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,660

Funders: China National Petroleum Corporation, National Key Research and Development Program of China