Early warning study of field station process safety based on VMD-CNN-LSTM-self-attention for natural gas load prediction
Wei Zhao, Bilin Shao, Ning Tian, Wen Zhang, Xue Zhao, Shuqiang Wu
Abstract
As a high-risk production unit, natural gas supply enterprises are increasingly recognizing the need to enhance production safety management. Traditional process warning methods, which rely on fixed alarm values, often fail to adequately account for dynamic changes in the production process. To address this issue, this study utilizes deep learning techniques to enhance the accuracy and reliability of natural gas load forecasting. By considering the benefits and feasibility of integrating multiple models, a VMD-CNN-LSTM-Self-Attention interval prediction method was innovatively proposed and developed. Empirical research was conducted using data from natural gas field station outgoing loads. The primary model constructed is a deep learning model for interval prediction of natural gas loads, which implements a graded alarm mechanism based on 85%, 90%, and 95% confidence intervals of real-time observations. This approach represents a novel strategy for enhancing enterprise safety production management. Experimental results demonstrate that the proposed method outperforms traditional warning models, reducing MAE, MAPE, MESE, and REMS by 1.13096 m 3 /h, 1.3504%, 7.6363 m 3 /h, 1.6743 m 3 /h, respectively, while improving R 2 by 0.04698. These findings are expected to offer valuable insights for enhancing safe production management in the natural gas industry and provide new perspectives for the industry’s digital and intelligent transformation.