Predictive combination model for CH4 separation and CO2 sequestration with CO2 injection into coal seams: VMD-STA-BiLSTM-ELM hybrid neural network modeling

Abstract

In the experimental process of separating coal seam gas using the CO2 displacement method, establishing a predictive model for key variables is essential to optimize displacement parameters, increase coal seam gas recovery, and improve CO2 sequestration efficiency. Traditional modeling methods often struggle with the complex nature of industrial data and are susceptible to overfitting due to multicollinearity caused by long-term datasets. This paper presents a hybrid predictive model based on variational mode decomposition (VMD), a spatiotemporal attention mechanism (STA), a bidirectional long short-term memory network (BiLSTM), and an extreme learning machine (ELM). During the offline phase, VMD is used to decompose raw data into intrinsic mode functions (IMFs). The hidden state from the last time step of the STA-BiLSTM is then added to the original data to enrich the features for ELM training. In the online prediction phase, the outputs from the VMD-STA-BiLSTM and VMD-STA-BiLSTM-ELM models are combined using an error reciprocal method to generate the final prediction. The proposed model is validated with experimental datasets from CO2 displacement for coal seam CH4 under various conditions, as well as with N2-ECBM and CO2-ECBM engineering datasets. The results show that the hybrid model surpasses VMD-ELM, STA-BiLSTM-ELM, BiLSTM, STA-BiLSTM, ELM, TCN, and Attention-TCN models in predictive accuracy. Even in multi-step and rolling predictions, the model exhibits minimal impact from cumulative errors, maintaining accurate forecasts with strong generalization and robustness. It effectively captures feature patterns across different datasets and accurately predicts unknown data. The model shows potential for application in diverse scenarios and complex environments, offering reliable support and decision-making for the field application of CO2 displacement in coal seam CH4 separation. It is an effective and promising predictive approach to enhance coal seam gas recovery and CO2 sequestration efficiency.