Modeling methane production prediction for energy optimization via improved long short-term memory network

Abstract

Methane, a highly essential industrial raw material, plays a pivotal role in safeguarding national energy security and advancing sustainable development. Due to the expansion of industrial scale and increased integration in modern methane production, the production data exhibits complex multiscale variability over time, which poses great challenges for accurate methane production prediction. Therefore, a novel production prediction model is proposed by employing an improved Long Short-Term Memory Network (LSTM) combining with the multiscale feature fusion method (MSFF) (MSFF-LSTM). The MSFF decomposes the raw industrial process data into multiple two-dimensional tensors based on periods, which can ravel out the complex temporal fluctuations into multiple intraperiod- and interperiod-variations. Then, the methane prediction model is constructed utilizing multiple LSTM models to extract interactive features at various scales. Finally, using a feature fusion module to fuse the prediction results at different scales can fully aggregate local and global features for complementary prediction. Experimental results demonstrate that, compared with other prediction models, the MSFF-LSTM achieves the state-of-the-art results with the mean absolute error (MAE), the mean square error (MSE), coefficient of determination (R²) and the root mean square error (RMSE) of 0.1056, 0.0300, 0.9199 and 0.1733, respectively, which offers the optimization direction for the anaerobic digestion process of straw for methane production.