Title: DARE-LSTMAE: LSTM autoencoder model with an attention mechanism and optimised reconstruction error for anomaly detection in biogas plants

Authors: K. Meena; U. Srinivasulu Reddy; Meena Krishnamoorthy; P. Ilangovan

Addresses: Machine Learning and Data Analytics Lab, Center of Excellence in Artificial Intelligence, Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620 015, Tamil Nadu, India ' Machine Learning and Data Analytics Lab, Center of Excellence in Artificial Intelligence, Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620 015, Tamil Nadu, India ' Department of Computer Science and Engineering, Periyar Maniammai Institute of Science and Technology, Thanjavur 613 403, Tamil Nadu, India ' Department of Computer Science and Engineering, Periyar Maniammai Institute of Science and Technology, Thanjavur 613 403, Tamil Nadu, India

Abstract: Anomaly detection in biogas plants is critical for ensuring optimal performance and preventing potential failures. Traditional monitoring methods fail to capture complex sensor interactions, optimise reconstruction error and focus on critical features for accurate anomaly detection. This study develops a deep LSTM autoencoder with attention (DARE-LSTMAE) to enhance anomaly detection accuracy and enable real-time fault detection. It is designed to learn the normal operational patterns of the biogas plant by encoding and reconstructing the input data. The attention mechanism helps the model focus on the most important properties, improving anomaly detection. The experimental results demonstrate that the LSTM autoencoder with an attention mechanism outperforms traditional methods in detecting anomalies. The proposed model achieves 97% precision, 100% recall and 98% F1 score, providing early warnings of potential issues. The optimised reconstruction error further enhances the system's reliability, making it a robust solution for real-time monitoring and fault detection in biogas plants.

Keywords: anomaly detection; biogas plants; LSTM; reconstruction error; threshold; attention mechanism.

DOI: 10.1504/IJISTA.2026.152034

International Journal of Intelligent Systems Technologies and Applications, 2026 Vol.24 No.1, pp.28 - 53

Published online: 04 Mar 2026 *

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