Title: A multivariate multi-headed deep LSTM model: a new approach to enhance the prediction of methane production in the biogas plant

Authors: K. Meena; U. Srinivasulu Reddy; 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

Abstract: Biogas is a sustainable method for converting diverse waste sources into energy. It is of utmost importance to develop a new approach for enhancing the prediction of methane production in the biogas plant. This study introduces a standalone multi-headed neural network architecture called multivariate multi-headed long short-term memory (MM-LSTM) to improve the prediction accuracy of methane production from biogas digesters. The proposed model treats each input variable independently, employing them as distinct LSTM network models (heads) to improve the prediction accuracy. In the MM-LSTM approach, the outputs of these independent LSTM models are concatenated to forecast high methane production. The present study evaluated the proposed model's predictive performance using sensor data from a biogas digester and compared it to state-of-the-art models and single-headed long short-term memory (LSTM) neural network architectures. Based on the experimental results, the MM-LSTM model achieves a prediction accuracy of 99.52%, which is best among all the existing models. The experimental results suggest that the proposed prediction model can be effectively used in biogas plants for biogas production processes and for advancing sustainable energy production.

Keywords: multivariate time series; multi-headed LSTM; prediction; neural network model; methane production; biogas plants.

DOI: 10.1504/IJIEI.2026.154021

International Journal of Intelligent Engineering Informatics, 2026 Vol.14 No.2, pp.150 - 172

Received: 12 Jul 2024
Accepted: 05 Oct 2024
Published online: 10 Jun 2026 *

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