Piadeh, Farzad, Offie, Ikechukwu, Behzadian, Kourosh, Bywater, Angela and Campos, Luiza C. (2024) Real-time operation of municipal anaerobic digestion using an ensemble data mining framework. Bioresource Technology, 392. p. 130017. ISSN 09608524

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Abstract

This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making
framework composed of weak learner data mining models. The framework utilises simple but practical features such as
waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is
validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates
a significant improvement in prediction accuracy, increasing from the range of 50–80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding.

Item Type:	Article
Identifier:	10.1016/j.biortech.2023.130017
Keywords:	Anaerobic digestion Biogas generation Data mining Ensemble modelling Organic waste Real-time operation
Subjects:	Computing Medicine and health
Depositing User:	Kourosh Behzadian
Date Deposited:	11 Dec 2023 13:42
Last Modified:	04 Nov 2024 11:05
URI:	https://repository.uwl.ac.uk/id/eprint/10538

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