Real-time operation of municipal anaerobic digestion using an ensemble data mining framework

Farzad Piadeh, Ikechukwu Offie, Kourosh Behzadian, Angela Bywater, Luiza C. Campos

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number130017
Pages (from-to)1-12
Number of pages12
JournalBioresource Technology
Volume392
Early online date13 Nov 2023
DOIs
Publication statusE-pub ahead of print - 13 Nov 2023

Keywords

  • Anaerobic digestion
  • Biogas generation
  • Data mining
  • Ensemble modelling
  • Organic waste
  • Real-time operation

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