Proceedings

Application of MeMo Predictive Machine Learning Software to predict and optimise AD performance: PRESENTATION ONLY

Harrison, D., Nexus Bioresource

(free)

Anaerobic digestion has experienced rapid expansion and challenges as a technology to convert organic waste material to renewable energy.  Where once anaerobic digestion was predominantly municipal now energy crops, industrial wastewater and food waste are common.  Furthermore, anaerobic digesters are being used to treat multiple wastes through co-digestion and operate at higher loadings.

At the same time, the rapidly developing technologies utilising advanced modelling and other data sciences are bringing new insights to the analysis of AD even allowing operators to predict in advance the plant performance.  This paper reviews the performance of the MeMo platform now operational on 18 AD sites treating industrial, agricultural and municipal organic sludges.

Predictive data models are only as good as the information they are fed and therefore and therefore significant progress has been made in process monitoring and feed waste characterisation.  The paper goes on to show how near IR scan technology developed by BioEnTech and site based automated analysis using SNAC can be combined with the MeMo model to accurately predict the digester performance and the impact of novel feed materials on existing plants.

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