Using machine learning techniques to optimise anaerobic digestion performance: PRESENTATION ONLY

Proceedings

Using machine learning techniques to optimise anaerobic digestion performance: PRESENTATION ONLY

Stephenson, M.¹ and Minall, R.², ¹Hal24k Water, UK, ²Aqua Enviro, UK
(free)

• What’s the concept?
• Why we need to consider AI across all parts of the water sector
• The benefit of Machine learning
• How we construct an ML solution
• Limitations of machine learning approach

Physical parameters to model for Machine Learning approach
CONTROL
Mixing rate
Throughput rate
External temperature
INPUT
Feedstock type
Feedstock characteristics
e.g. %Dry Solids, Volatile
Fatty Acids, pH , alkalinity
OUTPUT
GAS Yield
% CH4
CONTROL
Heat input
Reactor temperature
OUTPUT
Digestate Characteristics
e.g. Total Solids, Volatile
Solids, Volatile Fatty Acids,
pH
Aim To maximise CH4
yield under variable input conditions

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Proceedings