A pH-based control of ammonia in biogas during anaerobic digestion of artificial pig manure and maize silage

The purposes of this study were to prove that ammonia can be present in biogas from anaerobic digestion and to control this ammonia by reducing the reactor pH. Ammonia containing biogas was produced for a period of more than 100 days, with a maximum of 332 ppm. Especially during periods of high free ammonia concentrations in the reactor was ammonia present in biogas. The free ammonia was effectively reduced to less than the inhibition level by pH-based control and the ammonia in biogas concentration was reduced to 9 ppm. Simultaneously the CH4-yield was severely reduced. A pH-based control of ammonia in biogas with a satisfactory biogas production was thus so far proven not to be achievable. In the carrying out this study it was shown that high ammonia concentrations lead to a range of problems: process inhibition, decreased COD removal efficiency, reduced biogas production, malodour and a poor biogas quality that requires treatment

Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox

The outlook to apply the highly energetic biogas from anaerobic digestion into fuel cells will result in a significantly higher electrical efficiency and can contribute to an increase of renewable energy production. The practical bottleneck is the fuel cell poisoning caused by several gaseous trace compounds like hydrogen sulfide and ammonia. Hence artificial neural networks were developed to predict these trace compounds. The experiments concluded that ammonia in biogas can indeed be present up to 93 ppm. Hydrogen sulfide and ammonia concentrations in biogas were modelled successfully using the MATLAB Neural Network Toolbox. A script was developed which made it easy to search for the best neural network models' input/output-parameters, settings and architectures. The models were predicting the trace compounds, even under dynamical conditions. The resulted determination coefficients (R2) were for hydrogen sulfide 0.91 and ammonia 0.83. Several model predictive control tool strategies were introduced which showed the potential to foresee, control, reduce or even avoid the presence of the trace compounds

A fuzzy logic approach to control anaerobic digestion

One of the goals of the EU-Project AMONCO (Advanced Prediction, Monitoring and Controlling of Anaerobic Digestion Process Behaviour towards Biogas Usage in Fuel Cells) is to create a control tool for the anaerobic digestion process, which predicts the volumetric organic loading rate (Bv) for the next day, to obtain a high biogas quality and production. The biogas should contain a high methane concentration (over 50%) and a low concentration of components toxic for fuel cells, e.g. hydrogen sulphide, siloxanes, ammonia and mercaptanes. For producing data to test the control tool, four 20 l anaerobic Continuously Stirred Tank Reactors (CSTR) are operated. For controlling two systems were investigated: a pure fuzzy logic system and a hybrid-system which contains a fuzzy based reactor condition calculation and a hierachial neural net in a cascade of optimisation algorithm