RT Conference Proceedings
T1 A spiking neural network-based model for anaerobic digestion process
A1 Lo Sciuto, G.
A1 Susi, Gianluca
A1 Cammarata, G.
A1 Capizzi, G.
AB There are many conversion technologies for the transformation of biomass into usable energy forms. Among these technologies, anaerobic digestion is one of the most attractive. In many papers appeared in the literature it has been demonstrated that the application of efficient mathematical models is an essential requirement to improve digester’s performance. In this paper a spiking neural network-based model for anaerobic digestion process is proposed. This model performs a long-term prediction of the concentration of the biogas (CH4 and CO2) at the 100th day of the process, by analysing the concentration evolution of 6 measurable marker-molecules (MMM) namely CH4, CH4S, CO2, H2, H2S and NH3 during the first 10 days of the process. For the validation of the model, a small domestic digester was realized. The tests carried out show an excellent agreement between the predicted values and those obtained with the digester.
YR 2016
FD 2016-06
LK https://hdl.handle.net/20.500.14352/117520
UL https://hdl.handle.net/20.500.14352/117520
LA eng
NO G. Lo Sciuto, G. Susi, G. Cammarata and G. Capizzi, "A spiking neural network-based model for anaerobic digestion process," 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), Capri, Italy, 2016, pp. 996-1003, doi: 10.1109/SPEEDAM.2016.7526003.
NO Se deposita la versión enviada (preprint) de la ponencia
DS Docta Complutense
RD 5 abr 2025