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

Lo Sciuto, G., Susi, G., Cammarata, G., Capizzi, G. (2016). A spiking neural network-based model for anaerobic digestion process. In 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2016 (pp.996-1003). IEEE [10.1109/SPEEDAM.2016.7526003].

A spiking neural network-based model for anaerobic digestion process

SUSI, GIANLUCA;
2016-01-01

Abstract

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
2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2016
ita
2016
23.
Rilevanza internazionale
contributo
2016
2016
Settore ING-IND/31 - ELETTROTECNICA
Settore ING-INF/01 - ELETTRONICA
English
anaerobic process models; Biogas; biogas prediction for food waste; spiking neural network;
biogas; biogas prediction for food waste; anaerobic process models; spiking neural network
http://ieeexplore.ieee.org/document/7526003/
Intervento a convegno
Lo Sciuto, G., Susi, G., Cammarata, G., Capizzi, G. (2016). A spiking neural network-based model for anaerobic digestion process. In 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM 2016 (pp.996-1003). IEEE [10.1109/SPEEDAM.2016.7526003].
Lo Sciuto, G; Susi, G; Cammarata, G; Capizzi, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/189473
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