A diagnostic system for improving biomass quality based on a sensor network.

Losses during storage of biomass are the main parameter that defines the profitability of using preserved biomass as feed for animal husbandry. In order to minimize storage losses, potential changes in specific physicochemical properties must be identified to subsequently act as indicators of silage decomposition and form the basis for preventive measures. This study presents a framework for a diagnostic system capable of detecting potential changes in specific physicochemical properties, i.e., temperature and the oxygen content, during the biomass storage process. The diagnostic system comprises a monitoring tool based on a wireless sensors network and a prediction tool based on a validated computation fluid dynamics model. It is shown that the system can provide the manager (end-user) with continuously updated information about specific biomass quality parameters. The system encompasses graphical visualization of the information to the end-user as a first step and, as a second step, the system identifies alerts depicting real differences between actual and predicted values of the monitored properties. The perspective is that this diagnostic system will provide managers with a solid basis for necessary preventive measures.