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

Area of Science:

• Agricultural Engineering
• Biomass Management
• Animal Husbandry

Background:

• Biomass storage losses significantly impact the economic viability of using preserved biomass for animal feed.
• Identifying indicators of silage decomposition is crucial for implementing effective preventive measures.
• Physicochemical property changes during storage can signal decomposition.

Purpose of the Study:

• To develop a diagnostic system for monitoring biomass storage.
• To identify key physicochemical properties (temperature, oxygen content) as indicators of silage decomposition.
• To provide a framework for minimizing storage losses in animal husbandry.

Main Methods:

• Implementation of a monitoring tool utilizing a wireless sensor network.
• Development of a prediction tool based on a validated computational fluid dynamics (CFD) model.
• Integration of monitoring and prediction tools into a comprehensive diagnostic system.

Main Results:

• The diagnostic system provides continuous updates on biomass quality parameters to end-users.
• Graphical visualization of monitored properties enhances user understanding.
• Alerts are generated when actual values deviate from predicted values, indicating potential issues.

Conclusions:

• The developed diagnostic system offers a robust method for tracking biomass quality during storage.
• Continuous monitoring and prediction enable timely identification of silage decomposition risks.
• The system empowers managers with data-driven insights for effective preventive actions in animal feed preservation.