Gas emissions and respiration losses at the silo face:
A comprehensive study with novel in situ sensors and data model fusion

Project description

Large quantities of silage are used worldwide for feeding ruminants and for biogas production. The dynamics of gas exchange the opened silage during removal are still poorly understood. However, these findings have a direct effect on the assessment of silage quality (spoilage as a result of aerobic microbial activity) and the evaluation of the resulting environmental impacts (climate gas emissions). To this end, a fundamental and comprehensive study is being carried out focused on both issues, on the one hand the oxidation rate and energy losses at the silo face and on the other hand the emission of climate-relevant (greenhouse) gases. Two novel, automated multi-sensor systems will be used to record and allocate the gas emission from the silage. The two common types of silage will be considered: Flat silos for maize silage and round bales for ryegrass, lucerne and maize silage. The positive or negative effect of chemical and biological additives (silage additives) on the spoilage processes and gas emission from the silage will also be investigated. By means of digital signal processing, the released CO2 is divided into two sources, on the one hand the existing quantities which have formed in the silage during the preceding anaerobic ensiling process and have accumulated in the material, and on the other hand the newly formed quantities which arise in the course of the microbial, aerobic processes at the gating surface. While the first source has a decaying function, it covers the second source at the beginning of the measurements, which describes the actual oxidative process and is associated with the energy losses of the silage. Using these new findings, we will update and adapt the commonly used Pitt-Muck model, which simulates the CO2 flux from the open side of a bunker silo. Using a data model fusion approach, the Pitt-Muck model will be extended so that the in-situ measurements from experiments (on different study farms) can be validated and combined with the numerical results.
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