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UBC Theses and Dissertations

Preventive control of ammonia and odor emissions during the active phase of poultry manure composting Zhang, Wenxiu


Traditional measures used in the composting industry for ammonia and odor emissions control are those involving collection and treatment such as thermal oxidation, adsorption, wet scrubbing and biofiltration. However, these methods do not address the source of the odor generation problem. The primary objective of this thesis research was to develop preventive means to minimize ammonia and odor emissions, and maximize nitrogen conservation to increase the agronomic value of compost. Laboratory-scale experiments were performed to examine the effectiveness of various technologies to minimize these emissions during the active phase of composting. These techniques included precipitating ammonium into struvite in composting matrix before it release to outside environment; the use of chemical and biological additives in the form of yeast, zeolite and alum; and the manipulation of key operational parameters during the composting process. The fact that struvite crystals were formed in manure composting media, as verified by both XRD and SEM-EDS analyses, represents novel findings from this study. This technique was able to reduce ammonia emission by 40-84%, while nitrogen content in the finished compost was increased by 37-105%. The application of yeast and zeolite with dosages of 5-10% enhanced the thermal performance of composting and the degree of degradation, and ammonia emission was reduced by up to 50%. Alum was found to be the most effective additive for both ammonia and odor emission control; ammonia emission decreased by 45-90% depending on the dosage, and odor emission assessed via an dynamic dilution olfactometer was reduced by 44% with dosages above 2.5%. This study reaffirmed that aeration is the most influential factor to odor emission. An optimal airflow rate for odor control would be 0.6 L/min.kg dry matter with an intermittent aeration system. Quantitative relationships between odor emission and key operational parameters were determined, which would enable “best management practices” to be devised and implemented for composting. An empirical odor predictive model was developed to provide a simple and direct means for simulation of composting odor emissions. The effects of operating conditions were incorporated into the model with multiplicative algorithm and linearization approximation approach. The model was validated with experimental observations.

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