Design and Validation of Field-Scale Anaerobic Digesters Treating Dairy Manure for Small Farms

TRANSACTIONS OF THE ASABE(2015)

引用 2|浏览10
暂无评分
摘要
Six field-scale (FS) digesters designed using a plug-flow design used by millions of farmers in developing countries and reconfigured for a temperate climate were tested over an 18-week period. Digester efficiency was analyzed based on methane (CH4) production, volatile solids (VS) reduction, cost-benefit analysis, and energy production efficiency. Results were compared to the literature and a full-scale on-site anaerobic digestion system (BARC) that uses the same manure source. The average CH4 yield of the replicate FS digesters was 0.31 m(3) CH4 kg(-1) VS added, resulting in 0.34 m(3) CH4 m(-3) digester(-1) d(-1), with 67.2% CH4 and 4310 ppm H2S in the biogas. The digesters reduced VS in the solid-separated dairy manure by 43.3%. Weekly CH4 production values between the six replicate digesters varied < 18%. The results were comparable to the on-site BARC digestion system (0.31 m(3) CH4 kg(-1) VS). Both systems used separated liquid manure and operated in the lower mesophilic range (25 degrees C to 30 degrees C), yet functioned at the higher range of literature values. The total energy produced by the FS digesters was three times the energy needed to counteract heat loss from the digesters, but the heating kettle system was inefficient. The material cost of the system ($16,800) was much higher than in developing countries ($150 to $400) but lower than traditional U.S. digestion systems, illustrating the impact of the heating and insulation infrastructure on sustainability at the small-scale. The FS design presents an alternative for smaller-scale farmers in temperate climates without anaerobic digestion systems. However, while the design and insulation appeared to be sufficient, the heating efficiency should be improved and/or use of waste heat from an engine generator set should be explored.
更多
查看译文
关键词
Anaerobic digestion,Biogas,Dairy manure,Design,Methane,Small farm
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要