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Batch anaerobic digestion of five different livestock manures: Biogas productivity and fertilizer composition

机译:批次厌氧消化五种不同的牲畜饲养:沼气生产率和肥料组合物

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The aim of this study was to evaluate biochemical methane potential (BMP) of five different livestock manures (dairy manure (DM), horse manure (HM), goat manure (GM), chicken manure (CM) and swine manure (SM)) and predict the BMP using manure chemicalcomposition. Nutrients of the digested different manures were also monitored. The BMP tests were conducted under mesophilic temperatures with a manure loading rate of 3.5 g volatile solids (VS)/L and a feed to inoculum ratio (FA) of 0.5. Single variableand multiple variable regression models were developed using manure total carbohydrate (TC), crude protein (CP), crude fat (CF), lignin (LIG) and acid detergent fiber (ADF)), and measured BMP data. The BMP for DM, HM, GM, CM and SM were measured to be 204, 155, 159, 259, and 323 mUg VS, respectively. The effluents from the HM showed the lowest nitrogen, phosphorus and potassium concentrations. The effluents from the CM digesters showed highest nitrogen and phosphorus concentrations and digested SM showed highest potassium concentration. Based on the results of the regression analysis, the model using the variable of LIG showed the best (R2 = 0.851, p = 0.026) for BMP prediction among the single variable models, the model including variables of TC andTF showed the best prediction of BMP (R2 = 0.913, p = 0.068-0.075) comparing with other two- variable models, while the model including variables of CP, LIG and ADF performed the best in BMP prediction (R2 = 0.999, p = 0.009-0.017) if three-variable models were compared.
机译:本研究的目的是评估五种不同牲畜粪便的生化甲烷潜力(BMP)(乳制品粪便(DM),马粪(HM),山羊粪便(GM),鸡粪(CM)和猪粪(SM))并使用粪肥化学函数预测BMP。还监测消化的不同肥的营养素。 BMP试验在碘入嗜合核温度下进行,其粪便加载速率为3.5g挥发性固体(Vs)/ L和进料(Fa)为0.5。使用粪便总碳水化合物(TC),粗蛋白(CP),粗脂肪(CF),木质素(LIG)和酸性洗涤剂纤维(ADF))开发了单变量回归模型,并测量了BMP数据。为DM,HM,Gm,Cm和Sm的BMP分别测量为204,155,159,259和323杯Vs。来自HM的流出物显示出最低的氮,磷和钾浓度。来自CM消化器的流出物显示出最高的氮和磷浓度,并且消化的SM显示出最高钾浓度。基于回归分析的结果,使用LIG的可变的模型显示出对单变量模型中BMP预测的最佳(R2 = 0.851,P = 0.026),包括TC andTF变量模型显示BMP的最佳预测(R2 = 0.913,p = 0.068-0.075)与其他两个变量模型相比,而包括CP,LIG和ADF的变量的模型在BMP预测中最佳地执行(R2 = 0.999,P = 0.009-0.017),如果三 - 比较可变模型。

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