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首页> 外文期刊>Waste Management >Multivariate analysis and biodegradability test to evaluate different organic wastes for biological treatments: Anaerobic co-digestion and co-composting
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Multivariate analysis and biodegradability test to evaluate different organic wastes for biological treatments: Anaerobic co-digestion and co-composting

机译:多变量分析和生物降解性测试,以评估用于生物处理的各种有机废物:厌氧消化和共分解

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摘要

This study proposes the combination of statistical analysis and a biodegradability test to complement the composition of different wastes in order to find the optimal balance of nutrients for their joint bioconversion. Due to the need to determine the adequate balance of nutrients, the use of alternative techniques to experimental procedures could significantly reduce the cost and time of the process. With this aim, fifteen organic wastes (nine solid and six liquid wastes) were selected and different statistical analyses were performed on the physico-chemical characterization and respirometric variables. Liquid and solid wastes were analyzed separately using principal components analysis (PCA) (PC1 + PC2: 67% of total variance explained for solid substrates and PC1 + PC2: 85% of total variance explained for liquid substrates). The analysis provided considerable information about the predominant chemical composition of each substrate as well as their similarities and deficiencies to identify possible mixtures. In addition to PCA, cluster analyses (CA) were performed to group the substrates and identify the most significant differences between them. The joint evaluation of PCA and CA permitted identifying the optimal waste mixtures (i.e., glycerol-strawberry-fish waste) by correlating the loadings and scores plot, the cluster analysis dendograms and the COD/TKN ratio from the physico-chemical characterization. Moreover, multivariate regression was found to be an appropriate tool for predicting microbiological activity, as well as the soluble available biodegradable organic matter of each substrate. Inorganic carbon (C-IC and total organic carbon (C-TOC) were found to be the most influential parameters in the prediction correlation of oxygen consumption and oxygen uptake rate. (C) 2018 Elsevier Ltd. All rights reserved.
机译:这项研究提出了统计分析和生物降解性测试相结合的方法,以补充不同废物的组成,以便为它们的联合生物转化找到最佳养分平衡。由于需要确定营养物质的适当平衡,因此使用替代技术代替实验程序可以显着降低过程的成本和时间。为此目的,选择了15种有机废物(9种固体废物和6种液体废物),并对理化特性和呼吸测定变量进行了不同的统计分析。使用主成分分析(PCA)分别分析了液体和固体废物(PC1 + PC2:解释了固体基质的总变异的67%,PC1 + PC2:解释了液体基质的总变异的85%)。该分析提供了有关每种底物的主要化学组成及其相似性和不足之处的大量信息,以鉴定可能的混合物。除PCA外,还执行了聚类分析(CA)来对底物进行分组,并确定底物之间最显着的差异。 PCA和CA的联合评估允许通过将负荷和得分图,聚类分析树状图以及理化特性从COD / TKN比值中关联起来,从而确定最佳的废物混合物(即甘油-草莓-鱼废物)。此外,发现多元回归是预测微生物活性以及每种底物的可溶性可利用的可生物降解有机物的合适工具。无机碳(C-IC和总有机碳(C-TOC))被发现是耗氧量与吸氧率预测相关性中最有影响力的参数。(C)2018 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Waste Management》 |2018年第8期|819-828|共10页
  • 作者单位

    Univ Cordoba Spain, Dept Inorgan Chem & Chem Engn, Campus Univ Rabanales,Carretera N-4,Km 396, Cordoba 14071, Spain;

    Univ Cordoba Spain, Dept Inorgan Chem & Chem Engn, Campus Univ Rabanales,Carretera N-4,Km 396, Cordoba 14071, Spain;

    Univ Cordoba Spain, Dept Inorgan Chem & Chem Engn, Campus Univ Rabanales,Carretera N-4,Km 396, Cordoba 14071, Spain;

    Univ Cordoba Spain, Dept Inorgan Chem & Chem Engn, Campus Univ Rabanales,Carretera N-4,Km 396, Cordoba 14071, Spain;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Biological treatments; Principal components analysis; Cluster analysis; Multivariate regression; Biodegradability test;

    机译:生物处理;主成分分析;聚类分析;多元回归;生物降解性试验;

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