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Assessment of compost maturity by using an electronic nose

机译:使用电子鼻评估堆肥的成熟度

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摘要

The composting process produces and emits hundreds of different gases. Volatile organic compounds (VOCs) can provide information about progress of composting process. This paper is focused on the qualitative and quantitative relationships between compost age, as sign of compost maturity, electronic-nose (e-nose) patterns and composition of compost and composting gas at an industrial scale plant. Gas and compost samples were taken at different depths from composting windrows of different ages. Temperature, classical chemical parameters, O_2, CO, combustible gases, VOCs and e-nose profiles were determined and related using principal component analysis (PCA). Factor analysis carried out to a data set including compost physical-chemical properties, pile pore gas composition and composting time led to few factors, each one grouping together standard composting parameters in an easy to understand way. PCA obtained from e-nose profiles allowed the classifying of piles, their aerobic-anaerobic condition, and a rough estimation of the composting time. That would allow for immediate and in-situ assessment of compost quality and maturity by using an on-line e-nose. The e-nose patterns required only 3-4 sensor signals to account for a great percentage (97-98%) of data variance. The achieved patterns both from compost (chemical analysis) and gas (e-nose analysis) samples are robust despite the high variability in feedstock characteristics (3 different materials), composting conditions and long composting time. GC-MS chromatograms supported the patterns.
机译:堆肥过程产生并排放数百种不同的气体。挥发性有机化合物(VOC)可提供有关堆肥过程进展的信息。本文的重点是堆肥年龄之间的定性和定量关系,这是堆肥成熟度,电子鼻(e-nose)模式以及工业规模堆肥中堆肥和堆肥气体组成的标志。从不同年龄的堆肥堆中抽取不同深度的气体和堆肥样品。使用主成分分析(PCA)确定了温度,经典化学参数,O_2,CO,可燃气体,VOC和电子鼻轮廓,并将其关联。对包括堆肥物理化学性质,堆孔气组成和堆肥时间在内的数据集进行因子分析的因素很少,每个因子都以易于理解的方式将标准堆肥参数归为一组。从电子鼻轮廓获得的PCA可以对堆进行分类,它们的好氧-厌氧条件以及对堆肥时间的粗略估计。这将允许通过使用在线电子鼻来立即就地评估堆肥的质量和成熟度。电子鼻模式仅需要3-4个传感器信号即可占很大一部分数据差异(97-98%)。尽管原料特性(3种不同的材料),堆肥条件和堆肥时间长,但从堆肥(化学分析)和气体(电子鼻分析)样品中获得的模式都非常可靠。 GC-MS色谱图支持这些模式。

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  • 来源
    《Waste Management》 |2016年第2期|174-180|共7页
  • 作者单位

    Instituto de Recursos Naturales y Agrobiologia de Sevilla (IRNAS-CSIC), P.O. Box 1052, 41080 Sevilla, Spain;

    Departamento de Quimica y Ciencia de los Materiales, Facultad de Ciencias Experimentales, Univ. de Huelva, Campus Universitario El Carmen, Avenida de las Fuerzas Armadas, 21071 Huelva, Spain;

    Departamento de Ingenieria Quimica, Quimica Fisica y Quimica Organica, Facultad de Ciencias Experimentales, Univ. de Huelva, Campus Universitario El Carmen, Avenida de las Fuerzas Armadas, 21071 Huelva, Spain;

    Departamento de Ingenieria Quimica, Quimica Fisica y Quimica Organica, Facultad de Ciencias Experimentales, Univ. de Huelva, Campus Universitario El Carmen, Avenida de las Fuerzas Armadas, 21071 Huelva, Spain;

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

    Green wastes; Pruning residues; Manure; Biomass; Composting; Compost maturity; VOCs;

    机译:绿色废物;修剪残留物;肥料;生物质堆肥;堆肥成熟度;挥发性有机化合物;

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