Assessment of compost maturity by using an electronic nose
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-...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Recursos: | Universidad de Huelva (UHU) |
| Repositorio: | Arias Montano. Repositorio Institucional de la Universidad de Huelva |
| Idioma: | inglés |
| OAI Identifier: | oai:ariasmontano.uhu.es:10272/11453 |
| Acesso em linha: | http://hdl.handle.net/10272/11453 |
| Access Level: | acceso abierto |
| Palavra-chave: | Green wastes Pruning residues Manure Biomass Composting Compost maturity VOCs |
| Resumo: | 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, O2, 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. |
|---|