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Waste and Biomass Valorization
Article . 2012 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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Neural Models for Optimizing Lignocellulosic Residues Composting Process

Authors: Díaz, Manuel J.; Eugenio, María E.; López, Francisco; García, Juán C.; Yáñez, Rosa;

Neural Models for Optimizing Lignocellulosic Residues Composting Process

Abstract

Vegetable trimming residues are very plentiful and diverse; however, their recycling involves environmental problems. Producing high quality compost from these residues is a way to make a good use of them. The present work studies the influence of the operating conditions used during a composting process of vegetable trimming residues (aeration, moisture, particle size, composting time), on the evolution of the temperature, pH and CO2 while the compost is producing and on the physicochemical properties of the final compost (pH, organic matter, Kjeldahl-N, C/N ratio). An adaptive network based fuzzy inference system on basis of the four considered independent variables (aeration, moisture, particle size, composting time) was used to obtain the optima composting conditions which produce the best pH, temperature and CO 2 evolution and the highest quality compost. Low aeration contents (0.2 Lair (min kg-1), intermediate moisture content (55 %), medium-to-low particle size (1-3 cm) were the best operating conditions to obtain maximum temperatures and CO2 production. Moreover, under these conditions, it was obtained compost with satisfactory physico-chemical properties, useful for further agricultural application. © Springer Science+Business Media B.V. 2012.

Keywords

Aeration, Compost, Particle size, Composting parameters, Moisture, Optimum composting

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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