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Neural Models for Optimizing Lignocellulosic Residues Composting Process

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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 physico-chemical 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 CO2 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.

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Acknowledgments

Special thanks for the collaboration of the technical support at IRNAS (CSIC).

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Authors and Affiliations

  1. Agrifood Campus of International Excellence (CeiA3)-Pro2tecS Research Centre-Universidad de Huelva, 21071, Huelva, Spain

    M. J. Díaz, F. López, J. C. García & R. Yañez

  2. Centro Nacional de Investigación y Tecnología Agraria y Alimentaria, Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7,5, Madrid, Spain

    M. E. Eugenio

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  1. M. J. Díaz
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  2. M. E. Eugenio
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  4. J. C. García
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  5. R. Yañez
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Correspondence to M. J. Díaz.

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Díaz, M.J., Eugenio, M.E., López, F. et al. Neural Models for Optimizing Lignocellulosic Residues Composting Process. Waste Biomass Valor 3, 319–331 (2012). https://doi.org/10.1007/s12649-012-9121-y

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