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Please use this identifier to cite or link to this item: http://hdl.handle.net/10362/3648

Title: Location model for CCA-treated - wood waste remediation units
Authors: Gomes, Helena Isabel Caseiro Rego
Advisor: Lobo, Victor José de Almeida e Sousa
Ribeiro, Alexandra de Jesus Branco
Keywords: CCA-treated wood waste
Integrated waste management
Location models
Self-organizing maps (SOM)
K-means
Optimisation
Resíduos de madeira preservada com CCA
Gestão integrada de resíduos
Modelos de localização
Self-organizing maps (SOM)
K-means
Optimização
Issue Date: 3-Feb-2005
Series/Report no.: Mestrado em Ciência e Sistemas de Informação Geográfica;TSIG0002
Abstract: There is growing concern about the environmental impacts and increasing difficulty to dispose preservative treated wood products at the end of their service life. In the next decades, in Portugal, a significant increase is expected in the amounts of treated wood that annually needs to be properly disposed. The recycling of these wastes, containing chromium, copper and arsenic (in the case of CCA-treated wood), should only be made after its remediation, so planning and optimisation of the remediation units locations is of major importance. The objective of this study is the development of a location model to optimise the location of remediation plants for the treatment of CCA-treated wood waste for further recycling, minimizing costs and respecting environmental criteria. The location model was implemented with geographic information using Geographic Information Systems (ArcGIS 8.2 © ESRI). All the uses of treated wood products were considered, using soil occupation data and the results of a questionnaire sent to wood preservation industries. Two different clustering methods (Self-Organizing Maps and K-means) were tested in different conditions to solve the multisource Weber problem using SOMToolbox for MATLAB. The solutions obtained with our data and with both clustering methods make sense and could be used to decide on the location of these plants. SOM has provided more robust and reproducible results than k-means, with the disadvantage of longer computing times. The main advantage of k-means, compared to SOM, is the reduced computing time allied to the fact that it allows us to obtain the best solutions in the majority of the cases, in spite of bigger variances and more geographical dispersion.
Description: Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica
URI: http://hdl.handle.net/10362/3648
Appears in Collections:ISEGI - Dissertações de Mestrado em Ciência e Sistemas de Informação Geográfica

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