Location model for CCA-treated - wood waste remediation units

• Autor(es): Gomes, Helena Isabel Caseiro Rego
• Data: 2005
• Identificador Persistente: http://hdl.handle.net/10362/3648
• Origem: Repositório Institucional da UNL
• Assunto(s): 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
• Tipo de Documento: Dissertação de Mestrado
• Idioma: Inglês
• Orientador(es): Lobo, Victor José de Almeida e Sousa; Ribeiro, Alexandra de Jesus Branco

Descrição

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica 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.