Helena Gomes*¹, Alexandra B. Ribeiro** e Vitor Lobo***

*Engenheira do Ambiente

**Professora Auxiliar

Departamento de Ciências e Engenharia do Ambiente. Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Monte da Caparica, 2829-516 CAPARICA

***Professor Associado

Instituto Superior de Estatística e Gestão de Informação. Universidade Nova de Lisboa, Campus de Campolide, 1070-312 LISBOA

Sumário. O principal objectivo deste trabalho consiste em optimizar a localização de unidades de remediação de resíduos de madeira preservada, de forma a assegurar-lhes uma gestão adequada e a minimizar os custos. Prevêem-se aumentos significativos neste fluxo de resíduos nas próximas décadas, sendo que dezenas de milhares de m³ gerados anualmente em Portugal terão de ter um destino adequado. A reciclagem destes resíduos apenas deverá ser realizada após a sua remediação, pelo que o planeamento e optimização da localização de unidades de tratamento assumem uma grande importância.

As soluções obtidas com os dois métodos fazem sentido e podem ser utilizadas para decidir a localização das unidades de remediação. Enquanto que o SOM obteve resultados mais robustos e reprodutíveis, em tempos computacionais mais longos, o K-means obteve melhores soluções, apesar da maior variância e dispersão geográfica.

Palavras-chave: resíduos de madeira preservada com CCA; modelos de localização; Self-Organizing Maps; K-means; optimização

Optimisation Model for the Localization of Treated Wood Waste Remediation Units

Abstract. The objective of this study is to optimise the location of remediation plants for treating CCA-treated wood waste for further recycling, minimizing costs and respecting environmental criteria. In the next decades, the amounts of treated wood that annually needs to be properly disposed of in Portugal will increase considerably. The recycling of this waste, containing chromium, copper and arsenic, should only be made after its remediation, so planning and optimising the units' locations is of major importance.

The location model was implemented with geographic information using Geographic Information Systems (ArcGIS 8.2 © ESRI), 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 location problem.

The solution obtained with either clustering methods are valid and could be used to decide the location of these plants. SOM provided more robust and reproducible results than K-means, with the disadvantage of longer computing times. The main advantage of K-means is the reduced computing time. Additionally it allows us to obtain the best solutions in the majority of cases, in spite of bigger variances and more geographical dispersion.

Key words: CCA-treated wood waste; location models; Self-Organizing Maps; K-means; optimisation

Optimisation de la Localisation des Unités de Remédiation des Résidus de Bois Traités

Résumé. Le principal objectif de cette étude est d'optimiser la localisation des usines de remédiation pour traiter les déchets de bois imprégné avec CCA, en minimisant les coûts et en respectant les critères environnementaux. Dans les prochaines décennies, au Portugal, les quantités de bois traité, devant être annuellement correctement entreposés, augmenteront considérablement. Le recyclage de ces déchets, contenant chrome, cuivre et arsenic, devrait seulement être fait après leur remédiation, donc la planification et l'optimisation des sites des usines sont très importantes.

Le modèle de localisation a été implémenté grâce à l'information géographique en utilisant des systèmes d'information géographiques (ArcGIS 8.2 © ESRI), à l'occupation du sol et aux résultats d'un questionnaire envoyé aux industries d'imprégnation du bois.

Deux méthodes de groupage différentes (Self-organizing Maps et K-means) ont été examinées dans différentes conditions. Les solutions obtenues avec les deux méthodes sont viables et pourraient être employées pour décider de l'endroit de l'emplacement de ces usines. SOM a fourni des résultats plus robustes et reproductibles que K-means, avec l'inconvénient de durées de calcul plus longues. Le principal avantage de K-means, comparé à SOM, est la durée de calcul plus réduite et l'obtention de meilleures solutions dans la plupart des tests, malgré de grandes variations et une dispersion géographique.

Mots clés: déchets de bois traité avec CCA; modèles de localisation; Self-Organizing Maps; K-means; optimisation

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Entregue para publicação em Março de 2005

Aceite para publicação em Abril de 2005

¹ 1º Autor E-mail: helenaig@netcabo.pt