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Intelligent connection system for circular economy and industrial symbiosis using computational intelligence

Abstract

The linear and open economic system does not take into account the impacts generated in the use of resources and waste generation and the unsustainability of this process brings to light discussions about the need for a new closed model in which environment and economy have a circular relationship. Thus, the Circular Economy (CE) emerges in several countries to keep the product, its benefit, its materials and resources in the production chain, minimizing the generation of waste and the consumption of new natural resources. However, for the migration from a linear model to CE to be possible, there must be a collective engagement of different social sectors, together with the development of new business models and public policies that encourage this model and promote conditions for this transition. To this end, a point of great importance is the reinsertion of post-consumer products in the production chain through Reverse Logistics.In this context, the Industrial Symbiosis (SI) seeks to create a more competitive collective approach for industries, traditionally seen as separate entities, for exchanging materials and by-products. SI's focus is to encourage inter-organizational relationships throughout a supply chain that make it possible to work together towards more environmentally friendly activities. The IS principle encourages different parts of the supply chain to direct collaborative efforts to facilitate the development and implementation of more sustainable and mutually beneficial practices.Thus, this project aims to develop a platform to seek and establish connection and symbiosis between generators, collectors, destinations, cooperatives and other actors involved in waste management and reverse logistics, carrying out operations and searching for better disposal alternatives.For this, machine learning methodologies will be used, in order to extract characteristics of the obtained data and assist in the decision-making process. Added to this, the clustering of this information and the application of visualization techniques to produce heat maps of the highlighted points. (AU)

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