(517j) Used Cooking Oil Supply Chain Analysis, Optimization and Integration in the City of Bogotá, Colombia.

Global population growth involves increasing challenges regarding the sustainable development of our society, and in particular regarding the correct management of residues mainly in overpopulated areas. Without proper management regulations and practices, residues can cause a cascade of environmental, social and economic problems. Among the different urban residues, used cooking oil (UCO) is a highly problematic food waste which is typically disposed through syphons and drains. These disposal practices generate a variety of problems including infrastructure damage, flooding, vectors proliferation, ecosystems pollution, and even illegal collection and redistribution. A main reason for the mismanagement is the large economical cost involved in the mitigation of such practices.

The effects of UCOs mismanagement are sharply affecting densely populated urban centers mainly in developing countries. Particularly in Bogotá, the capitol city of Colombia, with nearly 10 million people in the metropolitan area, UCOs represent a major issue. According to recent estimations the city generates at least 45 kt UCO/yr. and the local government expends nearly 6 to 8 million dollar per year on corrective actions and maintenance related to UCOs mismanagement. Thus, urgent actions are required to promote the correct UCOs handling and possible exploitation. In this regard, this work deals with the study, characterization and optimization of a UCOs collection chain in the city of Bogotá for their further use as oleochemical feedstock.

First, data collection characterizing the UCOs logistic chain in the city of Bogotá was carried out. This task involved the location and nature of generators, available volumes, UCOs characteristics, characterization of collecting practices and routes, and the inventory of resources consumption during the collection process. Then, a model of the collection scheme was constructed and implemented in Python, using a Vehicle Routing Problem approach using weighted Hamiltonian paths. Weighting factors were defined according to economic and environmental indicators calculated as costs and equivalent-CO₂ emissions, respectively. Using a Genetic Algorithm with combinations as individuals, a reduced set of preferred collection routes were identified as optimal candidates. Finally, the scheme was integrated with the location of a hypothetical biorefinery facility with a potential portfolio of UCOs derived byproducts. Then, location of the facility and product portfolio distribution were simultaneously optimized using an Evolution Strategy algorithm. As result, an optimized collection scheme integrated with a facility whose optimal product portfolio and production capacity was identified.