One of the most controversial issues in the development of Life CycleInventory (LCI) is the allocation procedure, which consists in the partition anddistribution of economic flows and environmental burdens among to each of theproducts of a multi-output system. Because of the use of the allocation represents asource of uncertainty in the LCI results, the authors present a new approach basedon genetic algorithms (GAs) to solve the multi-output systems characterized by arectangular matrix of technological coefficients, without using computationalmethods such as the allocation procedure. In this Chapter, the GAs’ approach isapplied to an ancillary case study related to a cogeneration process. In detail, theauthors hypothesized that there are the following multi-output processes in thecase study: (1) cogeneration of electricity and heat; (2) co-production of diesel andlight fuel oil; (3) co-production of copper and recycled copper. The energy andmass balances are respected by means of specific bonds that limit the space inwhich the GA searches the solution. The results show low differences between theinventory vector derived from the GA application and that one obtained applying the substitution method and the allocation procedure based on the energy contentof the outputs. To avoid the allocation, the application of GA to calculate the LCIseems to be a promising method.

The use of Genetic Algorithms to solve the allocation problems in the Life Cycle Inventory

Mistretta M;
2013

Abstract

One of the most controversial issues in the development of Life CycleInventory (LCI) is the allocation procedure, which consists in the partition anddistribution of economic flows and environmental burdens among to each of theproducts of a multi-output system. Because of the use of the allocation represents asource of uncertainty in the LCI results, the authors present a new approach basedon genetic algorithms (GAs) to solve the multi-output systems characterized by arectangular matrix of technological coefficients, without using computationalmethods such as the allocation procedure. In this Chapter, the GAs’ approach isapplied to an ancillary case study related to a cogeneration process. In detail, theauthors hypothesized that there are the following multi-output processes in thecase study: (1) cogeneration of electricity and heat; (2) co-production of diesel andlight fuel oil; (3) co-production of copper and recycled copper. The energy andmass balances are respected by means of specific bonds that limit the space inwhich the GA searches the solution. The results show low differences between theinventory vector derived from the GA application and that one obtained applying the substitution method and the allocation procedure based on the energy contentof the outputs. To avoid the allocation, the application of GA to calculate the LCIseems to be a promising method.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/11130
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