The use of genetic algorithms to solve the allocation problems in the life cycle inventory

Abstract

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