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.
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Notes
- 1.
CLCA identifies and models all processes in the background system of a system in consequence of decisions made in the foreground system (European Union 2010).
- 2.
ALCA inventories the inputs and output flows for all processes of a system as they occur (European Union 2010).
- 3.
In this case, the complexity of the problem is linked to the dimension of the A matrix.
- 4.
Individuals are classified according to their fitness score. The position of an individual in the classification represents its rank.
- 5.
The expected value of an individual (number of times that is expected that the individual is chosen for the reproduction) is its fitness divided by the medium fitness of the population.
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Cellura, M., Longo, S., Marsala, G., Mistretta, M., Pucci, M. (2013). The Use of Genetic Algorithms to Solve the Allocation Problems in the Life Cycle Inventory. In: Cavallaro, F. (eds) Assessment and Simulation Tools for Sustainable Energy Systems. Green Energy and Technology, vol 129. Springer, London. https://doi.org/10.1007/978-1-4471-5143-2_13
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