Reducing the Dimensionality of Criteria in Multi-Objective Optimisation of Biomass Energy Supply Chains

This contribution presents a novel approach, by which the number of direct environmental footprints is reduced to a minimum number of “independent” ones (INDFs) through correlations among the footprints that show similar behaviour. The correlations are investigated between direct carbon, energy, water, water pollution, and land footprints. Those footprints that show similar behaviour are grouped in subsets of correlated footprints. In each subset only one footprint, an INDF is taken into the multiobjective optimisation, whilst the rest of the “dependent” footprints (DFs) are evaluated after the optimisation from the INDFs. In this way, the dimensionality of the criteria within the multi-objective optimisation is significantly reduced, so that a multi-parametric optimisation is performed with INDFs as parameters. The subjective weighting of environmental and social indicators or footprints is thus avoided. This novel approach is illustrated using a demonstration case study of different biomass energy supply chains.

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