Dimensionality Reduction Approach for Multi-Objective Optimisation Extended to Total Footprints
Lidija Čuček, Jiří Jaromír Klemeš, Petar Sabev Varbanov, Zdravko Kravanja · 2013
This contribution presents an extension of the novel criteria reduction method in multi-objective optimisation (MOO) – a Representative Footprint Method (RFM), from direct to total footprints. By applying RFM, the number of environmental criteria is reduced to a minimum number of representative footprints. Footprints with similar behaviour are grouped into subsets. Representative footprints are then selected, one for each subset, and MOO is carried out in respect to this reduced set of footprints. The similarities amongst footprints are investigated between different direct (direct burden on environment), indirect (unburdening by products’ substitutions, and utilising harmful raw-materials), and total footprints (the sum of direct and indirect footprints). Different footprints are considered, such as carbon (CF), energy (EF), water (WF), water pollution (WPF), and land (LF) footprints. The presented approach is illustrated using a demonstration case study of a mixed-integer linear programming (MILP) synthesis of biomass energy supply chains. This case study indicated that RFM is applicable for dimensionality reduction when considering total footprints.