Abstract
Abstract Development of fast, robust and reliable computational tools capable of addressing the process management under uncertain conditions, is an active topic in the current process systems engineering literature. In fact, scenario reduction strategies (for example SCENRED and OSCAR) have acquired a lot of attention to overcome the traditional issues associated to large-scale scenario-based problems. Thus, this work proposes a novel scenario-reduction alternative (henceforth known as SCANCODE approach) by combining Graph Theory to construct a network and community detection methods to identify the clusters within the network. The capabilities and limitations of the proposed approach were tested through the two-stage MILP optimisation of a bio-based energy network under raw material availability and energy demand uncertainties. For comparison purposes, the same problem was solved using the sets of scenarios obtained with SCENRED and OSCAR. This comparison demonstrates the quality of SCANCODE approach while states the potential benefits of the proposed approach over the current alternatives.
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