Abstract
In this study, an integrated Geographic Information System – Mixed Integer Linear Programming framework was developed for the spatial analysis and optimization of biomass-to-biofuels supply chains across four Italian regions, supporting European Renewable Energy Directive III and Italy's National Integrated Energy and Climate Plan targets for advanced biofuels by 2030. High-resolution data from Italian National Agency for New Technologies, Energy and Sustainable Economic Development's Atlante delle Biomasse mapped municipal-level availability of residual feedstocks—pruning residues (Emilia-Romagna), vegetable oils from contaminated soils (Marche), Organic Fraction of Municipal Solid Waste (Campania), and cereal straw (Puglia)—alongside transport networks and conversion sites. A multi-period Mixed Integer Linear Programming model then optimized biorefinery locations, capacities, and logistics to minimize production costs under resource, infrastructure, and scale constraints. Results reveal feedstock-driven topologies: low-density/wet Organic Fraction of Municipal Solid Waste favors distributed networks (7 plants at 80 kt/y in Campania); high-density oils enable single large-scale facilities (92 kt/y fatty acid methyl ester in Marche); concentrated prunings yield two 84 kt/y methanol plants (Emilia-Romagna); northern Puglia straw supports one 400 kt/y ethanol plant. Total output reaches 234 kt/y advanced biofuels from ~20% regional potentials, cutting logistics costs up to 40% of operating costs. The Web Geographic Information System – Mixed Integer Linear Programming approach captures interactions between biomass traits, terrain morphology, and supply-chain design, outperforming prior models through granular multi-feedstock flexibility. It provides actionable decision support for regional biofuel strategies, circular economy deployment, and net-zero infrastructure planning.
