Abstract
The energy recovery of agro-industrial residual biomass offers a pathway to reduce fossil fuel emissions in thermal processes while valorizing waste. In practice, however, the primary bottleneck is logistical: feedstocks are geographically dispersed, with low bulk density and high moisture content, driving up collection, pretreatment, and transport costs. This work combines geospatial processing with mathematical optimization to design a multi-stage logistics network. The model incorporates intermediate densification options and technology selection (chipping, pelletizing, or briquetting) to supply one or more final waste-to-energy plants. The case study focuses on Northeastern Uruguay, considering forestry residues, meat-processing waste, and rice husks. We formulate a multi-period Mixed-Integer Linear Programming (MILP) model aimed at minimizing the total annualized cost, encompassing transportation, logistical operations, capital investment, and plant O&M, subject to supply constraints, capacities, and maximum transport distances. Results show that the optimal configuration corresponds to the briquetting scenario, which includes a fixed densification facility and processes 200, 160 t/y of biomass from 18 sawmills. Under this configuration, the system generates 276, 954 MWh, with a net margin of USD 5.77/MWh and a net profit of USD 1, 596, 662 over the planning horizon. Sensitivity analysis indicates that the electricity selling price is a critical parameter, with a tolerance margin of only 8%, while mobile densification is not economically viable under current market conditions. Beyond the specific case study, the main contribution of this work is the optimization model itself, whose general and configurable formulation allows its application to other regions, residue types, and market conditions without reformulating its mathematical structure.