Multi-objective Robust Optimization for the Design of Biomass Co-firing Networks

Biomass co-firing in coal power plants is an immediate and practical approach to reduce coal usage and pollutant emissions because only minor modifications are required. With direct co-firing, biomass can be used directly as secondary fuel in power plants to partially displace coal. Although it requires minimal investments, it can lead to equipment corrosion from unconventional fuel properties of the biomass–coal blend. With indirect co-firing, the risk of damage is minimized by separately processing biomass. The solid biochar by-product can be used as soil fertilizer to achieve further reductions in GHG emissions through carbon sequestration. However, as this calls for a separate biomass energy conversion plant, its investment cost is higher. Moreover, this system faces uncertainties from the inherent variability in biomass quality. This must be accounted for because mixing fuels results in the blending of their properties. In this work, a robust optimization model is proposed to design cost and environmentally effective biomass co-firing networks that decides on appropriate co-firing configurations and fuel blends. A case study is solved to demonstrate validity. Results of Monte Carlo simulation show that the robust optimal network configuration is relatively immune to uncertainty realizations as compared with the optimum identified with deterministic models.