A spatially explicit techno-economic model of bioenergy and biofuels production in California

Abstract

a b s t r a c tThis study presents a spatially explicit techno-economic Bioenergy Siting Model (BSM) of the bioenergy production system in California. The model describes the bioenergy system in terms of facility siting and size, conversion technology, feedstock profile, and feedstock supply chain configuration for the year 2015. The BSM expands upon previous bioenergy siting work by optimizing the system using spatially explicit feedstock supply curves, multiple potential conversion technologies and geographically determined bioenergy demand. We present sensitivity analysis demonstrating the effect of market and policy change scenarios. The model couples transportation network analysis using a Geographic Information System (GIS) with a mixed integer-linear programming (MIP) optimization model. Scenario results show total biomass resource utilization between 18 and 25 million dry tons annually at biofuel prices from $2.20 to $4.00/gallon of gasoline equivalent.

Forest biomass supply chain optimization for a biorefinery aiming to produce high-value bio-based materials and chemicals from lignin and forestry residues: a review of literature

“…The majority of the optimal location studies focus on bioenergy production, including fuels, heat, and power (e.g., Leduc et al 2008;Velazquez-Marti and Fernandez-Gonzalez 2010;Parker et al 2010;Tittmann et al 2010;Bai et al 2011;Kim et al 2011;Natarajan et al 2012Natarajan et al , 2014Palander and Voutilainen 2013;Marvin et al 2013;Pantaleo et al 2014). However, some publications addressed BSC optimization for chemicals and materials production, including location problems, for example, Zhang and Wright (2014), Santibañez-Aguilar et al (2015), Kokossis et al (2015), , Sukumara et al (2014), Dansereau et al (2014) (Table 3).…”

BVCM: A comprehensive and flexible toolkit for whole system biomass value chain analysis and optimisation – Mathematical formulation

“…Zhang et al [13] also used three similar echelons, with harvested biomass in the form of raw forest residue and road-side chippings, both of which can be transported independently to the preprocessing sites or directy to integrated sites, which can process raw biomass or preprocessed biomass. In Tittmann et al [14], the pre-processing echelon is ignored, yielding a simpler model but one that is unable to explore the trade-off 120 between densifying the biomass at source and saving on transport costs, transporting biomass as-received and saving on investments into densification technologies or even converting the biomass on site. Lin et al [15] focused only on bio-ethanol production using a similar echelon structure and model formulation as Zhang et al but with only one form of raw biomass.…”

Optimizing ethanol and bioelectricity production in sugarcane biorefineries in Brazil

“…In addition, have simulated stand-alone and integrated second generation ethanol production from sugarcane biomass considering different technological scenarios [28]. Tittmann et al (2010) have presented a spatially explicit technoeconomic optimization model of bioenergy and biofuels production system in California, considering location/size of bioenergy plants, conversion technologies, and feedstock profile and its supply chain configuration [33]. The model aims at maximizing the profit of a biofuel industry at given feedstock price, transportation cost, conversion cost, and price for fuels, electricity, and coproducts.…”