The Impact of Biomass Availability and Processing Cost on Optimum Size and Processing Technology Selection

Abstract

Biomass processing plants have a trade-off between two competing cost factors: as size increases, the economy of scale reduces per unit processing cost, while a longer biomass transportation distance increases the delivered cost of biomass. The competition between these cost factors leads to an optimum size at which the cost of energy produced from biomass is minimized. Four processing options are evaluated: power production via direct combustion and via biomass integrated gasification and combined cycle (BIGCC), ethanol production via fermentation, and syndiesel via Fischer Tropsch. The optimum size is calculated as a function of biomass gross yield (the biomass available to the processing plant from the total surrounding area) and processing cost (capital recovery and operating costs). Higher biomass gross yield and higher processing cost each lead to a higher optimum size. For most cases, a small relaxation in the objective of minimum cost, 3%, leads to a halving of plant size. Direct combustion and BIGCC each produce power, with BIGCC having a higher capital cost and conversion efficiency. When the delivered cost of biomass is high, BIGCC produces power at a lower cost than direct combustion. The crossover point at which this occurs is calculated as a function of the purchase cost of biomass and the biomass gross yield.

Comparative Evaluation of Three Sugarcane Supply Strategies in Colombia: Logistics, Energy and GHG Emissions

“…The delivery of GHR to a projected bioethanol plant could be complex due to the low energy density, high moisture content and dispersed characteristics of the fuel which make it economically not feasible on long-distance travel [20,21]. Sugarcane GHR can be used as a potential feedstock for the second ethanol production and hence, hit the ethanol market in Colombia to meet the blending biofuel regulations set by the government.…”

“…The environmental load of the mobile and stationary operations is the summation of the calculated CO2 equivalent emissions through the following equations: (20) where, EL is the environmental load of each operation in kg CO2 equivalent per GHR dry-t; EM is the emissions of kg CO2 equivalent per functional unit of each operation (e.g. kg CO2 eq/ kWh electricity from mix grid), and GHGE is the total greenhouse gas emissions of the logistics operation in kg CO2 eq/dry-t GHR.…”

Modeling biomass procurement tradeoffs within a cellulosic biofuel cost model

“…Includes co-product credit but excludes capital depreciation and average return on investment. b dt denotes dry tons c Cameron et al (2007), De Wit et al (2010, Gan (2007), Kaylen et al (2000), Kumar et al (2003), Leboreiro and Hilaly (2011), Flynn (2009), andBrown (2007a). d Reported value includes biomass loading and unloading costs.…”

Biomass transportation model and optimum plant size for the production of ethanol

“…Following the approach of Nguyen and Prince (1996), Wright and Brown (2007) analyzed the optimum plant size of bio-refineries for several technologies including the production of ethanol from energy crops (with a relative high yield) via the bio-chemical route. Searcy and Flynn (2009) also investigated the optimum plant size for a bio-ethanol plant via fermentation using a simplified model based on regressing a capital investment power law function from data reported in the literature; the authors reported that allowing a 3% increase in production cost leads to a reduction of more than 50% in the optimum plant size.…”

“…Some ambiguity exists regarding the capital investment scaling exponent for bio-refineries. Previous research indicates that the investment for biomass-based plants scale with higher values of the exponent than petroleum refineries and are in the range of 0.70-0.94 (Fisher et al, 1986;Nguyen and Prince, 1996;Jenkins, 1997;Larson and Marrison, 1997;Bain et al, 1998;Kumar et al, 2003;Cameron et al, 2007;Searcy and Flynn, 2009). Even though higher scaling exponents for bio-based plants have been reported, several researchers have used petroleum based scaling exponents in the economic analysis of bio-fuel plants.…”