Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania19104, USA.
Environ Sci Technol. 2010 Nov 15;44(22):8773-80. doi: 10.1021/es102091a.
Renewable and low carbon fuel standards being developed at federal and state levels require an estimation of the life cycle carbon intensity (LCCI) of candidate fuels that can substitute for gasoline, such as second generation bioethanol. Estimating the LCCI of such fuels with a high degree of confidence requires the use of probabilistic methods to account for known sources of uncertainty. We construct life cycle models for the bioconversion of agricultural residue (corn stover) and energy crops (switchgrass) and explicitly examine uncertainty using Monte Carlo simulation. Using statistical methods to identify significant model variables from public data sets and Aspen Plus chemical process models,we estimate stochastic life cycle greenhouse gas (GHG) emissions for the two feedstocks combined with two promising fuel conversion technologies. The approach can be generalized to other biofuel systems. Our results show potentially high and uncertain GHG emissions for switchgrass-ethanol due to uncertain CO₂ flux from land use change and N₂O flux from N fertilizer. However, corn stover-ethanol,with its low-in-magnitude, tight-in-spread LCCI distribution, shows considerable promise for reducing life cycle GHG emissions relative to gasoline and corn-ethanol. Coproducts are important for reducing the LCCI of all ethanol fuels we examine.
在联邦和州层面制定的可再生和低碳燃料标准要求对替代汽油的候选燃料（如第二代生物乙醇）的生命周期碳强度（LCCI）进行估计。为了高度自信地估计此类燃料的 LCCI，需要使用概率方法来考虑已知的不确定性来源。我们为农业残留物（玉米秸秆）和能源作物（柳枝稷）的生物转化构建生命周期模型，并使用蒙特卡罗模拟法明确地检查不确定性。我们使用统计方法从公共数据集和 Aspen Plus 化学过程模型中识别重要模型变量，估算这两种原料与两种有前途的燃料转化技术相结合的随机生命周期温室气体（GHG）排放。该方法可以推广到其他生物燃料系统。我们的结果表明，柳枝稷-乙醇由于土地利用变化的 CO₂通量和 N 肥料的 N₂O 通量不确定，其 GHG 排放具有较高的不确定性。然而，玉米秸秆-乙醇的 LCCI 分布幅度小且范围窄，相对于汽油和玉米-乙醇，具有降低生命周期 GHG 排放的巨大潜力。对于我们研究的所有乙醇燃料，联产产品对于降低 LCCI 都很重要。
