1.5 Review of Related Technoeconomic Studies

Tao and Aden performed a survey of technoeconomic models of existing biofuels (corn ethanol, sugarcane ethanol, and conventional soy biodiesel) from the literature. These studies were normalized to a consistent year-dollar value and feedstock cost (where applicable) and compared to published market studies. This comparison, shown in Table 1, indicated that technoeconomic analysis was able to predict the actual cost of production of these biofuels within the expected accuracy of such models.

Developing a technoeconomic model for a pre-commercial technology, such as the corn-stover-to-ethanol process described in this report, requires a more ab initio approach, rooted in a thorough understanding of the state of the technology at the time of the analysis and good engineering practice.

In the years since the last NREL design report in 2002, several newer technoeconomic studies of biochemical cellulosic ethanol production have been published. Many of these studies were based to varying degrees on NREL’s previous design report, borrowing from its process assumptions, cost information, or both. This was in fact the principal goal of the earlier NREL design reports: to establish a baseline or “zero-point” technoeconomic benchmark from which process alternatives and improvements could be evaluated by others in the public realm. The present report now aims to establish a new zero-point and thereby serve a similar purpose for analyses going forward.

A brief survey of MESPs from recent technoeconomic studies of biochemical cellulosic ethanol production is presented in Table 2; note that these studies were not normalized to a consistent cost-year but were all published between 2008 and 2010, so cost-year differences should be minor. Clearly, there is a wide range of published MESP values within this subset of papers. For the most part, these are due to differences in feedstock cost, process assumptions, and co-product values, all of which vary considerably across the studies. For example, the analysis by Laser etс. assumes a fairly low feedstock cost and very high yields (indicating aggressive process assumptions) as well as improved economies of scale (if such a high feed rate can be sustained), while also receiving positive revenue from higher-value co-products such as protein and hydrogen. Furthermore, this study assumes a consolidated bioprocessing (CBP) approach, which—although less developed than separate saccharification and co-fermentation—could further improve economics by reducing enzyme costs. Conversely, studies on the high end of the MESP range such as Kazi etc. and Klein-Marcuschamer etc. assumed higher feedstock costs while achieving much lower ethanol yields. Compared to NREL’s 2002 design report (upon which many of its cost inputs were based), the Klein-Marcuschamer study’s base case assumes much longer batch times for saccharification and fermentation (thus higher associated capital costs), higher enzyme costs, and a lower carbohydrate fraction in the feedstock .

To further demonstrate the impact of such assumptions, we turn to the study of Kazi etc., a joint effort between Iowa State University, ConocoPhillips, and NREL. While this paper examined a variety of pretreatment and downstream processing alternatives, its baseline was essentially NREL’s 2008 State of Technology (SOT) model. The Kazi etc. study adjusted several key parameters from the 2008 SOT model to incorporate external public data. The feedstock cost, enzyme cost, indirect capital cost factors, and reaction conversions were all modified per data external to NREL analysis. While both analyses were intended to represent near-term or “state of technology” economics extrapolated to an n^th plant as discussed previously, the assumptions in the Kazi etc. study were generally more conservative than NREL’s 2008 SOT, raising the MESP considerably. Adjusting for these key economic assumptions, their MESP closely approaches that of the NREL 2008 SOT case. In many cases, economic variance between technoeconomic studies is easily explained by normalizing for a few important inputs.

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