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Производство оборудования и технологии

Techno-Economic Analysis and Future of Cellulosic Ethanol

17.1 Introduction

A few researchers from academic institutions, government labora­tories, and regulating agencies have studied the techno-economical aspects of large-scale cellulosic ethanol process to meet the global demand for fuel ethanol. Comprehensive techno-economic analy­sis for some cellulosic ethanol routes are found in technical reports from National Renewable Energy Laboratories (NREL), which is the national laboratory of the United States Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC. These technical reports are free to the public and can be downloaded at the NREL website: www. nrel. gov.

In addition to these, there is a small number of journal publica­tions on techno-economic analysis on individual cellulosic ethanol processes as well as comparison of two or more routes. Selected examples from recent literature on techno-economic analysis of cellulosic ethanol production will be discussed in this chapter. The accuracy and the ability of a techno-economic analysis to pre­dict the market price of biofuel is significant. Tao and Aden [1]

Table 17.1 Comparison of techno-economic analyses and market studies

for existing biofuels [



Market study ($/gal)

Techno-economic analysis prediction ($/gal)

Corn ethanol



Sugarcane ethanol



Soy biodiesel



performed a survey of techno-economic 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 and compared to published market studies. This comparison is shown in Table 17.1. As shown in this table, techno-economic analysis predictions are in reason­able agreement with the real market prices of corn or sugarcane ethanol and for soy biodiesel. Therefore, techno-economic analysis is a reliable tool in predicting the actual cost of production of these biofuels within the expected accuracy of such models.

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