271493 An Optimization-Based Technology Assessment Framework for Biofuel Production Strategies

Tuesday, October 30, 2012: 2:10 PM
323 (Convention Center )
Sercan Murat Sen1, Jiyong Kim1 and Christos T. Maravelias2, (1)Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, (2)Department of Chemical and Biological Engineering; DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI

In the course of the past few decades, various technologies have been developed to replace fossil-based routes for the production of chemicals and fuels with sustainable and environmentally friendly biomass-based strategies. There are many alternative ways to utilize a certain biomass feedstock as a carbon source to produce a certain fuel and trade-offs between these alternative technologies make the selection of a production strategy a challenging task. Therefore, systematic efforts are required to analyze biomass-based production in terms of cost, energy efficiency, environmental impact, and productivity. Towards this aim, we propose an optimization-based framework to evaluate biomass-to-fuels strategies. We use this framework to identify limitations in existing approaches as well as identify new promising strategies.

First, we identify the major reactants and products of each technology, and by interconnecting these technologies we generate a “technology superstructure" that contains multiple strategies. Second, we gather from the literature or evaluate based on similar technologies the required technical and economic parameters: conversion coefficients, unit production cost, energy requirement, feedstock supply availability, and final product demand. Third, we develop a linear programming (LP) model to represent the underlying network structure. The model consists of: (i) material balances for all compounds, (ii) equations describing the consumption/production of compounds by technologies, (iii) production capacity constraints, and (iv) feedstock availability and/or product demand satisfaction constraints. With minor modifications in this model, we can address various types of questions (e.g., which feedstock/strategy is best for a given product, how a given feedstock can be utilized) using different types of criteria (e.g., economic, environmental). The model yields the optimal strategy (i.e., selection of technologies) for a given question and objective function. Fourth, we develop a mixed-integer programming (MIP) model to identify alternative strategies. Finally, we assess the uncertainty in the parameters of the various technologies based on their levels of maturity and complexity. Using these indicators, we assign uncertainty levels to each technology, which are subsequently used to determine a range for the unit production cost of each alternative strategy.

We illustrate the capabilities of the proposed framework with a case study. Ethanol can be produced from hardwood via hydrolysis (using dilute acid, ammonia fiber expansion, or hot water pretreatment), direct or indirect gasification, or pyrolysis. We find that, although gasification-based strategies have higher capital and operating costs, their unit production costs are lower than fermentation-based strategies mainly due to their high ethanol yields. Moreover, the byproduct (acetic acid) credit obtained in the gasification-based strategies significantly decreases the production cost (reduction of 24.6%), whereas the electricity credit in fermentation-based strategies lowers production costs by only 5.8~7.3%. Finally, sensitivity analyses reveal that the economics of gasification-based strategies can be improved primarily through system modifications (e.g. cheaper catalysts), while the cost of fermentation-based strategies can be lowered if cheaper feedstock is used since feedstock cost is the major cost driver for these strategies.


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