(647a) Process Optimization of Bioethanol Production Via Hydrolysis of Switchgrass

Process
optimization of bioethanol production via Hydrolysis of Switchgrass

Mariano Martín^a,b,
Ignacio E. Grossmann^b[1]

mariano.m3@usal.es; grossmann@cmu.edu

^a Departamento
de Ingeniería química y textil. Universidad de Salamanca. Plz. Caidos
1-5 37008, Salamanca (Spain)

^bDepartment of Chemical
Engineering. Carnegie Mellon University 5000 Forbes Avd. Pittsburgh PA 15213 

The
Energy act in 2007 established the increase in the production of bioethanol
using lignocellulosic raw materials to help reduce the dependence on crude oil.
This was based on the fact that among the different possibilities only biomass
provides an alternative fuel that can be implemented in the short-term for the transportation
sector due to its compatibility with current
automobile engines (Cole, 2007) and to the fact that it can take advantage of
the existing supply chain of liquid fuels that is already well established. Currently
the production of ethanol from lignocellulosic raw materials still faces technical,
economic and commercial barriers (Huang, 2008).

Two
types of process technologies can be used to transform lignocellulosic raw
materials into ethanol. The first one is based on
the gasification of the raw material into syngas, which is used to obtain
ethanol either via Fischer-Tropsch based catalytic reaction or via fermentation
of the syngas (Phillips et al, 2007; Huhnke, 2008;
 Piccolo and Bezzo, 2009; Zhu
et al, 2009). The second one
is based on the hydrolysis of the raw material to break down the physical and
chemical structure of the crops to expose the sugars that are fermented to
ethanol. Due to its similarity with the current production of ethanol and the
expected lower capital cost, this technology has received the attention of many
researchers, e.g. Hamelinck et al. (2005); Cardona
& Sánchez (2006); Zhang et al. (2009); Keshwani
& Cheng (2009).

In this
paper we develop a conceptual design for the production of ethanol from
lignocellulosic raw materials with hydrolytic pretreatment of the biomass using
mathematical programming techniques (Daichendt & Grossmann, 1997, Grossmann
et al., 1999). The process consist of three steps, raw material pretreatment to
expose the sugars, sugar fermentation to ethanol and ethanol dehydration to
fuel grade. We
propose a superstructure optimization approach where we first construct a
flowsheet embedding the various process units involved in ethanol production
considering a number of alternatives for switcgrass pretreatment such as ammonia
fiber explosion or dilute acid pretreatment (Sung and Cheng, 2002; Keshwani & Cheng,
2009) and for different
technologies that can operated in parallel and/or in sequence for dehydrating
the ethanol like rectification, adsorption in corn grits, molecular sieves and
pervaporation. These units are interconnected to each other through network
flows and other utility streams. The goal is to optimize the structure by minimizing
the energy input in the ethanol production process. The optimization of the
system is formulated as a mixed-integer nonlinear programming (MINLP) problem,
where the model involves a set of constraints representing mass and energy
balances for all the units in the system. We then substitute the distillation columns
by multieffect column to reduce the consumption on cooling water and steam and we
design the optimal heat exchanger network using SYNHEAT. The heat recovery
network, together with a modified distillation column design, further reduces
the energy consumption in the plant and in turn decreases the unit production
cost of ethanol. Finally, we perform an economic evaluation. For the optimal
flowsheet we design the optimal water network based on the work by Ahmetovic,
et al (2010). First, we identify the sources of water within the process
(distillation columns, utilities units), the sinks (fermentor, pretreatment)
and the process units (unitities units), and optimize the corresponding
superstructure for reuse and recycle of water and treatment units so as to
minimize the fresh water consumption.

The
results of the optimized conceptual design indicates that under the current
yields reported for the pretreatment processes (Sung and Cheng, 2002) the optimal
process for the production of ethanol from switchgrass involves the use of
dilute acid pretreatment, followed by enzymatic hydrolysis and sugar
fermentation and the use of molecular sieves for the final dehydration of
ethanol to fuel grade. The predicted production cost of this process is $0.8/gal
with an investment cost of $161MM, while the consumption of water is
1.6gal/gal. These results are compared with the themal based processes that
requires a higher investment of $335MM, but has a reduced production cost of
$0.41/gal and water consumption of 1 gal/gal (Martin & Grossmann, 2011,
Martin et al ,2010)

References

Ahmetovic, E.; Martín, M.; Grossmann,
I.E. (2010) ?Optimization of Water Consumption in Process industry: Corn ?
based ethanol case study?  Ind. Eng. Chem Res.  49 (17) 7972- 7982

Cardona, C.A., Sánchez. O.J. (2006)
Energy consumption analysis of integrated flowsheets for production of fuel
ethanol from lignocellulosic biomass. Enrgy , 31, 2447- 2459

Cole, D. E. (2007)  Issues facing the
Auto Industry: Alternative Fuels, Technologies, and Policies ACP Meeting Eagle
Crest Conference Center June 20, 2007

Daichendt, M. M., and I. E. Grossmann,
(1997) Integration of Hierarchical Decomposition and Mathematical Programming
for the Synthesis of Process Flowsheets, Comp. Chem. Eng., 22, 147.

Grossmann, I. E.; Caballero, J. A.;
Yeomans, H. (1999) Mathematical Programming Approaches to the Synthesis of
Chemical Process Systems?, Korean J. Chem. Eng., 16, 407-426.

Keshwani, D. R., Cheng, J.J. (2009)
Switchgrass for bioethanol and other value-added applications: A review
Bioresource Technology 100, 1515?1523

Hamelinck, C. N., Geertje van
Hooijdonk, G., Faaij, A.P.C. (2005) Ethanol from lignocellulosic biomass:
techno-economic performance in short-, middle- and long-termBiomass and
Bioenergy 28, 384?410

Huang, J. Qiu,
H. and Scott Rozelle, S., (2008), More pain ahead for China's food prices, Far
Eastern Economic Review, 171, 5, 8?13.

Huhnke, R. L. (2008) Cellulosic
ethanol using gasification-fermentation. Resource:
Engineering & Technology for a Sustainable World

http://www.articlearchives.com/energy-utilities/renewable-energy-biomass...

Martín, M.; Ahmetovic, E.;  Grossmann,
I.E. (2010) ?Optimization of Water Consumption in Second Generation bio-Ethanol
Plants ?  I&ECR doi: 10.1021/ie101175p

Martín, M., Grossmann, I.E. (2011)
?Energy optimization of lignocellulosic bioethanol production via gasification?
accepted AIChE J. | DOI: 10.1002/aic.12544

Phillips, S., Aden, A., Jechura, J.
and Dayton, D., Eggeman, T (2007) Thermochemical Ethanol via Indirect
Gasification and Mixed Alcohol Synthesis of Lignocellulosic Biomass Technical
Report, NREL/TP-510-41168, April 2007

Piccolo, C., Bezzo, F., (2009) A
techno-economic comparison between two technologies for bioethanol production
from lignocelluloses. Biomass and Bioenergy 33 (2009) 478 ? 491

Sun, Y., Cheng, J., (2002) Hydrolysis
of lignocellulosic materials for ethanol production: a review. Bioresour.
Technol.,  83, 1-11

Zhu, Y., Gerber, M.A., Jones, S.B.,
Stevens, D.J (2009) Analysis of the effects of compositional and
configurational assumptions on product costs for the thermochemical cornversion
of lignocellulosic biomass to mixed alcohols  FY 2007 Progress report. DOE
PNNL.17949 Revision 1.