(264e) Multi-Objective Footprints-Based Synthesis of Integrated Bioethanol Production Systems

Energy consumption in the transport sector depends almost exclusively on fossil fuels – oil. The forecasts show a growing tendency of this sector in the following years. The price of oil on global markets continues to remain high. Peak oil production is predicted to start shortly or even that it has been already started (Narodoslawsky, 2007; Robelius, 2007; The Oil Drum). At the same time, a significant rise in carbon dioxide emissions from the burning of fossil fuels is raising the earth's temperature and is causing climate change (Hamilton, 2008). Therefore it is important to take actions towards energy efficiency, emissions reductions, fuel supply security, and low-carbon economy.

Biomass is renewable only if production is sustainable. Also, different research studies have showed that lower carbon footprints are achieved by utilizing biomass, when compared to energy generated from fossil sources. On the other hand higher amounts of nitrogen are emitted into air and water (Cherubini and Strømman, 2011; Bauer, 2008; Čuček et al., 2011).

This contribution focuses on the simultaneous integration of different technologies for converting starchy and lignocellulosic raw materials to bioethanol. Different paths are selected: biochemical, thermo-chemical and thermo-biochemical and integrated in one superstructure so that operating costs, energy and agricultural land are minimized and profit is maximized. The ethanol production is assumed to be centralized. Raw materials are assumed to grow on the agricultural area of the maximum of 50 000 hectares. The fossil energy used in the supply chain of bioethanol production is also calculated and analyzed. The degree of (un)sustainability of the bioethanol production system is evaluated by the carbon and nitrogen footprint and compared to gasoline supply chain. Multi-objective optimization is performed on bioethanol supply chain where net footprints are minimized. The optimization of the system is formulated as a mixed-integer non-linear programming (MINLP) problem. The problem is solved by the use of the Mixed-Integer Process Synthesizer MIPSYN using mass and energy balances and other constraints with profit maximization as the optimization criterion.

References

Bauer C., 2008, Life Cycle Assessment of Fossil and Biomass Power Generation Chains. An analysis carried out for ALSTOM Power services, PSI-report No. 08-05, Paul Scherrer Institute, Villigen, PSI, Switzerland

Cherubini F., and Strømman A. H., 2011, Life cycle assessment of bioenergy systems: State of the art and future challenges, Bioresource technology, 102, 437-451 

Čuček L., Klemeš J. J., and Kravanja Z., 2011, Overview of footprints and relations between carbon and nitrogen footprints, Chemical Engineering Transactions, 25, 923-928, DOI: 10.3303/CET1125154  

Hamilton L., 2008, Firewood and Woody Biomass and their Role in Greenhouse Gas Reduction, Agriculture Notes < www.homeheat.com.au> Accessed: April 2011

Narodoslawsky M., 2007, Sustainable Processes – The Challenge of the 21^st Century for Chemical Engineering, Proceedings of European Congress of Chemical Engineering (ECCE-6)

Robelius F., 2007, Giant Oil Fields -The Highway to Oil, Giant Oil Fields and Their Importance for Future Oil Production, Acta Universitatis Upsaliensis, Uppsala, Sweden

The Oil Drum <www.theoildrum.com> Accessed: April 2011