(226a) Process Technology Evaluation for Lignocellulosic Bioethanol Production: Plantwide Configurations Using a Dynamic Modeling Approach | AIChE

(226a) Process Technology Evaluation for Lignocellulosic Bioethanol Production: Plantwide Configurations Using a Dynamic Modeling Approach


Morales-Rodriguez, R. - Presenter, Technical University of Denmark
Meyer, A. S. - Presenter, Technical University of Denmark
Gernaey, K. V. - Presenter, Technical University of Denmark
Sin, G. - Presenter, Technical University of Denmark

Biofuels have the potential to provide a more sustainable transport fuel resource compared to fossil fuels in the future. A number of countries led by USA and EU have established ambitious targets for increasing the share of renewable energy in the transport sector, e.g. USA has set targets for replacement of 20% of total gasoline for road transport by biofuels by 2022.

Some hurdles need to be overcome to allow meeting these targets. One of them is the provision of an improved technical and economical feasibility of lignocellulose based biofuels production technologies (such as bioethanol, biohydrogen, biobutanol, biomethanol, fischer-tropsch diesel, etc). Most of the efforts for developing and evaluating bioethanol production have been based on experimental lab-scale studies and steady state modeling approaches. The experimental studies can be costly and inefficient in terms of time and resources[1]-[5], while steady state models have a number of limitations such as:

a) The analysis of the complete plant involving different operational scenarios (fed-batch, continuous, continuous-recycle) cannot be performed. b) Dynamic process responses to disturbances, e.g. changes in feedstock composition among others, could not be analyzed. c) Control strategies needed to optimize and operate the plant cannot be evaluated. d) Dynamic phenomena such as inhibition of enzymatic activity or growth/ decay of microorganisms involved in the different process units are not accounted for.

In order to overcome the above-mentioned limitations, a dynamic modelling framework (DMF)[6] consisting of two phases, is employed to generate and evaluate several technological configurations based on a conventional process flowsheet[5] for bioethanol production. In the first phase, a collection, analysis and identification of the most promising mathematical models is carried out. This includes unit operations such as pretreatment[7], enzymatic hydrolysis[8], co-fermentation[9], and downstream processes among others. In the second phase of the DMF, the generation of different process configurations is carried out employing diverse operational scenarios such as, fed-batch, continuous, continuous-recycle which are simulated using an object-oriented modeling tool (MatLab/Simulink). The benchmarking criteria used to find the best process configurations include the ethanol productivity and optimal operation of the complete process flowsheet for bioethanol production from lignocellulosic biomass. The dynamic model-based simulation framework can contribute to development of economically feasible bioethanol production strategies and process configurations. These strategies could be improved by generating and evaluating novel process flowsheet configurations and operational recipes. The simulation platform can also be used for operator training in the plant, e.g. to anticipate and predict possible responses of the plant as a consequence of different types of disturbances.

References [1]. America's Energy Future Panel on Alternative Liquid Transportation Fuels; National Academy of Sciences.(2009) Liquid Transportation Fuels from Coal and Biomass: Technological Status, Costs, and Environmental Impacts National Academy of Engineering; National Research Council. [2]. Aden, A., Ruth, M., Ibsen, K., Jechura, J., Neeves, K., Sheehan, J., Wallace, B., Montague, L., Slayton, A., Lukas, J., 2002. National Renewable Energy Laboratory Technical Report. NREL/TP-510-32438. [3]. Larsen, J., Petersen, M.Ø., Thirup, L., Li, H.W., Iversen, F.K., 2008. Chem. Eng. Technol. 31, 765-772. [4]. Cardona, C.A., Sánchez, O.J., 2007. Bioresources Technol. 98, 2471-2457. [5]. Margeot, A., Hahn-Hagerdal, B., Edlund, M. Slade, R., Monot, F., 2009. Curr. Opin. Biotechnol. 20, 371-380. [6]. Sin, G., Meyer, A. S., Gernaey, K. V., 2010. Comput. Chem. Eng. doi:10.1016/j.compchemeng.2010.02.012. [7]. Lavarack B.P., Griffin G.J. Rodman D., 2002. Biomass Bioenergy. 23, 367-380. [8]. Kadam, K.L. Rydholm, E.C. McMillan, J.D., 2004. Biotechnol. Prog. 20, 698-705. [9]. Krishnan M.S., Ho N.W.Y. Tsao G.T., 1999. Appl. Biochem. Biotechnol. 77-79, 373-388.