Dynamic model-based evaluation of process configurations for integrated operation of hydrolysis and co-fermentation for bioethanol production from lignocellulose Ricardo Morales-Rodriguez a , Anne S. Meyer b , Krist V. Gernaey c , Gürkan Sin a,⇑ a CAPEC, Dept. of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Lyngby, Denmark b Center of Bioprocess Engineering, Dept. of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Lyngby, Denmark c Center for Process Engineering and Technology, Dept. of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Lyngby, Denmark article info Article history: Received 21 June 2010 Received in revised form 3 September 2010 Accepted 9 September 2010 Available online 17 September 2010 Keywords: Bioethanol Process configuration Hydrolysis and co-fermentation Dynamic models SSCF abstract In this study a number of different process flowsheets were generated and their feasibility evaluated using simulations of dynamic models. A dynamic modeling framework was used for the assessment of operational scenarios such as, fed-batch, continuous and continuous with recycle configurations. Each configuration was evaluated against the following benchmark criteria, yield (kg ethanol/kg dry-biomass), final product concentration and number of unit operations required in the different process configura- tions. The results show that simultaneous saccharification and co-fermentation (SSCF) operating in con- tinuous mode with a recycle of the SSCF reactor effluent, results in the best productivity of bioethanol among the proposed process configurations, with a yield of 0.18 kg ethanol/kg dry-biomass. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction Biofuels can potentially contribute to alleviate the current cli- mate change and energy resource challenges, which today’s society is facing. However, turning biofuels production at industrial scale into a success story is only possible by solving a number of chal- lenges. This includes securing a sustainable feedstock supply as well as optimizing the techno-economic feasibility of cellulosic biomass conversion technologies by defining optimal process con- figurations (Gírio et al., 2010; Huber et al., 2006; Regalbuto, 2009). Thus far the transfer of these conversion technologies from proof-of-concept to industrial scale has been mainly done on an empirical basis that is typically inefficient and costly in terms of time and resource consumption (Aden et al., 2002; Gnansounou, 2010; Larsen et al., 2008). Although various flowsheet configura- tions have been reviewed and evaluated in the literature based on steady state models (Alvarado-Morales et al., 2009; Cardona and Sánchez, 2007; Lynd et al., 2008), quantitative modeling tools for the dynamic simulation and evaluation of different process flowsheet options have until now not been used for cellulosic ethanol production processes. The objective of this work was to develop a Dynamic Lignocel- lulosic Bioethanol (DLB1.0) modeling platform allowing the quan- titative simulation and comparison of different process configurations for 2nd generation (2G) bioethanol plants, thereby providing a basis for evaluation of the most promising process flowsheet. The study has taken a conventional process configura- tion (Margeot et al., 2009) as a base case using the dimensions and process conditions proposed by Aden et al. (2002). Dynamic models for each unit process operation, including pre-treatment, enzymatic hydrolysis and co-fermentation, have been imple- mented in one software platform (Matlab Simulink), and con- nected to obtain the plantwide dynamic model. This dynamic model has subsequently been used to simulate and evaluate differ- ent process configurations for 2G bioethanol production on the ba- sis of several benchmark criteria, notably the ethanol yield per unit biomass. 2. Methods 2.1. DLB1.0 mathematical models: pre-treatment, hydrolysis, co- fermentation and simultaneous saccharification and co-fermentation (SSCF) The model-based simulation framework involved two main parts: (1) the collection, analysis and identification of the most 0960-8524/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2010.09.045 ⇑ Corresponding author. Tel.: +45 4525 2806; fax: +45 4593 2906. E-mail address: gsi@kt.dtu.dk (G. Sin). Bioresource Technology 102 (2011) 1174–1184 Contents lists available at ScienceDirect Bioresource Technology journal homepage: www.elsevier.com/locate/biortech