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(596d) Enhancing Glucose Yield By Modeling of Cellulose Accessibility: Application of Model Predictive Control to Alkaline Pretreatment for Bioethanol Production

Choi, H. K. - Presenter, Texas A&M University
Son, S. H., Seoul National University
Kwon, J., Texas A&M University
As a sustainable alternative for petroleum-derived fuel, biofuels have attracted much attention over the last few decades [1-2]. However, since the conversion of lignocellulosic biomass to cellulosic biofuel is not economically viable with existing methods, optimization of biorefinery processes is a key to successful commercialization of cellulosic biofuel [3]. Typically, four main processes are involved in the biorefinery: pretreatment, enzymatic hydrolysis, fermentation and distillation. Among them, pretreatment has become a bottleneck for commercialization of cellulosic biofuel production due to the compact and rigid structure of lignocellulosic biomass known as recalcitrance cellulose; the sugar yield after enzymatic hydrolysis is primarily determined by the degree of pretreatment [1,4]. Specifically, during the pretreatment step, the recalcitrant structure of biomass is disrupted by delignification, thereby increasing the cellulose accessible surface area for improved biomass enzymatic digestibility [5-6]. Nevertheless, existing kinetic models for pretreatments in the literature are not able to capture the evolution of the accessible surface area of biomass during pretreatment [7-8].

We developed a multiscale model for biomass pretreatment that can describe the dynamic evolution of cellulose accessible area of biomass. Specifically, the degradation events of several biomass components under alkali chemical solvents (called pretreatment) are modeled by developing a new kMC algorithm, and the transport phenomena of the continuous phases are captured by a mathematical model that has been widely used to model pulp digesters; this is reasonable as pulp digester and pretreatment processes share many common features and fundamental chemical reactions. Then, we modeled enzymatic hydrolysis, which follows pretreatment and converts pretreated lignocellulosic biomass to fermentable sugars, by adopting a kinetic model from the literature to calculate the glucose yield under given enzymatic hydrolysis conditions (e.g., hydrolysis time, concentration of enzymes) [9-10]. Therefore, by integrating the proposed multiscale model of pretreatment and the existing kinetic model of enzymatic hydrolysis, we were able to predict the glucose production over different pretreatment temperatures. Once this integrated model was validated against experimental data from the literature [11], a reduced-order model was developed to design a model-based feedback controller for enhanced cellulose accessible area that ultimately leads to higher glucose yield. Within the controller, the solvent temperature that directly affects the biomass is manipulated while minimizing the energy used for heating during pretreatment. The implementation of the control framework demonstrated that the proposed modeling and control approaches improved the glucose yield by 46% while consuming only 7.2% more heat energy than a conventional constant-temperature pretreatment method.


[1] Zhao X.; Zhang L.; Liu D. Biomass recalcitrance. Part II: Fundamentals of different pre-treatments to increase the enzymatic digestibility of lignocellulose, Biofuels, Bioproducts and Biorefining, 2012, 6, 561–579.

[2] Kim JS.; Lee YY.; Kim TH. A review on alkaline pretreatment technology for bioconversion of lignocellulosic biomass, Bioresource Technology, 2016, 199, 42–48.

[3] Zhao X.; Zhang L.; Liu D. Biomass recalcitrance. Part I: the chemical compositions and physical structures affecting the enzymatic hydrolysis of lignocellulose, Biofuels, Bioproducts and Biorefining, 2012, 6, 465–482.

[4] Zhu W.; Zhu JY.; Gleisner R.; Pan XJ. On energy consumption for size-reduction and yields from subsequent enzymatic saccharification of pretreated lodgepole pine, Bioresources Technology, 2010, 101, 2782–2792

[5] Xu J.; Cheng JJ.; Sharma-Shivappa RR.; Burns JC. Sodium hydroxide pretreatment of switchgrass for ethanol production, Energy Fuels, 2010, 24, 2113–2119.

[6] Mosier N.; Wyman C.; Dale B.; Elander R.; Lee YY.; Holtzapple M.; Ladisch M. Features of promising technologies for pretreatment of lignocellulosic biomass, Bioresource Technology, 2005. 96, 673–686.

[7] Rocha MSRS.; Pratto B.; Junior RS.; Almeida RMRG.; Cruz AJG. A kinetic model for hydrothermal pretreatment of sugarcane straw, Bioresource Technology, 2017, 228, 176–185.

[8] Liu J.; Gong Z.; Yang G.; Chen L.; Huang L.; Zhou Y. Luo X. Novel kinetic models of xylan dissolution and degradation during ethanol based auto-catalyzed organosolv pretreatment of bamboo, Polymers, 2018, 10, 1149.

[9] Zheng Y.; Pan Z.; Zhang R.; Jenkins BM. Kinetic modeling for enzymatic hydrolysis of pretreated creeping wild ryegrass, Biotechnology & Bioengineering, 2009, 102, 1558-1569.

[10] Kadam KL.; Rydholm EC.; McMillan JD. Development and validation of a kinetic model for enzymatic saccharification of lignocellulosic biomass, Biotechnol. Prog., 2004, 20, 698-705.

[11] Xu J.; Cheng JJ.; Sharma-Shivappa RR.; Burns JC. Sodium hydroxide pretreatment of switchgrass for ethanol production, Energy Fuels, 2010, 24, 2113-2119.