(492c) A Computational Approach to Enhance the Economic Viability of Acid Fractionation Via Multiscale Modeling and Economic Model Predictive Control

During the past decade, the bioconversion of lignocellulose has been regarded as a potential alternative to conventional fossil fuel to meet the increasing energy demand and growing environmental concerns [1-3]. However, despite its availability and potentials, energy generated from the bioconversion of lignocellulose is relatively expensive compared to other conventional energy sources [4]. Therefore, several efforts have been made in the biorefinery process. First, to overcome the recalcitrance of lignocellulose toward chemical, enzymatic, and microbial conversions, diverse pretreatment strategies for economically favorable bioenergy production have been proposed. For instance, physical pretreatment (i.e., refining), acid or alkali pretreatments, oxidative or biological pretreatments, and auto-hydrolysis pretreatments have been investigated to enhance the accessible cellulose surface area of lignocellulose, which is a precondition for the intensified glucose yield [5-7]. Second, in an attempt to utilize the entire portion, lignocellulose is fractionated into its components such as lignin, cellulose, and hemicellulose followed by utilizing them as the raw materials for bioenergy, chemicals, and materials [8-10]. Particularly, although lignin has been considered as an undesired constituent because of its recalcitrant behavior, lignin valorization efforts have focused on lignin’s potential and turned the spotlight on its various usages such as dispersant, metal adsorbent, and carbon nanofibers [8, 11]. Likewise, fractionation is encouraged by both economic and ecological incentives. However, despite these given efforts, the economic viability still remains one of the major challenges that must be handled for the successful bioconversion, and therefore, an innovative method to address the techno-economic barrier is indeed required.

The economic feasibility of bioconversion is dependent on the cost of operating conditions (e.g., enzyme dosage and reaction temperature) and the revenue from the fractionated lignocellulose. However, finding an economically viable pathway of fractionation is impeded by the lack of understanding on the reaction process. For example, even though the recalcitrance of lignocellulose is determined by many factors such as cellulose accessibility, lignin content, and degree of polymerization (DP) of cellulose, the contribution of each one has not been fully elucidated, thereby veiling the profitability [12-13]. Moreover, even though the operating conditions play a crucial role in determining the quality of the fractionated lignocellulosic components (e.g., purity, DP, and yield), limited effort has been made to decipher the effect of the operating conditions on the economy of the lignocellulose fractionation [14-15]. It is attributed to the insufficient understanding of the nanoscale events during the fractionation, which is attributed to the fact that the previously developed deterministic models are not able to describe the potential pathways of fractionation at a molecular level, such as the evolution of DP and accessible surface areas.

Motivated by the limitation, in this work, a multiscale model was developed to capture the multidimensional phenomena during the acid fractionation under lignin-derivable phenol-4-sulfonic acid (PSA) solvent. First, a first-principle model was developed to describes the macroscopic properties such as concentrations of lignin, cellulose, and PSA during the fractionation process. Then, a kinetic Monte Carlo (kMC) algorithm, which is capable of capturing microscopic phenomena such as the evolution of DP and component accessibility, was integrated with the first-principle model. Specifically, since the lignin and cellulose are cleaved into smaller compounds during the fractionation process, these microscopic events were described by taking into account random and specific chain scission events in the kMC algorithm. Thereafter, reaction parameters were estimated, and the developed multiscale model was validated by experimental data sets. The developed model was then incorporated into the economic model predictive control (EMPC) system to compute the optimal input sequences which bring the maximum profit while balancing the operating cost (i.e., heat and PSA usage) and the expected revenue (i.e., glucose and lignin recovery).

References

[1] Satlewal, A., Agrawal, R., Bhagia, S., Sangoro, J., & Ragauskas, A. J. (2018). Natural deep eutectic solvents for lignocellulosic biomass pretreatment: recent developments, challenges, and novel opportunities. Biotechnology advances, 36(8), 2032-2050.

[2] Wyman, C. E., Dale, B. E., Elander, R. T., Holtzapple, M., Ladisch, M. R., & Lee, Y. Y. (2005). Coordinated development of leading biomass pretreatment technologies. Bioresource technology, 96(18), 1959-1966.

[3] Zhu, J. Y., & Pan, X. J. (2010). Woody biomass pretreatment for cellulosic ethanol production: technology and energy consumption evaluation. Bioresource technology, 101(13), 4992-5002.

[4] Loow, Y. L., Wu, T. Y., Jahim, J. M., Mohammad, A. W., & Teoh, W. H. (2016). Typical conversion of lignocellulosic biomass into reducing sugars using dilute acid hydrolysis and alkaline pretreatment. Cellulose, 23(3), 1491-1520.

[5] Patinvoh, R. J., Osadolor, O. A., Chandolias, K., Horváth, I. S., & Taherzadeh, M. J. (2017). Innovative pretreatment strategies for biogas production. Bioresource technology, 224, 13-24.

[6] Samuel, R., Cao, S., Das, B. K., Hu, F., Pu, Y., & Ragauskas, A. J. (2013). Investigation of the fate of poplar lignin during autohydrolysis pretreatment to understand the biomass recalcitrance. Rsc Advances, 3(16), 5305-5309.

[7] Den, W., Sharma, V. K., Lee, M., Nadadur, G., & Varma, R. S. (2018). Lignocellulosic biomass transformations via greener oxidative pretreatment processes: access to energy and value-added chemicals. Frontiers in chemistry, 6, 141.

[8] Renders, T., Van den Bosch, S., Koelewijn, S. F., Schutyser, W., & Sels, B. F. (2017). Lignin-first biomass fractionation: the advent of active stabilisation strategies. Energy & environmental science, 10(7), 1551-1557.

[9] Glasser, W. G., & Wright, R. S. (1998). Steam-assisted biomass fractionation. II. Fractionation behavior of various biomass resources. Biomass and Bioenergy, 14(3), 219-235.

[10] Sannigrahi, P., & Ragauskas, A. J. (2013). Fundamentals of biomass pretreatment by fractionation. Aqueous pretreatment of plant biomass for biological and chemical conversion to fuels and chemicals, 201-222.

[11] Ragauskas, A. J., Beckham, G. T., Biddy, M. J., Chandra, R., Chen, F., Davis, M. F., ... & Wyman, C. E. (2014). Lignin valorization: improving lignin processing in the biorefinery. science, 344(6185).

[12] Himmel, M. E., Ding, S. Y., Johnson, D. K., Adney, W. S., Nimlos, M. R., Brady, J. W., & Foust, T. D. (2007). Biomass recalcitrance: engineering plants and enzymes for biofuels production. science, 315(5813), 804-807.

[13] Zhao, X., Zhang, L., & Liu, D. (2012). Biomass recalcitrance. Part I: the chemical compositions and physical structures affecting the enzymatic hydrolysis of lignocellulose. Biofuels, Bioproducts and Biorefining, 6(4), 465-482.

[14] Park, E. D., & Lee, J. S. (1999). Effects of pretreatment conditions on CO oxidation over supported Au catalysts. Journal of Catalysis, 186(1), 1-11.

[15] Li, C., Knierim, B., Manisseri, C., Arora, R., Scheller, H. V., Auer, M., ... & Singh, S. (2010). Comparison of dilute acid and ionic liquid pretreatment of switchgrass: biomass recalcitrance, delignification and enzymatic saccharification. Bioresource technology, 101(13), 4900-4906