(458c) Kinetic Modeling and Operability Analysis for the Optimization and Advanced Control of Xanthan Gum Production

Xanthan gum is a highly viscous biopolymer produced by bacteria from the genus Xanthomonas with several applications in industries such as for food and cosmetics production, and oil extraction [1]. However, the high viscosity culture broth poses major obstacles for the production of xanthan gum. In particular, challenges arise due to the poor control of process mass transfer and oxygen supply, which ultimately result in process efficiency issues [2-3]. The conventional strategy for controlling oxygen supply in stirred-tank reactors is based on measuring the dissolved oxygen concentration and manipulating the impeller rotation speed, air and oxygen flows. Normally, bioreactor monitoring systems are already equipped with PID (Proportional-Integral-Derivative) controllers for this purpose [4-5]. Nevertheless, there is an inherent limitation of this method related to the biological and rheological conditions, which produce a heterogeneous culture medium and unstable delays in sensor signals. For example, at certain product concentrations, the system starts to show instability in the controlled and manipulated variables. Motivated by these challenges, this work addresses the development of a kinetic modeling and operability analysis framework for the optimization and advanced control of xanthan gum production.

Modeling the behavior of the process (assuming non-Newtonian broth behavior), enables the model-based optimization and control of the production of the Xanthan gum biopolymer. In particular, a kinetic model is developed for Process Operability and Model Predictive Control (MPC) purposes describing the evolution of biomass, product, substrate, viscosity, and oxygen consumption, for oxygen transfer rate control. In this approach, the culture broth rheology can be controlled and maintained by manipulating the feed and broth outlet rates of a CSTR, air, and O₂ inlet to keep the oxygen and substrate fluxes within their optimal ranges. Specifically, process operability provides a better understanding of the feasible operating regions during the design stage to help in the controllability assessment of nonlinear systems, including bioprocesses [6-8]. In this method, a feasible working region for the controller can be defined by the achievable output set (AOS), considering the kinetic model and the process inputs within the available inputs set (AIS). This AOS region can then be compared with the desired output set (DOS) that includes desired indicators, such as dissolved oxygen and broth viscosity, and a desired input set (DIS) is defined to satisfy process goals [9-10]. As motivated above, an advanced control algorithm is required to stabilize the operating conditions due to the complex nonlinear nature of this process. Results of the process simulation applying the operability analysis will be discussed toward the future application of the MPC.

[1] Rosalam S, England R. Review of xanthan gum production from unmodified starches by Xanthomonas comprestris sp . 2006;39:197–207.

[2] Amanullah A, Tuttiett B, Nienow AW. Agitator speed and dissolved oxygen effects in xanthan fermentations. Biotechnol Bioeng. 1998;57(2):198–210.

[3] García-Ochoa F, Castro EG, Santos VE. Oxygen transfer and uptake rates during xanthan gum production. Enzyme Microb Technol. 2000;27(9):680–90.

[4] Alford JS. Bioprocess control: Advances and challenges. Comput Chem Eng. 2006;30(10–12):1464–75.

[5] Diaz C, Dieu P, Feuillerat C, Lelong P, Salome M. Adaptive predictive control of dissolved oxygen concentration in a laboratory-scale bioreactor. J Biotechnol. 1995;43(1):21–32.

[6] Mesquita TJB, Campani G, Giordano RC, Ribeiro MPA, Horta ACL, Zangirolami TC, Lima FV. Operability and biomimetic control of a micro-aerated fermentation process. Comput Chem Eng. 2021;155:107511. Available from: https://doi.org/10.1016/j.compchemeng.2021.107511

[7] Demirel SE, Li J, Hasan MF. Systematic process intensification. Curr Opin Chem Eng. 2019;25:108–13. Available from: https://doi.org/10.1016/j.coche.2018.12.001

[8] Svensson, E., Eriksson, K., Wik, T. Reasons to apply operability analysis in the design of integrated biorefineries. Biofuels, Bioprod. Biorefining. 2015; doi:10.1002/bbb.1530.

[9] Gazzaneo V, Carrasco JC, Vinson DR, Lima FV. Process Operability Algorithms: Past, Present, and Future Developments. Ind Eng Chem Res. 2020;59(6):2457–70.

[10] Gazzaneo V, Lima FV. Multilayer Operability Framework for Process Design, Intensification, and Modularization of Nonlinear Energy Systems. Ind Eng Chem Res. 2019;58(15):6069–79