(452c) Online Monitoring and Real-Time Control of Lignin Molecular Properties

Closed-loop control of lignin properties represents a critical step toward reliable and sustainable lignin utilization. However, the dynamic control of lignin structural features–such as molecular weight distribution (MWd) and monolignol composition–has remained difficult due to the complexity inherent in biomasses [1, 2]. Moreover, limited online measurement becomes another barrier in real-time feedback control. Hence, we propose an integrated control framework that dynamically regulates lignin properties during fractionation.

First, the model predictive control (MPC) is enabled by a computationally efficient model. The population balance model (PBM) is used to capture key reaction dynamics including depolymerization and condensation based on chain length evolution [3]. Unlike previous modeling efforts, this framework explicitly tracks MWd, monolignol composition, and interunit bond configuration at reduced computational resources [4]. It offers a balance between structural resolution and computational efficiency, making it suitable for real-time implementation. However, acquiring these distributions in situ remains a significant challenge [5, 6]. To address this, solution viscosity was employed as an indicator of lignin MWd. Here, the molecular weights were calculated from solution viscosity, shear rate, and lignin concentration, and they were converted into MWd from the empirical dataset.

As a result, the MPC dynamically adjusted process inputs to maintain desired lignin properties under variable conditions. It drove the average molecular weight and monolignol ratio to the set-points within the error range of 5%, showcasing its excellent performance. Overall, this work introduces a scalable and generalizable framework for real-time lignin control, offering broader applicability to biopolymer processing systems.

Literature cited:

[1] Roberto R., Robin J., Matthew T.C., John R., Macro K., Pieter C.A.B., & Bert M.W., (2016). Paving the way for lignin valorisation: Recent advances in bioengineering, biorefining and catalysis. Angew. Chem. Int. Ed., 55, 8164-8215.

[2] Schutyser W., Renders T., Van den Bosch S., Koelewijn S. F., Beckham G. T., & Sels B. F., (2018). Chemical from lignin: an interplay of lignocellulose fractionation, depolymerisation, and upgrading. Chem. Soc. Rev., 47, 852-908.

[3] Bawareth B., Di Marino D., Nijhuis T.A., & Wessling M., (2018). Unravelling electrochemical lignin depolymerization. ACS Sustain. Chem. Eng., 6, 7565-7573.

[4] Kim J., Pahari S., Ryu J., Zhang M., Yang Q., Yoo C.G., & Kwon J.S.-I., (2024). Advancing biomass fractionation with real-time prediction of lignin content and MWd: A kMC-based multiscale model for optimized lignin extraction. Chem. Eng. J., 479, 147226.

[5] Khalili K.N.M., de Peinder P., Donkers J., Gosselink R.J.A., Bruijnincx P.C.A., & Weckhuysen B.M., (2021). Monitoring Molecular Weight Changes during Technical Lignin Depolymerization by Operando Attenuated Total Reflectance Infrared Spectroscopy and Chemometrics. ChemSusChem, 14, 5517-5524.

[6] Cui C., Zhu L., Ouyang J., Shen Y., Ren H., Yuan W., Zhou Z., & Qi F., (2022). Online investigation of lignin depolymerization via reactor-integrated electrospray ionization high-resolution mass spectrometry. Appl. Energ. Combust. S., 10, 100069.