Advancing biomass fractionation with real-time prediction of lignin content and MWd: A kMC-based multiscale model for optimized lignin extraction
Advancing biomass fractionation with real-time prediction of lignin content and MWd: A kMC-based multiscale model for optimized lignin extraction
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DOI：
10.1016/j.cej.2023.147226
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发表时间：
2023-11
影响因子：
15.1
通讯作者：
Juhyeon Kim;Silabrata Pahari;Jiae Ryu;Mairui Zhang;Qiang Yang;Chang Geun Yoo;J. Kwon
中科院分区：
文献类型：
--
作者：
Juhyeon Kim;Silabrata Pahari;Jiae Ryu;Mairui Zhang;Qiang Yang;Chang Geun Yoo;J. Kwon
近年来，木质素因其在自然界中的丰富含量而引起了广泛的研究关注。然而，木质素被视为一个顽固的因素，因为它阻碍了整个生物质分馏。在这方面，为了有效地分离生物质组分，采用了苛刻的操作条件，但它们可能会导致木质素的大量降解。另一个问题是，木质素反应的整体动力学仍然有限，因为目前的模型主要集中在纤维素纤维上。这些都对开发有效的工业木质素提取分馏策略提出了挑战。为此，我们提出了一个多尺度模型，并开发了一个控制器来确定最优的操作策略。在木质素方面，脱木素和解聚/再聚合同时发生，但在不同的长度和时间尺度上。我们采用双层结构的常微分方程组和动力学蒙特卡罗(KMC)算法，考虑了多尺度反应动力学。我们的模型提供了包括主体切片中的木质素含量和木质素分子量分布在内的关键输出，并通过实验进行了验证。随后，我们建立了一个用于软测量设计的降阶模型(ROM)，并建立了一个模型预测控制器(MPC)来确定最优的操作策略，从而最大化利润。
Recently, lignin has garnered significant research attention due to its abundance in nature. However, lignin is viewed as a recalcitrance factor as it impedes the overall biomass fractionation. In this regard, harsh operating conditions have been applied for the effective separation of the biomass components but they may cause substantial lignin degradation. Another problem is that the overall kinetics of lignin reactions remain limited since current models primarily focus on the cellulose fiber. These pose a challenge when developing effective fractionation strategies for industrial lignin extraction. To this end, we propose a multiscale model and develop a controller to determine the optimal operation strategy. In terms of lignin, delignification and de/repolymerization happen simultaneously but in different length and time scales. We adopted a bilayer structure of the ODEs and kinetic Monte Carlo (kMC) algorithm, accounting for the multiscale reaction kinetics. Our model provides the key outputs including the lignin content in the bulk chip and lignin molecular weight distribution, which were validated with the experiments. Subsequently, we developed a reduced-order model (ROM) for soft sensor design and formulated a model predictive controller (MPC) to determine the optimal operation strategy and then maximize the profitability.