Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
2 results
Search Results
Article Fractionation of Guaiacyl and Syringyl-Lignin Units Using Organic Solvent Nanofiltration(Elsevier, 2026) Croes, Tim; Dutta, Abhishek; Van Aelst, Korneel; Sels, Bert; Van der Bruggen, Bart; Cornet, IrisA major obstacle to employing the full potential of lignin-based aromatics is the fractionation of the monomers present in lignin, specifically the separation of guaiacyl (G) and syringyl (S) units, which possess nearly identical molecular weights (196 Da versus 166 Da) and dimensions, and identical functional groups. Such similarities make their separation highly challenging using conventional techniques and are generally considered beyond the capabilities of size-based membrane processes. This study examines the feasibility of organic solvent nano-filtration for fractionation of guaiacyl and syringyl units, and how membrane and process parameters affect separation of these two molecules. Sixteen commercially available membranes were tested with methanol and ethyl acetate as solvents. The results demonstrate that, despite the extreme similarity of the solutes, selective separation is achievable and is primarily governed by membrane material and solvent selection rather than the pore size-based molecular weight cut-off. Polyimide-based solvent-resistant membranes exhibited the highest selectivity, with a maximum observed separation factor of 3.33 obtained using a DuraMemTM 500 membrane in methanol. These findings demonstrate the potential of nanofiltration to address previously unresolved separation challenges in lignin valorization and provide a basis for further process development.Article Citation - WoS: 2Citation - Scopus: 1A Novel Framework for Droplet/Particle Size Distribution in Suspension Polymerization Using Physics-Informed Neural Network (PINN)(Elsevier Science Sa, 2025) Turan, Meltem; Dutta, AbhishekA Machine Learning (ML) based neural network can capture the complex evolution of polymer chain distributions, accounting for factors such as initiation, propagation, and termination steps in a suspension polymerization process, by integrating stagewise molar balance model (MBM) and population balance model (PBM) with Physics-Informed Neural Network (PINN). The integrated PINN framework is proposed to efficiently solve these equations, incorporating known physical laws as constraints and minimizing errors in both the distribution and dynamics of the polymer chains. By optimizing the neural network parameters such as weight matrices and bias vector, the model reproduces the moments of the polymer molecular weight distribution in close alignment with numerical solutions, and it generates population balance solutions that exhibit excellent agreement with their analytical counterparts. Sensitivity analyses for the depth of the neural network architecture to quantify how structural choices affect model fidelity has been performed. The resulting MBM-PINN and PBM-PINN integrated framework demonstrates robustness and versatility in accurately capturing (96-97%) droplet/particle dynamics. The proposed methodology has the capability to provide a powerful tool for faster and scalable simulations of polymerization reactions, enabling better prediction of product properties which could be used for optimizing reaction conditions in industrial applications.