Ye Seol Lee

Research Associate

Imperial College London

Dr Ye Seol (Lauren) Lee, Research Associate at Imperial College London Ye Seol Lee is a Research Associate of Chemical Engineering at Imperial College London. She joined the Molecular Systems Engineering research group in 2017 and received her Ph.D. in 2022. During her PhD, she focused on the development of computer-aided molecular and process design frameworks aiming to provide a reliable path to accelerate the identification of optimal solvents. Awards include a BFWG women graduate (2020) and Roger Sargent scholarship (2018). Before joining the group, she worked as a Research Engineer in Daewoo Shipbuilding and Marine Engineering Co. She led an R&D project for the development of a new concept of offshore oil production process. As a research associate, she is working on a project of the PharmaSEL-Prosperity partnership, which focuses on the design of reacting solvents for drug production. Her research interests include knowledge-based and data-driven molecular/materials design, spanning the development of design method, property prediction techniques and optimisation algorithms.  

Computer-aided molecular and Process Design: Algorithms and application to Optimal solvent design for CO2 chemical absorption processes

Computer-Aided Molecular Design (CAMD) has been put forward as a powerful and systematic technique that can accelerate the identification of new candidate molecules. Given the benefits of CAMD, the concept has been extended to Computer-Aided Molecular and Process Design (CAMPD). In CAMPD approaches, not only is the interdependence between the properties of the molecules and the process performance captured, but it is also possible to assess the optimal overall performance of a given fluid using an objective function that may be based on process economics or environmental criteria. Despite the significant advances made in the field of CAM(P)D, there are remaining challenges in handling the complexities arising from the large mixed-integer nonlinear structure-property and process models and the presence of conflicting performance criteria that cannot be easily merged into a single metric.  To overcome these challenges, a novel CAMPD optimisation framework is proposed in the context of identifying optimal amine solvents for carbon dioxide (CO2) chemical absorption. The efficiency of the proposed algorithm is demonstrated by applying it to the design of CO2 chemical absorption processes. The algorithm is found to converge successfully in all 150 runs carried out. Next, a robust algorithm for multi-objective optimisation (MOO), the SDNBI algorithm, is designed to address the theoretical and numerical challenges associated with the solution of general nonconvex and discrete MOO problems. The SDNBI found to provide the most evenly distributed approximation of the Pareto front as well as useful information on regions of the objective space that do not contain a nondominated point. The advances in these studies can accelerate the discovery of novel solvents for CO2 capture that can achieve improved process performance. More broadly, the modelling and algorithmic development presented extend the applicability of CAMPD and MOO based CAM(P)D to a wider range of applications.