Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

• Published in: Frontiers of chemical science and engineering Vol. 16; no. 2; pp. 221 - 236
• Main Authors: Dong, Yachao, Georgakis, Christos, Santos-Marques, Jacob, Du, Jian
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.02.2022; Springer Nature B.V
• Subjects: Case studies; Chemistry; Chemistry and Materials Science; Computing time; Design of experiments; Dynamic response; Industrial Chemistry/Chemical Engineering; Nanotechnology; Regression models; Research Article; Response surface methodology; pharmaceutical organic synthesis; Lasso regression; data-driven modeling; dynamic response surface methodology
• ISSN: 2095-0179; 2095-0187
• DOI: 10.1007/s11705-021-2061-y

To study the dynamic behavior of a process, time-resolved data are collected at different time instants during each of a series of experiments, which are usually designed with the design of experiments or the design of dynamic experiments methodologies. For utilizing such time-resolved data to model the dynamic behavior, dynamic response surface methodology (DRSM), a data-driven modeling method, has been proposed. Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used in several of previous publications, and Lasso regression, which is newly incorporated in this paper for the estimation of DRSM models. Here, we show that both approaches yield similarly accurate models, while the computational time of Lasso is on average two magnitude smaller. Two case studies are performed to show the advantages of the proposed method. In the first case study, where the concentrations of different species are modeled directly, DRSM method provides more accurate models compared to the models in the literature. The second case study, where the reaction extents are modeled instead of the species concentrations, illustrates the versatility of the DRSM methodology. Therefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety of organic synthesis datasets.