2026-04-14T03:35:56Zhttps://recercat.cat/oai/request
oai:recercat.cat:20.500.14342/46202025-05-22T14:28:47Zcom_2072_482405com_2072_183628col_2072_482414
RECERCAT author Forster, Tim author Vázquez Vázquez, Daniel author Guillén-Gosálbez, Gonzalo 2024-08-30 1873-4405 http://hdl.handle.net/20.500.14342/4620 https://doi.org/10.1016/j.ces.2024.120606 Identifying suitable kinetic models for bioprocesses is a complex task, particularly when interpretable models are sought. Classical machine learning algorithms are gaining wide interest to simulate complex bioprocesses that are hard to describe via first principles. However, they often rely on a priori assumptions of the model structure and lead to mathematical expressions that are hard to interpret. In this work, we apply an alternative approach based on symbolic regression to identify bioprocess models without assuming a pre-defined model structure. We obtain algebraic expressions for the kinetic rates from data consisting of concentration profiles. The model training was performed following a two-step approach that allows avoiding the iterative integration of differential equations for the parameter estimation step. The proposed procedure was found from numerical examples to slightly outperform neural network benchmarks. Moreover, the obtained algebraic expressions for the rate equations facilitate the model interpretation and enable the direct application of optimization algorithms. eng © L'autor/a Attribution 4.0 International Bioprocess Symbolic regression Optimization Machine learning uncovers analytical kinetic models of bioprocesses info:eu-repo/semantics/article