Enhanced parameter estimation with improved particle swarm optimization algorithm for cell culture process modeling

Abstract Mechanism models and model‐assisted process optimization play a vital role in biopharmaceutical industry. However, the parameter estimation remains a challenging task in modeling due to the presence of multiple local optima and ill‐conditioning. In this study, we firstly presented a comparative evaluation of particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) for parameter estimation and confirmed PSO was the most efficient approach. Then an improved PSO algorithm using second‐order oscillation and particle replacement strategies was developed to enhance fitting performance in fed‐batch cell culture modeling. The results of fitting and model validation demonstrated that the improved PSO could explore more reasonable parameters and leading to a significantly enhancement in predictive accuracy throughout the entire cell culture process under varying conditions. Moreover, 10 diverse fed‐batch experiments were conducted to validate the fitting abilities on different process condition and clones, the improved PSO method exhibited improvements in accuracy and universality of parameter estimation for modeling various cultivation processes, particularly those lacking any prior knowledge. This improved algorithm is implemented to make it available to both the scientific community and industry, offering customized solutions for specific projects.