In the biopharmaceutical industry, accurately predicting penicillin concentration during fermentation is key to boosting production efficiency and quality assurance. This study leverages the PenSim simulation data set and applies various machine learning and deep learning techniques to forecast penicillin fermentation concentration. Initially, through correlation analysis, nine feature variables with significant impacts on penicillin concentration were screened, and the data underwent preprocessing and standardization. Using grid search, we systematically optimize the hyperparameters of various prediction models. Results show that the ridge regression model excels, achieving a mean squared error of 0.0512 and a mean absolute error of 0.0361. This indicates a strong linear relationship between penicillin concentration and the selected features. Our study offers data-driven insights for intelligent monitoring and optimization of penicillin fermentation processes. It also showcases the potential of artificial intelligence in enhancing control of biotechnological facilities, paving the way for future research.
Keywords: concentration prediction; data analysis; deep learning; machine learning; penicillin fermentation.
© 2024 Wiley Periodicals LLC.