Model calibration was achieved by sequentially calibrating sensitive parameters. Among eight sensitive parameters, nutrient uptake and biopolymer storage rates were first calibrated using batch kinetic experiment data, and the remaining sensitive parameters were then calibrated using reconciled continuous online monitoring data (25 days of data for calibration; 20 days of data for validation). In particular, bounded calibration was performed by leveraging Optuna -an automatic hyperparameter optimization software framework in Python. The Optuna, employing Bayesian optimization, was used to narrow down the search space and to find an optimal combination of the sensitive parameters that returns the minimum objective values. The minimum and maximum bounds for each sensitive parameter can be found in Table .