Mathematical Modeling and Simulation of 1,3‐Propanediol Production by Klebsiella pneumoniae BLh‐1 in a Batch Bioreactor Using Bayesian Statistics

Abstract

Mathematical modeling and computer simulation are fundamental for optimizing biotechnological processes, enabling cost reduction and scalability, thereby driving advancements in the bioindustry. In this work, mathematical modeling and estimation of fermentative kinetic parameters were carried out to produce 1,3‐propanediol (1,3‐PDO) from residual glycerol and Klebsiella pneumoniae BLh‐1. The Markov chain Monte Carlo method, using the Metropolis‐Hastings algorithm, was applied to experimental data from a batch bioreactor under aerobic and anaerobic conditions. Sensitivity analysis and parameter evolution studies were conducted. The root‐mean‐square error (rRMSE) was chosen as the validation and calibration metric for the developed mathematical model. The results indicated that the average tolerance of glycerol was 174.68 and 44.85 g L−1, the inhibitory products was 150.95 g L−1 for ethanol and 35.56 g L−1 for 1,3‐PDO, and the maximum specific rate of cell growth was 0.189 and 0.275 h−1, for aerobic and anaerobic cultures, respectively. The model presented excellent fits in both crops, with rRMSE values between 0.09 − 33.74% and 3.58 − 31.82%, for the aerobic and anaerobic environment, respectively. With this, it was possible to evaluate and extract relevant information for a better understanding and control of the bioprocess.