Data-based dynamic compartment model: Modeling of E. coli fed-batch fermentation in a 600 m3 bubble column.

Mathematical modeling is a powerful and inexpensive approach to provide a quantitative basis for improvements that minimize the negative effects of bioreactor heterogeneity. For a model to accurately represent a heterogeneous system, a flow model that describes how mass is channeled between different zones of the bioreactor volume is necessary. In this study, a previously developed compartment model approach based on data from flow-following sensor devices was further developed to account for dynamic changes in volume and flow rates and thus enabling simulation of the widely used fed-batch process. The application of the dynamic compartment model was demonstrated in a study of an industrial fermentation process in a 600 m3 bubble column bioreactor. The flow model was used to evaluate the mixing performance by means of tracer simulations and was coupled with reaction kinetics to simulate concentration gradients in the process. The simulations showed that despite the presence of long mixing times and significant substrate gradients early in the process, improving the heterogeneity did not lead to overall improvements in the process. Improvements could, however, be achieved by modifying the dextrose feeding profile.

Flow-following sensor devices: A tool for bridging data and model predictions in large-scale fermentations.

Production-scale fermentation processes in industrial biotechnology experience gradients in process variables, such as dissolved gases, pH and substrate concentrations, which can potentially affect the production organism and therefore the yield and profitability of the processes. However, the extent of the heterogeneity is unclear, as it is currently a challenge at large scale to obtain representative measurements from different zones of the reactor volume. Computational fluid dynamics (CFD) models have proven to be a valuable tool for better understanding the environment inside bioreactors. Without detailed measurements to support the CFD predictions, the validity of CFD models is debatable. A promising technology to obtain such measurements from different zones in the bioreactors are flow-following sensor devices, whose development has recently benefitted from advancements in microelectronics and sensor technology. This paper presents the state of the art within flow-following sensor device technology and addresses how the technology can be used in large-scale bioreactors to improve the understanding of the process itself and to test the validity of detailed computational models of the bioreactors in the future.

Raw material quality assessment approaches comparison in pectin production.

Different opportunities are explored to evaluate quality variation in raw materials from biological origin. Assessment of raw materials attributes is an important step in a bio-based production as fluctuations in quality are a major source of process disturbance. This can be due to a variety of biological, seasonal, and supply scarcity reasons. The final properties of a product are invariably linked with the initial properties of the raw material. Thus, the operational conditions of a process can be tuned to drive the product to the required specification based on the quality assessment of the raw material being processed. Process analytical technology tools which enable this assessment in a far more informative and rapid manner than current industrial practices that rely on rule-of-thumb decisions are assessed. An example with citrus peels is used to demonstrate the conceptual and performance differences of distinct quality assessment approaches. The analysis demonstrates the advantage of characterization through multivariate data analysis coupled with a complementary spectroscopic technique, near-infrared spectroscopy. The quantitative comparative analysis of three different approaches, discriminant classification based on expert-knowledge, unsupervised classification, and spectroscopic correlation with reference physicochemical variables, is performed in the same dataset context. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2762, 2019.

Real-time model based process monitoring of enzymatic biodiesel production.

In this contribution we extend our modelling work on the enzymatic production of biodiesel where we demonstrate the application of a Continuous-Discrete Extended Kalman Filter (a state estimator). The state estimator is used to correct for mismatch between the process data and the process model for Fed-batch production of biodiesel. For the three process runs investigated, using a single tuning parameter, qx = 2 × 10(-2) which represents the uncertainty in the process model, it was possible over the entire course of the reaction to reduce the overall mean and standard deviation of the error between the model and the process data for all of the five measured components (triglycerides, diglycerides, monoglycerides, fatty acid methyl esters, and free fatty acid). The most significant reduction for the three process runs, were for the monoglyceride and free fatty acid concentration. For those components, there was over a ten-fold decrease in the overall mean error for the state estimator prediction compared with the predictions from the pure model simulations. It is also shown that the state estimator can be used as a tool for detection of outliers in the measurement data. For the enzymatic biodiesel process, given the infrequent and sometimes uncertain measurements obtained we see the use of the Continuous-Discrete Extended Kalman Filter as a viable tool for real time process monitoring.

Mechanistic modeling of biodiesel production using a liquid lipase formulation.

In this article, a kinetic model for the enzymatic transesterification of rapeseed oil with methanol using Callera™ Trans L (a liquid formulation of a modified Thermomyces lanuginosus lipase) was developed from first principles. We base the model formulation on a Ping-Pong Bi-Bi mechanism. Methanol inhibition, along with the interfacial and bulk concentrations of the enzyme was also modeled. The model was developed to describe the effect of different oil compositions, as well as different water, enzyme, and methanol concentrations, which are relevant conditions needed for process evaluation, with respect to the industrial production of biodiesel. The developed kinetic model, coupled with a mass balance of the system, was fitted to and validated on experimental results for the fed-batch transesterification of rapeseed oil. The confidence intervals of the parameter estimates, along with the identifiability of the model parameters were presented. The predictive capability of the model was tested for a case using 0.5% (wt. Enzyme/wt. Oil), 0.5% (wt. Water /wt. Oil) and feeding 1.5 times the stoichiometric amount of methanol in total over 24 h. For this case, an optimized methanol feeding profile that constrains the amount of methanol in the reactor was computed and the predictions experimentally validated. Monte-Carlo simulations were then used to characterize the effect of the parameter uncertainty on the model outputs, giving a biodiesel yield, based on the mass of oil, of 90.8 ± 0.55 mass %.