Application of modelling techniques for the improvement of industrial bioprocesses

Holistic Process Models: A Bayesian Predictive Ensemble Method for Single and Coupled Unit Operation Models

The coupling of individual models in terms of end-to-end calculations for unit operations in manufacturing processes is a challenging task. We present a probability distribution-based approach for the combined outcomes of parametric and non-parametric models. With this so-called Bayesian predictive ensemble, the statistical moments such as mean value and standard deviation can be accurately computed without any further approximation. It is shown that the ensemble of different model predictions leads to an uninformed prior distribution, which can be transformed into a predictive posterior distribution using Bayesian inference and numerical Markov Chain Monte Carlo calculations. We demonstrate the advantages of our method using several numerical examples. Our approach is not restricted to certain unit operations, and can also be used for the more robust interpretation and assessment of model predictions in general.

Application of modeling and simulation tools for the evaluation of biocatalytic processes: a future perspective

Modeling and simulation techniques have for some time been an important feature of biocatalysis research, often applied as a complement to experimental studies. In this short review, we report on the state-of-the-art process and kinetic modeling for biocatalysis with the aim of identifying future research needs. We have particularly focused on four aspects of modeling: (i) the model purpose, (ii) the process model boundary, (iii) the model structure, and (iv) the model identification procedure. First, one finds that most of the existing models describe biocatalyst behavior in terms of enzyme selectivity, mechanism, and reaction kinetics. More recently, work has focused on extending these models to obtain process flowsheet descriptions. Second, biocatalysis models remain at a relatively low level of complexity compared with the trends observed in other engineering disciplines. Hence, there is certainly room for additional development, i.e., detailed mixing and hydrodynamics, more process units (e.g., biorefinery). Third, biocatalysis models have been only partially subjected to formal statistical analysis. In particular, uncertainty analysis is needed to ascertain reliability of the predictions of the process model, which is necessary to make sound engineering decisions (e.g., the optimal process flowsheet, control strategy, etc). In summary, for modeling studies to be more mature and successful, one needs to introduce Good Modeling Practice and that asks for (i) a standardized and systematic guideline for model development, (ii) formal identifiability analysis, and (iii) uncertainty analysis. This will advance the utility of models in biocatalysis for more rigorous application within process design, optimization, and control strategy evaluation.

Polycarnitine?a new biomaterial

The natural product L-carnitine is--due to its biotechnological accessibility and specific properties--on the way to becoming an attractive biobased bulk product. L-carnitine is a natural betaine with vitamin properties. Carnitine is an essential part of the fatty acid metabolism of human beings and animals. Carnitine was first isolated in 1905 from meat extract and important recent developments include the biosyntheses of L-carnitine from L-lysine or gamma-butyrobetaine. Our synthesis routes are designed to maintain the primary structure and specific properties of carnitine, such as hydrophilicity and "stiffening" effects for polymeric structures and applications. L-carnitine is converted via lactonization or olefinization into polymerizable basic molecules. The properties and the applications of carnitine polymers are described.

Modeling of reaction kinetics for reactor selection in the case of L-erythrulose synthesis

To choose the most effective process design in enzyme process development it is important to find the most effective reactor mode of operation. This goal is achieved by modeling of the reaction kinetics as a tool of enzyme reaction engineering. With the example of the transketolase catalyzed L-erythrulose synthesis we demonstrate how the most effective reactor mode can be determined by kinetic simulations. This is of major importance if the biocatalyst deactivation is caused by one of the substrates as in this case by glycolaldehyde. The cascade of two membrane reactors in series with soluble enzyme is proposed as a solution for the enzyme deactivation by one of the substrates.

Modeling of the biotransformation of crotonobetaine into L-(-)-carnitine by Escherichia coli strains

A simple unstructured model, which includes carbon source as the limiting and essential substrate and oxygen as an enhancing substrate for cell growth, has been implemented to depict cell population evolution of two Escherichia coli strains and the expression of their trimethylammonium metabolism in batch and continuous reactors. Although the model is applied to represent the trans-crotonobetaine to L-(-)-carnitine biotransformation, it is also useful for understanding the complete metabolic flow of trimethylammonium compounds in E. coli. Cell growth and biotransformation were studied in both anaerobic and aerobic conditions. For this reason we derived equations to modify the specific growth rate, mu, and the cell yield on the carbon source (glycerol), Y(xg), as oxygen increased the rate of growth. Inhibition functions representing an excess of the glycerol and oxygen were included to depict cell evolution during extreme conditions. As a result, the model fitted experimental data for various growth conditions, including different carbon source concentrations, initial oxygen levels, and the existence of a certain degree of cell death. Moreover, the production of enzymes involved within the E. coli trimethylammonium metabolism and related to trans-crotonobetaine biotransformation was also modeled as a function of both the cell and oxygen concentrations within the system. The model describes all the activities of the different enzymes within the transformed and wild strains, able to produce L-(-)-carnitine from trans-crotonobetaine under both anaerobic and aerobic conditions. Crotonobetaine reductase inhibition by either oxygen or the addition of fumarate as well as its non-reversible catalytic action was taken into consideration. The proposed model was useful for describing the whole set of variables under both growing and resting conditions. Both E. coli strains within membrane high-density reactors were well represented by the model as results matched the experimental data.

Computer applications in bioprocessing

Biotechnologists have stayed at the forefront for practical applications for computing. As hardware and software for computing have evolved, the latest advances have found eager users in the area of bioprocessing. Accomplishments and their significance can be appreciated by tracing the history and the interplay between the computing tools and the problems that have been solved in bioprocessing.

Bioreactor performance: a more scientific approach for practice

In practice, the performance of a biochemical conversion process, i.e. the bioreactor performance, is essentially determined by the benefit/cost ratio. The benefit is generally defined in terms of the amount of the desired product produced and its market price. Cost reduction is the major objective in biochemical engineering. There are two essential engineering approaches to minimizing the cost of creating a particular product in an existing plant. One is to find a control path or operational procedure that optimally uses the dynamics of the process and copes with the many constraints restricting production. The other is to remove or lower the constraints by constructive improvements of the equipment and/or the microorganisms. This paper focuses on the first approach, dealing with optimization of the operational procedure and the measures by which one can ensure that the process adheres to the predetermined path. In practice, feedforward control is the predominant control mode applied. However, as it is frequently inadequate for optimal performance, feedback control may also be employed. Relevant aspects of such performance optimization are discussed.