Bioprocess Applications Research Articles

Fundamental bottlenecks in the application of continuous bioprocesses

Continuous culture is applied mainly as a research tool and much less as a production process. Fundamental bottlenecks in continuous culture are discussed to help shed light on this apparent contradiction. Based on a discussion of technical, process related, and economic/ market bottlenecks it is concluded that the often mentioned productivity argument in favor of continuous processing is much too simple. The optimal choice of a process mode is determined by a full understanding of the equipment and production plant factors and of the economic/market factors. Very often the resulting choice will be the fed batch and/or the cell retention process mode which is characterized by low growth rates. Therefore more research towards product formation at low growth rates (<0.05 h −1) is needed.

Bioprocess applications of model‐based estimation techniques

AbstractThe last decade has seen the development of a number of approaches for estimating those variables which are difficult to measure on‐line in industrial process situations. Whilst a range of techniques is available, a common element is the use of process knowledge in the form of a system model. In the case of bioprocess systems, although a large range of models has been presented in the literature, their use in estimation schemes on an industrial scale has been limited. A number of reasons can be identified for their low level of utilisation. Of particular significance is the uncertainty which exists in quantifying system performance and the process‐model mismatch which inevitably results. The level of ‘pre‐defined model’ uncertainty, together with the knowledge gained during the course of the fermentation, serves to dictate estimator structure. The paper considers a range of estimation strategies and contrasts, through industrial applications, their performance characteristics and utility.

Intelligent tuning of P+I controllers for bioprocess application

This paper outlines two methods of advanced control which can be utilised for tuning conventional controllers. A novel AI method based upon the use of artificial neural networks is contrasted with an advanced control method utilising the PIP philosophy. Both techniques are shown to be particularly effective advanced control approaches and with appropriate structuring can be used for tuning conventional P + I controllers. This is particularly advantageous from an industrial perspective. In order to demonstrate the potential of the methodologies, a simulation of a continuous, glucose limited Saccharomyces cerevisiae fermentation is employed.

An adaptive controller for batch feed bioprocess application to lysine production

This paper presents an adaptive algorithm designed for batch feed bioprocess control, The proposed algorithm is based on a non-linear ‘grey box’ model, which is adapted on-line to the process behaviour by the estimation of a key parameter. Stability and convergence analysis of this algorithm is presented, and from this, a practical tuning method is given. The proposed algorithm is compared to a standard adaptive generalized predictive controller approach, and is shown to exhibit similar performance while being easier to use. A real application of this adaptive algorithm to a batch feed lysine process is presented. Good results are obtained, which show that this control scheme is a worthwhile alternative for batch feed bioprocess control.

Concentration of L-phenylalanine with a reverse osmosis membrane

A high flux, thin film composite reverse osmosis (RO) membrane was used to concentrate L-phenylalanine (L-Phe) from clarified bioreactor harvest media. At pH 10±0.5 and 50°C, concentrations of 100 g l −1 were easily achieved and at fluxes from 17 to 119 1 m −2 h −1. Rejection coefficient for L-Phe was inversely proportional (as the log) to retentate concentration. A preliminary system study showed that stages in a cascade could be used to recover essentially all of the product from clarified harvests. The study shows the importance of empirical evaluation as the basis of design and suggests that bioprocess applications of RO are likely to be case specific.

Rapid bioprocess optimization using the ellipsoid algorithm

A new optimization technique, the ellipsoid method, has been programmed and tested for bioprocess applications. This algorithm uses gradient information to construct a series of ellipsoids with reducing volume that contract about an optimal point. Some modifications allow planning of sequential sets of experiments that quickly bound the optimal conditions for a bioprocess. An important feature is simple handling of inequality constraints, providing a new tool for empirical optimization studies. Several examples highlight the robust performance of ellipsoidal design.