Modeling and control of non-ideally mixed bioreactors

Abstract

Mixing plays a substantial role in determining the overall performance of a bioreactor. Well mixing in bioreactor, especially for ethanolic fermentation process is important for the homogenization of miscible and immiscible liquids, gas dispersion and suspension of solid particles. Improper mixing will eventually affect the biological and kinetics reactions occurring in the bioreactor and subsequently deteriorate the bioreactor performance. Currently, most modeling and control applications of bioreactors have been devoted to ideally mixed assumption, for simplicity. This is not realistic in practical applications. Furthermore, the strength and accuracy of the bioreactor models reflect their performance and subsequently its control strategy. Therefore, it is vital to consider the imperfect mixing for the control of bioreactor.In this study, a batch, micro-aerobic bioreactor for ethanolic fermentation process will be considered for modeling. Up to date, not much study has been conducted in exploiting the mixing mechanism for controlling this type of bioreactor. Traditionally, only the bioreactor conditions such as temperature and pH are controlled for such a batch bioreactor. Other parameters, such as aeration rate and stirrer speed are not used to control the bioreactor. Thus, it is difficult to improve the bioreactor performance as the bioreactor performance is less sensitive to both temperature and pH than to the mixing mechanism. However, the mixing behaviour of the bioreactor needs to be captured if we are to employ both aeration rate and stirrer speed for the control of such a batch bioreactor.It is known that aeration rate and stirrer speed could significantly affect the biological and kinetics reactions. Therefore, both aeration rate and stirrer speed are suggested in this work as manipulated variables in the modeling of batch bioreactor. Thus, with this approach the ideally mixed assumption will be relaxed.The models proposed will be implemented for control studies. New control strategies will be established for continuous bioreactor, whereby dilution rate and substrate concentration are considered as disturbance variables and both aeration rate and stirrer speed are suggested as manipulated variables. With this approach, the practicability of the proposed models could be investigated.The aims of this research have therefore been as follows: 1. To experimentally study the impact of aeration rate and stirrer speed on the bioreactor performances, i.e. yield and productivity. 2. To develop an integrated bioreactor model to allow us to employ the aeration rate and stirrer speed as manipulated variables for control design. 3. To establish new control strategies for bioreactor without the ideally mixed assumption.A systematic approach has been proposed to develop the non-ideally mixed bioreactor model and to design the control strategy of the lab-scale fermentation process. Three modeling approaches are employed, i.e. data-based, kinetics hybrid and kinetics multi-scale models for the analysis of the impacts of both aeration rate and stirrer speed on the performance of bioreactor. Using the three models, the aeration rate and stirrer speed are also used to analyze the mixing mechanism in the bioreactor.Furthermore, new control strategies are then proposed for the bioreactor. By using the proposed control strategies, the effect of both aeration rate and stirrer speed on the overall performance could be analyzed in the face of disturbances on other process parameters. Furthermore, the stability and achievable performance of the control strategies could be compared for different models. Hence, the proposed control strategies would lead to a better operation of the bioreactor.The study highlighted the following main findings: 1. It is identified that both aeration rate and stirrer speed could affect significantly the overall performance of the bioreactor. Therefore, both aeration rate and stirrer speed rather than temperature and pH could be used as manipulated variables for controlling the bioreactor. The ideally mixed assumption is relaxed where the mixing mechanism of the bioreactor is included in the proposed model.2. The main issue in modeling is the complexity of the microbial reactions and kinetics of the bioreactor performance for the non-ideally mixed behaviour of the bioreactor. Thus, it is important to identify the main reactions and kinetics which actually affect the bioreactor performance. In this study, Monod’s kinetics has been employed with the implementation of both aeration rate and stirrer speed. It is shown that the kinetics multi-scale model demonstrated good predictions of the mixing mechanism of bioreactor. Different conditions of aeration rate and stirrer speed influence the mixing mechanism and thus, contribute to the dynamics and kinetics within the bioreactor. These show that both aeration rate and stirrer speed play important role in studying the non-ideally mixed mechanism of the bioreactor.3. Optimization results, however, suggest that the kinetics hybrid model gives the most comparable values of maximum yield and productivity. Thus, this model is suggested for the determination of the optimum conditions of the bioreactor operation due to its simplicity in model construction, as compared to the kinetics multi-scale model.4. The control strategy of bioreactor using the data-based model does not always produce good performance, especially in the face of large disturbances. This implies that the use of models with ideally mixed assumptions would not always give good overall performance. Therefore, the controllability of the bioreactor performance is further improved with the implementation of the proposed non-ideally mixed bioreactor model. It is observed that both databased and kinetics hybrid models are able to keep the controlled variables in their set-point values by manipulating both aeration rate and stirrer speed for low disturbance changes.Hence, this research contributes on the understanding of mixing phenomena in micro-aerobic fermentation process from which a set of optimal operational conditions and control strategies to enhance its performance are developed.