La experiencia de construir el Reporte de Experiencia Profesional

OPTIMAL GLUCOSE FLOW ONLINE LEARNING qc,max CONTROLLER Fs,opt=qs,optxv/Sin qs,opt OPTIMIZATION MAX(J) Fs,opt J Y_{X S}/ O P( _dP) P x, s, e Measurements x, v Fs,opt

Fig. 7. Hybrid network for online optimization. The feature identification and optimal glucose flow modules are condensed representations of the diagrams in figs 3 and 5 respectively.

In order to further improve the control and to respond on modeling and measurement errors, the predetermined feature profile can be corrected on-line. Such tuning can be done with HYBNET in the way sketched in Fig. 7. The currently available measurement are compared with the model predictions and as soon as there is a significant deviation,

the feature qc max will be corrected in a error feed-back fashion. Any significant change then also requires an adapttion of the qs,opt profile. With the new data of qs opt, the set point for the Fopt valve is then corrected.

1 2 3 0,0 0,1 0,2 0,3 0,4 0,5 W ithout online correction W ith online correction Theoretical optimum Y_x/s(g-biom/g-gluc) Pobtained(g-biom/g-biom/h) Optimization procedure

Fig. 8. Comparison of the optimization procedures with a test fermentation: procedure 1) Theoretical optimum Yxs=0.49 Pobtained=0.23; procedure 2) online

implementation with correction Yxs=0.48 Pobtained=0.24, and procedure 3) online

implementation without correction Yxs=0.42 Pobtained=0.25.

The decisive advantage of this procedure (Oliveira et al. 1997) in comparison to the classical direct optimization of the feed rate profile F(t) is that the focus of the control is on the physiological key control quantity, the specific substrate consumption profile and the potential errors in assuming biomass and volume profiles (which are necessary to determine F(t)) are significantly reduced (viz Fig. 8). E.g., there are particularly problems in determining the v(t) profile in laboratory experiments and pilot plants where base addition, evaporation, feeding of antifoam agents etc. can lead to significant errors in the prediction of v(t). Also errors in the concentration sF can lead to significant errors in practice. With state estimation procedures using actual measurement data during the running process, the profiles of x and v can be determined with a considerably smaller error.

expenditures for development and maintenance in acceptable limits. HYBNET is a development done in order to cope with that problem.

HYBNET helps to reduce as much as possible the formal efforts to develop a process model that can be used for process optimization and control. It provides the corresponding guidance for the biochemical engineer. However, it is not possible what some vendors of software packages claim that modeling of complex biochemical processes is an easy task. The biochemical engineer must perform hard work to collect all the currently available knowledge which might be relevant to his particular task. It is also not correct that all problems can now be solved merely with artificial networks or fuzzy experts systems alone. For instance, mechanistic descriptions already available must be used directly, it does not make sense to learn them once more from noisy data. HYBNET will help him to decide whether or not it will have a significant impact on the process benefit/cost-ratio and in case of a positive decision to integrate it into the network constituting the current process model.

In HYBNET, however, the process model is merely considered a means to an end. The focus is on the task to be performed, e.g., the optimization of the productivity. Thus, the afford to construct a model must carefully rated with an eye on the benefit/cost-ratio. A central issue in HYBNET is process optimization, e.g., the determination of optimal feeding profiles in fed-batch processes as well as the start parameters like start volume, start substrate concentration. Several ways are provided to support such tasks depending on the knowledge that can be made available. In the beginning, when there are only a few data available the weight is more on knowledge from literature. When there is more data available the focus moves more and more to data driven techniques, since they more directly reflect the behavior of the particular process under consideration. The idea of fusion of models is not new, but HYBNET is a tool which allows to bring it into practice with a reasonable expenditure.

HYBNET was developed to support advanced control strategies in industrial production environments. There process control is one major issue. This problem has been solved in very much the same way than process modeling. The process is then viewed at with the controller being an integrated part of the process. Consequently, the hybrid techniques developed, could be simply extended. And, the determination of the controller parameters become an optimization problem, where the process performance is used as the optimization criterion.

HYBNET is used in different biochemical production processes as well as in laboratories and pilot plants. It is continuously being extended in particular concerning the user interface, which is necessary to reduce the activation barrier felt by many process engineers in industry for using software tools.

Acknowledgements. Discussions with many colleagues from industry are gratefully acknowledged. R. Oliveira acknowledges the Portuguese Science Foundation JNICT (BD/2501/93-RM)

Oliveira, R., G. Smolders, R. Simutis, A. Lübbert (1997). Improved on-line control of biochemical cultivation processes. (in preparation)

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Chapter 4