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Fermentation is the term used by microbiologists to describe any process for the production of a product by means of the mass culture of a microorganism [1]. The product can either be: i) The cell itself: referred to as biomass production. ii) A microorganism's own metabolite: referred to as a product from a natural or genetically improved strain. iii) A microorganism foreign product: referred to as a product from recombinant DNA technology or genetically engineered strain.

Optimizing a fed-batch fermentation of hybridoma cells using a GA is described in this chapter. Optimal single- and multi-feed rate trajectories are determined via the GA to maximize the final production of MAb. The results show that the optimal, varying, feed rate trajectories can significantly improve the final MAb concentration as compared to the optimal constant feed rate trajectory. Moreover, in comparison with DP, the GA- calculated feed trajectories yield a much higher level of MAb concentrations.

This chapter presents an on-line approach for identifying and optimizing fedbatch fermentation processes based on a series of real-valued GA. The model parameters are determined through on-line tuning. The final MAb concentration reaches 98% of the highest MAb concentration obtained in Chapter 2, wherein all the parameters are assumed to be known (i.e., no online tuning). The on-line method proved to be effective in coping with the problem of parameter variation from batch to batch.

One of the difficulties encountered in control and optimization of bioprocesses is the lack of reliable on-line sensors, which can measure the key processes' state variables. This chapter assesses the suitability of using RNNs for on-line biomass estimation in fed-batch fermentation processes. The proposed neural network sensor only requires the DO, feed rate and volume to be measured. The results show that RNNs are a powerful tool for implementing an on-line biomass softsensor in experimental fermentations.

A combination of a cascade RNN model and a modified GA for optimizing a fed-batch bioreactor is investigated in this chapter. The complex nonlinear relationship between the manipulated feed rate and the biomass product is described by two recurrent neural sub-models. Based on the neural model, the modified GA is employed to determine a smooth optimal feed rate profile. The final biomass quantity yields from the optimal feed rate profile based on the neural network model reaches 99.8% of the “real” optimal value obtained based on a mechanistic model.

This chapter examines cascade RNN models for modelling bench-scale fedbatch fermentation of Saccharomyces cerevisiae . The models are experimentally identified through training and validating using the data collected from experiments with different feed rate profiles. Data preprocessing methods are used to improve the robustness of the neural network models. The results show that the best biomass prediction ability is given by a DO cascade neural model.

This chapter deals with the problem of design and implementation of optimal control for a bench-scale fermentation of Saccharomyces cerevisiae . A modified GA is proposed for solving the dynamic constrained optimization problem. The optimal profiles are verified by applying them to the laboratory experiments. Among all 12 runs, the one that is controlled by the optimal feed rate profile based on the DO neural model yields the highest product. The main advantage of the approach is that the optimization can be accomplished without a priori knowledge or detailed kinetic models of the processes.

In this book, a number of results related to monitoring, modelling and optimization of fed-batch fermentation processes are presented. The study focuses on AI approaches, in particular, RNNs and GAs. These two techniques can be used either separately or together to fulfill various goals in the research. The great advantages that are offered by these approaches are the flexible implementation, fast prototype development and high benefit/cost ratio. Their applications to biotechnology process control provide a new inexpensive, yet effective way to improve the production yield and reduce the environmental impact.