In this paper, a 1D hydrodynamic model has been developed for gas hold-up and liquid circulation velocity prediction in air-lift reactors. The model is based on momentum balance equations and has been adjusted to experimental data collected on a pilot plant reactor equipped with two types of gas distributors and using water and water/butanol as the liquid phase. Different techniques of signal analysis have also been applied to pressure fluctuations in order to extract information about flow regimes and regime transitions. A good knowledge of the flow pattern is essential to establish adequate correlations for the hydrodynamic model.

Most advanced computer-aided control applications rely on good dynamics process models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-line identification experiments on the process. These experiments for identification often result in input-output data with small output signal-to-noise ratio, and using these data results in inaccurate model parameter estimates [1]. In this work, a multivariable adaptive self-tuning controller (STC) was developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN). A second approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge about the process being modeled) with artificial neural networks. In this case, kinetic parameters - which are very hard to be accurately determined in real time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varying and nonlinear models. This advanced control strategy was applied to a fermentation process to produce ethyl alcohol (ethanol) in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the proposed procedure in this work has a great potential for application.