State and parameter estimation based on a nonlinear filter applied to an industrial process control of ethanol production

Most advanced computer-aided control applications rely on good dynamics pro cess models. The performance of the control system depends on the accuracy of the model used. Typically, such models are developed by conducting off-l ine identification experiments on the process. These experiments for identi fication often result in input-output data with small output signal-to-nois e ratio, and using these data results in inaccurate model parameter estimat es [1]. In this work, a multivariable adaptive self-tuning controller (STC) was developed for a biotechnological process application. Due to the diffi culties involving the measurements or the excessive amount of variables nor mally found in industrial process, it is proposed to develop "soft-sensors" which are based fundamentally on artificial neural networks (ANN). A secon d approach proposed was set in hybrid models, results of the association of deterministic models (which incorporates the available prior knowledge abo ut the process being modeled) with artificial neural networks. In this case , kinetic parameters - which are very hard to be accurately determined in r eal time industrial plants operation - were obtained using ANN predictions. These methods are especially suitable for the identification of time-varyi ng and nonlinear models. This advanced control strategy was applied to a fe rmentation 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 condit ions. The results obtained show that the proposed procedure in this work ha s a great potential for application.