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

AUTOR(ES)

Meleiro, L.A.C., Maciel Filho, R.

FONTE

Brazilian Journal of Chemical Engineering

DATA DE PUBLICAÇÃO

2000-12

RESUMO

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.

Documentos Relacionados

• STATE ESTIMATION AND TRAJECTORY TRACKING CONTROL FOR A NONLINEAR AND MULTIVARIABLE BIOETHANOL PRODUCTION SYSTEM
• Feddforward control based on neural networks applied to an absorption column of ethanol production process
• Nonlinear predictive control of an industrial slurry reactor
• Controle preditivo baseado em modelo neuro-fuzzy de sistemas não-lineares aplicado em sistema de refrigeração = : Model predictive control based on neuro-fuzzy nonlinear systems applied to a refrigeration plant
• Predictive control based on neural networks: an application to a fluid catalytic cracking industrial unit