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 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

Constructive Neural Network In Model-based Control Of A Biotechnological Process

In the present work, a constructive learning algorithm is employed to design an optimal one-hidden layer neural network structure that best approximates a given mapping. The method determines not only the optimal number of hidden neurons but also the best activation function for each node. Here, the projection pursuit technique is applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is optimally defined for every approximation problem, better rates of convergence are achieved. The proposed constructive learning algorithm was successfully applied to identify a large-scale multivariate process, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model is then considered as part of a model-based predictive control strategy, with high-quality performance in closed-loop experiments.324062411Andrietta, S.R., Maugeri, F., Optimum design of a continuous fermentation unit of an industrial plant for alcohol production (1994) Advances in Bioprocess Engineering, , Kluwer Academic PublishersBärmann, F., Biegler-König, F., On a Class of Efficient Learning Algorithms for Neural Networks (1992) Neural Network, 5 (1), pp. 139-144Battiti, R., First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method (1992) Neural Computation, 4 (2), pp. 141-166Camacho, E.F., Bordons, C., (1999) Model Predictive Control, , Springer-Verlag"Clarke, D.W., (1994) Advances in Model Based Predictive Control, , Oxford University PressEdgar, T.F., Himmelblau, D.M., (1988) Optimization of Chemical Processes, , McGraw-HillFriedman, J.H., Stuetzle, W., Projection Pursuit Regression (1981) Journal of the American Statistical Association (JASA), 76, pp. 817-823Geman, S., Bienenstock, E., Doursat, R., Neural Networks and the Bias/Variance Dilemma (1992) Neural Computation, 4 (1), pp. 1-58Haykin, S., (1999) Neural Networks: A Comprehensive Foundation - 2nd Edition, , Prentice HallHornik, K., Multilayer feedforward networks are universal approximators (1989) Neural Networks, 2 (5), pp. 359-366Huber, P.J., Projection Pursuit (1985) The Annal a of Statistics, 13 (2), pp. 435-475Hwang, J.-N., Lay, S.-R., Maechler, M., Martin, R.D., Schimert, J., Regression Modeling in Back-Propagation and Projection Pursuit Learning (1994) IEEE Transactions on Neural Networks, 5 (3), pp. 342-353Jones, L.K., On a conjecture of Huber concerning the convergence of projection pursuit regression (1987) The Annals of Statistics, 15, pp. 880-882Kosko, B., (1997) Fuzzy Engineering, , Prentice HallKosko, B., (1992) Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, , Prentice HallKwok, T.Y., Yeung, D.Y., Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems (1997) IEEE Trans. on Neural Networks, 8 (3), pp. 630-645Meleiro, L.A.C., (2002) Design and Applications of Linear, Neural, and Fuzzy Model-Based Controllers, , PhD Thesis (in Portuguese)Meleiro, L.A.C., Campello, R.J.G.B., Maciel Filho, R., Von Zuben, F.J., Identification of a Multivariate Fermentation Process Using Constructive Learning (2002) Proc. SBRN'2002 - VII Brazilian Symposium on Neural Networks, , IEEE Computer SocietyMeleiro, L.A.C., Maciel Filho, R., Campello, R.J.G.B., Amaral, W.C., Hierarchical Neural Fuzzy Models as a Tool for Process Identification: A Bioprocess Application (2001) Application of Neural Networks and Other Learning Technologies in Process Engineering, , Mujtaba, I. M. and Hussain, M. A. (Editors). Imperial College PressNg, G.W., (1997) Application of Neural Networks to Adaptive Control of Nonlinear Systems, , Research Studies Press Ltd., John Wiley & Sons IncRoosen, C.B., Hastie, T.J., Automatic Smoothing Spline Projection Pursuit (1994) Journal of Computational and Graphical Statistics, 3, pp. 235-248Soeterboek, R., (1992) Predictive Control - A Unified Approach, , Prentice HallVon Zuben, F.J., Netto, M.L.A., Unit-growing learning optimizing the solvability condition for model-free regression (1995) Proceedings of the IEEE International Conference on Neural Networks, 2, pp. 795-800Von Zuben, F.J., Netto, M.L.A., Projection Pursuit and the Solvability Condition Applied to Constructive Learning (1997) Proceedings of the International Joint Conference on Neural Networks, pp. 1062-1067. , Houston - USA,

Hierarchical neural fuzzy models as a tool for process identification: a bioprocess application

Hierarchical structures have been introduced in the literature to deal with the dimensionality problem, which is the main drawback to the application of neural networks and fuzzy models to the modeling and control of large-scale systems. In the present work, hierarchical neural fuzzy models are reviewed focusing on an industrial application. The models considered here consist of a set of Radial Basis Function (RBF) networks formulated as simplified fuzzy systems and connected in a cascade fashion. These models are applied to the modeling of a Multi-Input/Multi-Output (MIMO) complex biotechnological process for ethyl alcohol (ethanol) production and show to adequately describe the dynamics of this process, even for long-range horizon predictions

Application of hierarchical neural fuzzy models to modeling and control of a bioprocess

Hierarchical structures have been introduced in the literature to deal with the dimensionality problem, which is the main drawback to the application of neural networks and fuzzy models to modeling and control of large-scale systems. In the present work, hierarchical neural fuzzy (HNF) models are reviewed, focusing on the model-based control of a biotechnological process. The model considered here consists of a set of neural fuzzy systems connected in cascade and is used in the modeling of an industrial plant for ethyl alcohol ( ethanol) production. Based on the HNF model of the process, a nonlinear model predictive controller (HNF-MPC) is designed and applied to control the process. The performance of the HNF-MPC is illustrated within servo and regulatory scenarios

Identification of a multivariate fermentation process using constructive learning

In the present work, a constructive learning algorithm is employed to design an optimal one-hidden neural network structure that best approximates a given mapping. The method determines not only the optimal number of hidden neurons but also the best activation function for each node. Here, the projection pursuit technique is applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is optimally defined for every approximation problem, better rates of convergence are achieved. Since the training process operates the hidden neurons individually, a pertinent activation function employing Hermite polynomials can he iteratively developed for each neuron as a function of the learning set. The proposed constructive learning algorithm was successfully applied to identify a large-scale multivariate process, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions

Hierarchical fuzzy models within the framework of orthonormal basis functions and their application to bioprocess control

Fuzzy models within the framework of orthonormal basis functions (OBF fuzzy models) have been introduced in previous works and shown to be a very promising approach to the areas of nonlinear system identification and control, since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. As fuzzy models, however, they exhibit the dimensionality problem which is the main drawback to the application of neural networks and fuzzy systems to the modeling and control of large-scale systems. This problem has successfully been dealt with in the literature by means of hierarchical structures composed of submodels connected in cascade. In the present paper a hierarchical fuzzy model within the OBF framework is presented. A data-driven hybrid identification method based on genetic and gradient-based algorithms is described in details. A model-based predictive control scheme is also presented and applied to control of a complex industrial process for ethyl alcohol (ethanol) production