Luiz Augusto da Cruz Meleiro

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was 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 defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.

Non-linear multivariable predictive control of an alcoholic fermentation process using functional link networks

In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs), identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.

Avaliação da eficiência de uma célula a combustível estacionária de ácido fosfórico

Operation and performance of a commercial PAFC power plant were analyzed. Processes influencing energy conversion efficiency were studied in each module of the fuel cell power plant. The main processes were simulated using mass and energy balance equations, and the results were validated by means of experimental data. It was concluded that the electrical efficiency is higher in comparison with microturbines. The main result achieved is a better understanding of balance of plant processes, knowledge necessary for fuel cell power plant development.

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.

Development of a hydrodynamic model for air-lift reactors

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.

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 be 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.