Christine Renotte

Numerical 3D analysis of oscillatory flow in the time-varying laryngeal channel.

Three-dimensional flow through an anatomically representative model of the human larynx has been numerically simulated. This model includes the vestibular folds, the vocal cords and the glottic and subglottic areas. Pseudo-time-varying glottic aperture and flow conditions have been considered during quiet breathing, with a peak volume flow rate of 0.75l/s and a frequency of 0.25Hz. Because of the severe constriction, jet-like configurations have been observed. Minor differences have been outlined between the inspiration and expiration profiles. Simulations demonstrated the presence of a backflow region which may extend to 60mm from the glottis at peak inspiration and occupy 20% of the tracheal cross section. Because of its rolling, this backflow region appears in the sagittal plane close to the anterior wall, only one diameter from the laryngeal constriction and extends over about 40mm. The evolution of the streamwise velocity contours and of the corresponding secondary vector plots at six critical stations, including the glottic section, has also been described. A double pair of counter-rotating vortices develops shortly downstream/upstream from the orifice respectively at inspiration/expiration and merges near the frontal plane about 25mm from the glottis. The effect of the incoming flow has been evaluated by including the pharyngeal channel; no major difference has been observed in the computed flow patterns.

Modeling and control of cement grinding processes

In this study, a nonlinear dynamic model of a cement grinding process, including a ball mill and an air separator in closed loop, is developed. This gray-box model consists of a set of algebraic and partial differential equations containing a set of unknown parameters. The selection of a model parametrization, the design of experiments, the estimation of unknown parameters from experimental data, and the model validation are discussed. Based on the resulting model, a dynamic simulator can be developed, which appears as a useful tool to analyze the process behavior and to understand the origin of instabilities observed in real-life operations. As a result, a cascaded control structure for regulating the mill flow rate, and a proportional integral controller for regulating the cement fineness are designed. Experimental data demonstrate the effectiveness of this control scheme. Alternatively, if on line measurements of the recirculated flow rate are available, a feedforward control of the feed flow rate is described, which ensures a better decoupling of mass flow rate and fineness regulation.

On the use of simultaneous perturbation stochastic approximation for neural network training

Learning, i.e., estimation of weights and biases in neural networks, involves the minimization of a quadratic error criterion, a problem which is usually solved using backpropagation algorithms. This study, which is essentially experimental, aims at assessing the potential of first- and second-order simultaneous perturbation stochastic approximation (SPSA) algorithms to handle this minimization problem. To this end, several application examples in identification and control of nonlinear dynamic systems are presented. Test results, corresponding to training of neural networks possessing different structures and sizes, are discussed in terms of efficiency, accuracy, ease of use (parameter tuning), and implementation.

Neural modeling and control of a heat exchanger based on SPSA techniques

The aim of the paper is twofold: first, we consider a variation of the first-order simultaneous perturbation stochastic approximation (SPSA) algorithm developed by Spall (1992, 1998) which makes use of several numerical artifices, including adaptive gain sequences, gradient smoothing and a step rejection procedure, to enhance convergence and stability. Second, we present numerical studies on a non-trivial test-example, i.e., the water cooling of sulfuric acid in a two-tank system. This numerical evaluation includes the development of a neural model as well as the design of a model-based predictive neural PID controller.

Biological reaction modeling using radial basis function networks

The difficulty associated with experimental studies of biochemical systems often makes the development of pure black-box neural network models particularly delicate. Hence, it is appealing to resort to a hybrid physical-neural network approach, which uses all the available a priori knowledge about the process, and combines a first-principles model with a partial neural network (NN) model describing the phenomena, which are (at least partly) unknown. In this work, this strategy is applied to a real-case experimental study, i.e. batch CHO animal cell cultures. Several alternative model formulations are considered, including serial model structures, in which neural networks are used to describe either the reaction kinetics or the complete reaction rates (globalizing pseudo-stoichiometry and kinetics), or parallel model structures, in which a NN compensates for the prediction errors of a first-principles model. Attention is focused on the procedure used to estimate the unknown NN parameters and initial conditions from experimental data, including a maximum likelihood approach to take account of all the measurement errors, and a weight decay technique to alleviate identifiability problems. The good model agreement is demonstrated with cross-validation tests.

Transient analysis of a wastewater treatment biofilter -- distributed parameter modelling and state estimation

This paper is concerned with a pilot-scale fixed-bed biofilter used for nitrogen removal from municipal wastewater. Process modelling yields a set of mass balance partial differential equations describing the evolution of the component concentrations along the biofilter. Based on sets of experimental data collected over several months, unknown model parameters are estimated by minimizing an output-error criterion. The resulting distributed parameter model and a few pointwise measurements of nitrate, nitrite, and ethanol concentrations are then used to design observers allowing the unmeasured biomass concentrations to be reconstructed on-line. First, it is demonstrated that asymptotic observers are not applicable to the given model structure. Then, a receding-horizon observer is designed and tested, showing a very satisfactory performance.

Application of stochastic approximation techniques in neural modelling and control

Learning, i.e. estimation of weights and biases in neural networks, involves the minimization of an output error criterion, a problem which is usually solved using back-propagation algorithms. This paper aims to assess the potential of simultaneous perturbation stochastic approximation (SPSA) algorithms to handle this minimization problem. In particular, a variation of the first-order SPSA algorithm that makes use of several numerical artifices including adaptive gain sequences, gradient smoothing and a step rejection procedure is developed. For illustration purposes, several application examples in the identification and control of nonlinear dynamic systems are presented. This numerical evaluation includes the development of neural network models as well as the design of a model-based predictive neural PID controller.

Stochastic approximation techniques applied to parameter estimation in a biological model

Simultaneous perturbation stochastic approximation (SPSA) is a class of optimization algorithms which compute an approximation of the gradient and/or the Hessian of the objective function by varying all the elements of the parameter vector simultaneously and therefore, require only a few objective function evaluations to obtain first or second-order information. Consequently, these algorithms are particularly well suited to problems involving a large number of design parameters. Their potentialities are assessed in the context of nonlinear system identification. To this end, a challenging modelling application is considered, i.e. dynamic modelling of batch animal cell cultures from sets of experimental data. The performance of the optimization algorithms are discussed in terms of efficiency, accuracy and ease of use

Some observations on modeling and control of cement grinding circuits

Based on system analysis and experimental data, a dynamic model of a closed-loop cement grinding circuit, which consists of a mixed set of algebraic and partial differential equations, is developed and validated. The model equations are solved numerically using the method of lines and the resulting simulation program is used to gain some insight into the process dynamics and to design and compare control loops to achieve product specifications. The influence of the model nonlinearities, which are related to the dependency of the rates of breakage on the mill hold-up, is highlighted. In particular, this nonlinearity introduces a strong coupling between PI control loops using the fresh feed flow rate and the louver position of the classifier as manipulated variables. Several variations of this basic control scheme are thoroughly analyzed, and the necessity of an efficient mill flow rate control for the stability of the fineness control loop is demonstrated.

Dynamic model of airway pressure drop

A multipath model of the mechanical behaviour of healthy lungs subject to a plethysmographic test (close to quiet breathing conditions) has been developed, which includes the main physiological nonlinearities. This model is built on a symmetric branching scheme based on Weibels data, and uses non-linear fluid equations for the upper and lower airways. The alveolar gas compression, the changes in airway dimensions related to lung volume and/or transmural pressure, and the respiratory swings in glottic aperture have been taken into account. As clinically observed, the behaviour of the lungs, taken as a whole, seems linear, but it is confirmed by simulation that this linearity is only apparent. Simplifications and linearisations therefore need to be made carefully, only after their impact on the global behaviour of the lung is evaluated.