Brigitte Grondin-Perez

Thermal Building Simulation and Computer Generation of Nodal Models

The designers preoccupation to reduce the energy needs and get a better thermal quality of ambiances helped in the development of several packages simulating the dynamic behaviour of buildings. This paper shows the adaptation of a method of thermal analysis, the nodal analysis, linked to the case of buildings thermal behaviour. We take successively an interest in the case of conduction into a wall, in the coupling with superficial exchanges and finally in the constitution of thermal state models of the building. Big variations existing from one building to another, its necessary to build the thermal model from the building description. This article shows the chosen method in the case of our thermal simulation program for buildings, CODYRUN

Mechanistic Model versus Artificial Neural Network Model of a Single-Cell PEMFC

Model-based controllers can significantly improve the performance of Proton Exchange Membrane Fuel Cell (PEMFC) systems. However, the complexity of these strategies constraints large scale implementation. In this work, with a view to reduce complexity without affecting performance, two different modeling approaches of a single-cell PEMFC are investigated. A mechanistic model, describing all internal phenomena in a single-cell, and an artificial neural network (ANN) model are tested. To perform this work, databases are measured on a pilot plant. The identification of the two models involves the optimization of the operating conditions in order to build rich databases. The two different models benefits and drawbacks are pointed out using statistical error criteria. Regarding model-based control approach, the computational time of these models is compared during the validation step.

An integrated model of a wireless power transportation for RFID and WSN applications

In this paper a simple and efficient model is proposed to predict the received direct current (DC) power in wireless power transportation (WPT) systems, for Radio Frequency Identification (RFID) and wireless sensor network applications. The conversion of radio frequency (RF) power into usable DC power is done by a rectenna (a rectifying circuit with an antenna). The DC power was estimated using a model that takes into account the distance between the antennas, the power transmitted, the reflection and the diffraction mechanism associated with the laboratory environment, and the load value. For this purpose, the analytical model is compared to measurement results. This model was implemented in a RF simulation environment.

Neural modelling and control of a crystal growth process in cane sugar industry

Abstract This paper describes the development of an original neural model-based control strategy to improve a low grad crystallization process through the crystal growth phase. The modelling, to control the growing phase, is one of our main objectives. Through the identification of a neural model of this non linear phase, a way to improve the process control is proposed. It consists in a linearization of the neural network model, used to extract the linear model and to update the controller parameters. Results obtained during present investigation carried out encouraging improvements.