SYMULACJA PRZEPŁYWU GRAWITACYJNEGO I ESTYMACJA JEGO PARAMETRÓW PRZY UŻYCIU ELEKTRYCZNEJ TOMOGRAFII POJEMNOŚCIOWEJ I SZTUCZNYCH SIECI NEURONOWYCH

The paper presents a new approach to monitoring changes of characteristic parameters of gravitational solids flow. Electrical Capacitance Tomography (ECT) is applied for non-invasive process monitoring. Artificial Neural Networks (ANN) are used to estimate important flow parameters knowing the measured capacitances. The proposed approach solves the ECT inverse problem in a direct manner and provides a rapid parameterization of the funnel flow. The simulation of the silo discharging process is performed relying on real flow behaviour obtained from the authors’ previous work. The simulated data are used to new approach testing and verification. The obtained results proved that proposed ANN-based method will allow for on-line gravitational solids flow monitoring.W artykule opisano nowe podejście do monitorowania zmian charakterystycznych parametrów przepływu grawitacyjnego. Do nieinwazyjnego monitorowania procesu stosowana jest Elektryczna Tomografia Pojemnościowa (ECT). Sztuczne Sieci Neuronowe wykorzystywane są do estymacji ważnych parametrów przepływu na podstawie mierzonych pojemności. Zaproponowane podejście pozwala na rozwiązanie problemu odwrotnego w ECT w sposób bezpośredni i umożliwia natychmiastową parametryzację przepływu kominowego. Symulacja procesu rozładowania silosu została wykonana na podstawie wyników wcześniejszych badań eksperymentalnych przeprowadzonych na rzeczywistym obiekcie. Dane symulacyjne wykorzystano do testowania i weryfikacji nowego podejścia. Uzyskane wyniki wykazały, iż zaproponowana metoda wykorzystująca Sztuczne Sieci Neuronowe pozwoli na monitorowanie on-line parametrów przepływu grawitacyjnego

METODY PARAMETRYCZNE W ROZWIĄZYWANIU PROBLEMU ODWROTNEGO DLA MONITOROWANIA PRZEPŁYWÓW MATERIAŁÓW SYPKICH

The article presents the parametrisation-based methods of monitoring of the process of gravitational silo discharging with aid of capacitance tomography techniques. Proposed methods cover probabilistic Bayes’ modelling, including spatial and temporal analysis and Markov chain Monte Carlo methods as well as process parametrisation with artificial neural networks. In contrast to classical image reconstruction-based methods, parametric modelling allows to omit this stage as well as abandon the associated reconstruction errors. Parametric modelling enables the direct analysis of significant parameters of investigated process that in turn results in easier incorporation into the control feedback loop. Presented examples are given for the gravitational flow of bulk solids in silos.Niniejszy artykuł przedstawia parametryczne metody rozwiązywania problemu odwrotnego w tomografii pojemnościowej na przykładzie monitorowania procesu przepływu materiałów sypkich przy użyciu tomografii pojemnościowej. Wybrane metody obejmują modelowanie probabilistyczne Bayesa, w tym przestrzenne i czasowe oraz metody Monte Carlo łańcuchów Markowa, a także parametryzację procesu z użyciem sztucznych sieci neuronowych. W odróżnieniu od klasycznych metod opartych na algorytmach rekonstrukcji obrazu parametryzacja pozwala na pominięcie tego etapu, a co za tym idzie brak dodatkowych błędów związanych z rekonstrukcją. Parametryzacja pozwala na bezpośrednią analizę istotnych parametrów badanego procesu, przez co łatwiejsze jest użycie tych wyników w pętli sprzężenia zwrotnego sterowania. Przykłady rozpatrywane w tekście są opisane dla procesu grawitacyjnego opróżniania materiałów sypkich przechowywanych w silosach

Electrostatic forces in fluidized bed reactors : numerical and experimental analysis

Fluidized bed reactors are one of the unit processes most commonly used in the industry. Plastic production, energy conversion, petroleum refining, and medicine manufacturing are just a few examples of the fields benefiting from this type of technology. Although important advances have been made towards the understanding and prediction of the dynamics of fluidized beds, many important questions remain unanswered. One of the most important open challenges is the study of the effects of electrostatic forces inside the reactor. This electrostatic interaction is known to be the cause of some important problems such as the accumulation of material at the reactor's wall, the risk of explosion, the perturbation of nearby electronic devices and even the complete loss of the fluidization state. Despite the important research efforts in the last few decades, many problems are still unsolved. Amongst these, we find the use of non-invasive measurements techniques to characterize the hydrodynamics effects of electrostatic forces inside the reactor; and the macroscopic mathematical modeling of the charging dynamics in the bed. These are the issues that this research program tries to address. As part of the project ANR-IPAF, this Ph.D. thesis aims at improving the understanding of the effects of electrostatic forces in a fluidized bed reactor. On the modeling front, we use the kinetic theory of rapid granular flow to derive the most complete Eulerian model of the particle electric charge dynamics in monodispersed gas-solid flow systems. In this work, we show how to lift some of the most restrictive hypotheses of previous models. We show that the transport equation for the mean particle electric charge can be obtained without assuming the shape of the particle electric charge probability density function. In addition to this, we also derive and close the transport equation for the second order terms: the particle charge-velocity covariance and the particle charge variance. Our results show that a correct modeling of the second order moments is needed in dilute or highly electrically charged regions. Given that this complete model also adds many more partial differential equations to be solved, we study possible simplifications. Two algebraic models, one neglecting the effects of the charge variance and one taking it into account are proposed. The former proved to be suitable in configurations with low electric potential energy. However, the latter must be use with caution as it can become nonphysical in high charged situations. Finally, a semi-algebraic model is also proposed to solve the important limitations of the coupled algebraic model. On the experimental front, we study the use of an ECVT system to characterize the dynamics inside the bed. We focus our attention to the image reconstruction algorithm. We test the traditional reconstruction algorithms found in the literature. However, our results show that they are, either too inaccurate, or too computationally expensive. For these reasons, we explore the use of a novel reconstruction technique using machine learning algorithms. In this thesis, we propose two different strategies to train a feed forward artificial neural network to handle the image reconstruction step in a ECVT device. The first strategy is based on CFD-generated data which is coupled with the sensitivity matrix model to deduce the capacitance measurements. The second approach relies exclusively on real experimental data and it seeks to reconstruct an image that could explain the capacitance measurements. Our results show that artificial neural networks can be as accurate as the best image reconstruction algorithms found in the literature. However, they can reduce the computational cost by several order of magnitudes