Hydrodynamics related performance evaluation of Upflow Moving Bed Hydrotreater reactor (MBR) using developed experimental methods and CFD simulation

Upflow Moving Bed Hydrotreater (MBR) reactor is used for hydrotreating resid crude oil. It is a two-phase upflow reactor having a catalyst bed with conical bottom, and plena. At industrial conditions the reactor is not performing at its best and encountering issues such as hot spots, catalyst agglomeration inside the catalyst bed leading to frequent shutdown of the reactor. The root cause of these problems are linked to the improper hydrodynamics inside the catalyst bed. To investigate this, the industrial scale MBR is scaled down to a pilot scale and indicative and key hydrodynamic parameters are investigated using developed experimental methods and CFD simulation. The local hydrodynamics is quantified using an experimental technique called two-tip optical probe (TTOP). Developed algorithms for TTOP to derive the local phase saturations, velocities, backmixing, maldistribution using the time series data of the probe. The results indicates high maldistribution zones inside the catalyst bed and found convincing evidence to link this to the conical design and plena of MBR. Overall Gas and Liquid dispersion/Mixing in the catalyst bed is investigated by tracer studies using a developed methodology based on residence time distribution (RTD), Convolution, Regression, and Catalyst Bed Models based on axial dispersion and wave model. Good gas/liquid dispersion is seen at the industrial scaled down operating condition. A CFD model is developed for the lower plenum of MBR and validated with gamma ray densitometry (GRD) for radial profile of line average phase volume fraction. The simulation indicates that the current design of lower plenum is enabling a dominant movement of phases only in the central region outlets of this plenum. A modification of the current design proves to perform better in terms of movement of phases along entire outlets of the lower plenum --Abstract, page iv

Annual report 2006 // Institute of Safety Research

[no abstract available

Annual report 2006 // Institute of Safety Research

[no abstract available

3D tomographic imaging using ad hoc and mobile sensors

The aim of this research is to explore the integration of ad hoc and mobile sensors into a conventional Electrical Resistance Tomography (ERT) system. This is motivated by the desire to improve the spatial resolution of 3D reconstructed images that are produced using ERT. The feasibility of two approaches, referred to as the Extended Electrical Tomography (EET) and Augmented Electrical Tomography (AET) are considered. The approaches are characterized according to the functionality of the sensors on the ad hoc 'pills'. This thesis utilizes spectral and numerical analysis techniques, with the goal of providing a better understanding of reconstruction limitations, including quality of measurements, sensitivity levels and spatial resolution. These techniques are applied such that an objective evaluation can be made, without having to depend heavily on visual inspection of a selection of reconstructed images when evaluating the performance of different set-ups. In EET, the sensors on the pills are used as part of the ERT electrode system. Localized voltage differences are measured on a pair of electrodes that are located on an ad hoc pill. This extends the number of measurements per data set and provides information that was previously unobtainable using conventional electrode arrangements. A standalone voltage measurement system is used to acquire measurements that are taken using the internal electrodes. The system mimics the situation that is envisaged for a wireless pill, specifically that it has a floating ground and is battery-powered. For the present exploratory purposes, the electronic hardware is located remotely and the measured signal is transmitted to the PC through a cable. The instrumentation and data acquisition circuits are separated through opto-isolators which essentially isolates both systems. Using a single pill located in the centre of a vessel furnished with 16 electrodes arranged in a single plane, spectral analysis indicates that 15 of the 16 extended measurements acquired using the adjacent current injection strategy are unique. Improvement is observed for both the sensitivity and spatial resolution for the voxels in the vicinity of the ad hoc pill when comparing the EET approach with the conventional ERT approach. This shows the benefit of the EET approach. However, visual inspection of reconstructed images reveals no apparent difference between images produced using a regular and extended dataset. Similar studies are conducted for cases considering the opposite strategy, different position and orientation of the pill, and the effect of using multiple pills. In AET, the sensors on the ad hoc pills are used as conductivity probes. Localized conductivity measurements provide conductivity values of the voxels in a discretized mesh of the vessel, which reduces the number of unknowns to be solved during reconstruction. The measurements are incorporated into the inverse solver as prior information. The Gauss-Newton algorithm is chosen for implementation of this approach because of its non-linear nature. Little improvement is seen with the inclusion of one localized conductivity measurement. The effect on the neighbouring voxels is insignificant and there is a lack of control over how the augmented measurement influences the solution of its neighbouring voxels. This is the first time that measurements using ad hoc and 'wireless' sensors within the region of interest have been incorporated into an electrical tomography system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Caractérisation de l'hydrodynamique des écoulements solide-liquide dans une conduite en forme de boucle

Caractérisation des suspensions -- Écoulements de suspensions dans une conduite -- Modélisation des écoulements de suspensions dans une conduite -- Défis de la modélisation et la validation des modèles polyphasiques -- Maquette froide -- Développement de stratégies pour l'interprétation des mesuses d'ERT -- Caractérisation des régimes d'écoulement et détermination des vitesses de transition dans la conduite horizontale a la sortie du coude -- Caractérisation du mélange dans une conduite en forme de boucle -- Algorithmes pour la mesure quantitative de concentration des écoulements -- Caractérisation des transitions entre les régimes d'écoulements d'une suspension avec ERT -- Influence d'un coude sur les transitions entre les régimes d'écoulements d'une suspension dans une conduite -- Mélange d'une suspension dans une conduite en forme de boucle

Bayesian Methods for Gas-Phase Tomography

Gas-phase tomography refers to a set of techniques that determine the 2D or 3D distribution of a target species in a jet, plume, or flame using measurements of light, made around the boundary of a flow area. Reconstructed quantities may include the concentration of one or more species, temperature, pressure, and optical density, among others. Tomography is increasingly used to study fundamental aspects of turbulent combustion and monitor emissions for regulatory compliance. This thesis develops statistical methods to improve gas-phase tomography and reports two novel experimental applications. Tomography is an inverse problem, meaning that a forward model (calculating measurements of light for a known distribution of gas) is inverted to estimate the model parameters (transforming experimental data into a gas distribution). The measurement modality varies with the problem geometry and objective of the experiment. For instance, transmittance data from an array of laser beams that transect a jet may be inverted to recover 2D fields of concentration and temperature; and multiple high-resolution images of a flame, captured from different angles, are used to reconstruct wrinkling of the 3D reacting zone. Forward models for gas-phase tomography modalities share a common mathematical form, that of a Fredholm integral equation of the first-kind (IFK). The inversion of coupled IFKs is necessarily ill-posed, however, meaning that solutions are either unstable or non-unique. Measurements are thus insufficient in themselves to generate a realistic image of the gas and additional information must be incorporated into the reconstruction procedure. Statistical inversion is an approach to inverse problems in which the measurements, experimental parameters, and quantities of interest are treated as random variables, characterized by a probability distribution. These distributions reflect uncertainty about the target due to fluctuations in the flow field, noise in the data, errors in the forward model, and the ill-posed nature of reconstruction. The Bayesian framework for tomography features a likelihood probability density function (pdf), which describes the chance of observing a measurement for a given distribution of gas, and prior pdf, which assigns a relative plausibility to candidate distributions based on assumptions about the flow physics. Bayes’ equation updates information about the target in response to measurement data, combining the likelihood and prior functions to form a posterior pdf. The posterior is usually summarized by the maximum a posteriori (MAP) estimate, which is the most likely distribution of gas for a set of data, subject to the effects of noise, model errors, and prior information. The framework can be used to estimate credibility intervals for a reconstruction and the form of Bayes’ equation suggests procedures for improving gas tomography. The accuracy of reconstructions depends on the information content of the data, which is a function of the experimental design, as well as the specificity and validity of the prior. This thesis employs theoretical arguments and experimental measurements of scalar fluctuations to justify joint-normal likelihood and prior pdfs for gas-phase tomography. Three methods are introduced to improve each stage of the inverse problem: to develop priors, design optimal experiments, and select a discretization scheme. First, a self-similarity analysis of turbulent jets—common targets in gas tomography—is used to construct an advanced prior, informed by an estimate of the jet’s spatial covariance. Next, a Bayesian objective function is proposed to optimize beam positions in limited-data arrays, which are necessary in scenarios where optical access to the flow area is restricted. Finally, a Bayesian expression for model selection is derived from the joint-normal pdfs and employed to select a mathematical basis to reconstruct a flow. Extensive numerical evidence is presented to validate these methods. The dissertation continues with two novel experiments, conducted in a Bayesian way. Broadband absorption tomography is a new technique intended for quantitative emissions detection from spectrally-convolved absorption signals. Theoretical foundations for the diagnostic are developed and the results of a proof-of-concept emissions detection experiment are reported. Lastly, background-oriented schlieren (BOS) tomography is applied to combustion for the first time. BOS tomography employs measurements of beam steering to reconstruct a fluid’s optical density field, which can be used to infer temperature and density. The application of BOS tomography to flame imaging sets the stage for instantaneous 3D combustion thermometry. Numerical and experimental results reported in this thesis support a Bayesian approach to gas-phase tomography. Bayesian tomography makes the role of prior information explicit, which can be leveraged to optimize reconstructions and design better imaging systems in support of research on fluid flow and combustion dynamics