Investigation of packed bed and moving bed reactors with benchmarking using advanced measurement and computational techniques

Trickle bed reactors (TBR), as typical packed bed reactors (PBR), are widely used in various fields. Very limited information regarding the flow behaviors, hydrodynamic, and mathematical models in extrudate catalyst shapes, such as cylinders, trilobes, and quadrilobes, can be found in literatures because the major focus was on spherical shape. Therefore, a hybrid pressure drops and liquid holdup phenomenological model for extrudate catalyst shapes was developed based on two-phase volume averaged equations, which showed high accuracy against experimental data. The maldistribution and dynamic liquid holdup were investigated in quadrilobe catalyst using gamma-ray computed tomography. A pseudo-3D empirical model was developed and compared with deep neural network predictions. Both models were in good agreement with experimental data. The accretion locations of heavy metal contaminants entrained with flow were tracked by the dynamic radioactive particle tracking technique in the packed beds of sphere, cylinder, trilobe, and quadrilobe, respectively. Kernel density estimator was used to indicate the accretion probability distribution, showing that pressure drop played an important role in heavy metal accretions. CFD simulations of random packed trilobe catalyst bed were conducted to obtain the local information and were validated by experimental data. Moving bed reactors (MBR), as a relatively new type of reactor, encounter many challenges due to the bed expansion because of the concurrent gas-liquid upflow. DEM simulation was used to generate expanded bed. A porosity distribution correlation was developed and implemented in CFD simulations to investigate the hydrodynamics --Abstract, page iv

Improving field management by machine vision - a review

Growing population of people around the world and thus increasing demand to food products as well as high tendency for declining the cost of operations and environmental preserving cares intensify inclination toward the application of variable rate systems for agricultural treatments, in which machine vision as a powerful appliance has been paid vast attention by agricultural researchers and farmers as this technology consumers. Various applications have introduced for machine vision in different fields of agricultural and food industry till now that confirms the high potential of this approach for inspection of different parameters affecting productivity. Computer vision has been utilized for quantification of factors affecting crop growth in field; such as, weed, irrigation, soil quality, plant nutrients and fertilizers in several cases. This paper presents some of these successful applications in addition to representing an introduction to machine vision

Transient Analysis of Full Scale and Experimental Downburst Flows

Downbursts are highly transient natural phenomena which produce strong downdrafts evolving from a cumulonimbus cloud They induce an outburst of damaging winds on or near to the ground causing an immense damage to the ground mounted structures and aircrafts. This study investigates the transient nature of downbursts using wind speed records from full scale downburst events employing an objective methodology. This method can detect the abrupt change points in a downburst time series based on statistical parameters such as mean, standard deviation and linear trend. In addition to the analysis of the full scale downburst events, several large scale experimental model downbursts are produced in the Wind Engineering, Energy and Environment (WindEEE) Dome at Western University by varying downdraft jet diameter and jet velocity to comprehensively characterize the downburst flow field. High resolution surface layer data is captured using Cobra probes and dynamics of the downburst vortices is investigated using Particle Image Velocimetry (PIV) Technique. The analysis of wind speed record is carried out deploying the moving mean approach with different averaging times. Statistical analysis on turbulence using reasonable averaging time shows the similarities of experimental model with full scale events. For the first time, an effort has been made to compare the primary vortex structure and its evolution with the limited full scale downburst records obtained using Doppler radar measurements

Blood Velocities Estimation using Ultrasound

This thesis consists of two parts. In the rst part, the iterative data-adaptive BIAA spectral estimation technique was extended to estimate lateral blood velocities using ultrasound scanners. The BIAA method makes no assumption on samples depth or sampling pattern, and therefore allows for transmission in duplex mode imaging. The technique was examined on a realistic Field II simulation data set, and showed fewer spectral artifacts in comparison with other techniques. In the second part of the thesis, another common problem in blood velocity estimation has been investigated, namely strong backscattered signals from stationary echoes. Two methods have been tested to examine the possibility of overcoming this problem. However, neither of these methods resulted in a better estimation of the blood velocities, most likely as the clutter characteristics in color ow images vary too rapidly to allow for this form of models. This might be a result of the non-stationary tissue motions which could be caused by a variety of factors, such as cardiac activities, respiration, transducer/patient movement, or a combination of them

Fluidodinámica de un reactor de lecho fluidizado de dos zonas con cambio de sección y su aplicación usando membrana permeoselectiva

El reactor de lecho fluidizado de dos zonas, recientemente patentado por la Universidad de Zaragoza (PCT/ES2009/070241), ha sido propuesto como una solución efectiva al problema de la desactivación catalítica que tiene lugar en procesos gas-sólido catalíticos en los que intervienen hidrocarburos gaseosos. El fundamento del reactor radica en la alimentación fraccionada de gas en el lecho. Por un punto intermedio de este se introduce una corriente de gas reactivo, mientras que por la parte inferior del mismo se alimenta una corriente de gas oxidante. De este modo se inducen dos zonas con atmósferas diferentes en un mismo lecho fluidizado (reductora y oxidante) y la circulación de partículas entre ambas zonas del lecho permite mantener una actividad catalítica constante a lo largo del tiempo. En la zona superior del lecho tiene lugar la reacción catalítica que genera, como subproducto, un depósito carbonoso (coque) sobre la superficie activa del catalizador mientras que en la zona inferior del lecho se produce la combustión de dicho coque obteniéndose partículas de catalizador regeneradas y nuevamente activas para desarrollar la actividad catalítica en la zona superior. A fin de ganar versatilidad y poder mantener el régimen de fluidización (la velocidad del gas) entre ambas zonas del lecho, aún trabajando con caudales muy diferentes de corriente reactiva y de regeneración, el diseño original del RFLDZ se modificó añadiendo un cambio de sección (RLFDZ-CS). Adicionalmente, se propuso la inclusión de membranas en la zona de superior del lecho para retirar selectivamente los productos de reacción (RLFDZ-CS+MB) con el objetivo de desplazar el equilibrio termodinámico en reacciones limitadas por éste. La presente tesis abarca la caracterización fluidodinámica del novedoso RLFDZ-CS+MB así como su demostración experimental en base al proceso productivo de propileno a partir de la deshidrogenación catalítica de propano. Las limitaciones de este proceso (bajas conversiones de equilibrio, elevada endotermicidad y rápida desactivación catalítica) pretenden ser mitigadas con el reactor multifuncional descrito. El estudio fluidodinámico del RLFDZ-CS+MB incluye la medición experimental del movimiento de las partículas catalíticas en el lecho, la caracterización del régimen de burbujeo en función de las condiciones de operación, el desarrollo de modelos matemáticos para predecir el grado de mezcla axial de sólidos y las propiedades de burbuja y la validación de códigos de fluidodinámica computacional, CFD, para simular el comportamiento del lecho. En concreto, se han utilizado técnicas de tratamiento digital de imagen y velocimetría de partículas para caracterizar el movimiento de sólidos y burbujas en el lecho, haciendo uso de trazadores ópticos fosforescentes para realizar el seguimiento del grado de mezcla axial entre las zonas superior e inferior del lecho catalítico. Se han desarrollado correlaciones hidrodinámicas para estimar la variación del tamaño y velocidad de burbujas de gas con la posición vertical en el lecho y se ha implementado el modelo de retromezcla a contracorriente (Counter-current Back-Mixing, CCBM) para predecir la evolución temporal del grado de mezcla axial. Se ha simulado el comportamiento fluidodinámico del RLFDZ-CS+MB mediante los códigos CFD comerciales Ansys CFX y Fluent, validando experimentalmente los resultados obtenidos por dichos modelos. Esencialmente, se ha evaluado el efecto del tipo de partícula, velocidad del gas, ángulo de cambio de sección y posición relativa de la alimentación superior en el comportamiento fluidodinámico del reactor. Adicionalmente se ha estudiado la influencia del cambio de escala, el uso y disposición de membranas en el lecho y la inclusión de obstáculos (internals) en el régimen de burbujeo y mezcla de sólidos. Se han analizado las limitaciones operacionales del sistema relativas la aparición de zonas muertas, by-pass de gas y regímenes de slugging y se ha descrito una ventana de operación para el correcto funcionamiento fluidodinámico del reactor. La demostración experimental del funcionamiento del RLFDZ-CS+MB, en base a la información recogida en la caracterización fluidodinámica, se ha llevado a cabo estudiando la deshidrogenación catalítica de propano en presencia de un catalizador activo y selectivo a la deshidrogenación (Pt-Sn/MgAl2O4) y de membranas densas basadas en paladio para la extracción selectiva de hidrógeno de los gases de reacción. Se ha establecido un rango de temperaturas de operación y fracciones de agente oxidante en la alimentación óptimos para maximizar la producción estacionaria de propileno. La intensificación de procesos llevada a cabo en el RLFDZ-CS+MB multifuncional ha mejorado las tasas de rendimiento a propileno de procesos comerciales, obteniéndose los mejores resultados (SC3H6 = 92%, YC3H6 = 58%) entre los reportados en literatura

Finite-Volume Filtering in Large-Eddy Simulations Using a Minimum-Dissipation Model

Large-eddy simulation (LES) seeks to predict the dynamics of the larger eddies in turbulent flow by applying a spatial filter to the Navier-Stokes equations and by modeling the unclosed terms resulting from the convective non-linearity. Thus the (explicit) calculation of all small-scale turbulence can be avoided. This paper is about LES-models that truncate the small scales of motion for which numerical resolution is not available by making sure that they do not get energy from the larger, resolved, eddies. To identify the resolved eddies, we apply Schumann’s filter to the (incompressible) Navier-Stokes equations, that is the turbulent velocity field is filtered as in a finite-volume method. The spatial discretization effectively act as a filter; hence we define the resolved eddies for a finite-volume discretization. The interpolation rule for approximating the convective flux through the faces of the finite volumes determines the smallest resolved length scale δ. The resolved length δ is twice as large as the grid spacing h for an usual interpolation rule. Thus, the resolved scales are defined with the help of box filter having diameter δ= 2 h. The closure model is to be chosen such that the solution of the resulting LES-equations is confined to length scales that have at least the size δ. This condition is worked out with the help of Poincarés inequality to determine the amount of dissipation that is to be generated by the closure model in order to counterbalance the nonlinear production of too small, unresolved scales. The procedure is applied to an eddy-viscosity model using a uniform mesh

Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures

Floods are the most recurrent, widespread and damaging natural disasters, and are ex-pected to become further devastating because of global warming. Blockage of cross-drainage hydraulic structures (e.g., culverts, bridges) by flood-borne debris is an influen-tial factor which usually results in reducing hydraulic capacity, diverting the flows, dam-aging structures and downstream scouring. Australia is among the countries adversely impacted by blockage issues (e.g., 1998 floods in Wollongong, 2007 floods in Newcas-tle). In this context, Wollongong City Council (WCC), under the Australian Rainfall and Runoff (ARR), investigated the impact of blockage on floods and proposed guidelines to consider blockage in the design process for the first time. However, existing WCC guide-lines are based on various assumptions (i.e., visual inspections as representative of hy-draulic behaviour, post-flood blockage as representative of peak floods, blockage remains constant during the whole flooding event), that are not supported by scientific research while also being criticised by hydraulic design engineers. This suggests the need to per-form detailed investigations of blockage from both visual and hydraulic perspectives, in order to develop quantifiable relationships and incorporate blockage into design guide-lines of hydraulic structures. However, because of the complex nature of blockage as a process and the lack of blockage-related data from actual floods, conventional numerical modelling-based approaches have not achieved much success. The research in this thesis applies artificial intelligence (AI) approaches to assess the blockage at cross-drainage hydraulic structures, motivated by recent success achieved by AI in addressing complex real-world problems (e.g., scour depth estimation and flood inundation monitoring). The research has been carried out in three phases: (a) litera-ture review, (b) hydraulic blockage assessment, and (c) visual blockage assessment. The first phase investigates the use of computer vision in the flood management domain and provides context for blockage. The second phase investigates hydraulic blockage using lab scale experiments and the implementation of multiple machine learning approaches on datasets collected from lab experiments (i.e., Hydraulics-Lab Dataset (HD), Visual Hydraulics-Lab Dataset (VHD)). The artificial neural network (ANN) and end-to-end deep learning approaches reported top performers among the implemented approaches and demonstrated the potential of learning-based approaches in addressing blockage is-sues. The third phase assesses visual blockage at culverts using deep learning classifi-cation, detection and segmentation approaches for two types of visual assessments (i.e., blockage status classification, percentage visual blockage estimation). Firstly, a range of existing convolutional neural network (CNN) image classification models are imple-mented and compared using visual datasets (i.e., Images of Culvert Openings and Block-age (ICOB), VHD, Synthetic Images of Culverts (SIC)), with the aim to automate the process of manual visual blockage classification of culverts. The Neural Architecture Search Network (NASNet) model achieved best classification results among those im-plemented. Furthermore, the study identified background noise and simplified labelling criteria as two contributing factors in degraded performance of existing CNN models for blockage classification. To address the background clutter issue, a detection-classification pipeline is proposed and achieved improved visual blockage classification performance. The proposed pipeline has been deployed using edge computing hardware for blockage monitoring of actual culverts. The role of synthetic data (i.e., SIC) on the performance of culvert opening detection is also investigated. Secondly, an automated segmentation-classification deep learning pipeline is proposed to estimate the percentage of visual blockage at circular culverts to better prioritise culvert maintenance. The AI solutions proposed in this thesis are integrated into a blockage assessment framework, designed to be deployed through edge computing to monitor, record and assess blockage at cross-drainage hydraulic structures

Aeronautical Engineering: A Continuing Bibliography with Indexes

This report lists reports, articles and other documents recently announced in the NASA STI Database