Intelligent laser cladding control system design and construction.

Imperial Users onl

Regulation of Powder Mass Flow Rate in Gravity-Fed Powder Feeder Systems

Precise regulation of powder mass flow in laser-based manufacturing processes is critical to achieving excellent part dimensional and microstructure quality. Control of powder mass flow is challenging because low flow rates, where nonlinear effects are significant, are typically required. Also, gravity-fed powder feeder systems have significant material transport delays, making the control of powder mass flow even more challenging. This paper presents a control strategy for regulating the powder mass flow rate in a gravity-fed powder feeder system. A dynamic model of the powder feeder system, including material transport delay, is constructed, and a modified proportional plus integral (PI) controller is designed. An observer is used to estimate powder mass flow rate using the powder feeder motor encoder signal. The control strategy is implemented in a Smith Predictor Corrector Structure, which has been adjusted such that it can be applied to the modified PI controller, to account for the inherent material transport delay. Experimental studies are conducted that validate the dynamic model and controller strategy

Electrostatic Sensors – Their Principles and Applications

Over the past three decades electrostatic sensors have been proposed, developed and utilised for the continuous monitoring and measurement of a range of industrial processes, mechanical systems and clinical environments. Electrostatic sensors enjoy simplicity in structure, cost-effectiveness and suitability for a wide range of installation conditions. They either provide unique solutions to some measurement challenges or offer more cost-effective options to the more established sensors such as those based on acoustic, capacitive, optical and electromagnetic principles. The established or potential applications of electrostatic sensors appear wide ranging, but the underlining sensing principle and resultant system characteristics are very similar. This paper presents a comprehensive review of the electrostatic sensors and sensing systems that have been developed for the measurement and monitoring of a range of process variables and conditions. These include the flow measurement of pneumatically conveyed solids, measurement of particulate emissions, monitoring of fluidised beds, on-line particle sizing, burner flame monitoring, speed and radial vibration measurement of mechanical systems, and condition monitoring of power transmission belts, mechanical wear, and human activities. The fundamental sensing principles together with the advantages and limitations of electrostatic sensors for a given area of applications are also introduced. The technology readiness level for each area of applications is identified and commented. Trends and future development of electrostatic sensors, their signal conditioning electronics, signal processing methods as well as possible new applications are also discussed

Electrodynamic sensors and neural networks for electrical charge tomography.

This research into the feasibility of imaging particulate processes using electrical charge tomography investigates four techniques: the multi-sensing of electrical charge in a cross-section, a neural network based classifier for flow regime identification, cross correlation based velocity determination and spectral analysis of electrodynamic signals. A single charged-particle model is developed to simulate the induction effect on a sensor by a charge. The spatial representation of the voltage induced onto sixteen sensors, placed on the boundary of a circular pipe, gives a flow distribution profile over the cross-section. A two charged-particle model is developed to simulate the electrodynamic effect of two particles on a tomographic sensor configuration. As in the single particle model, a spatial representation of the voltages induced onto the sensors is presented. This voltage profile is due to the combined effects of position and charge of the two particles. A multi-particle model is developed to predict the voltage profile of several flow regimes: full, annular, core, half and stratified. The model is extended to provide the loading and concentration of a given flow. A measurement system is constructed consisting of sixteen sensors equally spaced around the boundary of a circular 100mm pipe. Measurements on a bead drop system are designed to verify the single particle model. A sand flow system, consisting mainly of 300 micron sized particles, is used for measurements of the induced voltages due to different flow regimes. The latter are created artificially by using baffles of different shapes that obstruct the sand flow. The voltage profile from the sixteen sensors gives spatial information about the flow regime. These voltage profiles are normalised into patterns that are presented to a Kohonen neural network for classification. Two regime classification between well differentiated regimes gives an accuracy of identification of 95%. This is expanded to provide classification of three regimes with more variability in the input patterns giving success rates between 50% to 70%. A power spectral density analysis of the measured electrodynamic signals gives observable features for particle characterisation during flow. In full flow, with no baffles obstructing the sand flow, a consistently high frequency spectra of 550Hz is observed. At flow rates above 0.540 kgs-1, the frequency spectra shifts to a lower range of 200Hz. In obstructed flow, such as in stratified regime, an inhomogeneous phase is inferred from the drop in frequency of the power spectra at relatively low flowrates (0.36kgs-1). These results suggest a relationship between the observed spectra and the phenomenon of clustering of particles at higher concentrations. The potential of electrodynamic spectroscopy for particle characterisation in terms of size distribution is discussed. Knowledge of flow regime voltage profile, regime identification and concentration provided a basis for an empirically based image reconstruction algorithm. Finally the achievements of the thesis are discussed and suggestions made for further work

A tomographic imaging system for pneumatic conveyors using optical fibres.

This thesis presents an investigation into the application of optical fibre sensors to a tomographic imaging system.Several sensing mechanisms for measurement using non-intrusive techniques are discussed and there relevance to pneumatic conveying discussed. Optical systems are shown to be worthy of investigation. The optical sensor is modelled to predict the expected sensor output voltage profiles arising from different, artificially produced flow regimes. These artificial flow regimes are created by placing a shaped obstruction inside a gravity drop conveyor in the path of the flowing solids. It is shown that for two arrays, each consisting of sixteen transducers, approximately 30% of the measurement volume is sampled.An image reconstruction method for optical tomography is described, based on the back projection between view lines algorithm.The design of the optical tomography system is described, with emphasis on preparation of the ends of the optical fibre, beam collimation and design of the transmitter and receiver circuits.The optical sensors are evaluated singly and as a tomographic array. Results relating to concentration measurement are presented for solids flow using sand with a mean of 300 micron and plastic beads of 2 mm nominal diameter. Measurements were made with a single optical sensor using the gravity flow rig. The results demonstrate the suitability of the optical sensor for concentration measurement for lightly loaded flows (up to approximately 2% solids by volume in the test). The test is extended to all thirty-two sensors using a range of solids mass flow rates from 40 to 320 gm/s with both dry sand and plastic beads over a range of artificially created flow regimes. The results obtained by comparing the measured and predicted flowrates show good general agreement. The statistical parameters for the error of the sand flow measurement have been calculated as having a mean of 6.76% and standard deviation of 3.94% and for plastic beads is 5.43% and standard deviation of 0.21%. The results also demonstrates that the system is reasonably independent of flow regime and so the optical fibre system is suitable as a concentration meter.Back projection is used to generate tomographic images as an alternative representation of the data on concentration measurement. This provides a visual representation of optical density (concentration) information which is not obvious from the concentration measurements.Results from experiments on particles with different sizes are presented. The results are analysed using frequency spectrum techniques and shown to be dependent upon the particle size for approximately spherical particles with diameters between 600 |im and 5 mm.Suggestions for further work on optical fibre sensors and optical fibre tomographic measurements are made

Instrumentation of particle conveying using electrical charge tomography.

This thesis presents an investigation into the application of electrodynamic sensors to a tomographic imaging system. Several sensing mechanisms for measurement using non-intrusive techniques are discussed and their relevance to pneumatic conveying considered. Electrical charge tomography systems are shown to be worthy of investigation. Electrodynamic sensors are inherently low cost and simple in concept. This sensor is used to detect the inherent charge on dry, moving solids. Models are developed to predict the sensitivity of circular and rectangular electrodes. The spatial filtering effect of these sensors is investigated. Cross correlation is briefly reviewed and a software program is presented and tested. For tomographic imaging the forward problem for the individual sensors is modelled, used to solve the inverse problem and derive the linear back projection and filtered back projection algorithms.The design of the electronic circuitry which forms the transducer is presented. The gravity drop flow rig is described and the relationship between sand flow and plastic bead flow relative to the flow indicator setting determined. The dual 16-channel sensor array measurement section is described. Flow models are developed and used to predict the relative output voltage profiles expected from the sensor arrays.The linearity and frequency bandwidth of the sensor electronics is measured. The effect of sensor size on sensitivity and spatial filtering are investigated for circular and rectangular electrodes.Estimates of the solid concentration of flowing particles are made using individual sensors. Concentration profiles are generated and compared with predicted values. Peripheral velocities of the flowing material are determined from transit times calculated by cross correlation of upstream and downstream sensor signals.Concentration profiles are calculated using linear back projection and filtered back projection algorithms from data measured by the sensor arrays. Velocity profiles are obtained by cross correlation of upstream and downstream pixel concentration values. Estimates of the mass flow rate are obtained by combining concentration and velocity profiles.Suggestions for further work on electrodynamic sensors and tomographic measurements are made

Particle Entrainment Studies From Dry and Wet Bed

During the Fluid Coking Process™ bitumen is sprayed into a fluidized bed of hot coke particles. The bitumen undergoes thermal cracking and is converted into gasses, condensable vapors, and solid coke, which deposits on the coke particles. These vapors rise through the vessel and result in entrainment of coke particles. Wet fine coke particles from the freeboard region enter the cyclone and contribute to the fouling of the Coker cyclones which can lead to the premature shut-down of Fluid Cokers. The primary objective of this research is to determine how bed wetness affects entrainment. First, a new fluidized bed was constructed, and a novel pseudo-isokinetic sampling was developed and tested to collect entrained solids. Then the entrainment from the dry bed in the bubbling and the turbulent regime was investigated. The cluster analysis was performed and showed an improvement in predicting the flux of solids ejected from bed surface, above the TDH, and across the freeboard. In the next chapter, the effect of the different levels of bed wetness on flux and size distribution of the entrained particles was studied, and the results were compared to the ones obtained for the dry bed. It was observed that in the bubbling regime, the presence of liquid can change the bubble properties in the bed which will affect the entrainment. However, the effect of low liquid loading on entrainment in turbulent regime was found to be negligible. In the final chapter, RPT was employed and it was found that the motion of ejected clusters in the freeboard could not be tracked as the clusters move too fast for the strength of radioactive that was used. Moreover, a new model to measure the entrainment of clusters in the freeboard using a modified radioactive tracer technique in which the detectors are collimated was proposed. It was found that this method only provides the decay coefficient of the clusters flux decay

CFD Modeling of Complex Chemical Processes: Multiscale and Multiphysics Challenges

Computational fluid dynamics (CFD), which uses numerical analysis to predict and model complex flow behaviors and transport processes, has become a mainstream tool in engineering process research and development. Complex chemical processes often involve coupling between dynamics at vastly different length and time scales, as well as coupling of different physical models. The multiscale and multiphysics nature of those problems calls for delicate modeling approaches. This book showcases recent contributions in this field, from the development of modeling methodology to its application in supporting the design, development, and optimization of engineering processes