Particle sizing in the process industry using Hertz-Zener impact theory and acoustic emission spectra

The cost of implementing real-time monitoring and control of industrial processes is a significant barrier for many companies. Acoustic techniques provide complementary information to optical spectroscopic sensors and have a number of advantages: they are relatively inexpensive, can be applied non-invasively, are non-destructive, multi-point measurements are possible, opaque samples can be analysed in containers that are made from opaque materials (e.g. steel or concrete) and the analysis can be conducted in real-time. In this paper a new theoretical model is proposed which describes the transport of particles in a stirred reactor, their collision with the reactor walls, the subsequent vibrations which are then transmitted through the vessel walls, and their detection by an ultrasonic transducer. The particle-wall impact is modelled using Hertz-Zener impact theory. Experimental data is then used in conjunction with this (forward) model to form an inverse problem for the particle size distribution using a least squares cost function. Application of an integral smoothing operator to the power spectra greatly enhances the accuracy and robustness of the approach. One advantage of this new approach is that since it operates in the frequency domain, it can cope with the industrially relevant case of many particle-wall collisions. The technique will be illustrated using data from a set of controlled experiments. In the first instance a set of simplified experiments involving single particles being dropped in air onto a substrate are utilised. The second set of experiments involves particles in a carrier fluid being stirred in a reactor vessel. In each case the approach is able to successfully recover the associated particle size

Particle size distribution estimation of a mixture of regular and irregular sized particles using acoustic emissions

This works investigates the possibility of using Acoustic Emissions (AE) to estimate the Particle Size Distribution (PSD) of a mixture of particles that comprise of particles of different densities and geometry. The experiments carried out involved the mixture of a set of glass and polyethylene particles that ranged from 150-212 microns and 150-250microns respectively and an experimental rig that allowed the free fall of a continuous stream of particles on a target plate which the AE sensor was placed. By using a time domain based multiple threshold method, it was observed that the PSD of the particles in the mixture could be estimated

Particle size measurement using electrostatic sensor through spatial filtering method

Particle size measurement is important in powder and particle industries in which the particle size affects the productivity and efficiency of the machine, for example, in coal-fired power plants. An electrostatic sensor detects the electric charge from dry particles moving in a pipeline. Analysis of the detected signal can provide useful information about the particle velocity, mass flow rate, concentration and size. Using electrostatic sensors, previous researches studied particle sizing using magnitude dependent analysis which is a highly conditional method where the results can be affected by other parameters such as particle mass flow rate, velocity and concentration. This research proposes a magnitude independent analysis for particle sizing in the frequency domain called spatial filtering method. The solution was started by modeling and analysis of the charge induced to the ring electrode using finite-element analysis to find the sensitivity of electrode. A mathematical model was provided to compute particle position on the radial axis of the electrode and then a new technique was proposed to extract a single particle size from the calculated particle radial position. To validate the proposed method experimentally, a sensor was designed and five test particles ranging from 4 mm to 14 mm were selected for measurement. The results show a 0.44 mm estimation error between the estimated and expected results. The results also show that the method is promising for the establishment of a reliable and cost-effective solid particle sizing system

Particle size distribution estimation of a powder agglomeration process using acoustic emissions

Washing powder needs to undergo quality checks before it is sold, and according to a report by the partner company, these quality checks include an offline procedure where a reference sieve analysis is used to determine the size distributions of the powder. This method is reportedly slow, and cannot be used to measure large agglomerates of powders. A solution to this problem was proposed with the implementation of real time Acoustic Emissions (AE) which would provide the sufficient information to make an assessment of the nature of the particle sizes. From the literature reviewed for this thesis, it was observed that particle sizes can be monitored online with AE but there does not appear to be a system capable of monitoring particle sizes for processes where the final powder mixture ratio varies significantly. This has been identified as a knowledge gap in existing literature and the research carried out for this thesis contributes to closing that gap. To investigate this problem, a benchtop experimental rig was designed. The rig represented limited operating conditions of the mixer but retained the critical factors. The acquired data was analysed with a designed hybrid signal processing method based on a time domain analysis of impact peaks using an amplitude threshold approach. Glass beads, polyethylene and washing powder particles were considered for the experiments, and the results showed that within the tested conditions, the designed signal processing approach was capable of estimating the PSD of various powder mixture combinations comprising particles in the range of 53-1500 microns, it was also noted that the architecture of the designed signal processing method allowed for a quicker online computation time when compared with other notable hybrid signal processing methods for particle sizing in the literature

Flow measurement of pneumatically conveyed solids using intrusive electrostatic sensors

Particulate solids are commonly conveyed in industry by means of pneumatic pipelines. The particle flows often need to be controlled and maintained within certain bounds, but the development of instrumentation to monitor them remains a challenging area. A variety of techniques have been researched to measure various flow parameters. An overview of the existing technology is presented, along with advantages and limitations of each method. A detailed investigation is conducted into the use of electrostatic sensors with intrusive electrodes to measure the velocity of pneumatic particle flows. Previous work has been reported on the use of non-intrusive ring electrodes, but few studies of intrusive electrodes have been undertaken to date. Modelling, based on the finite element method, is used to determine the characteristics of the charge induced by solid particle flows onto intrusive electrodes. These are then compared with the properties of non-intrusive circular ring electrode elements. The effects of electrode intrusion depth are studied, and it is shown that whilst stability of the velocity measurements improves with intrusion depth, some types of flow are best measured using a particular intrusion that results in the most accurate average velocity reading. Electrode spacing, which must be close enough to allow a measurement to be taken but far enough to avoid unwanted interactive effects, is investigated, along with the effect of electrode cross sectional shape on sensor signals and the effect of common mode noise on cross correlation velocity measurement. This information is used in the development of a practical sensor system that uses embedded signal processing, which is then tested on laboratory and industrial flow rigs. The results are used to characterise the features of intrusive electrostatic sensors and their response to different flow conditions. Most significantly, intrusive electrodes are shown to be sensitive to localised flow regimes. Finally, suggestions on aspects of electrostatic sensors that would benefit from further development are discussed