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

Estimation of powder mass flow rate in a screw feeder using acoustic emissions

Screw feeders are widely used in powder processes to provide an accurate and consistent flow rate of particles. However this flow rate is rarely measured or controlled. This investigation explores the use of generalised norms and moments from structural-borne acoustic emission (AE) measurements as key statistics indicators for the estimation of powder mass flow rate in a screw feeder. Experimental work was carried out acquiring AE measurements from an industrial screw feeder working with four different types of material at different dispensation rates. Signal enveloping was used in first place to eliminate high frequency components while retaining essential information such as peaks or bursts caused by particle impacts. Secondly a set of generalised norms and moments is extracted from the signal, and their correlation with mass flow rate was studied and assessed. Finally a general model able to estimate mass flow rate for the four different types of powders tested was developed

Online particle size distribution estimation of a mixture of similar sized particles with acoustic emissions

Particle processing plants regard the Particle Size Distribution (PSD) as a key quality factor as it influences the bulk and flow properties of the particles. In this work, Acoustic Emission (AE) is used to estimate the PSD of a mixture that comprise of similar sized particles. The experiments involved the use of regular sized particles (glass beads) and with the aid of a time domain based threshold analysis of the particle impacts the PSD of the mixtures could be estimated

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

Estimation of fine and oversize particle ratio in a heterogeneous compound with acoustic emissions

The final phase of powder production typically involves a mixing process where all of the particles are combined and agglomerated with a binder to form a single compound. The traditional means of inspecting the physical properties of the final product involves an inspection of the particle sizes using an offline sieving and weighing process. The main downside of this technique, in addition to being an offline-only measurement procedure, is its inability to characterise large agglomerates of powders due to sieve blockage. This work assesses the feasibility of a real-time monitoring approach using a benchtop test rig and a prototype acoustic-based measurement approach to provide information that can be correlated to product quality and provide the opportunity for future process optimisation. Acoustic emission (AE) was chosen as the sensing method due to its low cost, simple setup process, and ease of implementation. The performance of the proposed method was assessed in a series of experiments where the offline quality check results were compared to the AE-based real-time estimations using data acquired from a benchtop powder free flow rig. A designed time domain based signal processing method was used to extract particle size information from the acquired AE signal and the results show that this technique is capable of estimating the required ratio in the washing powder compound with an average absolute error of 6%

Size differentiation of a continuous stream of particles using acoustic emissions

Procter and Gamble (P&G) requires an online system that can monitor the particle size distribution of their washing powder mixing process. This would enable the process to take a closed loop form which would enable process optimization to take place in real time. Acoustic emission (AE) was selected as the sensing method due to its non-invasive nature and primary sensitivity to frequencies which particle events emanate. This work details the results of the first experiment carried out in this research project. The first experiment involved the use of AE to distinguish sieved particle which ranged from 53 to 250 microns and were dispensed on a target plate using a funnel. By conducting a threshold analysis of the peaks in the signal, the sizes of the particles could be distinguished and a signal feature was found which could be directly linked to the sizes of the particles

On the Use of Deep Learning Decompositions and Physiological Measurements for the Prediction of Preterm Pregnancies in a Cohort of Patients in Active Labor †

Preterm pregnancies are one of the leading causes of morbidity and mortality amongst children under the age of five. This is a global issue and has been identified as an area requiring active research. The emphasis now is to identify and develop methods of predicting the likelihood of preterm birth. This paper uses physiological data from a group of patients in active labor. The dataset contains information about fetal heart rate (FHR) and maternal heart rate (MHR) for all patients and electrohysterogram (EHG) recordings for the measurement of uterine contractions. For the physiological data analysis and associated signal processing, we utilize deep wavelet scattering (DWS). This is an unsupervised decomposition and feature extraction method combining characteristics from deep learning convolutions, as well as the classical wavelet transform, to observe and investigate the extent to which active preterm labor can be accurately identified from an acquired physiological signal, the results of which were compared with the metaheuristic linear series decomposition learner (LSDL). Additional machine learning algorithms are tested on the acquired physiological data to allow for the identification of optimal model architecture for this specific physiological data

An artificial intelligence-based decision support system for early diagnosis of polycystic ovaries syndrome

Polycystic ovary syndrome (PCOS) can affect a female’s reproductive system and comes with associated complications to the endocrine system of the affected individual. The diagnosis success of the condition varies depending on the stage of the disease. Thus, there is a need for investment in additional technologies that can help bolster the overall diagnosis success of the condition and to cue in prompt care strategies. A substantial amount of work has been done on this, where artificial intelligence technology has been investigated around the exploitation of patient medical health records towards predicting whether a patient is carrying the PCOS condition. The shortcomings associated with this related literature are based on the use of an unbalanced dataset towards the training of the candidate models, which can induce a form of model bias and approach the problem as a binary-based prediction exercise. This study aims at providing a solution to this apparent gap in knowledge by designing a prediction model using the Kaggle PCOS dataset, which is initially balanced using a synthetic sample generation algorithm. Next, a probability-based inference system is designed to estimate and stage the degree of the PCOS condition in the patient