Size Differentiation Of A Continuous Stream Of Particles Using Acoustic Emissions

Nsugbe, Ejay; Starr, Andrew; Foote, Peter; Ruiz-Cárcel, Cristobal; Jennions, Ian K.

doi:10.1088/1757-899x/161/1/012090

Cited by 23 publications

(36 citation statements)

References 9 publications

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“…Although the sieving process has a set of distinct advantages, such as not requiring sample preparation and not requiring expert knowledge, there are also some distinct disadvantages of this measurement method [ 1 , 2 ]. Only a certain size of sample can be analysed by sieving and, therefore, the results offered by means of such analysis only reflect a localised area of the mixture [ 1 ].…”

Section: Methodsmentioning

confidence: 99%

“…Only a certain size of sample can be analysed by sieving and, therefore, the results offered by means of such analysis only reflect a localised area of the mixture [ 1 ]. Powder agglomerates with a considerable moisture content in certain cases cannot be analysed by sieving due to sieve blockage, and certain types of powders with strong electrostatic and van der Waals interactions between them are prone to erroneous sieve characterisation due to cohesive forces between the particles [ 2 ]. Another disadvantage is that being an offline process, sieve analysis does not allow the plant operators to have a closed-loop approach to their powder mixing process.…”

Section: Methodsmentioning

confidence: 99%

“…With the sensing frequency being in the ultrasonic region, it would provide primary sensitivity to particle events and insensitivity to the low-frequency acoustic activity produced by the process equipment. Another advantage is the low hardware cost associated with the implementation of the sensing method [ 2 , 5 ]. AE signals are acquired as the mixing process takes place and the powders make contact with the internal walls of the mixer—hence, sample extraction is not necessary.…”

Section: Methodsmentioning

confidence: 99%

“…For powder samples that contain particles of different sizes, the various sizes present in the mixture and their associated amounts are usually presented in the form of a particle size distribution (PSD). In powder processing plants, the PSD is regarded as a critical factor due to its influence on the final bulk and flow properties of the particles [ 2 , 3 , 4 ]. This has led to a high level of interest in particle size measurement technologies, which has been covered in the review study conducted by Nsugbe et al [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…In previous investigations, technologies such as electrostatic sensing, imaging, sieving and weighing, near-infrared spectroscopy (NIR), acoustic emission (AE), focused beam reflectance measurement (FBRM), microwave and vibration have been used [ 5 ]. For this investigation, acoustic emission (AE) was chosen as the sensing method to investigate the problem due to the benefit of being a non-invasive sensor that is insensitive to low-frequency acoustic activity and that it possesses primary sensitivity to frequencies at which particle events occur [ 2 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Estimation of Fine and Oversize Particle Ratio in a Heterogeneous Compound with Acoustic Emissions

Nsugbe

Ruiz-Cárcel

Starr

et al. 2018

Sensors

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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%.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of Fine and Oversize Particle Ratio in a Heterogeneous Compound with Acoustic Emissions

Nsugbe

Ruiz-Cárcel

Starr

et al. 2018

Sensors

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Towards an affordable magnetomyography instrumentation and low model complexity approach for labour imminency prediction using a novel multiresolution analysis

Nsugbe

Sanusi

2021

Applied AI Letters

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The ability to predict the onset of labour is seen to be an important tool in a clinical setting. Magnetomyography has shown promise in the area of labour imminency prediction, but its clinical application remains limited due to high resource consumption associated with its broad number of channels. In this study, five electrode channels, which account for 3.3% of the total, are used alongside a novel signal decomposition algorithm and low complexity classifiers (logistic regression and linear‐SVM) to classify between labour imminency due within 0 to 48 hours and >48 hours. The results suggest that the parsimonious representation comprising of five electrode channels and novel signal decomposition method alongside the candidate classifiers could allow for greater affordability and hence clinical viability of the magnetomyography‐based prediction model, which carries a good degree of model interpretability. The results showed around a 20% increase on average for the novel decomposition method, alongside a reduced group of features across the various classification metrics considered for both the logistic regression and support vector machine.

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Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

Nsugbe¹

2023

Applied AI Letters

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Schizophrenia is a psychiatric disorder which is prevalent in individuals around the world, where diagnosis methods for this disorder are done via a combination of interview style questioning of the patient alongside a review of their medical record; but these methods have been largely criticised for being subjective between psychiatrists and largely unreplicable. Schizophrenia also occurs in adolescent individuals who have been said to be even more challenging to diagnose largely due to delusions being mistaken for childhood fantasies, and established methods for adult patients being applied to diagnose adolescents. This work investigates the use of electroencephalography (EEG) signals acquired from adolescent patients in the age range of 10-14 years, alongside signal processing methods and machine learning modelling towards the diagnosis of adolescent schizophrenia. The results from the machine learning modelling showed that the linear discriminant analysis (LDA) and fine K-nearest neighbour (KNN) produced the best recognition results for models with easy and hard interpretability, respectively. Additionally, a computational method was applied towards contrasting the neuroanatomical activation patterns in the brain of the schizophrenic and normal adolescents, where it was seen that the neural activation patterns of the normal adolescents showed a greater consistency when compared with the schizophrenics.

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Size Differentiation Of A Continuous Stream Of Particles Using Acoustic Emissions

Cited by 23 publications

References 9 publications

Estimation of Fine and Oversize Particle Ratio in a Heterogeneous Compound with Acoustic Emissions

Estimation of Fine and Oversize Particle Ratio in a Heterogeneous Compound with Acoustic Emissions

Towards an affordable magnetomyography instrumentation and low model complexity approach for labour imminency prediction using a novel multiresolution analysis

Enhanced recognition of adolescents with schizophrenia and a computational contrast of their neuroanatomy with healthy patients using brainwave signals

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