Hydrocyclones

QR Code

Hydrocyclones

This chapter gives a brief synopsis of hydrocyclones in theory and in practice, including basic design, characterization of performance, models, scale-up and design, as well as monitoring and control of the equipment. The effect of various operational variables, such as pressure drop, cone angle, cy...

Full description

Bibliographic Details
Main Author:	Aldrich, Chris
Format:	Book Chapter
Published:	Elsevier Ltd 2014
Online Access:	http://hdl.handle.net/20.500.11937/24024

Similar Items

Detecting changes in the operational states of hydrocyclones
by: van Vuuren, J., et al.
Published: (2011)

On-line monitoring of hydrocyclones by use of image analysis
by: van Vuuren, J., et al.
Published: (2010)

Estimation of particle size in hydrocyclone underflow streams by use of Multivariate Image Analysis
by: Aldrich, Chris, et al.
Published: (2014)

Influence of bentonite on grinding, hydrocyclone and flotation performance
by: Xu, JingJing
Published: (2025)

Fine Particle Classification and Dewatering of Tailing Using Hydrocyclone
by: Kim, J., et al.
Published: (2015)

A neural fuzzy approach for well log and hydrocyclone data interpretation.
by: Wong, Kok W.
Published: (1999)

Computational fluid dynamic modelling of two phase flow on a hydrocyclone
by: Leeuwner, M., et al.
Published: (2008)

Monitoring and Control of Hydrocyclones by Use of Convolutional Neural Networks and Deep Reinforcement Learning
by: Giglia, Keith Carmelo
Published: (2022)

Consumption of steel grinding media in mills: A review
by: Aldrich, Chris
Published: (2013)

Fault detection in the Tennessee Eastman benchmark process with nonlinear singular spectrum analysis
by: Krishnannair, S., et al.
Published: (2017)

Automatic flotation froth bubble size distribution estimation using mean shift and watershed transforms.
by: Amankwah, A., et al.
Published: (2014)

On-line monitoring of aqueous base metal solutions with transmittance spectrophotometry
by: Phiri, M., et al.
Published: (2014)

Automatic estimation of bubble size distributions in flotation froths by use of a mean shift algorithm and watershed transforms
by: Amankwah, A., et al.
Published: (2014)

Detecting change in complex process systems with phase space methods
by: Aldrich, Chris, et al.
Published: (2014)

Froth image analysis by use of transfer learning and convolutional neural networks
by: Fu, Y., et al.
Published: (2018)

Automatic ore image segmention using mean shift and watershed transform
by: Amankwah, A., et al.
Published: (2011)

Development of neurocontrollers with evolutionary reinforcement learning
by: Conradie, A., et al.
Published: (2005)

Multivariate Image Processing in Minerals Engineering with Vision Transformers
by: Liu, Xiu, et al.
Published: (2024)

Using Convolutional Neural Networks to Develop State-of-the-Art Flotation Froth Image Sensors
by: Fu, Y., et al.
Published: (2018)

An IsaMilla™ Soft Sensor based on Random Forests and Principal Component Analysis
by: Napier, L., et al.
Published: (2017)

Deep Learning Approaches to Image Texture Analysis in Material Processing
by: Liu, Xiu, et al.
Published: (2022)

Explaining anomalies in coal proximity and coal processing data with Shapley and tree-based models
by: Liu, Xiu, et al.
Published: (2022)

Recognition of flotation froth conditions with k-shot learning and convolutional neural networks
by: Liu, Xiu, et al.
Published: (2023)

Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines
by: Groenewald, J., et al.
Published: (2015)

The estimation of platinum flotation grade from froth image features by using artificial neural networks
by: Marais, C., et al.
Published: (2011)

Effect of Preconditioning on the Flotation of Coal
by: Feng, D., et al.
Published: (2005)

Change point detection in time series data with random forests
by: Auret, L., et al.
Published: (2010)

Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods
by: Jemwa, G., et al.
Published: (2012)

Robust Block-Matching Motion Estimation of Flotation Froth Using Mutual Information
by: Amankwah, A., et al.
Published: (2012)

Fault detection and diagnosis with random forest feature extraction and variable importance methods
by: Aldrich, Chris, et al.
Published: (2010)

Monitoring of Mineral Processing Operations based on Multivariate Similarity Indices
by: Auret, L., et al.
Published: (2011)

Rock image segmentation using watershed with shape markers
by: Amankwah, A., et al.
Published: (2010)

Automated Online Estimation of Fines in Ore on Conveyer Belt Using Image Analysis
by: Amankwah, A., et al.
Published: (2013)

Monitoring of metallurgical plant performance with Bayesian change point detection algorithms
by: Aldrich, Chris, et al.
Published: (2012)

Estimation of particulate fines on conveyor belts by use of wavelets and morphological image processing
by: Amankwah, A., et al.
Published: (2011)

Unsupervised process monitoring and fault diagnoses with machine learning methods
by: Aldrich, Chris, et al.
Published: (2013)

Relationship between solids flux and froth features in batch flotation of sulphide ore
by: Yang, X., et al.
Published: (2005)

Motion Estimation in Flotation Froth Images Based on Edge Detection and Mutual Information
by: Amankwah, Anthony, et al.
Published: (2012)

Biosorption of heavy metals from aqueous solutions with tobacco dust
by: Qi, B., et al.
Published: (2008)

Empirical comparison of tree ensemble variable importance measures
by: Auret, L., et al.
Published: (2011)