Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series

Fault diagnosis in continuous dynamic systems can be challenging, since the variables in these systems are typically characterized by autocorrelation, as well as time variant parameters, such as mean vectors, covariance matrices, and higher order statistics, which are not handled well by methods designed for steady state systems. In dynamic systems, steady state approaches are extended to deal with these problems, essentially through feature extraction designed to capture the process dynamics from the time series. In this chapter, recent advances in feature extraction from signals or multivariate time series are reviewed. These methods can subsequently be considered in a classical statistical monitoring framework, such as used for steady state systems. In addition, an extension of nonlinear signal processing based on the use of unthresholded or global recurrence quantification analysis is discussed, where two multivariate image methods based on gray level co-occurrence matrices and local binary patterns are used to extract features from time series. When considering the well-known simulated Tennessee Eastman process in chemical engineering, it is shown that time series features obtained with this approach can be an effective means of discriminating between different fault conditions in the system. The approach provides a general framework that can be extended in multiple ways to time series analysis

Multivariate Image Processing in Minerals Engineering with Vision Transformers

Vision transformers (ViTs) are a new class of deep learning algorithms that have recently emerged as a competitive alternative to convolutional neural networks. In this investigation, their application to two operations previously studied in the mineral processing industry is considered. These are image recognition of fines in coal particles on conveyor belts and characterisation of the particle size in the underflow of a hydrocyclone. Promising results were achieved by use of vision transformers, as they performed as well as, or better than convolutional neural networks in these image recognition problems. In addition, features extracted from the best ViT model could be used to visualise its performance and these features could also serve as a basis for nonlinear process monitoring models. Furthermore, explainability techniques such as attention maps for ViTs were implemented to better understand the ViT models, similar to techniques such as occlusion sensitivity maps used with convolutional neural networks

Deep Learning Approaches to Image Texture Analysis in Material Processing

Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results

On-line monitoring of aqueous base metal solutions with transmittance spectrophotometry

Transmittance spectrophotometry was used to monitor copper, cobalt and zinc in solution in laboratory experiments. The samples simulated plant conditions encountered on the Skorpion zinc mine in Namibia and were prepared using a simplex centroid mixture design. Principal component, partial least squares and support vector regression models were calibrated from visible and near infrared absorption spectra. All models could accurately estimate the concentrations of all the metals in solution. Although these models were affected by nickel contamination, the Cu models were less sensitive to this contamination than the Co and Zn models. Likewise, elevated temperatures led to degradation of the calibrated models, particularly the Zn models. The effects of these conditions could be visualized by a linear discriminant score plot of the spectral data

A Fuzzy – Based Methodology for Aggregative Waste Minimization in the Wine Industry

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CFD modelling of global mixing parameters in a Peirce-Smith converter with comparison to physical modelling

The flow pattern and mixing in an industrial Peirce-Smith converter (PSC) has been experimentally and numerically studied using cold model simulations. The effects of air volumetric flow rate and presence of overlaying slag phase on matte on the flow structure and mixing were investigated. The 2-D and 3-D simulations of the three phase system were carried out using volume of fluid (VOF) and realizable k - ɛ turbulence model to account for the multiphase and turbulence nature of the flow respectively. These models were implemented using commercial Computational Fluid Dynamics (CFD) numerical code FLUENT. The cold model for physical simulations was a 1:5 horizontal cylindrical container made of Perspex with seven tuyeres on one side of the cylinder typifying a Peirce-Smith converter. Compressed air was blown into the cylinder through the tuyeres, simulating air or oxygen enriched air injection into the PSC. The matte and slag phases were simulated with water and kerosene respectively in this study. The influence of varying blowing conditions and simulated slag quantities on the bulk mixing was studied with five different air volumetric flow rates and five levels of simulated slag thickness. Mixing time results were evaluated in terms of total specific mixing power and two mixing time correlations were proposed for estimating mixing times in the model of PSC for low slag and high slag volumes. Both numerical and experimental simulations were in good agreement to predict the variation characteristics of the system in relation to global flow field variables set up in the converter through mathematical calculation of relevant integrated quantities of turbulence, Volume Fraction (VF) and velocity magnitudes. The findings revealed that both air volumetric flow rate and presence of the overlaying slag layer have profound effects on the mixing efficiency of the converter

A methodology for geomechanical modelling of in situ recovery (ISR) in fractured hard rocks

The extraction of geothermal energy, in situ minerals, liquid and gas hydrocarbons, and subsurface water are all constrained by the flow of fluid through fractured media in the earth’s crust, as is the viability of projects involving CO2 sequestration, nuclear and hazardous waste storage, hydrocarbon storage, and subsurface cavities. Subsurface fractures are the main fluid pathways as the matrix permeability is negligible in most rocks. In situ recovery (ISR) or in situ leaching (ISL), particularly in hard rock, poses some challenges currently. One of the main problems is the modelling of fluid flow in fractured rock masses, and this was the primary focus of this project. Modelling fluid flow in fractures can be done in many ways. The modelling showed that ISL in hard rock demonstrates potential. However, the modelling also exhibited the need for advancements in the fluid flow in fractures modelling area. In this paper comprehensive review of developed approaches for subsurface fracture mapping, processing and characterisation to build a fractured rock mass geometry and fluid flow simulation and mineral leachability along with examples were illustrated

Infrasonic backpulsed membrane cleaning micro-and ultrafiltration membranes fouled with alumina and yeast

Membrane fouling is universally considered to be one of the most critical problems in the wider application of membrane filtration. In this research microfiltration membranes were fouled during a cross-flow filtration process, using yeast and alumina suspensions in a flat cell. Infrasonic backpulsing directly into the permeate space was then used to clean the membrane, using both permeate water and soap solutions. Ultrasonic time domain reflectometry (UTDR) was used to detect and measure the growth of fouling on membrane surfaces, during the filtration and cleaning processes. The objective of this work was to examine the efficiency of back-pulse cleaning, using different combinations of membrane materials and foulants, in flat cells. The results show that a flux value of between 60% and 95% of the clean water value can be recovered after cleaning, by using a sequence of three 6.7 Hz backpulses, each pulse being 35 s long with a peak amplitude of about 140 kPa

Monitoring of carbon steel corrosion by use of electrochemical noise and recurrence quantification analysis

The corrosion of carbon steel in aqueous media resulting in uniform corrosion, pitting corrosion and passivation was investigated on a laboratory scale. Recurrence quantification analysis was applied to short segments of electrochemical current noise measurements. These segments were converted to recurrence variables, which could be used as reliable predictors in a multilayer perceptron neural network model to identify the type of corrosion. In addition, an automated corrosion monitoring scheme is proposed, based on the principal component scores of the recurrence variables. This approach used the uniform corrosion measurements as reference data and could differentiate between uniform and non-uniform corrosion

Exploratory analysis of multivariate drill core time series measurements

Demand for mineral resources is increasing, necessitating exploitation of lower grade and more heterogeneous orebodies. The high variability inherent in such orebodies leads to an increase in the cost, complexity and environmental footprint associated with mining and mineral processing. Enhanced knowledge of orebody characteristics is thus vital for mining companies to optimize profitability. We present a pilot study to investigate prediction of geometallurgical variables from drill sensor data. A comparison is made of the performance of multilayer perceptron (MLP) and multiple linear regression models (MLR) for predicting a geometallurgical variable. This comparison is based on simulated data that are physically realistic, having been derived from models fitted to the one available drill core. 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