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Producing consistent quality pre-baked carbon anodes for the Hall–Héroult aluminum reduction process is challenging due to the decreasing quality and increasing variability of anode raw materials. Non-destructive testing techniques (NDT) have been developed and recently implemented in manufacturing plants to establish better suited and more efficient quality control schemes than core sampling and characterization. These technologies collect measurements representing effective properties of the materials located along a pathway between two transducers (emitter and receiver), and not spatially-resolved distribution of properties within the anode volume. A method to interpolate pathway-based measurements and provide spatially-resolved distribution of properties is proposed in this work to help NDT technologies achieve their full potential. The interpolation method is tested by simulating acousto-ultrasonic data collected from a large number of 2D and 3D toy examples representing simplified anode internal structures involving randomly generated defects. Experimental validation was performed by characterizing core samples extracted from a set of industrial anodes and correlating their properties with interpolated speed of sound by the algorithm. The method is shown to be successful in determining the defect positions, and the interpolated results are shown to correlate significantly with mechanical properties. [ABSTRACT FROM AUTHOR]

• Novel control framework based on optimization in the latent space of a SMB-PLS model. • Real-time optimization of continuous process facing raw material fluctuation. • Overcomes parametric disturbances with model adaptation and closed-loop structure. • Adjustment of process operating conditions based on quality thresholds and economics. Reducing the impact of lot-to-lot raw material variability through optimization of operating conditions is key when the lots are already purchased, and available in inventory. The objective of this paper is to provide a framework to optimize operating conditions to maximize profitability while aiming at achieving product quality targets each time a new lot of raw material is fed to a continuous process. The proposed approach consists of solving an optimization problem in the latent space of a sequential multi-block partial least square model (SMB-PLS). Model updating and closed-loop operation are considered to overcome parametric disturbances. The approach is illustrated using a simulated grinding-flotation plant for a sequence of ore lots with variable properties. The case study shows that optimizing operating conditions with the proposed approach allows increasing biannual gain by 1.5 to 2 % compared to nominal operation. This represents between 59 and 75 % of the true achievable gain. [ABSTRACT FROM AUTHOR]

The presence of defects in baked carbon anodes can significantly affect aluminum production performance detrimentally, and cause increased emissions and cost through increased specific energy and carbon consumption. This study explores the use of principal component analysis coupled with linear discriminants to classify full-scale industrial anodes based on vibration frequency response data related to mechanical properties. Large structural changes, such as the anode's production stage, and smaller structural changes, such as the presence of external defects, are evaluated together with an assessment of the measurement's repeatability. The method proposed in this work tests the entire anode block, achieves superior performance compared to previously published results using modal analysis both in classification accuracy and repeatability, and is also potentially faster. The ultimate goal is to use modal analysis as an online anode quality control system. [ABSTRACT FROM AUTHOR]

A machine vision sensor was developed for predicting deviations from the optimum amount of pitch in anode formulations using paste texture analysis. It could help operators mitigate the impact of the increasing variability of anode raw materials (coke and pitch). Paste samples were formulated in the laboratory using dry aggregate mixes obtained using two cokes having different properties and various amounts of pitch. These were imaged, formed into small cylindrical anodes, and baked to measure their density. A combination of image texture methods was used for extracting relevant paste textural features. The latter were then used as inputs of partial least squares regression models to predict deviations from the maximum baked density. Good prediction results were obtained. Furthermore, the sensor was able to detect when the paste was at the optimal amount of pitch for both cokes and to measure deviations from it. [ABSTRACT FROM AUTHOR]

Decision tools to determine the acceptability of an incoming lot of raw materials prior to its purchase from a supplier is essential to ensure efficient quality control. They allow mitigating the propagation of the raw material variability through the process and, ultimately, in the final product quality. Establishing multivariate specifications regions for raw material properties to achieve high and consistent end product quality using latent variable models is key. However, these specifications are typically designed assuming the process operates at nominal conditions. The objective of this work is to enlarge the raw material acceptance region to accept materials having a wider range of properties, and possibly at a lower cost, while meeting final product quality requirements. Using a sequential multi-block partial least squares model (SMB-PLS), the design of multivariate specification regions is combined with optimization of process operating conditions to better cope with fluctuations in raw material properties. An economic criterion is used to assess whether accepting lower cost materials, at the expense of adjusting plant operation, which incurs additional costs, is economically justified. The approach is illustrated using a grinding-flotation plant simulator where the objective is to assess the profitability of different lots of raw materials (i.e. ores). The impact of measurement noise is also investigated. The case studies show that the raw material processability is improved since the number of correctly accepted lots is increased by 7.3 % in comparison with maintaining nominal process conditions. • Adjusting process conditions widens multivariate specifications for raw materials. • Optimization in the latent space of SMB-PLS model is performed for the first time. • Development of multivariate specifications includes an economic criterion. • A general SMB-PLS prediction equation is provided and demonstrated. [ABSTRACT FROM AUTHOR]

The potential and effectiveness of near-infrared (NIR) hyperspectral imaging was investigated for rapid and nondestructive spectral-spatial inspection of functional multilayer films. A series of film samples having different combinations of layers were used to demonstrate the sensor performance. Multivariate Image Analysis (MIA) was applied to NIR hyperspectral images of unprinted multilayer films with different compositions to distinguish them regarding absence/presence of particular layers. Since printing of commercial brands on the films may interfere with product inspection, the ability of MIA to detect the presence of the functional layer (barrier) in spite of film surface printing was also assessed. The results show that the proposed MIA approach is successful at classifying multilayer films, even in the presence of printing. [ABSTRACT FROM AUTHOR]