Abstract
Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. Reconstruction-based data-driven models are trained to reconstruct the characteristic patterns of hazardous event-preceding process data with minimal residuals, facilitating effective event prediction based on reconstruction residuals. This investigation evaluated one-dimensional convolutional auto-encoders as reconstruction-based data-driven models for predicting positive pressure events in industrial furnaces. A simple furnace model was used to generate dynamic multivariate process data with simulated positive pressure events to use as a case study. A one-dimensional convolutional auto-encoder was trained as a reconstruction-based model to recognize the data preceding the hazardous events, and its performance was evaluated by comparing it to a fully-connected auto-encoder as well as a principal component analysis reconstruction model. This investigation found that one-dimensional convolutional auto-encoders recognized event-preceding patterns with lower detection delays, higher specificities, and lower missed alarm rates, suggesting that the one-dimensional convolutional auto-encoder layout is superior to the fully connected auto-encoder layout for use as a reconstruction-based event prediction model. This investigation also found that the nonlinear auto-encoder models outperformed the linear principal component model investigated. While the one-dimensional auto-encoder was evaluated comparatively on a simulated furnace case study, the methodology used in this evaluation can be applied to industrial furnaces and other mineral processing applications. Further investigation using industrial data will allow for a view of the convolutional auto-encoder’s absolute performance as a reconstruction-based hazardous event prediction model.
Highlights
While the Dynamic PCA (dPCA) model showed inferior performance at each evaluated recognition threshold due to its limitations as a linear model, it should be noted that the computational requirements for developing and applying dPCA models are far lower than for AEs and convolutional auto-encoders (CAEs)
Kernel Principal component analysis (PCA) is a non-linear alternative to PCA that performs eigenvalue decomposition of the outer product of modelled data, but this is computationally infeasible on the larger datasets typically recorded on industrial furnaces
The AE- and CAE models evaluated in this project were not limited by computing requirements but scaling them in complexity may not always be feasible. dPCA may be more suitable for applications where time-consuming optimization algorithms are undesired
Summary
South Africa hosts the majority of the world’s platinum group metal (PGM)-reserves in the Bushveld Igneous Complex [1]. These PGMs are extracted from nickel-copper ores contained in the Bushveld Complex through a series of process steps. Mined ore undergoes comminution, liberating sulphides to create a sulphide concentrate that is concentrated through flotation. Flotation concentrates are smelted and converted, yielding a coppernickel matte rich in PGMs. Precious metals within the matte are separated from base metals through hydrometallurgical treatments before being refined into their pure forms [2]
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