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Dr Lidia Auret
Senior Lecturer

Short Bio


PhD (Extractive Metallurgical Engineering), Stellenbosch University, 2011
BEng (Chemical Engineering: Mineral Processing), Stellenbosch University, 2007

Professional experience

Postdoctoral Research Fellow – Claude Leon Fellowship
Department of Process Engineering, Stellenbosch University

Control Engineer
Anglo American Platinum Centre of Process Monitoring
Department of Process Engineering, Stellenbosch University

Honours and awards

Stellenbosch University Chancellor’s Medal for most outstanding final-year student, 2010
CSense Systems prize: Best Process Engineering PhD thesis, Stellenbosch University, 2010

Research Interests

Process monitoring
Process modelling

Selected Publications

Aldrich, C. & Auret, L. (2013). Unsupervised process monitoring with machine learning methods. Part of series: Advances in Computer Vision and Pattern Recognition. Springer. London.
Naudé, N., Lorenzen, L., Kolesnikov, A. V., Aldrich, C., & Auret, L. (2013). Observations on the separation of iron ore in a prototype batch jig. International Journal of Mineral Processing, 120, 43-47.
Auret, L., & Aldrich, C. (2012). Interpretation of nonlinear relationships between process variables by use of random forests. Minerals Engineering, 35, 27-42.
Auret, L., & Aldrich, C. (2011). Empirical comparison of tree ensemble variable importance measures. Chemometrics and Intelligent Laboratory Systems, 105(2), 157-170.
Auret, L., & Aldrich, C. (2011). Monitoring of mineral processing operations based on multivariate similarity indices. Paper presented at the IFAC Proceedings Volumes (IFAC-PapersOnline), 18(PART 1) 9923-9928.
Janse Van Vuuren, M. J., Aldrich, C., & Auret, L. (2011). Detecting changes in the operational states of hydrocyclones. Minerals Engineering, 24(14), 1532-1544.
Auret, L., & Aldrich, C. (2010). Change point detection in time series data with random forests. Control Engineering Practice, 18(8), 990-1002.
Auret, L., & Aldrich, C. (2010). Unsupervised process fault detection with random forests. Industrial and Engineering Chemistry Research, 49(19), 9184-9194.


Chemical Engineering 344 (Modelling and Optimization)
Chemical Engineering 426 (Process Control)