A Plenary on Biological Control Systems A tribute to Professor Babatunde Ogunnaike

It is with great sadness that I learnt of the passing of Babatunde Ogunnaike, a great son of Africa and a leading researcher in our field. I had the honour of introducing his plenary a the inaugural Control Conference Africa in 2017, where he gave a fascinating talk on Biological Control Systems. We are lucky to have the slides from this presentation, which I post today as a memorial to a great but humble man.

https://apc-smart.com/wp-content/uploads/2022/02/BiologicalControlSystems_Plenary.pdf

Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring

S. Joe Qin a, Yining Dong, Qinqin Zhu, Jin Wang, Qiang Li

At the recent Control Conference Africa 2021 we were very fortunate to have a plenary lecture delivered by Professor S. Joe QIn. This month’s paper of interest sets out the theory and practice behind dynamic latent variables and provides a unifying review of dynamic latent variable methods, dynamic factor models, subspace identification methods, dynamic feature extractions, and their uses for prediction and process monitoring.

The paper introduces the dimension reduction expression of state space (DRESS) framework. The mathematics is at times challenging but these methods will provide new insight into the dynamic correlated data that we deal with all the time in the process industries. The method has application in process monitoring, inferential sensors, quality relevant monitoring. It can also be used for sensor fault detection and reconstruction, an area I am particularly interested in.

The paper quoted here is the summation of the work, and there are other papers by Qin and co-workers that include case studies.

S. Joe Qin, Yining Dong, Qinqin Zhu, Jin Wang, Qiang Liu, Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring, Annual Reviews in Control, Volume 50, 2020, Pages 29-48

https://www.sciencedirect.com/science/article/abs/pii/S1367578820300602?via%3Dihub

Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods

For the first time, I am featuring a paper by me and colleagues from the Universities of Ilmenau, Pretoria and Anglo American. This paper extends work that Professor Shardt and I have been doing in the general area of using historical data for system identification, thus avoiding the step testing that is normally used. The example in this paper is a primary mill, and linear and non-linear models are built using the DQA methods.

For the case of a single input-output system, the method has been coded in a Matlab App. Further work will be done to extend this app to use heuristics to cut unsuitable data, as well as the extension to MIMO systems.

The paper is open access and can be found here https://www.mdpi.com/2075-163X/11/9/954

Brooks, K., le Roux, D., Shardt, Y. A., & Steyn, C. (2021). Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods. Minerals, 11(9), 954.

Automation in the Mining Industry: Review of Technology, Systems, Human Factors, and Political Risk

Rogers, W. P., Kahraman, M. M., Drews, F. A., Powell, K., Haight, J. M., Wang, Y., … & Sobalkar, M. (2019). Automation in the mining industry: Review of technology, systems, human factors, and political risk. Mining, Metallurgy & Exploration, 36(4), 607-631.

https://link.springer.com/article/10.1007/s42461-019-0094-2

Rogers and co-authors provide a review of the state of automation research is given. The review considers three critical areas: automation technology, system’s engineering and management processes around automation, and the role of human factors engineering in automated and semi-automated systems. The paper includes reviews of MPC in grinding and milling, flotation and other processes. It is section 4 of the paper that is the most interesting, covering human factors. Those of us working in the field would do well to consider this statement in the conclusions of the paper “the human resource left within the system has become a more critical element and absorbed much of the tolerance of system into a single process. … However, people and system engineering will continue to be the key bottleneck for deployment and success of automation projects.”

Machine learning applications in minerals processing: A review

McCoy, J. T., & Auret, L. (2019). Machine learning applications in minerals processing: A review. Minerals Engineering, 132, 95-109. For those with library access the paper can be found here:

https://www.sciencedirect.com/science/article/abs/pii/S0892687518305430

Dr Auret has kindly supplied a presentation from IFAC MMM2018 in Shanghai

https://apc-smart.com/wp-content/uploads/2021/08/L-Auret-IFAC_MMM.pdf

This review aims to equip both researchers and practitioners with structured knowledge on the state of machine learning applications in mineral processing. The period reviewed is from 2004 to 2018 with data-based modelling, fault detection and diagnosis and machine vision identified as the main application categories. The main process applications are flotation, ore sorting, milling and smelting. This is a very good and up to date review of the state of the art in mineral processing. The authors specifically do not discuss control and optimisation applications. Reinforcement learning is also committed; this is certainly a growing field of research – and the subject of our next post.