a) Data analytics in minerals processing b) Alignment of industry and university research in Scandinavia

Transcription

1 a) Data analytics in minerals processing b) Alignment of industry and university research in Scandinavia Magnus Nydén Ian Wark Research Institute ARC Special Research Centre for Particle and Material Interfaces

2 OREOMICS - A SYSTEMS APPROACH TO MINERALS PROCESSING Ian Wark Research Institute ARC Special Research Centre for Particle and Material Interfaces

3 VALUE PROPOSITION Unit operation (or processing line) always performing at its best given a change in feed composition (mineralogy, particle size, surface properties etc) New SENSORS Mill operating parameters (Energy, ph, Eh, DO, etc.) Feed characterisation (Mineralogy, PSD, liberation, surface properties, etc.) Flotation or leaching parameters (Reagents, ph, Jg, bubble size, froth depth, etc.) Process outputs (Recovery, grade)

4 VALUE PROPOSITION OREOMICS has the potential of transforming the industry FROM THIS TO THIS Plant flotation froth after a sudden change in feed properties Plant flotation froth after operating conditions are optimised

5 OREOMICS MINERALS PROCESSING WITH A SYSTEMS APPROACH Material and process parameters Understanding process performance as function of material and process parameters is key!

6 THE PROBLEM Robert J. Hunter: Introduction to Modern Colloid Science, 1992 Most attempts to describe the behavior of particles (ore) in mathematical terms assume that they are homogeneous Chapter 1: Microscopic colloidal behavior Chapter 2: Flow behavior Chapter 3: Thermodynamics of surfaces Chapter 4: Adsorption at interfaces Chapter 5: Electrically charged interfaces Chapter 6: Measuring surface charge and potential Chapter 7: Particle interactions and coagulations Final Chapter: Industrial applications of colloid and surface science (where particles never are homogeneous) Focus on predictive process models 0 (!) predictive process models We need new science/engineering!

7 HYPOTHESIS This kind of problem requires a paradigm shift: in addition to hypothesis driven research, towards a methodology that lets the data speak for itself TOP DOWN Engineering that builds on decades of bottom up strategies Science 1. Weinsten et al. PhysOrg, 2013 arxiv: v2 [physics.data-an]

8 INSPIRATION SYSTEMS BIOLOGY Year SYSTEMS BIOLOGY Big Data based Systems Approach

9 DEMONSTRATED SUCCESS New cancer treatments leading to significant increase in survival rates. Prediction of side effects of drugs and treatment of chronic angina. 2 Prediction of the re-emergence of the HIV virus in AIDS treatment. 3 Increased survival rates in chronic myeloid leukaemia. 4,5 Conclusion: Success was achieved when quantitative data became available. Human genome investments returned 65-fold ($65 for every $1) 1 Zhang W. Chin J Cancer. 2011, 30: Noble D. J. Pharmacol Sci. 2008, 107: , 3 Ho DD. Science, 1996, 271: Michor F. Nature, 2005, 435: Michor F: Br. J. Cancer, 2006, 95:

10 INSPIRATION MATERIALS GENOME MATERIALS GENOME INITIATIVE Materials structure-function relationships too complex Big data based Systems Approach for speeding up materials development.

11 THE PULSE OF THE APPROACH hydrodynamics, pulp chemistry etc Homogeneous materials Heterogeneous materials f S (D, S, P) R Known Big Data/Analytics Data Fusion Machine learning OREOMICS New f P(D), P(D, S), P(P) Known relationships for model materials New relationships for REAL MATERIALS ON-LINE ($10) SENSORS quantitative mineralogy/liberation recovery, grade and yield

12 Magnus Nydén Centre Director

13 We started in Partners: 7 companies and 2 academic organizations Hosted by Chalmers involving four departments

14 Stage 2-start Partners: 8 companies and 2 academic organizations Hosted by Chalmers involving four departments Gothenburg University-Institute for entrepreneurship and innovations

15 Soft biomaterial users Soft biomaterial producers Academic and industrial scientist

16 Hypothesis: Industry and academy together form the basis for innovations Be competitive upstream to be attractive downstream Open innovation Strategy: In symbiosis between industry and academy industrially driven Projects lead by academy together with reference groups from industry Mix between men and women Internationalisation Mobility between partners Strong links to other excellence centres Focus on strong links between projects

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