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Towards an improved PEPT triangulation routine
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Towards an improved PEPT triangulation routine
J Newling1, AJ Morrison1, N Fowkes2, I Govender1 and L Bbosa1
1 University of Cape Town, Cape Town, South Africa
2University of Western Australia, Perth, Australia
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Tumbling Mills
• Minerals industry (gold, platinum, copper, etc …)
• Main aim is size reduction of extracted ore
• Very energy-intensive, however inefficient
• Aggressive environment,in situ measurement not feasible
• Models are empirical– Mill specific– Ore specificMill diameter 0.3 – 5m
Rotational speed 15 – 40 rpm
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Positron Emission Particle Tracking
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γ
γ
Positron Emission Particle Tracking
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Positron Emission Particle Tracking
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Sources of false events
True Pairing
Scattered Pairing
Random Pairing
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Triangulation
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75% - 90% of recorded events are discarded
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Proposal 1: Minimum perpendicular distance method
Method•Find the midpoint of the perpendicular between successive lines of response•Use the median of these midpoints to estimate the particle location in that time interval
Motivation•Avoid iteration by using the median to weight true pairs
Shortcoming•No guarantee that the closest approach is in the area of the tracer particle
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Proposal 1: Minimum perpendicular distance method
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Proposal 2: Density of lines
Method•Discretise the field of the view into a 3D grid.•Use the number of intersections of the LoRs with each grid element to isolate the particle position
Motivation•Discriminate against random and scattered events
Shortcoming•Computationally expensive
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Proposal 2: Density of linesDino Giovannoni & Matthew Bickell (Physics Honours)
From detected lines to line density…
… to probability distributions…
… to particle position.
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Proposal 3: 2D triangulation
Method•Divide LoR into coplanar sets and use these to reduce the problem to a 2D one
Motivation•Simplify the 3D case into a 2D problem
Shortcoming•Drastically reduces the statistics•Does not discriminate between true and false lines.
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Proposal 4: Distance distribution
Method•Use the current iterative method to calculate the centroid•Use the distribution of LoR distances from the centroid to dynamically determine the fraction to discard•Recalculate the centroid and repeat until some convergence criteria is met.
Motivation•Avoid having to calibrate the routine for each experiment
Shortcoming•Does not reduce the computational expense
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Proposal 4: Distance distribution
Frequency of events
Distance from centroid /mm
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Conclusion