Open Collections

Sensing and sorting ore using a relational influence diagram

University of British Columbia

Mining companies typically process all the material extracted from a mine site using processes which are extremely consumptive of energy and chemicals. Sorting this material more effectively would reduce the resources required. A high-throughput rock-sorting machine developed by MineSense™ Technologies Ltd. provides the sensors and diverting equipment. After receiving noisy sensor data, the sorting system has 400 ms to decide whether to activate the diverters which will divert the rocks into either a keep or a discard bin. The problem tackled in this thesis is to sort an unknown number of rocks by sensing their mineralogy, position, and size using electromagnetic sensors and diverting them according to how valuable the mineral is to the mine. In real-time we must interpret the sensor data and compute the best action to take. We model the problem with a relational influence diagram which shows relations between random variables, decision variables, and utility nodes. We learn the model offline and do online inference. Inference is achieved using a combination of exhaustive and random search. The model parameters are learned using Sequential Model-based Algorithm Configuration (SMAC). We simulate the diverters for offline evaluation and evaluate our solution on recorded sensor data. Our result improves over the current state-of-the-art across the entire range of utility.

Text

2014-08-13

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/49998

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/