The excessive energy consumption from the mining industry are currently receiving international attention. A promising method able to enhance significantly the comminution process efficiency worldwide is by using electric pulse fragmentation treatment. However, to insure a minimum energy consumption in real scale operation, an online process monitoring is of utmost importance.
Research at Empa focused on developing a new online monitoring system able to classify with high confidences events such as discharge, pre-weakening and break-down of transparent samples or various weight losses of the rocks.
The three major achievements are (a) the possibility to detect online the different events taking place in the sample, (b) we develop supervised and unsupervised AI method to classify with high confidence the weight losses of rocks subjected to electrical discharge and finally (c) the demonstration our approach was successfully applied to real ores.

• Meylan B., Shevchik S.A., Parvaz D., Mosaddeghi A., Simov V., and Wasmer K., "Acoustic Emission and Machine Learning for In Situ Monitoring of a Gold–Copper Ore Weakening by Electric Pulse", Journal of Cleaner Production, Vol. 280, Issue: 1, Paper ID: 124348, pp: 1-12, 2021, https://doi.org/10.1016/j.jclepro.2020.124348

• Shevchik S.A., Meylan B., Mosaddeghi A. and Wasmer K., “Acoustic Emission for In Situ Monitoring of Solid Materials Pre-weakening by Electric Discharge: A Machine Learning Approach”, IEEE Access, Vol. 6, Issue 1, pp: 40313-40324, 2018,
  https://doi.org/10.1109/ACCESS.2018.2853666