From the bench outside the drinking hole, you see the skeletons of three old marine buildings. Just beyond that are four tug boats bobbing fat and squat on the tide. And beyond that elegant yachts grace the setting sun.
I sat quaffing large glasses of draft ale with a colleague from thirty years and more ago. He has grown over the years in stature and reputation. Now like me, he looks forward to finally educating his kids and spending time smoking & drinking, fishing & eating, and avoiding all the rest that it takes to be a profitable consultant to any industry, net alone the mining industry.
He has written books, published extensively, given talks to multitudes, and consulted on the most difficult problems. His ego is satiated. At least last night there was no ego and we laughed over old stories of clients and co-workers; stories that we never could put down on paper or even on a blog.
We exchanged thoughts on mentoring. He noted that very few of the people he had mentored had succeeded in the ways he had sought for them to succeed. Most had gone off and done something entirely different. Same has happened in my case. The real question was whether we had any responsibility at this stage of our lives to continue mentoring? We considered some other old friends still scampling into sinecures arguing that it was their duty to “teach the young and pass on their wisdom.” And we laughed at them and their illusions. For we agreed that we have nothing to teach the young, in mining or anywhere else.
Our generation does not read blogs, does not know how to iTune, is absent from Facebook, and avoids fuzzy logic decision trees. We need a person under thirty to keep out computers alive. What can we teach such people? Our focus on social justice is just a faint echo of the dreams & ideals of the sixties and seventies. We take refuge in easy phrases like sustainable development and fail to tackle the mathematics of perception and impact. Too many of our generation are wondering around hogging the lecturns repeating ideas we first generated thirty, twenty, and ten years ago.
In the clear light of this morning–well except for a lingering hangover–I took up his challenge to name the future leaders of mining academia. Here is my list. If you have others to nominate, let me know.
Sean Dessureault at the University of Arizona in Tucson approaches data mining of mining data with a new perspective. Here is the (edited) abstract of one of his paper to give you some idea of what he does:
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This paper reviews and critiques long-held managerial assumptions that dominate mine management and engineering decision making. It is submitted that these long-held beliefs should be abandoned now that detailed, objective information is available. The managerial practices that dominate the annual budgeting process and its use as a performance management and goal setting tool are questioned. Case studies are described where data from multiple sources are integrated using data mining techniques. We propose alternative and better ways forward.
From the University of Kentucky, new ideas swirl around Braden Lusk whose research stretches from quantification of blasting impacts on local communities through mine operations to public relations. I listened to a lively presentation he made at a conference in Denver earlier this year on modifying blasting practices in quarries adjacent to poor and rich neighbourhoods in order to reduce the “felt” or “experienced” impact of the blast on the rich versus the poor. He told us how different socio-economic classes perceive the magnitude of a blast. It seems it all depends on the word you use. If you quantify the blast via decibels, PSI, or energy units, you get a different response. Most people respond positively to descriptions of how big the blast will be if you use PSI. Seems most people have cars with tires that need pumping, and the PSI from most blasts is much lower than tire pressures.
Davide Elmo is at Simon Fraser University, Vancouver. He is revitalizing the study of block caving research by applying advanced mathematical techniques. He is doing this as a cooperative investigation of block caving between his institution, Diavik Diamond Mine, Rio Tinto, and the University of British Columbia. Seems the real problem is that persistent question: at what scale do you numerically model jointed rock. More than thirty years ago I struggled with the philosophical issue: do you model fluid flow through every joint around the open pit, or do you just assume the rock has a permeability tensor like any old anisotropic porous medium? The problem has only gotten worse over the years as computer codes have multiplied and it is possible to model every joint, deterministically or probabilistically, and track the further breaking of the joint to connect to adjacent breaking joints and then watch the whole rock mass fall down into a block caving drawpoint somewhere deep underground.
Rajive Ganguli is at the University of Alaska Fairbanks. His research is predominantly computer based mining technologies including application of artificial intelligence (neural networks and expert systems), statistical process control (such as with SAG mills), evolutionary computing (genetic algorithms) and operations research (production simulation etc), underground coal and power plant (coal burning) operations. I also use classical statistics, geostatistics and time series analysis in mining. His other interests include ground control, 3D ore modeling and on-line education.
I wish them all the best and put my faith for the future of mining in their hands.
