Tag Archives: team

Coperion K-Tron at Fakuma 2014

Coperion K-Tron cordially invites you to attend Fakuma 2014, Oct 14-18, 2014 in Friedrichshafen, Germany, Booth 6406, Hall A6 to learn more about the latest advances in feeding, weighing and pneumatic conveying solutions.

We will show you the patented BSP-135 Bulk Solids PumpTM Feeder on pivoting base frame which has been specially designed and engineered to provide gentle, precise feeding of free-flowing pellets, granules, and friable bulk materials.

Additionally on display will be a Gravimetric Batch Blender with integrated 2400 Series Vacuum Receivers and a Twin Screw Powder Feeder.

Stop by the booth and explore our innovative solutions.

We look forward to seeing you at Fakuma 2014!

Your team of Coperion K-Tron
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Bulk Solids PumpTM for precise feeding of free flowing bulk materials

First in the Industry: Positive Displacement Feeding Technology

 

The new Coperion K-Tron Bulk Solids Pump (BSP) feeders have been specifically designed and engineered to provide gentle, precise feeding of free-flowing materials

The Features

Coperion K-Tron Bulk Solids Pump (BSP) feeders have been specifically designed and engineered to provide gentle, precise feeding of free-flowing pellets, granules, and friable products.

Preview animations and video

The BSP feeders do not use the usual screws/augers, belts or vibratory trays to convey the material. The feeder utilizes positive displacement action to feed free flowing materials with astounding accuracy, offering uniform discharge, consistent volume and gentle handling.

The BSP feeders have vertical rotating discs that create a product lock-up zone, conveying the material smoothly from storage hopper to discharge outlet, achieving true linear mass flow.

Utilizing a simple design and the principle known as “lock up” (see demonstration), the material in the feeder is moved together in true positive displacement, producing excellent linearity and breakthrough accuracy levels.

With no pockets or screws and only one moving part, the compact feeder is cleaned in seconds, making it ideal for applications with frequent material changes.

 

Download the BSP brochure

Bulk Solids Pump Feeders:
Brochure highlights the features, principle of operation and anatomy of the Coperion K-Tron Bulk Solids Pump feeder line, including information on “Positive Displacement Feeding”, as well as information on various feed rates / models available.
picture of the 4 zones of a Bulk Solids Pump feeder

The Principle

Zone 1: CONSOLIDATION
Interparticle forces produce lock-up at the end of Zone 1

 

Zone 2: ROTATION
Material is in lock-up condition throughout Zone 2 and rotates as a solid body

 

Zone 3: RELAXATION
Interparticle forces fall below lock-up threshold

 

Zone 4: ACTIVE DISCHARGE
Material discharge occurs.

 

BSP technology demonstrations and video

The Anatomy of the BSP-100 and BSP-135

exploded view of BSP-100 and BSP-135

 


BSP-100

The BSP-100 features a single feeding duct formed by two rotating discs. It includes a conical inlet transition piece which can be combined with a variety of standard extension hoppers.

A slide gate on the inlet allows for material shut-off and removal of feeder for cleaning and hopper emptying. A low power stepper motor drive mechanism and controller provide excellent turndown and flexibility ensuring a very wide operating range.

 

The BSP-100 is designed for feed rates of 2 to 400 dm³/hr (0.07 to 14 ft³/hr). It is available as a volumetric unit or as a gravimetric unit with a choice of 2 platform scales, single point suspension scale or three-point suspension scale.

 


BSP-135

The BSP-135 is a slightly larger version of the BSP-100, with all the same features except that it has three feeding ducts instead of one.

The BSP-135 is designed for feed rates of 22 to 4,400 dm³/hr (0.8 to 155  ft³/hr).

Unit is available as volumetric or as gravimetric, with a choice of platform scale, single-point suspension scale, or three-point suspension scale.

 


BSP-150-S

BSP-150-S is designed with four feeding ducts, and is based on the same technology as the BSP-100, but manufactured of stainless steel.

The unit is designed with an inlet transisiotn piece, as well as a stepper motor and removable material discharge chute.

With only one moving part and no pockets or screws, the design provides ease of use and almost zero maintenance.

The BSP-150-S is designed for feed rates of 34 to 6,700 dm³/hr (1.2 to 237 ft³/hr).

It is available as a volumetric unit or as a gravimetric unit with a three-point suspension scale.

 


 

Benefits of the Revolutionary BSP Technology

  • True Positive Displacement Action
  • Linear Over Full Operating Range
  • Uniformity of Discharge
  • Active Discharge (minimal residual material)
  • Unaffected by Differential Pressures
  • Mechanical & Maintenance Simplicity

For video, animated demonstrations, brochures and technical articles click here.

 

Gravimetric Batch Blenders

Coperion K-Tron Gravimetric Batch Blenders are available in various sizes from 0.5 kg to 25 kg total batch size and include up to 8 main feed elements depending on the unit.

Application

Each one of the materials is dispensed sequentially into a common weighing hopper in the desired proportions. The weighed materials are then released into a separate mixing chamber, which provides a consistent homogeneous blend. It includes an advanced metering and weighing system that accurately controls every ingredient of every batch to the desired amounts and is not averaged over multiple batches.

Gravimetric Batch Blenders Design Features

The Coperion K-Tron range of blenders are compact with a robust design. They are designed and constructed of heavy gauge steel (11 gauge steel) to withstand the most rigorous of operating conditions. The load cell is overload protected and guarded from physical damage. Each blender is constructed with high load bearing continuous welding and is protected against vibration and shocks. All blenders include self-optimizing software to give you the most efficient output on every recipe. Continuous mixing of the bulk materials means the mixing times do not need to be adjusted and the consistency of every mix is guaranteed.

Standard Mechanical Specifications

  • Ingredient accuracy of +/- 0.02% per batch at highest accuracy setting
  • Robust design (11 gauge steel)
  • Color touch screen
  • 3 level password security
  • Ethernet port with remote service capability
  • 2 USB ports
  • Integrated loading platform for mounting material loaders/vacuum receivers
  • Simple quick drain models
  • Integrated slide valves on each material hopper
  • Side feeders – up to (4) available on most models

 

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For more information on Coperion K-Tron, please click here.

For more information on Coperion, please click here.

Keywords:
bulk-online Leader, Mechanical Feeders, Agitated Feeders, Dosing Feeders, Gravimetric Feeders, Loss-in-Weight Feeders, Proportioning Feeders, Sanitary Feeder, Screw Feeders, Vibratory Feeders, Volumetric Feeder, Weigh Belt Feeders, Pneumatic Conveyors, Modes of Pneumatic Conveying, Dilute Phase Conveying, Vacuum Conveying, Process Automation, Silo Storage Equipment & Systems, Silo Discharge Equipment, Live Bottom Flow Devices, Pneumatic Flow Devices, Weighing & Proportioning Equipment, Weigh Feeders, K-Tron Feeders: Volumetric & gravimetric feeders, K-Tron Premier: pneumatic conveying equipment

 

Watch this video

 

Coperion K-Tron is a  bulk-online Leader

The Inspector’s Field Sampling Manual

That’s printed above the title page! Smaller print lower on the same page refers to A Sampling Manual and Reference Guide for Environment Canada Inspectors, First Edition. I perused its 231 pages and thought it ought to be revised. The more so since geostatistics is an invalid variant of applied statistics. Here’s why! A French scholar in the 1970s stripped the variance off the distance-weighted average, and called what was left a kriged estimate. Infinite sets of kriged estimates, zero kriging variances and zero degrees of freedom underpin Matheron’s science of geostatistics. Incredibly, geostatistics is an integral part of 2.0 Sampling in the Field and of 2.1 Site Selection and Documentation. The question is then why a stratified systematic sampling is an integral part of its very First Edition!

In fact, 1.1.1 Message from the Deputy Minister, pointed out the present edition will be updated as soon as more information is available and more lessons have been learned. Good thinking but slow learning! What sort of lessons do the inspectors of Environment Canada have to be familiar with before this field sampling manual will be revised?

Mel Cappe, Deputy Minister, may not have been taught why geostatistics is an invalid variant of applied statistics. Here’s why in a nutshell! Professor George Matheron and a few of his students traveled to the USA in the 1970s. Matheron invoked Brownian motion along a straight line. Marechal and Serra derived a set of sixteen (16) values from a set of nine (9). Geostatistics is an invalid variant of applied statistics because Matheron and his disciples did not know that degrees of freedom ought to be counted and why!

Did Mel Cappe and his team know how to count degrees of freedom? Surely, his inspectors ought to know how to count degrees of freedom and why! The fact that Matheron and his students did not count degrees of freedom should have been addressed. Yet, neither Section 2.0 Sampling in the Field and nor Section 2.1 Site Selection and Documentation, explain how, when and why degrees of freedom ought to be counted.

Mel Cappe is Professor in the School of Public Policy and Governance. He teaches in the Masters Program and is Coordinator of the Undergraduate program in Public Policicy. He is an Officer of the Order of Canada. Mel Cappe, unlike Professor Georges Matheron, should be able to count degrees of freedom flawlessly! It’s printed above the title page! Smaller print on the same page refers to A Sampling Manual and Reference Guide for Environment Canada Inspectors, First Edition. I perused its 231 pages and thought it ought to be revised. The more so since geostatistics is an invalid variant of applied statistics. Here’s why! A French scholar in the 1970s stripped the variance off the distance-weighted average, and called what was left a kriged estimate. Infinite sets of kriged estimates and zero degrees of freedom underpin Matheron’s science of geostatistics. Incredibly, geostatistics is an integral part of 2.0 Sampling in the Field and of 2.1 Site Selection and Documentation. The question is why stratified systematic sampling is an integral part of this First Edition!

In fact, 1.1.1 Message from the Deputy Minister, has pointed out that the present edition will be updated as soon as more information is available and more lessons have been learned. Good thinking but slow learning! What lessons do Environment Canada Inspectors have to learn before this field sampling manual will be revised?

Mel Cappe, Deputy Minister, may not have been taught why geostatistics is an invalid variant of applied statistics. Here’s why in a nutshell! Professor George Matheron and a few of his students traveled to the USA in the 1970s. Matheron invoked Brownian motion along a straight line. Marechal and Serra derived a set of sixteen (16) values from a set of nine (9). Geostatistics is an invalid variant of applied statistics because Matheron and his disciples did not know that degrees of freedom ought to be counted and why!

Did Mel Cappe and his team know how to count degrees of freedom? Surely, his inspectors ought to know how to count degrees of freedom and why! The fact that Matheron and his students did not count degrees of freedom should have been addressed. Yet, neither Section 2.0 Sampling in the Field and nor Section 2.1 Site Selection and Documentation, explain how, when and why degrees of freedom ought to be counted.

Mel Cappe is Professor in the School of Public Policy and Governance. He teaches in the Masters Program and is Coordinator of the Undergraduate program in Public Policicy. He is an Officer of the Order of Canada. Mel Cappe, unlike Professor Georges Matheron, should be able to count degrees of freedom flawlessly!

What’s wrong with Matheron’s 1965 PhD Thesis

Once upon a time a young geologist in Algiers derived the degree of associative dependence between lead and silver grades of drill core samples. What he didn’t derive were length-weighted average lead and silver grades. Neither did he test for spatial dependence between metal grades of ordered core samples. This geologist did do it with a bit of applied statistics so he called his article Note statistique No1! In time, one of several scores of dedicated disciples decided to change it to Note géostatistique No1. Somebody do so after the Internet was born! The same disciple is still the custodian of Matheron’s magnum opus. He may well want to play with Matheron’s new science of geostatistics from the 1950s to eternity. Good grief! That’s long time! And it’s a headache already! The more so since Note géostatistique No28 shows “krigeage” in its title. Did Matheron ever ask Krige whether he wanted his name to become a genuine eponym?

Matheron was a master at working with mathematical symbols. He couldn’t possibly have taught his disciples how to test for spatial dependence between mathematical symbols. What’s more, he didn’t even know in the 1950s how to test for spatial dependence between measured values in ordered sets. Neither did he know how to test for spatial dependence in his 1965 PhD thesis! As a matter of fact, Matheron has never tested for spatial dependence between measured values in ordered sets. He did not know how to apply Fisher’s F-test to the variance of a set and the first variance term of the ordered set. Degrees of freedom for both sets ought to be counted and taken into account. Matheron is remembered either as the Founder of Spatial Statistics or as the Creator of Geostatistics. I don’t care what his disciples called him. What I care about is that he didn’t know how to test for spatial dependence by applying Fisher’s F-test! Why did Matheron strip the variances off distance-weighted averages cum kriged estimates? And why did he assume spatial dependence between measured values in ordered sets?

Those who were to judge Matheron’s PhD Thesis on November 10, 1965 may well have asked him to put in plain words the nitty-gritty of his thesis.  Matheron had called it “LES VARIABLES RÉGIONALISÉES ET LEUR ESTIMATION”. His PhD supervisors were Professor Dr Swartz, President, Professor Dr Fortet and Professor Dr Caileux, Examinators. This team proposed a second thesis with the title “PROPOSITIONS DONNÉES PAR LA FACULTÉ”. Did Matheron’s supervisors ask him to jump hoops? And how far would Matheron jump to defend variance-deprived distance-weighted averages cum kriged estimates? The very first page of a whopping 301 pages of Matheron’s 1965 thesis mesmerized me. Why had Matheron cooked up a pair of prime data sets? Why were both inserted under INTRODUCTION on the very first page? Why didn’t he show how to test for spatial dependence? Why didn’t PhD candidate George Matheron know how to test for spatial dependence and count degrees of freedom?

Matheron_Thesis_data
All it takes to test for spatial dependence is to compare observed F-values with tabulated F-values. Of course, degrees of freedom ought to counted and be taken into account. I have applied Fisher’s F-test to verify spatial dependence in sample spaces and sampling units alike. I have done so ever since I worked on ASTM and ISO Standards. Geostatistical software converted Bre-X’s bogus grade and Busang’s barren rock into a massive phantom gold resource.  I unscrambled the Bre-X salting scam by proving that the intrinsic variance of gold was statistically identical to zero. Of course, it is of critical importance to grasp the properties of variances.

Matheron_Stats
It became Matheron’s new science of geostatistics when the variance was stripped off the distance-weighted average and what was left was called it a kriged estimate. Did Matheron really think had created a new science. Geostatistocrats thought he really  did! Good grief!

False test for bias

Testing for bias plays a key role in science and engineering. Student’s t-test is the par excellence test for bias. The t-test for paired data has always played a key role in my work. A bias between test results at loading and discharge is a constant cause of conflict between trading partners. The question is then whose test results are biased. A matter of concern in 1967 was dry ash contents of anthracite shipments from the mines in Pennsylvania to the port of Rotterdam. I went to the USA and determined that loss of dust during sample preparation was the most probable cause of bias between dry ash contents. I had done time at TUDelft. So, I knew that carbonaceous shale is softer than anthracite, and that hammer mills tend to crush and grind autogenously. That’s why I thought loss of fine dust during preparation of primary samples at loading would cause test samples to show lower dry ash contents at discharge in the Port of Rotterdam.

Holmes hammer mill

SGS’s coal testing laboratory in Rotterdam, too, had a Holmes hammer mill. It was similar to the one at loading but ours was run with its spring-loaded container closed. Settlements between buyer and seller were based on test results determined at discharge. So, we couldn’t afford to mess up primary samples by running our hammer mill ajar. What we did do was prepare test samples for analysis in the usual manner. We would then pass the reject of each primary sample through the hammer mill with its container left slightly ajar. We collected dust on sheets of paper placed at 0.5 m and 1.5 m from the hammer mill. Dust that had settled at 0.5 m weighed 26.2 grams and contained 14.6% dry ash. Dust that had settled at 1.5 m weighed 12.1 g and contained 16.5% dry ash. The settlement sample showed 10.40% ash on dry basis whereas our messed-up sample showed 10.26% ash on dry basis. With but one degree of freedom our experiment was not much of a true test for bias. But it did prove the integrity of our settlement samples passed scrutiny. We didn’t determine dry ash in dust collected on our coveralls and face masks. I had a fine team to work with. But I wanted more than a team! I wanted SGS to build a new laboratory as far away as possible from where we were. But SGS was not ready yet. What SGS did do was ask me to set up a laboratory in Vancouver. Now guess what?

When I met Greg Gould for the first time at Rotterdam in 1967 he did already chair ASTM Committee D05 on coal and coke. Greg praised Volk’s Applied Statistics for Engineers so I bought my first copy. He told me about Dr Jan Visman, his work at the Dutch State Mines during the war, his 1947 PhD thesis, and his input in ASTM. I was pleased to meet him in person after we had moved to Canada in October 1969. Dr Jan Visman was an independent thinker. He was as much a true a scientist as Greg Gould was a professional engineer. And a  true PEng he was! I treasure my copy of Visman’s PhD thesis and our correspondence. With fondness I remember our talks. We talked about the composition and segregation components of his sampling variance. I pointed out the term “segregation” suggests that a sampling unit may have been more homogeneous in the past. That’s why the distribution variance and the composition variance added up to the sampling variance. It happened when two Dutchmen talked about sampling in a foreign language. But unlike French sampling experts we did grasp the properties of variances.

The odd reader of my blogs may think I’m a pack rat. I do plead guilty! I want to get back to testing for bias with Student’s t-test. But I need to tell one more tale before talking about false bias testing. Once upon a time Matheron’s new science of geostatistics somehow slipped into bias testing. It came about after ASTM awarded me in 1996 a plaque for 25 years of services. Greg Gould had asked ASTM’s Board of Directors to recognize Dr Jan Visman and his work. ASTM did so but misspelled Jan’s first name as Jane! It was the same year that Barrick Gold signed me on to figure out what kind of gold resource Bre-X Minerals had cooked up in the Kalimantan jungle. It was the time when Greg Gould sent me bias test data that Charles Rose had enhanced by kriging. Rose had taken to liking to geostatistics. So much so that one of his papers was approved for David’s 1993 bash at McGill University. Rose talked about A Fractal Correlation Function for Sampling Problems. But one of his many problems was that Mohan Srivastava lent him a helping hand. I met Rose for the first time in Colombia many years ago. He joined SGS after I had left in 1979. ASTM awarded Rose in 2004 the R A Glen Award. He represents the USA on ISO/TC27 on coal. He talked about his take on bias testing during the meeting at Vancouver in 2009. What a waste of time! SGS announced on April 24, 2008 the strategic acquisition of Geostat Systems International, Montreal, Canada. For crying out loud!

False test for bias

That’s why I decided to show how to apply a false bias test. Firstly, I got the set of paired dry ash contents determined in eleven (11) shipments of Pennsylvanian anthracite at loading and at discharge. Next, I played the kriging game by inserting a kriged estimate between each pair of measured values. Take a look at what I cooked up! The variance of differences between paired data dropped from var(Δx)=0.1078 for a set of eleven (11) measured values to var(Δx)=0.0396 for a set of eleven (11) measured values which was enriched with a set of ten (10) kriged estimates. So much for kriging when testing for bias. Stay tuned for a true test for bias.

Unscrambling the French sampling school

My grandma taught me not to put all my eggs in one basket. She was a caring matriarch who told inspiring stories. She played card games but odds were beyond her grasp. She played for pennies but not with other people’s pennies. She didn’t have a PhD in anything. But I took her word and never put all my eggs in one basket.

Dr Pierre Gy (1924-…) and Professor Dr Georges Matheron (1930-2000) put the French sampling school on the world map. Matheron never put core samples from a single hole in one basket so to speak. But Gy did put a set of primary increments taken from a sampling unit in one basket. So he didn’t even get a single degree of freedom. The interleaved sampling protocol is described in several ISO Standard Methods. It is also described in Chapter 6 Spatial Dependence in Material Sampling of a textbook on Approaches in Material Sampling. Dr Bastiaan Geelhoed edited the text. IOS Press published the book in 2010.

Matheron marched to a new low when he sampled in situ ores. So he didn’t put in one basket a set of core samples from a single borehole. But he failed to derive measures for precision, to test for spatial dependence between grades of ordered core sections, and to count degrees of freedom. Quelle dommage! Matheron thought that Gy knew a lot about sampling theory and sampling practice. Gy’s L’Échantillonage des Minerais en Vrac was printed in two parts and on 656 pages. Tome 1 is dated January 15, 1967, and Tome 2 hit the shelves on September 15, 1971.

Gy’s sampling slide rule

Gy pioneered a slide rule of sorts to simplify the sampling of mined ores. His sampling constant C is a function of c, the mineralogical composition factor, of l, the liberation factor, of f, the particle shape factor, and of g, the size range factor. Hence, Gy’s sampling “constant” is a function of a set of four (4) stochastic variables. As such, Gy’s constant C does have its own variance.

Some sampling constant!

Matheron wrote a three-page Synopsis to Gy’s Tome 1 Theory Generale. He praised Gy’s work for defining, “… accuracy and precision, bias and random error, etc…” Gy, in turn, praised Matheron’s 1965 PhD thesis. Gy did refer to Visman’s 1947 PhD Thesis and to his 1962 Towards a common basis for the sampling of materials. Gy didn’t mention Sir R A Fisher, Anders Hald, Carl Pearson, and William Volk. Why did Gy deserve Matheron’s praise?

Dr Pierre M Gy is a chemical engineer with a deterministic take on sampling. He is the most prolific author of works on sampling. He sent me a copy of his 1979 Sampling of Particulate Materials, Theory and Practice. It was marked Christmas 1979 and signed underneath. Gy pointed to degrees of freedom in Chapter 14. His Index does not list degrees of freedom between “degenerate splitting processes” and “degree of representativeness”. Another odd entry in this Index is “SF = Student-Fisher”. Student’s t-test proves or disproves bias between paired data. Fisher’s F-test proves or disproves whether two variances are statistically identical or differ significantly. Both statistical tests demand that degrees of freedom be counted!

Matheron praised Gy’s work in 1967 and Gy, in turn, praised Matheron’s work in 1979. Here’s what Gy wrote literally:

“The sampling of compact solids and more specifically mineral deposits
is covered by the science known as ”Geostatistics”. The fundamentals
of this science, established by Krige, Sichel, deWijs were developed by
Matheron and his team (references in appendix). Worked out in France,
Matheron’s theories are slowly but steadily gaining acceptance in
English speaking countries around the world thanks to
an increasing teaching and to technical textbooks such as
Michel David’s “Geostatistical Ore Reserve Estimation” (1977)”.

Now that’s a nice little tit-for-tat between scholars who created the French sampling school! Matheron and his disciples cooked up quite a variant of applied statistics! Thank goodness, his magnum opus is posted on CdG’s website. I have scanned his 1965 PhD Thesis for degrés de fidelité but didn’t find any at all in 301 pages of dense probability theory. But I did find two sets of numerical data. Matheron’s Set A looks a lot less variable than Set B but both sets have the same central value. So, I applied Fisher’s F-test to the variances of the sets and the first variance terms of the ordered sets.

Data sets in Matheron’s PhD thesis

I have pasted Matheron’s A- and B-sets on a truncated title page of his 1965 PhD Thesis. This title page and Fisher’s F-tests for his A- and B-sets are posted on my website. Matheron and Gy didn’t know how to test for spatial dependence in sampling units and sample spaces. The root of the problem is these scholars didn’t grasp the properties of variances. But then, neither did my grandma!

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You can turn teamwork into success: Plan…Do…Review

Planning and Doing (Implementation) come natural to us. However, setting time aside and reviewing what has been done or implemented is not common practice. Always stick with the process step that you are at the time. When you are planning stage, do not do any of the other two things. The same goes for the Doing and Reviewing part. You will save on nerves, effort and money if you do the well.

The most vital component to a successful team task is the review process. It is the most frequent overlooked task in any project you need to undertake. Some projects may make great progress at first and then later fail because along the course of anything you do in business there will always be some tweaking along the way and no one ever took the time to include this in a review phase. The “tweaking” changes the dynamics of a project and if no one takes time to record what, why and how it happened you will not be able to capture why the results were different than expected. This is extremely frustrating for everyone and wastes resources with a direct impact on the net bottom line because you will never know why you failed, or worse why you had success.

Think of how many team based projects you may have been involved in that did not provide the desired results. I venture saying that the vast majority of them failed because no one thought of drawing the line at the beginning of the project and ask that there be a time line, a set of criteria for the results be reviewed and measurements. This normally leads to so called open ended projects. This is one of the biggest morale busters as the involved people will not be able to conclude anything. Something that was supposed to be only a temporary or experimental measure now has become the permanent solution without openly and officially saying so. One of the most important items any employee is looking for in an organization is that his or her contributions are “worth” the effort and make a difference. How is the employee ever going to figure this out if you never conclude the project? A winning company turns the knowledge from a finished project into common practice. Working for a winning company is equally important to an employee.

Another such waste comes from not paying attention to what phase you are in. What I mean by that is that most teams get so far ahead of themselves and start doing without thoroughly finishing the planning session. Did you ever paint a room in your house or apartment? Well, then you know what I am talking about. It takes so much more effort and mostly time to prepare for the painting, than it does to do the actual painting. Make sure that you have covered the must-have items of a successful endeavor such as mission, goal, strategy, tactics, responsibilities, authority, timeline and the most important one: measurements. Take this into account when you form a team for a specific task. Make sure to allow ample time for the team to plan the course of the project and only then start thinking about the implementation part. Do not mix up the project phases. When you plan – plan; when you do – do; do not forget to review – just review and do not start revising your plan in the midst of it. Finish it well by recognizing the contributions made and that the project either did or did not turn into common practice.

A plan is only a plan as long as you stick to your plan. Otherwise it is a new one. Do not get me wrong here. I am not advocating that you go through with a project when a team recognizes that it makes no sense. But I am saying that you need to adhere to the distinctly different process phases of your project once you have all bought into the course of action. Otherwise you will diminish your success rate of any project.

Ralf Weiser

One more message to CIM’s President

CIM stands for Canadian Institute of Mining, Metallurgy and Petroleum. Once upon a time I was a proud CIM Member. Today I am the accidental CIM Life Member. My first message to CIM`s President was snail mailed on March 20, 1992. CIM`s President was William E Stanley of The Coopers & Lybrand Group in Vancouver. He was the first of many whom I had told why geostatistics is an invalid variant of applied statistics. We met, he listened to my story, and I wrote him a letter. CIM Bulletin of March 1989 had published Armstrong and Champigny’s A Study on kriging Small Blocks. Both authors were at that time geostatistical scholars at the Centre de Géostatistique, France. They thought up the study since, “The kriging variance rises up to a maximum and then drops off.” What they found out is that “…mine planners are often tempted to kriging very small blocks.” How about that? Smoothing a little is good but smoothing very small blocks is bad. That sort of a pass-the-buck study did pass David’s peer review with red flags blazing.

Early in 1990 we found out that Precision Estimates for Ore Reserves was rejected. Our paper showed how to test for spatial dependence between gold grades of ordered rounds in a drift. David’s 1977 textbook didn’t show how to test for spatial dependence, or how to count degrees of freedom. Neither did his work show how to derive unbiased confidence limits for metal contents and grades of in-situ ores. So, I put Geostatistics or Voodoo Science on paper, and The Northern Miner printed it on March 20, 1992. Champigny was no longer a geostatistical scholar at the Centre de Géostatistique in France but a Senior Consultant with The Coopers & Lybrand Group in Toronto. He never lost his passion for kriging and smoothing. As a matter of fact, he rounded up a team of anonymous ore reserve practitioners to stand on guard against the rise and fall of kriging variances. What he and his team did prove was that the properties of variances were far beyond their grasp. The Northern Miner put Champigny’s rambling tale in print on May 18, 1992. Armstrong went beyond the pale and lectured on scientific integrity in De Geostatisticis of July 1992.

Following is the text of my emessage of January 13, 2010, to Michael J Allen, CIM’s President, Vice President, Engineering, with Teck Corporation, Member of APEGBC and SME, and a CIM Fellow:

About twenty years ago I reported to CIM that geostatistics is an invalid variant of applied statistics. Geostatistocrats with CIM Bulletin promptly put up a spirited battle to salvage the new science of geostatistics. And a fine job they did! Matheron’s madness of surreal geostatistics even survived the Bre-X fraud. Statistics turned into geostatistics under the guidance of Professor Dr Georges Matheron, a French probabilist who became a self-made wizard of odd statistics in the 1950s. A brief history of my 20-year campaign against the geostatocracy and its army of degrees of freedom fighters is chronicled on my website.

Dr Frederik P Agterberg, Past President, International Association for Mathematical Geosciences formerly know as International Association for Mathematical Geology, called Matheron (1930-2000) the Founder of Spatial Statistics. Agterberg ranked Matheron on a par with giants of real statistics such as Sir Ronald A Fisher (1890-1962) and Professor Dr J W Tukey (1915-2000). Agterberg was wrong! Matheron fumbled the variance of the length-weighted average in 1954. Agterberg himself fumbled the variance of the distance-weighted average first in his 1970 Autocorrelation Functions in Geology and once more in his 1974 Geomathematics. Agterberg is Emeritus Scientist with Natural Resources Canada. He ought to but has yet to explain why his distance-weighted average point grade does not have a variance. After all, Gemcom‘s geostatistical software converted Bre-X’s bogus grades and Busang’s barren rock into a massive phantom gold resource. I applied Fisher’s F-test to prove that the intrinsic variance of Bre-X’s phantom gold resource was statistically identical to zero. Duplicate test results for gold by cyanide leaching determined in a few boreholes would have been enough to unravel the Bre-X fraud in a timely manner.

I make a clear and concise case for real statistics. Test for spatial dependence by applying Fisher’s F-test to the variance of a set of measured values and the first variance term of the ordered set. Chart a sampling variogram to show where spatial dependence in a sample space (or in a sampling unit) dissipates into randomness. We applied Fisher’s F-test in Precision Estimates for Ore Reserves. And we did it again in our APCOM 2009 paper entitled Metrology in Mineral Exploration.

Geostatisticians assume spatial dependence between measured values in ordered sets, interpolate by kriging, smooth some kind of least biased subset of an infinite set of Agterberg’s zero-dimensional and variance-deprived distance-weighted average point grades AKA kriged estimates or kriged estimators, and rig the rules of real statistics with reckless abandon. I urge CIM to investigate whether or not geostatistics is a scientific fraud. I do so as a CIM Life Member. Please do not assume that CIM need not resolve this matter.

To strip or not to strip?

CIM Bulletin approved Abuse of Statistics for publication. Dr Frits Agterberg wanted to know when and where Wells spoke so highly about statistical thinking. I wasn’t about when Wells said what he did. What I do know is that Darrell Huff said Wells did. That’s good enough for me. Huff did so in his 1954 How to Lie with Statistics. It was the very same year that young Matheron didn’t know how to test for spatial dependence between metal grades of ordered core samples, how to derive the variance of the set of metal grades, and how to derive the variance of the central value of the set. Huff never found out what Matheron did wrong. But then, neither did Matheron himself! And Agterberg, Armstrong, David, Journel and scores of geostatistocrats never broke rank with Matheron.

I want to move fast forward to the present. Michael J Allan, CIM President in 2010, writes under President’s Notes about A time of renewal. Let’s read what else he wrote. “Our work in providing standard reserve and resource definitions that are used by the country’s securities regulators is an example of the ongoing technical contributions CIM makes to the industry at large”. For heaven’s sake! Geostatistics is as alive and flawed as it was in the 1970s. So it seems that CIM is not about to kill the incredible kriging machine. For infinite sets of kriged estimates and zero kriging variances set the stage for boundless krige and smooth fests. APEGBC ‘s Code of Ethics is not written to rule against scientific fraud. What will kill the kriging machine is the study of climate dynamics on our little planet. No ifs and buts!

Who wants more Munk Debates?

Who wouldn’t! Debates beat apathy. The Munk Debates is cool. The more so since climate change was the theme for the Fourth Munk Debates. Climate change, just like continental drift, has been around for a few billion years. It took geologists from 1915 to 1950 to slow down to continental drift and call it plate tectonics. So, it’s about time to debate climate change. Why not call it weather dynamics? I work with metrology, the science of measurement. I took a crack at testing whether or not annual temperatures at several locations in Canada have changed significantly as a function of time. The average temperature of 6.57 centigrade in 2007 at Ottawa International Airport was significantly higher than the average temperature of 4.79 centigrade in 1939. Similarly, the average temperature of 8.30 centigrade in 2007 at Toronto International Airport was significantly higher than the average temperature of 6.04 centigrade in 1939. Average temperatures didn’t change at international airports in Calgary, Vancouver and Victoria. Neither did the average temperature in Coral Harbour and Iqaluit change significantly during the test period under examination.

Some grasp of statistics is required to apply Fisher’s F-test and verify spatial dependence between annual temperatures in ordered sets. Weather dynamics do change from day to day, from week to week, and from month to month. Such short-term changes in temperatures do not merit a Munk Debates. What does merit a Munk Debates is the question whether or not geostatistics is a scientific fraud.
Here’s in a nutshell my take on the Fourth Munk Debates. Elizabeth May is Leader of the Green Party of Canada. She is a gifted and confident speaker. She knows a lot of environmental stuff. She doesn’t know much about temperatures recorded by Environment Canada. Given that the Leader of the Green Party does speak a lot in public, she should know where temperatures went up or down, since when, and by how much.
George Monbiot was her partner in the Fourth Munk Debates. He is a superb scribe with the Guardian newspaper where his penchant for hyperboles runs rampant. How to measure climate change as a function of space and time is far beyond his grasp. Monbiot says cool things such as, “Canada is a cultured, peaceful nation, which every so often allows a band of Neanderthals to trample over it.” He doesn’t know Sir Ronald A Fisher ‘s work is trampled over by a tribe of statistically dysfunctional geoscientists bred in France, Great Britain, and elsewhere on this planet. The May/Monbiot side debated The Case For Climate Change.
Lord Nigel Lawson and Bjorn Lomborg debated The Case Against Climate Change. Lord Lawson is in a class apart when it comes to a life of public service in the United Kingdom of Great Britain. His work has done much to cool down global warming to climate change. He is the author of An Appeal to Reason, A Cool Look at Global Warming. He is the Chairman of Oxford Investment Partners, and of Central Europe Trust. As such, he knows all about mining conglomerates and mineral inventories in annual reports. He is bound to remember the Bre-X fraud. He may be unaware that geostatistical software converted Bre-X’s bogus grades and Busang’s barren rock into a huge phantom gold resource. Neither may Lord Lawson remember the cast of characters behind the Bre-X fraud.
Bjorn Lomborg’s claim to fame is based on The Skeptical Environmentalist and on Cool It. He is adjunct professor at the Copenhagen Business School. He also set up the Copenhagen Consensus Center to bring together those who set priorities for the world. I had brought to his attention in August 2008 that junk statistics underpins Matheron’s new science of geostatistics. I wanted to know whether he applies geostatistical data analysis. Environment Canada points to geostatistical data analysis in its handbook for inspectors. The skeptical environmentalist did not respond to my message.
The Merks and Merks team wants to debate The Case Against Geostatistics. Dr Frits P Agterberg, Emeritus Scientist with Natural Resources Canada, and Dr Roussos Dimitrakopoulos, Professor with McGill University, are highly qualified to debate The Case For Geostatistics. Both are serving in key positions with IAMG (International Association for Mathematical Geosciences). Once upon a time, IAMG stood for International Association for Mathematical Geology. Nowadays, our world needs more mathematical statistics.

Engineering and Project Teamwork

The cause is bigger than the individual and the product is more than the sum of the parts

I have had the privilege to be part of a crack engineering team that gelled and matured over a several year period and then continued to get better and better with time and with each challenge. As is natural, each team member revealed particular strengths. The individual strengths tended to be diverse and cumulatively greatly broadened the capability. When inspired by leadership, towards a common cause that is deemed bigger than the individual, the individual strengths dovetail, not only filling the gaps but with strengthened bonds produce a powerful force. This is not a case for promoting specialization, quite the contrary. Individual strengths and leanings happen naturally. Indeed, individuals that are trained and inspired to be well rounded and complete don’t lose their strengths and leanings but achieve greater versatility and productivity and through a broader understanding of all functions are able to enhance the performance of others by their support.

Construction at Victor Project in the harsh Canadian environment
Construction at Victor Project in the harsh Canadian environment

Recently I had the privilege of witnessing such exemplary teamwork in action at the Victor Diamond Mining project, in Northern Ontario, Canada. I was there for Dos Santos International, starting-up and commissioning our three DSI Snake Sandwich High-Angle Conveyors. At the morning launch meetings as at the evening recap, the enthusiasm and sense of purpose was contagious. Clearly the cause was larger than the individual and this sense was shared by all team members from management to labor of the participating companies; the owner, the EPCM, the installation contractor and the various suppliers. Assignments, both planned and unexpected were embraced with enthusiasm and performed with pride and purpose. It’s no wonder that the Victor project is an example of success, coming in ahead of schedule and under budget.

Twin DSI Snakes at the Victor Mine
Twin DSI Snakes at the Victor Mine

The accomplishments, product of the teamwork, are the more impressive when the size, location and schedule of the Victor Project are considered. The following stats are taken from “E&MJ Dec. 2005” and “Canadian Business Dec. 2005”:

  • Project cost, (US Dollars) $ 982 million
  • Project life is 17 years
  • Productive mine life is 12 years, based on only one of 16 pipes (grading 22.3 carats/100 t), 6 million carats. Exploration continues on others in order to extend the mine life
  • Mine will produce annual revenue of (US Dollars) $ 117 million
  • The Victor kimberlite has a surface area of 15 hectors
  • Mine is located in James Bay Lowlands of northern Ontario, 90 km west of the coastal community of Attawapiskat
  • Mine is accessible only by air, and supplied by ice roads during 2 to 2½ months in the winter
  • Project schedule:
    • Environmental permits were approved in late October, 2005
    • Construction began in early 2006
    • Mine production began in early 2008, nearly a full year ahead of schedule
  • Project manpower grew to more than 800 during construction and settled to 380 for the productive mine life


I was privileged and honored to be a part of (if only as a supplier and observer) this very successful and exemplary project and team.

Joseph A. Dos Santos, PE

Going gaga about confidence without limits

If truth be told I didn’t really miss the 2000 Millennium celebrations of the Canadian Institute of Mining, Metallurgy, and Petroleum (CIM) and the Prospectors & Developers Association of Canada (PDAC). For the masters of ceremonies didn’t pine for my paper on Applied Statistics and the Bre-X fraud. Most CIM and PDAC members play the kriging game and talk about confidence without limits. Most scientists on this planet work with real statistics and real confidence limits. I work mostly with 95% confidence intervals (95% CI) and 95% confidence ranges (95% CR) for metal contents and grades of mined ores, mineral concentrates, mineral reserves and mineral resources. The world’s mining industry blathers about confidence without limits for mineral reserves and mineral resources. Yet it did accept confidence intervals and ranges limits for mined ores and mineral concentrates. So what’s all that talk about confidence? It should be about risk! The risks between trading partners seem to matter a lot more than the risks mining investors run. That’s the real story behind Bre-X!

I thought all along the Ontario Securities Commission (OSC) would lose confidence in all the mumbo jumbo that replaced the 1998 Interim Report of the Mining Standards Task Force. For I couldn’t find a single scrap of sound statistics in National Instrument 43-101 Standards of Disclosure for Mineral Projects. Here’s what happened on September 10, 2004. “We, the Canadian Securities Administrators (CSA), are publishing for a 90-day review comment period the following documents…” How about that! Did the CSA really plan to repeal and replace that National Instrument rubbish? Did the CSA want real statistics in its standards? Some of its objectives were to “correct errors”, and to “generally make the Current Mining Rule more user-friendly and practical.” Correct errors? Did CSA’s mining experts finally figure out how many variances went missing? Were variances of weighted averages about to make a comeback? Did OSC’s Chief Mining Consultant figure out who lost what and when? I did find the answers but nobody gave a hoot. So I stayed in the trenches and watched CSA’s rulers rule.

Patricia Dillon, CIM Guidelines Coordinator, met with the CSA in Edmonton on May 11, 2004. The objective of this formal annual meeting was to clarify the source of various documents and guidance that underpins Reporting Standards and Guidelines. I like that kind of stuff! The CIM Standing Committee on Reserve Definitions consists of a team of eleven ore reserve practitioners. Normand Champigny, the coauthor of A Study on Kriging Small Blocks and a leading activist of sorts against oversmoothing, brought all of his insights to CIM’s reserve definitions team. Champigny didn’t grasp the additive property of the variances of metal contents for blocks of in situ ore when he spoke on behalf of “five anonymous ore reserve practitioners in Canada and abroad.” He did so in Geostatistics: A Tool that Works (The Northern Miner, May 18, 1992) in response to my Geostatistics or Voodoo Science (The Northern Miner, April 20, 1992). It did work all too well at Bre-X’s Busang property! I wonder whether or not any of Champigny’s anonymous buddies in 1992 served on Dillon’s definitions body in 2000.

CIM Council on December 11, 2005 adopted CIM Definition Standards for Mineral Resources and Mineral Reserves. The term confidence played a prominent role in statements such as the level of confidence, a lower level of confidence, a high level of confidence, a higher level of confidence, the highest degree of confidence, insufficient confidence, the level of geoscientific confidence, different levels of geological confidence, and confident interpretation. Such is the verbose burden of confidence without limits. What happened with confidence intervals and ranges in ore reserve estimation? Who repealed 95% CIs and 95% CR’s? Ten pages of mind numbing text with rambling nuggets such as reasonable assumptions, acting reasonable, conceptional estimates, order of magnitude estimates, reasonable prospects, reasonably assume the continuity of mineralization, and reasonably assumed but not verified. Was Dillon’s waffling squad really thinking?

It was easier to meet my Member of Parliament and talk about geostatistical data analysis of shellfish counts along a coastline than it was to meet Deborah McCombe during her trip to Vancouver and talk about real statistics. She granted me one hour of her time on January 22, 2005. We met at the office of the BC Securities Commission in the presence of Dr Gregory J Gosson, BCSC’s Chief Mining Advisor. I talked about the lost variance of the distance-weighted average, and why it should not have gone missing when the distance-weighted average was reborn as an honorific kriged estimate. I used Clark’s hypothetical uranium data to show how to test for spatial dependence in her sample space, and when the distance-weighted average converges on the arithmetic mean and its variance on the Central Limit Theorem. I also showed what happens when ore was inferred between Bre-X’s salted holes, and what happens when interpolation positions kriged holes between salted holes. There were no questions either during our meeting or thereafter.

BCSC’s former Chief Mining Advisor and OSC’s former Chief Mining Consultant present a $350 workshop at the University of Alberta Campus on Saturday, May 3, 2008. Nowadays, Deborah McCombe is the executive vice-president, Scott Wilson Mining Group, and Greg Gossan is the technical director of geology and geostatistics, AMEX Mining and Metals Consulting Group. They will talk about matters ranging from Setting the regulatory scene to Case studies of what went wrong. What Gossan and McCombe will not talk about is when Agterberg fumbled the variance of the distance-weighted average, and why it is too late to reunite them. For the name of the exploration game is to look forward with confidence without limits rather than take a step back to figure out what is really wrong with geostatistics. Geostatistical data analysis of shellfish counts in samples taken along a shoreline at 1-km intervals may kill the kriging game. The Harper Government may well agree that mineral reserves and mineral resources in annual reports and populations of shellfish along Canada’s shorelines should all be reported with unbiased confidence intervals and ranges.