Syndicated Project for Rope Shovel Benchmarking in Russia

GBI are formulating a syndicated benchmark specifically for rope shovels in Russia with a focus on the Kartex EKG range. If you have these machines and would like to participate in this benchmark to find out how your equipment is performing compared to Best Practice please contact me for more information on laura.seviour@gbimining.com.

More excellent feedback from our Mine Operating Standards Best Practice Course

Best Practice Standards Series - Top 10 Mine Operating Standards Feedback

Again really positive feedback from all participants on Day 2, with numerous in-depth discussions generated as a result of the topics presented.

"Very Interesting and informative"

"The Top 10 Best Practices were really interesting"

"I will encourage other people at my mine to come to this in the future. Would be good for maintenance people to come to this."

Achieved an overall course ranking of 4.57 out of 5. Where 1 is poor and 5 is excellent.

Ranking for Content = 4.37  out of 5.

The next Best Practice Standards Series are to be held on the following dates at Colorado School of Mines, Golden, CO 80401:

October 4th - Top 10 Mine Operating Standards

October 5th - Top 10 Dragline Operating Standards October

8th - Top 10 Mine Operating Standards

October 9th - Top 10 Operating Standards

Contact Karen.Trott@gbimining.com or go to http://www.gbimining.com/GBI-Training.html for more details.

Best Practice Standards Series - Top 10 Dragline Operating Standards Feedback

On 28th August 2012 GBI conducted its Best Practice Standards Series - Top 10 Dragline Operating Standards Course. We wanted to share with you some of our feedback and the course rankings:

"Overall very useful and informative course. Covered a broad range of topics well. Information was presented objectively with supporting data and facts which was very valuable."

"Very good dragline course. Bucket and rigging section was very involved obviously due to the large amount of knowledge GBI has amassed in this area. Well presented and the info was delivered at the right level for the target audience"

"Great workshop overall, very informative on a range of levels."

"Being relatively new to Dragline operations and management, this course has been really beneficial."

The course achieved an overall course ranking of 4.68 out of 5. Where 1 is poor and 5 is excellent.

The Ranking for Content achieved was 4.42  out of 5.

The next Best Practice Standards Series are to be held on the following dates at Colorado School of Mines, Golden, CO 80401:

October 4th - Top 10 Mine Operating Standards
October 5th - Top 10 Dragline Operating Standards
October 8th - Top 10 Mine Operating Standards
October 9th - Top 10 Operating Standards

Contact Karen.Trott@gbimining.com or go to http://www.gbimining.com/GBI-Training.html for more details.

Truck and Loader Optimisation

I have spent the last couple of blogs discussing issues relating to optimising truck and loader sizing and how common it is to find poor matches.  I find it incredible how you can go to two different mining companies and you get two completely different approaches to optimising output from their truck and loader fleets.  Some mining companies believe you undertruck to optimise cost and others believe you should overtruck to optimise output.  Both approaches are right but I am sure most companies don’t understand the link between strategy and actions on the ground.

Variation in performance can be a major contributor to reduction in efficiency and it is this variation which must be understood and controlled if the operation is to achieve their strategic goals. In statistics, a result within three standard deviations of the average is considered to be under control. Given a normal distribution of results, 0.14% of cycles should be expected to be more than 3 standard deviations above the average.  Under normal circumstances the key “interaction parameters” – Wait on Trucks and/or Wait on Loaders, will not be normally distributed and should be skewed strongly to the right or “positively skewed”. (Skew or skewness is the lack of symmetry in a frequency distribution. Positive skew has a long tail to the right of the peak – high percentage of results with a low result.) Most mines have both wait on truck and wait on loader very strongly and significantly skewed to the right. In truck and loader operations most wait on truck and wait on loader results, up to 5% of cycles can be more than three standard deviations above the average.  However, to meet one of the two key strategies our mines follow most of the time you actually only want one of these strongly skewed.

A high skew (maximum frequency of low values with a long tail to the right) on both parameters is required for optimising efficiency but this will deliver neither maximum output nor minimum cost. The higher the measure of skew the more efficient the operation.  It is not unusual for the value of skewness statistic divided by standard error of skew to be over 100.  A significant difference in skewness statistic / standard error of skew for wait on trucks cf wait on loader is an indicator of overtrucking (skew of wait on truck is stronger than wait on loader) or undertrucking (skew of wait on loader is stronger than wait on truck) and one of these is what is required for most mines.

Another way of measuring the efficiency of truck and loader usage is the proportion of time where the truck and loader wait for less than 30 seconds (excluding spotting).  To optimise the mine’s execution of strategy it is often necessary to have these two measures significantly different.  For example you might find that wait on trucks is less than 30 seconds 90% of the time and wait on loader is less than 30 seconds 35% of the time.  This demonstrates a strongly over-trucked scenario.  This will deliver high system output but will not be the most cost effective way to operate the fleet.  However, if it is your mine’s strategy to optimise output at any cost then being overtrucked is a good thing.  These results can be reported on a month by month basis to demonstrate the strength of the overtrucking (maximum wait on truck events less than 30 secs and minimum wait on loader events less than 30 secs).

As already discussed most operations usually follow one of two strategies in relation to matching number of trucks to the loading unit. These operations either follow an over trucked or an under trucked approach.  Under trucking is a lower cost option, delivering a higher utilisation on the trucks while sacrificing the loading units utilisation. Over trucking will cause a higher cost per tonne, will have a higher utilisation on the loading units and a lower utilisation on the truck fleet although moving more material.  Sometimes an approach will be taken to attempt to optimise output and cost but this usually ends in underperformance in profit and/or output.

The challenge for all mines is how best to represent this match of numbers of trucks reporting to each loading unit in a way which makes the outcome meaningful for ongoing optimisation.  The optimal matching of trucks is the critical element for a loading unit to achieve its required production rate and it is essential that the supply of trucks to the loading unit is sufficient to meet the required output.  In most cases the average haul distance varies from the beginning of a new bench to the end of the bench and truck numbers need to taken into account as well as managing the dump areas (long and short dumps).

Wait on truck delays (loading unit entered delays) that are less then 2 minutes in duration are generally considered to be part of normal operational delays.  Wait on truck is typically where there are no trucks available to be loaded by the loading unit and highlights one or more of the following problems in the circuit:

·         The loading unit is under trucked.

·         The trucks are being delayed in the circuit by one or more of the following:

·         Delays on the dump, waiting for dozer work or queuing.

·         Haul road grades too steep, poor road conditions, grading of roads etc. slowing trucks down.

·         Sub optimal Operator performance / speed / technique

·         Dust / weather / blasting etc.

If the trucks are queued, waiting to load, this is called wait on loader.  Most mines that have relatively high wait on loader time (queue time) are over trucked.

Some mines operate day-to-day using a Match Factor, i.e. where a MF of 1 means that the number of trucks are perfectly matched to the digger such that the cycle times are integrated and should no delays occur, trucks will arrive and depart in a perfect scenario exactly matching the digger’s truck requirement.  The method of calculating MF varies but the formula used by GBI is
MF = (1-%wait on loader time)*(1-%wait on truck time)

This is only of value where a mine is passing through this phase of balancing output and cost.  Fleets operating a strategy of optimising the balance between loaders and trucks should achieve an MF >=0.70.  This allows for the typical mining delays and means that as a result of them, some time the digger waits for trucks, and other times trucks are queued at the same digger (normal every day mining).  For most mines the important factor is a comparison of either wait on truck or wait on loader.  This will give them a direct indication of how well they are meeting their strategy.

Truck and Loader Matching Part 6

This blog I want to present a case study where a mine had a large shovel with 44 CuM dipper loading 218 tonne trucks perfectly in two and a half passes!  (Situation normal for most!) The dilemma, faced by multitudes of mines around the world, is do you put a third small pass in the truck or do you send it away 80% full?

The average payload of the shovel was 85 tonnes.  The original methodology for determining the match was not known but the performance of the dipper was quite good when looking around the industry.  It appears likely that the original aim was to fill the 218 tonne trucks in three passes.  Two passes sent trucks away with an average of 170 tonnes payload.  The decision was made not to put the third pass into the trucks due to the loss in productivity, damage caused to trucks by overloading and the increased spillage. 

The desired average payload was 218 tonnes per truck (109 tonnes per dipper).  The mine had a quote from the OEM to change the boom geometry of the two shovels and provide two new dippers. Quote was for $6M+.

Using a combination of data analysis and physical modelling four stages of work were undertaken with the following outcomes;

Stage 1         Analyse data. Process changes recommended.  Discussions held with operators.

Result - Payload increased to 95 tonnes on average which was in line with best practice dipper performance.

Stage 2         Physical modeling of the existing dipper, the supplier’s recommended dipper and two boom geometries.

Result – Modelling proved accurate.  Modelling demonstrated under-performance of supplier’s recommended dipper relative to existing dipper.  Recommendation made not to change boom geometry.  Recommendation not to purchase new dipper due to substantial under-performance.  Recommendation to test changes to existing dipper.

Stage 3         Physical modeling of changes to the dipper.

Result – A number of changes had a positive impact on payload but none gave enough by themselves to increase payload to 109 tonnes. Recommendation to conduct further testing combining various options to modify the dipper.

Stage 4         Four options were presented which met the target 109 tonne average

payload, (Figure 1).

The mine chose the preferred option with a slight change, engaged a structural engineer to design the modifications and a local business undertook the modifications to one dipper (Figure 2).

End Result    All up cost $350,000, Average Payload 111 tonnes. Value to mine at the time $8M per annum.

Consequently a second dipper was modified for the second shovel. 

All up cost was $470,000 with two dippers achieving 111 tonnes and 109 tonnes average payload. Cash saved on the project >$5.5M.  Value to the mine $15M per annum.

The most important lesson here is that you can’t achieve anything if you won’t have a go.  The four stages here took 18 months and were rigorously evaluated before proceeding, but the key is that they did it and they added real value.

Truck and Loader Matching Part 5

This blog continues to investigate the issue of why many trucks are being perfectly loaded in 2.5 or 3.5 passes.  In this discussion I am looking at rope shovel capacity and why we need so much steel to carry what is often a very poor payload.

How is it possible that best practice in dipper performance provides a payload of 2.16 times capacity but the dominant manufacturers provide dippers which only achieve around 1.70 times capacity?  This is more than 20% less payload for the same capacity and around the same weight of steel.  This rhetorical question actually has a real answer.  It is because the mines don’t care.  So long as it keeps going and is supported when it breaks then that is OK.  Many mines don’t even complain when the loader truck match is 2.5 or 3.5.  To someone who has worked in equipment productivity for over 20 years this is really depressing.

 

Looking at some issues which impact shovel payload.  Firstly, dipper issues which the mine can have some impact on.  The tooth attack angle is really important. Payload increases by around 0.5% per degree as the tooth attack angle is increased.  However, it is not possible to simply keep steepening the tooth attack angle of the dipper due to the interaction between the heel and the bank.  Relative heel wear rises exponentially after about 65 degrees tooth attack angle.  By 70 degrees the heel wear is probably unacceptably high.  Many buckets are in the range 50-55^o and are losing a lot of payload.

The concept of Bail vs Bail-less is a function of where the hoist connection is made to the dipper. The connection of hoist ropes at the rear of the dipper increases payload.  Where the connection is 25% along the dipper the difference is -10% which is significant. 

The width : height : depth ratios as well as teeth arrangements have an impact on payload but there is little impact site people can have on these issues once you have the dipper so I won’t expand on these issues here.

The other side of the payload issue is operational issues.  Many of these can be controlled by the mine.  What is being dug causes variation in average payload by up to 20% in the same dipper. Herein lies a significant issue relating to truck/shovel matches.  It is possible that the same dipper, even on the same minesite, can get differences in payload of 20% simply due to the spoil being dug.  The key to higher payload is the degree of fragmentation.  The highest payloads are achieved in spoil where there is a range of particle sizes; not all large and not all small.  The implication is that payload is significantly enhanced by good blasting practices.

The power made available to the operator has a major impact on payload.  In harder digging, ie. blocky, poorly shot, etc., increased power provides increased payload up to 120% of the standard power level.  In softer spoils the shovel dipper achieves higher payloads at lower power levels.  In summary, it is beneficial (in terms of payload) to increase power to the maximum.

Bench height plays a major role in determining payload.  At any bench height greater than 30% of boom point height a full payload can be achieved consistently.  Similarly, the distance from the face has a major impact on payload.  The variation from cycle to cycle is quite large but a consistent trend is seen for each digging position.  The first few digs have the loading unit very close to the face.  During these cycles the payloads are reduced possibly due to the inefficient application of power to the trajectory of the dipper / bucket.  The payload increases as the face “moves” away from the shovel.  Once the dipper starts having trouble reaching the face the payload reduces quite quickly.   The decision about when to move the loader is not an easy one to get right.  Generally the operator will decide to move the loader when they encounter difficulty in loading the truck in the designated number of cycles.  To optimise the productivity a range of factors need to be considered, including, payload, fill time, another truck waiting, what the face is like.  As a general observation, if the loader is under-trucked, it would appear prudent to move the loading unit frequently.  If the shovel is over-trucked it becomes a multi-dimensional equation as to when the most efficient time to move is.

                                                      

It became evident from a very early stage in the work on shovels that on some loading equipment the efficiency of the bucket / dipper was severely compromised by large voids inside the dipper / bucket (Figure 1).  These voids ranged from 5% inside a backhoe bucket up to 25% inside rope shovel buckets.  The impact of these voids is included in the previously described impacts on payload.

Finally I would direct your attention to Figure 2.  This shows the variation in dipper payload for P&H and Cat (previously Bucyrus), (both unidentified) and VR Mining Dippers.  I have spent my career helping mines be more productive and the VR Mining dipper is the most efficient dipper design I am aware of.  I am aware there are maintenance, support and financial issues to purchasing a dipper but speak to dipper manufacturers, not just the OEM, the next time you want a dipper.

Just so you know: I worked for VR Mining in 1997 and 1998; before they designed this dipper.  GBI has had a number of small consulting jobs from VR Mining over the last 10 years.  I had no input into the VR design.  Neither I nor GBI receive anything from anyone for the comments made here.  They are simply my honest opinion – the VR dipper is the best and the mines are costing themselves a bundle by not looking at it.  Even if the mines used this fact to put pressure on P&H and Caterpillar to do better, the industry would benefit.

Truck and Loader Matching Part 4

Over the last few weeks I have systematically pulled apart the issue of nominal truck capacities to demonstrate why big mining trucks achieve 5-15% below what the manufacturer says they should get on average.  I don’t believe this is an issue that too many truck manufacturers’ want to address and the cynical side of me suggests that this article won’t help.  Maybe a single voice in the wilderness can gain support to force change. 

My focus is on mines moving more for less and apart from the engineering design work to increase the capacity of trucks from the 150 tonne maximum size 25 years ago to the 360 tonne maximum size now I don’t think that the truck suppliers have helped the “move more for less” equation too much.  Even the notion of bigger trucks being a great innovation and assistance in efficiency enhancement is questionable.  I will repeat something from a previous blog.  On the whole bigger trucks are less efficient than smaller trucks.  They carry less payload (as a percentage of nominal capacity) and work less hours. However, this is not a consistent picture between OEM’s.  In terms of nominal capacity the 360 ton trucks are 50% bigger than a 240 ton truck. however, in terms of actual annual capacity, average 360 ton trucks move just 20% more than 240 ton trucks.  I am not pointing the finger at one supplier. 

Figure 1 shows the 2010 median performance for each major mining truck make and model.  Some of the older and newer models are not included due to lack of data.  Mining truck performance is presented in this analysis as annual tonnes (normalised for full year operation) * km travelled per tonne of nominal tray carrying capacity.

Trucks with different designations (usually A, B, etc used by Cat and Liebherr) have not been separated in this analysis.  The capacities for these “sub-models” are generally similar as is the output.   It is important to note that this plot does not attempt to say whether the make and model results actually reflect better trucks or the operating characteristics of the sites at which they are used.  The trends with increasing size of mining trucks are mixed.  The Liebherr trucks become more efficient with increasing size while the Cat trucks become less efficient with increasing size.  The Hitachi, Komatsu and Terex trucks achieve peak efficiency with the 240 ton (218 metric tonne) capacity size EH4500, 830E and 4400 respectively.  The larger capacity trucks are not as efficient with these OEM’s.  Of the larger trucks the Liebherr T282 is the highest performer with Terex and Komatsu both achieving 20% less annual tkm/t and Cat 23% less annual tkm/t.  It is not without precedent for larger equipment to have lower unit production (ie. draglines) however, the exceptional performance of the Liebherr T282 range demonstrates that this is not a necessary outcome.  Another clear finding from this plot is that the performance of the smaller Cat trucks (777 and 785) was, and continues to be, relatively high.  They however, are not suitable for loading with the larger loaders. 

This industry has lived in a world where bigger is better.  But frequently when bigger equipment is released it just doesn’t perform well.  Those of us who remember the release of 240 ton trucks would remember that they had real problems.  It seems too easy for a poorly performing mine to just get bigger equipment and that is what they tend to do.  They waste more millions of dollars when the improvements they need are available by just operating more efficiently and would actually cost very little.

To demonstrate this point I will set up a scenario of a PC8000 hydraulic shovel loading Cat793 trucks.  These have not been chosen for any particular reason except it should be a comfortable three pass match.  The average PC8000 loader will require 7.5 average Cat 793 trucks.  Four crews plus spares plus trainees (you should always have a pool of people training) probably means around 40 truck drivers.  If a mine then goes and purchases Cat797 trucks the typical method of determining number of trucks is to simply work out the proportional capacity.  New trucks = old trucks * 793 capacity / 797 capacity.  Using this formula five new Cat797 trucks would be purchased with the expectation that around 13 people would be saved along with reduced running and maintenance costs.  Unfortunately, this scenario is fictitious.  In the real world the PC8000 on average needs 5.8 * 797 trucks and only saves 9 people.  Bigger trucks cost more to buy and more to run, so how far ahead are you?

OK so returning to the real point of this column; technology is progressing fast.  We now know that trucks are not carrying the nominal payloads.  This has not gone unnoticed by companies which make their way in the world by making equipment work better.  For the OEM the real money seems to be in the chassis and tyres.  Improvements in payload are coming from specialist tray suppliers.  Truck trays are no different to most other mining equipment.  What the equipment carries is made up of steel and payload and the aim is to maximise the payload and minimise the steel while achieving acceptable life.  In the past with trucks this was a nothing equation because OEM’s told the mine what payload the truck would carry.  We now know this was almost always wrong.  Truck trays seem to be following where the industry has been with draglines.  Now Bucyrus and P&H build draglines and shovels but CQMS currently build the most efficient dragline buckets while VR Mining have the most efficient shovel dippers.  In trucks you have specialised truck tray manufacturers like DT HiLoad, Duratray, Esco, Philippi-Hagenbach, Westech, etc. who seem to get it; the chassis is built to carry a certain load and if you can reduce tonnes of steel and increase tonnes of payload then the mine must be ahead. 

It is my proposal that we must here and now dispose of SAE Standard J-1363 for calculating truck capacity the same way suppliers have disposed of the CIMA formula for dragline bucket capacity.  We must also stop rating trucks based on a nominal payload.  We should establish a rated capacity for the truck trays which is struck capacity (contained capacity with no heaping according to computer models) multiplied by a factor.  With dragline buckets the factor is 0.9 which I have always disagreed with but everyone knows it and accepts it.  I believe the rated capacity of a truck tray should be equal to the struck capacity, (factor = 1).  In the same way that we have a Bucket Efficiency Ratio for draglines and a Dipper Efficiency Ratio for shovels, which is payload / rated capacity, we need a Tray Efficiency Ratio (payload / rated capacity) for trucks - TER.  There is also a steel weight ratio (Tray Unit Weight (TUW)), which is the weight of the tray divided by the rated capacity.  The formula for the optimum truck tray rated capacity is then;

OTC    =        GVM – Chassis Wt

                      TER + TUW

Only then can we get the best tray design with the right capacity to meet the gross vehicle mass.  At least then we will be covering Step 1 in the optimisation process; mines will be selecting the right piece of gear.