Improved Monitoring of Dragline Operation

ACIRL's Australian Dragline Performance Centre (ADPC) was contracted to undertake a comparison of different dragline process variables as project C3001 under the Australian Coal Association Research Program.

Objectives

The specific objectives of the project were:

• Process the data captured by dragline monitors to a format whereby critical performance could be qualified and compared;
• Analyse sufficient data from a range of operational situations, (key cut, main bench, bridge, rehandle Dragline etc), and from different operations to:
• Provide relative information on individual dragline performance;

Allow productivity improvements to be sought, backed by quantitative data.

To achieve the objectives of this project data manipulation software was developed to handle the large volume of data captured by dragline monitors. With the data in a manageable form, analysis for a meaningful comparison and trends was commenced. The tool and methods facilitated speedy analysis of dragline monitor data. Dragline performance across draglines at one mine, and across a population was carried out.

The first objective has been met. The second objective could not be met due to two factors:

• reliability of operator imput of codes is not sufficient to provide an accurate differentiation between different dig modes;
• the quantity of data required to achieve the second objective is extremely large and requires more time, more computing power and capacity, and the capture of different data than is currently provided.

Project method

Differences between draglines have been quantified and information has been provided to the mines on the comparative performance of their machines.

Sufficient information has been provided to enable mines to identify areas of unacceptable performance and areas of potential productivity improvement. An individual is required to do their own assessment of the causes of unacceptable performance and how performance can be improved.

Participating mines are (in alphabetical order);

• Blair Athol 1 * BE1370W
• Capcoal 1 * M8050
• Howick 1 * BE1570
• Mt Thorley 1 * M8200
• Newlands 1 * BE1370W
• Norwick Park1 * M8050 & 3 *BE1370W
• Oaky Creek 1 * BE1370
• Ravensworth 1 * BE1370 & 1 * BE1570
• Tarong 1 * BE1370

In all cases, except Oaky Creek, the draglines have an on-board, permanent Tritronics 9000 Dragline Monitor. Oaky Creek had a monitor fitted between August 1992 and March 1993. These draglines were given a number from one to sixteen, chosen randomly to ensure confidentiality. Each of the mines have been advised which number(s) correspond to their dragline(s).

The analysis has been broken into two main parts; average values of selected process variables, and secondly, frequency histograms and standard deviations of selected process variables have been derived for each dragline and for the population of 16 draglines;

• Idle Time (% of Calendar Time)
• Idle Time (% of Total Non-Operating Time)
• Dig Time (% of Calendar Time)
• Walk Time (% of Calendar Time)
• Operating Time (% of Calendar Time)
• Swings per Day (#)
• Bucket efficiency Ratio(Payload / Rated Cap.)
• Fill Time (Secs)
• Repass Count (Total of Swings)
• Swing Time (secs)
• Hoist Dependent Swings (% of Total Swings)
• Swing Angle (deg)
• Return Time (secs)
• Spot Time (secs)
• Cycle Time (secs)

Measures of Productivity:

• Swings per Operating hour
• Tonnes per Operating Hour Per Cubic Metre of Rated Capacity

Frequency histograms have been plotted and standard deviations calculated for the following variables:

• Payload (t)
• Fill Time (secs)
• Swing Time (secs)
• Swing Angle (deg)
• Return Time (secs)
• Cycle Time (secs)

Full details of the results for each process variable have been included in Section 4.

No analysis of the monitor algorithms was undertaken. Interpretation of different process variables is assumed to be in line with the Tritronics manual. Accuracy of variables was not accounted for. Both these points are important and will need to be considered as part of any more detailed work using monitors to improve productivity.

The ultimate goal of the dragline must be to maximise the rate of coal uncovered at the lowest unit cost. The rate of coal uncovered is influenced by rehandle, dig depth, etc. Some of these factors have been taken into account and some have not. As a broad comparison between machines, the draglines were rated and scored from one to sixteen for each process variable which was believed to impact on productivity.

Due to inherent results, they were broken into values and percentages, scored as above, averaged for each process variable and the averages ranked. The lower the score the better, with the best possible score 16. The top ranking dragline scored 81, (which is an average of fifth position), with the bottom scoring 201, (which is an average of thirteenth position).

The machines ranked as follows:

It is emphasised that this assessment was limited to those variables measured by the monitor and analysed using the project software. No account was made of inherent differences between operations eg. equipment, geology, dig method, dig geometry, bucket type etc.

Conclusions & recommendations

The ultimate goal of the dragline must be to maximise the rate of coal uncovered at the lowest unit cost. The rate of coal uncovered is influenced by rehandle, dig depth, etc. Some of these factors have been taken into account and some have not, however, as a broad comparison between machines the draglines were rated and scored from one to sixteen for each process variable which was believed to impact on productivity.

Due to inherent variation occurring in the standard deviations which may have more influence in these results, they were broken into values and percentages, scored as above, averaged for each process variable and the averages ranked. The lower the score the better, with the best possible score 16. The top ranking dragline scored 81, (which is an average of fifth position), with the bottom scoring 201, (which is an average of thirteenth position).

It is emphasised that this assessment was limited to those variables which have been quantified in this report. No account was made of inherent differences between operations. It is suggested that each operation look at their own results in detail to determine what results are influenced by inherent causes and which ones may be controlled to give improvements in productivity.

An e-newsletter has also been published for this project, highlighting its significance for the industry.