Draw management system

Transcription

1 5th International Conference and Exhibition on Mass Mining, Luleå Sweden 9-11 June 2008 Draw management system A. Susaeta IAL LTDA, Chile G. Valenzuela Codelco Andina, Chile G. País Universidad de Chile, Chile D. Carkeet Independent Consultant, Chile Abstract The draw order design (daily three shift production plan) for a panel caving operation, in order to comply the monthly and annual production schedule and optimization of recovery of the broken reserves, requires the integration of the draw data: extracted grade, draw point productivity and dilution percentage, with the daily tonnage, grade target, and maximum dilution content requirements. An optimizing system has been designed and applied in El Salvador and Andina Mine Panel Caving operations of Codelco, in order to generate an achievable draw order, which allows managing coarse fragmentation. The system integrates the previous shifts performance of draw points (tonnage drawn, dilution content, and extraction grade), secondary blasting history, draw condition at the end of the shift, and physical condition of draw point, to generate a viable draw order with grade, dilution and uniformity of draw targets. The system is able to compute operational restrictions, which the user can specify (draw rates, secondary blasting resources, ore pass capacity, etc.). Historical results of the improvement obtained in uniformity of draw, minimizing of dilution entry and moisture entry as well as improvements in production and grade of the draw order, for a LHD operation are presented to illustrate the application of the system. 1 Introduction One of the most important issues in a panel caving operation is to achieve the planned tonnage with a reasonable uniformity of draw, so as to minimize dilution entry. This is relatively easy to do with secondary ore, where fragmentation is fine and all draw points flow. The problem appears with primary ore where the number of hang-ups, and secondary blasting increases dramatically. In panel caving mine planning, the draw strategy (draw control practice) defines the quality of the tonnage drawn and the draw point performance mainly defined in terms of oversize, stresses in pillar and moisture. All of them affect directly the value of the business. Codelco as most of the modern caving operations, have invested important amounts of resources in software and hardware to plan and control the extraction of the ore. The idea to generate an achievable 3 shift plan that complies with a grade, uniformity and dilution target was considered 8 years ago during a reengineering exercise of draw control practices done in El Salvador. Starting from this idea a computational system was developed and started its operational use in year 2000 in Salvador mine (Codelco Chile) and then was migrated and used in operations of Andina mine (Codelco Chile) in year This system generates a recommendation (during shift A) for the following 3 shifts (B,C &A), using all the information of the extracted tonnage until shift B (ie: tonnage extracted by draw point, sampled grades & dilution, hang-ups, secondary blasting, status of the drawpoints, status of the downgradient materials handling system, etc.). Due to this fact, the recommendation (draw order) is always achievable, because it considers the current production status of each draw point. The recommendation can be changes manually to introduce specific changes, and the system adjusts the call order to comply with its goal. The goals of the system are to achieve the tonnage, grade, dilution and uniformity index for the shift. The user can specify the priorities amongst these goals (considering that the tonnage target is always met). As it was mentioned above, the user has the option of edit interactively the draw call, and the system marks the

2 draw point/shift that user has modified, alerting him if the changes are in conflict with some constrains such as minimum and maximum production rate, number of active draw points in the same crosscut, ore-pass management, among others. The inclusion of the uniformity index (Susaeta, 2004) to calculate uniformity, and visualization of the tonnage that has been extracted with uniformity for any period, generated a very good tool to plan and control this variable. According to results of back analysis of uniformity versus dilution entry point (Susaeta, 2004), the recovery of the reserves are dependent on the uniformity of draw. The implementation of the system to obtain an extraction with uniformity in primary rock panels, such as Andina-LHD and Salvador-ICW sectors, requires having an efficient management for both secondary blasting of the oversize muck and hung up blasting. Planning of these activities are also included in the system. 2 Description of automatic draw order system (ADO) The draw control data base in El Salvador and Andina, includes for each shift the tonnage extracted from each active draw point, secondary blasting and hang up blasting, production situation of the draw point at the end of the shift (flowing, hanged up, with boulders to blast, etc.), operational situation (geotechnical damage, restrictions due to mud, restrictions due to geo metallurgical restrictions, etc.). The grade and dilution content of the draw points are also included in the data base, but are not generated every shift (every 500 tons approximately). All this information is used to generate a productive capacity per draw point every day. This productive capacity uses the historical production, the production and operational situation of the draw point and an improvement parameter (20%) to assess the potential production of every draw point, with its hang up and secondary blasting requirements. At the same time a grade and a dilution predictor is used to assess these variables for the next 3 shifts. Figure 1 ADO System Diagram. The operational restrictions for draw rates at different extraction percentages (minimum and maximum per shift and per month), restriction of maximum tonnage per production drift per shift, secondary blasting and hang up blasting are incorporated as general restrictions to the system. The shift tonnage aim is always searched with first priority by the ADO system (Susaeta 2000) function. The grade, dilution and uniformity of draw (Susaeta, 2004) aims, can be ordered with relative priorities between them, when the optimization function is operated to find the suggested draw order for the following 3 shifts. The optimization algorithm works with a finite elements incremental function. Figures 1 and 2 shows in a block diagram a general sequence of the ADO System. 258

3 Figure 2 ADO System Details. 2.1 The ADO Software Algorithm The ADO Software generates a suggestion for the draw for the next three shifts of the mine s life, using the historical data of the draw points performance and user specified restrictions and targets as the basis for its suggestion. The algorithm used by the system to generate the draw is described in the following paragraphs: For each active draw point in the sector of the mine being planned, the system extracts the historical operational data from the draw control database. This data delivers specific information regarding the draw point s current state (flowing, hung, etc.), the draw tonnages which have been achieved during the last few shifts, and the grades and dilution measured by the last few ore control samples. Using this historical information, the system calculates performance indices for each draw point for the following three shifts. The indices predict the probable dilution percentage, grades, and maximum draw tonnage using a simple capped linear extrapolation of the historical data. The system then requests that the user specifies the restrictions and targets for the following day s draw, which will control the automatic generation of the plan. First, the user must specify the hard restrictions on draw for the next day s production. These hard restrictions are specified in terms of maximum extraction velocity. The maximum velocity is keyed on current extraction percentage of each draw point (for example, the user may wish to specify a higher maximum extraction velocity for draw points with higher extraction percentages). The use specifies the production targets for the next three shifts. The specifiable targets include production tonnage, grades, percentage of dilution material, and the uniformity index permissible for the next day s production. The system will always attempt to achieve the tonnage target, given that this is typically fixed by the requirement of constant plant feed. However the other targets (grades, dilution, and uniformity index) are specified in order of preference; the system will give higher priority to the target which the user has specified with higher preference in the target hierarchy. It is often the case that not ALL the desired targets can be achieved based on the current performance of the available draw points. The hierarchical targets feature means that the system can respond to the realities of short term planning, where the specific planning requirements can change from day to day. The system then starts to construct a suggested plan. To do this it uses a finite element concept. The algorithm is the following. While the tonnage target has not been achieved 259

4 Find the target with highest preference which is not currently achieved in the plan. Look through the list of draw points to find the draw point (with tonnage available) which best improves this target. Draw a finite element from this draw point. The finite element is typically the tonnage of one LHD bucket Update the plan results with this draw. Repeat the process until the tonnage target has been reached, or, no more tonnage is available to be drawn The system presents the results of the suggested draw in both graphical and tabular form. The user may also manually modify the draw suggested by the ADO system, to reflect realities not reported in the draw control database. Figure N 3 shows part of the software s main screen to generate draw suggestions. Figure 3 ADO software interface The suggested draw order has the virtue that firstly is operationally achievable, and that it follows the medium term (monthly) program as per grade and dilution requirements, complying with a pre established uniformity of draw. Originally the system was designed to work with costs and income per draw point, thus a profit per draw point and per draw order could be calculated and aimed to. This tool that has not been implemented yet has great potential to optimize the economic control of a panel caving operation, closure of draw points, and economic optimization over longer periods of time. 3 Salvador Results The main objective in El Salvador (Codelco) was to obtain the required tonnage from the recently caved ICW Sector, and introduce uniform draw to minimize dilution entry. When the implementation started not only there was isolated draw, but the tonnage target was not met, because the call orders were used to force the mine personnel to draw the points that were hanged or with coarse rock. The system (ADO), that had a great more flexibility allowing production from many draw points per call, rapidly had the Sector flowing and with reasonable uniformity. Table 1, shows the improvement in uniformity after the introduction of the system (year 2000), comparing two areas, ICW ( ) and IN ( ).: 260

5 Table 1 Uniformity results for ICW and IN Sector Draw Uniform Semi Uniform Non Uniform Year Total Points Tonnage Tonnage Tonnage [#] [Mt] [Mt] [Mt] [Mt] % U+SU ICW % IN % As seen the improvement in tonnage extracted with uniformity and semi uniformity from a low primary/secondary ore sector (IN-Inca Norte) to a high primary ore column (ICW Inca Central Weste) is from 64% to 84% uniform and semi uniform draw. 4 Andina results Andina (Codelco) underground mine s, III Panel is composed by two sectors: LHD, with 570 LHD draw points in primary rock (mixed columns) and Parrillas, with 490 grizzly draw point in secondary rock. As part of draw control practice, two kinds of dilution are mapped by the geology department in the draw points: rhyolite rock and overburden (mix of original overburden, remnant ore and lateral dilution (rhyolite) from previous panels). The first one is a very good geologic marker of the material over the in-situ column of the III Panel because it can be easily detected, being historically measured since On the other hand, overburden is defined as all the broken material over the insitu column. Its grade has been estimated using mass balance calculations, from mining data of panels I and II. The ADO System started being used in the LHD sector in April 2003 and in Parrillas sector in January 2005, showing successful results, according to moisture, grade and dilution control (Valenzuela 2007). A schematic view of the underground mine is shown below. Figure 4 Andina Underground Mine. 261

6 4.1 Moisture Behavior Moisture is an important parameter for draw control. Andina internal reports have shown that the presence of over 3% moisture in the ore, specially when mixed with overburden (that has clay content), produces a phenomena call llampo, that generates packing of the fines in the whole of the materials handling system (LHD buckets, ore passes, trucks, hoppers, crushers, conveyor belts, etc.).. The graph below shows the mine to mill moisture and dilution (overburden) behavior for the underground mine. It can be seen that at the beginning of 2002 and 2003 the conditions are over the limit for a good operation (moisture over 3%). After the introduction of the system that improved the uniformity of draw, moisture has been controlled, even with a very high increase of the extracted overburden in critical periods of the year (snow melting season). Table 2 shows the average moisture obtained for the 3 periods defined by the use of the ADO System. Successive improvement is seen, even with a high increment in extraction of dilution (overburden). Table 2 Ore moisture averages in underground mine production Date Moisture Average (%) Jan2001-April April2003-Jan Jan2005-Oct Figure 5 Moisture and Dilution behavior, underground mine. 4.2 Grade behavior A grade model is used to predict the grade, for mine planning proposes in the underground mine. Figure 6 shows the correlation between the predicted (grade model) and real copper grade reported by the mill (values have been multiplied by a constant so they do not necessary reflect the real copper grade of the mine). There is obviously an under estimation of the grade in the prediction model, that is assumed in 0.13%. The graph shows that between January 2001 and April 2003, the model and the effective grade do not have a good correlation (0.074%). Between 2003 and 2005 there is an improvement (0.070%). After January 2005 when the Parrillas sector started to be planned with the ADO system, an important improvement between real and modeled grades can be seen (0.033%), so obviously the draw control to have good uniformity had an important effect in the dispersion between grade prediction model and reality 262

7 Figure 6 Modeled and Real grade sent to plant behavior, underground mine. 4.3 Sector A and G dilution entry results Sectors A and G are located in figure 1 within the LHD sector. They were extracted from and respectively. Sector A was extracted without using the ADO system, and sector G has used it in all periods. The uniformity index results of the tonnage drawn for periods mentioned above are listed below: Table 3 Uniformity results for sector G and A Sector # Draw Uniform Semi Uniform Non Uniform % Year Total Points Tonnage Tonnage Tonnage U+SU [#] [Mt] [Mt] [Mt] [%] [Mt] G A As seen in the table the improvement from 68.6% of the tonnage drawn with uniformity (U+SU) in sector A, to 94.5% in sector G is relevant. The production history of the sectors is presented as percentage of extraction (%E), where an extraction of 100% represents that of the tonnage of the in situ reserves. In both analyses, rhyolite was used as a dilution marker. Figure 7 Modeled and Real grade sent to plant behavior, underground mine. 263

8 Figure 7 and 8 shows the measured rhyolite dilution behavior for all draw points in Sector A and G respectively. It is important to note that the dilution value (% dilution) only represents part of the total dilution of the overburden (rhyolite as a marker implies overburden presence). The graph shows that in sector A, which was extracted without ADO system, the Pedza (Isolated draw dilution entry point -Susaeta, 2004) starts about at 20% extraction. In the other hand, sector G shows a Pedza at 50% extraction. This results show that the system can effectively improve uniformity of draw and that uniformity delays the dilution entry point, thus improves the total reserves recovery. Figure 8 Rhyolite behavior for Sector G, Andina LHD underground mine. 5 Conclusions The ADO (automatic draw order) System provides an efficient tool to program the short term extraction in a panel caving operation. The daily generation of a 3 shift draw order, using the draw control information to ensure an achievable production (tonnage) that complies with the aimed grade, maximum dilution and with a uniformity of draw target has been successfully done over 7 years in El Salvador and 4 years in Andina. The improvement in the uniformity of draw, with no sacrifice of production, has generated an increase in the % of extraction of dilution entry point, a more uniform behavior of the grade and a control over the moisture entry into the draw points. 6 Acknowledgements The authors of this paper would like to thank CODELCO for the permission to publish these results, and specially Mr. Fidel Baez, who as general manager of El Salvador supported the idea and made possible the change. References Susaeta, A. (2004) Theory of gravity flow (Part 2), MassMin Proceedings 2004, A.Karzulovic &M.Alfaro, Minería Chilena, Santiago, Susaeta, A. (2000) Informe Final Proyecto Minco 2001 Reingeniería del Tiraje IAL Ltda, División El Salvador- Codelco Chile, Internal report. Valenzuela, G. (2007) Draw control production data División Andina, Codelco Chile, Internal report. 264