There may be practical problems generating the required information. Computer software can produce most of the mining data required for open pits. Depending on the level of study, it may be necessary to do some designs with human engineering input in order to calibrate data generated automatically by software for a large number of cases. Transforming data so that it’s suitable to input into optimisation software may present challenges.
Automated software processes are nowhere near as advanced for underground operations as they are for open pits. At the time of writing, stope optimisation software that is capable of generating nested stopes is commercially available, but is not necessarily applicable to all styles of orebodies and mining methods, though developments continue. General purpose software for automatic generation of stope and stope access development is limited to in-house software owned by some companies and applicable to specific methods and styles of mineralisation. Commercially available applications are, however, being developed. Software to optimise the placement of declines7 has been
developed but, to the best of the author’s knowledge, is not commercially available. Generating adequate data for an underground strategy therefore requires an amount of human-generated mine design, and availability of technical staff to do this work may be a practical constraint on the overall strategy optimisation process.
Technical staff often produce rigorously engineered designs for every case to be evaluated, yet this might not be necessary. If the optimisation software requires graphical objects created by a mine planning software application as its inputs, each case may need to be designed, and this may not be a trivial task within the constraints of both the design and the optimisation software. If, however, the optimisation software requires only tables of data to define the mining requirements, it may well
7. For a simple single orebody, an experienced design engineer will be able to produce a design as good as any computer algorithm, though if multiple designs are needed (for example, to service the different orebodies defined by different cut-offs), an automated process could be useful. For many optimisation studies, knowing the depth of the mine and the gradient of the decline will be sufficient to derive development quantities at an acceptable level of accuracy. For multiple orebodies that could potentially require a complex network of access declines to link them, an access layout optimisation tool may be essential.
be that a small number of cases require engineering input, while data for other cases can be interpolated. The guiding principle for input data generation is: maximum information from minimum work. Exactly how this balance is achieved in each case depends on the complexities of the mineralisation and mining methods and on the engineering judgement of the technical staff. If the optimum solution is one for which engineered designs have not been done, there should be sufficient engineering input into other cases from which the data for the optimum case has been interpolated so that the results can be accepted with confidence within the study’s level of accuracy.
There is no general rule as to what constitutes sufficient rigour in the data – what is judged to be adequate by one team, or within a particular corporate culture, may be perceived to be inadequate by another. Despite this, if any input items are deemed to not be sufficiently accurate – perhaps because of time or availability of engineering resources constraints – those parameters may be treated as scenario parameters to ascertain whether changes have an impact on the decision to be made. If not, the additional accuracy might be nice to have, but it is not needed to make a sound decision.
METALLurGicAL inPuTS
Physical metallurgical inputs to a study will generally be related to both metallurgical performance (typically recovery and product quality) and processing constraints.
It is essential to specify recovery relationships that are reliable for the head grades resulting from the range of cut-offs to be evaluated. Other parameters such as blends of different rock types may also need to be accounted for. Other relationships, such as the mathematically rigorous four-way relationship between recovery, head grade, concentrate grade and tailings grade,8 or empirical relationships between recovery and
feed rate, may be relevant. Care must be taken to ensure that relationships are valid for the range of head grades encountered. Often, recovery relationships have been derived by regression of results in the current operating range, assuming a polynomial or some form of exponential function of head grade. Plotting recoveries over the wider range of head grades anticipated for the study may reveal unrealistic behaviours. For example, a cubic function may produce recoveries that rise, fall and rise again with increasing head grades, or that fall to zero or negative values with lower head grades that are actually economic to treat.9 Some metallurgical investigations may be needed to identify
better relationships to use for the study, or scenario analysis may be necessary over any uncertainty range in the recovery relationships to identify whether strategic decisions are likely to change if the recovery relationships vary.
8. recovery = [c.(h – t)] / [h.(c – t)], where h = head grade, c = concentrate grade and t = tailings grade.
this formula is particularly useful for base metals operations, where the tonnage of concentrate is a significant proportion of the ore feed tonnage. For free-milling precious metals, where head grades are low (a few parts per million), the tailings tonnage is the same as the ore tonnage, and the concentrate grade is very high relative to the tailings grade. then, as an approximation, c = (c – t) and the formula reduces to recovery = (h – t) / h. 9. the author has also encountered relationships that, with high head grades, generated concentrates with
contained product grades in excess of 100 per cent. the point of the discussion is that quoted recovery relationships (or any relationships) should not be blindly accepted simply because they are provided by technical experts. the optimisation team must ensure that any relationships used will be valid across the full range of the values that may be encountered, and the relevant technical experts should be intimately involved in this to ensure that relationships are accurate across the range of interest.
CHAPTER 8 | The Mine Strategy Optimisation Process Treatment plant production constraints must be identified. As a minimum, these should be for ore feed and each final product. Depending on the complexity of the treatment circuits, it might be necessary to specify constraints for some of the intermediate process streams. Any points in the circuit where it may be feasible to build stockpiles of in-process materials may potentially act as logical breaks into separate processes, each of which might have feed and output constraints, as well as metallurgical performance characteristics.
Ore and product streams must be considered separately, taking account of the real constraints in each independently. The ore constraint is often expressed as a maximum tonnage rate that reduces when the feed grade increases above a specified grade. This typically means that, at the specified grade, the ore and product streams are at full capacity, and any increase in head grade must result in reducing the ore feed rate to avoid overloading the product circuits. For strategy optimisation, this combined relationship should ideally be split out and ore and product stream constraints specified separately. The combined effects of both constraints will be taken into account in the optimisation process, but by specifying them separately, the effects of changes in each can be more easily handled.
Constraints may be more complex than simple ore and product tonnages. It may be useful to consider the constraint on throughput to be the number of operating hours available. Since different rock types in the ore feed may have different milling rates, the ore tonnage capacity of the plant will be dependent on the ore blend. In such circumstances, it may be preferable to apply a tonnes-per-mill-hour factor to the various components of the ore feed, change the primary quantity measure from tonnes to required mill hours and apply the annual operating hours as the constraint. If appropriate, this process could be extended to account for power draw required, with the constraint becoming the available milling power.
As for mining, options for removing constraints in the treatment plant will usually need to be specified, unless the scope of the project is clearly defined to be limited to optimising the operation’s value subject to existing constraints. Since a strategy optimisation study will be evaluating a range of cut-offs and hence head grades, some strategies investigated may be high production rate / low-grade cases, which may only encounter ore stream constraints, while others may be low production rate / high-grade cases, encountering only product stream constraints. Metallurgical staff will frequently have a list of sequential upgrades for the plant. Typically these will be a mix of ore stream and product stream upgrades, based on an assumption of increasing ore feed tonnages at the same head grade. To properly account for the distinction between ore and product in the analysis, it’s necessary to separate the list of plant upgrades into separate sequential lists for each process stream.
As well as identifying upgrades to be implemented, it will be necessary to specify any changes in metallurgical performance that result from capacity changes, such as recovery and product quality. Operating and sustaining capital cost changes may also need to be accounted for.
The increasing focus on geometallurgical properties of mineral resources is leading to an increase in the amount of information that may be taken into account to optimise operating strategies. The example of different milling rates for different rock types has been available to evaluation teams for many years, but more sophisticated relationships relating various metallurgical performance parameters to mineralogy and other rock
properties are being developed. The availability of extra information, however, while enabling extra opportunities to add value, potentially increases the complexity of the analysis that must be undertaken to do so.