Bottleneck Analysis for Single Source Mining Model Using Grids

"Classic engineering economics provides the mining engineer with a fundamental approach to choosing between capital spending alternatives. This approach has been heavily used over the years and is an accepted technique for most investment decisions. There is a shortcoming that I have run into over the 15 year period when I worked in business planning roles. The classic time value of money approaches such as Net Present Value, Return on Investment, and Internal Rate of Return provide good estimates, but the input assumptions may fall short of describing the real material differences required by these analysis techniques. If the cost benefit analysis is defined solely on the improvement of the item being replaced or improved, the true profitability of the decision may be overestimated. Productivity improvement based capital projects can yield more accurate forecasts when conducted in concert with a “bottleneck analysis”. INTRODUCTION A review of the Theory of Constraints, (Goldratt, Cox, 1984) Lean, (Womack, 2003) (Rattner, 2006) and Six Sigma (Cryger, DeFeo 2015) lead us to recognize that these process improvement methodologies are oriented to the manufacturing process. These systems generally involve a factory that the material moves through on the way to becoming a final product. The mining challenge has many similarities to manufacturing once the ore has been won and made ready for processing. Before that point, however, the process is somewhat inverted while the ore is being won. Instead of the material moving through the factory, the factory is moving through the material. The Theory of Constraints (TOC) was used in an underground context by Baafi, Cai and Porter at 2015 APCOM, but even this groups’ analysis did not carry the concept from mining to shipping of the final product. There can be spatially distributed data that can affect the mining throughput constraints, some of which can be determined from exploration results. Identifying constraints can lead us to mapping productivity bottlenecks on future mining. Knowing this may improve decision making by eliminating one of Jim Fishbein’s three deadly sins of strategic planning: incomplete planning. When evaluating capital investment alternatives you should evaluate the impact of any proposed improvements on the entire operation. At the core of any mining system is the materials handling system. This can be typically expressed as the system that handles the raw material from the “face” through processing and then handling the finished product as well as the waste material. A “Bottleneck Analysis” is an investigation into the factors limiting a material handling system’s productivity (Goldratt, Cox, 1984). Mining material handling systems are usually composed of several linked components such as draglines, shovels, loaders, hydraulic excavators, continuous miners, longwalls, highwall miners, bucket wheel excavators, LHDs, feeders, conveyor belts, stockpiles, crushers, processing plants with various circuits, and waste handling systems. Each of the aforementioned components has a slightly different throughput capacity. When these are linked in series, the one with the lowest throughput capacity limits all of the others, creating the bottleneck for the system. Every material handling system has a bottleneck. In the mining environment, a subtle, but very significant phenomenon may also occur. The bottleneck may change as a result of a change in the raw feed qualities, face recession, overburden depth, digability, learning curve, and many others factors which may be derived from the drillhole data. These parameters often change as a function of the location in the reserve base, and may also vary with time."