Process for the physical segregation of minerals

Data processing: generic control systems or specific application – Specific application – apparatus or process – Article handling

Reexamination Certificate

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C700S030000, C700S031000, C700S036000, C700S051000, C700S214000, C700S037000, C703S004000, C703S014000, C711S147000

Reexamination Certificate

active

06675064

ABSTRACT:

TECHNICAL FIELD
The present invention relates generally to the segregation of minerals into fractions depending on a certain characteristic and, more particularly, to a plurality of methods for improving the yield of a particular segregated fraction of a mineral stream.
BACKGROUND OF THE INVENTION
Upon extracting or recovering minerals from a source, further processing is often required prior to shipping for later use. For example, coal emanating from a mine, known as “run-of-mine” or “r.o.m.” coal, is usually washed to reduce the content of ash such that it meets the specifications of a particular customer. The cost of washing the coal runs anywhere from $3.00 to $5.00 per ton. Thus, it is a considerable expense associated with the coal mining process.
To reduce this expense, mine operators may physically segregate coal into wash and no wash “fractions” or piles. As should be appreciated, the coal segregated into the no wash pile must at a minimum meet the customer specification to be ready for shipment without washing. In contrast, coal sent to the wash pile is either washed to meet customer specifications prior to shipment or, in the case of extremely poor quality coal, completely rejected.
Central to the segregation strategy is an online analyzer for detecting a particular parameter of the coal stream at a given instant. Typically, the online analyzer is mounted on or above the main conveyor belt exiting the mine and detects a parameter that correlates to the presence of a particular component, such as ash, sulfur, BTU, or the like. Coal deemed “good quality” (i.e., at least meeting the customer specification for the selected parameter) is sent to the no wash pile, while that deemed “bad quality” is sent to the wash pile. Usually, the physical segregation of the coal is accomplished using a device such as a “flop” gate, which as its name connotes is a gate that “flops” to and fro over a portion of a divided chute positioned under the conveyor belt to direct the coal to the desired pile.
While the online analyzer recognizes the quality based on the detected parameter, the decision to send a segment of coal to the wash or no wash pile has in the past been made by a segregation control procedure that works in conjunction with the analyzer. Since the quantity and quality of the no wash pile affects processing economics significantly, it is imperative that the segregation algorithm is efficient. Of course, segregating r.o.m. coal in real-time into wash and no wash fractions is a simple matter if maximizing yield is not taken into account. For example, the algorithm could simply make the decision that only r.o.m. coal that at least meets the particular customer specification is accepted, i.e., the cutoff level of the detected parameter is set at the customer target, where cutoff level is defined as the lowest acceptable quality for a particular block of coal to be sent to the no wash pile. This strategy yields a no wash pile with average quality that is much better than the target quality because only coal that meets and exceeds target quality is placed in the no wash pile. However, since in reality the target needs only to be met on average, and not for every unit of coal in the shipment, this strategy will have poor yield. In other words, the coal sent to the no wash pile will have a much better quality than required, while the coal sent to the wash pile will increase as a result. This reduces efficiency and increases costs.
Present day industrial segregation algorithms make cutoff adjustments to improve yield. These algorithms are loosely based on conventional feedback control schemes that examine the error between the ash level of the no-wash pile and the quality target value. Based on the detected error, adjustments to the cutoff value are made. These adjustments involve the use of arbitrary numerical gains that are set exogenously by trial and error and are not linked to the monitored process. Moreover, no attempts are made to account for and characterize the stochastic, or random, nature of the process (which is an issue that, as will be understood from reviewing the description that follows, is central to segregation control). As a result, the current industrial algorithms leave much to be desired in terms of both accuracy and efficiency. This is especially true when the coal comes from multiple seams, or “sections” of the mine, having different values of the particular parameter under consideration (i.e., different ash levels).
The decision to send any block of coal to the wash or no wash pile should depend on two factors: (1) the average quality level of the no wash pile at the present time; and (2) the distribution of the quality of coal expected in the future. Using these criteria ensures maximization of the yield, while at the same time the average quality of the shipment meets the target value. The determination of the average composition of the no wash pile at a given instant is straightforward, as it is only a matter of recording the values corresponding to the quality of the coal or other mineral previously to the no wash pile and averaging those values.
The future quality, however, is not simple to predict. Frequent changes in the nature of the mining process or the quality of coal render making any such prediction difficult. Field observations demonstrate that the distribution of coal quality changes substantially and unpredictably over time. Accordingly, a practical coal segregation system needs to view the observations as a realization of a non-stationary stochastic process. Instead of predicting the future, segregation decisions could be based on the present stochastic nature of the process. This stochastic nature could be defined in terms of a statistical description, such as a distribution form for the desired or acceptable quality levels. If the segregation decision were consistently the best for the present nature of the process, then in the long run, high yields should be realized. Of course, yields with such a strategy will be lower than what might have been obtained could the long run distribution of coal quality somehow be forecast a priori. However, in the absence of stationarity, such forecasting is simply not possible. Moreover, if the process were, in fact, stationary, this strategy would still optimize yields because the present and long term distributions would be identical. Thus, for successful application, the segregation strategy must accurately estimate the current statistical nature of the process.
SUMMARY OF THE INVENTION
To fulfill the needs identified above, and to overcome the shortcomings of prior art methods of mineral segregation, the present invention comprises a plurality of methods of segregating a mineral, such as coal, based on the level of a particular component, such as ash, sulfur, or the like. Specifically, the method employs mathematical and statistical modeling techniques to segregate a flowing stream of minerals into at least two fractions: one of that may undergo further processing prior to shipment (or in some cases, may simply be discarded), and one that does not require further processing (that is, the level of the component substantially meets a customer specification as to the content of that component). By maximizing the amount of the mineral sent to the fraction that does not require further processing, while still meeting the customer target, the overall processing time and the concomitant processing expense are both advantageously reduced.
In accordance with a first aspect of the invention, a method of segregating a mineral stream into a first fraction substantially meeting a particular customer specification and a second fraction requiring further processing such that the proportion of the mineral stream in the first fraction is maximized is disclosed. The method comprises: (a) observing a value of a selected parameter for a plurality of segments of the mineral stream to establish an original minimum history of data values; (b) creating an existing model to fit the minimum history; (c) obtaining a new value

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