CN103514324B - A kind of method utilizing Wavelet temporal sequence to determine delivery batch iron ore grade fluctuation - Google Patents
A kind of method utilizing Wavelet temporal sequence to determine delivery batch iron ore grade fluctuation Download PDFInfo
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Abstract
The invention discloses a kind of method utilizing Wavelet temporal sequence to determine delivery batch iron ore grade fluctuation, adopt the analysis foundation that during unloading, the granularity data of iron ore fluctuates as delivery batch iron ore grade, on-line grain size measurement result when utilizing time series that iron ore is unloaded is analyzed, recycling wavelet analysis method, application MATLAB software is set up the ARIMA model grade fluctuation situation to iron ore and is predicted.In delivery batch iron mine, the granularity Detection result of each part of sample is arranged as nonstationary time series in temporal sequence, it is formed through additivity superposition by trend term, periodic term and steady item, the inventive method utilizes wavelet transformation to be carried out decomposing and analyzing, extract the characterization factor relevant to iron mine grade, comparison the unknown delivery batch part sample time series granularity, reaches the purpose of grade prediction.
Description
Technical field
The present invention relates to a kind of method determining delivery batch iron ore grade fluctuation, be specifically related to a kind of method utilizing Wavelet temporal sequence to determine delivery batch iron ore grade fluctuation.
Background technology
The grade of iron ore refers to the iron-holder in iron ore.The kind of iron ore is a lot, and its grade is different.Produce the reason of iron ore grade difference specifically include that the iron ore just adopted is not carried out batch mixing processing, when the some place of production, the ore deposit iron ore of different grade loads in mixture, iron ore powder agglomates grade difference is big and produces broken design improper etc..
The iron ore just adopted is not carried out batch mixing processing.No matter it is open work or underground mining, even the Commercial Ore that directly exploitation just can use, except the iron ore just adopted contains rich ore of high grade except part, all the other are the lean ores containing a large amount of stone-like pulses, sometimes rich ore also can cause grade inconsistent because iron ore mineral composition is different, therefore the iron ore grade that same ore deposit point is mined also can be uneven to some extent, the iron ore just adopted generally is carried out muck by the mine of certain operations specification, ore grinding, sorting, enrichment, the processes such as mixing, but also there is the iron ore mine of some low developed areas, owing to lacking the capital fund of mineral products early stage processing, adopt stripping level error, simply the first mining not carrying out any process is used directly to sale, its iron ore product is many, and to produce grade irregular.
The point place of production, the ore deposit iron ore of different grades loads in mixture.Some iron ore place of production is smaller because of mine capacity, and ore deposit company is many, iron ore quantity owing to once concluding the business is big, cause a large-tonnage contained iron ore of iron mine ship respectively from the mine of multiple different grades or company, and do not carried out mixing etc. when shipment and process, how the iron ore of even same cargo hold from, it is easy to causes whole ship iron ore grade irregular.In this case, the color of the levels iron ore of same cabin sometimes be can be observed also different, it is simply that because levels iron ore originates from different mines.
Iron ore powder agglomates grade difference is big.Transport at iron ore, load and unload, store up etc. in process, owing to jolting, wind-force, rolling, motion etc. can cause the separation of iron ore size particles.Mineralogical character according to different iron ores, the oarse-grained grade of some iron ores is higher than little granule, and the short grained grade having is more than bulky grain.Therefore the iron ore grade sometimes that powder agglomates difference is big also can be extremely uneven.The iron ore grade that generally granule is big is of a relatively high, this is because the mineral purity of bulky grain iron ore is higher, hardness is also big, especially magnetic iron ore.
During production, broken design is improper.Iron ore coarse crushing, in broken and fine crushing operation, ore grain size (coarse fraction), hardness (hard-type Ore) fluctuation have a certain impact to broken.To gyratory crusher, when feed preparation unit size diminishes, Ore is not only broken by disintegrating machine, and when feed preparation unit size is crossed thick, disintegrating machine treating capacity reduces；To standard or short head cone crusher, when feed preparation unit size diminishes, make the pack completeness in broken segment and broken space reduce, and cause the increase of breaking ores granularity.Make metalliferous mineral degree of dissociation not enough, all can cause that iron ore grade is uneven.It addition, in dry type and wet magnetic separation operation, ore grain size, iron-holder (ferrum in magnetic iron ore) fluctuation cause concentrate quality to reduce and in mine tailing, flowing molten iron loses and increases.In flotation operation, the change of granularity can cause that in concentrate quality and mine tailing, ferrous components runs off.
The method of traditional determination iron ore grade fluctuation adopts ISO3084 " Ironores-Experimentalmethodsforevaluationofqualityvariat ion " (namely, ISO3084 " test method(s) of iron ore evaluation quality fluctuation "), but utilize the ISO3084 workload determining that grade fluctuates quite big.According to ISO3084, there is traditional method two kinds different that iron ore can carry out grade fluctuation evaluation, it may be assumed that part sample replaces legal and quantity method.The quality fluctuation of iron ore represents with standard deviation.By the standard deviation of quality characteristic between each part sample taked in layerwRepresenting, it is to estimate to replace the deviation between duplicate sample, or to measure each part of sample and to determine by Linear intercept and the slope of quantity method deduction sample preparation and measurement deviation.Both of these case will do sample preparation and measurement deviation correction, and sample preparation and measurement deviation must be determined when quality fluctuation is tested determining simultaneously.Determine that the quality characteristic selected by grade fluctuation can be component content and moisture, particle size distribution and other physical qualities characteristics such as all iron content, dioxide-containing silica, aluminium sesquioxide content.Determine when the sample of quality characteristic is individually taked, then should correspond respectively to the quality fluctuation of each characteristic.If taken sample is for determining more than one quality characteristic, then should select the classification that in these characteristics, quality fluctuation is big.The sampling of evaluation quality fluctuation can combine with the daily sampling measuring delivery batch quality.
Summary of the invention
The technical problem to be solved is to provide a kind of simple to operate method utilizing Wavelet temporal sequence to determine delivery batch iron ore grade fluctuation, adopt the inventive method can save substantial amounts of manpower financial capacity, substitute the traditional method needing heavy manual physical labor, it is achieved fluctuate the manual intelligent determined to delivery batch iron ore grade.
The present invention solves that the technical scheme that above-mentioned technical problem adopts is: a kind of method utilizing Wavelet temporal sequence to determine delivery batch iron ore grade fluctuation, utilize time series, on-line grain size measurement result when iron ore is unloaded is analyzed, recycling wavelet analysis method, application MATLAB software is set up ARIMA model and time series is analyzed and the grade fluctuation situation of iron ore is predicted, and concretely comprises the following steps:
1) online granularity when iron ore being unloaded is measured, and forms the time series starting the granularity terminated to unloading from unloading；
2) ARIMA model set up by application MATLAB software, the trend term of time series superposition, periodic term and steady item can be easily separated by this model, and the random factor of analytical separation, in conjunction with trend and cycle analysis, respectively trend term, periodic term and steady item are predicted；
3) above-mentioned MATLAB software is installed on computers, the wavelet toolbox of MATLAB software selects one-dimensional discrete wavelet analysis, import on-line grain size measurement result during iron ore unloading, select db7 small echo to carry out 4 layers of decomposition, obtain random entry d1, periodic term d2-d3With steady item d4；
4) on the basis of above-mentioned db wavelet decomposition, time series is carried out denoising, to the high-frequency noise curve setting threshold value separated, obtain the threshold curve after denoising, according to random entry d1In the amplitude size that steady item is upper and lower, i.e. threshold amplitude, the grade fluctuation situation of iron ore being predicted and determined, the grade size for judging iron ore provides reference frame.
According to ISO3084, it is determined that the quality characteristic selected by grade fluctuation can be component content and moisture, particle size distribution and other physical qualities characteristics such as all iron content, dioxide-containing silica, aluminium sesquioxide content.The grade of iron ore is fluctuated and is determined by online particle size distribution when the inventive method utilizes iron ore to unload just.
The inventive method has been used time series.Time series analysis is an important application branch of probability statistics, is widely used in fields such as finance economy, meteorological model, signal processing, mechanical vibration.Time series is the one group of data sorted in chronological order, and it can reflect the time dependent rule of things.The sampling standard of iron ore needs to quote corresponding grade fluctuation result and determines taked sample part sample number, representation " greatly " that grade selects, " in " or " little ", it is big, medium or little for representing grade of ore fluctuation respectively, part sample number that different grades is calculated is different, but utilizes the ISO3084 grade workload determined quite big.Full-automatic mechanical sampling iron ore equipment is a kind of facility of energy on-line checking granularity in iron ore uninstall process, grade fluctuation situation according to delivery batch iron mine, in conjunction with delivery batch quantity set part sample number, can by machine automatically by wait the time or etc. weight interval take part sample and record the granulometery of each part of sample, finally calculate delivery batch overall grain size result.The granularity Detection result of each part of sample of iron mine is criticized in each delivery temporally process can form one group of time-sequencing data, and this creates condition for time series analysis iron mine grade volatility forecast.Wavelet transformation is one of maximally effective Data Mining Tools of time series analysis, and it can reduce the dimension of original complex time series signal, extracts effective information and carries out pattern recognition, is finally reached the purpose of the grade of prediction the unknown delivery batch iron ore.
The inventive method is applied MATLAB software and establishes ARIMA model.
Matlab language is different from other high-level language, and it is referred to as fourth-generation computer language, and Matlab language makes people free from red tape code.Its abundant function is without developer's overprogram, and Matlab allows to use mathematical form coding, is therefore more nearly us writes the mode of thinking of computing formula than FORTRAN, C language.Utilizing Matlab wavelet toolbox to carry out wavelet analysis and can utilize order line or the GUI two ways of Matlab, the expansion of GUI some function of mode limits to some extent, and order line needs to remember substantial amounts of order and function code, but complete function.In time series iron mine grade volatility forecast of the present invention is applied, main employing one-dimensional discrete Wavelet Denoising Method, one-dimensional discrete small echo also can be divided into one-dimensional wavelet transform and one-dimensional wavelet package transforms, and both are distinctive in that HFS can also be finely divided by wavelet packet.Therefore, to small echo iron ore grade volatility forecast, Matlab is fabulous analytical tool.
ARIMA model full name is difference ARMA model (AutoregressiveIntegratedMovingAverageModel), is the famous Time Series Forecasting Methods proposed the beginning of the seventies by Box and Jenkins.Refer to nonstationary time series is converted into stationary time series, then its lagged value and the present worth of stochastic error and lagged value are only returned the model set up by dependent variable.In the time series of real work, its trend term comprised, periodic term and steady item imply certain rule, when adopting, wavelet transformation is layers-separated by sophisticated signal, trend term after separation is large scale composition, and it is the smoothingtime sequence after the abnormity point in signal, random factor being eliminated；Random entry is little dimensional components, is generally the interference factor needing to filter；Periodic term is mesoscale composition.General trend term and periodic term just can meet the prediction needs of grade fluctuation, adopt this model prediction iron ore grade to fluctuate, mainly utilize each part of sample granularity seasonal effect in time series trend term of delivery batch, periodic term and the trend term of delivery batch of unknown grade fluctuation, the similarity of periodic term, relatedness comparison principle that known grade fluctuates.
Compared with prior art, advantages of the present invention is as follows: the inventive method adopts the analysis foundation that when unloading, the granularity data of iron ore fluctuates as delivery batch iron ore grade, on-line grain size measurement result when utilizing time series that iron ore is unloaded is analyzed, recycling wavelet analysis method, application MATLAB software is set up the ARIMA model grade fluctuation situation to iron ore and is predicted.In delivery batch iron mine, the granularity Detection result of each part of sample is arranged as nonstationary time series in temporal sequence, it is formed through additivity superposition by trend term, periodic term and steady item, the inventive method utilizes wavelet transformation to be carried out decomposing and analyzing, extract the characterization factor relevant to iron mine grade, comparison the unknown delivery batch part sample time series granularity, reaches the purpose of grade prediction.
Accompanying drawing explanation
Fig. 1 is the time series during part sample granularity change taking system in embodiment；
Fig. 2 is the wavelet decomposition curve of the granularity Detection result of 9-16mm in embodiment；
Fig. 3 is at d to the low-and high-frequency noise curve separated1To d4Set the threshold curve after threshold value denoising；
Fig. 4 is the steady item of time series and grade fluctuation situation resultant curve.
Detailed description of the invention
Below in conjunction with accompanying drawing embodiment, the present invention is described in further detail.
Criticizing pellet for the delivery of a collection of 42200t, it is 800t that sampling interval is criticized in this delivery, and the online granulometry of Mechanical Sampling and Preparation system selects, and once sieve is 9mm, and secondary sieve is 5mm, and three sieves are 16mm.Final result is expressed as-5mm, 5-9mm, 9-16mm ,+16mm four file data, the four file data corresponding sample times of each part of sample, can form a granularity time sequence starting to terminate from unloading to unloading.Owing to different small echo performance differences are very big, it is respectively arranged with its feature, it is necessary to choose according to practical situation.See Fig. 1.
ARIMA model set up by application MATLAB software, selects one-dimensional discrete wavelet analysis in the wavelet toolbox of MATLAB software, imports on-line grain size measurement result during iron ore unloading, selects db7 small echo to carry out 4 layers of decomposition, must such as Fig. 2 result.
In this example, the low-and high-frequency part (d when small echo db7 decomposes the 4th layer, in original signal (s)1-d4) separated, now low frequency part (a4) development trend that changes in chronological order for this granularity section, high frequency d1For random entry, can be used as grade volatility forecast, d2-d3For periodic term, d4For steady item.Gained threshold value is made no modifications, and time series is carried out denoising, obtain the threshold curve after denoising, obtain as shown in Figure 3.Then d1Threshold amplitude is grade coefficient, it is known that the grade that this delivery is criticized is " greatly ", then this place of production pellet other measurable with threshold amplitude be " greatly ".See Fig. 4, with reference to and according to noise in the upper and lower amplitude size of steady item, i.e. threshold amplitude, can determine whether the grade size of iron ore.In existing analysis method, it is generally the case that ARIMA model is to adopt difference to eliminate the trend term in sequence, periodic term, after its dimensionality reduction, then with the steady item of ARMA (Auto-RegressiveandMovingAverageModel) model analysis.But the using method of ARIMA model has been improved by the inventive method, the random entry of ARIMA model is mainly utilized to be analyzed.
In addition to the implementation, respectively the powder iron mine from Australian multiple places of production being tested, data source is: Australia powder iron mine, and 17 groups of grades are+8mm data, 8 groups of grades is the data of+6.3mm；Making every effort to open up powder iron mine, 13 groups of grades are the data of+6.3mm, 5 groups of grades is+9.5mm data；Hammersley powder iron mine, 14 groups of grades are+8mm data, 5 groups of grades be+9.5mm data, 2 groups of grades is the data of+6.3mm.The time series constituted by the Data-Link of these arrays carries out db7 small echo 4 layers decomposition as stated above, and the soft-threshold of each level after wavelet decomposition denoising does not do how manual intervention, obtains high pass soft-threshold as shown in table 1.D1 in table 1 is so-called noise, it is actually grade fluctuation, the d1 soft-threshold that the identical grade detected value time series fluctuated by known grade obtains knows, with the unknown, the d1 soft-threshold comparison that the identical grade detected value time series that grade fluctuates obtains, and draws the grade of unknown iron mine.As seen from Table 1, d1 soft-threshold 0～1 is little grade, and 1～2 is middle grade, and more than 2 is big grade.
Table 1
Claims (1)
1. one kind utilizes the method that Wavelet temporal sequence determines delivery batch iron ore grade fluctuation, it is characterized in that utilizing time series, on-line grain size measurement result when iron ore is unloaded is analyzed, recycling wavelet analysis method, application MATLAB software is set up ARIMA model and time series is analyzed and the grade fluctuation situation of iron ore is predicted, and concretely comprises the following steps:
1) online granularity when iron ore being unloaded is measured, and forms the time series starting the granularity terminated to unloading from unloading；
2) ARIMA model set up by application MATLAB software, the trend term of time series superposition, periodic term and steady item can be easily separated by this model, and the random factor of analytical separation, in conjunction with trend and cycle analysis, respectively trend term, periodic term and steady item are predicted；
3) above-mentioned MATLAB software is installed on computers, the wavelet toolbox of MATLAB software selects one-dimensional discrete wavelet analysis, import on-line grain size measurement result during iron ore unloading, select db7 small echo to carry out 4 layers of decomposition, obtain random entry d1, periodic term d2-d3With steady item d4；
4) on the basis of above-mentioned db wavelet decomposition, time series is carried out denoising, to the high-frequency noise curve setting threshold value separated, obtain the threshold curve after denoising, according to random entry d1In the amplitude size that steady item is upper and lower, i.e. threshold amplitude, the grade fluctuation situation of iron ore being predicted and determined, the grade size for judging iron ore provides reference frame.
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