CN1603986A - Grinding system intelligent optimization initialization method based on case ratiocination - Google Patents

Abstract

It is an intelligent optimization method based on case ration of mine grinding system and belongs to automatic control technique field, which comprises the following steps: overfall particle flexible measurement, aided variable selection, main variable selection, boundary condition identification, case expression, initial case acquiring, case ration, case storage and maintenance. This invention gives the optimization set values of control circuit of new mine given amount, overfall concentration, entrance water add amount according to the index of the mine particle concentration and makes mine grating system at optimized working state to get the qualified mine product.

Description

Grinding system intelligent optimizing set method based on reasoning by cases

Technical field

The invention belongs to the automatic control technology field, particularly the basic control loop of the wet grinding system that is made up of bowl mill and spiral classifier that is used for ground ore in the ore dressing plant ore grinding workshop section is carried out the method for intelligent optimizing set.

Background technology

With regard to ore dressing field, the basic task of grinding operation is exactly to make raw mineral materials be crushed to suitable granularity, makes valuable mineral composition monomer dissociation from gangue, or different valuable mineral composition is separated from each other, for follow-up ore dressing process is supplied raw materials.The wet grinding loop of being made up of bowl mill and spiral classifier is widely used in ore grinding to the particle size range of technological requirement, and granularity is excessive or too smallly all the follow-up process of sorting is had a negative impact.Because the beneficiating efficiency the during material of each beneficiation method and mineral processing circuit sorting different grain size composition is different, and different material sorting granularity lower limits is arranged all, therefore, for given ore and beneficiation flowsheet, it is very important to produce optimum size composition according to ore grinding.At present, conventional ore grinding control method only can realize the basic circuit controls of new mine-supplying quantity, effluent concentration, inlet amount of water, and can't provide the optimization setting value of basic control loop according to the target of grinding particle size index.

Summary of the invention

Deficiency for the control method that solves above grinding system, the invention provides a kind of intelligent optimizing set method based on reasoning by cases, target according to the grinding particle size index, provide the optimization setting value of current grinding system basis control loop, make grinding classification system be in the optimal working state, with the ore grinding product that obtains to form by release mesh.

Intelligent optimizing set method of the present invention depends on the grinding system hardware platform, is realized by intelligent optimization software.

Its hardware platform core is made up of bowl mill, spiral classifier and relevant device, has been equipped with measurement instrument simultaneously, topworks and the computer system of carrying out computed in software.The connection of its hardware is that input end and belt feeder, bowl mill inlet amount of water pipeline and the grader sand return mouth of bowl mill links, bowl mill output terminal and outlet are added water inlet and are joined with the spiral classifier input port simultaneously, spiral classifier sand return end and bowl mill inlet join detailed structure following (as shown in Figure 1):

With bowl mill and spiral classifier is the grinding system of core, also comprises following measurement instrument:

Be used for the new mine-supplying quantity Q of on-line measurement bowl mill _GKLWeighing instrument (as uclear scale or belt conveyer scale), be installed on the bowl mill feeding belt;

Be used for on-line measurement bowl mill inlet amount of water W _F1Flowmeter, be installed on the bowl mill inlet filler pipe;

Be used for on-line measurement overflow concentration in classifier D _YLNDDensitometer, be installed on the classifier overflow pipeline;

Two power meters or two galvanometer are used for on-line measurement ball mill power P _QM, the spiral classifier power P _FJJOr bowl mill electric current and spiral classifier electric current, the drive motor with bowl mill and grader joins respectively, because power signal and current signal equivalence, so P in this instructions _QMAnd P _FJJAlso can be used for representing bowl mill and spiral classifier current signal.

Its topworks comprises:

Two motor regulated valves are respectively applied for and regulate that the bowl mill inlet is added water and water is added in the bowl mill outlet, are installed on that the bowl mill inlet is added water pipe and the bowl mill outlet is added on the water pipe;

A frequency converter is used to regulate the batcher vibration frequency, joins with oscillating feeder;

This grinding system has disposed distributed computer control system (DCS) or programmable logic controller (PLC) (PLC) or industrial control computer (IPC) simultaneously, or the discrete industrial regulator, and forms the basic controlling loop according to following corresponding relation:

The new mine-supplying quantity Q of electric vibrating feeder frequency control _GKL

The bowl mill inlet is added the moving variable valve control of water power bowl mill inlet and is added discharge W _F1

The moving variable valve control of water power spiral classifier effluent concentration D is added in the bowl mill outlet _YLND

Intelligent optimizing set software of the present invention promptly can operate on the supervisory control comuter of computer control system, also can run on independently on the optimization setting computing machine, this software is by carrying out communication with control computer (distributed computer control system (DCS) or programmable logic controller (PLC) (PLC) or industrial control computer (IPC)), obtain real-time process data, and provide the optimization setting value in basic controlling loop.

Implementation method of the present invention comprises, the obtaining of the determining of the selection of the soft measurement of (1) overflow granularity, (2) auxiliary variable, the selection of (3) leading variable, (4) boundary condition, (5) case representation, the initial case of (6) case library, (7) reasoning by cases, the storage of (8) case and safeguard.Intelligent optimizing set method model structural drawing based on reasoning by cases of the present invention as shown in Figure 2.

(1) the soft measurement of overflow granularity.

Adopt " bowl mill grinding system overflow granularity index flexible measurement method ", this method patent applied for, its number of patent application are 03133951.4.This method comprises: the study of the obtaining of the selection of auxiliary variable, training data, neural network soft sensor model and use step estimates the spiral classifier overflow granularity of stable state according to the stable state real time data of process.

(2) selection of auxiliary variable.

Select ore grindability M _KMXAn auxiliary variable as model.But ore grindability can only be with the language description of relatively fuzzyyer character property, in order to carry out the convenience of case associative operation, not ore grindability M _KMXAs an attribute of case database, but, set up its corresponding case database according to the difference of different ore grindabilities.

The selected auxiliary variable of the present invention comprises:

Time variable T _JQThe T here _JQRefer at one and add ball within the cycle, from add ball begin constantly to current time hour being the time span of unit.

Classifier overflow granularity expectation value LD _EXP

The soft measured value LD of classifier overflow granularity _RCL

Bowl mill current detection value P _QM

Grader current detection value P _EJJ

(3) selection of leading variable.

According to three control loops of bottom, select the leading variable of following three variablees as model.

The new mine-supplying quantity Q of bowl mill _GKL

Bowl mill inlet amount of water W _F1

Overflow concentration in classifier D _YLND

(4) boundary condition determines.

In order to guarantee the parameter after the optimization setting, in normal working range, must carry out the boundary condition constraint to it, determine that promptly the bound of bowl mill mine-supplying quantity, bowl mill give the bound of mineral water and the bound of overflow concentration in classifier.Concrete numerical value as for bound is determined according to concrete technology.

(5) case representation.

The grinding system operating mode is organized according to certain structure and is stored in the case database with the form of case.Each case is described to conciliate by operating mode and is formed, and the operating mode of case is described auxiliary variable---the time variable T that promptly chooses _JQ, classifier overflow granularity expectation value LD _EXP, the soft measured value LD of classifier overflow granularity _RCL, bowl mill current detection value P _QM, grader current detection value P _FJJCase separate leading variable---the new mine-supplying quantity Q of bowl mill for choosing _GKL, bowl mill inlet amount of water W _F1, overflow concentration in classifier D _YLND

In addition, need in the case database table, increase by two attributes again for the ease of case retrieval and coupling and the operation of other case: time and similarity, wherein the time is that case obtains the time, similarity is the similarity of this case in current working description and the case library, in the case database table this property value of every case only carry out case retrieve, mate, just meaningful when reusing because the similarity of the case in the identical case library and different operating modes descriptions is different.So the case in the database is carried out following case representation:

Table 1 case representation structure

Time	Operating mode is described					Separate			Similarity
										Time variable	Classifier overflow granularity expectation value	The soft measured value of classifier overflow granularity	The bowl mill current detection value	The grader current detection value	The bowl mill mine-supplying quantity	Bowl mill is to the mineral water amount	Overflow concentration in classifier
	??T	????f 1	????f 2	????f 3	????f 4	????f 5	????j 1	????j 2										????j 3	?SIM

In order to carry out case correction and evaluation, the present invention has set up intelligent optimizing set real-time data base (following brief note is a real-time data base), write down time, operating mode description of intelligent optimizing set each time and separating of providing thereof, in addition, also be provided with " classifier overflow granularity laboratory values " attribute in order to compare real-time data base.The intelligent optimizing set real-time data base represents that structure is as follows:

Table 2 intelligent optimization real-time data base is represented structure

Time	Operating mode is described					Separate			Classifier overflow granularity laboratory values
										Time variable	Classifier overflow granularity expectation value	The soft measured value of classifier overflow granularity	The bowl mill current detection value	The grader current detection value	The bowl mill mine-supplying quantity	Bowl mill is to the mineral water amount	Overflow concentration in classifier
	T	????f 1	????f 2	????f 3	????f 4	????f 5	????j 1	????j 2										????j 3	????LD REAL

(6) the initial case of case library obtains.

At interval classifier overflow is carried out artificial sample once at regular intervals, with chemical examination classifier overflow granularity.In the corresponding sampling time each time, can find the process variable value and the soft measured value of classifier overflow granularity of the correspondence that stores in the computer control system.So just can obtain one group of case, its data comprise the classifier overflow granularity expectation value LD that measures sample _EXP(replacing), time variable T with laboratory values _JQ, the soft measured value LD of classifier overflow granularity _RCL, bowl mill current detection value P _QM, grader current detection value P _FJJ, the new mine-supplying quantity Q of bowl mill _GKL, bowl mill inlet amount of water W _F1With overflow concentration in classifier D _YLNDAnd sampling time T.After treating the data aggregation of m group, can obtain following data acquisition

M _V={ [T _i, T _JQ, LD _EXP, LD _RCL, P _QM, P _FJJ, Q _GKL, W _F1, D _YLND,] | i=1 ..., m} according to following rule pairing, promptly becomes case with above-mentioned data acquisition:

{[T _i，T _JQ，LD _EXP，LD _RCL，P _QM，P _EJJ]|i＝1，…，m}→{Q _GKL，W _F1，D _YLND|i＝1，…，m}

Usually, grinding-classification operation will be handled multiple ore by stages, and the hardness of each ore, grindability are different, should set up its corresponding case database respectively at the grindability difference of different ores.

(7) reasoning by cases

Reasoning by cases of the present invention adopts software to realize that its basic procedure block diagram as shown in Figure 3.Left column is the flow process that case retrieval and coupling and case are reused, and the flow process of case evaluation and correction is classified on the right side as.Its detailed step is as follows:

(A) initialization: carry out the initialization of all variablees.

(B) carry out intelligent optimizing set? if, then go to (C), carry out the process that case retrieval and coupling and case are reused; If not, then go to (K), carry out the process of case evaluation and correction.

Step (C) to (J) is reused flow process for the retrieval of case and coupling and case, adopts the nearest neighbor strategy in the case search strategy.

(C) select ore grindability;

Different ores has different grindabilitys, and the case database that is used for intelligent optimizing set of its correspondence also is different, so select ore grindability just to select case database.

(D) reading current working describes:

Just read operating mode characterising parameter or the online in real time that to carry out intelligent optimizing set and read the current working characterising parameter automatically.

(E) similarity is calculated:

If the current operating condition of grind grading process is M _GK, definition M _GKOperating mode be described as F=(f ₁, f ₂, f ₃, f ₄, f ₅), M _GKSeparate and be J _GK=(j ₁, j ₂, j ₃).Case is C in the definition case library ₁, C ₂C _n, case C wherein _k(k=1,2 ... n) operating mode is described as F _k=(f _{1, k}, f _{2, k}, f _{3, k}, f _{4, k}, f _{5, k}), C _kSeparate and be J _k=(j _{1, k}, j _{2, k}, j _{3, k}).

Current working is described M so _GKDescription feature f _i(i=1,2,3,4,5) and case C _k(k=1,2 ... n) description feature f _{I, k}Similarity function be:

$\mathrm{sim}(f_i,f_{i,k})=1-\frac{|f_i-f_{i,k}|}{\mathrm{Max}\left(f_{i,}{f}_{i,k}\right)},i=\mathrm{1,2,3,4,5},k=\mathrm{1,2},\·\·\·n$

Current working is described M _GKWith case C _k(k=1,2 ... n) similarity function is:

$\mathrm{SIM}(M_{\mathrm{GK}},C_k)=\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{\mathrm{\ω}}_i\mathrm{sim}(f_i,f_{i,k}),k=\mathrm{1,2}\·\·\·n$

ω wherein _iFor operating mode is described the weighting coefficient of feature, can determine ω according to concrete technology characteristics or experience _iSatisfy:

$\underset{i=1}{\overset{5}{\mathrm{\Σ}}}{\mathrm{\ω}}_i=1$

The similarity that each case and current working are described in the case library makes " similarity " property value of corresponding case in the case library equal its corresponding similarity value after calculating and finishing.

(F) determine threshold value:

If SIM _MaxThe maximal value of the similarity of trying to achieve for all are above-mentioned, that is: ${\mathrm{SIM}}_{\max }=\underset{k=\mathrm{1,2}\·\·\·n}{\mathrm{Max}}\left(\mathrm{SIM}(M_{\mathrm{GK}},C_k)\right),$ Threshold value SIM so _YzCan determine by following formula:

Threshold X wherein _YZDetermine by concrete technology or experience.

(G) case retrieval and coupling:

From case library, pick out case " similarity " property value SIM 〉=threshold value SIM _YzAll cases as the coupling case and successively by " similarity ", " time " (case storage time) property value descending sort.

(H) case is reused:

Do not exist in the case library generally speaking with current working and describe the case of coupling fully, thus the coupling operating mode that retrieves separate can not be directly as the separating of current working, this just need be reused the similar cases that retrieval obtains.Concrete grammar is as follows:

From the coupling case, pick out and have maximum similarity SIM _MaxCase and determine its number Num.

If Num=1, the case that promptly has maximum similarity has only one, and establishing this case is C _m, 1≤m≤n, case C in the note coupling case data table _mNext case be C _k, 1≤k≤n and since the coupling case when retrieving out by " similarity ", " time " (case storage time) property value descending sort, so C _kShould have second largest similarity and be up-to-date one of time.Note case C _mSeparate and be J _m, similarity is SIM _m, case C _kSeparate and be J _k, similarity is SIM _k, current working is described M so _GKSeparate J _GKFor:

$J_{\mathrm{GK}}=\frac{{\mathrm{SIM}}_m\×J_m+{\mathrm{SIM}}_k\×J_k}{{\mathrm{SIM}}_m+{\mathrm{SIM}}_k}$

If Num＞1, the case that promptly has an identical maximum similarity has a plurality of, might as well be provided with l, and (l＞1, l ∈ Z) is individual, supposes these cases C _i, i=1 ... l by property value descending sort " time " (case storage time) is: C ₁, C ₂C _l, J ₁, J ₂J _lFor it is separated accordingly, current working is described so separates J _GKFor:

$J_{\mathrm{GK}}=\frac{\underset{i=1}{\overset{l}{\mathrm{\Σ}}}{\mathrm{\θ}}_i\×J_i}{\underset{i=1}{\overset{l}{\mathrm{\Σ}}}{\mathrm{\θ}}_i}$

θ wherein _iTime weight coefficient for this case is reused satisfies θ ₁〉=θ ₂〉=... 〉=θ _l, can be as the case may be or experience determine.

(I) boundary condition constraint

In order to guarantee that the parameter after the optimization setting does not exceed normal working range, must reuse separating of back current working description to case and carry out the constraint of boundary condition separately, boundary condition constraint and adjustment algorithm thereof are determined according to concrete technology.

(J) adorn and preserve the intelligent optimizing set result down:

Boundary condition constraint back can be separating of current working the new mine-supplying quantity Q of bowl mill just _GKL, bowl mill inlet amount of water W _F1, overflow concentration in classifier D _YLNDUnder install in the middle of the slave computer circuit controls, if necessary can also suitably revise with artificial experience before the dress down.Simultaneously data such as the result of intelligent optimizing set and the description of current industrial and mineral, time are saved in the Relational database, manipulate for case correction and evaluation and other.

Step (K) to (U) is case evaluation and correction flow process.

(K) select ore grindability

Corresponding different ore grindabilities has been set up different case database, and corresponding different case database should be set up different intelligent optimizing set real-time data bases.Select ore grindability here, select its corresponding optimization setting real-time data base exactly.

(L) read classifier overflow granularity laboratory values and laboratory sampling time:

Note grader overflow granularity laboratory values is LD _Real, the laboratory sampling time is T _QY

(M) record retrieval:

Retrieval " time " property value T and laboratory sampling time T in real-time data base _QYImmediate data recording, the data recording that note retrieves is C _T, its corresponding operating mode is described: time variable, classifier overflow granularity expectation value, the soft measured value of classifier overflow granularity, bowl mill current detection value, grader current detection value are designated as respectively $F^T=(f_1^T,f_2^T,f_3^T,f_4^T,f_5^T),$ And note C _TClassifier overflow granularity expectation value be LD _EXP

(N) ask the poor of granularity expectation value and laboratory values:

The difference of note grader overflow granularity expectation value and laboratory values is Δ LD, so Δ LD=|LD _EXP-LD _Real|.

(O) whether satisfy accuracy requirement:

If Δ LD≤LD _HG(LD _HGBe the granularity criterion of acceptability), illustrate that then the granularity precision is qualified, do not need to carry out the case correction; If Δ LD＞LD _HG, illustrate that then the granularity precision is defective, need carry out the case correction.

(P) the input operating mode corresponding with sample time described:

In the real-time data base with T sample time _QYCorresponding record C _TOperating mode property value is described $F^T=(f_1^T,f_2^T,f_3^T,f_4^T,f_5^T)$ Import once more in the intelligent optimizing set model, carry out the operation of following (P)～(R) again.

(Q) similarity is calculated:

(E) is the same with step among Fig. 3.

(R) determine threshold value:

(F) is the same with step among Fig. 3.

(S) case retrieval and coupling

(G) is the same with step among Fig. 3.

(T) case correction

From the coupling case, pick out and have maximum similarity SIM _MaxCase and determine its number Num.

If SIM _Max＜X _XZYZ, then directly case C _TJoin in the case library.Threshold X wherein _XZYZDetermine according to concrete technology and actual conditions.

If SIM _Max〉=X _XZYZ, and Num=1, establishing this case is C _m, 1≤m≤n is so case C _mCorresponding operating mode is described F _m=(f _{1, m}, f _{2, m}, f _{3, m}, f _{4, m}, f _{5, m}) use $F^T=(f_1^T,f_2^T,f_3^T,f_4^T,f_5^T)$ Replace case C _mSeparate J _m=(j _{1, m}, j _{2, m}, j _{3, m}) use C _TSeparate $J_T=(j_1^T,j_2^T,j_3^T)$ Replace, and its " time " property value is made as T _QY

If SIM _Max〉=X _XZYZ, and Num＞1, it is individual to establish Num=l (l＞1, l ∈ Z), supposes that these cases are C _i, i=1 ... l, separating of its correspondence is J _i, i=1 ... l establishes J _j, 1≤j≤l is for making | J _j-J _Real| maximum one, so J _jPlace case C _jCorresponding operating mode is described F _j=(f _{1, j}, f _{2, j}, f _{3, j}, f _{4, j}, f _{5, j}) use $F^T=(f_1^T,f_2^T,f_3^T,f_4^T,f_5^T)$ Replace case C _jSeparate and use C _TSeparate $J_T=(j_1^T,j_2^T,j_3^T)$ Replace, and its " time " property value is made as T _QY

(U) case is preserved

Revised case is saved in the case library.

(V) finish

(8) case storage and maintenance.

As time goes on, the case in the case library constantly increases, if do not take adequate measures, probably occurs the overlapping big problem of case over time, has so promptly strengthened the time of reasoning, makes case lack typicalness again.For case library is controlled in the certain scale, must learn the case that adds in the case library.The study here can be regarded " merging " or " extensive " process as.Concrete operations are such:

To preparing to add the new case C in the case library _New,, calculate the similarity of all court case of long standing examples of storing in itself and the case library according to the calculating formula of similarity of front.If these similarities are respectively: SIM ₁, SIM ₂SIM _n(n is the number of court case of long standing example in the case library, 0≤SIM _i≤ 1).

If all similarities of obtaining all are less than or equal to some given threshold xi ₁, 0＜ξ ₁＜1, then add this new case C _NewIf exist a similarity greater than this threshold value at least, then abandon this new case, do not store.

The invention has the advantages that: according to the target of grinding particle size index, provide the optimization setting value of the basic control loop such as new mine-supplying quantity, effluent concentration, inlet amount of water of current grinding system by the reasoning by cases technology, make grinding classification system be in the duty of optimization, with the ore grinding product that obtains to form by release mesh.

Description of drawings

The flow process of Fig. 1 grinding circuit and measurement instrument, topworks, basic loop configuration and computer configuration figure

Fig. 2 is based on the intelligent optimizing set model structure figure of reasoning by cases

The FB(flow block) of Fig. 3 intelligent optimizing set software of the present invention

Used label symbol is as follows among Fig. 1 to Fig. 2:

Ball mill power (or electric current)---P _QM

The new mine-supplying quantity of bowl mill---Q _GKL

Bowl mill is to mineral water amount---W _F1

Grader electric current---P _FJJ

Overflow concentration in classifier---D _YLND

Classifier overflow granularity laboratory values---LD _REAL

The soft measured value of classifier overflow granularity---LD _RCL

Classifier overflow granularity expectation value---LD _EXP

Time variable---T _JQ

Power (or electric current) transmitter---PT

Consistency transmitter---DT

Flow transmitter---FT

Quality transmitter---WT

Solid arrow is represented logistics (raw ore, water and ore pulp) or signal flow;

Dotted line is represented being connected of sensor and transmitter.

Embodiment

Embodiments of the invention are the primary grinding series of the weak magnetic roasted ore of a large-scale iron ore beneficiating factory.The main iron ore in this ore dressing plant is a pyrite, limonite, gangue is with barite, quartzy, jasper and ferrodolomite are main, the actual ferrous grade 33% of ore, behind calcining process, transport to low intensity magnetic separation cylinder ore storage bin through the weak magnetic ore deposit after the sorting, the synoptic diagram of grinding system as shown in Figure 1, roasted ore in the low intensity magnetic separation cylinder ore storage bin is by the electricity rock feeder discharge that shakes, again by sending in the bowl mill for the ore deposit belt feeder, adding water with bowl mill inlet is blended in and is ground into ore pulp in the bowl mill, this section ore grinding adopts grate ball mill, bowl mill ore discharge and bowl mill outlet are added the water bout and are entered spiral classifier, ball milling is returned in the spiral classifier sand return one time, forms closed circuit with a ball milling.After entering the pump pond, spiral classifier overflow (being the final products of this procedure) is transported to subsequent handling.

The bowl mill model is Φ 3200 * 3500, useful volume 25.3m ³, drum speed 18.5r/min, 54 tons of maximum ball loads.

Spiral classifier is a 2FLG-2400 type duplex-spiral classifier.Revolution speed of screw 3.5r/min, the tank gradient 17 degree.

At first following measurement instrument is installed, is comprised in requirement according to this instructions:

Uclear scale is measured new mine-supplying quantity Q _GKL

Electromagnetic flowmeter survey bowl mill inlet is added discharge W _F1

Nuclear density gauge is measured spiral classifier effluent concentration D _YLND

Galvanometer is measured bowl mill electric current P _QM

Galvanometer is measured spiral classifier electric current P _FJJ

Frequency Converter Control electric vibrating feeder frequency

Two electric control valves control bowl mill inlet adds water and water is added in the bowl mill outlet

Realize the automatic control of basic control loop with Programmable Logic Controller (PLC).In slave computer, use the single-loop regulator configuration among the PLC to become following basic control loop:

The new mine-supplying quantity Q of electric vibrating feeder frequency control _GKL

The bowl mill inlet is added the moving variable valve control of water power bowl mill inlet and is added discharge W _F1

The moving variable valve control of water power spiral classifier effluent concentration D is added in the bowl mill outlet _YLND

Realize monitoring human-computer interface at host computer (supervisory control comuter) with RSView32 software.The normal range of operation of this grinding system is:

New mine-supplying quantity---75 ± 5 tons/hour

An ore milling concentration---78%～85%

The spiral classifier effluent concentration---45%～50%

Spiral classifier overflow granularity---55%～60% (200 order)

The medium filling rate---38%～42%

The VBA application software establishment that intelligent optimizing set program and the soft process of measurement of classifier overflow granularity provide with RSView32.Optimization setting software moves on independent optimizing computer, the RSLinx communication program is housed on this computing machine is responsible for carrying out data communication with PLC and host computer, carries out both-way communication by the DDE mode between RSLinx and the optimization setting program.

At first use patent---" bowl mill grinding system overflow granularity index flexible measurement method (patent No.: 03133951.4) " carry out the soft measurement of classifier overflow granularity: the soft measurement neural network of granularity adopts the single hidden layer BP network of seven inputs, one output.Seven inputs are respectively: new mine-supplying quantity Q _GKL, inlet adds discharge W _F1, overflow concentration in classifier D _YLND, bowl mill electric current P _QM, spiral classifier electric current P _FJJ, time variable T _JQ, constant threshold value (1).Above-mentioned seven variablees are formed the training set together with the laboratory values of correspondence overflow granularity constantly.Latent first number of neural network is chosen as 60, and the structure of network is expressed as with the form of matrix:

y＝NN(x)

=W ₁Sig (W ₂X) wherein, y is the output of network, and x contains threshold value at interior augmentation input vector, and activation function is chosen as the Sigmoid function, and its expression formula is:

$\mathrm{sig}\left(z\right)=\frac{e^z-e^{-z}}{e^z+e^{-z}}$

Output variable and output layer weights W ₁Between be linear relationship, adopt the gradient descent method to train, learning rate η gets 0.0001.When dropping to 2%, relative error stops training when following.

According to the described implementation method select time of this instructions variable T _JQ, classifier overflow granularity expectation value LD _EXP, the soft measured value LD of classifier overflow granularity _RCL, bowl mill current detection value P _QM, grader current detection value P _FJJBe auxiliary variable, the new mine-supplying quantity Q of selection bowl mill _GKL, bowl mill inlet amount of water W _F1, overflow concentration in classifier D _YLNDTake variable as the leading factor, set up the case database table after, set up the initial case of case library with the service data in 20 days.Carry out case retrieval and coupling and case when reusing involved weighting coefficient or dependent thresholds specifically determine as follows according to concrete technology characteristics and experience:

Operating mode is described the weighting coefficient of feature:

Time variable weighting coefficient---ω ₁=0.2

Classifier overflow granularity expectation value weighting coefficient---ω ₂=0.3

The soft measured value weighting coefficient of classifier overflow granularity---ω ₃=0.2

Bowl mill current detection value weighting coefficient---ω ₄=0.15

Grader current detection value weighting coefficient---ω ₅=0.15

The threshold X of when carrying out " determining threshold value " step in reusing of case retrieval and coupling and case, using _YZBe defined as 0.9, i.e. X _YZ=0.9.

Carrying out case when reusing, be provided with l, (l＞1, l ∈ Z) individual case with identical maximum similarity is supposed these cases C _i, i=1 ... l by property value descending sort " time " (case storage time) is: C ₁, C ₂C _l, their time weight coefficient is defined as respectively so: 10+l, and 10+ (l-1) ..., 10+[l-(l-1)].

When carrying out the case evaluation and revising, precision criterion of acceptability LD _HGBe defined as 2, i.e. J _HG=2, threshold X _XZYZBe defined as 0.9, i.e. X _XZYZ=0.9.

When carrying out the case storage and safeguarding, threshold xi ₁Be defined as 0.9, i.e. ξ ₁=0.9.

Boundary condition is definite and method of adjustment is as follows:

Overflow concentration in classifier D _YLNDThe upper limit 50% is limited to 45% down.

The new mine-supplying quantity Q of bowl mill _GKL80 tons/hour of the upper limits are limited to 70 tons/hour down.

Bowl mill is to mineral water amount W _F1On be limited to 16 tons/hour, down be limited to 10 tons/hour.

If a certain parameter has exceeded boundary condition after the optimization setting, so just make this parameter value equal this boundary condition.

Based on the grinding system intelligent optimizing set model of reasoning by cases at the grinding system run duration, can provide optimum basic control loop setting value according to the real time data of process, make that relative error is no more than 2% between classifier overflow granularity actual value and the expectation value, become one have very high practical value, grinding system optimization setting method cheaply.

Claims (6)

1, a kind of grinding system intelligent optimizing set method based on reasoning by cases is characterized in that this method depends on the grinding system hardware platform, is realized by intelligent optimizing set software, may further comprise the steps:

(1) the soft measurement of overflow granularity;

(2) selection of auxiliary variable;

(3) selection of leading variable;

(4) boundary condition determines;

(5) case representation;

(6) the initial case of case library obtains;

(7) reasoning by cases;

(8) case storage and maintenance.

2, grinding system intelligent optimizing set method based on reasoning by cases as claimed in claim 1, it is characterized in that the grinding system hardware platform that this method relies on, comprise: bowl mill, spiral classifier and relevant device are formed, be equipped with measurement instrument simultaneously, topworks and the computer system of carrying out computed in software, the input end of bowl mill and belt feeder, bowl mill inlet amount of water pipeline and grader sand return mouth link, bowl mill output terminal and outlet are added water inlet and are joined with the spiral classifier input port simultaneously, spiral classifier sand return end and bowl mill inlet join, on the bowl mill feeding belt weighing instrument is installed, on bowl mill inlet filler pipe, a flowmeter and a motor regulated valve are installed, motor regulated valve on bowl mill outlet filler pipe, packing density meter on the classifier overflow pipeline, difference installation power meter or galvanometer on the drive motor of bowl mill and grader, frequency converter is installed on oscillating feeder, above-mentioned measurement instrument and topworks are connected to control computer, and form the new mine-supplying quantity Q of electric vibrating feeder frequency control _GKL, bowl mill inlet adds the moving variable valve control of water power bowl mill inlet and adds discharge W _F1, bowl mill outlet adds the moving variable valve of water power and regulates bowl mill control spiral classifier effluent concentration D _YLNDThree basic control loops.

3, the grinding system intelligent optimizing set method based on reasoning by cases as claimed in claim 1 is characterized in that the selection of described auxiliary variable comprises: time variable T _JQ, classifier overflow granularity expectation value LD _EXP, the soft measured value LD of classifier overflow granularity _RCL, bowl mill current detection value P _QM, grader current detection value P _FJJ

4, the grinding system intelligent optimizing set method based on reasoning by cases as claimed in claim 1 is characterized in that the leading variable selection comprises: the new mine-supplying quantity Q of bowl mill _GKLBowl mill inlet amount of water W _F1Overflow concentration in classifier D _YLND

5, the grinding system intelligent optimizing set method based on reasoning by cases as claimed in claim 1, what it is characterized in that described boundary condition determines that promptly bound, the bowl mill of definite bowl mill mine-supplying quantity are given the bound of mineral water and the bound of overflow concentration in classifier.

6, the grinding system intelligent optimizing set method based on reasoning by cases as claimed in claim 1, it is characterized in that described reasoning by cases comprises the retrieval and the coupling of case and case is reused, case evaluation and correction, its flow process is: (A) initialization: carry out the initialization of all variablees; (B) carry out intelligent optimizing set? if, then go to (C), carry out the process that case retrieval and coupling and case are reused; If not, then go to (K), carry out the process of case evaluation and correction; (C) select ore grindability; (D) reading current working describes; (E) similarity is calculated; (F) determine threshold value; (G) case retrieval and coupling; (H) case is reused; (I) boundary condition constraint; (J) adorn and preserve the intelligent optimizing set result down; (K) select ore grindability; (L) read classifier overflow granularity laboratory values and laboratory sampling time; (M) record retrieval; (N) ask the poor of granularity expectation value and laboratory values; (O) whether satisfy accuracy requirement,, do not need to carry out the case correction, otherwise need carry out the case correction if the granularity precision is qualified; (P) the input operating mode corresponding with sample time described; (Q) similarity is calculated; (R) determine threshold value; (S) case retrieval and coupling; (T) case correction; (U) case is preserved; (V) finish.

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