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

Grinding system intelligent optimization initialization method based on case ratiocination Download PDF

Info

Publication number
CN1603986A
CN1603986A CN 200410050750 CN200410050750A CN1603986A CN 1603986 A CN1603986 A CN 1603986A CN 200410050750 CN200410050750 CN 200410050750 CN 200410050750 A CN200410050750 A CN 200410050750A CN 1603986 A CN1603986 A CN 1603986A
Authority
CN
China
Prior art keywords
case
bowl mill
grinding
classifier
reasoning
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN 200410050750
Other languages
Chinese (zh)
Other versions
CN1285977C (en
Inventor
赵大勇
岳恒
柴天佑
丁进良
周平
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northeastern University China
Original Assignee
Northeastern University China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northeastern University China filed Critical Northeastern University China
Priority to CN 200410050750 priority Critical patent/CN1285977C/en
Publication of CN1603986A publication Critical patent/CN1603986A/en
Application granted granted Critical
Publication of CN1285977C publication Critical patent/CN1285977C/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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, kSimilarity function be:
sim ( f i , f i , k ) = 1 - | f i - f i , k | Max ( f i , f i , k ) , i = 1,2,3,4,5 , k = 1,2 , · · ·n
Current working is described M GKWith case C k(k=1,2 ... n) similarity function is:
SIM ( M GK , C k ) = Σ i = 1 5 ω i sim ( f i , f i , k ) , k = 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:
Σ i = 1 5 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: SIM max = Max k = 1,2 · · · n ( SIM ( M GK , C k ) ) , 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 GK = SIM m × J m + SIM k × J k SIM m + 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 GK = Σ i = 1 l θ i × J i Σ i = 1 l 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:
sig ( z ) = 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.
CN 200410050750 2004-10-29 2004-10-29 Grinding system intelligent optimization initialization method based on case ratiocination Expired - Fee Related CN1285977C (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN 200410050750 CN1285977C (en) 2004-10-29 2004-10-29 Grinding system intelligent optimization initialization method based on case ratiocination

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN 200410050750 CN1285977C (en) 2004-10-29 2004-10-29 Grinding system intelligent optimization initialization method based on case ratiocination

Publications (2)

Publication Number Publication Date
CN1603986A true CN1603986A (en) 2005-04-06
CN1285977C CN1285977C (en) 2006-11-22

Family

ID=34665931

Family Applications (1)

Application Number Title Priority Date Filing Date
CN 200410050750 Expired - Fee Related CN1285977C (en) 2004-10-29 2004-10-29 Grinding system intelligent optimization initialization method based on case ratiocination

Country Status (1)

Country Link
CN (1) CN1285977C (en)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100401213C (en) * 2005-10-19 2008-07-09 东北大学 Intelligent optimized control method for comprehensive production index in ore dressing process
CN101950171A (en) * 2010-09-17 2011-01-19 中冶北方工程技术有限公司 Intelligent hierarchical control method and control device for ore grinding in concentration plant
CN101244403B (en) * 2008-03-17 2011-07-20 西安艾贝尔科技发展有限公司 Optimization control method for grind grading process
CN101694583B (en) * 2009-10-14 2011-10-26 东北大学 Ore grinding process operation control method based on multivariable decoupling (IMC) technology
CN102649097A (en) * 2012-04-09 2012-08-29 桐柏银矿有限责任公司 Ore sorting equipment
CN102652925A (en) * 2012-04-26 2012-09-05 中冶南方工程技术有限公司 System for measuring granularity of pulverized coal of blast furnace coal powder injection middle-speed milling system
CN103377247A (en) * 2012-04-28 2013-10-30 沈阳铝镁设计研究院有限公司 Intelligent extraction method for ore grinding control cases
CN103617456A (en) * 2013-12-04 2014-03-05 东北大学 Operating index optimization method in beneficiation process
CN103639033A (en) * 2013-11-25 2014-03-19 中冶长天国际工程有限责任公司 Method and device for obtaining optimum ore feeding amount of ore mill
CN104028364A (en) * 2014-04-30 2014-09-10 江西理工大学 Multi-metal ore-separating and ore-grinding grading optimization test method
CN104318313A (en) * 2014-09-19 2015-01-28 东北大学 Intelligent decision system and method for ore dressing total flow operation index on the basis of case-based reasoning
CN104549718A (en) * 2014-12-24 2015-04-29 中冶长天国际工程有限责任公司 Method and device for controlling concentration of ore pulp in process of grinding and grading ore
CN104898563A (en) * 2015-04-30 2015-09-09 长沙有色冶金设计研究院有限公司 Bowl mill control method
CN105214787A (en) * 2015-09-15 2016-01-06 首钢总公司 The production method of single driving type ball mill and application process thereof
CN106292292A (en) * 2016-10-17 2017-01-04 鞍钢集团矿业有限公司 The floatation of iron ore dosing Optimal Setting method and system of case-based reasioning
CN108906306A (en) * 2018-07-02 2018-11-30 山东世联环保科技开发有限公司 Cement slurry Vertical Mill variation-tracking control method
CN110180683A (en) * 2019-06-21 2019-08-30 安徽国兰智能科技有限公司 A kind of floatation system based on big data analysis

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100401213C (en) * 2005-10-19 2008-07-09 东北大学 Intelligent optimized control method for comprehensive production index in ore dressing process
CN101244403B (en) * 2008-03-17 2011-07-20 西安艾贝尔科技发展有限公司 Optimization control method for grind grading process
CN101694583B (en) * 2009-10-14 2011-10-26 东北大学 Ore grinding process operation control method based on multivariable decoupling (IMC) technology
CN101950171A (en) * 2010-09-17 2011-01-19 中冶北方工程技术有限公司 Intelligent hierarchical control method and control device for ore grinding in concentration plant
CN102649097A (en) * 2012-04-09 2012-08-29 桐柏银矿有限责任公司 Ore sorting equipment
CN102652925A (en) * 2012-04-26 2012-09-05 中冶南方工程技术有限公司 System for measuring granularity of pulverized coal of blast furnace coal powder injection middle-speed milling system
CN103377247A (en) * 2012-04-28 2013-10-30 沈阳铝镁设计研究院有限公司 Intelligent extraction method for ore grinding control cases
CN103377247B (en) * 2012-04-28 2016-11-02 沈阳铝镁设计研究院有限公司 The intelligent extract method of grind mineral control case
CN103639033A (en) * 2013-11-25 2014-03-19 中冶长天国际工程有限责任公司 Method and device for obtaining optimum ore feeding amount of ore mill
CN103639033B (en) * 2013-11-25 2015-08-05 中冶长天国际工程有限责任公司 A kind of method and apparatus obtaining the best mine-supplying quantity of ore mill
CN103617456A (en) * 2013-12-04 2014-03-05 东北大学 Operating index optimization method in beneficiation process
CN103617456B (en) * 2013-12-04 2016-08-17 东北大学 A kind of ore dressing process operating index optimization method
CN104028364A (en) * 2014-04-30 2014-09-10 江西理工大学 Multi-metal ore-separating and ore-grinding grading optimization test method
CN104318313A (en) * 2014-09-19 2015-01-28 东北大学 Intelligent decision system and method for ore dressing total flow operation index on the basis of case-based reasoning
CN104318313B (en) * 2014-09-19 2017-05-10 东北大学 Intelligent decision system and method for ore dressing total flow operation index on the basis of case-based reasoning
CN104549718A (en) * 2014-12-24 2015-04-29 中冶长天国际工程有限责任公司 Method and device for controlling concentration of ore pulp in process of grinding and grading ore
CN104898563A (en) * 2015-04-30 2015-09-09 长沙有色冶金设计研究院有限公司 Bowl mill control method
CN104898563B (en) * 2015-04-30 2018-02-02 长沙有色冶金设计研究院有限公司 A kind of ball mill control method
CN105214787A (en) * 2015-09-15 2016-01-06 首钢总公司 The production method of single driving type ball mill and application process thereof
CN105214787B (en) * 2015-09-15 2018-08-21 首钢集团有限公司 The production method of single driving type ball mill
CN106292292A (en) * 2016-10-17 2017-01-04 鞍钢集团矿业有限公司 The floatation of iron ore dosing Optimal Setting method and system of case-based reasioning
CN108906306A (en) * 2018-07-02 2018-11-30 山东世联环保科技开发有限公司 Cement slurry Vertical Mill variation-tracking control method
CN110180683A (en) * 2019-06-21 2019-08-30 安徽国兰智能科技有限公司 A kind of floatation system based on big data analysis
CN110180683B (en) * 2019-06-21 2021-01-26 安徽国兰智能科技有限公司 Flotation system based on big data analysis

Also Published As

Publication number Publication date
CN1285977C (en) 2006-11-22

Similar Documents

Publication Publication Date Title
CN1285977C (en) Grinding system intelligent optimization initialization method based on case ratiocination
CN1749891A (en) Intelligent optimized control method for comprehensive production index in ore dressing process
CN1598534A (en) Soft investigating method for overflow grain index of ore grinding system based on case inference
CN100346856C (en) Rectification tower automatic control and optimization method
CN1042267C (en) Controlling method and measuring mixer for liquids and powders
CN1525153A (en) Flexible measuring method for overflow particle size specification of ball mill grinding system
CN1016116B (en) Be used to measure the method and apparatus of fluid
CN1053247C (en) Apparatus for predicting flow of water supplying
CN1975611A (en) Constraint and limit feasibility handling in a process control system optimizer
CN1300651C (en) Constraint and limit feasibility process in process control system optimizer procedure
CN1086009A (en) Chemical process optimization method
CN101244403B (en) Optimization control method for grind grading process
CN1945602A (en) Characteristic selecting method based on artificial nerve network
CN1721050A (en) Operation supporting device for ultrafiltration treatment device
CN110605178B (en) Intelligent control system and method for heavy medium sorting process
CN108469797A (en) A kind of grinding process modeling method based on neural network and evolutionary computation
CN1643460A (en) Automobile manufacturing line input order planning apparatus
CN1769861A (en) Support vector machine method for measuring overflow granularity distribution of hydrocyclone for solid-liquid separation
CN1106601C (en) Method and system for inference using hierarchy model, and control system and method thereof
CN103617456A (en) Operating index optimization method in beneficiation process
CN102778843B (en) Operation control method of high magnetic grading process
CN1291312A (en) Method and system for controlling processes
CN1057175C (en) Instruction value determining device
CN1687921A (en) Rare-earth cascade extraction separation component content soft measuring method
CN1226249C (en) Method for optimizing operation condition of xylene isomerization reactor

Legal Events

Date Code Title Description
PB01 Publication
C06 Publication
SE01 Entry into force of request for substantive examination
C10 Entry into substantive examination
GR01 Patent grant
C14 Grant of patent or utility model
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20061122

Termination date: 20151029

EXPY Termination of patent right or utility model