CN1680945B - Method for proportioning ores for beneficiation - Google Patents

Abstract

A proportioning method of ore dressing includes collecting relevant parameters, confirming desired grade of fine iron ore at each selection period, setting up model and disposing parameters, evaluating adaptability of each particle, confirming the best adaptability for whole situation and outputting the proportioning ratio of original ore.

Description

The ore-proportioning method that is used for dressing Production Process

Technical field

The invention belongs to the ore dressing production technical field.

Background technology

Ore dressing is the requisite extremely important rings of industrial sector such as metallurgy, chemical industry, building materials, and iron ore also is one of primary raw material of Iron and Steel Production.The ore dressing production practices show that ore of different nature is under different ore proportioning rates, and its concentrate grade is also different, and reasonably concentrate grade is the important manufacturing parameter of enterprise.Joining the ore deposit is multistage technological process, the mutual coordination that is the mineral wealth quality and quantity is with balanced, be the mutual coupling and the control of tcrude ore processing processing power and product quality, quantity, joining the ore deposit process should be to continue in productive exploration to going into the whole mining activities period that stove is smelted.After the ore extraction, enter bowl mill through each system's feed bin of ore dressing (even middle storage ore storage bin), and these links can constitute ore blending system.From the document of openly reporting both at home and abroad, the existing miner of joining does research and only rests on the mining production phase, and the research in other stage of ore blending system yet there are no open report.Dressing Production Process join the ore deposit, for determining that the reasonable iron concentrate grade into blast furnace then is crucial.

Summary of the invention

Problem at prior art exists the invention provides a kind of ore-proportioning method that is used for dressing Production Process.

The inventive method comprises the collection correlation parameter; Determine the iron concentrate grade of per period screening expectation; Set up model, configuration parameter; Estimate the fitness of each particulate; Determine the fitness that the overall situation is best; Six steps such as output raw ore ore proportioning rate.

1, gathers correlation parameter

Need gathering all raw ore kinds and parameters such as stock, equipment capacity and concentrate demand, is the N kind as known raw ore kind of carrying out magnetic separation, wherein i (i=1,,,,, N) total amount of planting ore is G _i, the ratio μ of fine ore and lump ore _Ij, iron content accounts for ρ respectively _Il, metal recovery rate is η _Il, concentrate yield is σ _iThe equipment capacity rating that the t period is used to screen fine ore and lump ore in plan phase [1, T] scope ${\mathrm{CE}}_t^1(1=\mathrm{1,2});$ The primary reserves of iron ore concentrate is S among the stock ₀, the concentrate amount M (t) that the day part sintering plant is required.

2, determine the iron concentrate grade of per period screening expectation

The decision maker expects that the metal content of iron ore concentrate of screening of per period is as far as possible near P when formulating ore dressing process week plan ₀(t).

3, set up model, configuration parameter

Definition decision variable: X _i(t) represent the input amount of i kind raw ore at time t;

P (t) represents actual comprehensive concentrate grade, P ₀(t) be the metal balance grade;

$\min f\left(x\right)={\left(\frac{1}{T}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{[P\left(t\right)-P_0\left(t\right)]}^2\right)}^{1/2}---\left(1\right)$

s.t.

$\begin{array}{cc}\underset{i=1}{\overset{T}{\mathrm{\Σ}}}{x}_i\left(t\right)\≤G_i,& i=1,\·\·\·,N;\end{array}---\left(2\right)$

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i\left(t\right)\×{\mathrm{\μ}}_{\mathrm{il}}\≤{\mathrm{CE}}_l\left(t\right),l=\mathrm{1,2},\·\·\·,M;t=1,\·\·\·,T;---\left(3\right)$

$\underset{\mathrm{\τ}=1}{\overset{t}{\mathrm{\Σ}}}[\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i\left(\mathrm{\τ}\right)\underset{l=1}{\overset{L}{\mathrm{\Σ}}}({\mathrm{\μ}}_{\mathrm{il}}\×{\mathrm{\ρ}}_{\mathrm{il}}\×{\mathrm{\η}}_{\mathrm{il}})-M\left(\mathrm{\τ}\right)]+S_0\≥0,t=1,\·\·\·,T;---\left(4\right)$

$P\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i\left(t\right)\underset{l=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\μ}}_{\mathrm{il}}\×{\mathrm{\ρ}}_{\mathrm{il}}\×{\mathrm{\η}}_{\mathrm{il}})/\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i\left(t\right)\underset{l=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\μ}}_{\mathrm{il}}\×{\mathrm{\σ}}_l),t=1,\·\·\·,T;---\left(5\right)$

x _i(t)≥0，i＝1，…，N，t＝1，…，T；(6)

Specification of a model:

Objective function (1) is to make day part metal balance deviation minimum;

Constraint condition (2) is meant all kinds of raw ore total amount restrictions;

Constraint condition (3) is meant the capacity of equipment restriction that intermediate product are produced;

Constraint condition (4) guarantees the restriction of iron ore concentrate demand;

Constraint condition (5) is meant comprehensive iron concentrate grade;

Constraint condition (6) be meant i kind raw ore in the input amount of time t greater than 0.

Dressing Production Process is joined the ore deposit Optimization Model and is belonged to nonlinear programming and since the PSO algorithm simply be easy to realize that be fit to find the solution nonlinear problem, variable is the advantage of real number bounded problem, so, find the solution so attempt this algorithm of employing in conjunction with the characteristics of problem.

Configuration parameter table: particle number m, inertia weight, aceleration pulse, maximal rate/maximum operation algebraically;

4, estimate the fitness eval (x) of each particulate

In order to limit infeasible solution, adopt penalty function method.Definition Δ G _i, Δ CE _iWith the violation amount of each constraint of Δ M (t) representative, wherein:

$\mathrm{\Δ}{G}_i=\max \{\underset{i=1}{\overset{T}{\mathrm{\Σ}}}{x}_i\left(t\right)-G_{i,}0\}$

$\mathrm{\Δ}{\mathrm{CE}}_l\left(t\right)=\max \{\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i\left(t\right)\×{\mathrm{\η}}_{\mathrm{il}}-{\mathrm{CG}}_l\left(t\right),0\}$

$\mathrm{\ΔM}\left(t\right)=\max \{\underset{\mathrm{\τ}=1}{\overset{t}{\mathrm{\Σ}}}M\left(\mathrm{\τ}\right)-\underset{\mathrm{\τ}=1}{\overset{t}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_i\left(\mathrm{\τ}\right)\×{\mathrm{\η}}_i-S_0,0\}$

Evaluation function is selected following multiplication form: eval (x)=P ₀-f (x) * P (x), here:

$p\left(x\right)=\frac{1}{N+N\×T+T}(\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\frac{\mathrm{\Δ}{G}_i}{\mathrm{\Δ}{G}_i^{\max }}+\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\underset{\mathrm{\τ}=1}{\overset{T}{\mathrm{\Σ}}}\frac{\mathrm{\Δ}{\mathrm{CE}}_l\left(t\right)}{\mathrm{\Δ}{\mathrm{CE}}_l^{\max }\left(t\right)}+\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\frac{\mathrm{\ΔM}\left(t\right)}{\mathrm{\Δ}{M}^{\max }\left(t\right)})$

Wherein:

With Represent the maximum violation amount of each constraint in the current population;

$\mathrm{\Δ}{G}_i^{\max }=\max \{\mathrm{\ϵ},\mathrm{\Δ}{G}_i\};$

$\mathrm{\Δ}{\mathrm{CE}}_l^{\max }\left(t\right)=\max \{\mathrm{\ϵ},{\mathrm{\ΔCE}}_l\left(t\right)\};$

$\mathrm{\Δ}{M}^{\max }\left(t\right)=\max \{\mathrm{\ϵ},\mathrm{\ΔM}\left(t\right)\};$

For each particulate, its fitness and the desired positions that lives through are compared; If better, then with it as current desired positions;

5,, its fitness and the desired positions that the overall situation is lived through are compared for each particulate; If better, then reset call number;

Adjust the speed and the position of particulate according to formula; It is as follows that formula is adjusted in particle rapidity and position:

${\mathrm{\υ}}_{\mathrm{it}}={\mathrm{\ω}}_{\mathrm{it}}\×{\mathrm{\υ}}_{\mathrm{it}}+c_1\mathrm{rand}\left(\right)\×(g_{\mathrm{it}}^{\mathrm{best}}-x_{\mathrm{it}})+c_2\mathrm{rand}\left(\right)\×(h_{\mathrm{it}}^{\mathrm{best}}-x_{\mathrm{it}});$

x _i(t)＝x _i(t)+ν _it；

6, judge whether to reach the algorithm end condition according to maximum algebraically, as reach and then export raw ore ore proportioning rate value, then return step 4 as satisfying.

The present invention has changed the sum of errors that is caused by artificial calculating and has carried out the subjectivity error that the metal balance analysis causes with artificial experience for ore dressing production provides a kind of effective ore-proportioning method, and the science that has solved the ore dressing plant production run is joined the ore deposit problem.

Description of drawings

Fig. 1: be the software flow pattern of the inventive method

Embodiment

Real data according to production scene, wine steel ore dressing plant, proposed to instruct ore dressing production with the metal balance method, with intermediate bin and tank farm stock is minimum target, foundation is based on the material balance of rational ore matching, power balance, the ore dressing production planning optimization model of factors such as inputoutput, and utilization particle swarm optimization algorithm PSO (particle swarm optimization algorithm) algorithm is found the solution this problem, obviously it is separated and is feasible solution, the value of meeting the expectation, show that this invention is to solve ore dressing process to join an effective method of ore deposit problem and realization easily, can reduce because the artificial error of calculation that causes of calculating, with carry out the subjectivity error that the metal balance analysis causes with artificial experience, be the cost accounting in ore dressing plant, plan and operation instruction provide the most important theories foundation.

Example: according to the actual conditions in wine steel ore dressing plant, expect concentrate grade altogether 52.5-53.5%, 54.20% be high useless,＜52.20% for low useless.Known to plan phase T[1,4] in (all around) 5 kinds of raw ores (comprising: specularite (piece, powder), low-grade blackhawk mountain lump ore (piece, powder), high phosphorus blackhawk mountain lump ore (piece, powder), blackhawk mountain fine ore, raft fine ore, black fine ore) are screened the total amount G of various raw ores _i, the ratio μ of fine ore and lump ore _Il, and iron content accounts for ρ respectively _Il, metal recovery rate is η _Il, concentrate yield is σ _i, see Table 1; Production organizational mode, if capacity of equipment and concentrate demand then select 8 series to carry out in plan [1,2] scope, 4 weak magnetic bowl mill and 4 strong magnetic bowl mill operations just.If equipment and concentrate demand in its [3,4] scope of plan, are then selected 6 series operations, 3 weak magnetic bowl mill and 3 strong magnetic bowl mill operations just.Treatment capacity and other parameters when table 2 provides the ball milling platform.

Table 1 raw ore overall target and total amount

Table 2 known device ability and concentrate demand

We adopt the following parameters value to come control algolithm: particle number m=10, the maximum operation of C1=C2=2.0 inertia weight=0.5-0.9 aceleration pulse algebraically=60; Table 3 provides the simulation result of interior raw ore input amount of different plan phases and iron concentrate grade.

Each all raw ore input amount x of table 3 _i(t) comprehensive concentrate grade

Claims (1)

1. ore-proportioning method that is used for dressing Production Process is characterized in that comprising following six steps:

1) gathers correlation parameter;

The parameter of gathering comprises all raw ore kinds and stock, equipment capacity and concentrate demand;

2) determine the iron concentrate grade that per period screening is expected;

The decision maker expects that the metal content of iron ore concentrate of screening of per period is as far as possible near P when formulating ore dressing process week plan ₀(t);

3) set up model, configuration parameter;

The model of described foundation is:

s.t.

x _i(t)≥0，i＝1，...，N，t＝1，...，T； (6)

Wherein, N is the raw ore kind of carrying out magnetic separation, G _iBe the total amount of i kind ore, μ _IlBe the ratio of fine ore and lump ore, ρ _IlBe iron content, η _IlBe metal recovery rate, σ ₁Be concentrate yield;

Be the equipment capacity rating that the t period is used to screen fine ore and lump ore in plan phase [1, T] scope;

S ₀Be the primary reserves of iron ore concentrate among the stock, M (t) is the required concentrate amount of day part sintering plant;

X _i(t) represent the input amount of i kind raw ore at time t; P (t) represents actual comprehensive concentrate grade, P ₀(t) be the metal balance grade;

Its constraint condition is:

(1) be to make day part metal balance deviation minimum;

(2) be meant all kinds of raw ore total amount restrictions;

(3) be meant the capacity of equipment restriction that intermediate product are produced;

(4) guarantee the restriction of iron ore concentrate demand;

(5) be meant comprehensive iron concentrate grade;

(6) be meant i kind raw ore in the input amount of time t greater than 0;

Described configuration parameter comprises: particle number m, inertia weight, aceleration pulse, maximal rate/maximum operation algebraically;

4) estimate the fitness of each particulate;

Definition Δ G _i, Δ CE ₁With the violation amount of each constraint of Δ M (t) representative, wherein:

Evaluation function is selected following multiplication form: eval (x)=P ₀-f (x) * P (x), here:

Wherein: With Δ M ^Max(t) the maximum violation amount of each constraint in the current population of representative;

ΔM ^max(t)＝max{ε，ΔM(t)}；

For each particulate, its fitness and the desired positions that lives through are compared; If better, then with it as current desired positions;

5) determine the fitness that the overall situation is best;

For each particulate, its fitness and the desired positions that the overall situation is lived through are compared; If better, then reset call number;

Adjust the speed and the position of particulate according to formula; It is as follows that formula is adjusted in particle rapidity and position:

x _i(t)＝x _i(t)+υ _it；

6) output raw ore ore proportioning rate;

Judge whether to reach the algorithm end condition according to maximum algebraically, as reach and then export raw ore ore proportioning rate value, then return step 4) as satisfying.