CN103631140B - Based on the coke oven heating-combustion process fire path temperature Automatic adjustment method of Performance Evaluation - Google Patents

Abstract

The invention discloses a kind of coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation, by gathering the historical data that fire path temperature runs, set up the many attributes Performance Evaluation Model based on information entropy, gather the data that current fire path temperature runs, the many attributes Performance Evaluation Model based on information entropy is utilized to judge current control system performance rate, Model for Multi-Objective Optimization is set up according to current control system performance, genetic algorithm is adopted to solve Model for Multi-Objective Optimization, controller parameter in regulation and control system, realize automatically regulating fire path temperature, should utilize this method that the controller parameter of production scene can be regulated automatically, reach the object that fire path temperature regulates automatically, effectively improve automaticity and the coking economic benefit of producing that coking produces.By adopting the on-line performance evaluate model based on information entropy, have evaluated the ruuning situation of control system in coking production process accurately, for the optimization of controller parameter provides foundation.

Description

Based on the coke oven heating-combustion process fire path temperature Automatic adjustment method of Performance Evaluation

Technical field

The invention belongs to coke oven heating-combustion process control field, relate to a kind of coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation.

Background technology

Coke is widely used in the industrial circles such as blast furnace ironmaking, non-ferrous metal metallurgy, calcium carbide, casting, gasification as the raw materials for metallurgy of iron and steel, is the important substance basis of the national economic development.In steel industry, coke quality directly affects enterprise product cost, the quality of production, governs the growth of Business Economic Benefit.Design coke oven heating-combustion process Optimal Control System is stablized coke oven fire path temperature and is had very important effect to reduction coal chemical enterprise production cost with increasing economic efficiency.

Coke oven heating-combustion process fire path temperature affects coke quality and the most important procedure parameter of coking energy consumption, if fire path temperature is unstable, fluctuation large, coke will be caused to heat uneven, local green coke can cause over-emitting black exhaust when the discharging of the coke, and directly affects coke quality and converter life.Owing to coalingging frequently, coke pushing or the reason such as mechanical disorder, electric fault, coke oven heating-combustion process fire path temperature easily produces larger fluctuation.

Be directed to the method usually taked at this problem coking scene and adjust controling parameters to reduce the fluctuation of fire path temperature by artificial mistake, but this method depends on artificial experience very much.When adjustment control parameter, according to fire path temperature, first field technician can judge that whether system cloud gray model is normally, if system cloud gray model is abnormal, then by adjustment control parameter, reduces the fire path temperature fluctuation of control system, arrives steady state (SS) to make System recover.This manual shift controling parameters, the method stablizing fire path temperature depend on the experience of field technician, are unfavorable for the timely adjustment of control system, may have influence on coking and produce.Therefore, invent a kind of coke oven heating-combustion process fire path temperature Automatic adjustment method, intelligent to production run, enhancing productivity has great significance with the quality of production.

Summary of the invention

The invention provides a kind of coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation, the fire path temperature that its object is to overcome coke oven heating-combustion process of the prior art can only rely on artificial judgment to regulate, adjustment process depends on technician's experience, automatically cannot regulate, affect the problem of coking production efficiency and quality.

A kind of coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation, by gathering the historical data that fire path temperature runs, set up the many attributes Performance Evaluation Model based on information entropy, gather the data that current fire path temperature runs, the many attributes Performance Evaluation Model based on information entropy is utilized to judge current control system performance rate, Model for Multi-Objective Optimization is set up according to current control system performance, genetic algorithm is adopted to solve Model for Multi-Objective Optimization, controller parameter in regulation and control system, realizes automatically regulating fire path temperature;

The concrete steps of the controller parameter in regulation and control system are as follows:

Step 1: set up Model for Multi-Objective Optimization;

Step 2: with the controller parameter X=[x of control system ₁, x ₂, x ₃] as population at individual, x ₁and x ₂the quantizing factor of input quantity fire path temperature deviation in controller and fire path temperature deviation ratio respectively, x ₃be the scale factor of controller output quantity, adopt random mode to set up initialization population, and calculate each desired value in the step 1 of each individuality difference correspondence in population, the value of population at individual quantity NP is 40;

The method of the initialization population of differential evolution algorithm formation controller parameter is adopted to be shown below in this example:

$x_{\mathrm{qp}}^0=x_{q\min }+\mathrm{rand}\left(\mathrm{0,1}\right)*(x_{q\max }-x_{q\min })$

In formula, p represents p group controller parameter in population, and q represents q parameter in a group controller parameter, represent p parameter of q group controller parameter in the 0th generation population kind, x _qmax, x _qminrepresent the maximum occurrences of decision variable and minimum value, the span of p is [Isosorbide-5-Nitrae 0], and the span of q is [1,3];

Step 3: the controller parameter of two groups of different control system in random selecting population, adopts difference strategy, calculates of future generation individual, obtains middle population after individuality is carried out interlace operation;

Step 4: the interim population by the NP group parameter composition population scale in NP group controller parameter in middle population and previous generation being 2NP, sort according to the good and bad grade of individuality and crowding distance, from interim population, select the controller parameter being arranged in front NP group as population of future generation, complete an iteration;

Carrying out sort method according to the good and bad grade of individuality and crowding distance is the disclosed content of document " multi-Objective Chaotic differential evolution algorithm ", has a detailed description in its Literature [2] in 3.1.3 trifle

Step 5: when iterations is more than or equal to the maximum iteration time of setting, using good and bad grade be the controller parameter of 1 as the solution of Model for Multi-Objective Optimization, otherwise, return step 3;

Described Model for Multi-Objective Optimization comprises following two kinds:

1) when the value of control system performance rate B belongs to (0.2,0.6), then the coke oven heating-combustion process Optimized model be shown below is adopted:

$\left\{\begin{array}{c}\min f_1\left(x\right)=[f_{11}\left(X\right),f_{12}\left(X\right),f_{13}\left(X\right)]\\ f_{11}=\frac{1}{M}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}|T_j-R|\\ f_{12}=\frac{1}{M}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{(T_j-\frac{1}{j}\underset{p=1}{\overset{j}{\mathrm{\Σ}}}{T}_p)}^2\\ f_{13}=t_{s,i}\\ u_j=g_1(x_1,x_2,x_3,e,\mathrm{ec})\\ T_j=g_2\left(u_j\right)\end{array}\right.---\left(1\right)$

Wherein, X=[x ₁, x ₂, x ₃] be decision vector, x ₁, x ₂represent two quantizing factors of fuzzy controller respectively, x ₃represent the scale factor of fuzzy controller;

M be after determining fire path temperature interval time of measurement in 12 hours according to the number of times carrying out this interval time measuring;

After M represents parameters revision, system enters the stable state moment and rises, the time period of system cloud gray model; T _jbe expressed as by function g ₂the fire path temperature predicted value in the jth moment that () obtains, R is fire path temperature setting value, t _s,irepresent the system fading margin time in i-th moment, e represents the difference of fire path temperature setting value and actual measured value, and ec represents the rate of change of fire path temperature setting value and actual measured value difference, u _jrepresent the volume forecasting value of a jth moment heating gas; T _p=T _j, p be from 1 value until the integer of j;

F ₁₁() represents the deviation of control system, f ₁₂() represents the mean square deviation that control system exports, f ₁₃() represents control system regulating time;

G ₁() represents with e and ec for input, u _jfor the two-dimensional fuzzy controller exported; The fuzzy domain of input quantity is [-6,6], and the fuzzy domain of output quantity is [-6,6], the word set of fuzzy variable all elects 7 as: { NB, NM, NS, ZO, PS, PM, PB}, in subset element represent respectively negative large, negative in, negative little, zero, just little, center, honest, input quantity and output quantity in fuzzy domain [-6 ,-5] they are negative large NB, (-5,-3] be negative middle NM, (-3 ,-1] be negative little NS, (-1,1] be zero ZO, (1,3] be just little PS, (3,5] be center PM, (5,6] be honest PB; Designed fuzzy reasoning table 1 is as shown in the table:

Table 1

G ₂() is the relational expression that employing timing learning algorithm sets up between fire path temperature and heating gas flow;

2) when the value of control system performance rate B is less than 0.2, then the coke oven heating-combustion process Optimized model be shown below is adopted:

$\left\{\begin{array}{c}\min f_2\left(x\right)=[f_{21}\left(X\right),f_{22}\left(X\right)]\\ f_{21}=\frac{1}{\min (\frac{\mathrm{USL}-\stackrel{\‾}{T}}{3S},\frac{\stackrel{\‾}{T}-\mathrm{LSL}}{3S})}\\ f_{22}=t_{s,i}\\ u_j=g_1(x_1,x_2,x_3,e,\mathrm{ec})\\ T_j=g_2\left(u_j\right)\end{array}\right.---\left(2\right)$

Wherein, X=[x ₁, x ₂, x ₃] be decision vector, x ₁, x ₂represent two quantizing factors of fuzzy controller respectively, x ₃represent the scale factor of fuzzy controller; M is the constant between 3 ~ 5, represents after parameters revision, and system enters the stable state moment and rises, the time period of system cloud gray model, t _s,irepresent the system fading margin time in i-th moment, e represents the difference of fire path temperature setting value and actual measured value, and ec represents the rate of change of fire path temperature setting value and actual measured value difference, u _jrepresent the volume forecasting value of a jth moment heating gas;

represent fire path temperature standard deviation, m fire path temperature mean value, USL is (the pusher side ceiling temperature 1200 DEG C of specification value on fire path temperature, coke side ceiling temperature 1400 DEG C) and LSL be (the pusher side lower limit temperature 1000 DEG C of specification value under fire path temperature, coke side lower limit temperature 1200 DEG C), T (i) measures the fire path temperature value obtained at i-th moment;

F ₂₁() represents the process capability that control system is current, f ₂₂() represents control system regulating time;

Control system performance rate B is obtained by the many attributes Performance Evaluation Model based on information entropy;

Described fire path temperature interval time of measurement is 3 ~ 4 hours.

The establishment step of the described Performance Evaluation Model of many attributes based on information entropy is as follows:

Step 1: calculate each performance index J respectively _x, according to membership function, determine the relational matrix Q of control system performance index;

In formula, r _xyrepresent that an xth performance index are under the jurisdiction of the degree value to y performance rate, obtain according to membership function, 1≤x≤6,1≤y≤3;

Step 2: the information entropy calculating each performance index:

$E_x=-\frac{1}{\ln 6}\underset{y=1}{\overset{3}{\mathrm{\Σ}}}{p}_{\mathrm{xy}}\ln p_{\mathrm{xy}}---\left(3\right)$

In formula $p_{\mathrm{xy}}=r_{\mathrm{xy}}/\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{r}_{\mathrm{xk}}(x=\mathrm{1,2},...,6;y=\mathrm{1,2,3});$ Work as r _xy=0, make r _xyfor infinitesimal;

Step 3: determine the weight of each performance index in performance evaluation process:

$h_x=(1-E_x)/(6-\underset{x=1}{\overset{6}{\mathrm{\Σ}}}{E}_x),(x=1,...,6)---\left(4\right)$

0≤h in formula _x≤ 1, and

Step 4: calculate the relative importance r between each index _xy':

${r_{\mathrm{xy}}}^{\′}=h_x{r}_{\mathrm{xy}}/\underset{k=1}{\overset{6}{\mathrm{\Σ}}}{h}_k{r}_{\mathrm{ky}}---\left(5\right)$

Step 5: the model obtaining coke oven heating-combustion process performance evaluation is:

In formula, ∨ (b ₁, b ₂)=max (b ₁, b ₂), B is the performance rate of control system, and its span is 0 ~ 1;

Wherein, described performance index comprise 6, are respectively two one-level performance index and four secondary performance index, and described control system performance rate span is 0 ~ 1;

Described performance rate comprises 3, and the first to three performance rate is followed successively by defective, good and qualified;

Described one-level performance index refer to one-level fire path temperature Deviation Indices J ₁with one-level fire path temperature deviation variation rate J ₂;

$J_1=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}|T\left(i\right)-R|---\left(7\right)$

$J_2=\frac{1}{N_1-1}\underset{i=1}{\overset{N_1-1}{\mathrm{\Σ}}}\left(\right|T(i+1)-R|-|T\left(i\right)-R\left|\right)---\left(8\right)$

Wherein, N ₁be fire path temperature fire path temperature data amount check measured in 12 hours, T(i) be fire path temperature measured value, R is fire path temperature setting value (coke side fire path temperature setting value is 1310 DEG C), and i represents i-th fire path temperature measured value;

Described secondary performance index refer to process capability index J respectively ₃, economic performance index J ₄, secondary fire path temperature Deviation Indices J ₅and secondary fire path temperature deviation variation rate J ₆;

$J_3=\min (\frac{\mathrm{USL}-\stackrel{\‾}{T}}{3S},\frac{\stackrel{\‾}{T}-\mathrm{LSL}}{3S})---\left(9\right)$

$J_4=\frac{J_4^{\mathrm{hist}}}{\mathrm{\φ}},\mathrm{\φ}=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\mathrm{\Σ}}}({(T\left(i\right)-R)}^2+\mathrm{\λ\ΔU}{\left(i\right)}^2)---\left(10\right)$

$J_5=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\mathrm{\Σ}}}|T\left(i\right)-R|---\left(11\right)$

$J_6=\frac{1}{N_2-1}\underset{i=1}{\overset{N_2-1}{\mathrm{\Σ}}}\left(\right|T(i+1)-R|-|T\left(i\right)-R\left|\right)---\left(12\right)$

Wherein, represent fire path temperature standard deviation, it is assessment cycle internal-quirk temperature averages, USL is gauge wire on fire path temperature (pusher side ceiling temperature 1200 DEG C, coke side ceiling temperature 1400 DEG C) and LSL is gauge wire under fire path temperature (pusher side lower limit temperature 1000 DEG C, coke side lower limit temperature 1200 DEG C), T is fire path temperature detected value, N ₂it is fire path temperature data volume measured in 24 hours; R represents fire path temperature setting value, and during production, the usual pusher side of the setting of fire path temperature is 1260 degree, and coke side is 1310 degree, and T (i) represents the fire path temperature value in i-th moment, and Δ U (i) represents the change of the heating gas flow in i-th moment; for the performance reference value of control system, from history data, choose fire path temperature deviation be less than ± 7, deviation variation rate is less than ± 7% time economic performance desired value;

Described fire path temperature interval time of measurement is 3 ~ 4 hours.

Described membership function refers to:

As the performance index value J of control system _xwhen being less than a, control system herein means and puts on the degree being under the jurisdiction of " defective " is 1;

As the performance index value J of control system _xwhen being greater than c, control system herein means and puts on the degree being under the jurisdiction of " well " is 1;

As the performance index value J of control system _xtime between a to b, control system herein means to be put on the degree being under the jurisdiction of " defective " and is the degree being under the jurisdiction of " qualified " is

As the performance index value J of control system _xtime between b to c, control system herein means to be put on the degree being under the jurisdiction of " qualified " and is the degree being under the jurisdiction of " well " is

Wherein, the value of a is the value of c is the value of b is with be respectively performance index J _xmaximal value and the minimum value of this index is calculated respectively according to the service data of production scene;

The collection of described fire path temperature refers to the mean value coke side fire path temperature of m firing chamber or pusher side fire path temperature being gathered simultaneously to acquisition;

$T=\frac{1}{m}\underset{m=1}{\overset{56}{\mathrm{\Σ}}}{T}_m---\left(13\right)$

Wherein, T _mrepresent coke side fire path temperature or the pusher side fire path temperature of m firing chamber.

The performance reference value of described control system in the operational process of control system, upgrade with the maximum economic performance desired value of the history obtained in current control system

Beneficial effect

The invention provides a kind of coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation, by gathering the historical data that fire path temperature runs, set up the many attributes Performance Evaluation Model based on information entropy, gather the data that current fire path temperature runs, the many attributes Performance Evaluation Model based on information entropy is utilized to judge current control system performance rate, Model for Multi-Objective Optimization is set up according to current control system performance, genetic algorithm is adopted to solve Model for Multi-Objective Optimization, controller parameter in regulation and control system, realize automatically regulating fire path temperature, should utilize this method that the controller parameter of production scene can be regulated automatically, reach the object that fire path temperature regulates automatically, effectively improve automaticity and the coking economic benefit of producing that coking produces.Meanwhile, by the present invention proposes the on-line performance evaluate model based on information entropy, have evaluated the ruuning situation of control system in coking production process accurately, for the optimization of controller parameter provides foundation.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of the method for the invention;

The membership function of Fig. 2 performance index;

Fig. 3 interlace operation process example figure;

Fig. 4 pusher side fire path temperature self-adjusting schematic diagram;

Fig. 5 coke side fire path temperature self-adjusting schematic diagram.

Embodiment

Below in conjunction with drawings and Examples, the present invention is described further.

Be adjusted to certain iron company new 1# coke oven coke side temperature the explanation that example carries out embodiment, the fire path temperature of this iron company is measured once every four hours.

As shown in Figure 1, for the process flow diagram of a kind of coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation of the present invention, by gathering the historical data that fire path temperature runs, set up the many attributes Performance Evaluation Model based on information entropy, gather the data that current fire path temperature runs, the many attributes Performance Evaluation Model based on information entropy is utilized to judge current control system performance rate, Model for Multi-Objective Optimization is set up according to current control system performance, genetic algorithm is adopted to solve Model for Multi-Objective Optimization, controller parameter in regulation and control system, realize automatically regulating fire path temperature,

The concrete steps of the controller parameter in regulation and control system are as follows:

Step 1: set up Model for Multi-Objective Optimization;

Step 2: with the controller parameter X=[x of control system ₁, x ₂, x ₃] as population at individual, x ₁and x ₂the quantizing factor of input quantity fire path temperature deviation in controller and fire path temperature deviation ratio respectively, x ₃be the scale factor of controller output quantity, adopt random mode to set up initialization population, and calculate each desired value in the step 1 of each individuality difference correspondence in population, the span of population at individual quantity NP is 20 ~ 50;

Step 3: the controller parameter of two groups of different control system in random selecting population, adopts difference strategy, see document [1], calculates of future generation individual, obtains middle population after individuality is carried out interlace operation;

Interlace operation process as shown in Figure 3, v in figure _q,p(g+1) individuality for producing after variation, x _q,pg () is previous generation individuality.The basis of first step interlace operation takes out v at random _q,p(g+1) q in _randindividual parameter is as u after intersection _q,p(g+1) q _randfor parameter (what get in the example of Fig. 3 is primary parameter).Follow-up crossover process then gets 0.8 by crossover probability CR(the present invention) to choose be x _q,p(g) or v _q,p(g+1) u is used as _q,p(g+1) equipotential parameter, r is the random number between [0,1];

The population produced after variation, intersection is called middle population, is directed to each group controller parameter calculating target function value of middle population;

Step 4: the interim population by the NP group parameter composition population scale in NP group controller parameter in middle population and previous generation being 2NP, sort according to the good and bad grade of individuality and crowding distance, from interim population, select the controller parameter being arranged in front NP group as population of future generation, complete an iteration, iterations adds 1;

Good and bad higher grade, the sequence of the less individuality of crowding distance in population more rearward, carrying out sort method according to the good and bad grade of individuality and crowding distance is the disclosed content of document [2] " multi-Objective Chaotic differential evolution algorithm ", has a detailed description in the 3.1.3 trifle in its Literature [2];

Step 5: when iterations is more than or equal to the maximum iteration time of setting, using good and bad grade be the controller parameter of 1 as the solution of Model for Multi-Objective Optimization, otherwise, return step 3;

Described Model for Multi-Objective Optimization comprises following two kinds:

1) when the value of control system performance rate B belongs to (0.2,0.6), then the coke oven heating-combustion process Optimized model be shown below is adopted:

$\left\{\begin{array}{c}\min f_1\left(x\right)=[f_{11}\left(X\right),f_{12}\left(X\right),f_{13}\left(X\right)]\\ f_{11}=\frac{1}{M}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}|T_j-R|\\ f_{12}=\frac{1}{M}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}{(T_j-\frac{1}{j}\underset{p=1}{\overset{j}{\mathrm{\Σ}}}{T}_p)}^2\\ f_{13}=t_{s,i}\\ u_j=g_1(x_1,x_2,x_3,e,\mathrm{ec})\\ T_j=g_2\left(u_j\right)\end{array}\right.---\left(1\right)$

Wherein, X=[x ₁, x ₂, x ₃] be decision vector, x ₁, x ₂represent two quantizing factors of fuzzy controller respectively, x ₃represent the scale factor of fuzzy controller;

M be after determining fire path temperature interval time of measurement in 12 hours according to the number of times carrying out this interval time measuring;

After M represents parameters revision, system enters the stable state moment and rises, the time period of system cloud gray model; T _jbe expressed as by function g ₂the fire path temperature predicted value in the jth moment that () obtains, R is fire path temperature setting value, t _s,irepresent the system fading margin time in i-th moment, e represents the difference of fire path temperature setting value and actual measured value, and ec represents the rate of change of fire path temperature setting value and actual measured value difference, u _jrepresent the volume forecasting value of a jth moment heating gas; T _p=T _j, p be from 1 value until the integer of j;

F ₁₁() represents the deviation of control system, f ₁₂() represents the mean square deviation that control system exports, f ₁₃() represents control system regulating time;

G ₁() represents with e and ec for input, u _jfor the two-dimensional fuzzy controller exported; The fuzzy domain of input quantity is [-6,6], and the fuzzy domain of output quantity is [-6,6], the word set of fuzzy variable all elects 7 as: { NB, NM, NS, ZO, PS, PM, PB}, in subset element represent respectively negative large, negative in, negative little, zero, just little, center, honest, input quantity and output quantity in fuzzy domain [-6 ,-5] they are negative large NB, (-5,-3] be negative middle NM, (-3 ,-1] be negative little NS, (-1,1] be zero ZO, (1,3] be just little PS, (3,5] be center PM, (5,6] be honest PB; Designed fuzzy reasoning table 1 is as shown in the table:

Table 1

G ₂() is the relational expression that employing timing learning algorithm sets up between fire path temperature and heating gas flow; Timing study is a kind of machine learning algorithm having supervision, but does not shift to an earlier date training sample, until need the output estimating that certain input value is corresponding, and this input value [y _j-1, y _j-2, u _j-1, u _j-2, u _j-3] be commonly called query point, for this query point, find similar data heating gas flow to set up dynamic sample storehouse according to fire path temperature in existing sample set, the local according to setting up in the annex 1 after 3.8 joints in document [3] maps, and obtains the output of respective queries point.

2) when the value of control system performance rate B is less than 0.2, then the coke oven heating-combustion process Optimized model be shown below is adopted:

$\left\{\begin{array}{c}\min f_2\left(x\right)=[f_{21}\left(X\right),f_{22}\left(X\right)]\\ f_{21}=\frac{1}{\min (\frac{\mathrm{USL}-\stackrel{\‾}{T}}{3S},\frac{\stackrel{\‾}{T}-\mathrm{LSL}}{3S})}\\ f_{22}=t_{s,i}\\ u_j=g_1(x_1,x_2,x_3,e,\mathrm{ec})\\ T_j=g_2\left(u_j\right)\end{array}\right.---\left(2\right)$

Wherein, X=[x ₁, x ₂, x ₃] be decision vector, x ₁, x ₂represent two quantizing factors of fuzzy controller respectively, x ₃represent the scale factor of fuzzy controller; M is the constant between 3 ~ 5, represents after parameters revision, and system enters the stable state moment and rises, the time period of system cloud gray model, t _s,irepresent the system fading margin time in i-th moment, e represents the difference of fire path temperature setting value and actual measured value, and ec represents the rate of change of fire path temperature setting value and actual measured value difference, u _jrepresent the volume forecasting value of a jth moment heating gas;

represent fire path temperature standard deviation, m fire path temperature mean value, USL is (the pusher side ceiling temperature 1200 DEG C of specification value on fire path temperature, coke side ceiling temperature 1400 DEG C) and LSL be (the pusher side lower limit temperature 1000 DEG C of specification value under fire path temperature, coke side lower limit temperature 1200 DEG C), T (i) measures the fire path temperature value obtained at i-th moment;

F ₂₁() represents the process capability that control system is current, f ₂₂() represents control system regulating time;

Control system performance rate B is obtained by the many attributes Performance Evaluation Model based on information entropy;

Described fire path temperature interval time of measurement is 3 ~ 4 hours.

The establishment step of the described Performance Evaluation Model of many attributes based on information entropy is as follows:

Step 1: calculate each performance index J respectively _x, according to membership function, determine the relational matrix Q of control system performance index;

In formula, r _xyrepresent that an xth performance index are under the jurisdiction of the degree value to y performance rate, obtain according to membership function, 1≤x≤6,1≤y≤3;

Step 2: the information entropy calculating each performance index:

$E_x=-\frac{1}{\ln 6}\underset{y=1}{\overset{3}{\mathrm{\Σ}}}{p}_{\mathrm{xy}}\ln p_{\mathrm{xy}}---\left(3\right)$

In formula $p_{\mathrm{xy}}=r_{\mathrm{xy}}/\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{r}_{\mathrm{xk}}(x=\mathrm{1,2},...,6;y=\mathrm{1,2,3});$ Work as r _xy=0, make r _xyfor infinitesimal;

Information entropy is less, shows that the uncertainty of these performance index in Performance Evaluation process is less, and weight shared in Performance Evaluation process is larger; Otherwise information entropy is larger, show that the uncertainty of this index in Performance Evaluation process is larger, weight shared in Performance Evaluation process is less;

Step 3: determine the weight of each performance index in performance evaluation process:

$h_x=(1-E_x)/(6-\underset{x=1}{\overset{6}{\mathrm{\Σ}}}{E}_x),(x=1,...,6)---\left(4\right)$

0≤h in formula _x≤ 1, and

Step 4: calculate the relative importance r between each index _xy':

${r_{\mathrm{xy}}}^{\′}=h_x{r}_{\mathrm{xy}}/\underset{k=1}{\overset{6}{\mathrm{\Σ}}}{h}_k{r}_{\mathrm{ky}}---\left(5\right)$

Step 5: the model obtaining coke oven heating-combustion process performance evaluation is:

In formula, ∨ (b ₁, b ₂)=max (b ₁, b ₂), ∨ is for getting large symbol, and B is the performance rate of control system, and its span is 0 ~ 1;

Wherein, described performance index comprise 6, are respectively two one-level performance index and four secondary performance index, and described control system performance rate span is 0 ~ 1;

Described performance rate comprises 3, and the first to three performance rate is followed successively by defective, good and qualified;

Described one-level performance index refer to one-level fire path temperature Deviation Indices J ₁with one-level fire path temperature deviation variation rate J ₂;

$J_1=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}|T\left(i\right)-R|---\left(7\right)$

$J_2=\frac{1}{N_1-1}\underset{i=1}{\overset{N_1-1}{\mathrm{\Σ}}}\left(\right|T(i+1)-R|-|T\left(i\right)-R\left|\right)---\left(8\right)$

Wherein, N ₁be fire path temperature fire path temperature data amount check measured in 12 hours, T(i) be fire path temperature measured value, R is fire path temperature setting value (coke side fire path temperature setting value is 1310 DEG C), and i represents i-th fire path temperature measured value;

Described secondary performance index refer to process capability index J respectively ₃, economic performance index J ₄, secondary fire path temperature Deviation Indices J ₅and secondary fire path temperature deviation variation rate J ₆;

$J_3=\min (\frac{\mathrm{USL}-\stackrel{\‾}{T}}{3S},\frac{\stackrel{\‾}{T}-\mathrm{LSL}}{3S})---\left(9\right)$

The process capability of the conventional criterion industrial system as shown in table 1 of industry, as can be seen from Table 1, when the Distribution value of process capability index is between 1.33 to 2.0, control system is in good running status.In this example, the upper limit of process capability indexed basis is made the lower limit of process capability indexed basis is made to be

Table 1

J 3Value	System performance
		J 3∈[2.0,+∞]	System cloud gray model spy is excellent, can consider to reduce costs
J 3∈[1.67,2.0)	System cloud gray model is good, continues to keep
		J 3∈(1.33,1.67]	System cloud gray model is good, but performance still has the space of lifting
J 3∈(0.67,1.33]	System performance is poor, needs lifting controller performance immediately
		J 3∈[0,0.67]	System performance is unacceptable, need redesign controller

Heating gas is the valuable source in coking production process, and under ensureing that fire path temperature meets the prerequisite of production requirement, reduce gas flow is the important means reducing energy consumption as far as possible, adopts φ to represent economic performance function, J ₄represent the economic performance index of control system:

$J_4=\frac{J_4^{\mathrm{hist}}}{\mathrm{\φ}},\mathrm{\φ}=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\mathrm{\Σ}}}({(T\left(i\right)-R)}^2+\mathrm{\λ\ΔU}{\left(i\right)}^2)---\left(10\right)$

The performance reference value of described control system initial value is 25.384, in the operational process of control system, upgrades with the maximum economic performance desired value of the history obtained in current control system the economic performance of system is judged according to table 2.

Table 2

$J_5=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\mathrm{\Σ}}}|T\left(i\right)-R|---\left(11\right)$

$J_6=\frac{1}{N_2-1}\underset{i=1}{\overset{N_2-1}{\mathrm{\Σ}}}\left(\right|T(i+1)-R|-|T\left(i\right)-R\left|\right)---\left(12\right)$

Wherein, represent fire path temperature standard deviation, it is assessment cycle internal-quirk temperature averages, USL is gauge wire on fire path temperature (pusher side ceiling temperature 1200 DEG C, coke side ceiling temperature 1400 DEG C) and LSL is gauge wire under fire path temperature (pusher side lower limit temperature 1000 DEG C, coke side lower limit temperature 1200 DEG C), T is fire path temperature detected value, N ₂it is fire path temperature data volume measured in 24 hours; R represents fire path temperature setting value, during production, the usual pusher side of the setting of fire path temperature is 1260 degree, coke side is 1310 degree, and T (i) represents the fire path temperature value in i-th moment, and Δ U (i) represents the variable quantity of the heating gas flow in i-th moment; for the performance reference value of control system, from history data, choose fire path temperature deviation be less than ± 7, deviation variation rate is less than ± 7% time economic performance desired value;

According to design of the present invention, the assessment cycle of one-level performance index is 24 hours, and the assessment cycle of secondary performance index is 12 hours.Therefore, when the assessment cycle of secondary performance index arrives, need to carry out one-level Performance Evaluation simultaneously.In the present invention, when secondary performance index are assessed, assess one-level fire path temperature Deviation Indices and fire path temperature deviation variation rate index, the computing method of these two performance index are identical with the computing method of carrying out when one-level performance index are assessed simultaneously.

Described fire path temperature interval time of measurement is 4 hours.

Described membership function is Triangleshape grade of membership function as shown in Figure 2, according to maximum membership grade principle:

As the performance index value J of control system _xwhen being less than a, control system herein means and puts on the degree being under the jurisdiction of " defective " is 1;

As the performance index value J of control system _xwhen being greater than c, control system herein means and puts on the degree being under the jurisdiction of " well " is 1;

As the performance index value J of control system _xtime between a to b, control system herein means to be put on the degree being under the jurisdiction of " defective " and is the degree being under the jurisdiction of " qualified " is

As the performance index value J of control system _xtime between b to c, control system herein means to be put on the degree being under the jurisdiction of " qualified " and is the degree being under the jurisdiction of " well " is

Wherein, the value of a is the value of c is the value of b is with be respectively performance index J _xmaximal value and the minimum value of this index is calculated respectively according to the service data of production scene; According to analyzing, provide following instance data above:

$J_2^{\mathrm{hist}1}=7\%,$ $J_3^{\mathrm{hist}1}=0.67,$ $J_4^{\mathrm{hist}1}=0.2,$ $J_2^{\mathrm{hist}1}=14\%,$ $J_3^{\mathrm{hist}1}=1.67,$ $J_4^{\mathrm{hist}1}=0.8$

In the present embodiment, coke-oven plant's 1# stove of certain iron company has 56, firing chamber, therefore, each thermometric obtain being data be, 56 groups of coke side fire path temperatures, the collection of described fire path temperature refers to that the coke side fire path temperature to 56 firing chambers gathers the mean value of acquisition simultaneously;

$T=\frac{1}{56}\underset{m=1}{\overset{56}{\mathrm{\Σ}}}{T}_m---\left(13\right)$

Wherein, T _mrepresent coke side fire path temperature or the pusher side fire path temperature of m firing chamber.

The present invention adopts matlab software to carry out simulating, verifying to the method proposed, and pusher side fire path temperature self-adjusting schematic diagram as shown in Figure 4, (in Fig. 4 before 24h) controller parameter is x before optimization ₁=1.5, x ₂=2, x ₃=10, after optimization, controller parameter is x ₁=1.05, x ₂=11.32, x ₃=5.49.Be illustrated in figure 5 coke side fire path temperature self-adjusting schematic diagram, (in Fig. 5 before 24h) controller parameter is x before optimization ₁=1.13, x ₂=2.8, x ₃=5.03, after optimization, controller parameter is x ₁=1.75, x ₂=8.96, x ₃=7.63.Emulation shows, the optimization method of the present invention's design makes fire path temperature return to normal condition when fire path temperature fluctuation is larger immediately.

List of references

[1] Yang Qiwen, Cai Liang, Xue Yuncan. differential evolution algorithm summary [J]. Intelligent Recognition and artificial intelligence .2008,21 (4): 506-511.

[2] Niu great Peng, Wang Fuli, what is wealthy greatly, Jia Mingxing. multi-Objective Chaotic differential evolution algorithm [J]. control and decision-making .2009,24 (3): 361-365.

[3] Pan Tianhong, based on nonlinear system multi-model modeling and the control [D] of local learning strategy, Shanghai Communications University .2007.

Claims (5)

1. the coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation, it is characterized in that, by gathering the historical data that fire path temperature runs, set up the many attributes Performance Evaluation Model based on information entropy, gather the data that current fire path temperature runs, the many attributes Performance Evaluation Model based on information entropy is utilized to judge current control system performance rate, Model for Multi-Objective Optimization is set up according to current control system performance, genetic algorithm is adopted to solve Model for Multi-Objective Optimization, controller parameter in regulation and control system, realize automatically regulating fire path temperature,

The concrete steps of the controller parameter in regulation and control system are as follows:

Step 1: set up Model for Multi-Objective Optimization;

Step 2: with the controller parameter X=[x of control system ₁, x ₂, x ₃] as population at individual, x ₁and x ₂the quantizing factor of input quantity fire path temperature deviation in controller and fire path temperature deviation ratio respectively, x ₃be the scale factor of controller output quantity, adopt random mode to set up initialization population, and calculate each desired value in the step 1 of each individuality difference correspondence in population, the span of population at individual quantity NP is 20 ~ 50;

Step 3: the controller parameter of two groups of different control system in random selecting population, adopts difference strategy, calculates of future generation individual, obtains middle population after individuality is carried out interlace operation;

Step 4: the interim population by the NP group parameter composition population scale in NP group controller parameter in middle population and previous generation being 2NP, sort according to the good and bad grade of individuality and crowding distance, from interim population, select the controller parameter being arranged in front NP group as population of future generation, complete an iteration;

Step 5: when iterations is more than or equal to the maximum iteration time of setting, using good and bad grade be the controller parameter of 1 as the solution of Model for Multi-Objective Optimization, otherwise, return step 3;

Described Model for Multi-Objective Optimization comprises following two kinds:

1) when the value of control system performance rate B belongs to (0.2,0.6), then the coke oven heating-combustion process Optimized model be shown below is adopted:

$\left\{\begin{array}{c}\min f_1\left(x\right)=\[f_{11}\left(X\right),f_{12}\left(X\right),f_{13}\left(X\right)\]\\ f_{11}=\frac{1}{M}\underset{j=1}{\overset{M}{\Σ}}|T_j-R|\\ f_{12}=\frac{1}{M}\underset{j=1}{\overset{M}{\Σ}}{(T_j-\frac{1}{j}\underset{p=1}{\overset{j}{\Σ}}{T}_p)}^2\\ f_{13}=t_{s,i}\\ u_j=g_1(x_1,x_2,x_3,e,ec)\\ T_j=g_2\left(u_j\right)\end{array}\right.---\left(1\right)$

Wherein, X=[x ₁, x ₂, x ₃] be decision vector, x ₁, x ₂represent two quantizing factors of fuzzy controller respectively, x ₃represent the scale factor of fuzzy controller;

M be after determining fire path temperature interval time of measurement in 12 hours according to the number of times carrying out this interval time measuring;

T _jbe expressed as by function g ₂the fire path temperature predicted value in the jth moment that () obtains, R is fire path temperature setting value, t _s,irepresent the system fading margin time in i-th moment, e represents the difference of fire path temperature setting value and actual measured value, and ec represents the rate of change of fire path temperature setting value and actual measured value difference, u _jrepresent the volume forecasting value of a jth moment heating gas; T _p=T _j, p be from 1 value until the integer of j;

F ₁₁() represents the deviation of control system, f ₁₂() represents the mean square deviation that control system exports, f ₁₃() represents control system regulating time;

G ₁() represents with e and ec for input, u _jfor the two-dimensional fuzzy controller exported; The fuzzy domain of input quantity is [-6,6], and the fuzzy domain of output quantity is [-6,6], the subset of fuzzy variable all elects 7 as: { NB, NM, NS, ZO, PS, PM, PB}, in subset element represent respectively negative large, negative in, negative little, zero, just little, center, honest, input quantity and output quantity in fuzzy domain [-6 ,-5] they are negative large NB, (-5,-3] be negative middle NM, (-3 ,-1] be negative little NS, (-1,1] be zero ZO, (1,3] be just little PS, (3,5] be center PM, (5,6] be honest PB; Designed fuzzy reasoning table 1 is as shown in the table:

Table 1

G ₂() is the relational expression that employing timing learning algorithm sets up between fire path temperature and heating gas flow;

2) when the value of control system performance rate B is less than 0.2, then the coke oven heating-combustion process Optimized model be shown below is adopted:

$\left\{\begin{array}{c}\min f_2\left(x\right)=\[f_{21}\left(X\right),f_{22}\left(X\right)\]\\ f_{21}=\frac{1}{min(\frac{USL-\stackrel{\‾}{T}}{3S},\frac{\stackrel{\‾}{T}-LSL}{3S})}\\ f_{22}=t_{s,i}\\ u_j=g_1(x_1,x_2,x_3,e,ec)\\ T_j=g_2\left(u_j\right)\end{array}\right.---\left(2\right)$

Wherein, X=[x ₁, x ₂, x ₃] be decision vector, x ₁, x ₂represent two quantizing factors of fuzzy controller respectively, x ₃represent the scale factor of fuzzy controller; M is the constant between 3 ~ 5, represents after parameters revision, and system enters the stable state moment and rises, the time period of system cloud gray model, t _s,irepresent the system fading margin time in i-th moment, e represents the difference of fire path temperature setting value and actual measured value, and ec represents the rate of change of fire path temperature setting value and actual measured value difference, u _jrepresent the volume forecasting value of a jth moment heating gas;

represent fire path temperature standard deviation, be M fire path temperature mean value, USL is specification value on fire path temperature, pusher side ceiling temperature 1200 DEG C, coke side ceiling temperature 1400 DEG C and LSL are specification value under fire path temperature, pusher side lower limit temperature 1000 DEG C, coke side lower limit temperature 1200 DEG C, T (i) is the fire path temperature value in i-th moment;

F ₂₁() represents the process capability that control system is current, f ₂₂() represents control system regulating time;

Control system performance rate B is obtained by the many attributes Performance Evaluation Model based on information entropy;

Described fire path temperature interval time of measurement is 3 ~ 4 hours.

2. the coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation according to claim 1, is characterized in that, the establishment step of the described Performance Evaluation Model of many attributes based on information entropy is as follows:

Step 1: calculate each performance index J respectively _x, according to membership function, determine the relational matrix Q of control system performance index;

In formula, r _xyrepresent that an xth performance index are under the jurisdiction of the degree value to y performance rate, obtain according to membership function, 1≤x≤6,1≤y≤3;

Step 2: the information entropy calculating each performance index:

$E_x=-\frac{1}{ln6}\underset{y=1}{\overset{3}{\Σ}}{p}_{xy}\ln p_{xy}---\left(3\right)$

In formula x=1,2 ..., 6; Y=1,2,3; Work as r _xy=0, make r _xyfor infinitesimal;

Step 3: determine the weight of each performance index in performance evaluation process:

$h_x=(1-E_x)/(6-\underset{x=1}{\overset{6}{\Σ}}{E}_x),(x=1,...,6)---\left(4\right)$

0≤h in formula _x≤ 1, and

Step 4: calculate the relative importance r between each index _xy':

${r_{xy}}^{\′}=h_x{r}_{xy}/\underset{k=1}{\overset{6}{\Σ}}{h}_k{r}_{ky}---\left(5\right)$

Step 5: the model obtaining coke oven heating-combustion process performance evaluation is:

In formula, ∨ (b ₁, b ₂)=max (b ₁, b ₂), B is the performance rate of control system, and its span is 0 ~ 1;

Wherein, described performance index comprise 6, are respectively two one-level performance index and four secondary performance index, and described control system performance rate span is 0 ~ 1;

Described performance rate comprises 3, and the first to three performance rate is followed successively by defective, good and qualified;

Described one-level performance index refer to one-level fire path temperature Deviation Indices J ₁with one-level fire path temperature deviation variation rate J ₂;

$J_1=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\Σ}}\left|T\right(i)-R|---\left(7\right)$

$J_2=\frac{1}{N_1-1}\underset{i=1}{\overset{N_1-1}{\Σ}}\left(\right|T\left(i+1\right)-R|-|T\left(i\right)-R\left|\right)---\left(8\right)$

Wherein, N ₁be fire path temperature fire path temperature data amount check measured in 12 hours, T (i) is the fire path temperature value in i-th moment, and R is fire path temperature setting value, and coke side fire path temperature setting value is 1310 DEG C, and i represents i-th moment;

Described secondary performance index refer to process capability index J respectively ₃, economic performance index J ₄, secondary fire path temperature Deviation Indices J ₅and secondary fire path temperature deviation variation rate J ₆;

$J_3=min(\frac{USL-\stackrel{\‾}{T}}{3S},\frac{\stackrel{\‾}{T}-LSL}{3S})---\left(9\right)$

$J_4=\frac{J_4^{hist}}{\mathrm{\φ}},\mathrm{\φ}=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\Σ}}({\left(T\left(i\right)-R\right)}^2+\mathrm{\λ}\mathrm{\Δ}U{\left(i\right)}^2)---\left(10\right)$

$J_5=\frac{1}{N_2}\underset{i=1}{\overset{N_2}{\Σ}}|T\left(i\right)-R|---\left(11\right)$

$J_6=\frac{1}{N_2-1}\underset{i=1}{\overset{N_2-1}{\Σ}}\left(\right|T(i+1)-R|-|T\left(i\right)-R\left|\right)---\left(12\right)$

Wherein, represent fire path temperature standard deviation, be assessment cycle internal-quirk temperature averages, USL is gauge wire on fire path temperature, pusher side ceiling temperature 1200 DEG C, and coke side ceiling temperature 1400 DEG C and LSL are gauge wire under fire path temperature, pusher side lower limit temperature 1000 DEG C, coke side lower limit temperature 1200 DEG C, N ₂it is fire path temperature data volume measured in 24 hours; R represents fire path temperature setting value, and during production, the setting pusher side of fire path temperature is 1260 degree, and coke side is 1310 degree, and T (i) represents the fire path temperature value in i-th moment, and Δ U (i) represents the change of the heating gas flow in i-th moment; for the performance reference value of control system, from history data, choose fire path temperature deviation be less than ± 7 DEG C, deviation variation rate is less than ± 7% time economic performance desired value;

Described fire path temperature interval time of measurement is 3 ~ 4 hours.

3. the coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation according to claim 2, it is characterized in that, described membership function refers to:

As the performance index value J of control system _xwhen being less than a, control system herein means and puts on the degree being under the jurisdiction of " defective " is 1;

As the performance index value J of control system _xwhen being greater than c, control system herein means and puts on the degree being under the jurisdiction of " well " is 1;

As the performance index value J of control system _xtime between a to b, control system herein means to be put on the degree being under the jurisdiction of " defective " and is the degree being under the jurisdiction of " qualified " is

As the performance index value J of control system _xtime between b to c, control system herein means to be put on the degree being under the jurisdiction of " qualified " and is the degree being under the jurisdiction of " well " is

Wherein, the value of a is the value of c is the value of b is with be respectively performance index J _xmaximal value and the minimum value of this index is calculated respectively according to the service data of production scene.

4. the coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation according to any one of claim 1-3, it is characterized in that, described fire path temperature value T (i) refers to the mean value coke side fire path temperature of m firing chamber or pusher side fire path temperature being gathered simultaneously to rear acquisition;

$T\left(i\right)=\frac{1}{m}\underset{m=1}{\overset{56}{\Σ}}{T}_m---\left(13\right)$

Wherein, T _mrepresent coke side fire path temperature or the pusher side fire path temperature of m firing chamber.

5. the coke oven heating-combustion process fire path temperature Automatic adjustment method based on Performance Evaluation according to any one of claim 1-3, is characterized in that, the performance reference value of described control system in the operational process of control system, upgrade with the maximum economic performance desired value of the history obtained in current control system