CN116859720A - Multi-objective optimization control method for grate cooler considering efficiency and energy consumption - Google Patents
Multi-objective optimization control method for grate cooler considering efficiency and energy consumption Download PDFInfo
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Abstract
The invention discloses a multi-objective optimization control method of a grate cooler, which takes efficiency and energy consumption into consideration, and comprises the following steps: 1. establishing a grate cooler energy evaluation model on the basis of considering efficiency and energy consumption; 2. establishing a grate cooler efficiency target and an energy consumption target based on a grate cooler energy efficiency evaluation model, and establishing a grate cooler multi-target optimization model; 3. solving a multi-objective optimization model of the grate cooler by using an MOEA/D algorithm to obtain an optimal solution of a set value of the grate down pressure and a set value of the fan air quantity; 4. and rolling optimization of the grate down pressure and the fan air quantity is realized by using a generalized predictive control algorithm and an incremental PID algorithm. According to the invention, on the premise of considering the efficiency and the energy consumption of the grate cooler, the set value of the grate cooler grate down pressure and the set value of the fan air quantity are synchronously optimized, so that the regulation and control level of the grate cooler parameters is improved, and the production energy consumption is reduced.
Description
Technical Field
The invention belongs to the technical field of grate cooler control, and particularly relates to a multi-objective optimization control method of a grate cooler, which takes efficiency and energy consumption into consideration.
Background
The grate cooler is used as one of the main equipment in cement clinker production, and has the tasks of cooling cement clinker and recovering high temperature, and the running condition of the grate cooler has direct influence on clinker quality and production energy consumption. In order to improve the efficiency of the grate cooler and reduce the energy consumption of production, operators can increase the temperature of secondary air and tertiary air as much as possible by controlling the thickness of the grate cooler material layer, adjusting the cooling air quantity and the like, and reduce the temperature of outlet clinker, so that the clinker is fully cooled and the recovery heat is increased to the greatest extent. However, the parameter regulation and control of the grate cooler system is relatively complex, mainly depends on manual experience, and the overall control level is generally low. Moreover, the operation level of the staff is uneven, so that the running state of the grate cooler cannot be kept stable and continuous, the clinker cooling condition cannot be effectively controlled, and the energy efficiency of the system is difficult to reach the optimal level.
The target selection is one of key problems of multi-target optimal control of the grate cooler. At present, most multi-target optimization researches of the grate cooler take the grate cooler efficiency as a target, and the energy consumption of the grate cooler is rarely considered, and in actual production, the grate cooler efficiency and the energy consumption are required to be considered simultaneously so as to ensure the rationality of the adjustment of the parameters of the grate cooler. In addition, in the process of optimizing parameters of the grate cooler, people mostly take the direct control quantity of the grate cooler as an optimized variable, and when the working quantity of the grate cooler changes, the control quantity can change severely, so that the stable operation of the grate cooler system can not be well maintained.
Disclosure of Invention
Aiming at the problems, the invention provides a multi-objective optimizing control method of the grate cooler, which considers efficiency and energy consumption, so that the set value of the grate down pressure of the grate cooler and the set value of the air quantity of a fan can be synchronously optimized on the premise of meeting the efficiency objective and the energy consumption objective, thereby improving the parameter regulation level of the grate cooler, reducing the production energy consumption and ensuring the efficient operation of the grate cooler.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention relates to a multi-target optimizing control method of a grate cooler considering efficiency and energy consumption, which comprises n cooling fans, wherein the air quantity of m fans can be freely adjusted and is sequentially marked as FA＝{FA 1 ,FA 2 ,…,FA l ,…,FA m }，FA l The first fan is represented as l which is more than or equal to 1 and less than or equal to m, and the air quantity of the other fans is constant or controlled by an operator; the multi-target optimizing control method for the grate cooler is characterized by comprising the following steps of:
step 1, establishing a grate cooler energy efficiency evaluation model on the basis of considering efficiency and energy consumption;
step 1.1, data acquisition:
respectively acquiring the operation data of the grate cooler in N time periods with the time length of t, and calculating the average operation data of the grate cooler in the kth time period, and recording the average operation data as { y } 1 (k),y 2 (k),y 3 (k),u 1 (k),u 2 (k),…,u l (k),…,u m (k),u m+1 (k),u m+2 (k) K=1, 2, …, N, where y 1 (k) Represents the average secondary air temperature, y of the grate cooler in the kth time period 2 (k) Represents the average tertiary air temperature, y of the grate cooler in the kth time period 3 (k) Indicating the average outlet clinker temperature, u, of the grate cooler in the kth time period l (k) First fan FA for indicating grate cooler in kth time period l Average fan air quantity u m+1 (k) Represents the average grate down pressure, u, of the grate cooler in the kth time period m+2 (k) Representing the average raw material feeding amount of the grate cooler in the kth time period;
calculating the power consumption of the grate cooler in the kth time period and marking as y 4 (k)；
Step 1.2, processing the grate cooler operation data in the kth time period by utilizing a moving average filtering method to obtain filtered grate cooler operation dataWherein (1)>The value Wen Lvbo of overgrate air representing the kth period,/->A tertiary air Wen Lvbo value representing the kth period,Outlet clinker temperature filter value representing the kth time period, ->A power consumption amount filter value representing the kth period,First fan FA of grate cooler in kth time period l Fan air quantity filtering value, < >>The grate down pressure filter value representing the kth time period,/->A raw material feed amount filter value representing a kth period;
step 1.3, establishing an energy efficiency evaluation model of the grate cooler based on the filtered grate cooler operation data:
to be used forAnd->The ith output y of the energy efficiency evaluation model respectively i (k) I=1, 2,3,4, in +.>The j-th input of the energy efficiency evaluation model respectivelyThereby constructing an energy efficiency evaluation model of the grate cooler in the kth time period by using the formula (1):
in the formula (1), z -1 For a delay operator, representing a lag of 1 step;an input parameter matrix representing a kth time period and obtained from formula (2); a is that i (z -1 ) Represents the i-th output +.>The output coefficient polynomial of (2) is obtained by the formula (3); t (T) i (z -1 ) Represents the i-th output +.>Is obtained from the formula (4); b (B) i (z -1 ) Represents the i-th output +.>The input coefficient polynomial matrix of (2) is obtained by a formula (5); epsilon i (k) Represents the i-th output +.>A noise term at a kth time period;
in the formula (3), the amino acid sequence of the compound,respectively is output coefficient polynomial A i (z -1 ) Coefficients of the sub-term of>Represents the i-th output +.>Output order of (2);
in the formula (4), the amino acid sequence of the compound,represents the i-th output +.>The j-th input->Corresponding hysteresis order τ j For j' th input->Is a hysteresis step number of (2);
B i (z -1 )＝[b i,1 ,b i,2 ,…,b i,j ,…,b i,m+2 ] (5)
in formula (5), b i,j Representing the ith outputThe j-th input->And has:
in the formula (6), the amino acid sequence of the compound,respectively, are input coefficient polynomialsb i,j Coefficients of the sub-term of>Represents the i-th output +.>The j-th input->Is a function of the input order of (a);
step 1.4, identifying parameters of a grate cooler energy evaluation model;
initializing structural parameters of a grate cooler energy assessment model, comprising: ith outputOutput order +.>Ith output->The j-th input->Input order +.>Input hysteresis matrix T i (z -1 )；
Inputting the filtered grate cooler operation data into an energy efficiency evaluation model of the grate cooler, and identifying parameters of the model through a recursive least square algorithm, wherein the method comprises the following steps: output coefficient polynomial A i (z -1 ) Coefficients of the sub-terms of (2)Input coefficient polynomial b i,j Coefficients of the sub-terms of->
Step 2, constructing a multi-objective optimization model of the grate cooler;
2.1, establishing an efficiency target and an energy consumption target of the grate cooler in the kth time period by using a formula (7), wherein the efficiency target comprises a secondary air temperature evaluation index, a tertiary air temperature evaluation index and an outlet clinker temperature evaluation index, and the energy consumption target is an electric consumption evaluation index;
minF(X(k))＝(f 1 (X(k)),f 2 (X(k)),f 3 (X(k)),f 4 (X(k))) T (7)
in the formula (7), f 1 (X (k)) represents the secondary air temperature evaluation index in the kth time zone, andf 2 (X (k)) represents the tertiary air temperature evaluation index in the kth period, and +.>f 3 (X (k)) represents an outlet clinker temperature evaluation index in the kth period, and +.>f 4 (X (k)) represents the power consumption evaluation index of the kth period, and +.>X (k) represents a decision variable of the kth time period, anda j 'th decision variable representing a kth time period, 1.ltoreq.j'. Ltoreq.m+1;
step 2.2, establishing constraint conditions of a multi-objective optimization model of the grate cooler;
step 3, solving a multi-objective optimization model of the grate cooler in the kth time period based on an MOEA/D algorithm to obtain an optimal solution of the multi-objective optimization model of the grate cooler in the kth+1th time period, and using m fans FA corresponding to the optimal solution 1 ～FA m The fan air quantity and the grate down pressure of the air conditioner are respectively taken as m-stage fans FA in the (k+1) th time period 1 ～FA m A fan air quantity set value and a grate lower pressure set value;
step 4, according to the m-table fan FA under the k+1th time period 1 ～FA m The fan air quantity set value and the grate down pressure set value of the air conditioner are utilized, and m fans FA based on an incremental PID algorithm are utilized 1 ～FA m The fan air quantity controller based on generalized predictive control algorithm controls m fans FA in the (k+1) th time period respectively by the grate lower pressure controller 1 ～FA m The air quantity and the grate down pressure of the air conditioner are adaptively adjusted to realize multi-objective optimal control of the grate cooler.
The multi-objective optimizing control method of the grate cooler, which is provided by the invention and takes efficiency and energy consumption into consideration, is also characterized in that the step 2.2 comprises the following steps:
step 2.2.1, constructing the operation constraint of the grate cooler by utilizing the step (8):
in the formula (8), u j′min Represents the j' th decision variable u j′ (k) U, the minimum value of (2) j′max Represents the j' th decision variable u j′ (k) Is the maximum value of (2);
2.2.2, constructing stability constraint of the grate cooler by utilizing the formula (9):
in formula (9), SP j′ (k) The j' th decision variable representing the k-th time periodIs set to Deltau j′ Represents the j' th decision variable +.>Set point SP of the same j′ (k) Maximum deviation between.
The step 3 comprises the following steps:
step 3.1, initializing algorithm parameters:
step 3.1.1, initializing population size to be P, neighborhood size to be T, current iteration number to be G=0, maximum iteration number to be G max The non-dominant solution set EP of the kth time period is an empty set;
step 3.1.2, randomly generating an initial population as a G generation population, constructing P weight vectors and sequentially distributing the P weight vectors to each individual of the G generation population;
step 3.1.3, for each individual in the G generation population, calculating Euclidean distance between the weight vector of each individual and the weight vectors of other individuals, and selecting the individual corresponding to the first T weight vectors with the minimum Euclidean distance as a neighbor set of the corresponding individual;
step 3.1.4, calculating to obtain secondary air temperature evaluation indexes, tertiary air temperature evaluation indexes, outlet clinker temperature evaluation indexes and power consumption evaluation indexes corresponding to all individuals in the G generation population by using a formula (7), and selecting the minimum values of the secondary air temperature evaluation indexes, the tertiary air temperature evaluation indexes, the outlet clinker temperature evaluation indexes and the power consumption evaluation indexes of all the individuals as an ideal point set of the G generation;
step 3.2, updating MOEA/D solution set:
step 3.2.1, for each individual in the G generation population, randomly selecting two neighbor individuals from a neighbor set of each individual, and performing differential evolution on the corresponding individual by using the selected two neighbor individuals to generate a new individual in the G generation population;
step 3.2.2, updating the ideal point set according to the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the outlet clinker temperature evaluation index and the power consumption evaluation index of each new individual in the G generation population to obtain an ideal point set of the G+1th generation;
step 3.2.3, updating the neighbor set:
updating the neighbor set of each individual in the G generation population by using a Chebyshev polymerization method to obtain the G+1th generation population and the neighbor set of each individual in the G+1th generation population;
3.2.5, generating a new non-dominant solution according to the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the outlet clinker temperature evaluation index and the power consumption evaluation index of each new individual in the G generation population, adding the new non-dominant solution into a non-dominant solution set EP of the kth time period, and removing the dominant solution governed by the new non-dominant solution from the EP;
step 3.3, G+1 is given to G, and G is less than G max If yes, executing step 3.2, otherwise, indicating that G is completed max Outputting a non-dominant solution set EP of the multi-objective optimization model of the grate cooler in the kth time period through iteration;
and 3.4, selecting one non-dominant solution from the non-dominant solution set EP of the kth time period as an optimal solution of the multi-objective optimization model of the grate cooler of the (k+1) th time period.
The electronic equipment comprises a memory and a processor, wherein the memory is used for storing a program for supporting the processor to execute the multi-target optimizing control method of the grate cooler, and the processor is configured to execute the program stored in the memory.
The invention relates to a computer readable storage medium, which is stored with a computer program, wherein the computer program is executed by a processor to execute the steps of the multi-objective optimizing control method of the grate cooler.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, on the premise of considering the efficiency and the energy consumption of the grate cooler, the set value of the grate cooler grate down pressure and the set value of the fan air quantity are synchronously optimized, and the self-adaptive adjustment of the parameters of the grate cooler is realized by combining a predictive control method, so that the defect of manual operation in parameter adjustment is overcome. In an actual application scene, a user can flexibly select the set value of the grate cooler grate down pressure and the set value of the fan air quantity according to the actual control requirement of the grate cooler and focus on different targets, so that the control requirement under different working conditions is met.
2. According to the invention, the MOEA/D algorithm is used for solving the multi-objective optimization model of the grate cooler, so that the calculation complexity is low, the convergence speed is high, the set value parameters of the grate cooler can be optimized offline, and the online control requirement of the grate cooler can be well met.
Drawings
FIG. 1 is a schematic diagram of the operation of the grate cooler of the present invention;
FIG. 2 is a block diagram of a multi-objective optimizing control system of the grate cooler of the invention.
Detailed Description
In this embodiment, a grate cooler in a grate cooler multi-objective optimization control method considering efficiency and energy consumption has n cooling fans, where the air volumes of m fans can be freely adjusted, and the m fans are denoted as fa= { FA 1 ,FA 2 ,…,FA l ,…,FA m }，FA l The first fan is represented as l which is more than or equal to 1 and less than or equal to m, and the air quantity of the other fans is constant or controlled by an operator; FIG. 1 is a schematic diagram of the operation of a grate cooler of a 5000t/d production line of a cement plant, wherein the grate cooler shown in FIG. 1 is provided with 11 cooling fans, namely an F1A fan, an F1B fan, an F2L fan, an F2R fan, an F3 fan, an F4 fan, an F5 fan, an F6 fan, an F7 fan and an F8 fan; under normal working conditions, the air volumes of the F1A fan, the F2L fan and the F2R fan can be freely adjusted according to the on-site production condition, so that the F1A fan, the F2L fan and the F2R fan can be respectively marked as FA 1 Blower fan, FA 2 Blower fan, FA 3 Fan and FA 4 The air quantity of the other fans is kept constant; the multi-objective optimizing control method of the grate cooler considering efficiency and energy consumption is carried out according to the following steps:
step 1, establishing a grate cooler energy efficiency evaluation model on the basis of considering efficiency and energy consumption;
step 1.1, data acquisition:
respectively acquiring the operation data of the grate cooler in N time periods with the time length of t, and calculating the average operation data of the grate cooler in the kth time period, and recording the average operation data as { y } 1 (k),y 2 (k),y 3 (k),u 1 (k),u 2 (k),…,u l (k),…,u m (k),u m+1 (k),u m+2 (k) K=1, 2, …, N, where y 1 (k) Represents the average secondary air temperature, y of the grate cooler in the kth time period 2 (k) Represents the average tertiary air temperature, y of the grate cooler in the kth time period 3 (k) Indicating the average outlet clinker temperature, u, of the grate cooler in the kth time period l (k) First fan FA for indicating grate cooler in kth time period l Average fan air quantity u m+1 (k) Represents the average grate down pressure, u, of the grate cooler in the kth time period m+2 (k) Representing the average raw material feeding amount of the grate cooler in the kth time period;
calculating the power consumption of the grate cooler in the kth time period and marking as y 4 (k)；
Because the production time delay of the grate cooler is required to be a certain time interval for the set value optimization of the grate cooler, namely, the grate cooler can keep the same set value in the time interval t of two adjacent optimizations, the average operation data of the grate cooler in a plurality of time periods are collected by taking t as the time length, the control effect of different set values is reflected, and the data support is provided for the subsequent set value optimization;
the power consumption is the total power consumption of the grate cooler in each time period;
step 1.2, processing the grate cooler operation data in the kth time period by utilizing a moving average filtering method to obtain filtered grate cooler operation dataWherein, the liquid crystal display device comprises a liquid crystal display device,the value Wen Lvbo of overgrate air representing the kth period,/->A tertiary air Wen Lvbo value representing the kth period,Indicating the exit clinker temperature for the kth time periodFiltered value, < >>A power consumption amount filter value representing the kth period,First fan FA of grate cooler in kth time period l Fan air quantity filtering value, < >>The grate down pressure filter value representing the kth time period,/->A raw material feed amount filter value representing a kth period;
step 1.3, establishing an energy efficiency evaluation model of the grate cooler based on the filtered grate cooler operation data:
step 1.3.1And->Ith output of energy efficiency evaluation model respectivelyi=1, 2,3,4, in +.>The j-th input of the energy efficiency evaluation model respectivelyJ is more than or equal to 1 and less than or equal to m+2, so that an energy efficiency evaluation model of the grate cooler in the kth time period is constructed by using the formula (1):
in the formula (1), z -1 For a delay operator, representing a lag of 1 step;an input parameter matrix representing a kth time period and obtained from formula (2); a is that i (z -1 ) Represents the i-th output +.>The output coefficient polynomial of (2) is obtained by the formula (3); t (T) i (z -1 ) Represents the i-th output +.>Is obtained from the formula (4); b (B) i (z -1 ) Represents the i-th output +.>The input coefficient polynomial matrix of (2) is obtained by a formula (5); epsilon i (k) Represents the i-th output +.>A noise term at a kth time period;
in the formula (3), the amino acid sequence of the compound,respectively is output coefficient polynomial A i (z -1 ) Coefficients of the sub-term of>Represents the i-th output +.>Output order of (2);
in the formula (4), the amino acid sequence of the compound,represents the i-th output +.>The j-th input->Corresponding hysteresis order τ j For j' th input->Is a hysteresis step number of (2);
B i (z -1 )＝[b i,1 ,b i,2 ,…,b i,j ,…,b i,m+2 ] (5)
in formula (5), b i,j Representing the ith outputThe j-th input->And has:
in the formula (6), the amino acid sequence of the compound,respectively is an input coefficient polynomial b i,j Coefficients of the sub-term of>Represents the i-th output +.>The j-th input->Is a function of the input order of (a);
step 1.4, identifying parameters of a grate cooler energy evaluation model;
initializing structural parameters of a grate cooler energy assessment model, comprising: ith outputOutput order +.>Ith output->The j-th input->Input order +.>Input hysteresis matrix T i (z -1 )；
Inputting the filtered grate cooler operation data into an energy efficiency evaluation model of the grate cooler, and identifying parameters of the model through a recursive least square algorithm, wherein the method comprises the following steps: output coefficient polynomial A i (z -1 ) Coefficients of the sub-terms of (2)Input coefficient polynomial b i,j Coefficients of the sub-terms of->
Step 2, constructing a multi-objective optimization model of the grate cooler;
2.1, establishing an efficiency target and an energy consumption target of the grate cooler in the kth time period by using a formula (7), wherein the efficiency target comprises a secondary air temperature evaluation index, a tertiary air temperature evaluation index and an outlet clinker temperature evaluation index, and the energy consumption target is a power consumption evaluation index; the secondary air temperature evaluation index and the tertiary air temperature evaluation index are used for measuring the thermal efficiency of the grate cooler, and the outlet clinker temperature is used for measuring the cooling efficiency of the grate cooler;
minF(X(k))＝(f 1 (X(k)),f 2 (X(k)),f 3 (X(k)),f 4 (X(k))) T (7)
in the formula (7), f 1 (X (k)) represents the secondary air temperature evaluation index in the kth time zone, andf 2 (X (k)) represents the tertiary air temperature evaluation index in the kth period, and +.>f 3 (X (k)) represents an outlet clinker temperature evaluation index in the kth period, and +.>f 4 (X (k)) represents the power consumption evaluation index of the kth period, and +.>X (k) represents a decision variable of the kth time period, anda j 'th decision variable representing a kth time period, 1.ltoreq.j'. Ltoreq.m+1;
step 2.2, establishing constraint conditions of a multi-objective optimization model of the grate cooler:
step 2.2.1, constructing the operation constraint of the grate cooler by utilizing the step (8):
in order to ensure the safe operation of the grate cooler, the parameter values of the grate cooler represented by the decision variables are required to be within the allowable range of the grate cooler;
in the formula (8), u j′min Representing the j' th decision variableU, the minimum value of (2) j′max Representing the j' th decision variableIs the maximum value of (2);
2.2.2, constructing stability constraint of the grate cooler by utilizing the formula (9):
in the embodiment, in order to keep the change of the set value of the grate cooler relatively stable, in the optimization process, the decision variables of each time period and the deviation between the set values of the decision variables should be kept within a certain range;
in formula (9), SP j′ (k) The j' th decision variable representing the k-th time periodIs set to Deltau j′ Represents the j' th decision variable +.>Set point SP of the same j′ (k) Maximum deviation between;
step 3, solving a multi-objective optimization model of the grate cooler in the kth time period based on an MOEA/D algorithm;
step 3.1, initializing algorithm parameters:
step 3.1.1, initializing population size to be P, neighborhood size to be T, current iteration number to be G=0, maximum iteration number to be G max The non-dominant solution set EP of the kth time period is an empty set;
step 3.1.2, randomly generating an initial population as a G generation population, constructing P weight vectors and sequentially distributing the P weight vectors to each individual of the G generation population;
step 3.1.3, for each individual in the G generation population, calculating Euclidean distance between the weight vector of each individual and the weight vectors of other individuals, and selecting the individual corresponding to the first T weight vectors with the minimum Euclidean distance as a neighbor set of the corresponding individual;
step 3.1.4, calculating to obtain secondary air temperature evaluation indexes, tertiary air temperature evaluation indexes, outlet clinker temperature evaluation indexes and power consumption evaluation indexes corresponding to all individuals in the G generation population by using a formula (7), and selecting the minimum values of the secondary air temperature evaluation indexes, the tertiary air temperature evaluation indexes, the outlet clinker temperature evaluation indexes and the power consumption evaluation indexes of all the individuals as an ideal point set of the G generation;
step 3.2, updating MOEA/D solution set:
step 3.2.1, for each individual in the G generation population, randomly selecting two neighbor individuals from a neighbor set of each individual, and performing differential evolution on the corresponding individual by using the selected two neighbor individuals to generate a new individual in the G generation population;
step 3.2.2, updating the ideal point set according to the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the exit clinker temperature evaluation index and the power consumption evaluation index of each new individual in the G generation population to obtain a G+1th generation ideal point set;
specifically, the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the outlet clinker temperature evaluation index and the power consumption evaluation index of each new individual in the G generation population are compared with the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the outlet clinker temperature evaluation index and the power consumption evaluation index in the ideal point set, and the minimum value of the secondary air temperature evaluation index, the minimum value of the tertiary air temperature evaluation index, the minimum value of the outlet clinker temperature evaluation index and the minimum value of the power consumption evaluation index are taken as the ideal point set of the G+1th generation;
step 3.2.3, updating the neighbor set:
updating the neighbor set of each individual in the G generation population by using a Chebyshev polymerization method to obtain the G+1th generation population and the neighbor set of each individual in the G+1th generation population;
for each individual in the G generation population, calculating the Chebyshev aggregation function value of all neighbor individuals of each individual and the Chebyshev aggregation function value of a new individual generated by each individual, if the Chebyshev aggregation function value of a certain neighbor individual of the corresponding individual is larger than the Chebyshev aggregation function value of the new individual generated by the corresponding individual, replacing the corresponding neighbor individual with the corresponding new individual, and finally obtaining the neighbor set of all the individuals in the G+1th generation population and the G+1th generation population;
3.2.5, generating a new non-dominant solution according to the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the outlet clinker temperature evaluation index and the power consumption evaluation index of each new individual in the G generation population, adding the new non-dominant solution into a non-dominant solution set EP of the kth time period, and removing the dominant solution governed by the new non-dominant solution from the EP;
step 3.3, G+1 is given to G, and G is less than G max If yes, executing step 3.2, otherwise, indicating that G is completed max Outputting a non-dominant solution set EP of the multi-objective optimization model of the grate cooler in the kth time period through iteration;
step 3.4, selecting one non-dominant solution from the non-dominant solution set EP of the kth time period as an optimal solution of a multi-objective optimization model of the (k+1) th time period grate cooler, and using m fans FA corresponding to the optimal solution 1 ～FA m The fan air quantity and the grate down pressure of the air conditioner are respectively taken as m-stage fans FA in the (k+1) th time period 1 ～FA m A fan air quantity set value and a grate lower pressure set value;
the selection of the optimal solution needs to be considered in combination with the actual production requirements and the emphasis degree of different targets, in this embodiment, in order to ensure the stable change of the running state of the grate cooler before and after the set value optimization, each of the following is calculatedM fans FA corresponding to non-dominant solutions 1 ～FA m M-table fan FA with fan air quantity, grate down pressure and current time period 1 ～FA m The mean square error between the fan air quantity set value and the grate lower pressure set value is selected, and the non-dominant solution corresponding to the minimum value of the mean square error is used as the optimal solution;
step 4, according to the m-table fan FA under the k+1th time period 1 ～FA m The fan air quantity set value and the grate down pressure set value of the air conditioner are utilized, and m fans FA based on an incremental PID algorithm are utilized 1 ～FA m The fan air quantity controller based on generalized predictive control algorithm controls m fans FA in the (k+1) th time period respectively by the grate lower pressure controller 1 ～FA m The air quantity and the grate down pressure of the air conditioner are adaptively adjusted to realize multi-objective optimal control of the grate cooler, as shown in figure 2.
In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method described above.
Claims (5)
1. A multi-objective optimizing control method of a grate cooler considering efficiency and energy consumption comprises n cooling fans, wherein the air quantity of m fans can be freely adjusted and is sequentially marked as FA= { FA 1 ,FA 2 ,…,FA l ,…,FA m }，FA l The first fan is represented as l which is more than or equal to 1 and less than or equal to m, and the air quantity of the other fans is constant or controlled by an operator; the multi-objective optimizing control method for the grate cooler is characterized by comprising the following steps of:
step 1, establishing a grate cooler energy efficiency evaluation model on the basis of considering efficiency and energy consumption;
step 1.1, data acquisition:
for N time periods of time length tRespectively collecting the operation data of the grate cooler, calculating the average operation data of the grate cooler in the kth time period, and recording as { y } 1 (k),y 2 (k),y 3 (k),u 1 (k),u 2 (k),…,u l (k),…,u m (k),u m+1 (k),u m+2 (k) K=1, 2, …, N, where y 1 (k) Represents the average secondary air temperature, y of the grate cooler in the kth time period 2 (k) Represents the average tertiary air temperature, y of the grate cooler in the kth time period 3 (k) Indicating the average outlet clinker temperature, u, of the grate cooler in the kth time period l (k) First fan FA for indicating grate cooler in kth time period l Average fan air quantity u m+1 (k) Represents the average grate down pressure, u, of the grate cooler in the kth time period m+2 (k) Representing the average raw material feeding amount of the grate cooler in the kth time period;
calculating the power consumption of the grate cooler in the kth time period and marking as y 4 (k)；
Step 1.2, processing the grate cooler operation data in the kth time period by utilizing a moving average filtering method to obtain filtered grate cooler operation dataWherein (1)>The value Wen Lvbo of overgrate air representing the kth period,/->Tertiary air Wen Lvbo value indicating the kth period, +.>Outlet clinker temperature filter value representing the kth time period, ->A power consumption amount filter value representing a kth period, for example>First fan FA of grate cooler in kth time period l Fan air quantity filtering value, < >>The grate down pressure filter value representing the kth time period,/->A raw material feed amount filter value representing a kth period;
step 1.3, establishing an energy efficiency evaluation model of the grate cooler based on the filtered grate cooler operation data:
to be used forAnd->The i-th output of the energy efficiency evaluation model respectively +.>i=1, 2,3,4, in +.>The j-th input of the energy efficiency evaluation model>J is more than or equal to 1 and less than or equal to m+2, so that an energy efficiency evaluation model of the grate cooler in the kth time period is constructed by using the formula (1):
in the formula (1), z -1 For a delay operator, representing a lag of 1 step;an input parameter matrix representing a kth time period and obtained from formula (2); a is that i (z -1 ) Represents the i-th output +.>The output coefficient polynomial of (2) is obtained by the formula (3); t (T) i (z -1 ) Represents the i-th output +.>Is obtained from the formula (4); b (B) i (z -1 ) Represents the i-th output +.>The input coefficient polynomial matrix of (2) is obtained by a formula (5); epsilon i (k) Represents the i-th output +.>A noise term at a kth time period;
in the formula (3), the amino acid sequence of the compound,respectively is output coefficient polynomial A i (z -1 ) Coefficients of the sub-term of>Representing the ith outputOutput order of (2);
in the formula (4), the amino acid sequence of the compound,represents the i-th output +.>The j-th input->Corresponding hysteresis order τ j For the j-th inputIs a hysteresis step number of (2);
B i (z -1 )＝[b i,1 ,b i,2 ,…,b i,j ,…,b i,m+2 ] (5)
in formula (5), b i,j Representing the ith outputThe j-th input->And has:
in the formula (6), the amino acid sequence of the compound,respectively is an input coefficient polynomial b i,j Coefficients of the sub-term of>Representing the ith outputThe j-th input->Is a function of the input order of (a);
step 1.4, identifying parameters of a grate cooler energy evaluation model;
initializing structural parameters of a grate cooler energy assessment model, comprising: ith outputOutput order +.>Ith output->The j-th input->Input order +.>Input hysteresis matrix T i (z -1 )；
Inputting the filtered grate cooler operation data into an energy efficiency evaluation model of the grate cooler, and identifying parameters of the model through a recursive least square algorithm, wherein the method comprises the following steps: output coefficient polynomial A i (z -1 ) Coefficients of the sub-terms of (2)Input systemPolynomial b of the number i,j Coefficients of the sub-terms of->
Step 2, constructing a multi-objective optimization model of the grate cooler;
2.1, establishing an efficiency target and an energy consumption target of the grate cooler in the kth time period by using a formula (7), wherein the efficiency target comprises a secondary air temperature evaluation index, a tertiary air temperature evaluation index and an outlet clinker temperature evaluation index, and the energy consumption target is an electric consumption evaluation index;
min F(X(k))＝(f 1 (X(k)),f 2 (X(k)),f 3 (X(k)),f 4 (X(k))) T (7)
in the formula (7), f 1 (X (k)) represents the secondary air temperature evaluation index in the kth time zone, andf 2 (X (k)) represents the tertiary air temperature evaluation index in the kth period, and +.>f 3 (X (k)) represents an outlet clinker temperature evaluation index in the kth period, and +.>f 4 (X (k)) represents the power consumption evaluation index of the kth period, and +.>X (k) represents a decision variable of the kth time period, and the j' th decision variable representing the k-th time period is 1.ltoreq.j′≤m+1；
Step 2.2, establishing constraint conditions of a multi-objective optimization model of the grate cooler;
step 3, solving a multi-objective optimization model of the grate cooler in the kth time period based on an MOEA/D algorithm to obtain an optimal solution of the multi-objective optimization model of the grate cooler in the kth+1th time period, and using m fans FA corresponding to the optimal solution 1 ～FA m The fan air quantity and the grate down pressure of the air conditioner are respectively taken as m-stage fans FA in the (k+1) th time period 1 ～FA m A fan air quantity set value and a grate lower pressure set value;
step 4, according to the m-table fan FA under the k+1th time period 1 ～FA m The fan air quantity set value and the grate down pressure set value of the air conditioner are utilized, and m fans FA based on an incremental PID algorithm are utilized 1 ～FA m The fan air quantity controller based on generalized predictive control algorithm controls m fans FA in the (k+1) th time period respectively by the grate lower pressure controller 1 ～FA m The air quantity and the grate down pressure of the air conditioner are adaptively adjusted to realize multi-objective optimal control of the grate cooler.
2. The multi-objective optimizing control method of the grate cooler considering efficiency and energy consumption according to claim 1, wherein the step 2.2 comprises:
step 2.2.1, constructing the operation constraint of the grate cooler by utilizing the step (8):
in the formula (8), u j′min Representing the j' th decision variableU, the minimum value of (2) j′max Represents the j' th decision variable u j′ (k) Is the maximum value of (2);
2.2.2, constructing stability constraint of the grate cooler by utilizing the formula (9):
in formula (9), SP j′ (k) The j' th decision variable representing the k-th time periodIs set to Deltau j′ Represents the j' th decision variable +.>Set point SP of the same j′ (k) Maximum deviation between.
3. The multi-objective optimizing control method of grate cooler according to claim 2, wherein the step 3 comprises:
step 3.1, initializing algorithm parameters:
step 3.1.1, initializing population size to be P, neighborhood size to be T, current iteration number to be G=0, maximum iteration number to be G max The non-dominant solution set EP of the kth time period is an empty set;
step 3.1.2, randomly generating an initial population as a G generation population, constructing P weight vectors and sequentially distributing the P weight vectors to each individual of the G generation population;
step 3.1.3, for each individual in the G generation population, calculating Euclidean distance between the weight vector of each individual and the weight vectors of other individuals, and selecting the individual corresponding to the first T weight vectors with the minimum Euclidean distance as a neighbor set of the corresponding individual;
step 3.1.4, calculating to obtain secondary air temperature evaluation indexes, tertiary air temperature evaluation indexes, outlet clinker temperature evaluation indexes and power consumption evaluation indexes corresponding to all individuals in the G generation population by using a formula (7), and selecting the minimum values of the secondary air temperature evaluation indexes, the tertiary air temperature evaluation indexes, the outlet clinker temperature evaluation indexes and the power consumption evaluation indexes of all the individuals as an ideal point set of the G generation;
step 3.2, updating MOEA/D solution set:
step 3.2.1, for each individual in the G generation population, randomly selecting two neighbor individuals from a neighbor set of each individual, and performing differential evolution on the corresponding individual by using the selected two neighbor individuals to generate a new individual in the G generation population;
step 3.2.2, updating the ideal point set according to the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the outlet clinker temperature evaluation index and the power consumption evaluation index of each new individual in the G generation population to obtain an ideal point set of the G+1th generation;
step 3.2.3, updating the neighbor set:
updating the neighbor set of each individual in the G generation population by using a Chebyshev polymerization method to obtain the G+1th generation population and the neighbor set of each individual in the G+1th generation population;
3.2.5, generating a new non-dominant solution according to the secondary air temperature evaluation index, the tertiary air temperature evaluation index, the outlet clinker temperature evaluation index and the power consumption evaluation index of each new individual in the G generation population, adding the new non-dominant solution into a non-dominant solution set EP of the kth time period, and removing the dominant solution governed by the new non-dominant solution from the EP;
step 3.3, G+1 is given to G, and G is less than G max If yes, executing step 3.2, otherwise, indicating that G is completed max Outputting a non-dominant solution set EP of the multi-objective optimization model of the grate cooler in the kth time period through iteration;
and 3.4, selecting one non-dominant solution from the non-dominant solution set EP of the kth time period as an optimal solution of the multi-objective optimization model of the grate cooler of the (k+1) th time period.
4. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program for supporting the processor to execute the grate cooler multi-objective optimization control method of claim 1 or 2 or 3, and the processor is configured to execute the program stored in the memory.
5. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being run by a processor, performs the steps of the multi-objective optimization control method of a grate cooler according to claim 1 or 2 or 3.
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