Search

CN-116859720-B - Multi-objective optimization control method for grate cooler considering efficiency and energy consumption

CN116859720BCN 116859720 BCN116859720 BCN 116859720BCN-116859720-B

Abstract

The invention discloses a grate cooler multi-objective optimization control method considering efficiency and energy consumption, which comprises the steps of 1, establishing a grate cooler energy efficiency evaluation model based on the consideration of the efficiency and the energy consumption, 2, establishing a grate cooler efficiency target and an energy consumption target based on the grate cooler energy efficiency evaluation model, establishing a grate cooler multi-objective optimization model, 3, solving the grate cooler multi-objective optimization model by utilizing an MOEA/D algorithm to obtain an optimal solution of a grate down pressure set value and a fan air quantity set value, and 4, utilizing a generalized predictive control algorithm and an incremental PID algorithm to realize rolling optimization of the grate down pressure and the fan air quantity. 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.

Inventors

  • CHEN WEI
  • LIU YONG
  • YE LEI
  • TAO JIE

Assignees

  • 合肥工业大学

Dates

Publication Date
20260508
Application Date
20230607

Claims (5)

  1. 1. The multi-target optimizing control method of the grate cooler considering efficiency and energy consumption comprises n cooling fans, wherein the air quantity of m fans can be freely adjusted, and the air quantity of m fans is marked as FA= { FA 1 ,FA 2 ,…,FA l ,…,FA m },FA l to represent the first fan, 1≤l≤m, and the air quantity of the rest 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: Respectively acquiring operation data of the grate cooler in N time periods with the time length of t, and calculating average operation data of the grate cooler in the k time period, wherein y 1 (k) represents average secondary air temperature of the grate cooler in the k time period, y 2 (k) represents average tertiary air temperature of the grate cooler in the k time period, y 3 (k) represents average clinker outlet temperature of the grate cooler in the k time period, u l (k) represents average fan air quantity of a first fan FA l of the grate cooler in the k time period, u m+1 (k) represents average grate down pressure of the grate cooler in the k time period, and u m+2 (k) represents average raw material feeding quantity of the grate cooler in the k 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 data Wherein, the A secondary air Wen Lvbo value representing the kth period, A tertiary air Wen Lvbo value representing the kth period, An outlet clinker temperature filter value representing a kth time period, A power consumption amount filter value representing the kth period, The fan air quantity filtering value of the first fan FA l of the grate cooler in the kth time period, A filter value of the grate down pressure representing the kth 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 for And Ith output of energy efficiency evaluation model respectively I=1, 2,3,4 to The j-th input of the energy efficiency evaluation model respectively 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 is a delay operator, which represents a lag of 1 step; An input parameter matrix representing the kth time period and obtained from equation (2), A i (z -1 ) representing the ith output And is obtained from equation (3), T i (z -1 ) represents the ith output Is obtained from equation (4) and B i (z -1 ) represents the ith output Is obtained from equation (5), epsilon i (k) represents the ith output A noise term at a kth time period; in the formula (3), the amino acid sequence of the compound, The coefficients of the respective terms of the output coefficient polynomial a i (z -1 ) are respectively, Representing the ith output Output order of (2); In the formula (4), the amino acid sequence of the compound, Representing the ith output The j-th input of (3) Corresponding hysteresis order, τ j is the 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 represents the ith output The j-th input of (3) Is used for the input coefficient polynomial of (a), and has the following components: In the formula (6), the amino acid sequence of the compound, The coefficients of the respective terms of the input coefficient polynomial b i,j , Representing the ith output The j-th input of (3) 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 the grate cooler energy evaluation model, including the ith output Output order of (2) Ith output The j-th input of (3) Input order of (2) An input hysteresis matrix T i (z -1 ); the operation data of the filtered grate cooler is input into an energy efficiency evaluation model of the grate cooler, and parameters of the model are identified through a recursive least square algorithm, wherein the parameters comprise coefficients of each order of an output coefficient polynomial A i (z -1 ) Coefficients of respective terms of the input coefficient polynomial b i,j Step2, 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, and F 2 (X (k)) represents a 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 an electric power consumption evaluation index in the kth time zone, an X (k) represents a decision variable of the kth time period, and A j 'th decision variable representing a kth time period, 1≤j'. Ltoreq.m+1; step 2.2, establishing constraint conditions of a multi-objective optimization model of the grate cooler; step3, 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, wherein the fan air quantity and the grate down pressure of m fans FA 1 ～FA m corresponding to the optimal solution are respectively used as a fan air quantity set value and a grate down pressure set value of m fans FA 1 ～FA m in the kth+1th time period; And 4, according to the fan air quantity set value and the grate down pressure set value of the m fans FA 1 ～FA m in the k+1th time period, utilizing a fan air quantity controller of the m fans FA 1 ～FA m based on an incremental PID algorithm, and respectively carrying out self-adaptive adjustment on the fan air quantity and the grate down pressure of the m fans FA 1 ～FA m in the k+1th time period by using a grate down pressure controller based on a generalized predictive control algorithm so as to realize multi-target optimal control of the grate cooler.
  2. 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 formula (8), u j′min represents the j' th decision variable U j′max represents the maximum value of the j' th decision variable u j′ (k); 2.2.2, constructing stability constraint of the grate cooler by utilizing the formula (9): In formula (9), SP j′ (k) represents the j' th decision variable of the k-th time period Deltau j′ represents the j' th decision variable And the maximum deviation between its set points SP j′ (k).
  3. 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 as P, neighborhood size as T, current iteration number as G=0, maximum iteration number as G max , non-dominant solution set EP of kth time period as 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; Step 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, assigning G+1 to G, judging whether G < G max is satisfied, if so, executing step 3.2, otherwise, finishing G max iterations, and outputting a non-dominant solution set EP of the grate cooler multi-objective optimization model in the kth time period; 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. 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. 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.

Description

Multi-objective optimization control method for grate cooler considering efficiency and energy consumption 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 is characterized in that the multi-target optimizing control method of the grate cooler is provided with n cooling fans, wherein the air quantity of m fans can be freely adjusted, and the air quantity of m fans is marked as FA= { FA 1,FA2,…,FAl,…,FAm},FAl to represent the first fan, 1≤l≤m, and the air quantity of the rest fans is constant or controlled by an operator, and the multi-target optimizing control method of the grate cooler is characterized by comprising 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 operation data of the grate cooler in N time periods with the time length of t, and calculating average operation data of the grate cooler in the k time period, wherein y 1 (k) represents average secondary air temperature of the grate cooler in the k time period, y 2 (k) represents average tertiary air temperature of the grate cooler in the k time period, y 3 (k) represents average clinker outlet temperature of the grate cooler in the k time period, u l (k) represents average fan air quantity of a first fan FA l of the grate cooler in the k time period, u m+1 (k) represents average grate down pressure of the grate cooler in the k time period, and u m+2 (k) represents average raw material feeding quantity of the grate cooler in the k 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 data Wherein, the A secondary air Wen Lvbo value represent