A rolling mill schedule optimization method based on multi-objective whale optimization algorithm

Abstract

The present invention discloses a rolling mill rolling schedule optimization method based on a multi-objective whale optimization algorithm, which is applicable to an 18-roller reversible rolling mill and belongs to the field of plate and strip production technology. The method comprises: collecting basic parameters of the rolling mill equipment; collecting strip steel parameters and rolling process parameters in the secondary system; determining the maximum number of passes required for rolling according to the strip steel parameters and the rolling process parameters; determining decision variables, objective functions and constraints, and constructing a rolling mill rolling schedule optimization model; based on the basic parameters of the rolling mill equipment, strip steel parameters, rolling process parameters and the maximum number of passes required for rolling, a multi-objective whale optimization algorithm is used to solve the rolling mill rolling schedule optimization model to obtain an optimal rolling schedule. The present invention establishes an optimization model for the rolling process of an 18-roller single stand, which effectively improves the optimization operation speed, calculation accuracy, product quality, and reduces production costs.

Description

Technical Field

[0001] The present invention relates to the technical field of plate and strip production, and in particular to a rolling schedule optimization method for an eighteen-roll reversible rolling mill based on a multi-objective whale optimization algorithm. Background Art

[0002] The 18-roller reversible mill has the characteristics of small production scale, low investment, fast variety and specification change and small batch, and the support roll system is used in the structure to make up for the smaller working roll diameter, which makes it superior in serving the needs of personalized users, especially some high value-added thin strip products, and has a wide range of applications. The setting of rolling schedule is the primary link in rolling production. Reasonable rolling schedule design can increase the rolling speed, reduce equipment maintenance costs, increase the service life of the rolling mill, and improve product quality, thereby increasing greater economic benefits; on the contrary, unreasonable rolling schedule will lead to an unsmooth production process, affecting the output of the rolling mill and the quality of the product. Therefore, it is of great practical significance to study the optimization method of the rolling schedule of the 18-roller reversible mill suitable for online use.

[0003] In response to the above problems, the industry has proposed many methods, which are currently mainly concentrated in two aspects: empirical method and multi-objective optimization. In terms of empirical method, relying on the production experience of the operator, according to the incoming material thickness range, strip width range, pressing method, etc., the tension, pressing amount, etc. are stored in the computer in the format of the database to form a rolling specification table as the basis for load distribution. In terms of multi-objective optimization, scholars consider multiple objective functions, such as taking rolling force, plate shape, motor load, energy consumption, etc. as considerations, so as to meet all aspects of plate and strip production.

[0004] However, there are still many problems with the above methods. Although the empirical method can be quickly calculated according to the rolling specification table, the formation of the rolling schedule table requires a large amount of actual on-site data and the experience of the operators, which is time-consuming, subjective and not very accurate. Although the current multi-objective optimization method takes into account various factors to meet the special production requirements, it still has some shortcomings. For example, the linear weighted algorithm used in the invention patent application with application number 202111088692.6 cannot take into account the accuracy of each objective function well, and the calculation iteration speed is slow and the calculation time is long, which is not conducive to online use and reduces production efficiency. In addition, for the optimization design of rolling schedules, the industry currently focuses on hot rolling and cold rolling, while the attention to reversible mills is obviously insufficient, especially the optimization of rolling schedules for 18-roll reversible mills is almost non-existent. Therefore, it is urgent to develop an optimization design scheme for rolling schedules for 18-roll reversible mills with high precision, fast calculation speed, and the ability to take into account all aspects and be used online, so as to improve product production quality and production efficiency and create greater economic benefits. Summary of the invention

[0005] The present invention provides a rolling schedule optimization method for an 18-roller reversible rolling mill based on a multi-objective whale optimization algorithm, so as to solve the technical problem that the existing rolling schedule optimization method cannot meet the rolling schedule optimization requirements of the 18-roller reversible rolling mill.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] On the one hand, the present invention provides a rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm, and the rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm comprises:

[0008] Collect basic parameters of rolling mill equipment;

[0009] Collect strip parameters and rolling process parameters in the secondary system;

[0010] Determine the maximum number of passes required for rolling according to the strip parameters and rolling process parameters;

[0011] Determine the decision variables, objective function and constraints, and build a rolling schedule optimization model for the rolling mill;

[0012] Based on the basic parameters of the rolling mill equipment, strip parameters, rolling process parameters and the maximum number of passes required for rolling, the multi-objective whale optimization algorithm is used to solve the rolling schedule optimization model of the rolling mill to obtain the optimal rolling schedule.

[0013] Furthermore, the basic parameters of the rolling mill equipment include: the roll diameters of the upper and lower working rolls, the rated power of the main motor, the maximum rolling force of each pass, the maximum roll speed, the minimum roll speed and the transmission ratio of the motor.

[0014] Furthermore, the strip steel parameters include: strip steel grade, strip steel width, deformation resistance parameters corresponding to different steel grades, and material constant coefficients of different steel grades.

[0015] Furthermore, the rolling process parameters include: incoming raw material thickness, outlet finished product thickness, initial roll speed of each pass, maximum reduction rate of each pass, minimum reduction rate of each pass, friction coefficient and initial reduction rate of each pass.

[0016] Furthermore, the maximum number of passes required for rolling is determined by: finding a value that makes H(1-ε1)(1-ε2)(1-ε3)…(1-ε n )≤h is the maximum value of n, which is taken as the maximum number of passes N required for rolling; where H represents the thickness of the incoming raw material; h represents the thickness of the exported finished product; ε i Represents the initial reduction rate of the i-th pass, i=1,2,…,n.

[0017] Further, the decision variable is the reduction rate of each pass;

[0018] The objective function takes proportional rolling force, minimum energy consumption and proportional load as the objectives and is expressed as:

[0019]

[0020]

[0021]

[0022] Among them, a i represents the rolling force ratio of the i-th pass; P i represents the rolling force of the i-th pass; W i represents the motor power of the i-th pass; b i Indicates the motor power ratio of the i-th pass;

[0023] The constraints include equipment constraints and process constraints; wherein,

[0024] The device constraints are expressed as:

[0025]

[0026] Among them, P i-max represents the maximum rolling force of the i-th pass; W max Indicates the maximum power of the motor;

[0027] The process constraints are expressed as:

[0028]

[0029] Among them, ε i represents the reduction rate of the i-th pass; ε i-min represents the minimum reduction rate of the i-th pass; ε i-max represents the maximum reduction rate of the i-th pass; V i represents the roller speed of the i-th pass; V i-min represents the minimum roller speed of the ith pass; V i-max Indicates the maximum roll speed of the i-th pass.

[0030] Furthermore, based on the basic parameters of the rolling mill equipment, the parameters of the strip steel, the rolling process parameters and the maximum number of passes required for rolling, a multi-objective whale optimization algorithm is used to solve the rolling schedule optimization model of the rolling mill to obtain the optimal rolling schedule, including:

[0031] Based on the basic parameters of the rolling mill equipment, the parameters of the strip steel, the rolling process parameters and the maximum number of passes required for rolling, the front and rear unit tension of the strip steel, the rolling force and the motor power under the initial condition are determined;

[0032] The decision variables are set as the independent variables of the optimization model, the objective function is set as the objective function of the optimization model, the constraints are set as the constraint functions of the optimization model, the basic parameters of the rolling mill equipment, strip parameters, rolling process parameters, the maximum number of passes required for rolling, and the determined initial unit tension of the strip before and after, rolling force and motor power are substituted into the optimization model, and the multi-objective whale optimization algorithm is used to solve the rolling schedule optimization model of the rolling mill, and the optimal decision variables are output as the optimal rolling schedule.

[0033] Furthermore, the calculation formula of the front and rear unit tension of the strip is:

[0034] t f =σ 11 *k f +σ 21

[0035] t b =σ 21 *k b +σ 22

[0036] Among them, t f is the front unit tension of the strip; t b is the rear unit tension of the strip; σ 11 , σ 21 , σ 22 are the material constant coefficients of different steel grades; k f Indicates the static deformation resistance of the inlet; k b Indicates the static deformation resistance at the outlet.

[0037] Furthermore, the calculation formula of the rolling force is:

[0038]

[0039]

[0040] A3=1.08-1.02·ε all

[0041]

[0042]

[0043]

[0044] Where μ represents the friction coefficient; ε allrepresents the total reduction rate; R represents the upper and lower working roll diameters; kp represents the dynamic deformation resistance; K represents the tension influence coefficient; C H is the Hitchcock coefficient; H represents the thickness of the incoming raw material; ε i represents the reduction rate of the i-th pass; B represents the strip width.

[0045] Furthermore, the calculation formula of the motor power is:

[0046]

[0047] G T =R*B(tb*H i -tf*h i )

[0048]

[0049] G M =G R +G r +G L

[0050] W i =0.2193×10 -3 ·(V i / R)·G M

[0051] Among them, g L0 ~g L4 are all constants; R represents the diameter of the upper and lower working rolls; ε i represents the i-th pass reduction rate; P represents the rolling force; H i represents the entrance thickness of the i-th pass; tb represents the rear unit tension; tf represents the front unit tension; h i represents the exit thickness of the i-th pass; V i represents the initial roll speed of the i-th pass; W i It represents the motor power of the i-th pass; R′ represents the elastic pressure diameter of the roller; B represents the width of the strip.

[0052] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above method.

[0053] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the above method.

[0054] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0055] 1. Compared with the traditional empirical table method for calculating the rolling schedule, the present invention can greatly reduce the influence of manual experience and improve the accuracy of the rolling schedule calculation;

[0056] 2. Compared with other multi-objective function optimization methods, the multi-objective whale optimization algorithm used in the present invention has a faster calculation speed, can better take into account various objective functions, and can be used online;

[0057] 3. Compared with other cold rolling process optimizations, the present invention is particularly targeted at eighteen-roll reversible rolling mills, and can improve production efficiency and product quality through a more reasonable rolling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 It is a schematic diagram of the execution flow of a rolling mill rolling schedule optimization method based on a multi-objective whale optimization algorithm provided by an embodiment of the present invention;

[0060] Figure 2 It is a schematic diagram of a calculation flow of determining initial tension, rolling force, and motor power by decision variables ε provided in an embodiment of the present invention;

[0061] Figure 3 The embodiment of the present invention provides the method of searching for the optimal solution by three methods: bubble attack, prey search, and encirclement and predation. * =[ε1,ε2,ε3,…,ε j ] is a flow chart;

[0062] Figure 4 It is a system block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0064] First of all, it should be noted that in the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplarily" is intended to present the concept in a concrete way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0065] Moreover, in the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0066] In addition, in the embodiments of the present invention, sometimes a subscript (such as W1) may be mistakenly written as a non-subscript form (such as W1). When the difference is not emphasized, the meanings to be expressed are consistent.

[0067] First embodiment

[0068] An unreasonable design of the rolling schedule before rolling production of an 18-roller reversible rolling mill will lead to reduced product quality and output, thereby affecting the production of subsequent processes. In this regard, in order to optimize the rolling schedule, this embodiment provides a rolling schedule optimization method for an 18-roller reversible rolling mill based on a multi-objective whale optimization algorithm, which can be implemented by an electronic device, which can be a terminal or a server.

[0069] The execution flow of this method is as follows Figure 1 As shown, the following steps are included:

[0070] S1, collect basic parameters of rolling mill equipment;

[0071] Among them, the basic parameters of the rolling mill equipment include: the roll diameters of the upper and lower working rolls, the rated power of the main motor, the maximum rolling force of each pass, the maximum roll speed, the minimum roll speed and the transmission ratio of the motor.

[0072] S2, collecting strip parameters and rolling process parameters in the secondary system;

[0073] The strip steel parameters include: strip steel grade, strip steel width, deformation resistance parameters corresponding to different steel grades, and material constant coefficients of different steel grades. The rolling process parameters include: incoming raw material thickness, export finished product thickness, initial roll speed of each pass, maximum reduction rate of each pass, minimum reduction rate of each pass, friction coefficient, and initial reduction rate of each pass.

[0074] S3, determining the maximum number of passes required for rolling according to the strip parameters and rolling process parameters;

[0075] The determination of the maximum number of passes required for rolling is specifically as follows: the maximum number of passes required for rolling is determined according to the strip parameters obtained in the secondary system and the initial reduction rate of each pass, and the maximum number of rolling passes is calculated: H i (1-ε1)(1-ε2)(1-ε3)...(1-ε N )≤h i ; The calculated N is the maximum number of passes in the rolling procedure.

[0076] S4, determine the decision variables, objective function and constraints, and build a rolling schedule optimization model for the rolling mill;

[0077] Among them, under the given number of rolling schedule passes, the decision variables, objective functions and constraints of the multi-objective whale optimization algorithm are determined. The specific process is as follows:

[0078] S4-1: Determine the decision variables of the multi-objective whale optimization algorithm, specifically: to ensure the accuracy of the model calculation, select the reduction rate of each pass that has the greatest impact on the rolling force as the decision variable;

[0079] S4-2: Determine the multi-objective function of the multi-objective whale optimization algorithm, specifically:

[0080] S4-2-1: According to the equipment limitations and process requirements of the 18-high reversible rolling mill, in order to maintain the quality of the plate shape and take into account the mechanical properties of the strip, a large reduction should be used in the first pass to give full play to the transmission power due to the good plasticity of the strip. Then, as the work hardening intensifies, the reduction of each pass is restricted by the equipment capacity, and the rolling force is not completely equal or approximate. Therefore, in order to give full play to the equipment capacity of the 18-high rolling mill, the objective function of the rolling force equal proportion distribution method is adopted, and the formula is:

[0081]

[0082] Among them, a i represents the rolling force ratio of the i-th pass; P i represents the rolling force of the i-th pass;

[0083] S4-2-2: According to the equipment limitations and process requirements of the 18-high reversing mill, and taking into account the national policy of energy conservation and environmental protection, the minimum rolling energy consumption is used as the objective function, and the formula is:

[0084]

[0085] Among them, Wi represents the motor power of the i-th pass;

[0086] S4-2-3: According to the equipment limitations and process requirements of the 18-high reversible rolling mill, in order to ensure that the capacity of the 18-high rolling mill can be fully utilized and the load of each pass can reach the maximum capacity, the proportional load objective function is adopted, and the formula is:

[0087]

[0088] Among them, W i represents the motor power of the i-th pass; b i Indicates the motor power ratio of the i-th pass;

[0089] S4-3: Determine the constraints of the multi-objective whale optimization algorithm, specifically:

[0090] S4-3-1: According to the equipment limitation and process limitation of the 18-high reversing mill, the constraint conditions include equipment limitation and process limitation. The equipment limitation refers to the performance limitation of the 18-high mill, which is based on equipment parameters and power conditions, and is specifically:

[0091]

[0092] Among them, P i-max represents the maximum rolling force of the i-th pass; W max Indicates the maximum power of the motor;

[0093] S4-3-2: According to the equipment limitations and process limitations of the 18-high reversing rolling mill, the process constraints refer to the constraints that need to be met during the rolling production process, specifically:

[0094]

[0095] Among them, ε i represents the reduction rate of the i-th pass; ε i-min represents the minimum reduction rate of the i-th pass; ε i-max represents the maximum reduction rate of the i-th pass; V i represents the roller speed of the i-th pass; V i-min represents the minimum roller speed of the ith pass; V i-max Indicates the maximum roll speed of the i-th pass.

[0096] S5, based on the basic parameters of the rolling mill equipment, strip parameters, rolling process parameters and the maximum number of passes required for rolling, the multi-objective whale optimization algorithm is used to solve the rolling schedule optimization model of the rolling mill to obtain the optimal rolling schedule.

[0097] Specifically, in this embodiment, the implementation process of the above S5 is as follows:

[0098] S5-1, under a given rolling procedure number of passes, determine the initial tension, rolling force, and motor power;

[0099] S5-1-1: According to the characteristics of the 18-high rolling mill and the mathematical model of the cold rolling single stand, the calculation formula for the front and rear tension of the strip is as follows:

[0100] ε b =ln{H A / H i}

[0101] ε f =ln{H A / h i}

[0102] t f =σ 11 *k f +σ 21

[0103] t b =σ 21 *k b +σ 22

[0104] Where: ε b is the inlet strain rate, ε f is the outlet strain rate, ε i is the reduction rate of each pass, t f is the front unit tension, t b is the rear unit tension, σ ij is the material constant coefficient of different steel grades, k f is the static deformation resistance of the inlet, k b is the static deformation resistance of the outlet, specifically:

[0105]

[0106]

[0107] Where: l A 、m A 、n A are the deformation resistance parameters corresponding to different steel grades;

[0108] S5-1-2: According to the characteristics of the 18-high rolling mill and the mathematical model of the cold rolling single stand, the strip rolling force calculation formula is as follows:

[0109]

[0110]

[0111] A3=1.08-1.02·ε all

[0112]

[0113]

[0114]

[0115] Where μ is the friction coefficient, ε all is the total reduction rate, R is the diameter of the upper and lower working rolls, CH is the Hitchcock coefficient (≡0.214×10 -3 ), kp is the dynamic deformation resistance, κ is the tension influence coefficient, and P is the rolling force, specifically:

[0116]

[0117]

[0118]

[0119] Where: V i is the roller speed of each pass, H i is the entrance thickness of each pass, R′ is the elastic pressure diameter of the roller, is the strip strain rate, k s is the static deformation resistance, tb is the rear unit tension, tf is the front unit tension, and α is the coefficient, specifically:

[0120]

[0121]

[0122]

[0123] S5-1-3: According to the characteristics of the 18-high rolling mill and the mathematical model of the cold rolling single stand, the motor power is calculated by the motor torque, and the motor torque consists of three parts: rolling force torque, tension torque, and mechanical loss torque;

[0124] S5-1-3-1: The rolling force torque is related to the contact arc length and the rolling force. The specific formula is as follows:

[0125]

[0126] S5-1-3-2: The tension torque is related to the front tension, back tension and the exit and entrance thickness of the strip. The specific formula is as follows:

[0127] G T =R*B(tb*H i-tf*h i )

[0128] S5-1-3-3: The specific formula for calculating mechanical loss torque is:

[0129]

[0130] Where: g L0 ~g L4 They are all constants, related to the mechanical properties, and are generally determined at the initial stage of commissioning.

[0131] S5-1-3-4: The motor torque calculated from the above steps is:

[0132] G M =G R +G T +G L

[0133] S5-1-3-5: The motor power is related to the roller speed, roller radius and motor torque. The specific formula is:

[0134] W i =0.2193×10 -3 ·(V i / R)·G M

[0135] S5-2: After determining the decision variables, objective function, constraints, initial tension, rolling force, and motor power of the multi-objective whale optimization algorithm, a multi-objective whale optimization algorithm model is constructed, specifically:

[0136] S5-2-1: Relevant parameters of the multi-objective whale optimization algorithm model, including population size S = 40, maximum number of iterations i max =100, coefficient a affecting whale position update = 1.2, probability threshold of exploration or development P = 0.5, population selection probability SR = 0.2, population mutation probability MR = 0.6, population crossover probability CR = 0.7;

[0137] S5-2-2: According to the initial relevant parameters of the multi-objective whale algorithm, the decision variable ε determined in step S4-1 is j = [35%, 29%, 31%, 16%], set as the independent variable x of the model, that is, x = ε j ; Set the rolling related parameters and initial calculation results determined in steps S1, S2, and S5 as the initialization function of the model, set the objective function in the model as the multi-objective quantity determined in step S4-2; set the constraint conditions determined in step S4-3 as the model constraint function;

[0138] S5-3: According to the multi-objective whale optimization algorithm model established above, iterative calculations are performed to finally obtain the optimal solution x * =[ε1,ε2,ε3,...,ε j ], and output the optimal rolling schedule parameters, specifically:

[0139] S5-3-1: During the iterative operation of the multi-objective whale optimization algorithm model, the optimal solution is quickly found through three methods: bubble attack, prey search, and encirclement and predation. * ;

[0140] S5-3-2: The optimization strategy of the multi-objective whale optimization algorithm surrounds predation, that is, the optimization space of the algorithm is the global solution space. Since the position of the prey preyed by the whale is random in the entire space, each whale needs to communicate and transfer information to randomly swim to find prey. The specific mathematical expression is:

[0141] D=|C·x * (t)-x(t)|

[0142] x(t+1)=x(t)-A·D

[0143] Where: D is the distance vector between the current optimal solution and the target prey, t is the number of iterations of the current position algorithm, x * (t) is the position vector of the optimal solution currently obtained, x(t) is the current position vector of the whale, and A and C are the encirclement steps, specifically:

[0144]

[0145] A=random(2)

[0146] Where: random represents a random number uniformly generated in the range [0, 1], i is the current iteration number, i max is the maximum number of iterations;

[0147] S5-3-3: Bubble attack, the optimization strategy in the multi-objective whale optimization algorithm, attacks the prey by moving in an upward spiral and continuously shrinking the encirclement, approaching the optimal solution, specifically:

[0148] x(t+1)=D′·e bl ·cos(2πl)+x * (t)

[0149] Where: b is a constant that limits the logarithmic spiral form, 1 represents a random number between [-1, 1];

[0150] S5-3-4: The optimization strategy prey search in the multi-objective whale optimization algorithm randomly selects the position of a whale individual in the population as the target direction to find prey. Its mathematical expression is:

[0151] D=|C·x rand -x(t)|,

[0152] x(t+1)=x rand -A.D

[0153] Where: x rand represents the position vector of a randomly selected whale in the population.

[0154] When the 18-high mill is operating normally, it is necessary to ensure that the rolling schedule of each pass is equal to or infinitely close to x * =[ε1,ε2,ε3,...,ε j ], with this goal in mind, the rolling schedule of the unit is optimized, which can effectively bring into play the capacity of the 18-high rolling mill equipment, reduce energy consumption and improve economic benefits.

[0155] Below, a practical application example is used to further illustrate the implementation effect of the method of the present invention, and the steps are as follows:

[0156] S1: Collect basic parameters of rolling mill equipment, including: upper and lower working roll diameter 80mm, rated power of main motor of each pass 5000KW, maximum rolling force of each pass 1800t, maximum roll speed of each pass 13.3m / s, minimum roll speed of each pass 5m / s, and transmission ratio of motor 1.32;

[0157] S2: Collect the strip parameters and rolling process parameters in the secondary system; including: strip steel grade is LWB550, incoming raw material thickness is 2.3mm, export finished product thickness is 0.6mm, strip steel width is 1082mm, maximum reduction rate of each pass is 40%, minimum reduction rate of each pass is 5%, and deformation resistance parameter l corresponding to steel grade LWB550 A =130, m A =0.01, n A =0.28, material constant coefficient σ of steel grade LWB550 11 =200,σ 12 =120,σ 21 =225,σ 22 =150, friction coefficient 0.03, initial reduction rate of each pass [30%, 27%, 24.3%, 21.9%];

[0158] S3: According to the strip parameters obtained in the secondary system and the initial reduction rate of each pass [30%, 27%, 24.3%, 21.9%], the maximum number of passes required for rolling is determined: H (1-0.3) (1-0.27) (1-0.43) ... (1-ε N )≤h; where: H is the thickness of the incoming raw material 2.3mm; h is the thickness of the exported finished product 0.6mm; the result calculated according to the above formula is: N=4, that is, the maximum number of rolling passes is 4 times;

[0159] S4: Under the given rolling schedule pass number, determine the decision variables, objective functions and constraints of the multi-objective whale optimization algorithm;

[0160] S4-1: Determine the decision variables of the multi-objective whale optimization algorithm: To ensure the accuracy of the model calculation, select the reduction rate of each pass that has the greatest impact on the rolling force as the decision variable.

[0161] ε j =[35%, 29%, 31%, 16%];

[0162] S4-2: Determine the multi-objective function of the multi-objective whale optimization algorithm as follows:

[0163] S4-2-1: According to the equipment limitations and process requirements of the 18-high reversible rolling mill, in order to maintain the quality of the plate shape and take into account the mechanical properties of the strip, a large reduction should be used in the first pass to fully utilize the transmission power due to the good plasticity of the strip. Then, as the work hardening intensifies, the reduction of each pass is restricted by the equipment capacity, and the rolling force is not completely equal or approximate. Therefore, in order to fully utilize the equipment capacity of the 18-high rolling mill, the objective function of the rolling force proportional distribution method is adopted;

[0164] S4-2-2: Based on the equipment limitations and process requirements of the 18-high reversing mill and the national policy of energy conservation and environmental protection, the minimum rolling energy consumption is adopted as the objective function;

[0165] S4-2-3: According to the equipment limitation and process requirements of the 18-high reversing mill, in order to ensure that the capacity of the 18-high mill can be fully utilized and the load of each pass can reach the maximum capacity, the proportional load objective function is adopted;

[0166] S4-3: Determine the constraints of the multi-objective whale optimization algorithm as follows:

[0167] S4-3-1: According to the equipment limitation and process limitation of the 18-high reversing mill, the constraint conditions include equipment limitation and process limitation. The equipment limitation refers to the performance limitation of the 18-high mill, which is based on equipment parameters and power conditions, and is specifically:

[0168]

[0169] S4-3-2: According to the equipment limitations and process limitations of the 18-high reversing rolling mill, the process constraints refer to the constraints that need to be met during the rolling production process, specifically:

[0170]

[0171] S5: Under a given number of rolling passes, proportional rolling force, minimum energy consumption, proportional load are used as the objective function and the decision variable ε j = [35%, 29%, 31%, 16%], determine the strip unit tension, rolling force, and motor power under the initial conditions, Figure 2 The figure shows the specific process calculation diagram, taking the first pass as an example: from the data collected above, the initial front unit tension of LWB550 is calculated to be 13.8 tons, and the rear unit tension is 5.1 tons; from the data collected above, the initial pass rolling force of LWB550 is calculated to be 875.52 tons; from the data collected above, the initial pass motor power of LWB550 is calculated to be 1943.73 tons;

[0172] S6: Construct a model based on the multi-objective whale optimization algorithm after determining the decision variables, objective function, constraints, initial tension, rolling force, and motor power of the multi-objective whale optimization algorithm;

[0173] S6-1: Relevant parameters of the multi-objective whale optimization algorithm model, including population size S = 40, maximum number of iterations i max =100, coefficient a affecting whale position update = 1.2, probability threshold of exploration or development P = 0.5, population selection probability SR = 0.2, population mutation probability MR = 0.6, population crossover probability CR = 0.7;

[0174] S6-2: According to the initial relevant parameters of the multi-objective whale algorithm, the decision variable ε determined in step S4-1 is j = [35%, 29%, 31%, 16%], set as the independent variable x of the model, that is, x = ε j ; Set the rolling related parameters and initial calculation results determined in steps S1, S2, and S5 as the initialization function of the model, set the objective function in the model as the multi-objective quantity determined in step S4-2; set the constraint conditions determined in step S4-3 as the model constraint function;

[0175] S7: According to the multi-objective whale optimization algorithm model established above, iterative calculation is performed to finally obtain the optimal solution, and the optimal rolling schedule parameters are outputted based on it. Figure 3This is the flowchart of algorithm optimization. The specific steps are:

[0176] S7-1: Furthermore, during the iterative operation of the multi-objective whale optimization algorithm model, the optimal solution is quickly found through three methods: bubble attack, prey search, and encirclement predation;

[0177] S7-1-1: The optimization strategy of the multi-objective whale optimization algorithm surrounds predation, that is, the optimization space of the algorithm is the global solution space. Since the position of the prey preyed by the whale is random in the entire space, each whale needs to communicate and transfer information to randomly swim to find prey. The specific mathematical expression is:

[0178] D=|C·x * (t)-x(t)|

[0179] x(t+1)=x(t)-A·D

[0180] Where: D is the distance vector between the current optimal solution and the target prey, t is the number of iterations of the current position algorithm,

[0181] x * (t) is the position vector of the optimal solution currently obtained, x(t) is the current position vector of the whale, and A and C are the encirclement steps, specifically:

[0182]

[0183] A=random(2a)-(2a)

[0184] A=random(2)

[0185] Where: random represents a random number uniformly generated in the range [0, 1], i is the current iteration number, i max is the maximum number of iterations;

[0186] S7-2-1: Bubble attack, a strategy for optimizing the multi-objective whale optimization algorithm, attacks prey by moving in an upward spiral and continuously shrinking the encirclement, approaching the optimal solution. Specifically:

[0187] x(t+1)=D′·e bl ·cos(2πl)+x * (t)

[0188] Where: b is a constant that limits the logarithmic spiral form, l represents a random number between [-1, 1];

[0189] S7-2-1: The optimization strategy prey search in the multi-objective whale optimization algorithm randomly selects the position of a whale individual in the population as the target direction to find prey. Its mathematical expression is:

[0190] D=|C·x rand -x(t)|,

[0191] x(t+1)=x rand -A.D

[0192] Where: x rand represents the position vector of a randomly selected whale in the population;

[0193] The multi-objective whale optimization algorithm was implemented in MATLAB. The initial conditions were iteratively updated to obtain the optimal output parameters as shown in Table 1.

[0194] Table 1 Optimal output parameters

[0195]

[0196]

[0197] After optimization, the rolling force ratio of each pass is [0.9021:1.000:1.003:0.8400], which can better exert the capacity of the 18-high rolling mill equipment, and the operating speed is 5.1s, which can meet the online use goals.

[0198] In summary, this embodiment provides a rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm. By obtaining the basic parameters of the rolling mill equipment, rolling process parameters, and strip parameters, a rolling schedule optimization model with proportional distribution of rolling force, minimum rolling energy consumption, and proportional load as objective functions is constructed to obtain the optimal rolling schedule, give full play to the equipment capacity, improve product quality, and increase production efficiency.

[0199] Second embodiment

[0200] This embodiment provides an electronic device, such as Figure 4 As shown, the electronic device includes: a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method of the first embodiment. In addition, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0201] Next, combine Figure 4 The following is a detailed introduction to the various components of the electronic device:

[0202] Among them, the processor is the control center of the electronic device, and the electronic device may include multiple processors, each of which may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or other general-purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor may execute various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0203] In a specific implementation, as an embodiment, the processor may include one or more CPUs, such as Figure 4 The CPU0 and CPU1 shown in the figure are, of course, only exemplary.

[0204] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0205] Optionally, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and accessed through the interface circuit ( Figure 4 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0206] The transceiver may include a receiver and a transmitter ( Figure 4 The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function. The transceiver can be integrated with the processor or exist independently and communicate with the electronic device through the interface circuit ( Figure 4 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0207] In addition, it should be noted that Figure 4 The structure of the electronic device shown in the figure does not constitute a limitation on the device, and the actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In addition, the technical effects achieved by the electronic device when executing the method of the first embodiment above can refer to the technical effects described in the first embodiment above, so they are not repeated here.

[0208] Third embodiment

[0209] This embodiment provides a computer-readable storage medium, which stores at least one instruction, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer-readable storage medium may be a ROM, a random access memory, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. The instructions stored therein may be loaded by a processor in a terminal to execute the method.

[0210] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiment of the present invention can be in the form of a full or partial hardware embodiment, a full or partial software embodiment or an embodiment combining software and hardware. Moreover, when implemented using software, the embodiment of the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center containing one or more available media sets. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium. The semiconductor medium may be a solid state hard disk.

[0211] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0212] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0213] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements. In addition, the term "and / or" is only an association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone, wherein A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding. "At least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can be represented by: a, b, c, ab, ac, bc or abc, where a, b, c can be single or plural.

[0214] In addition, it can be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0215] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0216] In several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of functional modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0217] If the method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0218] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A rolling mill rolling schedule optimization method based on a multi-objective whale optimization algorithm, suitable for an 18-high reversing rolling mill, characterized in that: The rolling mill rolling schedule optimization method comprises: Collect basic parameters of rolling mill equipment; Collect strip parameters and rolling process parameters in the secondary system; Determine the maximum number of passes required for rolling based on strip parameters and rolling process parameters; Determine the decision variables, objective function and constraints, and build a rolling schedule optimization model for the rolling mill; Based on the basic parameters of the rolling mill equipment, strip parameters, rolling process parameters and the maximum number of passes required for rolling, the multi-objective whale optimization algorithm is used to solve the rolling schedule optimization model of the rolling mill to obtain the optimal rolling schedule; The decision variable is the reduction rate of each pass; The objective function takes proportional rolling force, minimum energy consumption and proportional load as the goals and is expressed as: Among them, a i represents the rolling force ratio of the i-th pass; P i represents the rolling force of the i-th pass; W i represents the motor power of the i-th pass; b i Indicates the motor power ratio of the i-th pass; N is the maximum number of passes required for rolling; The constraints include equipment constraints and process constraints; wherein, The device constraints are expressed as: Among them, P i-max represents the maximum rolling force of the i-th pass; W max Indicates the maximum power of the motor; The process constraints are expressed as: Among them, ε i represents the reduction rate of the i-th pass; ε i-min represents the minimum reduction rate of the i-th pass; ε i-max Indicates the maximum reduction rate of the i-th pass; V i Indicates the roller speed of the i-th pass; V i-min Indicates the minimum roll speed of the i-th pass; V i-max represents the maximum roll speed of the i-th pass; The method uses a multi-objective whale optimization algorithm to solve the rolling schedule optimization model of the rolling mill based on the basic parameters of the rolling mill equipment, the parameters of the strip steel, the rolling process parameters and the maximum number of passes required for rolling to obtain the optimal rolling schedule, including: Based on the basic parameters of the rolling mill equipment, strip parameters, rolling process parameters and the maximum number of passes required for rolling, the strip front and rear unit tension, rolling force and motor power under the initial conditions are determined; The decision variables are set as the independent variables of the optimization model, the objective function is set as the objective function of the optimization model, the constraints are set as the constraint functions of the optimization model, the basic parameters of the rolling mill equipment, strip parameters, rolling process parameters, the maximum number of passes required for rolling, and the determined initial unit tension of the strip, rolling force and motor power are substituted into the optimization model, and the multi-objective whale optimization algorithm is used to solve the rolling schedule optimization model of the rolling mill, and the optimal decision variables are output as the optimal rolling schedule.

2. The rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm according to claim 1, characterized in that: The basic parameters of the rolling mill equipment include: the diameter of the upper and lower working rolls, the rated power of the main motor, the maximum rolling force of each pass, the maximum roll speed, the minimum roll speed and the transmission ratio of the motor.

3. The rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm according to claim 2, characterized in that: The strip steel parameters include: strip steel grade, strip steel width, deformation resistance parameters corresponding to different steel grades, and material constant coefficients of different steel grades.

4. The rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm according to claim 3, characterized in that: The rolling process parameters include: incoming raw material thickness, outlet finished product thickness, initial roll speed of each pass, maximum reduction rate of each pass, minimum reduction rate of each pass, friction coefficient and initial reduction rate of each pass.

5. The rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm according to claim 4, characterized in that: The maximum number of passes required for rolling is determined as follows: n )≤h, which is the maximum value of n required for rolling; where H represents the thickness of the incoming raw material; h represents the thickness of the finished product; ε i Represents the initial reduction rate of the i-th pass, i = 1, 2,…, n.

6. The rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm according to claim 1, characterized in that: The calculation formula of the front and rear unit tension of the strip is: t f =s 11 *k f +s 21 t b =s 21 *k b +s 22 Among them, t f is the front unit tension of the strip; t b is the unit tension of the strip; σ 11 , σ 21 , σ 22 are the material constant coefficients of different steel grades; k f Indicates the static deformation resistance of the inlet; k b Indicates the static deformation resistance of the outlet.

7. The rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm according to claim 1, characterized in that: The calculation formula of the rolling force is: A3=1.08-1.02·e all Where μ represents the friction coefficient; ε all Indicates the total reduction rate; R indicates the upper and lower working roll diameters; kp indicates the dynamic deformation resistance; K indicates the tension influence coefficient; C H is the Hitchcock coefficient; H represents the thickness of the incoming raw material; ε i represents the reduction rate of the i-th pass; B represents the strip width.

8. The rolling schedule optimization method for a rolling mill based on a multi-objective whale optimization algorithm according to claim 1, characterized in that: The calculation formula of the motor power is: G T =R*B(tb*H i -tf*h i ) G M =G R +G T +G L W i =0.2193×10 -3 ·(V i / R)·G M Among them, g L0 ~g L4 are all constants; R represents the diameter of the upper and lower working rolls; ε i represents the reduction rate of the i-th pass; P represents the rolling force; H i represents the thickness of the entrance of the i-th pass; tb represents the rear unit tension; tf represents the front unit tension; h i represents the exit thickness of the i-th pass; V i represents the initial roll speed of the i-th pass; W i represents the motor power of the i-th pass; R′ represents the elastic pressure diameter of the roll; B represents the strip width.

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