A method for constructing and solving an optimization scheduling model of an oxygen system of a steel enterprise

Abstract

The application provides a kind of steel enterprise oxygen system optimization scheduling model construction and solution method, it is related to industrial information technology field, establishes steel enterprise oxygen system optimization scheduling model, considers oxygen demand constraint, unit production process constraint, minimum switch-on and switch-off time constraint and climbing constraint.The model decides the production running state of each unit and oxygen production in each period, to reduce the total operation cost of oxygen system.According to the characteristics of the model, a Lagrangian relaxation algorithm is designed to solve it.The method relaxes the material balance constraint to the objective function by Lagrange multiplier, and decomposes the original problem into air separation unit subproblem, gasifier unit subproblem and storage system subproblem.In order to speed up the solution speed of subproblem, an optimal property acceleration strategy is proposed, a subgradient algorithm is designed, and a multiplier updating strategy is proposed.The application can significantly reduce the oxygen dispersion, reduce the production operation cost of oxygen system, and ensure the safe and stable operation of oxygen system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial information technology, in particular to a steel enterprise oxygen system optimization scheduling model construction and solving method. BACKGROUND

[0002] Oxygen is an essential energy medium in the steel production process, mainly used in blast furnace ironmaking, converter steelmaking and other production processes. In order to ensure the smooth progress of the steel production process, oxygen systems are generally configured in steel enterprises to ensure the oxygen demand of ironmaking, steelmaking and other processes. The preparation of oxygen requires a large amount of electric energy, and the power consumption of the oxygen system accounts for about 20% of the total power consumption of the plant, belonging to a class of power load.

[0003] The oxygen emission rate of China's steel enterprises is generally high, with most enterprises having an oxygen emission rate of 2% to 8%. The main reason for the high oxygen emission rate is the imbalance between the intermittent oxygen consumption of the converter and the continuous production of the air separation unit. When the blowing rhythm is accelerated, the supply is less than the demand, and the oxygen supply is insufficient; while the converter is out of order, the supply is greater than the demand, and the oxygen needs to be dispersed. At present, the relevant research on the optimization scheduling of the oxygen system of the steel enterprise at home and abroad is as follows:

[0004] Jia Tianyun et al. (A steel enterprise oxygen optimization scheduling system and method, application number: 201510997648.5) uses a series of equipment working condition modules to solve the oxygen fine scheduling problem under complex working condition combination, provides an oxygen scheduling system easy to interact, thereby reducing oxygen cost, reducing oxygen equipment adjustment frequency, and improving production stability. However, its scheduling strategy is limited to the equipment level, lacks comprehensive consideration of the constraints of the whole system, and it is difficult to obtain a globally optimal scheduling scheme;

[0005] Zhang Ziyang et al. (A method for optimizing distribution of oxygen production system in steel enterprise, application number 201811181626.1) establishes a two-stage model to obtain the global optimal solution or the optimal solution of the distribution problem. The optimal solution is used to plan the distribution scheme, improve the distribution efficiency, and at the same time, within the range of the oxygen pipe network pressure regulation, improve the oxygen utilization rate and save energy; this method does not fully consider the matching between the operation specification and the actual process in the constraint processing of the oxygen pipe network, which may cause the optimization result to be inconsistent with the actual demand, affecting the scheduling effect;

[0006] Chen Guiling et al. (Research on energy-saving optimization of air separation oxygen production in steel enterprises[D]. Shandong Jianzhu University, 2015.) establishes a mathematical model with the objectives of minimizing the oxygen emission rate and maximizing the economic benefits of the oxygen system, and solves the model by combining the improved multi-objective particle swarm algorithm. Numerical experiments show the feasibility of the optimization method. However, this model lacks comprehensive consideration of the safety of equipment operation and energy utilization efficiency, and it is difficult to meet the overall demand under complex working conditions;

[0007] Zhang Qianqian et al. (Two-stage balance scheduling of oxygen system in steel enterprises[D]. Dalian University of Technology, 2016.) considers both oxygen and nitrogen products of the oxygen unit, establishes a mathematical model and solves it with the minimum oxygen and nitrogen emission rate as the target. In the numerical experiment part, the superiority of the scheduling scheme is illustrated under four typical oxygen and nitrogen supply and demand imbalance conditions. This research is limited to the static optimization scheduling of two products, and the dynamic supply and demand balance problem of the oxygen system is not considered, which is difficult to deal with sudden changes caused by demand changes in the actual production process.

[0008] The above researches focus on the optimization of specific scenarios or targets, lack of overall consideration of the interaction between oxygen production, storage, transportation and use, and the equipment operation restrictions and process specifications are not fully included in the scheduling model, which affects the landing of the scheduling scheme and the executability of the scheduling decision. SUMMARY

[0009] In view of the deficiencies of the prior art, the purpose of the present application is to provide an optimization scheduling model construction and solving method for an oxygen system of a steel enterprise, comprising:

[0010] Step 1: establishing an optimization scheduling model for the oxygen system of the steel enterprise, and considering the actual production constraint conditions, the optimization scheduling model comprising actual production constraint conditions and an objective function for optimizing the oxygen system, the actual production constraint conditions comprising upper and lower limit constraints of oxygen production of the air separation unit and the gasifier unit, climbing constraints of the air separation unit and the gasifier unit, air separation unit start-stop constraints, storage system related constraints and oxygen system material balance constraints;

[0011] Step 2: relaxing the oxygen system material balance constraints to the objective function by a Lagrange multiplier vector {λ t},{μ t}, generating a Lagrange relaxation problem, denoted as (LR), and constructing a Lagrange dual problem, denoted as (LD), the Lagrange dual problem being the maximum value of the Lagrange relaxation problem; for a given multiplier vector {λ t},{μ t}, the Lagrange relaxation problem (LR) is decomposed into an air separation unit sub-problem, a gasifier unit sub-problem and a storage system sub-problem;

[0012] Step 3: calculating the solutions of the air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem, and then obtaining a scheduling schedule, the scheduling schedule comprising the start-stop time of the air separation unit and the gasifier unit, and the production of gaseous oxygen and the production of liquid oxygen of the air separation unit, and the production of gaseous oxygen of the gasifier unit;

[0013] Step 3.1: setting an initial iteration number, λt , the initial value of μ t , and the maximum value of iteration number, the dual function value of the Lagrange dual problem is initialized, the initial iteration number is taken as the current iteration number, the initial value of λ t is taken as the current value of λ t , the initial value of μ t is taken as the current value of μ t , and the initial dual function value of the Lagrange dual problem is taken as the current dual function value of the Lagrange dual problem;

[0014] Step 3.2: solving the air separation unit sub-problem, the gasifier unit sub-problem, and the storage system sub-problem solution;

[0015] Step 3.3: taking the solution of the air separation unit sub-problem, the solution of the gasifier unit sub-problem, and the solution of the storage system sub-problem as the solution of the Lagrange relaxation problem, and further calculating the numerical value of the Lagrange relaxation problem, in the numerical value of the relaxation problem at the current iteration number and the current dual function value, the one with the maximum numerical value is taken as the new current dual function value, and the new current dual function value is taken as the lower bound of the objective function;

[0016] Step 3.4: in the case of setting the scheduling time, taking the solution of the Lagrange relaxation problem as the initial solution, using a heuristic algorithm to convert the initial solution into a feasible solution of the optimization scheduling model to obtain the start-stop sequence of the air separation unit and the gasifier unit;

[0017] Step 3.5: according to the start-stop sequence, using GUROBI to establish a model and solve it to obtain the scheduling schedule at the current iteration number;

[0018] Step 3.6: according to the feasible solution and the objective function, calculating the objective function value, and taking the objective function value as the upper bound of the objective function;

[0019] Step 3.7: calculating the dual gap GAP of the current iteration number k, and judging whether the dual gap GAP is less than the preset threshold value, in the case that the dual gap GAP is less than the set value, outputting the scheduling schedule at the current iteration number; in the case that the dual gap GAP is not less than the set value, executing step 3.8;

[0020] Step 3.8: judging whether the current iteration number is less than or equal to the maximum value of iteration number, in the case that the current iteration number is greater than the maximum value of iteration number, outputting the scheduling schedule at the current iteration number; in the case that the current iteration number is less than or equal to the maximum value of iteration number, the current iteration number is incremented by one, and step 3.9 is executed;

[0021] Step 3.9: using the subgradient algorithm to update the Lagrange multiplier, and returning to execute step 3.2.

[0022] Optionally, step 1 specifically comprises:

[0023] Step 1.1: According to the operation of the air separation unit and the gasifier unit in the oxygen system of the iron and steel enterprise, the upper and lower limit constraints of the oxygen output of the air separation unit and the gasifier unit are established, including:

[0024] (1) Upper and lower limit constraints of gaseous oxygen output of air separation unit:

[0025]

[0026] Where, i represents the air separation unit, I represents the set of air separation units; t represents the scheduling time period, T represents the set of discrete time points in the scheduling planning period; u i,t represents the start-stop state of the air separation unit i at period t, 0 represents shutdown, and 1 represents startup; pg i,t represents the gaseous oxygen output of the air separation unit i at period t; represents the lower limit of the gaseous oxygen output of the air separation unit i, represents the upper limit of the gaseous oxygen output of the air separation unit i;

[0027] (2) Upper and lower limit constraints of liquid oxygen output of air separation unit:

[0028]

[0029] Where, pl i,t represents the liquid oxygen output of the air separation unit i at period t; represents the lower limit of the liquid oxygen output of the air separation unit i, represents the upper limit of the liquid oxygen output of the air separation unit i;

[0030] (3) Upper and lower limit constraints of gaseous oxygen output of gasifier unit:

[0031]

[0032] Where, j represents the gasifier unit, J represents the set of gasifier units; u j,t represents the start-stop state of the gasifier unit j at period t, 0 represents shutdown, and 1 represents startup, pg j,t represents the gaseous oxygen output of the gasifier unit j at period t; represents the lower limit of the gaseous oxygen output of the gasifier unit j, represents the upper limit of the gaseous oxygen output of the gasifier unit j;

[0033] Step 1.2: According to the oxygen output rate of change when the air separation unit and the gasifier unit are running, the ramping constraints of the air separation unit and the gasifier unit are established, including:

[0034] (1) The gaseous oxygen production ramping constraint of the air separation unit:

[0035]

[0036] wherein, represents the upper limit of the oxygen production rate of change when the air separation unit i is running, represents the upper limit of the oxygen production rate of change when the air separation unit i is started, represents the upper limit of the oxygen production rate of change when the air separation unit i is shut down, pg i,t-1 represents the gaseous oxygen production of the air separation unit i at the t-1 period, u i,t-1 represents the start-stop state of the air separation unit i at the t-1 period;

[0037] (2) The liquid oxygen production ramping constraint of the air separation unit:

[0038]

[0039] wherein, represents the upper limit of the liquid oxygen production rate of change when the air separation unit i is running; represents the upper limit of the liquid oxygen production rate of change when the air separation unit i is started; represents the upper limit of the liquid oxygen production rate of change when the air separation unit i is shut down, pl i,t-1 represents the liquid oxygen production of the air separation unit i at the t-1 period;

[0040] (3) The gaseous oxygen production ramping constraint of the gasifier unit:

[0041]

[0042] wherein, represents the upper limit of the gaseous oxygen production rate of change when the gasifier unit j is running; represents the upper limit of the gaseous oxygen production rate of change when the gasifier unit j is started; represents the upper limit of the gaseous oxygen production rate of change when the gasifier unit j is shut down; pg j,t-1 represents the gaseous oxygen production of the gasifier unit j at the t-1 period, u j,t-1 represents the start-stop state of the gasifier unit j at the t-1 period;

[0043] Step 1.3: According to the on-off machine gap of the air separation unit in actual operation, the on-off machine constraint of the air separation unit is established as follows:

[0044]

[0045] wherein, represents the continuous on-machine time of the air separation unit i before the t period, denotes the minimum on-time of air separation unit i, T i on denotes the minimum on-time of air separation unit i, T i off denotes the minimum off-time of air separation unit i, T

[0046] Step 1.4: Establish storage system related constraints for liquid and gaseous oxygen storage characteristics;

[0047] (1) Establish gaseous oxygen storage related constraints;

[0048]

[0049] wherein sg t denotes the storage amount of gaseous oxygen at time t, sg min denotes the lower limit of gaseous oxygen inventory, sg max denotes the upper limit of gaseous oxygen inventory;

[0050] (2) Establish liquid oxygen storage related constraints;

[0051]

[0052] wherein sl t denotes the storage amount of liquid oxygen at time t, sl min denotes the lower limit of liquid oxygen inventory, sl max denotes the upper limit of liquid oxygen inventory;

[0053] Step 1.5: Establish oxygen system material balance constraints for liquid and gaseous oxygen;

[0054] (1) Establish gaseous oxygen material balance constraints:

[0055]

[0056] wherein denotes the demand for gaseous oxygen at time t, sg t-1 denotes the storage amount of gaseous oxygen at time t-1;

[0057] (2) Establish liquid oxygen material balance constraints:

[0058]

[0059] wherein denotes the conversion factor of gaseous oxygen to liquid oxygen, sl t-1 denotes the storage amount of liquid oxygen at time t-1, denotes the demand for liquid oxygen at time t;

[0060] Step 1.6: Optimize the objective function of the oxygen system and establish a minimized objective function. The objective function consists of the operating and start-up / shutdown costs of the air separation unit, the operating and start-up / shutdown costs of the vaporizer unit, and the inventory costs of the oxygen system.

[0061] (1) Operating and start-up / shutdown costs of air separation units:

[0062] C i,t =(α0) i pg i,t +α1 i pl i,t )·u i,t +S i ·u i,t (1-u i,t-1 (12)

[0063] Among them, C i,t S represents the cost of air separation unit i during time period t. i α0 represents the startup cost of air separation unit i. i ,α1 i This represents the operating cost coefficient of air separation unit i;

[0064] (2) Operating and start-up / shutdown costs of the vaporizer unit:

[0065] C j,t =β j ·pg j,t ·u j,t +S j ·u j,t (1-u j,t-1 (13)

[0066] Among them, C j,t S represents the cost of gasifier unit j during time period t. j β represents the startup cost of gasifier unit j. j This represents the operating cost coefficient of gasifier unit j;

[0067] (3) Establish oxygen system inventory costs;

[0068]

[0069] Among them, C t γ represents the oxygen system inventory cost during time period t, including the inventory cost of gaseous oxygen and the inventory cost of liquid oxygen. g γ represents the inventory cost coefficient for gaseous oxygen. l The inventory cost coefficient for liquid oxygen;

[0070] Based on this, the objective function can be summarized as follows:

[0071]

[0072] Based on this, the upper and lower limit constraints of oxygen production of the air separation unit and the gasifier unit, the ramping constraints of the air separation unit and the gasifier unit, the air separation unit start-stop constraints, the storage system related constraints, the oxygen system material balance constraints and the objective function of optimizing the oxygen system are obtained.

[0073] Optionally, the Lagrange relaxation problem is specifically represented by the following formula:

[0074] (LR)L(λ,μ)=minZ LR ; (16)

[0075] Wherein, Z LR Can be represented by the following formula:

[0076]

[0077] Wherein, the parameters in formula (17) satisfy the constraint conditions of formula (1)-formula (9);

[0078] Wherein, the Lagrange dual problem is represented by the following formula:

[0079] (LD)maxL(λ,μ);(18)

[0080] Based on this, the Lagrange relaxation problem, i.e. formula (17), and the Lagrange dual problem, i.e. formula (18), are obtained.

[0081] Optionally, the air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem in step 2 are represented by the following formula:

[0082] (1) Air separation unit sub-problem

[0083]

[0084] Wherein, the parameters in formula (19) satisfy the constraint conditions of formula (1), formula (2), formula (4), formula (5) and formula (7);

[0085] (2) Gasifier unit sub-problem

[0086]

[0087] Wherein, the parameters in formula (20) satisfy the constraint conditions of formula (3) and formula (6);

[0088] (3) Storage system sub-problem

[0089]

[0090] where the parameters in equation (21) satisfy the constraints of equation (8) and equation (9).

[0091] Optionally, step 3.2 specifically comprises:

[0092] Step 3.2.1: solving equation (19) for the air separation unit subproblem, where the values of a0 i , a1 i and S i are known, l t is the current value of l t , m t is the current value of m t , solving the minimum of equation (19) and obtaining the corresponding value of pg i,t , the value of pl i,t , the value of u i,t and the value of u i,t-1 , taking the value of pg i,t , the value of pl i,t , the value of u i,t and the value of u i,t-1 as the solution of the air separation unit subproblem, i.e., the solution of equation (19);

[0093] Step 3.2.2: solving equation (20) for the gasifier unit subproblem, where the values of b0 j , and S j are known, l t is the current value of l t , m t is the current value of m t , solving the minimum of equation (20) and obtaining the corresponding value of pg j,t , the value of u j,t and the value of u j,t-1 , taking the value of pg j,t , the value of u j,t and the value of u j,t-1 as the solution of the gasifier unit subproblem, i.e., the solution of equation (20);

[0094] Step 3.2.3: solving equation (21) for the storage system subproblem, where l t is the current value of l t , m t is the current value of m t , solving the minimum of equation (21) and obtaining the corresponding value of C t , the value of sg t , the value of sg t-1 , the value of sg t and the value of sl t-1the value of C t the value of sg t the value of sg t-1 the value of sg t the value of sl t-1 the value of sl as the solution of the storage system sub-problem, i.e., the solution of formula (21).

[0095] Optionally, step 3.4 specifically comprises:

[0096] Step 3.4.1: for each time period, taking the solution of the Lagrangian relaxation problem as an initial solution, and bringing the initial solution into the formula on the left side of the inequality formula (10) to obtain the total production of gaseous oxygen, judging the size of the total production of gaseous oxygen and the demand of gaseous oxygen, specifically, in the case that the total production of gaseous oxygen is greater than the demand of gaseous oxygen, sorting the gasifier units according to the running cost from high to low to obtain a first sequence, according to the sequence number from small to large in the first sequence, sequentially closing the gasifier units, sorting the air separation units satisfying the minimum start-up time constraint in formula (7) according to the running cost from high to low to obtain a second sequence, at the same time, according to the sequence number from small to large in the second sequence, sequentially closing the air separation units in the second sequence, and storing more gaseous oxygen than the demand of gaseous oxygen until the release; in the case that the total production of gaseous oxygen is less than the demand of gaseous oxygen, according to the sequence number from large to small in the first sequence, sequentially starting the gasifier units, at the same time, according to the sequence number from large to small in the second sequence, sequentially starting the air separation units in the second sequence, until the total production of oxygen is greater than or equal to the demand of gaseous oxygen;

[0097] Step 3.4.2: for each time period, taking the solution of the Lagrangian relaxation problem as an initial solution, and bringing the initial solution into the formula on the left side of the inequality formula (11) to obtain the total production of liquid oxygen, judging the size of the total production of liquid oxygen and the demand of liquid oxygen, specifically, in the case that the total production of liquid oxygen is greater than the demand of liquid oxygen, storing more liquid oxygen than the demand of liquid oxygen; in the case that the total production of liquid oxygen is less than the demand of liquid oxygen, increasing the production of gaseous oxygen of the air separation units being operated to further increase the production of liquid oxygen, until the total production of liquid oxygen is greater than the demand of liquid oxygen, or the production of gaseous oxygen of all the air separation units being operated reaches the upper limit, when the production of gaseous oxygen of all the air separation units being operated reaches the upper limit, and the total production of liquid oxygen is less than the demand of liquid oxygen, sorting the air separation units satisfying the minimum shutdown time constraint in formula (7) according to the running cost from low to high to obtain a third sequence, according to the sequence number from small to large in the third sequence, starting the air separation units, at the same time, increasing the production of gaseous oxygen of the started air separation units, until the total production of liquid oxygen is greater than or equal to the demand of liquid oxygen;

[0098] Step 3.4.3: Based on the adjustment of the air separation unit and the gasifier unit in step 3.4.1 and step 3.4.2, the start-stop sequence of the air separation unit and the gasifier unit is determined, and the initial solution is updated based on the parameters involved in step 3.4.1 and step 3.4.2 to obtain a feasible solution.

[0099] Optionally, the dual gap GAP of the current iteration number k in step 3.7 is calculated, which is realized by the following formula:

[0100]

[0101] Wherein, L * is the minimum objective function value up to the current iteration number k, L k is the current dual function value of the current iteration number k.

[0102] Optionally, in step 3.9, the subgradient algorithm is used to update the Lagrange multiplier, which is realized by the following formula:

[0103] μ k+1 = μ k + t k g k (23)

[0104]

[0105] Wherein, μ k+1 is the Lagrange multiplier at iteration number k+1, μ k is the Lagrange multiplier at iteration number k, t k is the step size of the current iteration number k, g k is the subgradient of the current iteration number k, and γ is the step size coefficient.

[0106] The beneficial effects produced by the above technical solutions are:

[0107] The present application provides a kind of steel enterprise oxygen system optimization scheduling model construction and solution method, this method practicality is strong, actual production problem is reduced to unit combination problem, consider the multiple production constraints involved in practical application, can effectively obtain deployment result.Application this method can effectively improve the utilization rate of steel enterprise oxygen system, reduce the diffusion of oxygen, reduce the total cost of oxygen system production operation, can meet the requirements of user demand, pipe network pressure safety and stability and economic benefit etc., in the energy saving and emission reduction, guarantee the economic stable operation of oxygen system. BRIEF DESCRIPTION OF DRAWINGS

[0108] Figure 1A flowchart of a steel enterprise oxygen system optimization scheduling model construction and solution method in an embodiment of the present application is shown.

[0109] Figure 2 A flowchart of a scheduling plan obtaining process in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0110] The specific embodiments of the present application are described in further detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not used to limit the scope of the present application.

[0111] In view of the problems in the prior art, the present application provides a steel enterprise oxygen system optimization scheduling model construction and solution method to realize the stability, reliability and accuracy of the steel enterprise oxygen system, consider various constraints in the actual steel enterprise operation, provide support for energy management personnel to formulate an oxygen scheduling plan, minimize the cost under the premise of meeting the oxygen demand of downstream users, help enterprises to save energy and reduce consumption, and reduce the diffusion. The specific operation method is combined Figure 1 , which can include the following steps:

[0112] Step 1: Establish an optimization scheduling model of the steel enterprise oxygen system, and consider the actual production constraint conditions, the optimization scheduling model includes the actual production constraint conditions and the objective function of the optimized oxygen system, the actual production constraint conditions include the upper and lower limit constraints of the oxygen production of the air separation unit and the gasifier unit, the climbing constraints of the air separation unit and the gasifier unit, the air separation unit start-stop constraints, the storage system related constraints and the oxygen system material balance constraints;

[0113] Step 1.1: According to the operation of the air separation unit and the gasifier unit in the steel enterprise oxygen system, the upper and lower limit constraints of the oxygen production of the air separation unit and the gasifier unit are established, including:

[0114] (1) Upper and lower limit constraints of gaseous oxygen production of air separation unit:

[0115]

[0116] Wherein, i represents the air separation unit, I represents the set of air separation units; t represents the scheduling time period, T represents the set of discrete time points in the scheduling horizon; u i,t represents the start-stop state of the air separation unit i at t period, 0 represents shutdown, 1 represents start; pg i,t represents the gaseous oxygen production of the air separation unit i at t period; represents the lower limit of the gaseous oxygen production of the air separation unit i, represents the upper limit of the gaseous oxygen production of the air separation unit i;

[0117] (2) Upper and lower limit constraints of liquid oxygen production of air separation unit:

[0118]

[0119] wherein, pl i,t represents the liquid oxygen production of the air separation unit i at time t; represents the lower limit of the liquid oxygen production of the air separation unit i, represents the upper limit of the liquid oxygen production of the air separation unit i;

[0120] (3) Upper and lower limit constraints of the gaseous oxygen production of the gasifier unit:

[0121]

[0122] wherein, j represents the gasifier unit, J represents the set of gasifier units; u j,t represents the start-stop state of the gasifier unit j at time t, 0 represents shutdown, and 1 represents startup, pg j,t represents the gaseous oxygen production of the gasifier unit j at time t; represents the lower limit of the gaseous oxygen production of the gasifier unit j; represents the upper limit of the gaseous oxygen production of the gasifier unit j;

[0123] Step 1.2: Establish the ramping constraints of the air separation unit and the gasifier unit according to the oxygen production rate of the air separation unit and the gasifier unit when running, including:

[0124] (1) Gaseous oxygen production ramping constraint of the air separation unit:

[0125]

[0126] wherein, represents the upper limit of the oxygen production rate of the air separation unit i when running, represents the upper limit of the oxygen production rate when the air separation unit i starts, represents the upper limit of the oxygen production rate when the air separation unit i is shut down, pg i,t-1 represents the gaseous oxygen production of the air separation unit i at time t-1, u i,t-1 represents the start-stop state of the air separation unit i at time t-1;

[0127] (2) Liquid oxygen production ramping constraint of the air separation unit:

[0128]

[0129] wherein, represents the upper limit of the liquid oxygen production rate of the air separation unit i when running; represents the upper limit of the liquid oxygen production rate when the air separation unit i starts; pl represents the upper limit of the liquid oxygen production rate change of the air separation unit i when it is closed; i,t-1 pl represents the liquid oxygen production rate change of the air separation unit i at the t-1 period;

[0130] (3) Gaseous oxygen production ramping constraint of the gasifier unit:

[0131]

[0132] wherein, pg represents the upper limit of the gaseous oxygen production rate change of the gasifier unit j when it is running; pg represents the upper limit of the gaseous oxygen production rate change of the gasifier unit j when it is started; pg represents the upper limit of the gaseous oxygen production rate change of the gasifier unit j when it is closed; j,t-1 u represents the gaseous oxygen production of the gasifier unit j at the t-1 period; j,t-1 u represents the start-stop state of the gasifier unit j at the t-1 period;

[0133] Step 1.3: According to the on-off gap of the air separation unit during actual operation, the on-off constraints of the air separation unit are established as follows:

[0134]

[0135] wherein, T represents the continuous on-time of the air separation unit i before the t period, T represents the continuous off-time of the air separation unit i before the t period; i on T represents the minimum on-time of the air separation unit i; i off T represents the minimum off-time of the air separation unit i;

[0136] Step 1.4: The storage system related constraints are established for the storage characteristics of liquid oxygen and gaseous oxygen;

[0137] (1) Establish gaseous oxygen storage related constraints;

[0138]

[0139] wherein, sg t sg represents the storage amount of gaseous oxygen at the t period; min sg represents the lower limit of the gaseous oxygen inventory; max sg represents the upper limit of the gaseous oxygen inventory;

[0140] (2) Establish liquid oxygen storage related constraints;

[0141]

[0142] Among them, sl t sl represents the amount of liquid oxygen stored during time period t. min This indicates the lower limit of liquid oxygen inventory, sl max This indicates the upper limit of liquid oxygen inventory;

[0143] Step 1.5: Establish material balance constraints for the oxygen system for both liquid and gaseous oxygen;

[0144] (1) Establishing material balance constraints for gaseous oxygen:

[0145]

[0146] in, sg represents the demand for gaseous oxygen during time period t. t-1 This represents the amount of gaseous oxygen stored during the time period t-1.

[0147] (2) Establish liquid oxygen material balance constraints:

[0148]

[0149] in, The conversion coefficient, sl, represents the conversion of gaseous oxygen to liquid oxygen. t-1 This indicates the amount of liquid oxygen stored during the time period t-1. This represents the amount of liquid oxygen required during time period t;

[0150] Step 1.6: Optimize the objective function of the oxygen system and establish a minimized objective function. The objective function consists of the operating and start-up / shutdown costs of the air separation unit, the operating and start-up / shutdown costs of the vaporizer unit, and the inventory costs of the oxygen system.

[0151] (1) Operating and start-up / shutdown costs of air separation units:

[0152] C i,t =(α0) i pg i,t +α1 i pl i,t )·u i,t +S i ·u i,t (1-u i,t-1 (12)

[0153] Among them, C i,t S represents the cost of air separation unit i during time period t. i α0 represents the startup cost of air separation unit i. i ,α1 i This represents the operating cost coefficient of air separation unit i;

[0154] (2) Operating and start-up / shutdown costs of the vaporizer unit:

[0155] C j,t = β j · pg j,t · u j,t + S j · u j,t (1-u j,t-1 ); (13)

[0156] wherein C j,t represents the cost of the gasifier unit j at the t period, S j represents the start-up cost of the gasifier unit j, β j represents the operation cost coefficient of the gasifier unit j;

[0157] (3) Establishing the inventory cost of the oxygen system;

[0158]

[0159] wherein C t represents the inventory cost of the oxygen system at the t period, including the inventory cost of gaseous oxygen and the inventory cost of liquid oxygen, γ g represents the inventory cost coefficient of gaseous oxygen, γ l represents the inventory cost coefficient of liquid oxygen;

[0160] Based on this, the objective function can be summarized as follows:

[0161]

[0162] Based on this, the upper and lower limit constraints of the oxygen production of the air separation unit and the gasifier unit, the ramping constraints of the air separation unit and the gasifier unit, the start-stop constraints of the air separation unit, the related constraints of the storage system, the material balance constraints of the oxygen system and the objective function of the optimized oxygen system are obtained.

[0163] wherein step 1, when implemented by software, since formula (1)-formula (15) belong to a nonlinear programming model, it cannot be directly solved by optimization software, and therefore needs to be converted into a linear programming model;

[0164] Formula (4) is linearized as follows:

[0165]

[0166] Formula (5) is linearized as follows:

[0167]

[0168] Formula (7) is linearized as follows:

[0169]

[0170] The formula (6) gasifier unit gaseous oxygen production ramp constraint is linearized as:

[0171]

[0172] According to the established oxygen scheduling mathematical model can be divided into the structure characteristics of the model in the coupling constraint using Lagrange multiplier relaxation to the objective function, so that the original problem can be divided into easy to solve the air separation unit, gasifier unit, storage system three kinds of single unit sub-problems, through step 2 to achieve.

[0173] Step 2: through the Lagrange multiplier vector {λ t},{μ t}, the oxygen system material balance constraint relaxation to the objective function, generate Lagrange relaxation problem, and recorded as (LR), the construction of Lagrange dual problem, recorded as (LD), the Lagrange dual problem is the maximum value of the Lagrange relaxation problem; for given multiplier vector {λ t},{μ t}, the Lagrange relaxation problem (LR) is divided into air separation unit sub-problems, gasifier unit sub-problems and storage system sub-problems;

[0174] Wherein, the Lagrange relaxation problem is specifically expressed by the following formula:

[0175] (LR)L(λ,μ)=minZ LR ; (16)

[0176] Wherein, Z LR can be expressed by the following formula:

[0177]

[0178] Wherein, the parameters in formula (17) satisfy the constraint conditions of formula (1)-formula (9);

[0179] Wherein, the Lagrange dual problem is expressed by the following formula:

[0180] (LD)maxL(λ,μ);(18)

[0181] Based on this, the Lagrange relaxation problem, that is, formula (17), and the Lagrange dual problem, that is, formula (18), are obtained.

[0182] Based on this, for given multiplier vector {λ t},{μ t} will be decomposed into an air separation unit sub-problem, a gasifier unit sub-problem and a storage system sub-problem, wherein the air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem are represented by the following formulas:

[0183] (1) Air separation unit sub-problem

[0184]

[0185] wherein the parameters in formula (19) satisfy the constraint conditions of formula (1), formula (2), formula (4), formula (5) and formula (7);

[0186] (2) Gasifier unit sub-problem

[0187]

[0188] wherein the parameters in formula (20) satisfy the constraint conditions of formula (3) and formula (6);

[0189] (3) Storage system sub-problem

[0190]

[0191] wherein the parameters in formula (21) satisfy the constraint conditions of formula (8) and formula (9).

[0192] Step 3: calculating the air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem, and then obtaining a scheduling plan table, wherein the scheduling plan table comprises the on-off time of the air separation unit and the gasifier unit, the production of gaseous oxygen and the production of liquid oxygen of the air separation unit, and the production of gaseous oxygen of the gasifier unit; combining Figure 2 , specifically comprising the following steps:

[0193] Step 3.1: setting the initial iteration number, the initial values of λ t , μ t and the maximum iteration number, initializing the dual function value of the Lagrange dual problem, taking the initial iteration number as the current iteration number, taking the initial value of λ t as the current value of λ t , taking the initial value of μ t as the current value of μ t , and taking the initial dual function value of the Lagrange dual problem as the current dual function value of the Lagrange dual problem;

[0194] Step 3.2: solving the air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem; wherein the air separation unit sub-problem and the gasifier unit sub-problem are solved by dynamic programming, and the storage system sub-problem is solved by GUROBI.

[0195] Step 3.2.1: Solve equation (19) for the air separation unit subproblem, where the values of a0 i , a1 i , and S i are known, λ t is the current value of λ t , μ t is the current value of μ t , solve for the minimum of equation (19) and obtain its corresponding value of pg i,t , value of pl i,t , value of u i,t , and value of u i,t-1 , take the value of pg i,t , value of pl i,t , value of u i,t , and value of u i,t-1 as the solution to the air separation unit subproblem, i.e., the solution to equation (19);

[0196] Step 3.2.2: Solve equation (20) for the gasifier unit subproblem, where the values of β j , , and S j are known, λ t is the current value of λ t , μ t is the current value of μ t , solve for the minimum of equation (20) and obtain its corresponding value of pg j,t , value of u j,t , and value of u j,t-1 , take the value of pg j,t , value of u j,t , and value of u j,t-1 as the solution to the gasifier unit subproblem, i.e., the solution to equation (20);

[0197] Step 3.2.3: Solve equation (21) for the storage system subproblem, where λ t is the current value of λ t , μ t is the current value of μ t , solve for the minimum of equation (21) and obtain its corresponding value of C t , value of sg t , value of sg t-1 , value of sg t , and value of sl t-1 , take the value of C t , value of sg t , value of sg t-1 , value of sgt The value and sl t-1 The value is taken as the solution to the storage system subproblem, that is, the solution to formula (21).

[0198] To accelerate the solution of subproblems, this invention proposes an acceleration strategy based on optimality conditions. When the optimality conditions are met, the optimal solution to the subproblem can be directly obtained. The optimality conditions and the optimal solution under these conditions are described below:

[0199] 1) Sub-problem of air separation unit:

[0200] Optimal property: when α0 i -λ t >0 and α1 i -μ t When the power is greater than 0, if the unit's continuous operating time is less than the minimum start-up time when the power is reduced to the minimum, it will continue to operate at low power. When the continuous operating time is equal to the minimum start-up time, it will be shut down directly. If the unit's continuous operating time is greater than the minimum start-up time when the power is reduced to the minimum, it will be shut down directly.

[0201] 2) Sub-problem with the vaporizer unit:

[0202] Optimal property: when When the load is reduced, the machine will slow down at the fastest possible rate and then shut down.

[0203] Step 3.3: Take the solutions to the air separation unit subproblem, the gasifier unit subproblem, and the storage system subproblem as the solutions to the Lagrange relaxation problem, and then calculate the numerical value of the Lagrange relaxation problem. Among the numerical value of the relaxation problem at the current iteration number and the current dual function value, take the one with the largest value as the new current dual function value, and take the new current dual function value as the lower bound of the objective function.

[0204] Because the relaxation problem relaxes both the oxygen supply and demand balance constraints and the liquid oxygen supply and demand balance constraints, the solution to the relaxation problem is often infeasible for the original problem. Therefore, this chapter designs a two-stage heuristic algorithm to transform the solution to the relaxation problem into a feasible solution to the original problem. The construction method is as follows:

[0205] The first stage considers the minimum start-up and shutdown time constraints and ramp-up constraints of the air separation unit, as well as the ramp-up constraints of the gasifier, to determine the start-up and shutdown sequence of the air separation and gasification equipment.

[0206] The second stage, based on the start-up and shutdown sequence given in the first stage, calculates the oxygen and liquid oxygen production of the air separation unit, the oxygen production of the vaporizer, and the storage of oxygen and liquid oxygen at each time period.

[0207] The first stage determines the start-stop state of each device by oxygen demand, and only when liquid oxygen is insufficient, the air separation unit is started to prevent dead loop caused by circulation regulation.

[0208] Step 3.4: In the case of setting the scheduling time, the solution of the Lagrangian relaxation problem is used as the initial solution, and the initial solution is converted into a feasible solution of the optimization scheduling model by using the heuristic algorithm to obtain the start-stop sequence of the air separation unit and the gasifier unit;

[0209] Step 3.4.1: For each time period, the solution of the Lagrangian relaxation problem is used as the initial solution, and the initial solution is brought into the formula on the left side of the inequality formula (10) to obtain the total production of gaseous oxygen, and the size of the total production of gaseous oxygen and the demand of gaseous oxygen is judged. Specifically, in the case that the total production of gaseous oxygen is greater than the demand of gaseous oxygen, the gasifier unit is sorted in descending order of operating cost to obtain a first sequence, and the gasifier unit is closed in order according to the sequence number in the first sequence from small to large, and the air separation unit satisfying the minimum start-up time constraint in formula (7) is sorted in descending order of operating cost to obtain a second sequence, and the air separation unit in the second sequence is closed in order according to the sequence number in the second sequence from small to large, and more than the demand of gaseous oxygen is stored until the dispersion; in the case that the total production of gaseous oxygen is less than the demand of gaseous oxygen, the gasifier unit is started in order according to the sequence number in the first sequence from large to small, and the air separation unit in the second sequence is started in order according to the sequence number in the second sequence from large to small until the total production of oxygen is greater than or equal to the demand of gaseous oxygen;

[0210] Step 3.4.2: For each time period, the solution of the Lagrangian relaxation problem is taken as the initial solution, and the initial solution is brought into the formula on the left side of the inequality formula (11) to obtain the total production of liquid oxygen, and the size of the total production of liquid oxygen and the demand for liquid oxygen is judged. Specifically, in the case where the total production of liquid oxygen is greater than the demand for liquid oxygen, more than the demand for liquid oxygen is stored; in the case where the total production of liquid oxygen is less than the demand for liquid oxygen, the production of gaseous oxygen of the air separation unit being operated is increased, and then the production of liquid oxygen is increased, until the total production of liquid oxygen is greater than the demand for liquid oxygen, or the production of gaseous oxygen of all air separation units being operated reaches the upper limit. When the production of gaseous oxygen of all air separation units being operated reaches the upper limit and the total production of liquid oxygen is less than the demand for liquid oxygen, the air separation units that meet the minimum shutdown time constraint in formula (7) are sorted in order of running cost from low to high to obtain a third sequence, and the air separation units are started in the order of the sequence from small to large in the third sequence, and the production of gaseous oxygen of the started air separation units is increased until the production of liquid oxygen is greater than or equal to the demand for liquid oxygen;

[0211] It should be noted that the air separation units and the gasifier units are arranged in ascending order according to the running cost after reading the data, to ensure that the heuristic algorithm prioritizes starting the devices with lower running costs when oxygen or liquid oxygen is insufficient; when shutting down the devices, the devices with higher running costs are prioritized.

[0212] Step 3.4.3: Based on the adjustments of the air separation units and the gasifier units in steps 3.4.1 and 3.4.2, determine the start-stop sequence of the air separation units and the gasifier units, update the initial solution based on the parameters involved in steps 3.4.1 and 3.4.2 to obtain a feasible solution.

[0213] Step 3.5: According to the start-stop sequence, use GUROBI to establish a model and solve it to obtain the scheduling plan at the current iteration number;

[0214] Step 3.6: According to the feasible solution and the objective function, calculate the value of the objective function, and take the value of the objective function as the upper bound of the objective function;

[0215] Step 3.7: Calculate the dual gap GAP of the current iteration number k, and judge whether the dual gap GAP is less than the preset threshold value. In the case where the dual gap GAP is less than the set value, output the scheduling plan at the current iteration number; in the case where the dual gap GAP is not less than the set value, execute step 3.8;

[0216] Wherein, the dual gap GAP of the current iteration number k is calculated, which is realized by the following formula:

[0217]

[0218] wherein, L * is the minimum objective function value up to the current iteration number k, L k is the current dual function value at the current iteration number k.

[0219] Step 3.8: determining whether the current iteration number is less than or equal to the maximum iteration number, in the case that the current iteration number is greater than the maximum iteration number, outputting the scheduling plan table at the current iteration number; in the case that the current iteration number is less than or equal to the maximum iteration number, increasing the current iteration number by one and executing step 3.9;

[0220] Step 3.9: updating the Lagrange multiplier by using the subgradient algorithm, and returning to execute step 3.2.

[0221] wherein, the Lagrange multiplier is updated by using the subgradient algorithm, and the updating is realized by the following formula:

[0222] μ k+1 = μ k + t k g k (23)

[0223]

[0224] wherein, μ k+1 is the Lagrange multiplier at iteration number k+1, μ k is the Lagrange multiplier at iteration number k, t k is the step length at the current iteration number k, g k is the subgradient at the current iteration number k, and γ is the step length coefficient.

[0225] An embodiment is given below to further describe the specific implementation of the present application in detail. A certain steel enterprise has four sets of air separation units, two sets of gasifier units, one set of storage system, and the oxygen required by the workshop is supplied by the whole plant pipe network, wherein the is 19985 cubic meters, the of the 2# air separation unit is 18281 cubic meters, the of the 3# air separation unit is 18301 cubic meters, and the of the 4# air separation unit is 18156 cubic meters, the of the 1# air separation unit is 26185 cubic meters, the of the 2# air separation unit is 26200 cubic meters, the of the 3# air separation unit is 27631 cubic meters, and the of the 4# air separation unit is 26030 cubic meters. The 1100 cubic meters, 2900 cubic meters. Taking the dispatching period T = 24 as an example, the basic dispatching data and parameters of the steel enterprise are sorted out, and the iterative operation is carried out by using the solving algorithm. The experimental results are shown in Table 1, mainly including the switching-on and switching-off time, storage capacity and diffusion capacity and other information of a time period.

[0226] Table 1 24h air separation equipment scheduling results

[0227]

[0228]

[0229] According to the above constraints, the corresponding oxygen distribution scheme can be obtained by solving the algorithm, and compared with the results obtained by the solver, the results are shown in Table 2, wherein ACT represents the average calculation time of each scale example, and GAP represents the dual gap.

[0230] Table 2 comparison results of LR algorithm and solver

[0231]

[0232] After the entire distribution target is completed, from the utilization of oxygen, under the condition of meeting the demand of oxygen and liquid oxygen users, the production and operation cost of oxygen system can be effectively reduced. The economic dispatching of oxygen system in steel enterprise is based on the oxygen optimization distribution model. Under the concept of advocating efficient energy, different dispatching schemes can be formulated for different users. Under the condition of ensuring the safe and stable operation, the operation cost of oxygen system can be effectively reduced, and the oxygen diffusion can be reduced.

[0233] The above description is only the preferred embodiments of the present disclosure and the explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features and the technical features disclosed in the embodiments of the present disclosure (but not limited to) having similar functions are replaced with each other to form a technical solution.

Claims

1. A method for constructing and solving an optimization scheduling model of an oxygen system in a steel enterprise, characterized in that, The method comprises the following steps: Step 1: establishing an optimized scheduling model of an oxygen system of a steel enterprise, and considering actual production constraints, the optimized scheduling model comprising actual production constraints and an objective function of optimizing the oxygen system, the actual production constraints comprising upper and lower limit constraints of oxygen production of an air separation unit and a gasifier unit, climbing constraints of the air separation unit and the gasifier unit, start-stop constraints of the air separation unit, storage system-related constraints and oxygen system material balance constraints; Step 2: Relax the oxygen system material balance constraints into the objective function by means of a Lagrange multiplier vector {λ t},{μ t}, generate a Lagrangian relaxation problem, denoted as (LR), and construct a Lagrangian dual problem, denoted as (LD), which is the maximum of the Lagrangian relaxation problem; for a given multiplier vector {λ t},{μ t}, decompose the Lagrangian relaxation problem (LR) into an air separation unit subproblem, a gasifier unit subproblem, and a storage system subproblem; Step 3: calculating solutions of the air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem, and then obtaining a scheduling table, the scheduling table comprising start-stop times of the air separation unit and the gasifier unit, and oxygen production of the air separation unit in gaseous and liquid states and oxygen production of the gasifier unit in gaseous state; Step 3.1: Set initial iteration number, initial value of λ t , initial value of μ t , and maximum iteration number, initialize the dual function value of the Lagrangian dual problem, take the initial iteration number as the current iteration number, take the initial value of λ t as the current value of λ t , take the initial value of μ t as the current value of μ t , and take the initial dual function value of the Lagrangian dual problem as the current dual function value of the Lagrangian dual problem; Step 3.2: solving the air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem; Step 3.3: taking the solution of the air separation unit sub-problem, the solution of the gasifier unit sub-problem and the solution of the storage system sub-problem as a solution of the Lagrangian relaxation problem, and then calculating a numerical value of the Lagrangian relaxation problem, obtaining one with the maximum numerical value from the numerical value of the relaxation problem at the current iteration number and the current dual function value as a new current dual function value, and taking the new current dual function value as a lower bound of the objective function; Step 3.4: taking the solution of the Lagrangian relaxation problem as an initial solution under the condition that the scheduling time is set, and using a heuristic algorithm to convert the initial solution into a feasible solution of the optimized scheduling model to obtain a start-stop sequence of the air separation unit and the gasifier unit; Step 3.5: using GUROBI to establish a model and solve the model according to the start-stop sequence to obtain a scheduling table at the current iteration number; Step 3.6: calculating an objective function value according to the feasible solution and the objective function, and taking the objective function value as an upper bound of the objective function; Step 3.7: calculating a dual gap GAP at the current iteration number k, and judging whether the dual gap GAP is less than a preset threshold value, and outputting the scheduling table at the current iteration number in the case that the dual gap GAP is less than the set value; In the case that the dual gap GAP is not less than the set value, step 3.8 is performed; Step 3.8: judging whether the current iteration number is less than or equal to a maximum iteration number, and outputting the scheduling table at the current iteration number in the case that the current iteration number is greater than the maximum iteration number; In the case that the current iteration number is less than or equal to the maximum iteration number, the current iteration number is increased by one, and step 3.9 is performed; Step 3.9: using a subgradient algorithm to update the Lagrangian multiplier, and returning to step 3.

2.

2. The method of claim 1, wherein the method further comprises: Step 1 specifically comprises: Step 1.1: establishing upper and lower limit constraints of oxygen production of an air separation unit and a gasifier unit according to running conditions of the air separation unit and the gasifier unit in the oxygen system of the steel enterprise, comprising: (1) upper and lower limit constraints of gaseous oxygen production of the air separation unit: wherein i denotes an air separation unit, I denotes a set of air separation units; t denotes a dispatch time period, T denotes a set of discrete time points within a dispatch horizon; u i,t denotes a start-stop state of air separation unit i at time period t, 0 denotes shut down, 1 denotes start up; pg i,t denotes gaseous oxygen production of air separation unit i at time period t; denotes a lower bound of gaseous oxygen production of air separation unit i, denotes an upper bound of gaseous oxygen production of air separation unit i; (2) upper and lower limit constraints of liquid oxygen production of the air separation unit: where pl i,t represents the liquid oxygen production of the air separation unit i at time t; represents the lower limit of the liquid oxygen production of the air separation unit i, represents the upper limit of the liquid oxygen production of the air separation unit i; (3) upper and lower limit constraints of gaseous oxygen production of the gasifier unit: wherein j denotes a gasifier unit, J denotes a set of gasifier units; u j,t denotes the start-stop state of the gasifier unit j at time period t, 0 denotes shutdown, 1 denotes startup, pg j,t denotes the gaseous oxygen production of the gasifier unit j at time period t; denotes the lower limit of the gaseous oxygen production of the gasifier unit j; denotes the upper limit of the gaseous oxygen production of the gasifier unit j; Step 1.2: According to the oxygen production rate change of the air separation unit and the gasifier unit when running, the ramping constraints of the air separation unit and the gasifier unit are established, including: (1) Air separation unit gaseous oxygen production ramping constraint: wherein, represents an upper limit of the oxygen production rate of change when the air separation train i is running, represents an upper limit of the oxygen production rate of change when the air separation train i is started, represents an upper limit of the oxygen production rate of change when the air separation train i is shut down, pg i,t-1 represents the gaseous oxygen production of the air separation train i at the t-1 period, u i,t-1 represents the start-stop state of the air separation train i at the t-1 period; (2) Air separation unit liquid oxygen production ramping constraint: wherein, represents the upper limit of the rate of change of liquid oxygen production when the air separation unit i is running; represents the upper limit of the rate of change of liquid oxygen production when the air separation unit i is started; represents the upper limit of the rate of change of liquid oxygen production when the air separation unit i is stopped, pl i,t-1 represents the liquid oxygen production of the air separation unit i at the t-1 period; (3) Gasifier unit gaseous oxygen production ramping constraint: wherein, represents the upper limit of the gaseous oxygen production rate of change when the gasifier unit j is operating; represents the upper limit of the gaseous oxygen production rate of change when the gasifier unit j is starting up; represents the upper limit of the gaseous oxygen production rate of change when the gasifier unit j is shutting down; j,t-1 represents the gaseous oxygen production of the gasifier unit j at the t-1 period, u j,t-1 represents the start-stop state of the gasifier unit j at the t-1 period; Step 1.3: According to the on-off interval of the air separation unit when actually running, the on-off constraints of the air separation unit are established as follows: wherein, represents the continuous on-time of the air separation unit i before the t period, represents the continuous off-time of the air separation unit i before the t period; T i on represents the minimum on-time of the air separation unit i, T i off represents the minimum off-time of the air separation unit i; Step 1.4: According to the storage characteristics of liquid oxygen and gaseous oxygen, the related constraints of the storage system are established; (1) Establish gaseous oxygen storage related constraints; wherein sg t represents the storage amount of gaseous oxygen at time t, sg min represents the lower limit of the inventory amount of gaseous oxygen, sg max represents the upper limit of the inventory amount of gaseous oxygen; (2) Establish liquid oxygen storage related constraints; wherein sl t represents the storage amount of liquid oxygen at time t, sl min represents the lower limit of the liquid oxygen inventory, sl max represents the upper limit of the liquid oxygen inventory; Step 1.5: For liquid oxygen and gaseous oxygen, establish oxygen system material balance constraints; (1) Establish gaseous oxygen material balance constraints: where D t g denotes the demand for gaseous oxygen for the period t, sg t-1 denotes the storage of gaseous oxygen for the period t-1; (2) Establish liquid oxygen material balance constraints: wherein, represents a conversion factor of conversion of gaseous oxygen into liquid oxygen, sl t-1 represents a storage amount of liquid oxygen at a t-1 period, represents a demand amount of liquid oxygen at a t period; Step 1.6: Optimize the objective function of the oxygen system, establish a minimization objective function, which is composed of air separation unit running and start-stop cost, gasifier unit running and start-stop cost, and oxygen system inventory cost; (1) Air separation unit running and start-stop cost: C i,t = (a0 i pg i,t + a1 i pl i,t ) · u i,t + S i · u i,t (1 - u i,t-1 ); (12) where C i,t denotes the cost of the air separation unit i at time period t, S i denotes the start-up cost of the air separation unit i, a0 i , a1 i denotes the operating cost coefficient of the air separation unit i; (2) Gasifier unit running and start-stop cost: C j,t = β j · pg j,t · u j,t + S j · u j,t (1 - u j,t-1 ); (13) where C j,t represents the cost of gasifier unit j at time period t, S j represents the start-up cost of gasifier unit j, β j represents the operating cost coefficient of gasifier unit j; (3) Establish oxygen system inventory cost; wherein C t represents the oxygen system inventory cost of the t period, including the gaseous oxygen inventory cost and the liquid oxygen inventory cost, γ g represents the inventory cost coefficient of gaseous oxygen, γ l represents the inventory cost coefficient of liquid oxygen; Based on this, the objective function can be summarized as follows: Based on this, the upper and lower limit constraints of the oxygen production of the air separation unit and the gasifier unit, the ramping constraints of the air separation unit and the gasifier unit, the on-off constraints of the air separation unit, the related constraints of the storage system, the oxygen system material balance constraints and the objective function of optimizing the oxygen system are obtained.

3. The method of claim 2, wherein the method further comprises: The Lagrangian relaxation problem is specifically represented by the following formula: (LR)L(λ,μ) = min Z LR ; (16) wherein Z LR is represented by the following equation: Wherein, the parameters in formula (17) satisfy the constraint conditions of formula (1)-formula (9); Wherein, the Lagrangian dual problem is represented by the following formula: (LD)maxL(λ,μ); (18) Based on this, the Lagrangian relaxation problem, i.e. formula (17), and the Lagrangian dual problem, i.e. formula (18), are obtained.

4. The method of claim 3, wherein the method further comprises: The air separation unit sub-problem, the gasifier unit sub-problem and the storage system sub-problem in step 2 are represented by the following formula: (1) Air separation unit sub-problem Wherein, the parameters in formula (19) satisfy the constraint conditions of formula (1), formula (2), formula (4), formula (5) and formula (7); (2) Gasifier unit sub-problem Wherein, the parameters in formula (20) satisfy the constraint conditions of formula (3) and formula (6); (3) Storage system sub-problem Wherein, the parameters in formula (21) satisfy the constraint conditions of formula (8) and formula (9).

5. The method of claim 4, wherein the method further comprises: Step 3.2 specifically includes: Step 3.2.1: Solve equation (19) for the air separation unit subproblem, where the values of a0 i , a1 i , and S i are known, l t is the current value of l t , and m t is the current value of m t , solve equation (19) for its minimum value and obtain the corresponding value of pg i,t , the value of pl i,t , the value of u i,t , and the value of u i,t-1 , take the value of pg i,t , the value of pl i,t , the value of u i,t , and the value of u i,t-1 as the solution to the air separation unit subproblem, i.e., the solution to equation (19); Step 3.2.2: Solve equation (20) for the gasifier unit subproblem, where the values of β j , and S j are known, λ t is the current value of λ t , μ t is the current value of μ t , solve equation (20) for its minimum value and obtain its corresponding value of pg j,t , value of u j,t and value of u j,t-1 , take the value of pg j,t , value of u j,t and value of u j,t-1 as the solution of the gasifier unit subproblem, i.e. the solution of equation (20); Step 3.2.3: Solve for formula (21) of the storage system subproblem, where λ t For λ t The current value of μ t For μ t Given the current value, solve for the minimum value of formula (21) and obtain its corresponding C. t The value, sg t The value, sg t-1 The value, sg t The value and sl t-1 The value of C t The value, sg t The value, sg t-1 The value, sg t The value and sl t-1 The value is used as the solution to the storage system subproblem, that is, the solution to formula (21).

6. The method of claim 5, wherein the method further comprises: Step 3.4 specifically includes: Step 3.4.1: for each time period, taking the solution of the Lagrangian relaxation problem as the initial solution, taking the initial solution into the formula on the left side of the inequality formula (10) to obtain the total production of gaseous oxygen, judging the size of the total production of gaseous oxygen and the demand of gaseous oxygen, specifically, in the case that the total production of gaseous oxygen is greater than the demand of gaseous oxygen, the gasifier unit is sorted in descending order of operating cost to obtain a first sequence, and the gasifier unit is closed in turn according to the sequence number in the first sequence from small to large, and the air separation unit that meets the minimum start-up time constraint in formula (7) is sorted in descending order of operating cost to obtain a second sequence, and the air separation unit in the second sequence is turned off in turn according to the sequence number in the second sequence from small to large, and the gaseous oxygen more than the demand of gaseous oxygen is stored until it is released; in the case that the total production of gaseous oxygen is less than the demand of gaseous oxygen, the gasifier unit is started in turn according to the sequence number in the first sequence from large to small, and the air separation unit in the second sequence is started in turn according to the sequence number in the second sequence from large to small, until the total production of oxygen is greater than or equal to the demand of gaseous oxygen; Step 3.4.2: for each time period, taking the solution of the Lagrangian relaxation problem as the initial solution, taking the initial solution into the formula on the left side of the inequality formula (11) to obtain the total production of liquid oxygen, judging the size of the total production of liquid oxygen and the demand of liquid oxygen, specifically, in the case that the total production of liquid oxygen is greater than the demand of liquid oxygen, the liquid oxygen more than the demand of liquid oxygen is stored; in the case that the total production of liquid oxygen is less than the demand of liquid oxygen, the production of gaseous oxygen of the air separation unit being operated is increased, and then the production of liquid oxygen is increased, until the total production of liquid oxygen is greater than the demand of liquid oxygen, or the production of gaseous oxygen of all air separation units being operated reaches the upper limit, when the production of gaseous oxygen of all air separation units being operated reaches the upper limit and the total production of liquid oxygen is less than the demand of liquid oxygen, the air separation unit that meets the minimum shutdown time constraint in formula (7) is sorted in descending order of operating cost to obtain a third sequence, and the air separation unit is started according to the sequence number in the third sequence from small to large, and the gaseous oxygen production of the started air separation unit is increased, until the total production of liquid oxygen is greater than or equal to the demand of liquid oxygen; Step 3.4.3: based on the adjustment of the start and stop sequence of the air separation unit and the gasifier unit in steps 3.4.1 and 3.4.2, the start and stop sequence of the air separation unit and the gasifier unit is determined, and the initial solution is updated based on the parameters involved in steps 3.4.1 and 3.4.2 to obtain a feasible solution.

7. The method of claim 1, wherein the method further comprises: In step 3.7, the dual gap GAP of the current iteration number k is calculated, which is realized by the following formula: where L * is the smallest objective function value up to the current iteration number k, L k is the current dual function value for the current iteration number k.

8. The method of claim 1, wherein the method further comprises: In step 3.9, the Lagrange multiplier is updated by using the subgradient algorithm, which is realized by the following formula: μ k+1 = μ k + t k g k (23) wherein μ k+1 is the Lagrange multiplier at iteration k+1, μ k is the Lagrange multiplier at iteration k, t k is the step size at current iteration k, g k is the subgradient at current iteration k, and γ is the step size factor.

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