CN110867219A

CN110867219A - Sintering material optimization control method and device based on ISAA algorithm

Info

Publication number: CN110867219A
Application number: CN201911014041.5A
Authority: CN
Inventors: 王俊凯; 马玉敏; 乔非; 翟晓东; 卢弘; 刘鹃
Original assignee: Shanghai Keyue Information Technology Co Ltd; Tongji University
Current assignee: Shanghai Keyue Information Technology Co Ltd; Tongji University
Priority date: 2019-10-23
Filing date: 2019-10-23
Publication date: 2020-03-06
Anticipated expiration: 2039-10-23
Also published as: CN110867219B

Abstract

The invention relates to an ISAA algorithm-based sintering batching optimization control method and a control device, wherein the control method comprises the following steps: 1) establishing a chemical composition constraint equation, a sinter alkalinity constraint equation, a raw material proportion constraint equation and an optimized batching target function, and establishing a sintering batching mathematical model; 2) solving the sintering burdening mathematical model by adopting an ISAA algorithm to obtain an optimal sintering burdening scheme; 3) controlling the sintering process based on the optimal sintering batching scheme. Compared with the prior art, the invention has the advantages of high precision, stable control and the like while reducing the cost.

Description

Sintering material optimization control method and device based on ISAA algorithm

Technical Field

The invention belongs to the field of industrial automation, relates to an intelligent optimization control method, and particularly relates to an ISAA algorithm-based sintering material proportioning optimization control method and a control device.

Background

The sintering process is one of the most energy-consuming processes of iron and steel enterprises and accounts for 8-10% of the whole iron and steel production process. Iron ore powder, iron oxide scales, return ores and other iron-rich raw materials, coke powder, a flux (such as quicklime and dolomite), gas ash and OG sludge are converted into sintered blocks with porous structures, and qualified sintered ores are provided for blast furnace ironmaking. The energy saving problem of the sintering process has been the focus of the industry and research institutions. On the one hand, many energy saving techniques for process improvement are continuously emerging in the present year, such as Complex Agglomeration Process (CAP), High-proportion Flue Gas Recirculation Sintering (FGRS), sinter cooling sensible heat Recovery process (RSSC), and the like. On the other hand, ingredient optimization is a key problem in realizing the energy-efficient sintering process. The energy consumption and the cost are high and the physical and chemical properties of the sinter are mainly determined by the raw material components and the feeding proportion.

Many scholars are working on the problem of batching in the sintering process. Most of these studies have focused on reducing the cost of the sintering process. Dauter et al focused on the primary batching stage to optimize the iron ore mixing ratio by analyzing the sintering properties of different iron ores. However, they did not consider other raw materials than iron ore. Then, they propose a "two-stage" method on this basis, dealing with different constraints in the two dosing stages. In the first burdening stage, the burdening proportion of different types of iron ore powder is only optimized; in the secondary burdening stage, the proportion of other raw materials such as flux, coke powder and the like is further considered. And the two-time optimization aims at reducing the cost, and a simplex method is adopted to solve the linear programming model. Wu et al propose a sintering batch integration optimization model to minimize sintering cost and SO₂Discharge amount ofTo optimize the objective, linear programming and GA-PSO (genetic-particle swarm) algorithm are used for solving. Wu et al designed an improved genetic algorithm based on a homogeneous model with an adaptive penalty function and an elite-retention learning strategy. Wang et al also propose a linear programming model to optimize sintering cost, quality and yield, and predict the composition and performance of sinter using a BP neural network. However, none of these studies described above consider the dimension of energy.

With the increasing prominence of the energy efficiency problem of the sintering process, the realization of energy consumption reduction in the batching process has recently received much attention, and some scholars have studied from different perspectives. Chen et al studied a Comprehensive Carbon Ratio (CCR) modeling and optimization problem. Before establishing the CCR model, a BP neural network is adopted to establish models of various operation modes, and then the optimal parameters of the selected CCR are obtained through a PSO algorithm. However, CCR is a measure of carbon efficiency, and the resulting optimization solution can only be used as a reference and not as a guide for sintering ingredients. When Shen et al systematically analyze the sintering matching process to the hot rolling output, the batching proportion and the new energy saving technology are studied in the optimization of the whole production process, and energy consumption is used as a target to establish linear and nonlinear programming models in the production process of each unit. However, they focus on only a single target. Liu et al uses the minimized sinter energy value as the target optimized sintering ratio. Waste heat and energy recovery constraints are further considered on the basis of a traditional proportioning optimization model, and an optimal batching scheme is obtained by adopting linear programming. However, this study only targets energy values as optimization, not considering the cost of raw materials. Based on the correlation between energy consumption and cost, obtaining a balanced and feasible sintering batching scheme requires comprehensive consideration of the two indexes.

So far, the related research considering both cost and energy consumption indexes is still less common. Some preliminary research efforts have been directed to optimizing costs while taking into account energy considerations. Wu et al studied the optimum ratio for the first batch stage based on the basic sintering characteristics of Yangdie. The conclusion shows that both cost and coke powder consumption reductions can be achieved when the yankee reaches 40%. However, they do not focus on the secondary batch stage and do not consider all of the raw materials of the sintering process. Wu et al propose a feasible optimization scheme for coke powder ratio by calculating the theoretical value of the coke powder ratio through energy flow analysis. They developed an optimization model with the goal of minimizing costs and considered the coke powder ratio as a constraint. Wang and Qiao propose a multi-objective ingredient optimization model for minimizing cost and energy consumption, and the two objectives are weighted to further convert the problem into a single-objective linear programming model.

Furthermore, data-driven models built from historical production data have been of great interest in recent years, since energy consumption and some other indicators are difficult to model by first-line principles. Zhang et al establishes prediction models of three indexes, namely cost, energy consumption and drum index, based on a BP neural network, and selects effective input variables for each prediction model by adopting GA; and simultaneously analyzing the incidence relation between the input variable and the three indexes. Wang et al propose an integrated prediction model for sintering energy consumption based on SVR and ELM. Wu et al used a mechanistic model and ELM to predict the quality of the sinter. Based on these studies, artificial neural networks have been widely used and have shown good generalization ability.

These studies described above provide some feasible solutions for achieving energy saving in sintering batch optimization, but have some disadvantages, such as insufficient accuracy of the batch calculation results.

Disclosure of Invention

The invention aims to overcome the defects in the prior art and provides a sintering material optimization control method and a sintering material optimization control device based on an ISAA algorithm.

The purpose of the invention can be realized by the following technical scheme:

an ISAA algorithm-based sintering batching optimization control method comprises the following steps:

1) establishing a chemical composition constraint equation, a sinter alkalinity constraint equation, a raw material proportion constraint equation and an optimized batching target function, and establishing a sintering batching mathematical model;

2) solving the sintering burdening mathematical model by adopting an ISAA algorithm to obtain an optimal sintering burdening scheme;

3) controlling the sintering process based on the optimal sintering batching scheme.

Further, the mathematical model of the sintering burden is described as:

0≤x_imin≤x_i≤x_imax≤1,i＝1,2,···,n (6)

wherein, formula (1) is an objective function based on cost optimization, formulas (2) and (3) represent chemical composition constraint equations, formula (4) represents a sintering ore alkalinity constraint equation, formula (5) represents a raw material proportion constraint equation, formula (6) represents a raw material proportioning constraint equation, h (x) represents the total cost of raw materials, n represents the number of raw materials, C represents the total cost of raw materials_iDenotes the unit price, x, of the i-th material_iDenotes the mixing ratio of the i-th raw material, b_jmax、b_jminRespectively represent the upper and lower limits of the jth chemical composition of the qualified sinter, a_jiRepresents the proportion of the jth chemical component in the ith raw material, d_iDenotes the burn-out rate of the i-th material during sintering, R (x) denotes the basicity of the sintered ore, R₁、R₂Respectively representing the upper and lower limits of basicity, CaO_iAnd SiO_2iDenotes the content of calcium oxide and silica in the i-th raw material, x_imin、x_imaxThe upper and lower limits of the ratio of each raw material are indicated.

Further, solving the sintering burdening mathematical model by using the ISAA algorithm specifically includes:

converting each constraint equation into a penalty function to construct a fitness function, and generating an initial population by taking the fitness function as an antigen and an antibody as a solution obtained;

and updating the population by adopting a crossover and mutation operator based on a simulated annealing algorithm, and iterating by adopting an immune algorithm to obtain an optimal sintering batching scheme.

Further, when the population is updated by adopting a crossover and mutation operator based on a simulated annealing algorithm, the old antibody is replaced according to the Metropolis criterion.

The invention also provides a sintering batching optimization control device based on the ISAA algorithm, which comprises the following components:

the optimization model building module is used for obtaining a chemical composition constraint equation, a sinter alkalinity constraint equation, a raw material proportion constraint equation and an optimization batching target function and building a sintering batching mathematical model;

the optimal scheme acquisition module is used for solving the sintering burdening mathematical model by adopting an ISAA algorithm to obtain an optimal sintering burdening scheme;

and the control module is used for controlling the sintering process based on the optimal sintering batching scheme.

Further, the mathematical model of the sintering burden is described as:

0≤x_imin≤x_i≤x_imax≤1,i＝1,2,···,n (6)

Further, the optimal solution obtaining module includes an ISAA algorithm storage unit and an ISAA algorithm execution unit, the ISAA algorithm storage unit stores a computer program, and the ISAA algorithm execution unit calls the computer program to execute the following steps:

Compared with the prior art, the invention has the following beneficial effects:

1) according to the invention, the ISAA algorithm is adopted to obtain the optimal batching scheme, the advantage of global search of the immune algorithm and the advantage of local search of the simulated annealing algorithm are combined, the degradation problem possibly caused by calculation errors and irrelevant constraints is solved, the solving precision is improved, and an effective guiding effect is generated on the actual production process.

2) According to the invention, through the optimization solution of the sintering burdening mathematical model, the cost is controlled, and meanwhile, the burdening in the sintering process can be accurately calculated, so that the sintering process is effectively and stably controlled.

Drawings

FIG. 1 is a schematic flow diagram of the present invention;

fig. 2 illustrates the convergence process of the ISAA algorithm of the present invention.

Detailed Description

The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.

Example 1

The embodiment provides a sintering material optimizing control method based on an ISAA algorithm, which comprises the following steps:

s101, establishing a chemical composition constraint equation, a sinter alkalinity constraint equation, a raw material proportion constraint equation and an optimized batching target function, and constructing a sintering batching mathematical model;

s102, solving the sintering burdening mathematical model by adopting an ISAA algorithm to obtain an optimal sintering burdening scheme;

and S103, controlling the sintering process based on the optimal sintering burdening scheme.

1. Mathematical model for sintering and batching

In this embodiment, a cost-based mathematical model of sintering mixture (CBPM) is established, which is specifically described as follows:

0≤x_imin≤x_i≤x_imax≤1,i＝1,2,···,n (6)

wherein, formula (1) is an objective function based on cost optimization, formulas (2) and (3) represent constraint equations of chemical components, formula (2) represents chemical components which are not volatilized during sintering, such as FeO and CaO, formula (3) represents components which are volatilized, such as sulfur (S), formula (4) represents constraint equation of basicity of sintering ore, formula (5) represents constraint equation of proportion of raw materials, which is constraint of '1', namely the sum of all raw material proportions is 1, formula (6) represents constraint equation of proportion of raw materials, h (x) represents total cost of raw materials, n represents number of raw materials, C (x) represents total cost of raw materials, C (x) represents total amount of raw materials_iDenotes the unit price, x, of the i-th material_iDenotes the mixing ratio of the i-th raw material, b_jmax、b_jminRespectively represent the upper and lower limits of the jth chemical composition of the qualified sinter, a_jiRepresents the proportion of the jth chemical component in the ith raw material, d_iDenotes the burn-out rate of the i-th material during sintering, R (x) denotes the basicity of the sintered ore, R₁、R₂Respectively representing the upper and lower limits of basicity, CaO_iAnd SiO_2iDenotes the content of calcium oxide and silica in the i-th raw material, x_imin、x_imaxThe upper and lower limits of the ratio of each raw material are indicated.

2. Immune-simulated annealing algorithm (ISAA algorithm)

The CBPM model can be converted into a linear programming problem and can be solved by a simplex method. However, sometimes computational errors and irrelevant constraints may cause degradation of the algorithm; at the same time, there may not be a feasible solution under so many strict constraints. In this case, a better non-feasible solution minimization fitness function needs to be found and used as a guide for the actual production process.

And in the process of solving the sintering batching mathematical model by adopting an ISAA algorithm, converting each constraint equation into a penalty function to construct a fitness function, taking the fitness function as an antigen and an antibody as a solution to be solved, updating the population by adopting a crossover and mutation operator based on a simulated annealing algorithm, and iterating by adopting an immune algorithm to obtain an optimal sintering batching scheme. The solving process comprises the following key steps:

1) and generating an initial population.

The population scale is set to NIND, the number of decision variables is NVAR, and a binary coding mode is adopted. Assuming that each variable is represented by a PRECI bit binary number, the Length of each antibody is NVAR × PRECI.

2) Antigen recognition and vaccine extraction.

The antigen of the sintering compounding problem is the fitness function and the antibody is the solution to the problem. The vaccination process was performed according to the constraints of CBPM. First, the vaccine of formula (6) requires that the values of each variable be limited to be within its upper and lower bounds; secondly, the vaccine of formula (5) needs to keep the sum of all variables at "1".

3) Antibody fitness evaluation and memory cell differentiation.

To facilitate solving the CBPM, other constraints are converted into a penalty function for processing. The fitness function may be represented by the following equation:

here, target values according to individuals (lowest target according to cost)Function calculation) order them from small to large, Pos represents the order number in a series of antibody orderings, sp is [1,2 ]]A scalar quantity within, β > 0 for a given selected pressure differential, called the penalty parameter, f_i(x) Are the constraint equations (2) - (6).

For memory cell differentiation, antibodies with low fitness values will be replaced by antibodies with high fitness values. Meanwhile, the similarity between antibodies was calculated using the entropy theory, and is shown by the following formula:

where h (nind) is the mean entropy value of the entire population; h_j(NIND) is the entropy of the j antibody, obtained from the formula

Here, P_ijIs the probability that the symbol i (0, 1 in binary) appears at the locus j (here, the value ranges from 1 to Length).

4) Crossover and mutation operations based on simulated annealing algorithms

In the crossover and mutation phases, the SA is used to perform a better search on the local region of the solution space. This procedure first generates an initial temperature by generating an initial antibody, after calculating its fitness value, performs crossover and mutation operations, the old antibody will be replaced according to the Metropolis guidelines; then, if the current temperature is lower than the termination temperature, the process is ended; otherwise, the next temperature is updated to T (i +1) ═ kt (i), where k is a constant coefficient that can be set.

This example uses the chemical composition, the burning loss rate, the upper and lower limits of the raw material and the unit price, the upper and lower limits of the chemical composition, etc. In the document "Cost and Energy Consumption optimization for the Sintering and Steel industry" (J.K. Wang, and F.Qiao, In: Proc.of the 2014 IEEE International Conference on Automation science and Engineering (CASE), Taipei, Taiwan, Aug.18-22,2014, pp.486-491).

Setting parameters NIND, NVAR and PRECI of the ISAA algorithm as 60, 8 and 20 respectively based on a trial and error method; the maximum iteration number is set to 500; the memory cell refresh rate was set to 10%; the selection, crossover and mutation probabilities were set to 0.6, 0.7 and 0.05, respectively. The similarity threshold and the similarity coefficient are set to 0.15 and 0.9, respectively. The convergence process of the algorithm is shown in fig. 2, and the algorithm can quickly converge on the optimal solution. Table 1 lists the optimal solution obtained by ISAA and compares it with the actual 2 true matching solutions, where S1-S8 represent the matching value of scene I (considered as the reference matching value) and C represents the total cost. As can be seen from table 1, the cost value is reduced by nearly 6%, which also verifies the validity of the present algorithm. Table 2 shows the chemical composition of the sintered ore obtained from ISAA.

TABLE 1 comparison of sintering batch optimization results

	M1	M2	M3	M4	M5
						Actu.#1	S1	S2	S3	S4	S5
Actu.#2	1.0018S1	1.2077S2	0.9672S3	0.9414S4	0.9901S5
						ISAA	0.9494S1	2.2568S2	0.1358S3	2.1641S4	0.9138S5
	M6	M7	M8	Cost
						Actu.#1	S6	S7	S8	C
Actu.#2	S6	0.9167S7	0.9278S8	0.9991C
						ISAA	0.7706S6	0.7700S7	1.0222S8	0.9441C

TABLE 2 sintered mineralogy by ISAA

Comp.	TFe	FeO	SiO₂	CaO	MgO	Al₂O₃	S	R
									ISAA	56.75	9.99	5.11	9.05	2.09	1.8	0.099	1.77

Example 2

The embodiment provides a sintering batching optimization control device based on an ISAA algorithm, which comprises an optimization model construction module, an optimal scheme acquisition module and a control module, wherein the optimization model construction module is used for acquiring a chemical composition constraint equation, a sintering ore alkalinity constraint equation, a raw material proportion constraint equation, a raw material proportioning constraint equation and an optimization batching objective function and constructing a sintering batching mathematical model; the optimal scheme acquisition module is used for solving the sintering burdening mathematical model by adopting an ISAA algorithm to obtain an optimal sintering burdening scheme; the control module is used for controlling the sintering process based on the optimal sintering batching scheme. The rest of this example is the same as example 1.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions that can be obtained by a person skilled in the art through logic analysis, reasoning or limited experiments based on the prior art according to the concept of the present invention should be within the protection scope determined by the present invention.

Claims

1. An ISAA algorithm-based sintering ingredient optimization control method is characterized by comprising the following steps:

2. The ISAA algorithm based sintering batch optimization control method according to claim 1, wherein the sintering batch mathematical model is described as:

0≤x_imin≤x_i≤x_imax≤1,i＝1,2,···,n (6)

wherein, formula (1) is an objective function based on cost optimization, formulas (2) and (3) represent chemical composition constraint equations, formula (4) represents a sintering ore alkalinity constraint equation, formula (5) represents a raw material proportion constraint equation, formula (6) represents a raw material proportioning constraint equation, h (x) represents the total cost of raw materials, n represents the number of raw materials, C represents the total cost of raw materials_iDenotes the unit price, x, of the i-th material_iDenotes the mixing ratio of the i-th raw material, b_jmax、b_jminRespectively represent the upper and lower limits of the jth chemical composition of the qualified sinter, a_jiRepresents the proportion of the jth chemical component in the ith raw material, d_iDenotes the burn-out rate of the i-th material during sintering, R (x) denotes the basicity of the sintered ore, R₁、R₂Individual watchUpper and lower limits of basicity, CaO_iAnd SiO_2iDenotes the content of calcium oxide and silica in the i-th raw material, x_imin、x_imaxThe upper and lower limits of the ratio of each raw material are indicated.

3. The ISAA algorithm-based sintering batch optimization control method according to claim 1, wherein the solving of the sintering batch mathematical model by using the ISAA algorithm specifically comprises:

4. The ISAA algorithm-based sintering batch optimization control method according to claim 3, wherein when the population is updated by adopting crossover and mutation operators based on the simulated annealing algorithm, old antibodies are replaced according to Metropolis criteria.

5. An ISAA algorithm-based sintering proportioning optimization control device is characterized by comprising:

6. The ISAA algorithm based sintering batch optimization control device according to claim 5, wherein the sintering batch mathematical model is described as:

0≤x_imin≤x_i≤x_imax≤1,i＝1,2,···,n (6)

7. The ISAA algorithm-based sintering burden optimization control device according to claim 5, wherein the optimal solution obtaining module comprises an ISAA algorithm storage unit and an ISAA algorithm execution unit, the ISAA algorithm storage unit stores a computer program, and the ISAA algorithm execution unit calls the computer program to execute the following steps:

8. The ISAA algorithm based sintering batch optimization control device as claimed in claim 6, wherein when the population is updated by the crossover and mutation operators based on the simulated annealing algorithm, the old antibody is replaced according to Metropolis criterion.