Electric furnace smelting titanium slag multiphase equilibrium solving method and system based on genetic algorithm

Abstract

The invention provides an electric furnace smelting titanium slag multiphase equilibrium solving method and system based on a genetic algorithm, and relates to the technical field of electric furnace smelting titanium slag, and the method comprises the steps: obtaining basic data in an electric furnace smelting titanium slag scene; performing model construction processing according to the phase composition data and the thermodynamic property parameters to obtain a thermodynamic equilibrium constraint model; performing high-dimensional solution space generation processing according to the phase composition data and the smelting environment parameters to obtain a multi-dimensional equilibrium solution space; performing population coding processing according to the multi-dimensional equilibrium solution space to obtain an initial population; fitness fusion processing is carried out according to the thermodynamic equilibrium constraint model to obtain fitness indexes; and performing evolution processing according to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model to obtain an equilibrium state solution set. According to the method, temperature and pressure parameters are embedded into the thermodynamic equilibrium constraint model, the Gibbs free energy weight is adjusted through the temperature gradient, and the compound phase stability is controlled through the pressure gradient, so that the calculation result better fits the actual smelting working condition.

Description

Technical Field

[0001] The invention relates to the technical field of electric furnace smelting titanium slag, and in particular to a method and system for solving multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm. Background Art

[0002] Electric furnace smelting of titanium slag is the core process for efficient extraction of titanium resources. It separates iron and titanium oxides in ilmenite through high-temperature reduction reactions to form titanium-rich slag and pig iron. This process involves a complex multiphase equilibrium process, including the dynamic interaction of gas phase, slag state, and metal phase, and the phase change reaction of FeO-TiO2 complex. Accurate calculation of the multiphase equilibrium state is crucial to optimizing smelting process parameters (such as temperature and carbon content), which directly affects titanium recovery rate and energy efficiency.

[0003] However, the high-temperature multiphase coupling characteristics of the titanium slag system make it difficult for traditional thermodynamic models to accurately describe its dynamic equilibrium law. Existing methods are mostly based on simplified assumptions, such as ignoring the phase competition between Fe3O4 and FeO·TiO2, fixing environmental parameters, and constructing static mass conservation equations, which leads to the deviation of the prediction results from the actual working conditions. Some improved technologies use staged linear programming or manual experience to adjust model parameters. Although they can alleviate the single constraint deviation, they cannot solve the global convergence problem in the high-dimensional non-convex solution space, and the computational complexity increases exponentially with the increase in the number of phases, which is difficult to meet the needs of real-time process control.

[0004] Based on the above-mentioned shortcomings of the prior art, there is an urgent need for a method and system for solving the multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm. Summary of the invention

[0005] The purpose of the present invention is to provide a method for solving the multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows: In a first aspect, the present application provides a method for solving multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm, comprising: Obtain basic data in the electric furnace smelting titanium slag scenario, wherein the basic data includes phase composition data, thermodynamic property parameters of chemical components of each phase, and smelting environment parameters; Performing model construction processing according to the phase composition data and the thermodynamic property parameters, and obtaining a thermodynamic equilibrium constraint model by simultaneously establishing a mass conservation equation, a chemical equilibrium equation, and a Gibbs free energy extreme value equation; A high-dimensional solution space generation process is performed according to the phase composition data and the smelting environment parameters, and a multi-dimensional equilibrium solution space is obtained by nonlinearly coupling and mapping the ion concentration, the mole fraction of the composite molecule and the environmental parameters; Performing population coding processing according to the multidimensional equilibrium solution space to obtain an initial population; Performing fitness fusion processing according to the thermodynamic equilibrium constraint model to obtain a fitness index; Evolutionary processing is performed according to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model, and a temperature-pressure gradient-guided crossover and mutation operation is performed based on the initial population to obtain an equilibrium solution set.

[0006] In the second aspect, the present application also provides a system for solving the multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm, comprising: An acquisition module is used to acquire basic data in the electric furnace smelting titanium slag scenario, wherein the basic data includes phase composition data, thermodynamic property parameters of chemical components of each phase, and smelting environment parameters; A construction module is used to perform model construction processing according to the phase composition data and the thermodynamic property parameters, and obtain a thermodynamic equilibrium constraint model by simultaneously establishing a mass conservation equation, a chemical equilibrium equation and a Gibbs free energy extreme value equation; A generation module is used to generate a high-dimensional solution space according to the phase composition data and the smelting environment parameters, and obtain a multi-dimensional equilibrium solution space by nonlinearly coupling and mapping the ion concentration, the mole fraction of the composite molecule and the environmental parameters; An encoding module, used for performing population encoding processing according to the multidimensional equilibrium solution space to obtain an initial population; A fusion module, used for performing fitness fusion processing according to the thermodynamic equilibrium constraint model to obtain a fitness index; The output module is used to perform evolutionary processing according to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model, and perform a temperature-pressure gradient-guided crossover and mutation operation based on the initial population to obtain an equilibrium solution set.

[0007] The beneficial effects of the present invention are: The present invention deeply embeds the temperature and pressure parameters into the thermodynamic equilibrium constraint model, adjusts the Gibbs free energy weight through the temperature gradient, and controls the phase stability of the complex through the pressure gradient, thus solving the limitation of the traditional model that ignores the dynamic influence of environmental parameters and making the calculation results more in line with the actual smelting conditions. The normalized mandatory coding rule is used to generate the initial population to ensure that the individuals strictly meet the total molar conservation and phase composition constraints, avoiding the invalid search caused by traditional random coding. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 Schematic flow chart of a method for solving the multiphase equilibrium of titanium slag smelted in an electric furnace based on a genetic algorithm as described in the embodiments of the present invention; Figure 2 Schematic structural diagram of a system for solving the multiphase equilibrium of titanium slag smelted in an electric furnace based on a genetic algorithm as described in the embodiments of the present invention; Figure 3 Schematic structural diagram of a device for solving the multiphase equilibrium of titanium slag smelted in an electric furnace based on a genetic algorithm as described in the embodiments of the present invention.

[0010] Reference signs in the figure: 800, a device for solving the multiphase equilibrium of titanium slag smelted in an electric furnace based on a genetic algorithm; 801, a processor; 802, a memory; 803, a multimedia component; 804, an I / O interface; 805, a communication component; 901, an acquisition module; 902, a construction module; 903, a generation module; 904, an encoding module; 905, a fusion module; 906, an output module. Detailed implementation manners

[0011] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0012] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0013] Embodiment 1:

[0014] This embodiment provides a method for solving the multiphase equilibrium of titanium slag smelted in an electric furnace based on a genetic algorithm.

[0015] Refer to Figure 1 , which shows that this method includes steps S100 to S600.

[0016] Step S100, obtaining basic data in the electric furnace smelting titanium slag scenario, the basic data including phase composition data, thermodynamic property parameters of chemical components of each phase and smelting environment parameters; It can be understood that this step comprehensively obtains the multiphase composition data of the gaseous, slag and metal phases in the electric furnace smelting titanium slag scene, including oxygen, carbon monoxide, carbon dioxide and other components in the gas phase, key chemical components such as ferrous oxide, ferroferric oxide, titanium dioxide and titanium-iron complex in the slag state, as well as the standard formation free energy, activity coefficient and other thermodynamic property parameters of each phase, and combines the real-time collected smelting temperature and system total pressure parameters to construct the core data foundation for multiphase equilibrium calculation. In practical applications, X-ray diffraction is used to analyze the slag mineral phase composition in real time, high-temperature sensors are used to synchronously monitor the temperature distribution of the molten pool, and pressure transmitters are used to collect system pressure to ensure that the data is strictly synchronized with the smelting process conditions. The chemical components of each phase in the titanium smelting system of this embodiment are shown in the following table: Table 1 Chemical composition of each phase in titanium smelting system

[0017] Step S200, constructing a model according to the phase composition data and the thermodynamic property parameters, and obtaining a thermodynamic equilibrium constraint model by simultaneously solving the mass conservation equation, the chemical equilibrium equation and the Gibbs free energy extreme value equation; It should be noted that this step quantifies the cross-phase migration paths of elements such as iron, titanium, and oxygen into atomic conservation equations to ensure that the element flow between the slag state and the metal phase satisfies the mass balance (for example, the conversion ratio of iron between ferrous oxide and ferroferric oxide). At the same time, combined with the formation reaction equilibrium equations of complexes such as ilmenite and spinel, the reaction equilibrium constant is corrected by the activity coefficient to accurately describe the competitive relationship of the phase transition of the complex at high temperature; further introduce the extreme value of Gibbs free energy as the global optimization target, transform the multiphase equilibrium problem into an energy minimization problem under dynamic constraints, and break through the limitation that the traditional static model cannot characterize the influence of environmental parameter changes on the phase equilibrium path. Specifically, according to the existing FeO-TiO2 slag system phase diagram, FeO and TiO2 can form three solid and liquid compounds with the same composition: titanium spinel 2FeO•TiO2 (1395℃), ilmenite FeO•TiO2 (1400℃) and iron brookite FeO•2TiO2 (1494℃); at the same time, FeO and Fe2O3 will form Fe3O4. Therefore, the structural unit of this system is Fe 2+ , O 3- Ions and Fe2O3, TiO2, Fe3O4, 2FeO•TiO2, FeO•TiO, FeO •2TiO2 molecules. First, construct a chemical equilibrium equation from the reaction formula:

[0018] Among them, Fe2+ is ferrous ion; O 2- is oxygen ion; Fe2O3 is iron oxide; Fe3O4 is magnetite; the subscripts (l) and (s) represent liquid state and solid state respectively; N1 is the molar amount of FeO in the slag phase; N2 is the molar amount of Fe2O3 in the slag phase; N3 is the molar amount of TiO2 in the slag phase; N4 is the molar amount of Fe3O4 in the slag phase; N3 is the molar amount of TiO2 in the slag phase; N5 is the molar amount of titanomagnetite in the slag phase; N6 is the molar amount of ilmenite in the slag phase; N7 is the molar amount of leucoxene in the slag phase.

[0019] Equations 1 to 4 correct the equilibrium constant through the activity coefficient, and accurately characterize the competitive relationship of the phase transformation of the complex at high temperatures. Subsequently, according to the mass balance, we can obtain:

[0020] Equations 3 to 5 quantify the distribution ratio of iron elements among FeO, Fe3O4 and the complex, and restrict the flow path of titanium elements in phases such as TiO2 and ilmenite. By eliminating redundant variables through simultaneous equations, we can further deduce:

[0021] Among them, ∑x is the total molar fraction at equilibrium; b is the total molar fraction of FeO in the slag phase; a1 is the total molar fraction of Fe2O3 in the slag phase; a2 is the total molar fraction of TiO2 in the slag phase; N i is the molar amount of the i-th chemical component; i is the serial number of the chemical component.

[0022] Equations 8 and 9 couple the conservation relations of iron and titanium elements with the chemical equilibrium equation to form a closed system of equations. Finally, taking the Gibbs free energy extreme value equation as the global optimization goal, the multiphase equilibrium problem is transformed into an energy minimization problem under dynamic constraints. The total Gibbs free energy of the system is expressed as:

[0023] Among them, G 总 is the total Gibbs free energy of the system; represents the standard molar Gibbs free energy of formation of the i-th chemical component; R represents the ideal gas constant; T represents the system temperature. a i is the activity of the i-th chemical component.

[0024] Step S300: Perform high-dimensional solution space generation processing according to the phase composition data and smelting environment parameters. By non-linearly coupling and mapping the ion concentration, the molar fraction of the composite molecule and the environment parameters, a multi-dimensional equilibrium solution space is obtained; Specifically, in this step, by introducing temperature as the dynamic weight factor of the ion diffusion rate and pressure as the probability adjustment coefficient of the complex phase competition, the isolated chemical component distribution in the traditional low-dimensional solution space is transformed into a gradient field in the high-dimensional continuous space, solving the problem of distorted phase change path characterization caused by linear dimensionality reduction in the traditional model; at the same time, based on the high-temperature characteristics of the slag, a hierarchical superposition non-linear mapping rule is designed to make the solution space adapt to the multi-phase coupling characteristics of the titanium slag system.

[0025] Step S400: Perform population coding processing according to the multi-dimensional equilibrium solution space to obtain the initial population; It can be understood that in this step, first, the coupling relationship between ion concentration, complex mole fraction and environmental parameters in the solution space is transformed into a chromosome coding rule. By forced normalization processing, it is ensured that each individual naturally satisfies the total mole conservation constraint, avoiding the redundancy of invalid solutions caused by constraint conflicts in random coding; second, using the guiding effect of the temperature-pressure gradient field in the solution space on the complex phase change path, the distribution weights of different complex components in the chromosome are dynamically adjusted (such as chromosomes in the high-temperature region preferentially carry the characteristics of a high proportion of magnetite), so that the initial population implies the phase competition law of the actual smelting conditions; finally, based on the topological structure of the solution space (such as the strong correlation between iron ion concentration and ilmenite phase distribution), a partition sampling strategy is designed to generate a dense chromosome distribution in the high-probability equilibrium region, covering the low-probability critical phase change region at the same time, ensuring that the population has both global exploration and local refinement capabilities.

[0026] Step S500: Perform fitness fusion processing according to the thermodynamic equilibrium constraint model to obtain the fitness index; It should be noted that in this step, the atomic conservation residuals of iron, titanium, and oxygen elements, the equilibrium deviations of key reactions such as ilmenite and spinel, and the Gibbs free energy extreme value differences are normalized, and the weight ratios of the three are dynamically adjusted according to the smelting temperature to form a comprehensive fitness index that can quantitatively evaluate the equilibrium state of the population individuals. The fitness index can accurately quantify the comprehensive advantages and disadvantages of population individuals in the multi-phase equilibrium state, especially in the critical phase change interval where ilmenite and magnetite compete to generate, significantly improving the algorithm's ability to identify the advantageous solution domain, providing a high-discrimination optimization guidance for subsequent evolutionary calculations, and ensuring that the population quickly converges to the global optimal solution set that meets the actual smelting conditions.

[0027] Step S600: Perform evolutionary processing according to the fitness index and the convergence conditions of the thermodynamic equilibrium constraint model, and perform temperature-pressure gradient-guided crossover and mutation operations based on the initial population to obtain the equilibrium state solution set.

[0028] It is understandable that this step regulates the search range of the crossover operation based on temperature parameters (such as expanding the crossover range in the high-temperature zone to accelerate the global exploration of the Gibbs free energy decline trend), and adjusts the mutation step size using pressure parameters (such as reducing the step size under high-pressure conditions to finely approximate the stable region of ilmenite phase), so that the evolutionary process closely adapts to the dynamic changes of smelting environment parameters; at the same time, integrating multi-criterion convergence conditions of mass conservation residuals, chemical equilibrium deviation, and Gibbs free energy change rate to ensure that the solution set simultaneously meets the requirements of atomic conservation, phase reaction equilibrium, and energy extremum. In the complex multi-phase coupled titanium slag smelting process, the evolutionary algorithm significantly improves the positioning efficiency of the global optimal solution through the combined action of gradient-guided directional search and multi-objective convergence criteria. Especially in the critical working conditions where ilmenite and magnetite phases compete, the fitness of the solution set to the actual process conditions is improved, providing a high-confidence decision-making basis for dynamically regulating the furnace temperature and optimizing the batching ratio.

[0029] Furthermore, step S200 includes steps S210 to S230.

[0030] Step S210: Based on the initial molar distribution of slag iron-titanium complexes and the elemental conservation characteristics in the phase composition data, construct multi-element atomic conservation equations. By establishing the atomic flow equilibrium relationship of iron, titanium, and oxygen elements among multiple phases, obtain the mass conservation equation set. Specifically, in this step, the migration paths of iron, titanium, and oxygen elements among the gas-slag-metal phases are quantified as atomic flow equilibrium relationships. For example, the reduction reaction of iron element between slag ferrous oxide and pig iron in the metal phase, and the solid solution transformation of titanium element between slag titanium dioxide and ilmenite, etc., to form a linear constraint equation set with complex mole fractions as variables. By introducing multi-element joint conservation modeling and synchronously considering the interactive distribution of iron and titanium between the slag phase and the metal phase, such as the dynamic transformation ratio of iron element between ferrous oxide and magnetite, and the solid solution equilibrium of titanium element in titanium dioxide and ilmenite, ensure that the equation fully characterizes the complexity of multi-phase interaction.

[0031] Step S220: Based on the standard formation free energy and activity dynamic correlation characteristics in the thermodynamic property parameters, construct multi-phase equilibrium equations. By dynamically coupling and calculating the thermodynamic equilibrium constant and complex activity, obtain the chemical equilibrium equation set. It should be noted that in this step, aiming at the non-linear variation characteristics of activity with temperature and pressure in high-temperature molten slag of titanium slag, the activity coefficient correction mechanism is embedded in the equilibrium constant calculation to dynamically adjust the phase stability thresholds of key reactions such as spinel and ilmenite. For example, when the temperature rises, the increase in the activity of ferrous oxide directly affects the tendency of magnetite formation, enabling the model to accurately depict the dynamic law of phase competition in actual smelting.

[0032] Step S230. Perform model construction processing according to the mass conservation equations and chemical equilibrium equations. By constructing a dynamic optimization framework with minimizing Gibbs free energy as the goal and multi-phase constraints as the boundary, a thermodynamic equilibrium constraint model for driving the evolution of the multi-phase equilibrium state is obtained.

[0033] It can be understood that in this step, by introducing a weight adjustment mechanism driven by temperature gradient, the energy extreme value is preferentially optimized in the high-temperature region, and the chemical equilibrium constraint is strengthened in the low-temperature region, enabling the model to adapt to the fluctuations of the smelting conditions. This progressive modeling logic, from atomic conservation to phase change equilibrium, ultimately realizes global energy optimization and provides a high-precision theoretical tool for solving the multi-phase equilibrium of titanium slag smelting.

[0034] Furthermore, step S300 includes steps S310 to S330.

[0035] Step S310. Perform ion-environment coupling processing according to the ion concentration distribution in the phase composition data and the temperature gradient characteristics of the smelting environment parameters. By non-linearly associating and mapping the concentrations of iron ions and oxygen ions with the temperature parameter, an initial coupling space reflecting the ion migration characteristics of high-temperature molten slag is obtained. It can be understood that in this step, through the dynamic regulation of the ion diffusion rate by temperature, the influence of the temperature gradient on the ion enrichment trend is quantified, enabling the initial coupling space to accurately represent the driving effect of temperature fluctuations on the ion migration path. The initial space can real-time map the regulatory effect of temperature changes on the ion distribution in the molten slag, providing an ion migration benchmark for physical field coupling for subsequent solution space expansion.

[0036] Step S320. Perform complex-pressure expansion according to the initial coupling space and the composite molecule mole fraction in the phase composition data. Using the constraint relationship of the pressure parameter on the composite molecule formation reaction, non-linearly expand the composite molecule mole fraction and the pressure parameter in dimensions to obtain a multi-dimensional correlation space containing the phase change path of the composite. It should be noted that in this step, by using the correction effect of pressure on the free energy of composite formation (such as high pressure inhibiting the decomposition of magnetite), the mole fractions of composites such as ilmenite and spinel are dynamically correlated with the pressure gradient to form an independent dimension to characterize the change of phase stability. The solution space breaks through the traditional one-dimensional pressure simplification assumption and realizes the refined modeling of the phase change competition law under multi-parameter coupling, such as the gradient characteristics of the dense distribution of the ilmenite phase in the high-pressure region.

[0037] Step S330. Perform high-dimensional space topology construction according to the multi-dimensional correlation space. Through multi-level non-linear superposition and normalization constraints of ion-composite-environment parameters, a multi-dimensional equilibrium solution space is obtained.

[0038] It is understandable that based on the strong correlation between ion concentration and complex distribution, the topological structure is defined (for example, the iron ion enrichment region corresponds to the high-probability distribution of the spinel phase), and the dimensional difference is eliminated through normalization to form a continuous solution domain. The solution space is globally covered with the complex phase transition paths of the titanium slag system (such as the gradient transition in the critical phase transition region of ilmenite - magnetite) through hierarchical topological optimization, providing a high-resolution search space for the genetic algorithm and significantly improving the positioning efficiency of the multiphase equilibrium solution.

[0039] Furthermore, step S400 includes steps S410 to S430.

[0040] Step S410: Perform normalized encoding processing in the multi-dimensional equilibrium solution space. By mapping the randomly generated complex distribution to a real-number encoded chromosome that satisfies the total mole fraction of 1, a candidate individual set is obtained. It should be noted that in this step, the randomly generated complex distribution is mapped to a real-number encoded chromosome that satisfies the total mole fraction of 1, ensuring that each individual naturally conforms to the basic thermodynamic constraints. Preferably, the mole fractions of complexes such as ilmenite and spinel randomly generated are scaled and corrected through a normalization function to eliminate invalid solutions caused by the mole fraction exceeding the limit. The technical effect of this step is that the feasibility ratio of the candidate individual set is significantly improved, avoiding invalid iterations caused by violating the total mole conservation in traditional random encoding, and providing a high-quality initial data basis for subsequent evolutionary calculations.

[0041] Step S420: Perform environmental parameter coupling processing according to the candidate individual set and the temperature-pressure gradient distribution in the multi-dimensional equilibrium solution space. By dynamically adjusting the influence of temperature on the generation probability of the dominant phase and pressure on the stability of the complex, a diverse population is generated. It is understandable that in this step, the temperature parameter is mapped to a dynamic adjustment factor for the generation probability of the dominant phase (such as magnetite), and at the same time, the pressure parameter is used to control the stability threshold of the complex (such as ilmenite). The proportion of magnetite in the individuals of the population in the high-temperature region increases with the increase in temperature, and the distribution density of ilmenite is enhanced under high-pressure conditions.

[0042] Step S430: Perform topological uniformity processing according to the diverse population and the topological correlation of the multi-dimensional equilibrium solution space. By defining the correlation partition between ion concentration and complex distribution, and performing topological uniform sampling and recombination on the chromosome, an initial population is generated.

[0043] It should be noted that in this step, the high-probability equilibrium region and the critical phase transition region are divided according to the topological structure of the solution space, and chromosomes are uniformly sampled proportionally within each partition to ensure that the population covers the potential solution domain of multiphase equilibrium. Preferably, intensive sampling is implemented in the critical phase transition region between ilmenite and magnetite, while the sampling density is reduced in the stable phase region. The global coverage ability of the generated initial population for the complex solution domain is significantly enhanced. Especially in the gradient transition region of the ilmenite-magnesioferrite phase transition path, the population diversity is increased by more than 50% compared with traditional random sampling.

[0044] Furthermore, step S500 includes steps S510 to S530.

[0045] Step S510: Perform multi-element residual fusion processing according to the thermodynamic equilibrium constraint model. By calculating the atomic conservation residuals of iron, titanium, and oxygen elements between different phases of the slag state and weighted summation, generate a mass conservation fitness component. It can be understood that in this step, weights are assigned to the element conservation residuals according to the priority of the smelting process to form an index that can quantitatively evaluate the degree of deviation of an individual from atomic conservation. Through the calculation of multi-element joint residuals, accurately identify the problem of atomic distribution imbalance caused by complex phase changes (such as the loss of titanium element solid solution due to the decomposition of ilmenite), providing a strict element conservation guidance for subsequent optimization.

[0046] Step S520: Perform dynamic deviation processing of the equilibrium constant according to the thermodynamic equilibrium constraint model. By introducing the dynamic correction of the complex activity coefficient to the equilibrium constant, calculate the equilibrium deviation of the key reactions of spinel and ilmenite, and generate a chemical equilibrium fitness component reflecting the competition relationship between phases. It should be noted that considering the inhibitory effect of the increase in the activity of ferrous oxide at high temperatures on the formation tendency of spinel, this step introduces a dynamic correction mechanism of the activity coefficient with temperature and pressure, making the equilibrium deviation calculation fit the actual smelting conditions. Furthermore, accurately quantify the stability of the complex phase change path, and solve the problem of misjudgment of the phase competition relationship caused by traditional models ignoring the dynamic changes of activity.

[0047] Step S530: Perform optimization fusion processing according to the mass conservation fitness component and the chemical equilibrium fitness component, adjust the optimization weight of the Gibbs free energy extremum based on the temperature gradient, and generate a fitness index.

[0048] It should be noted that in this step, a higher weight is given to the Gibbs free energy in the high-temperature region to preferentially satisfy the energy optimal solution, while in the low-temperature region, the combined constraint weight of mass conservation and chemical equilibrium is strengthened. When the furnace temperature exceeds the critical value of ilmenite phase change, reduce the weight of the mass conservation residual and focus on the phase composition optimization driven by energy. The generated fitness index can adapt to the dynamic changes of the smelting conditions, guiding the genetic algorithm to quickly converge to the global optimal solution set in complex multi-objective optimization, and significantly improving the efficiency and accuracy of solving the multiphase equilibrium of titanium slag.

[0049] Further, step S600 includes steps S610 to S630.

[0050] Step S610: According to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model, perform weighted parent selection processing for environmental parameters. Adjust the fitness sorting weight through the temperature gradient and control the population selection pressure through the pressure gradient to generate a parent population adapted to the smelting conditions. It can be understood that in this step, the priority weight in the fitness sorting is adjusted through the temperature gradient, and at the same time, the population selection pressure is controlled by using the pressure gradient to ensure that the distribution of the parent population is strictly adapted to the smelting condition parameters (such as the real-time temperature and pressure fluctuations in the furnace). The biased characteristics driven by the environmental parameters carried by the parent population significantly improve the tracking efficiency of the subsequent evolution process for the actual phase change path and avoid the problem of deviation in the search direction caused by traditional random selection.

[0051] Step S620: Perform gradient-guided crossover and mutation processing based on the parent population. Control the crossover range through the temperature gradient and adjust the mutation step size through the pressure gradient to generate an offspring population that searches along the direction of the decrease in Gibbs free energy. It should be noted that based on the parent population, this step transforms the physical field gradient (such as the temperature-energy correlation and the pressure-phase stability correlation) into the dynamic regulation logic of genetic operators. Preferably, when the temperature rises, the crossover operation tends to be the chromosome combination with a higher probability of generating magnetite, and the mutation operation under high-pressure conditions preferentially fine-tunes the mole fraction of ilmenite. Through this step, the search path of the offspring population closely conforms to the thermodynamic evolution law of actual smelting, significantly improving the positioning accuracy and convergence speed of the algorithm for complex phase equilibrium solutions.

[0052] Step S630: Perform multi-objective dynamic convergence processing based on the offspring population. Generate an equilibrium state solution set through the fusion of the criteria of the mass conservation residual threshold, the chemical equilibrium deviation tolerance, and the change rate of the Gibbs free energy extreme value.

[0053] It can be understood that this step sets the combined conditions of the mass conservation residual threshold (preferably, such as the deviation of iron and titanium element cross-phase migration ≤ 1%), the chemical equilibrium tolerance (preferably, such as the activity deviation of the spinel formation reaction ≤ 5%), and the change rate of the Gibbs free energy (preferably, such as the change rate of three consecutive generations < 0.1%) to ensure that the solution set simultaneously satisfies the thermodynamic constraints and process feasibility. Through the fusion of multi-objective criteria, the risk of truncating local solutions caused by traditional single convergence conditions (such as only relying on residuals or energy) is avoided, ensuring that the solution set comprehensively covers the feasible phase equilibrium states in titanium slag smelting and improving the reliability of process decision-making.

[0054] Example 2:

[0055] Such as Figure 2As shown, this embodiment provides a system for solving multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm, and the system includes: Acquisition module 901 is used to acquire basic data in the electric furnace smelting titanium slag scenario, the basic data including phase composition data, thermodynamic property parameters of chemical components of each phase and smelting environment parameters; A construction module 902 is used to perform model construction processing according to phase composition data and thermodynamic property parameters, and obtain a thermodynamic equilibrium constraint model by simultaneously solving the mass conservation equation, the chemical equilibrium equation and the Gibbs free energy extreme value equation; A generation module 903 is used to generate a high-dimensional solution space according to the phase composition data and the smelting environment parameters, and obtain a multi-dimensional equilibrium solution space by nonlinearly coupling and mapping the ion concentration, the mole fraction of the composite molecule and the environmental parameters; The encoding module 904 is used to perform population encoding processing according to the multi-dimensional equilibrium solution space to obtain an initial population; Fusion module 905, used to perform fitness fusion processing according to the thermodynamic equilibrium constraint model to obtain a fitness index; The output module 906 is used to perform evolutionary processing according to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model, and perform a temperature-pressure gradient-guided crossover and mutation operation based on the initial population to obtain an equilibrium solution set.

[0056] In some embodiments disclosed in this application, the building block 902 includes: The first construction unit is used to construct multi-element atomic conservation equations according to the initial molar distribution of the slag-state iron-titanium composite and the element conservation characteristics in the phase composition data, and obtain the mass conservation equations by establishing the atomic flow equilibrium relationship between iron, titanium and oxygen elements in multiple phases; The second construction unit is used to construct a multiphase equilibrium equation according to the standard generation free energy and activity dynamic correlation characteristics in the thermodynamic property parameters, and obtain a chemical equilibrium equation group by dynamically coupling the thermodynamic equilibrium constant and the activity of the complex; The third construction unit is used to carry out model construction processing according to the mass conservation equations and the chemical equilibrium equations. By constructing a dynamic optimization framework with Gibbs free energy minimization as the goal and multiphase constraints as the boundaries, a thermodynamic equilibrium constraint model that drives the evolution of the multiphase equilibrium state is obtained.

[0057] In some embodiments disclosed in this application, the generation module 903 includes: The first generation unit is used to perform ion-environment coupling processing according to the ion concentration distribution in the phase composition data and the temperature gradient characteristics of the smelting environment parameters, and obtain an initial coupling space reflecting the ion migration characteristics of the high-temperature slag by mapping the iron ion and oxygen ion concentrations with the temperature parameters according to nonlinear weight association; A second generation unit, configured to perform complex-pressure expansion according to the initial coupling space and the molar fraction of composite molecules in the phase composition data, and use the constraint relationship between the pressure parameter and the composite molecule generation reaction to perform non-linear dimensional expansion on the composite molecule fraction and the pressure parameter, so as to obtain a multi-dimensional correlation space including the phase change path of the composite; A third generation unit, configured to perform high-dimensional space topology construction according to the multi-dimensional correlation space, and obtain a multi-dimensional equilibrium solution space through multi-level non-linear superposition and normalization constraint of ion-composite-environment parameters;

[0058] In some embodiments disclosed in the present application, the encoding module 904 includes: A first encoding unit, configured to perform normalization encoding processing in the multi-dimensional equilibrium solution space, and map the randomly generated composite distribution to a real-number encoded chromosome satisfying the total molar fraction of 1, so as to obtain a candidate individual set; A second encoding unit, configured to perform environment parameter coupling processing according to the candidate individual set and the temperature-pressure gradient distribution in the multi-dimensional equilibrium solution space, and generate a diverse population by dynamically adjusting the influence of temperature on the dominant phase generation probability and the influence of pressure on the composite stability; A third encoding unit, configured to perform topology uniformity processing according to the diverse population and the topological correlation of the multi-dimensional equilibrium solution space, and generate an initial population by defining the correlation partition of the ion concentration and the composite distribution, and performing topological uniform sampling and recombination on the chromosome;

[0059] In some embodiments disclosed in the present application, the fusion module 905 includes: A first fusion unit, configured to perform multi-element residual fusion processing according to the thermodynamic equilibrium constraint model, and generate a mass conservation fitness component by calculating the atomic conservation residuals of iron, titanium, and oxygen elements between different phases of the slag state and performing weighted summation; A second fusion unit, configured to perform dynamic deviation processing of the equilibrium constant according to the thermodynamic equilibrium constraint model, and calculate the equilibrium deviation of the key reactions of spinel and ilmenite by introducing the dynamic correction of the equilibrium constant by the composite activity coefficient, so as to generate a chemical equilibrium fitness component reflecting the competition relationship between phases; A third fusion unit, configured to perform optimization fusion processing according to the mass conservation fitness component and the chemical equilibrium fitness component, and generate a fitness index by adjusting the optimization weight of the Gibbs free energy extreme value based on the temperature gradient;

[0060] In some embodiments disclosed in the present application, the output module 906 includes: The first output unit is configured to perform weighted parent selection processing on environmental parameters according to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model, adjust the fitness sorting weight through the temperature gradient, control the population selection pressure through the pressure gradient, and generate a parent population adapted to the smelting conditions; The second output unit is configured to perform gradient-guided crossover and mutation processing according to the parent population, control the crossover range through the temperature gradient, adjust the mutation step size through the pressure gradient, and generate an offspring population that searches along the direction of the Gibbs free energy decrease; The third output unit is configured to perform multi-objective dynamic convergence processing according to the offspring population, and generate an equilibrium state solution set through the fusion of the mass conservation residual threshold, the chemical equilibrium deviation tolerance, and the criterion of the change rate of the Gibbs free energy extreme value.

[0061] Embodiment 3:

[0062] Corresponding to the above method embodiment, in this embodiment, a multi-phase equilibrium solving device for electric furnace smelting of titanium slag based on a genetic algorithm is also provided. The multi-phase equilibrium solving device for electric furnace smelting of titanium slag based on a genetic algorithm described below can be mutually corresponding and referred to the method for solving multi-phase equilibrium of electric furnace smelting of titanium slag based on a genetic algorithm described above.

[0063] Figure 3 It is a block diagram of a multi-phase equilibrium solving device 800 for electric furnace smelting of titanium slag based on a genetic algorithm shown according to an exemplary embodiment. As Figure 3 shown, the multi-phase equilibrium solving device 800 for electric furnace smelting of titanium slag based on a genetic algorithm may include: a processor 801, a memory 802. The multi-phase equilibrium solving device 800 for electric furnace smelting of titanium slag based on a genetic algorithm may further include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0064] Among them, the processor 801 is used to control the overall operation of the apparatus 800 for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm, so as to complete all or part of the steps in the above-mentioned method for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm. The memory 802 is used to store various types of data to support the operation of the apparatus 800 for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm. These data may include, for example, instructions for any application program or method operating on the apparatus 800 for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm, as well as application program-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. Among them, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signal may be further stored in the memory 802 or sent through the communication component 805. The audio component further includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the apparatus 800 for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0065] In an exemplary embodiment, a multi-phase equilibrium solving device 800 for electric furnace smelting of titanium slag based on a genetic algorithm can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned multi-phase equilibrium solving method for electric furnace smelting of titanium slag based on a genetic algorithm.

[0066] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned multi-phase equilibrium solving method for electric furnace smelting of titanium slag based on a genetic algorithm are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above program instructions can be executed by the processor 801 of a multi-phase equilibrium solving device 800 for electric furnace smelting of titanium slag based on a genetic algorithm to complete the above-mentioned multi-phase equilibrium solving method for electric furnace smelting of titanium slag based on a genetic algorithm.

[0067] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A method for solving the multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm, characterized in that, include: Obtain basic data in the electric furnace smelting titanium slag scenario, wherein the basic data includes phase composition data, thermodynamic property parameters of chemical components of each phase, and smelting environment parameters; Performing model construction processing according to the phase composition data and the thermodynamic property parameters, and obtaining a thermodynamic equilibrium constraint model by simultaneously establishing a mass conservation equation, a chemical equilibrium equation, and a Gibbs free energy extreme value equation; A high-dimensional solution space generation process is performed according to the phase composition data and the smelting environment parameters, and a multi-dimensional equilibrium solution space is obtained by nonlinearly coupling and mapping the ion concentration, the mole fraction of the composite molecule and the environmental parameters; Performing population coding processing according to the multidimensional equilibrium solution space to obtain an initial population; Performing fitness fusion processing according to the thermodynamic equilibrium constraint model to obtain a fitness index; Evolutionary processing is performed according to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model, and a temperature-pressure gradient-guided crossover and mutation operation is performed based on the initial population to obtain an equilibrium solution set.

2. The method for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm according to claim 1, wherein The model is constructed according to the phase composition data and the thermodynamic property parameters, and a thermodynamic equilibrium constraint model is obtained by simultaneously combining the mass conservation equation, the chemical equilibrium equation and the Gibbs free energy extreme value equation, including: According to the initial molar distribution of the slag-state iron-titanium composite and the element conservation characteristics in the phase composition data, a multi-element atomic conservation equation is constructed, and a mass conservation equation group is obtained by establishing an atomic flow equilibrium relationship between iron, titanium and oxygen elements in multiple phases; According to the standard formation free energy and the dynamic correlation characteristics of the activity in the thermodynamic property parameters, a multiphase equilibrium equation is constructed, and a chemical equilibrium equation group is obtained by dynamically coupling the thermodynamic equilibrium constant and the activity of the complex; According to the mass conservation equations and the chemical equilibrium equations, a model construction process is performed, and by constructing a dynamic optimization framework with Gibbs free energy minimization as the goal and multiphase constraints as the boundaries, a thermodynamic equilibrium constraint model that drives the evolution of multiphase equilibrium states is obtained.

3. The method for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm according to claim 1, characterized in that A high-dimensional solution space generation process is performed according to the phase composition data and the smelting environment parameters, and a multi-dimensional equilibrium solution space is obtained by nonlinearly coupling and mapping the ion concentration, the mole fraction of the composite molecule and the environmental parameters, including: According to the ion concentration distribution in the phase composition data and the temperature gradient characteristics of the smelting environment parameters, ion-environment coupling processing is performed, and the initial coupling space reflecting the ion migration characteristics of the high-temperature slag is obtained by mapping the iron ion and oxygen ion concentrations with the temperature parameters according to nonlinear weight association; According to the initial coupling space and the mole fraction of the composite molecules in the phase composition data, a composite-pressure expansion is performed, and the constraint relationship of the pressure parameter on the composite molecule generation reaction is used to perform nonlinear dimensional expansion on the mole fraction of the composite and the pressure parameter to obtain a multidimensional correlation space containing the phase change path of the composite; A high-dimensional space topology is constructed according to the multi-dimensional correlation space, and a multi-dimensional equilibrium solution space is obtained through multi-level nonlinear superposition and normalization constraints of ion-complex-environment parameters.

4. The method for solving the multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm according to claim 1, characterized in that, Performing population coding processing according to the multidimensional equilibrium solution space to obtain an initial population includes: According to the normalized coding process in the multidimensional equilibrium solution space, a set of candidate individuals is obtained by mapping the randomly generated complex distribution into a real number coded chromosome satisfying a total molar fraction of 1; According to the candidate individual set and the temperature-pressure gradient distribution in the multidimensional equilibrium solution space, environmental parameter coupling processing is performed to generate a diverse population by dynamically adjusting the influence of temperature on the probability of generating a dominant phase and the influence of pressure on the stability of the complex; According to the topological correlation between the diverse population and the multidimensional equilibrium solution space, topological uniformity processing is performed, and an initial population is generated by defining the correlation partitions between ion concentration and complex distribution and performing topological uniform sampling and reorganization on chromosomes.

5. The method for solving the multiphase equilibrium of electric furnace smelting titanium slag based on genetic algorithm according to claim 1, characterized in that, The fitness fusion process is performed according to the thermodynamic equilibrium constraint model to obtain the fitness index, including: Perform multi-element residual fusion processing according to the thermodynamic equilibrium constraint model, calculate the atomic conservation residuals of iron, titanium and oxygen elements between various phases in the slag state and perform weighted summation to generate mass conservation fitness components; According to the thermodynamic equilibrium constraint model, dynamic deviation processing of equilibrium constants is performed, and by introducing the complex activity coefficient to dynamically correct the equilibrium constants, the equilibrium deviations of key reactions of spinel and ilmenite are calculated to generate chemical equilibrium fitness components reflecting the competition relationship between phases; According to the mass conservation fitness component and the chemical equilibrium fitness component, an optimization fusion process is performed, and the optimization weight of the Gibbs free energy extreme value is adjusted based on the temperature gradient to generate a fitness index.

6. A multiphase equilibrium solving system for electric furnace smelting of titanium slag based on genetic algorithm, characterized in that, include: An acquisition module is used to acquire basic data in the electric furnace smelting titanium slag scenario, wherein the basic data includes phase composition data, thermodynamic property parameters of chemical components of each phase, and smelting environment parameters; A construction module is used to perform model construction processing according to the phase composition data and the thermodynamic property parameters, and obtain a thermodynamic equilibrium constraint model by simultaneously solving the mass conservation equation, the chemical equilibrium equation and the Gibbs free energy extreme value equation; A generation module is used to generate a high-dimensional solution space according to the phase composition data and the smelting environment parameters, and obtain a multi-dimensional equilibrium solution space by nonlinearly coupling and mapping the ion concentration, the mole fraction of the composite molecule and the environmental parameters; An encoding module, used for performing population encoding processing according to the multidimensional equilibrium solution space to obtain an initial population; A fusion module, used for performing fitness fusion processing according to the thermodynamic equilibrium constraint model to obtain a fitness index; The output module is used to perform evolutionary processing according to the fitness index and the convergence condition of the thermodynamic equilibrium constraint model, and perform a temperature-pressure gradient-guided crossover and mutation operation based on the initial population to obtain an equilibrium solution set.

7. The multi-phase equilibrium solving system for electric furnace smelting of titanium slag based on genetic algorithm according to claim 6, characterized in that, The building blocks include: The first construction unit is used to construct a multi-element atomic conservation equation according to the initial molar distribution of the slag-state iron-titanium composite and the element conservation characteristics in the phase composition data, and obtain a mass conservation equation group by establishing an atomic flow equilibrium relationship between iron, titanium and oxygen elements in multiple phases; The second construction unit is used to construct a multiphase equilibrium equation according to the dynamic correlation characteristics of the standard Gibbs free energy and activity in the thermodynamic property parameters, and obtain a chemical equilibrium equation set through the dynamic coupling calculation of the thermodynamic equilibrium constant and the complex activity. The third construction unit is used to perform model construction processing according to the mass conservation equation set and the chemical equilibrium equation set, and obtain a thermodynamic equilibrium constraint model that drives the evolution of the multiphase equilibrium state by constructing a dynamic optimization framework with the minimization of Gibbs free energy as the goal and multiphase constraints as the boundary.

8. The multi-phase equilibrium solving system for electric furnace smelting of titanium slag based on genetic algorithm according to claim 6, characterized in that, The generation module includes: The first generation unit is used to perform ion-environment coupling processing according to the ion concentration distribution in the phase composition data and the temperature gradient characteristics of the smelting environment parameters, and obtain an initial coupling space reflecting the ion migration characteristics of high-temperature molten slag by nonlinearly associating and mapping the concentrations of iron ions and oxygen ions with temperature parameters. The second generation unit is used to perform complex-pressure expansion according to the initial coupling space and the complex molecule mole fraction in the phase composition data, and nonlinearly expand the complex mole fraction and pressure parameters by using the constraint relationship of the pressure parameter on the complex molecule generation reaction to obtain a multi-dimensional correlation space including the complex phase transition path. The third generation unit is used to construct a high-dimensional space topology according to the multi-dimensional correlation space, and obtain a multi-dimensional equilibrium solution space through the multi-level nonlinear superposition and normalization constraint of ion-complex-environment parameters.

9. The system for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm according to claim 6, characterized in that, The encoding module includes: The first encoding unit is used to perform normalization encoding processing in the multi-dimensional equilibrium solution space, and obtain a candidate individual set by mapping the randomly generated complex distribution to a real-number encoded chromosome satisfying the total mole fraction of 1. The second encoding unit is used to perform environment parameter coupling processing according to the candidate individual set and the temperature-pressure gradient distribution in the multi-dimensional equilibrium solution space, and generate a diverse population by dynamically adjusting the influence of temperature on the dominant phase generation probability and pressure on the complex stability. The third encoding unit is used to perform topological uniformity processing according to the diverse population and the topological correlation of the multi-dimensional equilibrium solution space, and generate an initial population by defining the correlation partition of ion concentration and complex distribution and performing topological uniform sampling and recombination on the chromosome.

10. The system for solving the multiphase equilibrium of electric furnace smelting titanium slag based on the genetic algorithm according to claim 6, characterized in that, The fusion module includes: The first fusion unit is used to perform multi-element residual fusion processing according to the thermodynamic equilibrium constraint model, and generate a mass conservation fitness component by calculating and weighted summing the atomic conservation residuals of iron, titanium, and oxygen elements between different phases of the slag state. The second fusion unit is used to perform dynamic deviation processing of the equilibrium constant according to the thermodynamic equilibrium constraint model, and calculate the equilibrium deviation of the key reactions of spinel and ilmenite by introducing the dynamic correction of the complex activity coefficient to the equilibrium constant, and generate a chemical equilibrium fitness component reflecting the inter-phase competition relationship. The third fusion unit is used to perform optimization fusion processing according to the mass conservation fitness component and the chemical equilibrium fitness component, and generate a fitness index by adjusting the optimization weight of the Gibbs free energy extreme value based on the temperature gradient.

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