Culture medium parameter optimization method and system based on steel slag leachate

Abstract

The invention relates to the technical field of culture medium preparation, in particular to a culture medium parameter optimization method and system based on steel slag lixivium.The method comprises the steps that culture medium components are obtained based on microorganisms, leaching operation is conducted on a steel slag set based on the culture medium components, and multiple steel slag lixiviums are obtained; performing the following operations on each of the plurality of steel slag lixivium: performing full-component quantitative analysis on the steel slag lixivium to obtain a plurality of lixivium components and a plurality of component concentrations, and performing safety assessment operation on the plurality of lixivium components and the plurality of component concentrations, and performing characteristic matching operation based on culture medium components on the treated leachate to obtain a plurality of supplemented component contents, performing parameter optimization on the plurality of supplemented component contents to obtain a plurality of optimized component contents, and constructing the culture medium based on the plurality of optimized component contents. According to the method, the steel slag can be safely and efficiently utilized, and parameter optimization of the steel slag leachate culture medium is realized.

Description

Technical Field

[0001] This invention relates to the field of culture medium preparation technology, and in particular to a method and system for optimizing culture medium parameters based on steel slag leachate. Background Technology

[0002] Steel slag is a major solid waste generated by the steel industry, with a huge annual output and low utilization rate. Steel slag contains various nutrients necessary for microbial growth, such as calcium, magnesium, iron, and silicon. Using steel slag leachate as a raw material for microbial culture media can reduce media costs and achieve resource utilization of solid waste. However, the composition of steel slag leachate is complex and unstable, containing not only beneficial nutrients but also potentially harmful components such as heavy metals. Direct use for microbial culture may inhibit microbial growth or even lead to microbial death.

[0003] Currently, methods for optimizing culture medium parameters mainly target synthetic culture media with well-defined components, and achieve the optimization of culture medium parameters through trial and error or single-factor experiments.

[0004] Although the above methods can effectively optimize synthetic culture media with well-defined components, when applied to steel slag leachates with complex and unstable compositions, they often fail to systematically assess and treat harmful components, posing safety risks. Furthermore, they do not fully consider the precise matching between steel slag leachate and microbial needs, neglecting the synergistic optimization of multiple key parameters such as the steel slag leachate fraction, culture medium pH, and nutrient supplementation. This results in inefficient optimization processes, unreliable results, and difficulty in achieving efficient utilization of steel slag leachate. Therefore, how to safely and efficiently utilize steel slag and optimize the parameters of the steel slag leachate culture medium has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method for optimizing culture medium parameters based on steel slag leachate and a computer-readable storage medium. Its main purpose is to utilize steel slag safely and efficiently, and the optimization of the parameters of the steel slag leachate culture medium has become an urgent problem to be solved.

[0006] To achieve the above objectives, the present invention provides a method for optimizing culture medium parameters based on steel slag leaching solution, comprising:

[0007] Microorganisms are obtained by acquiring culture medium components from a pre-constructed microbial nutrient database.

[0008] A steel slag set was obtained, and a leaching operation was performed on the steel slag set based on the culture medium composition to obtain multiple steel slag leachates;

[0009] A target steel slag leachate is obtained by sequentially extracting one steel slag leachate from multiple steel slag leachates. The following operations are then performed on the target steel slag leachate:

[0010] A full-component quantitative analysis was performed on the target steel slag leachate to obtain multiple leachate components and their concentrations;

[0011] Safety assessment procedures were performed on multiple leachate components and their concentrations to obtain assessment results.

[0012] If the assessment results indicate that pre-constructed harmful components are present in the target steel slag leachate, then the target steel slag leachate will undergo safety treatment to obtain treated leachate.

[0013] A feature matching operation based on the culture medium composition was performed on the treated leachate to obtain the contents of multiple supplementary components;

[0014] Parameter optimization was performed on the contents of multiple supplementary ingredients to obtain multiple optimized ingredient contents;

[0015] Culture media were constructed based on the optimized content of multiple components.

[0016] Optionally, the process of performing a feature matching operation on the treated leachate based on the culture medium components to obtain the contents of multiple supplementary components, including:

[0017] A full-component quantitative analysis was performed on the treated leachate to obtain the contents of multiple treated components.

[0018] The content of multiple nutrients is obtained from the composition of the culture medium;

[0019] Component matching and ratio calculation operations were performed on the contents of multiple treatment components and multiple nutrient components to obtain multiple enrichment and loss indices, wherein the enrichment and loss indices correspond one-to-one with the nutrient component contents.

[0020] Based on multiple enrichment and loss indices, the contents of multiple supplementary components are calculated using a pre-constructed supplementation content calculation formula.

[0021] Optionally, the parameter optimization of the content of multiple supplementary ingredients to obtain multiple optimized ingredient contents includes:

[0022] The volume fraction of the target steel slag leachate and the pH of the culture medium were obtained, and the volume fraction, the pH of the culture medium, and the content of multiple supplementary components were used as multiple initial influencing factors.

[0023] Determine the high-impact factor and low-impact factor of each initial impact factor from multiple initial impact factors to obtain multiple high-impact factors and multiple low-impact factors;

[0024] Multiple high-impact factors and multiple low-impact factors are combined to obtain multiple impact factor combinations, and multiple impact culture media are configured based on these multiple impact factor combinations.

[0025] Microbial culture was performed on multiple influencing media, and biomass was measured on each of the influencing media after the microbial culture was performed to obtain multiple influencing biomass.

[0026] Perform the following operations on multiple initial impact factors:

[0027] Based on the initial impact factor, multiple impact biomasses are divided into multiple high-impact biomasses and multiple low-impact biomasses, and the impact level is calculated based on the multiple high-impact biomasses and multiple low-impact biomasses.

[0028] If the absolute value of the impact level is greater than the preset impact threshold, the initial impact factor corresponding to the impact level will be recorded as the key impact factor.

[0029] By summarizing the key impact factors, multiple key impact factors are obtained;

[0030] Parameter optimization based on culture medium components was performed on several key influencing factors to obtain the optimized component contents.

[0031] Optionally, the parameter optimization of multiple key influencing factors based on culture medium components is performed to obtain multiple optimized component contents, including:

[0032] Extract one key impact factor sequentially from multiple key impact factors to obtain the target impact factor, and perform the following operations on the target impact factor:

[0033] Determine the impact level of the target impact factor to obtain the target impact level;

[0034] If the target impact level is positive, then the high impact factor of the target impact factor is recorded as the starting point of the target factor, and the direction of the factor is determined.

[0035] If the target impact level is negative, then the low impact factor of the target impact factor is recorded as the starting point of the target factor, and the direction of the factor is determined.

[0036] By summarizing the starting points and directions of the target factors, multiple target starting point factors and multiple factor directions are obtained.

[0037] Step A: Configure the starting culture medium based on multiple target starting factors, perform microbial culture on the starting culture medium, and perform biomass measurement on the starting culture medium after microbial culture to obtain the starting biomass. Update the factor content of multiple target starting factors based on the preset step size and multiple factor directions to obtain multiple updated factors. Use the multiple updated factors as the multiple target starting factors, return to the step of configuring the starting culture medium based on multiple target starting factors to obtain the updated biomass. Calculate the difference between the starting biomass and the updated biomass to obtain the biomass difference.

[0038] Repeat step A until the biomass difference is negative, then stop step A and obtain the starting biomass when the biomass difference is negative as the undetermined optimal biomass.

[0039] Multiple potential optimal influencing factors for determining the optimal biomass are obtained, and the factor content of multiple key influencing factors is optimized using these multiple potential optimal influencing factors to obtain the content of multiple optimized components.

[0040] Optionally, the step of optimizing the factor content of multiple key influencing factors using multiple undetermined optimal influencing factors to obtain multiple optimized component contents includes:

[0041] Extract one key impact factor from multiple key impact factors sequentially to obtain the target key factor, and then perform the following operations on the target key factor:

[0042] By identifying the target key factor among multiple undetermined optimal impact factors, the intermediate key factors are obtained.

[0043] Calculate the high critical factor and low critical factor based on the step size and medium critical factor, respectively.

[0044] Low criticality factors, medium criticality factors, and high criticality factors are summarized separately to obtain multiple low criticality factors, multiple medium criticality factors, and multiple high criticality factors;

[0045] An experimental matrix was constructed based on multiple low-criticality factors, multiple medium-criticality factors, and multiple high-criticality factors.

[0046] Multiple key vectors were extracted from the experimental matrix, and multiple key culture media were constructed based on the multiple key vectors. Microbial culture operations were performed on all multiple key culture media, and biomass measurement operations were performed on all multiple key culture media after microbial culture operations to obtain multiple key biomass.

[0047] By utilizing multiple key biomass and multiple key vectors, the content of multiple key influencing factors was optimized, resulting in the content of multiple optimized components.

[0048] Optionally, the optimization of factor content for multiple key influencing factors using multiple key biomass and multiple key vectors yields multiple optimized component contents, including:

[0049] An initial fitness function is constructed using several key influencing factors, as shown below:

[0050] ,

[0051] in, Indicates biomass. Indicates standard biomass. Indicates the first Index of key influencing factors This represents the total number of multiple key influencing factors. Indicates the first The coefficients of the first-order terms of the key influencing factors, Indicates the first The key influencing factors and the first The quadratic coefficients of the key influencing factors, Indicates the first The key influencing factors and the first The interaction coefficients of the key influencing factors, Indicates the first Index of key influencing factors Indicates the first One key influencing factor, Indicates the first Key influencing factors.

[0052] The fitness function is obtained by performing parameter fitting on the initial fitness function based on multiple key vectors and multiple key biomass.

[0053] The fitness function was used to optimize the factor content of several key influencing factors, resulting in the optimized component content.

[0054] Optionally, the step of optimizing the factor content of multiple key influencing factors using a fitness function to obtain multiple optimized component contents includes:

[0055] The multiple undetermined optimal influence factors are used as multiple initial search centers, and the product of the step size and the pre-constructed proportion is calculated to obtain the search radius. An initial search space is constructed with multiple initial search centers and search radii, wherein the initial search space includes multiple initial search intervals, and each initial search interval corresponds one-to-one with an initial search center.

[0056] Multiple key influencing factors are initialized based on multiple initial search intervals to obtain multiple initial search recipes, wherein the initial search recipes include multiple initial search factors;

[0057] By using a fitness function and multiple initial search formulations, the content of multiple key influencing factors was optimized, resulting in the content of multiple optimized components.

[0058] Optionally, the optimization of factor content for multiple key influencing factors using a fitness function and multiple initial search formulations yields multiple optimized component contents, including:

[0059] Multiple initial biomasses for multiple initial search recipes are calculated using fitness functions, and standard deviations are calculated for multiple initial search recipes to obtain multiple standard deviations;

[0060] Based on multiple initial biomass and multiple standard deviations, the formulation components of multiple initial search formulations are updated to obtain updated component formulations;

[0061] The updated component formulation was validated, and the validation results were obtained. Based on the validation results, the updated component formulation was confirmed as having multiple optimized component contents.

[0062] Optionally, the verification of the updated component formulation to obtain verification results includes:

[0063] Based on the updated component formulation and the components of the culture medium, updated culture medium and synthetic culture medium were respectively prepared;

[0064] Microbial culture operations were performed on both the renewal culture medium and the synthetic culture medium to obtain the renewal culture process and the synthetic culture process.

[0065] Biomass recording and product recording operations were performed on the renewal culture process and the synthesis culture process, respectively, to obtain a set of recording curves;

[0066] Based on the set of recorded curves, calculate the growth rate set and the yield set respectively. Perform size comparison operation on the growth rate set and the yield set respectively to obtain the comparison result set. Record the comparison result set as the verification result.

[0067] To achieve the above objectives, the present invention also provides a culture medium parameter optimization system based on steel slag leaching solution, comprising:

[0068] The leachate acquisition module is used to acquire microorganisms, obtain culture medium components from a pre-constructed microbial nutrient database based on the microorganisms, acquire a steel slag set, and perform leaching operations on the steel slag set based on the culture medium components to obtain multiple steel slag leachates.

[0069] The leachate treatment module is used to sequentially extract one steel slag leachate from multiple steel slag leachates to obtain a target steel slag leachate. The following operations are performed on the target steel slag leachate: full-component quantitative analysis is performed on the target steel slag leachate to obtain multiple leachate components and their concentrations; a safety assessment is performed on the multiple leachate components and their concentrations to obtain the assessment results; if the assessment results indicate that pre-constructed harmful components are present in the target steel slag leachate, then the target steel slag leachate is subjected to safety treatment to obtain a treated leachate.

[0070] The component matching module is used to perform feature matching operations based on the culture medium components on the treated leachate to obtain the contents of multiple supplementary components;

[0071] The parameter optimization module is used to perform parameter optimization on the content of multiple supplementary components, obtain multiple optimized component contents, and construct the culture medium based on the multiple optimized component contents.

[0072] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0073] Memory, storing at least one instruction; and

[0074] The processor executes the instructions stored in the memory to implement the above-described method for optimizing culture medium parameters based on steel slag leachate.

[0075] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described method for optimizing culture medium parameters based on steel slag leachate.

[0076] To address the problems described in the background art, this invention obtains microorganisms, acquires culture medium components from a pre-constructed microbial nutrient database based on these microorganisms, obtains a steel slag set, and performs a leaching operation on the steel slag set based on the culture medium components to obtain multiple steel slag leachates. This invention achieves efficient utilization of steel slag by selecting different leaching methods for the steel slag set using different microorganisms. From the multiple steel slag leachates, a target steel slag leachate is obtained by sequentially extracting one leachate. The target steel slag leachate is then subjected to the following operations: a full-component quantitative analysis is performed on the target steel slag leachate to obtain multiple leachate components and their concentrations; a safety assessment is performed on the multiple leachate components and their concentrations to obtain the assessment results; if the assessment results indicate the presence of pre-constructed harmful components in the target steel slag leachate, the target steel slag leachate is subjected to safety treatment to obtain a treated leachate. Furthermore, this invention achieves safe utilization of steel slag by performing full-component quantitative analysis on the steel slag leachate and performing safety treatment on the steel slag leachate. The leachate is subjected to a feature matching operation based on the culture medium composition to obtain the content of multiple supplementary components. Parameter optimization is then performed on these multiple supplementary component contents to obtain multiple optimized component contents. A culture medium is then constructed based on these optimized component contents. By determining the culture medium composition through microorganisms and then performing feature matching operations on the leachate based on these components, this invention fully considers the differences between the requirements of steel slag leachate and microorganisms. Furthermore, during the parameter optimization process for the multiple supplementary component contents, this invention also considers the synergistic optimization of several key parameters, such as the steel slag leachate fraction, culture medium pH, and nutrient supplementation amount. Therefore, this invention can safely and efficiently utilize steel slag and achieve parameter optimization of the steel slag leachate culture medium. Attached Figure Description

[0077] Figure 1 This is a flowchart illustrating a method for optimizing culture medium parameters based on steel slag leachate according to an embodiment of the present invention.

[0078] Figure 2A functional block diagram of a culture medium parameter optimization system based on steel slag leachate provided in an embodiment of the present invention;

[0079] Figure 3 This is a schematic diagram of an electronic device for implementing the method for optimizing culture medium parameters based on steel slag leachate, according to an embodiment of the present invention.

[0080] Explanation of reference numerals in the attached figures:

[0081] 1. Electronic device; 10. Processor; 11. Storage device; 12. Bus.

[0082] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0083] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0084] This application provides a method for optimizing culture medium parameters based on steel slag leaching solution. The execution entity of this method includes, but is not limited to, at least one electronic device configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0085] Reference Figure 1 The diagram shown is a flowchart illustrating a method for optimizing culture medium parameters based on steel slag leaching solution according to an embodiment of the present invention. In this embodiment, the method for optimizing culture medium parameters based on steel slag leaching solution includes:

[0086] S1. Obtain microorganisms. Based on the microorganisms, obtain culture medium components from a pre-constructed microbial nutrient database to obtain a steel slag set. Perform leaching operations on the steel slag set based on the culture medium components to obtain multiple steel slag leachates.

[0087] Understandably, "microorganisms" refers to bacteria cultured using steel slag leachate, such as *Bacillus pasteurellii* and *Bacillus subtilis*. The microbial nutrient database refers to a database storing various nutrients required for microbial growth, such as the databases of the China Industrial Microbial Culture Collection and the German National Culture Collection. Culture medium components refer to the various nutrients included in the culture medium used to cultivate microorganisms, such as peptone and sodium chloride.

[0088] It should be understood that steel slag is a byproduct of the steelmaking process, mainly composed of various oxides formed by the oxidation of impurities such as silicon, manganese, phosphorus, and sulfur in pig iron during smelting, as well as salts formed by the reaction of these oxides with solvents. Steel slag contains free oxides such as calcium oxide. During subsequent leaching operations and the preparation of culture media, these oxides react with water to continuously generate strongly alkaline substances, continuously raising the pH of the steel slag leachate and the culture medium. High pH (the definition of high pH varies among different microorganisms) inhibits microbial growth and development, rendering the culture medium unsuitable for microbial cultivation. For example, when the pH of the culture medium is greater than 9.0, it inhibits the growth and development of Bacillus subtilis. The normal pH range for both the leachate and the culture medium obtained from untreated steel slag is above 11. Therefore, oxides in the steel slag leachate must be removed before leaching.

[0089] Furthermore, the acquisition of the steel slag set includes:

[0090] Steel slag is obtained and ground to produce multiple ground steel slag particles;

[0091] A diameter detection operation is performed on multiple ground steel slag particles to obtain multiple steel slag particle diameters. If the diameters of multiple steel slag particles are all smaller than the pre-constructed standard diameter, then the multiple ground steel slag particles are recorded as multiple initial steel slag particles.

[0092] The following operation is performed on each of the multiple initial steel slag particles;

[0093] The initial steel slag particles were subjected to free oxide removal treatment to obtain treated steel slag particles;

[0094] The free oxide mass of the treated steel slag particles was tested to obtain the oxide mass.

[0095] If the mass of the oxide is less than or equal to the mass of the pre-constructed standard oxide, the treated steel slag particles are recorded as steel slag particles.

[0096] By summing the steel slag particles, a steel slag aggregate is obtained.

[0097] It is understood that the grinding operation of steel slag refers to grinding the steel slag through a pre-constructed grinding mill (steel slag ball mill). It should be noted that for steel slag with a diameter larger than the standard diameter, it should be fed into a crusher (e.g., a jaw crusher). The steel slag is crushed by the crusher's fixed jaw plate (fixed jaw) and movable jaw plate (moving jaw) extruding it until the diameter of the steel slag is smaller than the standard grinding diameter. The standard grinding diameter is the diameter of steel slag that can be fed into the grinding mill, which is generally known in the industry. It is usually 10 mm as the standard diameter. Steel slag larger than 10 mm entering the grinding mill will cause damage to the grinding mill.

[0098] Specifically, the grinding process refers to the collision between the grinding media (high chromium alloy cast balls) of the grinding mill and the steel slag to reduce the diameter of the steel slag and obtain multiple ground steel slag particles.

[0099] Importantly, the standard diameter refers to the diameter of the steel slag particles that are set by the manufacturer. Existing industry consensus and experimental data indicate that oxides in steel slag particles with a standard diameter of 75 micrometers can be removed. Therefore, the standard diameter of this invention is set to 75 micrometers.

[0100] In detail, the process of removing free oxides from the initial steel slag particles to obtain treated steel slag particles includes:

[0101] A pickling solution is obtained, and the initial steel slag particles are pickled using the pickling solution to obtain the pickling process;

[0102] Conductivity was measured during the pickling process to obtain the process conductivity.

[0103] The amount of reactant consumed is calculated based on the process conductivity, and the amount of stirring power adjustment is calculated based on the amount of reactant consumed.

[0104] The heat released is calculated based on the amount of reactants consumed, and the temperature adjustment is calculated based on the heat released to obtain the temperature adjustment amount.

[0105] The pickling temperature and stirring power of the pickling process are obtained, and the pickling temperature and stirring power are compared with the preset standard temperature and preset standard power respectively to obtain the temperature error and power error.

[0106] Based on temperature error and power error, a pre-constructed temperature and power control network is used to predict the temperature compensation amount and stirring power compensation amount.

[0107] Perform summation operations on the stirring power adjustment and stirring power compensation, and the temperature adjustment and temperature compensation, respectively, to obtain the temperature control and stirring power control values.

[0108] The pickling temperature and stirring power are numerically adjusted based on the temperature control quantity and the stirring power control quantity to obtain the adjusted power and the adjusted temperature.

[0109] The initial steel slag particles are subjected to free oxide removal treatment by adjusting the power and temperature to obtain treated steel slag particles.

[0110] It should be explained that obtaining the pickling solution refers to obtaining an acidic solution capable of removing oxides from the initial steel slag particles, such as purchasing pickling solutions like hydrochloric acid or acetic acid. The pickling operation using the pickling solution to pickle the initial steel slag particles refers to mixing and stirring the pickling solution with the initial steel slag particles at a set temperature to obtain the pickling process. The conductivity detection of the pickling process refers to detecting the conductivity during the pickling process using a conductivity meter (e.g., a Rosemount 400 series online conductivity analyzer) to obtain the process conductivity.

[0111] Importantly, the calculation of reactant consumption based on process conductivity refers to substituting the process conductivity into a pre-constructed formula for reactant consumption to calculate the reactant consumption. Here, reactant consumption refers to the consumption of initial steel slag particles. The formula for reactant consumption is common knowledge in the field of electrochemistry, namely, the higher the ion concentration, the greater the conductivity. The ion concentration is the concentration of ions generated by the reaction of the initial steel slag particles with the pickling solution. In other words, the higher the ion concentration, the greater the reactant consumption, and thus the greater the conductivity.

[0112] Furthermore, the relationship between the conductivity consumption is as follows:

[0113] ,

[0114] in, Indicates the amount of reactants consumed. Represents the relationship coefficient. Indicates the process conductivity. This represents the fitted parameters.

[0115] It should be understood that the conductivity consumption formula in this invention is a relationship between the consumption of steel slag and the conductivity. However, the composition of steel slag is not fixed; therefore, a relationship coefficient and fitting parameters need to be determined through a relational experiment. The specific process of the relational experiment is as follows: Steel slag particle samples are obtained from the initial steel slag particles. The oxide content in the steel slag particle samples is determined using existing techniques such as ethylene glycol extraction. The steel slag particle samples are then acid-washed using an acid washing solution. Conductivity is collected and the content of remaining oxides is detected at set time intervals (e.g., every 2 minutes). The oxide consumption is obtained by subtracting the remaining oxide content from the oxide content in the steel slag particle samples. Based on the collected conductivity and the calculated oxide consumption, the relationship coefficient and fitting parameters are fitted using the least squares method. The least squares method is existing technology and will not be elaborated upon here.

[0116] It should be noted that the calculation of the stirring power adjustment based on reactant consumption involves calculating the remaining reactant content based on the reactant consumption, and then using a least squares method to fit the relationship between reactant content and stirring power based on a power experiment. This determines the appropriate stirring power for different reactant contents to maximize the efficiency of the acid washing reaction. For example, a first stirring power should be set for a first reactant content, and a second stirring power should be set for a second reactant content. It is clear that the specific process of fitting the relationship between reactant content and stirring power using the least squares method based on the power experiment is consistent with the experimental procedure, and will not be elaborated upon here.

[0117] Specifically, the calculation of the heat release based on the reactant consumption refers to calculating the heat release by multiplying the reactant consumption and the corresponding molar enthalpy change based on the molar enthalpy change of the reactant consumption. The molar enthalpy change of the reactant is a modern chemical principle, which will not be elaborated here.

[0118] In particular, if the amount of reactant consumed is the sum of the amounts of multiple reactants consumed, for example, the sum of the amounts of magnesium oxide and calcium oxide consumed, then in the relationship experiment, a relationship between the sum of the amounts of multiple reactants consumed and the conductivity can be established according to the types of reactants. When calculating the heat release, the amount of multiple reactants consumed is calculated separately, and the product of the amount of each reactant consumed and the corresponding molar enthalpy change of the reaction is summed to obtain the heat release.

[0119] In detail, the calculation of temperature regulation based on heat release refers to calculating the temperature regulation using a heat calculation formula and the amount of heat released. The heat calculation formula is as follows:

[0120] ,

[0121] in, Indicates the release of heat. Indicates specific heat capacity. Indicates the quality of steel slag. This indicates the amount of temperature adjustment.

[0122] The heat calculation formula indicates how many degrees the temperature will rise during the pickling process based on the different amounts of heat released, i.e., how many degrees the temperature adjustment amount is.

[0123] Specifically, obtaining the pickling temperature and stirring power in the pickling process refers to detecting the temperature of the steel slag during the pickling process using a temperature sensor (e.g., an infrared sensor) and obtaining the stirring power of the mixer (e.g., an anchor mixer).

[0124] Understandably, the temperature error is the difference between the pickling temperature and the standard temperature. The standard temperature is the temperature at which the efficiency of the pickling process is maximized, determined experimentally based on the aforementioned relationship; that is, the temperature at which the chemical reaction rate between the pickling solution and the initial steel slag particles is maximized. The chemical reaction rate is prior art and will not be elaborated upon here. The power error is the difference between the stirring power and the standard power, where the standard power is also the stirring power at which the efficiency of the pickling process is maximized, determined experimentally based on the aforementioned relationship.

[0125] It should be explained that the temperature power control network is composed of an extended state observer and a radial basis function neural network. Both the extended state observer and the radial basis function neural network are existing technologies and will not be described in detail here.

[0126] In detail, the specific process of predicting the temperature compensation amount using the pre-constructed temperature power control network is as follows: the pickling temperature and the previous temperature control amount are used as inputs to the extended state observer. The extended state observer outputs a temperature disturbance value (e.g., temperature changes such as feed temperature fluctuations). The temperature error is used as input to the radial basis function neural network, which outputs an intermediate temperature value. The intermediate temperature value is subtracted from the temperature disturbance value to obtain the temperature compensation amount.

[0127] It's clear that the temperature control quantity refers to the sum of the temperature adjustment quantity and the temperature compensation quantity. It represents the change in the numerical adjustment of the pickling temperature. For example, if the temperature control quantity is the first temperature control quantity, then the adjusted temperature is obtained by adding the first temperature control quantity to the pickling temperature. The temperature control quantity can be positive or negative. If the temperature control quantity is positive, it means the pickling temperature is lower than the standard temperature, and the temperature control quantity needs to be added to the pickling temperature to make it equal to the standard temperature. If the temperature control quantity is negative, it means the pickling temperature is higher than the standard temperature, and the temperature control quantity needs to be added to the pickling temperature to make it equal to the standard temperature. The stirring power control quantity refers to the sum of the stirring power adjustment quantity and the stirring power compensation quantity. It represents the change in the numerical adjustment of the stirring power. The adjusted power is obtained by adding the stirring power control quantity to the stirring power.

[0128] Furthermore, the temperature regulation value refers to the numerical value that represents the heat released by the chemical reaction in a defined pickling process, resulting in a temperature change. The temperature perturbation value represents the uncertain but roughly estimable temperature change caused by changes in factors such as feed material during the pickling process. This is because the entire pickling system (including the instruments and reactants used in the pickling process) remains relatively constant throughout the pickling process. The temperature median value represents the numerical value of the temperature change caused by uncertainties such as environmental factors, predicted using a radial basis function neural network, under the current temperature error during the pickling process.

[0129] It should be understood that this invention divides the temperature control quantity into temperature regulation quantity, temperature disturbance value, and temperature intermediate value in order to improve the efficiency of the pickling process. If the temperature control quantity is directly predicted through a radial basis function neural network, the radial basis function neural network also needs to consider the influence of definite or estimable temperature changes such as temperature regulation quantity and temperature disturbance value on the overall temperature control quantity. However, by separating the temperature control quantity into temperature regulation quantity, temperature disturbance value, and temperature intermediate value, the radial basis function neural network only needs to consider the influence of fluctuations in uncertain factors such as the environment on the temperature, thereby improving the training and prediction efficiency of the radial basis function neural network, which is equivalent to improving the efficiency of the pickling process.

[0130] Importantly, after the initial steel slag particles undergo free oxide removal treatment based on power and temperature adjustments, the free oxide mass of the treated steel slag particles needs to be tested to obtain the oxide mass. The method for free oxide mass testing is ethylene glycol extraction-EDTA titration, which is existing technology and will not be elaborated upon here. The standard oxide mass refers to the mass of oxides contained in steel slag as specified by national and industry standards. If the oxide mass is less than or equal to the pre-constructed standard oxide mass, it indicates that the oxide mass contained in the treated steel slag particles is within a stable range. A stable range refers to a range where the oxide mass is less than or equal to the standard oxide mass, and will not cause the treated steel slag particles to continuously generate strongly alkaline substances upon contact with water.

[0131] Furthermore, if the mass of the oxide is greater than the mass of the pre-constructed standard oxide, then the treated steel slag particles are used as the initial steel slag particles, and the step of removing free oxides from the initial steel slag particles is returned until the mass of the oxide is less than or equal to the mass of the standard oxide, thus obtaining steel slag particles.

[0132] It should be noted that the leaching operation of the steel slag collection based on the culture medium composition refers to the process of performing different leaching operations on the steel slag collection according to different culture medium compositions. For example, Bacillus subtilis requires silicon and potassium as nutrients, so alkaline leaching is chosen to leach the steel slag collection. However, Thiobacillus ferrooxidans requires iron and sulfur as nutrients, and alkaline leaching cannot leach iron and sulfur from the steel slag collection, so bioleaching can be chosen to leach the steel slag collection. Alkaline leaching and bioleaching are existing technologies, and will not be described in detail here. Multiple steel slag leachates refer to the liquids obtained after performing leaching operations on the steel slag collections (multiple steel slag material flows) and containing the nutrients required by the microorganisms (the nutrients required by the microorganisms present in the steel slag collection).

[0133] S2. Extract one steel slag leachate sequentially from multiple steel slag leachates to obtain the target steel slag leachate. Perform the following operations on the target steel slag leachate: perform full-component quantitative analysis on the target steel slag leachate to obtain multiple leachate components and their concentrations.

[0134] It should be explained that the full-component quantitative analysis of the target steel slag leachate involves measuring the components and concentrations in the leachate using methods such as inductively coupled plasma atomic emission spectrometry (ICP-AES). For example, ICP-AES can be used to measure the multiple leachate components (e.g., potassium, calcium, sodium, and magnesium) and their concentrations (e.g., potassium concentration of 120 mg / L, calcium concentration of 800 mg / L, sodium concentration of 2500 mg / L, and magnesium concentration of 85 mg / L). ICP-AES and similar methods are existing technologies and will not be elaborated upon here.

[0135] Specifically, the application process for full-component quantitative analysis of target steel slag leachate is as follows: a leachate sample is obtained from the target steel slag leachate, and the leachate sample is digested (the solid particles in the leachate sample are dissolved into ions using acid or alkali) to form a homogeneous test solution. Inductively coupled plasma is used to excite each element in the homogeneous test solution to emit characteristic spectral lines. The composition is determined by detecting the wavelength of the characteristic spectral lines, and the accurate concentration of each element is quantitatively calculated based on the linear relationship between the intensity of the characteristic spectral lines and the concentration.

[0136] It is understood that the leachate sample refers to a portion of the target steel slag leachate; for example, 1 ml of target steel slag leachate is extracted from 10 ml of target steel slag leachate as the leachate sample. Acid refers to a compound that produces only hydrogen ions (H⁺) as cations upon ionization, such as sulfuric acid or hydrochloric acid. Base refers to a compound that produces only hydroxide ions as anions upon ionization, such as sodium hydroxide or potassium hydroxide. Homogeneous test solution refers to the leachate after digestion of the leachate sample; the elements and concentrations contained in the leachate sample can be determined by inductively coupled plasma (ICP). ICP is existing technology and will not be elaborated upon here.

[0137] Importantly, the characteristic spectral lines are resonance lines formed when atoms of different elements absorb or emit specific energies during transitions between the ground state and the first excited state. Different elements in a homogeneous test solution, after inductively coupled plasma (ICP) treatment, exhibit short lines of varying brightness at different wavelengths. For example, the characteristic spectral line for iron is at 589.0 nm, and for lead it is at 283.3 nm. If iron is present in the homogeneous test solution, a short line (characteristic spectral line) will appear at 589.0 nm after ICP treatment, and the higher the iron content, the brighter and thicker the line. Furthermore, the elemental concentration in the leachate sample can be determined based on the characteristic spectral lines using existing techniques such as X-ray photoelectron spectroscopy and laser-induced breakdown spectroscopy; these details are not elaborated upon here.

[0138] S3. Perform a safety assessment on multiple leachate components and their concentrations to obtain the assessment results. If the assessment results indicate that there are pre-constructed harmful components in the target steel slag leachate, then perform safety treatment on the target steel slag leachate to obtain treated leachate.

[0139] Specifically, during the leaching operation, it is difficult to leach only the nutrients required by the microorganisms, which affects the leaching efficiency. However, if the steel slag is leached by a general leaching operation, the target steel slag leachate will contain a variety of leachate components. Among these components, there may be beneficial components (components that promote microbial growth), inhibitory components (components that inhibit microbial growth), and ineffective components (components that neither promote nor inhibit microbial growth).

[0140] Furthermore, different microorganisms exhibit varying tolerances to inhibitory components. For instance, *Bacillus subtilis* tolerates lead at a concentration of 1.0 mg / L, while *Thiobacillus ferrooxidans* tolerates lead at a concentration of 5.0 mg / L. If the concentration of a leachate component exceeds its tolerance (i.e., it is a harmful component), the inhibitory component will significantly inhibit microbial growth. However, leachate components with concentrations less than or equal to the tolerance have negligible inhibitory effects on microorganisms. Therefore, the safety assessment operation for multiple leachate components and their concentrations refers to the process of identifying inhibitory components and their concentrations based on the differences in the microorganisms present. For example, if lead is present in multiple leachate components, and the lead concentration is 2.0 mg / L, then the target steel slag leachate needs to undergo safety treatment to reduce the lead concentration in the target steel slag leachate to a concentration less than or equal to the lead tolerance concentration.

[0141] Importantly, the aforementioned safety treatment of the target steel slag leachate refers to the process of removing harmful components present in the target steel slag leachate through methods such as chemical precipitation. For example, an alkaline solution (such as NaOH or Na2CO3) is slowly added to the target steel slag leachate to adjust the pH to the pH range for the precipitation of harmful components, generating precipitates of harmful components. These precipitates are then filtered, reducing the concentration of harmful components in the target steel slag leachate to less than or equal to the tolerance level, thus achieving safety treatment and obtaining treated leachate. The pH range for the precipitation of harmful components is determined based on existing chemical principles. Safety treatment methods such as chemical precipitation are existing technologies and will not be elaborated upon here.

[0142] S4. Perform a feature matching operation based on the culture medium components on the treated leachate to obtain the contents of multiple supplementary components. Perform parameter optimization on the contents of multiple supplementary components to obtain the contents of multiple optimized components.

[0143] Specifically, the process of performing a feature matching operation based on the culture medium components on the treated leachate yields the contents of multiple supplementary components, including:

[0144] A full-component quantitative analysis was performed on the treated leachate to obtain the contents of multiple treated components.

[0145] The content of multiple nutrients is obtained from the composition of the culture medium;

[0146] Component matching and ratio calculation operations were performed on the contents of multiple treatment components and multiple nutrient components to obtain multiple enrichment and loss indices, wherein the enrichment and loss indices correspond one-to-one with the nutrient component contents.

[0147] Based on multiple enrichment and loss indices, the contents of multiple supplementary components are calculated using a pre-constructed supplementation content calculation formula.

[0148] Understandably, since the concentrations of various components in the treated leachate and the target steel slag leachate will differ due to the safety treatment (different concentrations), accurate concentrations of various components are required as input in the feature matching operation and parameter optimization steps. This is also to verify whether the safety treatment removes harmful components. Therefore, this invention performs full-component quantitative analysis on the treated leachate. The content of the treated components refers to the concentration of components contained in the treated leachate. Generally, the process of performing full-component quantitative analysis on the treated leachate is the same as that of performing full-component quantitative analysis on the target steel slag leachate, and will not be repeated here.

[0149] It should be understood that if the harmful components are present in the contents of multiple treatment components, a second safety treatment must be performed on the treatment leachate until the harmful components are no longer present in the contents of the multiple treatment components.

[0150] In detail, the contents of the multiple nutrients are the contents of the nutrients recorded in the culture medium composition. For example, the required peptone (nutrient) content recorded in the culture medium composition of Bacillus subtilis is 5 g / L.

[0151] It should be noted that the component matching and ratio calculation operation for the contents of multiple treatment components and multiple nutrients refers to the process of determining the content of the corresponding treatment component among the multiple treatment component contents based on the contents of multiple nutrients, and calculating the ratio of each nutrient content to the corresponding treatment component content. For example, the contents of multiple treatment components include potassium (5 g / L) and silicon (4 g / L), and the contents of multiple nutrients include potassium (10 g / L) and silicon (5 g / L). Then, the enrichment / deletion index of potassium is... And the enrichment / depletion index of silicon is .

[0152] Specifically, if there are nutrient contents among the multiple nutrient contents that do not match the contents of the multiple treatment components, then a treatment component content of 0 g / L is constructed for the unmatched nutrient contents, and the enrichment loss index is calculated to be 0.

[0153] Importantly, the formula for calculating the supplementary content is as follows:

[0154] ,

[0155] in, Indicates the first The content of the supplementary components of each enrichment missing index, Indicates the content of multiple nutrients. The content of nutrients corresponding to each enrichment-deficiency index. Indicates the first of multiple enrichment missing indices A single enrichment missing index. Indicates the first Bioavailability index with enrichment missing index.

[0156] In detail, the bioavailability index refers to the proportion of the treated component content corresponding to the enrichment / depletion index that can be effectively utilized by the microorganisms. The form in which elements exist in steel slag leachate affects the content of elements that can be utilized by microorganisms. For example, microorganisms can usually only utilize soluble inorganic phosphates. However, in steel slag leachate, elements may exist in the form of soluble inorganic phosphates, precipitates, or complexes. Therefore, this invention establishes a bioavailability index.

[0157] For example, the phosphorus concentration (treatment component content) in the steel slag leachate is 10 mg / L, of which soluble inorganic phosphate (in a form that can be utilized by microorganisms, in the form of...) If the concentration of phosphorus is 6 mg / L, and the remainder is in precipitate or organic complex form (difficult to be utilized by microorganisms), then the bioavailability index of phosphorus is 6 / 10 = 0.6.

[0158] Furthermore, the parameter optimization of the content of multiple supplementary ingredients yields multiple optimized ingredient contents, including:

[0159] The volume fraction of the target steel slag leachate and the pH of the culture medium were obtained, and the volume fraction, the pH of the culture medium, and the content of multiple supplementary components were used as multiple initial influencing factors.

[0160] Determine the high-impact factor and low-impact factor of each initial impact factor from multiple initial impact factors to obtain multiple high-impact factors and multiple low-impact factors;

[0161] Multiple high-impact factors and multiple low-impact factors are combined to obtain multiple impact factor combinations, and multiple impact culture media are configured based on these multiple impact factor combinations.

[0162] Microbial culture was performed on multiple influencing media, and biomass was measured on each of the influencing media after the microbial culture was performed to obtain multiple influencing biomass.

[0163] Perform the following operations on multiple initial impact factors:

[0164] Based on the initial impact factor, multiple impact biomasses are divided into multiple high-impact biomasses and multiple low-impact biomasses, and the impact level is calculated based on the multiple high-impact biomasses and multiple low-impact biomasses.

[0165] If the absolute value of the impact level is greater than the preset impact threshold, the initial impact factor corresponding to the impact level will be recorded as the key impact factor.

[0166] By summarizing the key impact factors, multiple key impact factors are obtained;

[0167] Parameter optimization based on culture medium components was performed on several key influencing factors to obtain the optimized component contents.

[0168] It is understood that the volume fraction of the target steel slag leachate refers to the percentage of the volume of the steel slag leachate relative to the volume of the pre-constructed culture medium. The pH of the culture medium refers to the pH of the culture medium constructed using the treated leachate. Volume fraction and pH are existing chemical terms, and will not be elaborated upon here. The pre-constructed culture medium refers to the culture medium constructed based on an initial culture medium formula predetermined according to the composition of the steel slag leachate, the composition of the culture medium, and the content of the plurality of supplementary components, and then based on the initial culture medium composition.

[0169] It should be noted that the initial influencing factor refers to the factor initially determined to affect the cultivation of the microorganisms, which is one of the following: volume fraction, culture medium pH, and the content of multiple supplementary components. In the process of preparing the pre-constructed culture medium, to efficiently utilize the steel slag leachate, this invention selects volume fraction as the initial influencing factor. For example, the silicon concentration in the steel slag leachate is 1 g / L, but the required silicon concentration in the pre-constructed culture medium is 500 mg / L. Therefore, if 1 liter of culture medium is prepared, only 0.5 liters of steel slag leachate are needed for silicon, at which point the target volume fraction of the steel slag leachate is 50%. Thus, when constructing the culture medium, precisely using 0.5 liters of steel slag leachate avoids insufficient steel slag leachate, which would result in insufficient concentration of the target nutrient (such as silicon) in the culture medium, ensuring the needs of microbial cultivation. It also avoids ineffective consumption and waste of the steel slag leachate due to excessive use, thereby achieving efficient utilization of the steel slag leachate.

[0170] Furthermore, the effect of culture medium pH varies for different microorganisms; therefore, this invention selects culture medium pH as an initial influencing factor. For example, a culture medium pH range of 6.5-8.5 promotes the growth of Bacillus subtilis, while a strongly alkaline environment (culture medium pH > 8.5) inhibits its growth. Generally, the content of multiple supplementary components determines the final composition of the culture medium, and different component contents have different effects on microbial growth (different tolerance levels). Therefore, this invention also uses the content of multiple supplementary components as an initial influencing factor.

[0171] Importantly, a high impact factor refers to a high level of the initial impact factor, while a low impact factor refers to a low level. High and low levels represent the two extremes of the initial impact factor's range. Specifically, the volume fraction and pH of the culture medium both have defined ranges, while the ranges for the content of multiple supplementary components are determined by existing chemical principles. That is, if the content of multiple supplementary components is higher than the range, it will kill the microorganisms; if it is lower than the range, it will result in insufficient nutrients in the culture medium, making microbial cultivation impossible. Based on existing chemical principles, each component in the microbial culture medium has a lethal concentration and a minimum concentration of culturable microorganisms. Therefore, this invention can determine a range for the content of a supplementary component by using the lethal concentration and the minimum concentration of culturable microorganisms.

[0172] Furthermore, multiple high and low levels are selected from the ranges of added component content, volume fraction, and culture medium pH as high and low influence factors. For example, if the volume fraction range is 0 to 100%, and the midpoint 50% is taken as the boundary between high and low levels, then the volume fraction of 50%-100% is considered the high-level range, and the volume fraction of 0-50% is considered the low-level range. Therefore, a value within the 50%-100% range can be selected as the high-influence factor, and a value within the 0-50% range can be selected as the low-influence factor.

[0173] It should be understood that multiple high-impact factors and multiple low-impact factors only differ in direction. The values ​​should be close to the center of the range to avoid the extreme values ​​of high-impact factors and low-impact factors causing the biomass of microorganisms to be similar under high-impact factors and low-impact factors. This would result in multiple biomass values ​​failing to reflect the true situation (the difference in the number of microorganisms cultured in different culture media under high-impact factor and low-impact factor conditions), and thus having no reference value.

[0174] For example, taking 100% as the volume fraction, if iron concentration exists in the steel slag leachate, even without adding iron, the iron concentration exceeds the concentration in the culture medium components. High iron concentrations (high-impact factors) will inhibit microbial culture, and the higher the concentration, the greater the inhibitory effect. This results in high and low iron concentrations having the same effect on microbial cultivation, meaning the high-impact biomass and low-impact biomass of high and low-impact factors are similar. Therefore, when calculating the impact level, an impact level with a value less than a preset impact threshold is calculated, thus considering iron not as a critical impact factor. Therefore, this invention does not take extreme values ​​for multiple high-impact factors and multiple low-impact factors among multiple initial impact factors.

[0175] Specifically, the multiple influencing factor combinations are combinations of multiple initial influencing factors obtained by performing combination operations on multiple high-influence factors and multiple low-influence factors. For example, if there are high-volume-fraction and low-volume-fraction high-volume-fraction factors and low-volume-fraction medium pH factors, respectively, and high-medium-pH and low-medium-pH- respectively, then the multiple influencing factor combinations are {high-volume-fraction, high-medium-pH}, {high-volume-fraction, low-medium-pH}, {low-volume-fraction, high-medium-pH}, and {low-volume-fraction, low-medium-pH}.

[0176] Understandably, each combination of influencing factors can be configured with a different culture medium as the influencing medium. The process of performing microbial culture operations on multiple influencing media, and then performing biomass measurement operations on each of the multiple influencing media after microbial culture, involves cultivating the same microorganism using multiple influencing media and measuring the number of microorganisms in the multiple influencing media after cultivation to obtain multiple influencing biomasses.

[0177] It should be explained that the division of multiple influencing biomass into multiple high-influence biomass and multiple low-influence biomass based on the initial influencing factors refers to dividing multiple influencing biomass into multiple high-influence biomass and multiple low-influence biomass according to the influencing culture media corresponding to the high-influence and low-influence factors of the initial influencing factors. For example, multiple influencing factor combinations such as {high volume fraction, high culture medium pH}, {high volume fraction, low culture medium pH}, {low volume fraction, high culture medium pH}, and {low volume fraction, low culture medium pH} can be configured into 4 influencing culture media, thus allowing the measurement of 4 influencing biomass. Then, based on the high and low volume fractions, the 4 influencing biomass can be divided into two high-influence biomass and two low-influence biomass by volume fraction. Similarly, based on the high and low culture medium pH, the 4 influencing biomass can be divided into two high-influence biomass and two low-influence biomass by culture medium pH.

[0178] In detail, the calculation of the impact level based on multiple high-impact biomass and multiple low-impact biomass is performed using an impact level calculation formula, which is shown below:

[0179] ,

[0180] in, Indicates the level of influence. This represents the total number of multiple high-impact biomass. Indicating the first among multiple high-impact biomass A high-impact index on biomass Indicating the first among multiple high-impact biomass A high-impact biomass This represents the total amount of multiple low-impact biomass. Indicating the first among multiple low-impact biomass A low-impact biomass index This represents the m-th low-impact biomass among multiple low-impact biomass.

[0181] It should be understood that the influence level represents the degree to which the initial influencing factor affects microbial growth. The larger the absolute value of the influence level, the greater the degree of influence of the initial influencing factor on microbial growth. An influence threshold can be determined based on historical experience and compared with the influence level to determine whether the initial influencing factor is a decisive factor for microbial growth, i.e., a critical influencing factor.

[0182] Furthermore, the parameter optimization based on culture medium components is performed on multiple key influencing factors to obtain the content of multiple optimized components, including:

[0183] Extract one key impact factor sequentially from multiple key impact factors to obtain the target impact factor, and perform the following operations on the target impact factor:

[0184] Determine the impact level of the target impact factor to obtain the target impact level;

[0185] If the target impact level is positive, then the high impact factor of the target impact factor is recorded as the starting point of the target factor, and the direction of the factor is determined.

[0186] If the target impact level is negative, then the low impact factor of the target impact factor is recorded as the starting point of the target factor, and the direction of the factor is determined.

[0187] By summarizing the starting points and directions of the target factors, multiple target starting point factors and multiple factor directions are obtained.

[0188] Step A: Configure the starting culture medium based on multiple target starting factors, perform microbial culture on the starting culture medium, and perform biomass measurement on the starting culture medium after microbial culture to obtain the starting biomass. Update the factor content of multiple target starting factors based on the preset step size and multiple factor directions to obtain multiple updated factors. Use the multiple updated factors as the multiple target starting factors, return to the step of configuring the starting culture medium based on multiple target starting factors to obtain the updated biomass. Calculate the difference between the starting biomass and the updated biomass to obtain the biomass difference.

[0189] Repeat step A until the biomass difference is negative, then stop step A and obtain the starting biomass when the biomass difference is negative as the undetermined optimal biomass.

[0190] Multiple potential optimal influencing factors for determining the optimal biomass are obtained, and the factor content of multiple key influencing factors is optimized using these multiple potential optimal influencing factors to obtain the content of multiple optimized components.

[0191] It should be noted that the target impact level refers to the impact level of the target impact factor calculated based on multiple high-impact biomass and multiple low-impact biomass. A positive target impact level indicates that the target impact factor promotes microbial growth, meaning that the target impact factor value should be as high as possible. Therefore, the high impact factor of the target impact factor is recorded as the starting point of the target factor, and the factor direction is determined to be from the starting point towards a larger value. If the target impact level is negative, it indicates that the target impact factor inhibits microbial growth, meaning that the target impact factor value should be as low as possible. Therefore, the low impact factor of the target impact factor is recorded as the starting point of the target factor, and the factor direction is determined to be from the starting point towards a smaller value.

[0192] It is understood that the process of configuring the starting culture medium based on multiple target starting factors refers to determining multiple target starting factors as multiple values ​​of multiple key influencing factors, and then configuring the starting culture medium according to the multiple values ​​of multiple key influencing factors. For example, if the multiple values ​​of multiple key influencing factors are 50% by volume, the pH of the culture medium is 7.0, and the silicon content is 7 grams, then to prepare 1L of culture medium, 0.5L of steel slag leachate needs to be added to the container, and at the same time, 7 grams of silicon needs to be added. The pH of the solution containing 7 grams of silicon and 0.5L of steel slag leachate needs to be adjusted by acid or alkali until the pH of the solution is the same as the pH of the culture medium.

[0193] Specifically, the process of performing microbial culture on the starting culture medium and measuring the biomass of the starting culture medium after microbial culture is the same as the process of performing microbial culture on multiple influencing culture media and measuring the biomass of the multiple influencing culture media after microbial culture. This invention will not elaborate further here.

[0194] It should be understood that updating the factor content of multiple target starting factors based on a preset step size and multiple factor directions refers to updating the parameters of each of the multiple target starting factors according to the preset step size and the corresponding factor direction. For example, if the pH of the culture medium is 8.0, and the factor direction is a value greater than 8.0, and the preset step size is 0.5, then the updated pH of the culture medium will be 8.5. It is understood that the multiple target starting factors are all normalized values, thus ensuring that the preset step size can be applied to multiple target starting factors. The example of culture medium pH is only for visual demonstration. The preset step size is an increment of value set manually based on experience and the tolerance of the microorganisms.

[0195] It should be explained that the biomass difference is the difference between the starting biomass and the renewed biomass. If the biomass difference is negative, it indicates that the starting biomass is greater than the renewed biomass, meaning that the culture medium constructed with multiple renewal factors has a less effective cultivation effect on microorganisms than the starting culture medium. This indicates that, compared to the multiple renewal factors, the multiple target starting factors at this time represent an optimized value. Therefore, in this invention, when the biomass difference is negative, step A is stopped.

[0196] It is clear that the undetermined optimal biomass refers to the starting biomass when the biomass difference is negative. The phrase "obtaining multiple undetermined optimal influencing factors for the undetermined optimal biomass" refers to obtaining multiple target starting factors corresponding to the starting biomass when the biomass difference is negative, as multiple undetermined optimal influencing factors. For example, step A is repeated 5 times, resulting in 5 biomasses: 1, 2, 3, 6, and 5. Using 1 as the starting biomass, then 2 is the updated biomass, and the biomass difference is 1. Further, using 2 as the starting biomass, then 3 is the updated biomass, and so on. Using 6 as the starting biomass, then 5 is the updated biomass, and the biomass difference at this point is -1. Therefore, 6 is the starting biomass when the biomass difference is negative, and thus 6 is the undetermined optimal biomass. In particular, the previous biomass can be recorded as the preceding biomass of the next biomass, and the next biomass can be recorded as the following biomass of the previous biomass. That is, 1 is the preceding biomass of 2, 2 is the following biomass of 1, 2 is the preceding biomass of 3, 3 is the following biomass of 2, and so on. The process is the same and will not be described in detail here.

[0197] It should be understood that, in order to avoid the negative biomass difference being a random error (such as an error caused by improper operation), this invention requires multiple experiments to verify that the negative biomass difference is a random error.

[0198] Furthermore, the optimization of the factor content of multiple key influencing factors using multiple undetermined optimal influencing factors yields multiple optimized component contents, including:

[0199] Extract one key impact factor from multiple key impact factors sequentially to obtain the target key factor, and then perform the following operations on the target key factor:

[0200] By identifying the target key factor among multiple undetermined optimal impact factors, the intermediate key factors are obtained.

[0201] Calculate the high critical factor and low critical factor based on the step size and medium critical factor, respectively.

[0202] Low criticality factors, medium criticality factors, and high criticality factors are summarized separately to obtain multiple low criticality factors, multiple medium criticality factors, and multiple high criticality factors;

[0203] An experimental matrix was constructed based on multiple low-criticality factors, multiple medium-criticality factors, and multiple high-criticality factors.

[0204] Multiple key vectors were extracted from the experimental matrix, and multiple key culture media were constructed based on the multiple key vectors. Microbial culture operations were performed on all multiple key culture media, and biomass measurement operations were performed on all multiple key culture media after microbial culture operations to obtain multiple key biomass.

[0205] By utilizing multiple key biomass and multiple key vectors, the content of multiple key influencing factors was optimized, resulting in the content of multiple optimized components.

[0206] It should be explained that the intermediate key factor is the undetermined optimal influence factor corresponding to the target key factor among multiple undetermined optimal influence factors. Since the undetermined optimal influence factor is the value of the key influence factor of the undetermined optimal biomass, and the biomass difference corresponding to the undetermined optimal biomass is negative, it indicates that there exists a maximum biomass value between the preceding and following biomass of the undetermined optimal biomass, that is, the value of multiple key influence factors is optimal. Meanwhile, to avoid the biomass difference being negative due to random errors causing the maximum biomass value to not fall between the preceding and following biomass of the undetermined optimal biomass, this invention identifies the intermediate key factor, that is, using the undetermined optimal influence factor corresponding to the target key factor as the median value of the target key factor, and calculating the high and low key factors respectively using the step size and the intermediate key factor. Random errors refer to operational mistakes that cause the added amount to deviate from the calculated update factor.

[0207] For example, if 8 grams of iron were actually added when 7 grams should have been added, the measured biomass would be the biomass with 8 grams of iron added. Assuming this biomass is the undetermined optimal biomass, the calculated target key factor would be 7 grams of iron. However, due to an operational error, it should actually be 8 grams of iron. Therefore, the search range needs to be expanded. Using the calculated 7 grams of iron as a medium key factor, a high key factor (5) and a low key factor (9) can be calculated, including 8 grams of iron within the range of multiple key influencing factors, thus avoiding losing the optimal value of the target key factor.

[0208] Specifically, the calculation of high and low critical factors based on the step size and medium critical factor is done by adding a certain proportion of the step size to the medium critical factor. This certain proportion of the step size is set manually based on historical experience. For example, if the medium critical factor is 4 and the step size is 0.5, the step size can be doubled based on experience, meaning the high critical factor is 4 + 0.5 × 2 = 5, and the low critical factor is 4 - 0.5 × 2 = 3.

[0209] In detail, since there exists a maximum value of biomass between the pre- and post-biological biomass of the undetermined optimal biomass, which also represents the optimal values ​​for multiple key influencing factors, it is necessary to expand the range of values ​​for these key influencing factors. Therefore, this invention adds a certain percentage step size to the key factors. This certain percentage step size can be adjusted based on historical experience. For example, if the found undetermined optimal biomass is close to the optimal biomass in historical experience, and the values ​​of multiple key influencing factors are also close to the optimal values ​​in historical experience, the certain percentage can be set to a number less than 1, i.e., taking values ​​within a small range to improve the efficiency of parameter optimization. If the found undetermined optimal biomass is far from the optimal biomass in historical experience, or the values ​​of multiple key influencing factors are not close to the optimal values ​​in historical experience (possibly due to random errors between the calculated and actual values), then the certain percentage can be set to a number greater than 1, taking values ​​within a larger range; the greater the distance, the larger the certain percentage.

[0210] Therefore, this invention adds a certain percentage of step size to the key factors. If the step size is...

[0211] In detail, constructing an experimental matrix based on multiple low-key factors, multiple medium-key factors, and multiple high-key factors refers to combining multiple low-key factors, multiple medium-key factors, and multiple high-key factors to construct the experimental matrix. Extracting multiple key vectors from the experimental matrix refers to extracting all row vectors from multiple experimental matrices to form multiple key vector matrices.

[0212] For example, suppose there are multiple low critical factors, designated as the first low critical factor and the second low critical factor; multiple medium critical factors, designated as the first medium critical factor and the second critical factor; and multiple high critical factors, designated as the first high critical factor and the second high critical factor. Then the experimental matrix is ​​as follows:

[0213] ,

[0214] in, Indicates the first low critical factor. This indicates the second lowest critical factor. This represents the first key factor. This indicates the second key factor. Indicates the highest critical factor. This indicates the second highest key factor. As a key vector, and so on. All row vectors can be used as key vectors, which will not be elaborated further in this invention.

[0215] It is understood that the process of constructing multiple key culture media based on multiple key vectors, performing microbial culture operations on multiple key culture media, and performing biomass measurement operations on multiple key culture media after performing microbial culture operations is consistent with the process of configuring a starting culture medium based on multiple target starting factors, performing microbial culture operations on the starting culture medium, and performing biomass measurement operations on the starting culture medium after performing microbial culture operations. This invention will not elaborate further here.

[0216] Furthermore, the optimization of factor content for multiple key influencing factors using multiple key biomass and multiple key vectors yields multiple optimized component contents, including:

[0217] An initial fitness function is constructed using several key influencing factors, as shown below:

[0218] ,

[0219] in, Indicates biomass. Indicates standard biomass. Indicates the first Index of key influencing factors This represents the total number of multiple key influencing factors. Indicates the first The coefficients of the first-order terms of the key influencing factors, Indicates the first The key influencing factors and the first The quadratic coefficients of the key influencing factors, Indicates the first The key influencing factors and the first The interaction coefficients of the key influencing factors, Indicates the first Index of key influencing factors Indicates the first One key influencing factor, Indicates the first Key influencing factors.

[0220] The fitness function is obtained by performing parameter fitting on the initial fitness function based on multiple key vectors and multiple key biomass.

[0221] The fitness function was used to optimize the factor content of several key influencing factors, resulting in the optimized component content.

[0222] It should be explained that the initial fitness function represents the relationship between multiple key influencing factors and biomass; that is, different values ​​of the multiple key influencing factors correspond to different biomass values. The standard biomass is the biomass when the values ​​of the multiple key influencing factors are 0, that is, the biomass when no components are added to the steel slag leachate, and the microorganisms are cultivated using only the steel slag leachate. The coefficients of the first term, the quadratic term, and the interaction term are all coefficients of the initial fitness function, used to calculate the biomass based on the values ​​of the multiple key influencing factors. Simultaneously, during parameter fitting, the sum of squared residuals between the fitness biomass calculated by the fitness function and the corresponding key biomass is minimized by adjusting the coefficients of the first term, the quadratic term, and the interaction term.

[0223] It should be understood that the parameter fitting of the initial fitness function based on multiple key vectors and multiple key biomass refers to substituting multiple key vectors and multiple key biomass into the initial fitness function, and using the least squares method to solve the initial fitness function after substitution to obtain the specific values ​​of the coefficients of the first term, the coefficients of the second term, and the coefficients of the interaction term. The specific values ​​of the coefficients of the first term, the coefficients of the second term, and the coefficients of the interaction term are then substituted into the initial fitness function to construct the fitness function.

[0224] Specifically, the process of solving the initial fitness function using the least squares method is as follows: Substituting multiple key vectors into the initial fitness function yields multiple fitness biomasses. The least squares method is then used to perform regression fitting on the coefficients of the first term, the coefficients of the second term, and the coefficients of the interaction term in the initial fitness function, minimizing the sum of squared residuals between each fitness biomass and its corresponding key biomass, thereby obtaining the fitted coefficients of the first term, the coefficients of the second term, and the coefficients of the interaction term.

[0225] Furthermore, the fitness function is used to optimize the factor content of multiple key influencing factors, resulting in multiple optimized component contents, including:

[0226] The multiple undetermined optimal influence factors are used as multiple initial search centers, and the product of the step size and the pre-constructed proportion is calculated to obtain the search radius. An initial search space is constructed with multiple initial search centers and search radii, wherein the initial search space includes multiple initial search intervals, and each initial search interval corresponds one-to-one with an initial search center.

[0227] Multiple key influencing factors are initialized based on multiple initial search intervals to obtain multiple initial search recipes, wherein the initial search recipes include multiple initial search factors;

[0228] By using a fitness function and multiple initial search formulations, the content of multiple key influencing factors was optimized, resulting in the content of multiple optimized components.

[0229] It is understood that the multiple initial search centers are multiple undetermined optimal influence factors. The process of calculating the product of the step size and the pre-constructed proportion as the search radius is consistent with the step size of the certain proportion, and will not be elaborated here. The initial search space is a set of multiple initial search intervals, which refer to the range of values ​​of key influence factors constructed based on the search radius with the initial search center as the center. Each initial search interval corresponds to one key influence factor.

[0230] It needs to be explained that the initialization of multiple key influencing factors based on multiple initial search intervals refers to dividing each of the multiple initial search intervals equally to obtain multiple initial equal interval sets, and extracting multiple values ​​corresponding to the key influencing factors from each of the multiple initial equal interval sets to obtain multiple value sets of multiple key influencing factors. Then, a value matrix is ​​constructed based on the multiple value sets, and all row vectors are extracted from the value matrix as multiple initial search recipes.

[0231] For example, there exists a first initial search interval [0,1] corresponding to the first key influencing factor. This first initial search interval is divided into four equal parts using Latin hypercube sampling, resulting in a first initial equal interval set including the first initial equal interval [0,0.25], the second initial equal interval [0.25,0.5], the third initial equal interval [0.5,0.75], and the fourth initial equal interval [0.75,1]. Then, a value is extracted from each of the four initial equal intervals using Latin hypercube sampling as multiple values ​​corresponding to the first key influencing factor. Latin hypercube sampling is a prior art technique, and will not be elaborated upon here.

[0232] It should be understood that the process of constructing a value matrix based on multiple value sets and extracting all row vectors from the value matrix as multiple initial search recipes is consistent with the process of constructing an experimental matrix based on multiple low key factors, multiple medium key factors and multiple high key factors and the process of extracting multiple key vectors from the experimental matrix. This invention will not elaborate further here.

[0233] Specifically, an initial search formula includes the values ​​of multiple key influencing factors (i.e., multiple initial search factors), and each initial search factor corresponds one-to-one with a key influencing factor.

[0234] Furthermore, the optimization of the factor content of multiple key influencing factors is performed using a fitness function and multiple initial search formulations to obtain the content of multiple optimized components, including:

[0235] Multiple initial biomasses for multiple initial search recipes are calculated using fitness functions, and standard deviations are calculated for multiple initial search recipes to obtain multiple standard deviations;

[0236] Based on multiple initial biomass and multiple standard deviations, the formulation components of multiple initial search formulations are updated to obtain updated component formulations;

[0237] The updated component formulation was validated, and the validation results were obtained. Based on the validation results, the updated component formulation was confirmed as having multiple optimized component contents.

[0238] Understandably, calculating multiple initial biomasses using the fitness function for multiple initial search formulations refers to substituting the values ​​of multiple key influencing factors for each initial search formulation into the fitness function to calculate multiple biomasses, and then using these multiple biomasses as multiple initial biomasses. Performing standard deviation calculation on multiple initial search formulations refers to identifying the various component types (e.g., potassium, calcium, sodium, and magnesium) contained in the multiple initial search formulations, extracting multiple values ​​corresponding to each component type from the multiple initial search formulations, and then calculating the standard deviation of the initial search formulation for these multiple values.

[0239] For example, multiple initial search formulations are a first initial search formulation and a second initial search formulation. The first initial search formulation includes a first supplementary ingredient content of 7 and a second supplementary ingredient content of 9, and the second initial search formulation includes a first supplementary ingredient content of 8 and a second supplementary ingredient content of 9. Multiple ingredient types are then identified as the first supplementary ingredient and the second supplementary ingredient. Multiple values ​​for the first supplementary ingredient are extracted as 7 and 8, and multiple values ​​for the second supplementary ingredient are extracted as 9 and 9. The standard deviations of 7 and 8 are then calculated, yielding a standard deviation of 0.707 for the first supplementary ingredient. The standard deviations of 9 and 9 are calculated, yielding a standard deviation of 0 for the second supplementary ingredient. Standard deviations are existing technology and will not be elaborated upon here.

[0240] It should be noted that the step of updating the formulation components of multiple initial search formulations based on multiple initial biomass and multiple standard deviations refers to: judging multiple standard deviations; if multiple standard deviations are all less than or equal to a preset standard deviation threshold, then the multiple initial search formulations at this time are confirmed as multiple updated search formulations; using the fitness function to calculate multiple biomass of the multiple updated search formulations; and extracting the maximum value among the multiple biomass of the multiple updated search formulations to obtain the maximum updated biomass, thereby confirming the updated search formulation corresponding to the maximum updated biomass as the updated component formulation.

[0241] It should be understood that when cultivating the microorganisms, the larger the biomass, the higher the microbial reproduction efficiency and the more vigorous the growth under the updated search formula. Therefore, the present invention identifies the updated search formula corresponding to the maximum updated biomass as the updated component formula.

[0242] Importantly, if any one of the multiple standard deviations exceeds a preset standard deviation threshold, the initial search formula corresponding to the maximum value of the multiple initial biomass is extracted to obtain the target search formula. This target search formula is then removed from the multiple initial search formulas, resulting in multiple retained search formulas. Based on the target search formula, multiple search directions for the multiple initial search formulas are determined, and a random step size is obtained. Based on the random step size and the multiple search directions, the factor content of the multiple retained search formulas is updated to obtain multiple updated formulas. Using the multiple updated formulas and the target search formula as the multiple initial search formulas, the step of calculating the multiple initial biomass of the multiple initial search formulas using the fitness function is returned to obtain multiple updated biomass. The maximum value is extracted from the multiple updated biomass to obtain the updated maximum biomass.

[0243] It should be explained that determining multiple search directions for multiple initial search recipes based on the target search recipe involves using multiple values ​​of multiple key influencing factors in the target search recipe as multiple search directions. The process of obtaining the random step size is consistent with obtaining the certain proportion of the step size, and the process of updating the factor content of multiple retained search recipes based on the random step size and multiple search directions is consistent with the process of updating the factor content of multiple target starting factors based on a preset step size and multiple factor directions. These details will not be elaborated further here.

[0244] For example, if the target search formula includes a first target search factor 5 and a second target search factor 8, and a first initial search formula {first initial search factor 4, second initial search factor 9} also exists, then the first target search factor 5 and the second target search factor 8 are identified as two search directions, and a random step size of 0.3 is obtained. Then, the factor content of the first initial search formula is updated to obtain the first updated formula {first updated search factor 4 + 0.3 = 4.3, second updated search factor 9 - 0.3 = 8.7}.

[0245] It should be understood that the standard deviation threshold is a manually set threshold, which can be determined based on historical experience and the biomass requirements of the culture medium constructed with multiple optimized component contents. If the standard deviation is less than the standard deviation threshold, it is confirmed that multiple values ​​of the corresponding key influencing factors converge to a definite value, that is, the multiple values ​​of the updated factor content are close to the optimal value. At this time, the value of the corresponding key influencing factor can be confirmed as the optimal value. Therefore, only when multiple standard deviations are all less than or equal to the preset standard deviation threshold is it confirmed that the multiple sets of values ​​of multiple key influencing factors in the multiple initial search formulations have converged to the optimal value, and thus the multiple initial search formulations are recorded as multiple updated search formulations.

[0246] Furthermore, the verification of the updated component formulation, and the resulting verification results, include:

[0247] Based on the updated component formulation and the components of the culture medium, updated culture medium and synthetic culture medium were respectively prepared;

[0248] Microbial culture operations were performed on both the renewal culture medium and the synthetic culture medium to obtain the renewal culture process and the synthetic culture process.

[0249] Biomass recording and product recording operations were performed on the renewal culture process and the synthesis culture process, respectively, to obtain a set of recording curves;

[0250] Based on the set of recorded curves, calculate the growth rate set and the yield set respectively. Perform size comparison operation on the growth rate set and the yield set respectively to obtain the comparison result set. Record the comparison result set as the verification result.

[0251] It is understood that the process of configuring the updated culture medium based on the updated component formulation is the same as the process of configuring the starting culture medium based on multiple target starting factors, and will not be described again here. In particular, the synthetic culture medium is a culture medium configured according to the components of the culture medium through chemical synthesis.

[0252] It should be noted that the recorded curve set includes renewal biomass curves, synthetic biomass curves, renewal yield curves, and synthetic yield curves. The renewal biomass curve and renewal yield curve correspond to the renewal culture process, while the synthetic biomass curve and synthetic yield curve correspond to the synthetic culture process. The renewal biomass curve is a curve recording the change in biomass over time during the renewal culture process, with time as the horizontal axis and biomass as the vertical axis. The renewal yield curve is a curve recording the change in yield over time during the renewal culture process, with time as the horizontal axis and yield as the vertical axis. The synthetic biomass curve is a curve recording the change in biomass over time during the synthetic culture process, with time as the horizontal axis and biomass as the vertical axis. The synthetic yield curve is a curve recording the change in yield over time during the synthetic culture process, with time as the horizontal axis and yield as the vertical axis.

[0253] It should be explained that yield refers to the amount of metabolic products produced by the microorganism during the cultivation process. For example, the metabolic product of Bacillus pasteurellis is calcium carbonate, and yield is the mass of calcium carbonate.

[0254] Furthermore, the process of calculating the growth rate set and yield set based on the recorded curve set refers to calculating the updated growth rate set using the updated growth rate calculation formula and the synthetic growth rate set using the synthetic growth rate calculation formula, and then extracting the maximum value of the updated growth rate set and the maximum value of the synthetic growth rate set as the maximum update rate and the maximum synthetic rate, respectively. Additionally, the process of calculating the updated yield set using the updated yield calculation formula and the synthetic yield set using the synthetic yield calculation formula, and then performing maximum value extraction on both the updated yield set and the synthetic yield set to obtain the maximum updated yield and the maximum synthetic yield, and recording the maximum update rate and the maximum synthetic rate as the growth rate set and the maximum updated yield and the maximum synthetic yield as the yield set.

[0255] Specifically, the formula for calculating the update growth rate is as follows:

[0256] ,

[0257] in, Indicates the first The rate of change of growth over time Indicating the first [item] in the updated biomass curve Biomass at any given time Indicates the first Index of time, Indicating the first [item] in the updated biomass curve Biomass at any given time Indicating the first [item] in the updated biomass curve biomass, This indicates the time interval between two biomass records.

[0258] The formula for calculating the synthetic growth rate is as follows:

[0259] ,

[0260] in, Indicates the first Synthetic growth rate at time t, Indicating the first [item] in the synthetic biomass curve Biomass at any given time Indicating the first [item] in the synthetic biomass curve Biomass at any given time Indicating the first [item] in the synthetic biomass curve Biomass.

[0261] The formula for calculating the renewal yield is as follows:

[0262] ,

[0263] in, Indicates the first Real-time updated productivity This indicates the first [item] in the updated production curve. Production per moment This indicates the first [item] in the updated production curve. production, This indicates the time interval between two production records.

[0264] The formula for calculating the synthesis yield is as follows:

[0265] ,

[0266] in, Indicates the first Synthesis yield at time t, The first in the synthetic yield curve Production per moment The first in the synthetic yield curve Production.

[0267] In detail, the update growth rate, synthetic growth rate, update yield, and synthetic yield at each time point are calculated using the update growth rate calculation formula, synthetic growth rate calculation formula, update yield calculation formula, and synthetic yield calculation formula, respectively, to obtain the update growth rate set, synthetic growth rate set, update yield set, and synthetic yield set. The maximum values ​​are extracted from the update growth rate set, synthetic growth rate set, update yield set, and synthetic yield set to obtain the maximum update rate, maximum synthetic rate, maximum update yield, and maximum synthetic yield.

[0268] Furthermore, a comparison result set is obtained by comparing the maximum update rate with the maximum synthesis rate, and the maximum update yield with the maximum synthesis yield. If the maximum update rate is less than the maximum synthesis rate or the maximum update yield is less than the maximum synthesis yield, the component formulation is updated to the contents of the multiple supplementary components, and the process of optimizing the parameters for the contents of the multiple supplementary components is returned. Optimized component contents are obtained only when the maximum update rate is greater than or equal to the maximum synthesis rate and the maximum update yield is greater than or equal to the maximum synthesis yield.

[0269] It should be understood that the maximum synthesis rate and maximum synthesis yield are the maximum rate and maximum yield of the synthetic medium, and the maximum renewal rate and maximum renewal yield are the maximum rate and maximum yield of the renewing medium. The maximum rate refers to the fastest rate of microbial reproduction. The maximum yield refers to the fastest rate of microbial secretion. If the maximum renewal rate is less than the maximum synthesis rate, it indicates that the renewing medium cultivates microorganisms at a slower rate than the synthetic medium, meaning the renewing medium is less effective at cultivating microorganisms than the synthetic medium. Therefore, this invention considers that the renewing component formulation corresponding to this renewing medium does not contain the multiple optimized component contents. Similarly, if the maximum renewal yield is less than the maximum synthesis yield, it indicates that the renewing medium is less effective at producing microbial secretions than the synthetic medium. Therefore, this invention considers that the renewing component formulation corresponding to this renewing medium does not contain the multiple optimized component contents.

[0270] Importantly, only when the maximum renewal rate is greater than or equal to the maximum synthesis rate and the maximum renewal yield is greater than or equal to the maximum synthesis yield does it indicate that the optimized renewal medium, using steel slag leachate as the initial culture medium, has a greater effect on microbial culture and microbial secretion products than the synthetic medium. This demonstrates that the present invention efficiently utilizes steel slag leachate and achieves efficient microbial cultivation.

[0271] S5. Construct a culture medium based on the content of multiple optimized components.

[0272] It is understood that the process of constructing the culture medium based on the content of multiple optimized components is the same as the process of configuring the starting culture medium based on multiple target starting factors, and will not be described again in this invention.

[0273] To address the problems described in the background art, this invention obtains microorganisms, acquires culture medium components from a pre-constructed microbial nutrient database based on these microorganisms, obtains a steel slag set, and performs a leaching operation on the steel slag set based on the culture medium components to obtain multiple steel slag leachates. This invention achieves efficient utilization of steel slag by selecting different leaching methods for the steel slag set using different microorganisms. From the multiple steel slag leachates, a target steel slag leachate is obtained by sequentially extracting one leachate. The target steel slag leachate is then subjected to the following operations: a full-component quantitative analysis is performed on the target steel slag leachate to obtain multiple leachate components and their concentrations; a safety assessment is performed on the multiple leachate components and their concentrations to obtain the assessment results; if the assessment results indicate the presence of pre-constructed harmful components in the target steel slag leachate, the target steel slag leachate is subjected to safety treatment to obtain a treated leachate. Furthermore, this invention achieves safe utilization of steel slag by performing full-component quantitative analysis on the steel slag leachate and performing safety treatment on the steel slag leachate. The leachate is subjected to a feature matching operation based on the culture medium composition to obtain the content of multiple supplementary components. Parameter optimization is then performed on these multiple supplementary component contents to obtain multiple optimized component contents. A culture medium is then constructed based on these optimized component contents. By determining the culture medium composition through microorganisms and then performing feature matching operations on the leachate based on these components, this invention fully considers the differences between the requirements of steel slag leachate and microorganisms. Furthermore, during the parameter optimization process for the multiple supplementary component contents, this invention also considers the synergistic optimization of several key parameters, such as the steel slag leachate fraction, culture medium pH, and nutrient supplementation amount. Therefore, this invention can safely and efficiently utilize steel slag and achieve parameter optimization of the steel slag leachate culture medium.

[0274] like Figure 2 The diagram shown is a functional block diagram of a culture medium parameter optimization system based on steel slag leachate provided in an embodiment of the present invention.

[0275] The culture medium parameter optimization system 100 based on steel slag leachate of this invention can be installed in an electronic device. Depending on the functions implemented, the culture medium parameter optimization system 100 based on steel slag leachate may include a leachate acquisition module 101, a leachate processing module 102, a component matching module 103, and a parameter optimization module 104. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0276] The leachate acquisition module 101 is used to acquire microorganisms, acquire culture medium components from a pre-constructed microbial nutrient database based on the microorganisms, acquire a steel slag set, and perform a leaching operation on the steel slag set based on the culture medium components to obtain multiple steel slag leachates.

[0277] The leachate treatment module 102 is used to sequentially extract one steel slag leachate from multiple steel slag leachates to obtain a target steel slag leachate. The target steel slag leachate is then subjected to the following operations: full-component quantitative analysis is performed on the target steel slag leachate to obtain multiple leachate components and their concentrations; a safety assessment is performed on the multiple leachate components and their concentrations to obtain the assessment results; if the assessment results indicate that a pre-constructed harmful component exists in the target steel slag leachate, then the target steel slag leachate is subjected to safety treatment to obtain a treated leachate.

[0278] The component matching module 103 is used to perform a feature matching operation based on the culture medium components on the treated leachate to obtain the contents of multiple supplementary components.

[0279] The parameter optimization module 104 is used to perform parameter optimization on the content of multiple supplementary components to obtain multiple optimized component contents, and to construct a culture medium based on the multiple optimized component contents.

[0280] In detail, the modules in the culture medium parameter optimization system 100 based on steel slag leachate described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method used here is the same as the method for optimizing culture medium parameters based on steel slag leachate, and can produce the same technical effect, so it will not be repeated here.

[0281] like Figure 3 The diagram shown is a schematic diagram of an electronic device for implementing a method for optimizing culture medium parameters based on steel slag leachate, according to an embodiment of the present invention.

[0282] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for optimizing culture medium parameters based on steel slag leachate.

[0283] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a method program for optimizing culture medium parameters based on steel slag leachate, but also to temporarily store data that has been output or will be output.

[0284] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device via various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a method for optimizing culture medium parameters based on steel slag leachate) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0285] The bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0286] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0287] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0288] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0289] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0290] The program for optimizing culture medium parameters based on steel slag leachate, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0291] Microorganisms are obtained by acquiring culture medium components from a pre-constructed microbial nutrient database.

[0292] A steel slag set was obtained, and a leaching operation was performed on the steel slag set based on the culture medium composition to obtain multiple steel slag leachates;

[0293] A target steel slag leachate is obtained by sequentially extracting one steel slag leachate from multiple steel slag leachates. The following operations are then performed on the target steel slag leachate:

[0294] A full-component quantitative analysis was performed on the target steel slag leachate to obtain multiple leachate components and their concentrations;

[0295] Safety assessment procedures were performed on multiple leachate components and their concentrations to obtain assessment results.

[0296] If the assessment results indicate that pre-constructed harmful components are present in the target steel slag leachate, then the target steel slag leachate will undergo safety treatment to obtain treated leachate.

[0297] A feature matching operation based on the culture medium composition was performed on the treated leachate to obtain the contents of multiple supplementary components;

[0298] Parameter optimization was performed on the contents of multiple supplementary ingredients to obtain multiple optimized ingredient contents;

[0299] Culture media were constructed based on the optimized content of multiple components.

[0300] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0301] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0302] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0303] Microorganisms are obtained by acquiring culture medium components from a pre-constructed microbial nutrient database.

[0304] A steel slag set was obtained, and a leaching operation was performed on the steel slag set based on the culture medium composition to obtain multiple steel slag leachates;

[0305] A target steel slag leachate is obtained by sequentially extracting one steel slag leachate from multiple steel slag leachates. The following operations are then performed on the target steel slag leachate:

[0306] A full-component quantitative analysis was performed on the target steel slag leachate to obtain multiple leachate components and their concentrations;

[0307] Safety assessment procedures were performed on multiple leachate components and their concentrations to obtain assessment results.

[0308] If the assessment results indicate that pre-constructed harmful components are present in the target steel slag leachate, then the target steel slag leachate will undergo safety treatment to obtain treated leachate.

[0309] A feature matching operation based on the culture medium composition was performed on the treated leachate to obtain the contents of multiple supplementary components;

[0310] Parameter optimization was performed on the contents of multiple supplementary ingredients to obtain multiple optimized ingredient contents;

[0311] Culture media were constructed based on the optimized content of multiple components.

[0312] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0313] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0314] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0315] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0316] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing culture medium parameters based on steel slag leaching solution, characterized in that, The method includes: Microorganisms are obtained by acquiring culture medium components from a pre-constructed microbial nutrient database. A steel slag set was obtained, and a leaching operation was performed on the steel slag set based on the culture medium composition to obtain multiple steel slag leachates; A target steel slag leachate is obtained by sequentially extracting one steel slag leachate from multiple steel slag leachates. The following operations are then performed on the target steel slag leachate: A full-component quantitative analysis was performed on the target steel slag leachate to obtain multiple leachate components and their concentrations; Safety assessment procedures were performed on multiple leachate components and their concentrations to obtain assessment results. If the assessment results indicate that pre-constructed harmful components are present in the target steel slag leachate, then the target steel slag leachate will undergo safety treatment to obtain treated leachate. A feature matching operation based on the culture medium composition was performed on the treated leachate to obtain the contents of multiple supplementary components; Parameter optimization was performed on the contents of multiple supplementary ingredients to obtain multiple optimized ingredient contents; Culture media were constructed based on the optimized content of multiple components.

2. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 1, characterized in that, The process of performing a feature matching operation based on the culture medium components on the treated leachate yields the contents of multiple supplementary components, including: A full-component quantitative analysis was performed on the treated leachate to obtain the contents of multiple treated components. The content of multiple nutrients is obtained from the composition of the culture medium; Component matching and ratio calculation operations were performed on the contents of multiple treatment components and multiple nutrient components to obtain multiple enrichment and loss indices, wherein the enrichment and loss indices correspond one-to-one with the nutrient component contents. Based on multiple enrichment and loss indices, the contents of multiple supplementary components are calculated using a pre-constructed supplementation content calculation formula.

3. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 2, characterized in that, The parameter optimization of the content of multiple supplementary ingredients yields multiple optimized ingredient contents, including: The volume fraction of the steel slag leachate and the pH of the culture medium were obtained, and the volume fraction, the pH of the culture medium, and the content of multiple supplementary components were used as multiple initial influencing factors. Determine the high-impact factor and low-impact factor of each initial impact factor from multiple initial impact factors to obtain multiple high-impact factors and multiple low-impact factors; Multiple high-impact factors and multiple low-impact factors are combined to obtain multiple impact factor combinations, and multiple impact culture media are configured based on these multiple impact factor combinations. Microbial culture was performed on multiple influencing media, and biomass was measured on each of the influencing media after the microbial culture was performed to obtain multiple influencing biomass. Perform the following operations on multiple initial impact factors: Based on the initial impact factor, multiple impact biomasses are divided into multiple high-impact biomasses and multiple low-impact biomasses, and the impact level is calculated based on the multiple high-impact biomasses and multiple low-impact biomasses. If the absolute value of the impact level is greater than the preset impact threshold, the initial impact factor corresponding to the impact level will be recorded as the key impact factor. By summarizing the key impact factors, multiple key impact factors are obtained; Parameter optimization based on culture medium components was performed on several key influencing factors to obtain the optimized component contents.

4. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 3, characterized in that, The parameter optimization of multiple key influencing factors based on culture medium components yielded optimized component contents, including: Extract one key impact factor sequentially from multiple key impact factors to obtain the target impact factor, and perform the following operations on the target impact factor: Determine the impact level of the target impact factor to obtain the target impact level; If the target impact level is positive, then the high impact factor of the target impact factor is recorded as the starting point of the target factor, and the direction of the factor is determined. If the target impact level is negative, then the low impact factor of the target impact factor is recorded as the starting point of the target factor, and the direction of the factor is determined. By summarizing the starting points and directions of the target factors, multiple target starting point factors and multiple factor directions are obtained. Step A: Configure the starting culture medium based on multiple target starting factors, perform microbial culture on the starting culture medium, and perform biomass measurement on the starting culture medium after microbial culture to obtain the starting biomass. Update the factor content of multiple target starting factors based on the preset step size and multiple factor directions to obtain multiple updated factors. Use the multiple updated factors as the multiple target starting factors, return to the step of configuring the starting culture medium based on multiple target starting factors to obtain the updated biomass. Calculate the difference between the starting biomass and the updated biomass to obtain the biomass difference. Repeat step A until the biomass difference is negative, then stop step A and obtain the starting biomass when the biomass difference is negative as the undetermined optimal biomass. Multiple potential optimal influencing factors for determining the optimal biomass are obtained, and the factor content of multiple key influencing factors is optimized using these multiple potential optimal influencing factors to obtain the content of multiple optimized components.

5. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 4, characterized in that, The process involves optimizing the factor content of multiple key influencing factors using multiple undetermined optimal influencing factors, resulting in multiple optimized component contents, including: Extract one key impact factor from multiple key impact factors sequentially to obtain the target key factor, and then perform the following operations on the target key factor: By identifying the target key factor among multiple undetermined optimal impact factors, the intermediate key factors are obtained. Calculate the high critical factor and low critical factor based on the step size and medium critical factor, respectively. Low criticality factors, medium criticality factors, and high criticality factors are summarized separately to obtain multiple low criticality factors, multiple medium criticality factors, and multiple high criticality factors; An experimental matrix was constructed based on multiple low-criticality factors, multiple medium-criticality factors, and multiple high-criticality factors. Multiple key vectors were extracted from the experimental matrix, and multiple key culture media were constructed based on the multiple key vectors. Microbial culture operations were performed on all multiple key culture media, and biomass measurement operations were performed on all multiple key culture media after microbial culture operations to obtain multiple key biomass. By utilizing multiple key biomass and multiple key vectors, the content of multiple key influencing factors was optimized, resulting in the content of multiple optimized components.

6. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 5, characterized in that, The optimization of factor content for multiple key influencing factors using multiple key biomass and multiple key vectors yields multiple optimized component contents, including: An initial fitness function is constructed using several key influencing factors, as shown below: ， in, Indicates biomass. Indicates standard biomass. Indicates the first Index of key influencing factors This represents the total number of multiple key influencing factors. Indicates the first The coefficients of the first-order terms of the key influencing factors, Indicates the first The key influencing factors and the first The quadratic coefficients of the key influencing factors, Indicates the first The key influencing factors and the first The interaction coefficients of the key influencing factors, Indicates the first Index of key influencing factors Indicates the first One key influencing factor, Indicates the first One key influencing factor; The fitness function is obtained by performing parameter fitting on the initial fitness function based on multiple key vectors and multiple key biomass. The fitness function was used to optimize the factor content of several key influencing factors, resulting in the optimized component content.

7. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 6, characterized in that, The fitness function is used to optimize the factor content of multiple key influencing factors, resulting in multiple optimized component contents, including: The multiple undetermined optimal influence factors are used as multiple initial search centers, and the product of the step size and the pre-constructed proportion is calculated to obtain the search radius. An initial search space is constructed based on the multiple initial search centers and the search radius. The initial search space includes multiple initial search intervals, and each initial search interval corresponds one-to-one with an initial search center. Multiple key influencing factors are initialized based on multiple initial search intervals to obtain multiple initial search recipes, wherein the initial search recipes include multiple initial search factors; By using a fitness function and multiple initial search formulations, the content of multiple key influencing factors was optimized, resulting in the content of multiple optimized components.

8. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 7, characterized in that, The optimization of the component content of multiple key influencing factors is performed using a fitness function and multiple initial search formulations, resulting in multiple optimized component contents, including: Multiple initial biomasses for multiple initial search recipes are calculated using fitness functions, and standard deviations are calculated for multiple initial search recipes to obtain multiple standard deviations; Based on multiple initial biomass and multiple standard deviations, the formulation components of multiple initial search formulations are updated to obtain updated component formulations; The updated component formulation was validated, and the validation results were obtained. Based on the validation results, the updated component formulation was confirmed as having multiple optimized component contents.

9. The method for optimizing culture medium parameters based on steel slag leaching solution as described in claim 8, characterized in that, The verification of the updated component formulation, and the resulting verification results, include: Based on the updated component formulation and the components of the culture medium, updated culture medium and synthetic culture medium were respectively prepared; Microbial culture operations were performed on both the renewal culture medium and the synthetic culture medium to obtain the renewal culture process and the synthetic culture process. Biomass recording and product recording operations were performed on the renewal culture process and the synthesis culture process, respectively, to obtain a set of recording curves; Based on the set of recorded curves, calculate the growth rate set and the yield set respectively. Perform size comparison operation on the growth rate set and the yield set respectively to obtain the comparison result set. Record the comparison result set as the verification result.

10. A system for optimizing culture medium parameters based on steel slag leaching solution, characterized in that, The system includes: The leachate acquisition module is used to acquire microorganisms, obtain culture medium components from a pre-constructed microbial nutrient database based on the microorganisms, acquire a steel slag set, and perform leaching operations on the steel slag set based on the culture medium components to obtain multiple steel slag leachates. The leachate treatment module is used to sequentially extract one steel slag leachate from multiple steel slag leachates to obtain a target steel slag leachate. The following operations are performed on the target steel slag leachate: full-component quantitative analysis is performed on the target steel slag leachate to obtain multiple leachate components and their concentrations; a safety assessment is performed on the multiple leachate components and their concentrations to obtain the assessment results; if the assessment results indicate that pre-constructed harmful components are present in the target steel slag leachate, then the target steel slag leachate is subjected to safety treatment to obtain a treated leachate. The component matching module is used to perform feature matching operations based on the culture medium components on the treated leachate to obtain the contents of multiple supplementary components; The parameter optimization module is used to perform parameter optimization on the content of multiple supplementary components, obtain multiple optimized component contents, and construct the culture medium based on the multiple optimized component contents.

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