CN116738767A - Modeling method of process unit and generating method of process unit - Google Patents
Modeling method of process unit and generating method of process unit Download PDFInfo
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- PXHVJJICTQNCMI-UHFFFAOYSA-N nickel Substances [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 54
- 229910052759 nickel Inorganic materials 0.000 description 29
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 21
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- QAOWNCQODCNURD-UHFFFAOYSA-N Sulfuric acid Chemical compound OS(O)(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-N 0.000 description 2
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- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
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Abstract
The application discloses a modeling method of a process unit and a generating method of the process unit, wherein the modeling method of the process unit comprises the following steps: dividing the process flow according to the working procedures to obtain a plurality of process units to be built; constructing an equipment group model corresponding to each process unit, wherein the equipment group model comprises at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection; constructing a flow data configuration model, wherein the flow data configuration model configures the acquired flow data for the process unit; and constructing an environment data configuration model, wherein the environment data configuration model configures the acquired environment data for the process units, and completes modeling of the process units corresponding to each process to obtain a process unit digital model. The modeling method can model the process flow of the complex system, and simplifies the construction process of the complex production system.
Description
Technical Field
The present application relates to the field of nonferrous metal metallurgy, and in particular, to a modeling method of a process unit, a generating device of a process unit, a storage medium, and an electronic apparatus.
Background
In nonferrous metallurgy research and design, research on basic metallurgy and boundary metallurgy technology on microscopic level (molecular, atomic and ionic microscopic level) is relatively more, research on nonferrous metallurgy flow on macroscopic level is relatively less, and for a production system with relatively complex nonferrous metallurgy flow, insufficient flow research often causes that front and back process units cannot cooperatively and continuously operate, surplus coefficients of the process units are overlapped, bottleneck links are relatively more, the system is in a chaotic disordered state, normal production is often caused, production cannot be achieved, and even investment fails. Currently, the metallurgical process simulation method is very simple, generally only comprises material flow, information flow and the like, and cannot simulate a complex production system.
Disclosure of Invention
In view of the above, the present application provides a modeling method of a process unit and a generating method of a process unit, and is mainly aimed at solving the problem that the existing metallurgical process simulation method cannot simulate a complex production system.
To solve the above problems, the present application provides a modeling method of a process unit, including:
Dividing the process flow according to the working procedures to obtain a plurality of process units to be built;
constructing an equipment group model corresponding to each process unit, wherein the equipment group model comprises at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection;
constructing a flow data configuration model corresponding to each process unit, wherein the flow data configuration model configures acquired flow data for the process units, the flow data comprises inlet flow data and/or outlet flow data, and the data types in the flow data at least comprise one or more of material flow data, energy flow data, value flow data and information flow data;
and constructing an environment data configuration model corresponding to each process unit, wherein the environment data configuration model configures the acquired environment data for the process units, and the data types of the environment data comprise one or more of water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data, so as to complete the modeling of the process units corresponding to each process and obtain a process unit digital model.
Optionally, before modeling the process unit is completed to obtain a digital model of the process unit, the method further comprises:
determining a plurality of chemical reaction equations for realizing the functions according to the functions of the process units to be molded, and constructing and obtaining a process unit reaction set model;
a process unit field model is constructed, wherein the process unit field model is reaction parameter information for realizing each chemical reaction equation in the reaction set, and the reaction parameter information comprises: one or more of volume information, temperature information, pressure information, acid-base value information, substance information, concentration information, viscosity information, flow field flow velocity information and gradient information are stored.
Optionally, before modeling the process unit is completed to obtain a digital model of the process unit, the method further comprises:
analyzing the reaction parameter information by adopting a preset factor analysis method, and determining to obtain target reaction parameter information and a value range corresponding to the target reaction parameter information;
taking the target reaction parameter information as a system state variable to construct a system state function corresponding to the current process unit;
Determining a plurality of parameter values respectively corresponding to the system state variables in the value ranges;
and matching the parameter values to construct a system state set model meeting the preset design requirement.
Optionally, before modeling the process unit is completed to obtain a digital model of the process unit, the method further comprises:
constructing a process unit fault set model, wherein the process unit fault set model configures fault information of each functional device and pipeline for the process unit, and the fault information comprises: the fault type and maintenance information corresponding to the fault type occur;
the maintenance information includes: parking frequency information, distribution rule information and maintenance time information.
Optionally, before modeling the process unit is completed to obtain a digital model of the process unit, the method further comprises:
constructing a process unit clock model, wherein the process unit clock model configures time domain information and rhythm time information for the process unit;
the time domain information is the time that each of the flow data has elapsed from the start of entering the process unit to the end of exiting the process unit;
The rhythm time information is a time length corresponding to a basic action in a chemical reaction process for realizing the function, and the basic action is as follows: one or more of a predetermined quantitative reaction, a separation of a predetermined amount of a stream, and a leaching of a predetermined amount of a stream.
To solve the above problems, the present application provides a process unit generating method, which is a process unit digital model constructed by using the modeling method of a process unit, and the method includes:
acquiring first flow parameter information output by a preamble linking unit of the process unit and auxiliary flow parameter information corresponding to the process unit;
calculating based on the first flow parameter information and the auxiliary flow parameter information to obtain second flow parameter information input to the process unit;
calculating based on the second flow parameter information and the chemical reaction equations to obtain third flow parameter information of the output process unit;
and configuring the process unit based on each of the first flow parameter information, each of the second flow parameter information, and each of the third flow parameter information to generate a target process unit.
Optionally, the first flow parameter information, the second flow parameter information, and the third flow parameter information include: material flow data, energy flow data, value flow data, and information flow data;
wherein the material flow data includes: flow direction information of the material; the energy flow data includes: at least one of energy information of electric energy, steam energy and reaction heat; the value stream data includes: executing one or more of cost information and revenue information of the process unit, the information stream data comprising: one or more of material flow information, control operation information and detection information.
The present application provides a process unit generating device for solving the above problems, comprising:
input flow acquisition module: the method comprises the steps of acquiring first flow parameter information output by a preamble linking unit of the process unit and auxiliary flow parameter information corresponding to the process unit;
the calculation module: for performing calculation processing based on each of the first flow parameter information and each of the auxiliary flow parameter information to obtain each of the second flow parameter information input to the process unit;
the output flow obtaining module is used for: the calculating process is performed based on the second flow parameter information and the chemical reaction equations to obtain third flow parameter information of the output process unit;
The generation module is used for: for configuring the process unit based on each of the first flow parameter information, each of the second flow parameter information, and each of the third flow parameter information to generate a target process unit.
In order to solve the above-mentioned problems, the present application provides a storage medium storing a computer program which, when executed by a processor, implements the steps of the process unit modeling method and the steps of the process unit generating method described above.
The present application provides an electronic device for solving the above-mentioned problems, at least comprising a memory, a processor, the memory storing a computer program, the processor implementing the steps of the process unit modeling method and the steps of the process unit generating method described above when executing the computer program on the memory.
The method comprises the steps of dividing a process flow according to procedures to obtain a plurality of process units to be built; constructing an equipment group model corresponding to each process unit, wherein the equipment group model comprises at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection; constructing a flow data configuration model, wherein the flow data configuration model configures acquired flow data for the process unit, the flow data comprises inlet flow data and/or outlet flow data, and the data type in the flow data at least comprises one or more of substance flow data, energy flow data, value flow data and information flow data; and constructing an environment data configuration model, wherein the environment data configuration model configures the acquired environment data for the process unit, and the data types of the environment data comprise one or more of water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy replenishing data, so that the modeling of the process unit is completed, and a digital model corresponding to the process unit is obtained. The modeling method can model the technological process of a complex system and simplify the construction process of the complex production system by constructing the equipment group model, the flow data configuration model and the environment data configuration model to complete the construction of the process unit digital model.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flow chart of a modeling method for a process unit according to an embodiment of the present application;
FIG. 2 is a flow chart of a modeling method for a process unit according to yet another embodiment of the present application;
FIG. 3 shows a flow chart of a laterite-nickel ore hydrometallurgical process incorporating process elements of the application;
FIG. 4 shows a graph of the effect of temperature on nickel leaching in an embodiment of the present application;
FIG. 5 shows a graph of the effect of acid mine comparison on nickel leaching in an embodiment of the present application;
FIG. 6 shows a graph of the effect of liquid-solid ratios on nickel leaching effects for an embodiment of the present application;
FIG. 7 is a graph showing the effect of incubation time on nickel leaching in an embodiment of the present application;
FIG. 8 shows a graph of the effect of ore particle size on nickel leaching effect for an embodiment of the present application;
FIG. 9 is a flow chart of a process unit generation method according to another embodiment of the present application;
fig. 10 is a block diagram showing a process unit generating apparatus according to another embodiment of the present application.
Detailed Description
Various aspects and features of the present application are described herein with reference to the accompanying drawings.
It should be understood that various modifications may be made to the embodiments of the application herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of the application will occur to persons of ordinary skill in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and, together with a general description of the application given above, and the detailed description of the embodiments given below, serve to explain the principles of the application.
These and other characteristics of the application will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It is also to be understood that, although the application has been described with reference to some specific examples, those skilled in the art can certainly realize many other equivalent forms of the application.
The above and other aspects, features and advantages of the present application will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the application, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the application.
An embodiment of the present application provides a modeling method of a process unit, as shown in fig. 1, including:
step S101: dividing the process flow according to the working procedures to obtain a plurality of process units to be built;
in the specific implementation process, the function, the production capacity parameter information and the like of each process unit are determined based on the basic metallurgy of a microcosmic level and the professional metallurgy technology of a boundary level in nonferrous metal metallurgy application. The process unit is a process step, i.e. procedure, in the nonferrous metal metallurgy process. The function of a process unit refers to the specific function that is achieved in the nonferrous metallurgical production process, such as: comparing the functions of mineral or element leaching, washing, purifying, enriching, separating, oxidizing, reducing, refining, electrolyzing and the like. Specifically, the metallurgical process is divided into process units according to the sequence of the process steps according to the process flow, and each process unit corresponds to one procedure.
Step S102: constructing an equipment group model corresponding to each process unit, wherein the equipment group model comprises at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection;
in the specific implementation process, a functional equipment group model is built, and the functional equipment group model is configured according to the function and the production capacity parameter information of a process unit to be built, wherein the equipment group model comprises at least one intelligent equipment, and the connection mode among the intelligent equipment is serial or parallel; specifically, the type of the intelligent agent device and the number of intelligent agent devices corresponding to the target process unit are determined according to the function and the production capacity parameter information of the target process unit.
Step S103: constructing a flow data configuration model corresponding to each process unit, wherein the flow data configuration model configures acquired flow data for the process units, the flow data comprises inlet flow data and/or outlet flow data, and the data types in the flow data at least comprise one or more of material flow data, energy flow data, value flow data and information flow data;
in a specific implementation process of this step, the material flow data includes: flow direction information of the material; the energy flow data includes: energy information such as electric energy, steam energy, reaction heat and the like; the value stream data includes: and executing the cost information, the income information and the like of the process unit, wherein the information flow data comprises the following information: material flow information, control operation information, detection information, and the like.
Step S104: and constructing an environment data configuration model corresponding to each process unit, wherein the environment data configuration model configures the acquired environment data for the process units, and the data types of the environment data comprise one or more of water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data, so that the modeling of the process units is completed, and a process unit digital model is obtained.
In the implementation process of the step, the environmental data configuration model configures the acquired environmental data for the process unit, wherein the data type of the environmental data comprises at least one of the following: water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data. For example, the environmental data configuration model may configure water supply data; heating data and power supply data can also be configured; water supply data, heat supply data, power supply data, steam supply data and material data can also be configured.
The modeling method can model the technological process of a complex system and simplify the construction process of the complex production system by constructing the equipment group model, the flow data configuration model and the environment data configuration model to complete the construction of the process unit digital model.
Yet another embodiment of the present application provides another method of modeling a process unit, as shown in FIG. 2, comprising:
step S201: dividing the process flow according to the working procedures to obtain a plurality of process units to be built;
in the specific implementation process, in nonferrous metal metallurgy application, the functions, the production capacity parameter information and the like of each process unit are determined based on basic metallurgy at a microscopic level and professional metallurgy technology at a boundary level. The process unit is a process step, i.e. procedure, in the nonferrous metal metallurgy process. The function of a process unit refers to the specific function that is achieved in the nonferrous metallurgical production process, such as: comparing the functions of mineral or element leaching, washing, purifying, enriching, separating, oxidizing, reducing, refining, electrolyzing and the like. Specifically, the metallurgical process is divided into process units according to the sequence of the process steps according to the process flow, and each process unit corresponds to one process step, namely a working procedure.
Step S202: constructing an equipment group model corresponding to each process unit, wherein the equipment group model comprises at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection;
in the specific implementation process, the type of the intelligent agent equipment and the number of the intelligent agent equipment corresponding to the process unit are determined according to the function and the production capacity parameter information of the process unit. For example: the three-stage preheating process unit determines that the type of the intelligent body equipment of the three-stage preheating process unit is a preheater and three intelligent body equipment preheaters form the equipment group aiming at the three-stage preheating function, and the three preheaters are connected in series; aiming at the functions of the ore grinding process units and the production capacity parameter information of the ore grinding process units, the type of the intelligent body equipment corresponding to the ore grinding process units is determined to be ore grinding equipment such as a ball mill, and the number of the ore grinding equipment is one, so that the equipment group of the ore grinding process units is a ball mill.
Step S203: constructing a flow data configuration model corresponding to each process unit, wherein the flow data configuration model configures acquired flow data for the process units, the flow data comprises inlet flow data and/or outlet flow data, and the data types in the flow data at least comprise one or more of material flow data, energy flow data, value flow data and information flow data;
In the specific implementation process, firstly, a material flow model is constructed; secondly, constructing an energy flow model and a value flow model according to the material flow; and finally, constructing an information flow model according to the material flow, the energy flow and the value flow. The material flow data includes: flow direction information of the material; the energy flow data includes: energy information such as electric energy, steam energy, reaction heat and the like; the value stream data includes: and executing the cost information, the income information and the like of the process unit, wherein the information flow data comprises the following information: material flow information, control operation information, detection information, and the like. The existing logistics simulation and system simulation software can perform flow simulation of material flow and limited information flow, but cannot meet the actual requirements of information flow, energy flow and value flow simulation. The modeling method fully considers the material flow, the energy flow, the value flow and the information flow, and can digitally display various flow data information.
Step S204: constructing an environment data configuration model corresponding to each process unit, wherein the environment data configuration model configures the acquired environment data for the process units, and the data types of the environment data comprise one or more of water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data;
In the implementation process of the step, the environmental data configuration model configures the acquired environmental data for the process unit, wherein the data type of the environmental data comprises at least one of the following: water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data. For example, the environmental data configuration model may configure water supply data; heating data and power supply data can also be configured; water supply data, heat supply data, power supply data, steam supply data and material data can also be configured.
Step S205: determining a plurality of chemical reaction equations for realizing the functions according to the functions of the process units to be molded, and constructing and obtaining reaction set models corresponding to the process units respectively;
in the implementation process, determining a plurality of chemical reaction equations for realizing the functions of the process units; for example: the metallurgical chemical reaction equation for the high pressure acid leaching process unit includes:
and constructing and obtaining a process unit reaction set model corresponding to the high-pressure acid leaching process unit according to each chemical reaction equation.
Step S206: constructing a process unit field model corresponding to each process unit, wherein the process unit field model is reaction parameter information for realizing each chemical reaction equation in the reaction set, and the reaction parameter information comprises: storing one or more of volume information, temperature information, pressure information, acid-base value information, substance information, concentration information, viscosity information, flow field flow velocity information and gradient information;
In the implementation process of the step, the reaction parameter information comprises: and storing information such as volume information, temperature information, pressure information, acid-base value information, substance information, concentration information, viscosity information, flow field flow velocity information, gradient information and the like. According to different functions, the information of each reaction parameter contained in the reaction data set is different.
Step S207: constructing a system state set model corresponding to each process unit respectively;
in the specific implementation process, the construction of the system state set model comprises the following steps:
step A: analyzing the reaction parameter information by adopting a preset factor analysis method, and determining to obtain target reaction parameter information and a value range corresponding to the target reaction parameter information;
in the specific implementation process of the step, the preset factor analysis method can be a theoretical derivation method, an experimental method, a production actual measurement data method, a neural network and the like. Each target influencing factor may be determined experimentally. For example: figure 3 shows a flow chart of the laterite-nickel ore hydrometallurgical process of the application, including the various process elements. In the laterite-nickel ore wet smelting, a single-factor experiment method is adopted to determine that the process unit is the target influencing factor of the high-pressure acid leaching process, and the specific process is as follows: first,: and determining the influence of the temperature on leaching by adopting a single factor analysis method. With the rise of the temperature, the leaching rate of nickel is obviously improved, which indicates that the improvement of the temperature is favorable for increasing the leaching rate of nickel, and when the leaching temperature is 260 ℃, the leaching rate of nickel reaches 95.6 percent. Meanwhile, as the temperature rises, the leaching rate of iron is reduced, and after the leaching temperature reaches 230 ℃, the leaching rate of iron is lower than 5%, and the concentration of iron in the solution is lower. The temperature is increased, which is favorable for selectively and directionally leaching laterite-nickel ore, namely, the leaching rate of nickel can be increased, and the leaching rate of iron can be inhibited. The effect curve of temperature on nickel leaching effect is shown in fig. 4. Then: and the influence of the acid-ore ratio on leaching is determined by adopting a single-factor analysis method, the acid-ore ratio is an important factor influencing the nickel leaching rate, and the nickel leaching rate can be improved by increasing the acid-ore ratio. When the acid-ore ratio is increased from 0.4:1 to 0.5:1, the leaching rate is greatly increased, when the acid-ore ratio is 0.65:1, the nickel leaching rate reaches the highest value of 97.1%, and the leaching rate of iron is also increased along with the increase of the acid-ore ratio, but at a lower level. The acid consumption in the pressure leaching process is mainly determined by the magnesium content in the ore, enough sulfuric acid is added for completely leaching nickel in the ore, but the cost is increased due to the excessively high acid-ore ratio, and the excessive free acid in the leaching solution is also unfavorable for the hydrolysis of Fe and the subsequent treatment of the leaching solution. In the experiment, the leaching rate of nickel reaches 95% when the acid-ore ratio is 0.6:1, the leaching rate is only improved from 95% to 97.1% when the acid-ore ratio is improved from 0.6:1 to 0.5:1, and the optimal acid-ore ratio is selected as 0.6:1 in consideration of the cost and the acidity of the leaching solution. The effect curve of acid mine comparison on nickel leaching effect is shown in fig. 5. Further, a single factor analysis method is used to determine the effect of the liquid-solid ratio on leaching. The leaching rate of nickel is reduced along with the increase of the liquid-solid ratio, because the increase of the liquid-solid ratio reduces free acid in the solution, which is unfavorable for the leaching of nickel, and the leaching rate of iron is reduced along with the increase of the liquid-solid ratio, on the one hand, because the increase of the liquid-solid ratio reduces acidity, which is unfavorable for the leaching of iron, and on the other hand, because the low acidity is favorable for the hydrolysis of iron, so that the total leaching rate of iron is reduced. The effect curve of the liquid-solid ratio on the leaching effect of nickel is shown in fig. 6. Further, a single factor analysis method was used to determine the effect of time on leaching. The leaching rate of nickel and iron is not changed greatly with the increase of time, which indicates that the leaching reaction is performed quickly under the high-temperature and high-pressure conditions, and only enough reaction time is ensured. The effect of incubation time on nickel leaching effect is shown in figure 7. Further, a single factor analysis method was used to determine the effect of ore particle size on leaching. The leaching rate is not greatly influenced by the ore degree, the leaching rate in each degree range can basically reach 90%, and the nickel leaching rate is not influenced as long as the granularity is within a certain range. The effect curve of ore particle size on nickel extraction is shown in fig. 8. As shown by a single factor test, the main influencing factors of the high-pressure leaching are temperature, acid-ore ratio and liquid-solid ratio which are 3. The time and the ore granularity can meet a certain range, the time is more than 1h, and the ore granularity is-0.075 mm. According to the test conclusion and the process requirement, determining the temperature, the acid-ore ratio, the liquid-solid ratio, the pressure, the volume of the autoclave and the stirring speed as all the intermediate influencing factors. The value range of each intermediate influencing factor is as follows: the temperature T=250-270 ℃, the acid-ore ratio K=200-230 Kg/T ore, the liquid-solid ratio F=1.5:1-3:1, the pressure P=4-5 MPa, the autoclave volume V=800 m3, the diameter 5.6m, the length 42m, the stirring speed n=80-120 revolutions per minute, the time T >1h, the ore granularity: -0.075mm. Screening the intermediate influencing factors to obtain a plurality of target influencing factors for determining the state simulation parameters of the target system; specifically, the intermediate influencing factors determined by the single factor analysis method are screened to obtain the main influencing factors of the nickel leaching rate: temperature, acid-ore ratio, liquid-solid ratio to obtain the target influencing factors.
And (B) step (B): taking the target reaction parameter information as a system state variable to construct a system state function corresponding to the current process unit;
in the implementation process of this step, the system status function may be represented by the following formula 1:
（1）
where S is the system state, and where S is the system state,representing the nickel leaching rate, the system state can be represented by the following equation 2:
（2）
wherein T represents temperature, K represents acid-ore ratio, F represents liquid-solid ratio, T, K, F is a target influencing factor, and in practical application, the types and the amounts of the target influencing factors of nonferrous metal metallurgy processes of different ores are different, and the state variables and the amounts of the system need to be adjusted according to the practical metallurgy process.
Step C: determining a plurality of parameter values respectively corresponding to the system state variables in the value ranges;
in the specific implementation process, comprehensive tests are carried out according to the target influence factor temperature, the acid-ore ratio and the liquid-solid ratio selected by the single factor experiment. Experimental data are shown in table 1:
TABLE 1
And carrying out 27 groups of tests, wherein each group of test results corresponds to one system state, the total number of the system states is 27, and the specific function value in each system state is calculated by adopting an interpolation method according to the actual values of variables such as temperature, acid-mine ratio, liquid-solid ratio and the like. The 27 sets of test data are shown in table 2:
TABLE 2
Step D: and matching the parameter values to construct a system state set model meeting the preset design requirement.
Specifically, determining from the system state information according to each leaching rate result to obtain a target system state model meeting the preset design requirement.
Step S208: constructing a fault set model corresponding to each process unit, wherein the fault set model configures fault information of each functional device and pipeline for the process unit, and the fault information comprises: the fault type and maintenance information corresponding to the fault type occur;
in the specific implementation process, the fault set is a set of various possible faults and corresponding parameter information such as frequency, distribution rule, overhaul time and the like. For example, the autoclave failure set is shown in table 3 below for an autoclave acid leaching process unit:
TABLE 3 Table 3
Step S209: and constructing clock models respectively corresponding to the process units, wherein the process unit clock models are used for configuring time domain information and rhythm time information for the process units, and modeling the process units corresponding to the working procedures is completed to obtain a process unit digital model.
In a specific implementation of this step, the time domain information is the time point at which the unit material flow leaves the current process unit minus the time point at which the unit material flow enters the current sub-process unit. The process unit time domain information comprises a plurality of rhythm times. The cadence time is the time required for the process unit to complete a primary action of nonferrous metallurgy, which may be a quantity of chemical reaction, a primary action may be a quantity of separation of a substance flow, a primary action may be a quantity of leaching of a substance flow, etc. And establishing a time sequence by taking the rhythm time as a basic unit time, namely triggering one nonferrous metallurgy action for each rhythm time to promote the nonferrous metallurgy process. To complete the modeling of the process unit corresponding to each of the procedures, and to obtain a process unit digital model.
The modeling of the process unit is completed through constructing an equipment group model, constructing a flow data configuration model, constructing an environment data configuration model, constructing a reaction set model, constructing a process unit field model, constructing a system state set model, constructing a process unit fault set model and constructing a process unit clock model, so as to obtain a process unit digital model. The modeling method can model the process flow of the complex system, simplifies the construction process of the complex production system, comprehensively considers various factors to model the process unit, and ensures that the constructed model is more accurate.
In yet another embodiment of the present application, a process unit generation method is provided that uses a process unit digital model constructed in accordance with the modeling method of a process unit described above. As shown in fig. 9, includes:
step S301: acquiring first flow parameter information output by a preamble linking unit of the process unit and auxiliary flow parameter information corresponding to the process unit;
in the implementation process, each piece of first flow parameter information is material flow data, energy flow data, information flow data and value flow data which are output to the process unit after being processed by the preamble link unit; the auxiliary flow parameter information is auxiliary material flow data added for realizing the functions of the process unit, wherein the auxiliary material flow data comprises material flow data of water, a catalyst, an oxidant and the like.
Step S302: calculating based on the first flow parameter information and the auxiliary flow parameter information to obtain second flow parameter information input to the process unit;
in the implementation process, an auxiliary material flow is added to the process unit, and the auxiliary material flow is combined with the material flow output by the preamble link unit of the process unit to obtain each second flow parameter information input to the process unit.
Step S303: calculating based on the second flow parameter information and the chemical reaction equations to obtain third flow parameter information of the output process unit;
in the implementation process, determining the material flow information produced when each chemical reflection equation reaches an equilibrium state based on the second flow parameter information and each chemical reaction equation, and obtaining the material flow information in the third flow parameter information; calculating the produced material flow information to obtain value flow data in the third flow parameter information; and finally, obtaining information flow data in the third flow parameter information from the material flow information, the energy flow information and the value flow information.
Step S304: and configuring the process unit based on each of the first flow parameter information, each of the second flow parameter information, and each of the third flow parameter information to generate a target process unit.
In this step, in a specific implementation process, the first flow parameter information, the second flow parameter information, and the third flow parameter information include: material flow data, energy flow data, value flow data, and information flow data; wherein the material flow data includes: flow direction information of the material; the energy flow data includes: energy information such as electric energy, steam energy, reaction heat and the like; the value stream data includes: and executing the cost information, the income information and the like of the process unit, wherein the information flow data comprises the following information: material flow information, control operation information, detection information, and the like.
Yet another embodiment of the present application provides a process unit generating device, as shown in fig. 10, comprising:
input flow acquisition module 1: the method comprises the steps of acquiring first flow parameter information output by a preamble linking unit of the process unit and auxiliary flow parameter information corresponding to the process unit;
calculation module 2: for performing calculation processing based on each of the first flow parameter information and each of the auxiliary flow parameter information to obtain each of the second flow parameter information input to the process unit;
Output flow obtaining module 3: the calculating process is performed based on the second flow parameter information and the chemical reaction equations to obtain third flow parameter information of the output process unit;
generating module 4: for configuring the process unit based on each of the first flow parameter information, each of the second flow parameter information, and each of the third flow parameter information to generate a target process unit.
In a specific implementation process, the first flow parameter information, the second flow parameter information, and the third flow parameter information include: material flow data, energy flow data, value flow data, and information flow data; wherein the material flow data includes: flow direction information of the material; the energy flow data includes: at least one of energy information of electric energy, steam energy and reaction heat; the value stream data includes: executing one or more of cost information and revenue information of the process unit, the information stream data comprising: one or more of material flow information, control operation information and detection information.
Another embodiment of the present application provides a storage medium storing a computer program which, when executed by a processor, performs the method steps of:
Dividing the process flow according to the working procedures to obtain a plurality of process units to be built;
step two, constructing equipment group models respectively corresponding to the process units, wherein the equipment group models comprise at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection;
step three, constructing a flow data configuration model corresponding to each process unit, wherein the flow data configuration model configures acquired flow data for the process units, the flow data comprises inlet flow data and/or outlet flow data, and the data types in the flow data at least comprise one or more of material flow data, energy flow data, value flow data and information flow data;
and fourthly, constructing an environment data configuration model corresponding to each process unit, wherein the environment data configuration model configures the acquired environment data for the process units, and the data types of the environment data comprise one or more of water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data, so that modeling of the process units corresponding to each process is completed, and a process unit digital model is obtained.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The specific implementation process of the above method steps may refer to the embodiment of the modeling method of any process unit, and this embodiment is not repeated here.
The modeling of the process unit is completed by constructing a functional equipment group model, a flow data configuration model, an environment data configuration model, a reaction set model, a process unit field model, a system state set model, a process unit fault set model and a process unit clock model, so as to obtain a process unit digital model. The modeling method can model the process flow of the complex system, simplifies the construction process of the complex production system, comprehensively considers various factors to model the process unit, and ensures that the constructed model is more accurate.
Another embodiment of the present application provides an electronic device, which may be a server, that includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes non-volatile and/or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external client through a network connection. The electronic device program, when executed by a processor, performs a function or step on the service side of a modeling method for a process unit.
In one embodiment, an electronic device is provided, which may be a client. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external server through a network connection. The electronic device program, when executed by a processor, performs a function or step of a modeling method client side of a process unit.
Another embodiment of the present application provides an electronic device, at least including a memory, a processor, where the memory stores a computer program, and the processor when executing the computer program on the memory implements the following method steps:
dividing the process flow according to the working procedures to obtain a plurality of process units to be built;
step two, constructing equipment group models respectively corresponding to the process units, wherein the equipment group models comprise at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection;
step three, constructing a flow data configuration model corresponding to each process unit, wherein the flow data configuration model configures acquired flow data for the process units, the flow data comprises inlet flow data and/or outlet flow data, and the data types in the flow data at least comprise one or more of material flow data, energy flow data, value flow data and information flow data;
and fourthly, constructing an environment data configuration model corresponding to each process unit, wherein the environment data configuration model configures the acquired environment data for the process units, and the data types of the environment data comprise one or more of water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data, so that modeling of the process units corresponding to each process is completed, and a process unit digital model is obtained.
The specific implementation process of the above method steps may refer to the embodiment of the modeling method of any process unit, and this embodiment is not repeated here.
The modeling of the process unit is completed by constructing a functional equipment group model, a flow data configuration model, an environment data configuration model, a reaction set model, a process unit field model, a system state set model, a process unit fault set model and a process unit clock model, so as to obtain a process unit digital model. The modeling method can model the process flow of the complex system, simplifies the construction process of the complex production system, comprehensively considers various factors to model the process unit, and ensures that the constructed model is more accurate.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements of this application will occur to those skilled in the art, and are intended to be within the spirit and scope of the application.
Claims (10)
1. A method of modeling a process unit, comprising:
Dividing the process flow according to the working procedures to obtain a plurality of process units to be built;
constructing an equipment group model corresponding to each process unit, wherein the equipment group model comprises at least one intelligent body equipment, and the connection mode among the intelligent body equipment is serial connection or parallel connection;
constructing a flow data configuration model corresponding to each process unit, wherein the flow data configuration model configures acquired flow data for the process units, the flow data comprises inlet flow data and/or outlet flow data, and the data types in the flow data at least comprise one or more of material flow data, energy flow data, value flow data and information flow data;
and constructing an environment data configuration model corresponding to each process unit, wherein the environment data configuration model configures the acquired environment data for the process units, and the data types of the environment data comprise one or more of water supply data, heat supply data, power supply data, steam supply data, material data, energy data and kinetic energy supply data, so as to complete the modeling of the process units corresponding to each process and obtain a process unit digital model.
2. The method of claim 1, wherein prior to completing modeling the process unit to obtain a digital model of the process unit, the method further comprises:
determining a plurality of chemical reaction equations for realizing the functions according to the functions of the process units to be molded, and constructing and obtaining a process unit reaction set model;
a process unit field model is constructed, wherein the process unit field model is reaction parameter information for realizing each chemical reaction equation in the reaction set, and the reaction parameter information comprises: one or more of volume information, temperature information, pressure information, acid-base value information, substance information, concentration information, viscosity information, flow field flow velocity information and gradient information are stored.
3. The method of claim 2, wherein prior to completing modeling the process unit to obtain a digital model of the process unit, the method further comprises:
analyzing the reaction parameter information by adopting a preset factor analysis method, and determining to obtain target reaction parameter information and a value range corresponding to the target reaction parameter information;
taking the target reaction parameter information as a system state variable to construct a system state function corresponding to the current process unit;
Determining a plurality of parameter values respectively corresponding to the system state variables in the value ranges;
and matching the parameter values to construct a system state set model meeting the preset design requirement.
4. The method of claim 1, wherein prior to completing modeling the process unit to obtain a digital model of the process unit, the method further comprises:
constructing a process unit fault set model, wherein the process unit fault set model configures fault information of each functional device and pipeline for the process unit, and the fault information comprises: the fault type and maintenance information corresponding to the fault type occur;
the maintenance information includes: parking frequency information, distribution rule information and maintenance time information.
5. The method of claim 2, wherein prior to completing modeling the process unit to obtain a digital model of the process unit, the method further comprises:
constructing a process unit clock model, wherein the process unit clock model configures time domain information and rhythm time information for the process unit;
the time domain information is the time that each of the flow data has elapsed from the start of entering the process unit to the end of exiting the process unit;
The rhythm time information is a time length corresponding to a basic action in a chemical reaction process for realizing the function, and the basic action is as follows: one or more of a predetermined quantitative reaction, a separation of a predetermined amount of a stream, and a leaching of a predetermined amount of a stream.
6. A process unit generation method, characterized by a process unit digital model constructed using the modeling method of a process unit according to claim 5, the method comprising:
acquiring first flow parameter information output by a preamble linking unit of the process unit and auxiliary flow parameter information corresponding to the process unit;
calculating based on the first flow parameter information and the auxiliary flow parameter information to obtain second flow parameter information input to the process unit;
calculating based on the second flow parameter information and the chemical reaction equations to obtain third flow parameter information of the output process unit;
and configuring the process unit based on each of the first flow parameter information, each of the second flow parameter information, and each of the third flow parameter information to generate a target process unit.
7. The process unit generation method of claim 6, wherein the first flow parameter information, second flow parameter information, and third flow parameter information comprise: material flow data, energy flow data, value flow data, and information flow data;
wherein the material flow data includes: flow direction information of the material; the energy flow data includes: at least one of energy information of electric energy, steam energy and reaction heat; the value stream data includes: executing one or more of cost information and revenue information of the process unit, the information stream data comprising: one or more of material flow information, control operation information and detection information.
8. A process unit generation apparatus, comprising:
input flow acquisition module: the method comprises the steps of acquiring first flow parameter information output by a preamble linking unit of the process unit and auxiliary flow parameter information corresponding to the process unit;
the calculation module: for performing calculation processing based on each of the first flow parameter information and each of the auxiliary flow parameter information to obtain each of the second flow parameter information input to the process unit;
The output flow obtaining module is used for: the calculating process is performed based on the second flow parameter information and the chemical reaction equations to obtain third flow parameter information of the output process unit;
the generation module is used for: for configuring the process unit based on each of the first flow parameter information, each of the second flow parameter information, and each of the third flow parameter information to generate a target process unit.
9. A storage medium storing a computer program which, when executed by a processor, performs the steps of the process unit modeling method of any of the preceding claims 1-5 and the steps of the process unit generation method of any of the claims 6-7.
10. An electronic device comprising at least a memory, a processor, the memory having stored thereon a computer program, the processor, when executing the computer program on the memory, implementing the steps of the process unit modeling method of any of the preceding claims 1-5 and the steps of the process unit generation method of any of the claims 6-7.
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