CN110716516A - Intelligent chemical system based on material selection - Google Patents
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- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention provides an intelligent chemical system based on material selection, which comprises a front-end display module, a front-end display module and a control module, wherein the front-end display module is used for displaying the current requirements and the selected corresponding material related information in real time; a control module, the control module comprising: the demand unit is input into the control module according to an actual chemical engineering result; the material modeling unit is used for selecting a plurality of corresponding materials meeting preset conditions according to requirements; the processing module is used for acquiring various specific characteristic materials meeting the requirements according to the requirements of users; and the optimization module is used for obtaining a certain final material as an optimal material according to the material selection result. The intelligent chemical system based on material selection establishes a two-dimensional database by the material performance and the corresponding numerical value, inputs two-dimensional matrix information based on the material performance according to the user requirement in the selection process, and compares and optimizes the selected material selection value through a weighting and iteration algorithm to obtain the material type corresponding to the optimal material selection value.
Description
Technical Field
The invention relates to the technical field of intelligent chemical control, in particular to an intelligent chemical system based on material selection.
Background
In the existing chemical production and manufacturing process, when a specific material is produced, the technical process and optional equipment are fixed, the material selection is also fixed, and enterprises can only select the corresponding material when producing a specific product or adopting a specific process.
Chinese patent publication No.: CN107315399B discloses an automatic control system and control method based on artificial intelligence, it includes chemistry pilot plant, chemical raw materials warehouse, first mobile device, transfer car (buggy) and controlling means: the chemical pilot plant comprises a plurality of pilot plant posts; the chemical raw material warehouse comprises a plurality of raw material storage sites, the chemical raw material warehouse is connected with the chemical pilot plant by a first rail, and a traveling path is formed between one raw material storage site and the upstream end of the first rail; the first moving device is movably arranged on the first track; the transfer trolley can move along the trajectory route; the control device is respectively connected with the first moving device and the transfer trolley in a control mode. Can couple together chemical raw materials warehouse and chemical pilot plant through the setting of first track, first mobile device can send the raw materials in the warehouse directly to in the chemical pilot plant.
The intelligent factory scheme can only solve the problem of butt joint of chemical raw materials in a pilot plant test process, and cannot solve a systematic intelligent chemical scheme based on raw material selection.
Disclosure of Invention
In view of this, the invention provides an intelligent chemical system based on material selection, and aims to solve the technical problem that an intelligent chemical scheme based on material selection cannot be realized in the prior art.
The invention provides an intelligent chemical system based on material selection, which comprises,
the front-end display module is used for displaying the current requirements and the selected corresponding material related information in real time;
a control module, the control module comprising:
the demand unit is input into the control module according to an actual chemical engineering result;
the material modeling unit is used for selecting a plurality of corresponding materials meeting preset conditions according to requirements; the processing module is used for acquiring various specific characteristic materials meeting the requirements according to the requirements of users; the optimization module is used for obtaining a final certain material as an optimal material according to a material selection result;
the material modeling unit sets a material performance matrix F1 according to the type and performance of a material and a numerical value corresponding to certain performance when modeling, the demand unit inputs a corresponding material selection value A (k, M), a large number of material types are selected within a certain range based on the material selection value A in the process of material selection, the optimization module sets a basic material selection value A (k, N), current line material selection value information is compared with data information of the basic material selection value, and an optimal material is obtained by introducing an increment and weighting algorithm.
Further, a material matrix W is arranged in the material modeling unit, various materials are classified according to attributes in the material modeling unit, and a material selection value a and a material matrix W (C1, C2, C3 and C4) are set, wherein C1 represents a metal material, C2 represents an inorganic non-metal material, C3 represents a high molecular material, C4 represents a composite material, and a metal material matrix C1j, an inorganic non-metal material matrix C2j, a high molecular material matrix C3j and a composite material matrix C4j are respectively formed, wherein j represents a serial number, and each type represents one material; in the polymer material matrix C3j, j is 1,2,3, C31i represents a plastic material, i represents a serial number, for example, C311 represents polyethylene; c32i denotes a rubber material, C33i denotes a fiber material;
the material modeling unit forms a material matrix W (C1ji, C2ji, C3ji, C4ji) for each material, and quantifies a performance of each material, determining a material selection value a.
Further, the material modeling unit sets a material performance matrix F1 and a material function matrix F2, the material performance matrix F1(X1, X2, X3, X4, X5, X6), where X1 represents thermal performance, X2 represents mechanical performance, X3 represents electrical performance, X4 represents magnetic performance, X5 represents optical performance, and X6 represents chemical performance.
Further, the material modeling unit sets a thermal performance matrix X1 representing a specific parameter based on the thermal performance, and sets quantitative values, and a thermal performance matrix X (1i, a1) in which i represents a serial number, a1 represents a corresponding thermal performance value, X (11, a1) represents a heat capacity value of a corresponding certain material, X (12, a1) represents a heat conductivity value of a corresponding certain material, X (13, a1) represents a heat melting value of a corresponding certain material, X (14, a1) represents a heat expansion value of a corresponding certain material, and X (15, a1) represents a boiling point value of a corresponding certain material.
Further, the material modeling unit sets a mechanical property matrix X (2i, a2), where i represents a serial number, a2 represents a corresponding mechanical property value, X (21, a2) represents an elastic modulus value of a corresponding certain material, X (22, a2) represents a tensile strength value of the corresponding certain material, X (23, a2) represents an impact strength value of the corresponding certain material, X (24, a2) represents a yield strength value of the corresponding certain material, and X (25, a2) represents a fatigue strength value of the corresponding certain material.
Further, the material modeling unit sets an electrical property matrix X (3i, A3), wherein i represents a serial number, A3 represents a corresponding electrical property value, X (31, A3) represents a conductivity value of a corresponding certain material, X (32, A3) represents a resistivity value of the corresponding certain material, X (31, A3) represents a dielectric property value of the corresponding certain material, and X (31, A3) represents a breakdown voltage value of the corresponding certain material.
Further, the material modeling unit sets a magnetic performance matrix X (4i, a4), where i denotes a serial number, a4 denotes a corresponding magnetic value, X (41, a4) denotes a paramagnetic value of a corresponding certain material, which has paramagnetic properties and has a definite value, X (42, a4) denotes a diamagnetic value of the corresponding certain material, and X (43, a4) denotes a ferromagnetic value of the corresponding certain material.
Further, the material modeling unit sets an optical performance matrix X (5i, a5), i denotes a serial number, a5 denotes a corresponding optical parameter value, X (51, a5) denotes a reflection parameter value of light of a corresponding certain material, X (52, a5) denotes a refraction parameter value of light of a corresponding certain material, X (53, a5) denotes an absorption parameter value of light of a corresponding certain material, X (54, a5) denotes a projection parameter value of light of a corresponding certain material, X (55, a5) denotes a luminescence parameter value of light of a corresponding certain material, and X (55, a5) denotes a fluorescence parameter value of light of a corresponding certain material.
Further, the material modeling unit sets a chemical property matrix (6i, a6), i represents a serial number, a6 represents a corresponding chemical property parameter value, X (61, a6) represents a corresponding light corrosion resistance value of a certain material, X (62, a6) represents a corresponding light catalytic resistance value of a certain material, and X (63, a6) represents a corresponding light ion exchange property value of a certain material.
Further, the optimization module sets a basic material selection value A (k, N), compares the current material selection value information with the data information of the basic material selection value, the basic material selection value can be determined according to the existing database and is obtained by arranging according to the data of the materials in advance,
wherein the content of the first and second substances,
a comparison value, M, of data information representing material selection value information and said base material selection valueiCorresponding database information representing the ith material, NjMaterial selection value F, U representing a certain existing materialijThe relevance of the data information of the table basic material selection value and the ith basic database is shown, i represents the number of the basic material selection value, d represents the correction coefficient, and the value of d is 0.998; in this embodiment, a basis function a (ki, Mi) is determined, i represents a serial number, i represents a basic numerical value corresponding to each of i material properties, and Mi represents a corresponding basic value;
gain value of gain value A of material performance
The degree of association CijCalculated by the following formula:
wherein x represents the base data in the ith base database, y represents the existing data of the base database j, MiRepresenting the amount of data of the ith base database, NjA material selection value A representing data information of an existing base database;
step 1, when M isi＞NjWhen the temperature of the water is higher than the set temperature,
an output value of
When M isi≤NjWhen the temperature of the water is higher than the set temperature,
the output values of (1) are all 0;
step 2, if
If the output value is 0, directly replacing the next basic database, repeating the step 1 for comparison, and finally obtaining the final result
When the output value of (2) is not 0, entering the next step;
step 3, when
When the output value is not 0, the database processing module will
Storing the output value of (1) when the output value is not less thanWhen a function value zeta is set, establishing a base material performance database according to the base material selection value at the moment; and if the output function is smaller than the preset function value zeta, returning to the step 1 to reselect the base material selection value until the output function is not smaller than the preset function value zeta.
Compared with the prior art, the intelligent chemical system based on material selection has the advantages that the two-dimensional database is established by the material performance and the corresponding numerical value, two-dimensional matrix information based on the material performance is input according to user requirements in the selection process, and the selected material selection values are compared and optimized through a weighting and iteration algorithm to obtain the material type corresponding to the optimal material selection value.
Particularly, a material performance matrix F1 is set, the demand unit inputs a corresponding material selection value a (k, M), a large number of material types are selected in a certain range based on the material selection value a, the optimization module sets a basic material selection value a (k, N), current line material selection value information is compared with data information of the basic material selection value, and an optimal material is obtained by introducing an increment and weighting algorithm.
Particularly, a material matrix W is provided in the material modeling unit, in which various materials are classified according to attributes, and a material selection value a and a material matrix W (C1, C2, C3, C4) are set, and classified according to each material attribute, respectively. The material modeling unit sets a material performance matrix F1 and a material function matrix F2, wherein the material performance matrix F1(X1, X2, X3, X4, X5 and X6), X1 represents thermal performance, X2 represents mechanical performance, X3 represents electrical performance, X4 represents magnetic performance, X5 represents optical performance, and X6 represents chemical performance. Specific values are set for each performance requirement so as to intelligently operate based on big data.
Particularly, the invention establishes a process database, the database classifies according to the process, establishes three-dimensional or multi-dimensional, establishes a corresponding function model for the corresponding materials, process conditions, equipment and achieved process effect, selects the process through weighting and terminal feedback modes and determines the optimal selectable process. Setting a process effect matrix P (qi, Ci, Hi, Ji), wherein qi represents a corresponding process effect parameter, Ci represents a processable material selection value of a certain corresponding equipment, Hi represents a usable process condition selection value of the certain corresponding equipment, Ji represents a usable equipment selection value of the certain corresponding equipment, and i represents a serial number. The intelligent chemical system based on the effect selects the corresponding materials, process conditions and equipment according to the weighting relation between the process effect and the materials, the process conditions and the equipment.
The equipment modeling unit is used for calculating quantifiable parameters through a weighting algorithm, a quantification model is established based on equipment selection, the equipment modeling unit is provided with an equipment matrix S which classifies all the equipment, but due to the existence of a plurality of parameters, the optimal values are respectively selected to hardly achieve the overall effect, a preset parameter Z0 is arranged in a processing module, the weight parameter Z calculated in real time is compared with a preset parameter Z0, if the weight parameter Z is within a preset error interval, each selected optimal value is determined to be usable, corresponding equipment, materials and process conditions can be adopted, if the error space exceeds a preset error space, the equipment, process conditions and material selection sequence is adjusted, equipment selection is preferentially adjusted, then the process condition selection is adjusted, and finally the materials are selected until the optimal values are achieved.
In particular, the invention establishes a process condition modeling unit, which is internally provided with a process selection matrix H, classifies various chemical reactions and processes, and sets a process selection value B, wherein the process selection matrix H (D, E), D represents an inorganic chemical reaction type, E represents an organic chemical type, and the embodiment classifies the chemical reactions and processes in an inorganic and organic chemical manner; in each of the chemical reaction matrices D1, D2, E, a reaction condition matrix is nested, and as an example of an inorganic chemical reaction matrix, reaction condition matrices D1(fi, Ci, gi, Bi), D2(fi, Ci, gi, Bi), E (fi, Ci, gi, hi, Bi) are set. The invention establishes a two-dimensional database by corresponding and quantizing the process conditions and the corresponding reaction parameter values one by one, combines the materials and realizes intelligent selection based on the process conditions and the multidimensional matrix combination mode of the materials.
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 invention. Also, like reference numerals are used to refer to like parts throughout the drawings.
Fig. 1 is a functional block diagram of an intelligent chemical system based on material selection according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an intelligent chemical system based on process condition selection according to an embodiment of the present invention;
fig. 3 is a functional block diagram of an intelligent chemical system based on device selection according to an embodiment of the present invention;
fig. 4 is a functional block diagram of an intelligent chemical system based on effect selection according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 is a functional block diagram of an intelligent chemical system based on material selection according to an embodiment of the present invention; the intelligent chemical system comprises a front-end display module, a data processing module and a data processing module, wherein the front-end display module is used for displaying the current requirements and the selected corresponding material related information in real time; the system also comprises a control module, wherein the control module comprises a demand unit and is input into the control module according to an actual chemical engineering result; the material modeling unit is used for selecting a plurality of corresponding materials meeting preset conditions according to requirements; the processing module is used for acquiring various specific characteristic materials meeting the requirements according to the requirements of users; and the optimization module is used for obtaining a final certain material as an optimal material according to the material selection result.
Specifically, the demand unit in the embodiment of the present invention includes a specific process effect, a process environment, and a corresponding material attribute, and a user determines the required material information according to a result of a specific process parameter preset by the user.
Specifically, the material modeling unit sets a material performance matrix F1 according to the type and performance of the material and a value corresponding to a certain performance, and assigns a value to a characteristic of a certain material. The material modeling unit is internally provided with a material matrix W, various materials are classified according to attributes in the material matrix W, and a material selection value A is set, wherein the material matrix W (C1, C2, C3 and C4) is characterized in that C1 represents a metal material, C2 represents an inorganic non-metal material, C3 represents a high polymer material and C4 represents a composite material, and the material modeling unit defines chemical materials into the four categories, and is specifically subdivided. Respectively forming a metal material matrix C1j, an inorganic non-metal material matrix C2j, a high polymer material matrix C3j and a composite material matrix C4j, wherein j represents a serial number, and each represents a material. In the polymer material matrix C3j, j is 1,2,3, C31i represents a plastic material, i represents a serial number, for example, C311 represents polyethylene; c32i denotes a rubber material, and C33i denotes a fiber material. Therefore, a material matrix W (C1ji, C2ji, C3ji, C4ji) is formed for each material, and a material selection value a is determined by quantifying the performance of each material.
Specifically, in the embodiment of the present invention, the material modeling unit sets a material performance matrix F1 and a material function matrix F2, where the material performance matrix F1(X1, X2, X3, X4, X5, and X6), where X1 represents thermal performance, X2 represents mechanical performance, X3 represents electrical performance, X4 represents magnetic performance, X5 represents optical performance, and X6 represents chemical performance, and the embodiment classifies the materials according to their own specific performance.
Specifically, the material modeling unit thermal performance matrix X1 represents a specific parameter based on thermal performance, and sets quantitative values, and the thermal performance matrix X (1i, a1), where i represents a serial number, a1 represents a corresponding thermal performance value, X (11, a1) represents a thermal capacity value of a corresponding certain material, X (12, a1) represents a thermal conductivity value of a corresponding certain material, X (13, a1) represents a heat melting value of a corresponding certain material, X (14, a1) represents a thermal expansion value of a corresponding certain material, and X (15, a1) represents a heat melting point value of a corresponding certain material. The mechanical property matrix X (2i, A2), wherein i represents a serial number, A2 represents a corresponding mechanical property value, X (21, A2) represents an elastic modulus value of a corresponding certain material, X (22, A2) represents a tensile strength value of a corresponding certain material, X (23, A2) represents an impact strength value of a corresponding certain material, X (24, A2) represents a yield strength value of a corresponding certain material, and X (25, A2) represents a fatigue strength value of a corresponding certain material. The electrical property matrix X (3i, A3) is provided, wherein i represents a serial number, A3 represents a corresponding electrical property value, X (31, A3) represents a conductivity value of a corresponding certain material, X (32, A3) represents a resistivity value of a corresponding certain material, X (31, A3) represents a dielectric property value of a corresponding certain material, and X (31, A3) represents a breakdown voltage value of a corresponding certain material. The magnetic performance matrix X (4i, A4), wherein i represents a serial number, A4 represents a corresponding magnetic value, X (41, A4) represents a paramagnetic value of a corresponding certain material, which has paramagnetic property and a definite value, X (42, A4) represents a diamagnetic value of the corresponding certain material, and X (43, A4) represents a ferromagnetic value of the corresponding certain material. The optical performance matrix X (5i, A5), i represents serial numbers, A5 represents corresponding optical parameter values, X (51, A5) represents corresponding reflection parameter values of light of a certain material, X (52, A5) represents corresponding refraction parameter values of light of the certain material, X (53, A5) represents corresponding absorption parameter values of light of the certain material, X (54, A5) represents corresponding projection parameter values of light of the certain material, X (55, A5) represents corresponding luminescence parameter values of light of the certain material, and X (55, A5) represents corresponding fluorescence parameter values of light of the certain material. The chemical property matrix (6i, A6), i represents serial numbers, A6 represents corresponding chemical property parameter values, X (61, A6) represents corresponding light corrosion resistance values of a certain material, X (62, A6) represents corresponding light catalytic resistance values of a certain material, and X (63, A6) represents corresponding light ion exchange property values of a certain material.
Specifically, the material function matrix F (2j, a7) is a matrix in which F (21, a7) represents a value of a thermo-electric conversion property of a certain material, F (22, a7) represents a value of a photo-thermal conversion property of a certain material, F (23, a7) represents a value of a photo-electric conversion property of a certain material, F (24, a7) represents a value of a force-electric conversion property of a certain material, F (25, a7) represents a value of a magneto-optical conversion property of a certain material, F (26, a7) represents a value of an electric-optical conversion property of a certain material, and F (26, a7) represents a value of an acousto-optical conversion property of a certain material.
In the embodiment of the invention, a corresponding material selection value A (k, M) is input into a material selection-based intelligent chemical system demand unit, wherein k represents the input required material performance such as magnetism and tensile strength, M represents a corresponding material performance value, and in the material selection-based process, a large number of material types are selected from the material selection value A within a certain range, so that the optimal material is selected. Setting a basic material selection value A (k, N), comparing the current material selection value information with the data information of the basic material selection value, determining the basic material selection value according to the existing database, sorting the basic material selection value according to the data of the materials in advance,
wherein the content of the first and second substances,
a comparison value, M, of data information representing material selection value information and said base material selection valueiCorresponding database information representing the ith material, NjMaterial selection value F, U representing a certain existing materialijThe relevance of the data information of the table basic material selection value and the ith basic database is shown, i represents the number of the basic material selection value, d represents the correction coefficient, and the value of d is 0.998; in this embodiment, a basis function a (ki, Mi) is determined, i represents a serial number, and represents that there are i material properties respectively corresponding to the i material propertiesThe base value, Mi, represents the corresponding base value.
Gain value of gain value A of material performance
In this embodiment, the average value is obtained by averaging the respective basic values.
The degree of association CijCalculated by the following formula:
wherein x represents the base data in the ith base database, y represents the existing data of the base database j, MiRepresenting the amount of data of the ith base database, NjA material selection value A representing data information of an existing base database;
when M isi＞NjWhen the temperature of the water is higher than the set temperature,
an output value of
When M isi≤NjWhen the temperature of the water is higher than the set temperature,the output values of (a) are all 0.
If it isIf the output value is 0, the next basic database is directly replaced, and the step 2 is repeated to compareFinally, finally
If the output value of (3) is not 0, the next step is performed.
When in use
When the output value is not 0, the database processing module willWhen the output value is not less than a preset function value zeta, establishing a base material performance database according to the base material selection value at the moment; and if the output function is smaller than the preset function value zeta, returning to the step 2 to reselect the base material selection value until the output function is not smaller than the preset function value zeta.
Specifically, the preset function value ζ may be set by an administrator according to an actual requirement, and generally takes a value of 0.98.
Specifically, in the intelligent chemical engineering system based on material selection, a two-dimensional database is established by the material performance and the corresponding numerical value, two-dimensional matrix information based on the material performance is input according to the user requirement in the selection process, and the selected material selection value is compared and optimized through a weighting and iteration algorithm to obtain the material type corresponding to the optimal material selection value.
Specifically, after the selection based on the materials, the embodiment of the invention can also perform comprehensive selection of the multidimensional matrix on the process conditions, the reaction types, the equipment selection and the catalyst types through the multidimensional matrix.
Referring to fig. 2, which is a functional block diagram of an intelligent chemical system based on process condition selection according to an embodiment of the present invention, the present embodiment further includes a process condition modeling unit, in which a process selection matrix H is disposed, the process selection matrix H classifies various chemical reactions and processes, and sets a process selection value B, wherein the process selection matrix H (D, E) represents an inorganic chemical reaction type, and E represents an organic chemical type, and the present embodiment classifies the chemical reactions and processes according to inorganic and organic chemical manners. Nested inorganic chemical reaction matrixes D1(f1, f2, f3, f4) in the process selection matrix H (D, E), f1 represents a combination reaction, f2 represents a decomposition reaction, f3 represents a substitution reaction, and f4 represents a metathesis reaction; the inorganic chemical reaction matrix D2(f5i, f6), f5 represents redox reaction, f6 represents non-redox reaction, i represents serial number, f51 represents self-redox reaction, and f52 represents reaction of reducing agent and oxidizing agent.
Specifically, an inorganic chemical reaction matrix E (fi) is nested in the process selection matrix H (D, E), wherein i represents an integer with the serial number and i >6 and respectively corresponds to a free radical reaction, an ionic reaction, an electrophilic reaction and a nucleophilic reaction; nitration, halogenation, sulfonation, amination, acylation, cyanation, addition, elimination, substitution, polyaddition, polycondensation, i.e. f7 represents a radical reaction, f8 represents an ionic reaction, and so on.
Specifically, in each of the chemical reaction matrices D1, D2, E, a reaction condition matrix is nested, taking an inorganic chemical reaction matrix as an example, setting reaction condition matrices D1(fi, Ci, gi, Bi), D2(fi, Ci, gi, Bi), E (fi, Ci, gi, hi, Bi), wherein i represents a serial number, fi represents a corresponding specific reaction, for example, f1 represents a chemical combination reaction, f2 represents a decomposition reaction, Ci represents a corresponding specific material, different materials can generate different specific chemical reactions, the materials correspond to the specific reactions one by one, for example, C31i represents a plastic material, gi represents a specific condition corresponding to a particular material, such as heating and high temperature, hi represents a parameter consideration matrix of a material under a corresponding reaction condition in a corresponding chemical reaction type, and Bi represents a specific value or a range of values of a corresponding material under a corresponding reaction condition.
Specifically, g1, g2, g3, g4, g5, g6, g7, and the like are set to indicate heating conditions, ignition conditions, high temperature conditions, humidifying conditions, pressurizing conditions, point decomposition conditions, ultraviolet conditions, and the like, and g0 indicates catalyst conditions.
Specifically, when specific reaction conditions are met, the corresponding parameter matrix hi is set, where h1 represents the reactant state, the specific numerical values B under the corresponding reaction conditions represent (h1, Bi), i represents the serial number, the corresponding reaction condition values are (h1, B11) represent the solid state, (h1, B12) represent the liquid state, and (h1, B13) represent the gas state. (h2, B2) represents the shape, size and specific numerical value of the reactant, and the like, h3 represents the concentration of the reactant, h4 represents the reaction temperature range and range, h5 represents the reaction pressure, h6 represents the strength of the metal, h7 represents the strength of the nonmetal, h8 represents the reaction humidity, h9 represents the reaction atmosphere, h10 represents the catalyst, and h (10, B10) represents the corresponding certain amount of the catalyst.
Specifically, g0 shows the use of a catalyst, and a catalyst matrix g0j (i, Bi) is set, i shows the numbers and progresses according to the types of reaction to be catalyzed, j shows the classification of the catalyst, wherein g01(i, Bi) shows the state catalyst matrix under the state classification, g01(1, B1) shows the liquid catalyst and the amount thereof, and g01(2, B2) shows the solid catalyst and the amount thereof. The phase catalyst matrix g02(i, Bi), g02(1i, B1i) represents a homogeneous catalyst having an acid, a base, a soluble transition metal compound and a peroxide catalyst; g02(1i, B2i) represents a heterogeneous catalyst and is specifically classified as g02(11, B21) represents a solid acid catalyst, g02(12, B22) represents an organic base catalyst, g02(13, B23) represents an organic base catalyst, g02(14, B24) represents a metal oxide catalyst, g02(15, B25) represents a complex catalyst, g02(16, B26) represents a rare earth catalyst, g02(17, B27) represents a molecular sieve catalyst, g02(18, B28) represents a molecular sieve catalyst, and g02(19, B29) represents a nanocatalyst. The reaction type catalyst matrix g03(i, Bi), where i corresponds to various types of polymerization, polycondensation, esterification, acetalization, hydrogenation, dehydrogenation, oxidation, reduction, alkylation, isomerization, and Bi corresponds to various catalyst amounts based on the reaction type. The catalyst matrix g04(i, Bi), g04(1, B1) represents the main catalyst and the amount used, and g04(2, B2) represents the cocatalyst.
Specifically, the catalyst matrices g0j (i, Bi) are classified according to various ways, and the process condition modeling unit obtains the optimal catalyst by overlapping and considering the catalyst matrices, and in this embodiment, the process condition modeling unit obtains the optimal catalyst type and number by the weighting algorithm of the above equations (1) - (4) when selecting the catalyst to be used.
Specifically, in the embodiment, the process conditions and the corresponding reaction parameter values are in one-to-one correspondence and quantized, a two-dimensional database is established, the materials are combined, and intelligent selection is realized based on a multidimensional matrix combination mode of the process conditions and the materials. The demand unit inputs a corresponding chemical process selection value B (k, m), wherein k represents the input chemical process condition such as halogenation reaction, m represents a corresponding condition value such as heating temperature, and the optimal mode is selected through the value B. Setting a process selection value B (k, N) based on the weighting algorithm of the above equations (1) - (4), comparing the current process selection value information with the process selection data information,
wherein the content of the first and second substances,
a comparison value, M, of process selection value information and data information of the basic process selection valueiCorresponding database information, N, representing the ith processjRepresenting a process selection value F, U of a certain existing processijThe relevance of the data information of the table basic process selection value and the ith basic database, i represents the number of the basic process selection value, d represents the correction coefficient, and the value of d is 0.997; in this embodiment, a basis function B (ki, Mi) is determined, i represents a serial number, i represents a basic numerical value corresponding to each of the i types of process performance, and Mi represents a corresponding basic value.
Gain value of gain value B of process performance
In this embodiment, the average value is obtained by averaging the respective basic values.
The degree of association CijCalculated by the following formula:
wherein x represents the base data in the ith base database, y represents the existing data of the base database j, MiRepresenting the amount of data of the ith base database, NjA process selection value B representing data information of an existing base database;
when M isi＞NjWhen the temperature of the water is higher than the set temperature,
an output value of
When M isi≤NjWhen the temperature of the water is higher than the set temperature,the output values of (a) are all 0.
If it is
If the output value is 0, directly replacing the next basic database, repeating the step 2 for comparison, and finally obtaining the final result
If the output value of (3) is not 0, the next step is performed.
When in use
When the output value is not 0, the database processing module will
When the output value is not less than a preset function value zeta, the basic process performance database is established according to the basic process selection value at the moment; and if the output function is smaller than the preset function value zeta, returning to the step 2 to reselect the basic process selection value until the output function is not smaller than the preset function value zeta.
The preset function value ζ can be set by an administrator according to actual requirements, and is generally 0.99.
Specifically, when the demand unit selects the process conditions, the reaction condition matrix D1(fi, Ci, gi, Bi), D2(fi, Ci, gi, Bi), E (fi, Ci, gi, hi, Bi) has material options, in the selection process, the process conditions are used as preferred parameters, after the process conditions are determined, the demand unit selects corresponding optimal materials according to the above formulas (1) - (4) according to the corresponding selectable materials corresponding to the process conditions, and details are not repeated.
Referring to fig. 3, which is a functional block diagram of an intelligent chemical system based on device selection according to an embodiment of the present invention, a device modeling unit is provided in this embodiment, which establishes a quantitative model based on device selection, the device modeling unit sets a device matrix S, which classifies each device, S (Si, J1i, J2i, J3i, J4i, Ci, Hi), where Si represents a device usage classification matrix, S1i represents a chemical container class, S11 represents a tank, S12 represents a tank, S13 represents a kettle, and so on; s2i represents a separation tower, S21 represents a packed tower, S22 represents a float valve tower, S23 represents a bubble column, S24 represents a rotating disc tower and the like; s3i represents a reactor class, S31 represents a tubular reactor, an S32 fluidized reactor, an S33 stirred tank reactor and the like; s4i represents heat exchangers, S41 represents a tube type, S42 represents a plate type heat exchanger, S43 represents a coil type heat exchanger, and the like; s5i represents heating furnaces, S51 represents an electric heating furnace, S52 represents an electric tube cracking furnace, and S53 represents an electric waste heat boiler; s6i represents a crystallization device, S61 represents a solution crystallizer, and S62 represents a melt crystallizer; s7i represents various specialized chemical plants.
Specifically, J1i indicates a manufacturer of a corresponding certain equipment, J2i indicates a price of the corresponding certain equipment, J3i indicates a spatial size of the corresponding certain equipment as a factor of user parameter selection, J4i indicates a service life of the corresponding certain equipment, Ci indicates a processable material of the corresponding certain equipment, and is determined by a material matrix W (C1, C2, C3, C4), Hi indicates a process condition usable by the corresponding certain equipment, and is determined by a process selection matrix H (D, E), and a reaction condition matrix D1(fi, Ci, gi, Bi), D2(fi, Ci, gi, Bi), E (fi, gi, Hi, Bi) therein. Therefore, the embodiment of the invention corresponds the material selection and the process selection of a certain corresponding device to form a material and process model based on the device selection.
Specifically, in the embodiment of the present invention, when selecting the reference parameters of each device, the reference algorithm is referred to for processing, the demand unit inputs a corresponding device selection value J (k, M), where k represents the input required device performance, such as lifetime and size, and M represents a corresponding device parameter value, and in the process of device selection, the device selection value J is within a certain range, a large number of device types are selected, and an optimal device is selected from the device types. Setting basic equipment selection value J (k, N), comparing the current equipment selection value information with the data information of the basic equipment selection value, wherein the basic equipment selection value can be determined according to the existing database and is obtained by arranging the data of the previous equipment,
wherein the content of the first and second substances,
a comparison value of data information representing device selection value information and the basic device selection value, MiCorresponding database information representing the ith device, NjDevice selection value F, U representing an existing deviceijThe relevance of the data information of the table basic equipment selection value and the ith basic database, i represents the number of the basic equipment selection value, d represents the correction coefficient, and the value of d is 0.995; j represents the gain value of the performance of the device, in this caseIn the embodiment, a basic function J (ki, Mi) is determined, i represents a serial number, and represents a basic value corresponding to each of i types of equipment performance, and Mi represents a corresponding basic value.
Gain value of gain value J of device performance
In this embodiment, the average value is obtained by averaging the respective basic values.
The degree of association CijCalculated by the following formula:
wherein x represents the base data in the ith base database, y represents the existing data of the base database j, MiRepresenting the amount of data of the ith base database, NjA device selection value A representing data information of an existing underlying database;
when M isi＞NjWhen the temperature of the water is higher than the set temperature,
an output value of
When M isi≤NjWhen the temperature of the water is higher than the set temperature,the output values of (a) are all 0.
If it is
If the output value is 0, directly replacing the next basic database, repeating the step 2 for comparison, and finally obtaining the final result
If the output value of (3) is not 0, the next step is performed.
When in useWhen the output value is not 0, the database processing module will
When the output value is not less than a preset function value zeta, the performance database of the basic equipment is established according to the selection value of the basic equipment at the moment; and if the output function is smaller than the preset function value zeta, returning to the step 2 to reselect the basic equipment selection value until the output function is not smaller than the preset function value zeta.
Specifically, the preset function value ζ may be set by an administrator according to an actual requirement, and generally takes a value of 0.96.
Specifically, in the embodiment of the present invention, all quantifiable parameters are calculated by a weighting algorithm, but since there are a plurality of parameters and it is difficult to achieve the overall effect by respectively taking the optimal values, in the embodiment, the weighting parameter Z is set, the preset parameter Z0 is set in the processing module,
in the above formula, J2i represents the price of a corresponding device, J20The price standard quantity of a corresponding certain device is represented, J3i represents the space size of the corresponding certain device, J30 represents the space size standard quantity of the corresponding certain device, J4i represents the service life of the corresponding certain device, J4i represents the service life standard quantity of the corresponding certain device, Ci represents a processable material selection value of the corresponding certain device, C0 represents a processable material selection standard value of the corresponding certain device, and Hi represents the processable material selection standard value of the corresponding certain deviceThe process condition selection value is used, and H0 represents a process condition selection standard value usable by a corresponding equipment.
Specifically, a preset parameter Z0 is set in the processing module, the weight parameter Z calculated in real time is compared with a preset parameter Z0, if the weight parameter Z is within a preset error interval, each selected optimal value is determined to be usable, corresponding equipment, materials and process conditions can be adopted, if the weight parameter Z exceeds a preset error space, the selection is adjusted according to the selection sequence of the equipment, the process conditions and the materials, the equipment selection is preferentially adjusted, then the selection of the process conditions is adjusted, and finally the materials are selected until the optimal value is reached.
Referring to fig. 4, which is a functional block diagram of an intelligent chemical system based on effect selection according to an embodiment of the present invention, a process effect modeling unit is provided in the embodiment, and a process effect matrix P, P (q1, q2, q3, q4, q5, qi) is provided in the embodiment, q1 represents a productivity index, q2 represents an environmental pollution degree, q3 represents a conversion rate, q4 represents a yield, q5 represents a yield, and so on. Each technological effect parameter can be quantified, and if (q2, M), the technological effect is comprehensively selected according to materials, technological conditions and equipment. The demand unit inputs a process effect, and if a specific conversion rate is required, the process effect modeling unit sends preset information to the equipment modeling unit, the company condition modeling unit and the material modeling unit.
Specifically, in this embodiment, a process effect matrix P (qi, Ci, Hi, Ji) is set, qi represents a corresponding process effect parameter, Ci represents a processable material selection value of a certain corresponding equipment, Hi represents a usable process condition selection value of the certain corresponding equipment, Ji represents a usable equipment selection value of the certain corresponding equipment, and i represents a serial number. According to the intelligent chemical system based on the effect, the corresponding materials, process conditions and equipment are selected according to the weighting relation among the process effect, the materials, the process conditions and the equipment.
In this embodiment, the process effect weighting factor Q is set, the preset parameter Q0 is set in the processing module,
in the formula, Ci represents a processable material selection value of a corresponding certain equipment, C0 represents a processable material selection standard value of a corresponding certain equipment, Hi represents a usable process condition selection value of a corresponding certain equipment, H0 represents a usable process condition selection standard value of a corresponding certain equipment, qi represents a corresponding process effect parameter, q0 represents a corresponding process effect standard value, Zi represents a usable equipment selection value of a corresponding certain equipment, and Z0 represents a usable preset selection value of a corresponding certain equipment. Hi, qi and Zi are obtained through the calculation, and H0, q0 and Z0 are preset.
Specifically, a preset parameter Q0 is set in the processing module, the real-time calculated process effect weighting coefficient Q is compared with a preset parameter Q0, if the process effect weighting coefficient Q is within a preset error interval, each selected optimal value is determined to be usable, corresponding equipment, materials and process conditions can be adopted, if the process effect weighting coefficient Q exceeds a preset error space, the process effect weighting coefficient Q is adjusted according to the selection sequence of the process effect, the equipment, the process conditions and the materials, the process effect weighting coefficient Q and the process condition weighting coefficient Q are preferentially adjusted, the process effect weighting coefficient Q and the process condition weighting coefficient Q are adjusted in real time.
The invention establishes a process database, the database classifies according to the process, establishes three-dimensional or multi-dimensional, establishes a corresponding function model for the corresponding material, process condition, equipment and achieved process effect, selects the process by weighting and terminal feedback mode, and determines the best selectable process.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An intelligent chemical system based on material selection is characterized by comprising,
the front-end display module is used for displaying the current requirements and the selected corresponding material related information in real time;
a control module, the control module comprising:
the demand unit is input into the control module according to an actual chemical engineering result;
the material modeling unit is used for selecting a plurality of corresponding materials meeting preset conditions according to requirements; the processing module is used for acquiring various specific characteristic materials meeting the requirements according to the requirements of users; the optimization module is used for obtaining a final certain material as an optimal material according to a material selection result;
the material modeling unit sets a material performance matrix F1 according to the type and performance of a material and a numerical value corresponding to certain performance when modeling, the demand unit inputs a corresponding material selection value A (k, M), a large number of material types are selected within a certain range based on the material selection value A in the process of material selection, the optimization module sets a basic material selection value A (k, N), current line material selection value information is compared with data information of the basic material selection value, and an optimal material is obtained by introducing an increment and weighting algorithm.
2. The intelligent chemical industry system based on material selection as claimed in claim 1, wherein a material matrix W is arranged in the material modeling unit, in which various materials are classified according to attributes, and a material selection value a is set, and a material matrix W (C1, C2, C3, C4), wherein C1 represents a metal material, C2 represents an inorganic non-metal material, C3 represents a polymer material, C4 represents a composite material, and a metal material matrix C1j, an inorganic non-metal material matrix C2j, a polymer material matrix C3j, and a composite material matrix C4j are respectively formed, wherein j represents a serial number, and each represents a material; in the polymer material matrix C3j, j is 1,2,3, C31i represents a plastic material, i represents a serial number, for example, C311 represents polyethylene; c32i denotes a rubber material, C33i denotes a fiber material;
the material modeling unit forms a material matrix W (C1ji, C2ji, C3ji, C4ji) for each material, and quantifies a performance of each material, determining a material selection value a.
3. The intelligent chemical industry system based on material selection as claimed in claim 2, wherein the material modeling unit sets a material performance matrix F1 and a material function matrix F2, the material performance matrix F1(X1, X2, X3, X4, X5, X6), wherein X1 represents thermal performance, X2 represents mechanical performance, X3 represents electrical performance, X4 represents magnetic performance, X5 represents optical performance, and X6 represents chemical performance.
4. The intelligent chemical industry system based on material selection as claimed in claim 3, wherein the material modeling unit sets a thermal performance matrix X1 representing a specific parameter based on thermal performance and sets quantitative values, and the thermal performance matrix X (1i, A1) wherein i represents a serial number, A1 represents a corresponding thermal performance value, X (11, A1) represents a corresponding thermal capacity value of a certain material, X (12, A1) represents a thermal conductivity value of a corresponding certain material, X (13, A1) represents a corresponding melting heat value of a certain material, X (14, A1) represents a corresponding thermal expansion value of a certain material, and X (15, A1) represents a corresponding melting point value of a certain material.
5. The intelligent chemical industry system based on material selection as claimed in claim 3, wherein the material modeling unit sets a mechanical property matrix X (2i, A2), wherein i represents a serial number, A2 represents a corresponding mechanical property value, X (21, A2) represents an elastic modulus value of a corresponding certain material, X (22, A2) represents a tensile strength value of a corresponding certain material, X (23, A2) represents an impact strength value of a corresponding certain material, X (24, A2) represents a yield strength value of a corresponding certain material, and X (25, A2) represents a fatigue strength value of a corresponding certain material.
6. The intelligent chemical industry system based on material selection as claimed in claim 3, wherein the material modeling unit sets an electrical property matrix X (3i, A3), wherein i represents a serial number, A3 represents a corresponding electrical property value, X (31, A3) represents a conductivity value of a corresponding certain material, X (32, A3) represents a resistivity value of a corresponding certain material, X (31, A3) represents a dielectric property value of a corresponding certain material, and X (31, A3) represents a breakdown voltage value of a corresponding certain material.
7. The intelligent chemical industry system based on material selection as claimed in claim 3, wherein the material modeling unit sets a magnetic performance matrix X (4i, A4), wherein i represents a serial number, A4 represents a corresponding magnetic value, X (41, A4) represents a paramagnetic value of a corresponding certain material, which has a paramagnetic magnetism and a definite value, X (42, A4) represents a diamagnetic value of a corresponding certain material, and X (43, A4) represents a ferromagnetic value of a corresponding certain material.
8. The intelligent chemical industry system based on material selection as claimed in claim 3, wherein the material modeling unit sets an optical performance matrix X (5i, A5), i represents a serial number, A5 represents a corresponding optical parameter value, X (51, A5) represents a reflection parameter value of light of a corresponding certain material, X (52, A5) represents a refraction parameter value of light of a corresponding certain material, X (53, A5) represents an absorption parameter value of light of a corresponding certain material, X (54, A5) represents a projection parameter value of light of a corresponding certain material, X (55, A5) represents a luminescence parameter value of light of a corresponding certain material, and X (55, A5) represents a fluorescence parameter value of light of a corresponding certain material.
9. The intelligent chemical industry system based on material selection as claimed in claim 3, wherein the material modeling unit sets a chemical property matrix (6i, A6), i represents a serial number, A6 represents a corresponding chemical property parameter value, X (61, A6) represents a corresponding light corrosion resistance value of a certain material, X (62, A6) represents a corresponding light catalytic resistance value of a certain material, and X (63, A6) represents a corresponding light ion exchange performance value of a certain material.
10. The intelligent chemical industry system based on material selection as claimed in claim 3, wherein the optimization module sets a base material selection value A (k, N), compares the current material selection value information with the data information of the base material selection value, the base material selection value can be determined according to the existing database and is obtained by arranging according to the data of the materials in advance,
wherein the content of the first and second substances,
a comparison value, M, of data information representing material selection value information and said base material selection valueiCorresponding database information representing the ith material, NjMaterial selection value F, U representing a certain existing materialijThe relevance of the data information of the table basic material selection value and the ith basic database is shown, i represents the number of the basic material selection value, d represents the correction coefficient, and the value of d is 0.998; in this embodiment, a basis function a (ki, Mi) is determined, i represents a serial number, i represents a basic numerical value corresponding to each of i material properties, and Mi represents a corresponding basic value;
gain value of gain value A of material performance
The degree of association CijCalculated by the following formula:
wherein x represents the base data in the ith base database, y represents the existing data of the base database j, MiRepresenting the amount of data of the ith base database, NjA material selection value A representing data information of an existing base database;
step 1, when M isi＞NjWhen the temperature of the water is higher than the set temperature,
an output value ofWhen M isi≤NjWhen the temperature of the water is higher than the set temperature,
the output values of (1) are all 0;
step 2, if
If the output value is 0, directly replacing the next basic database, repeating the step 1 for comparison, and finally obtaining the final result
When the output value of (2) is not 0, entering the next step;
step 3, whenWhen the output value is not 0, the database processing module will
When the output value is not less than a preset function value zeta, establishing a base material performance database according to the base material selection value at the moment; and if the output function is smaller than the preset function value zeta, returning to the step 1 to reselect the base material selection value until the output function is not smaller than the preset function value zeta.
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