CN116595163A - Intelligent self-growing coal blending system based on dynamic data model rule - Google Patents
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- 239000003245 coal Substances 0.000 title claims abstract description 198
- 238000002156 mixing Methods 0.000 title claims abstract description 66
- 238000013499 data model Methods 0.000 title claims abstract description 15
- 238000002485 combustion reaction Methods 0.000 claims abstract description 26
- 238000004519 manufacturing process Methods 0.000 claims abstract description 19
- 238000012790 confirmation Methods 0.000 claims abstract description 17
- 238000012360 testing method Methods 0.000 claims abstract description 17
- 238000004364 calculation method Methods 0.000 claims abstract description 13
- 238000007711 solidification Methods 0.000 claims abstract description 13
- 230000008023 solidification Effects 0.000 claims abstract description 13
- 238000012216 screening Methods 0.000 claims abstract description 11
- 238000000034 method Methods 0.000 claims description 30
- 239000000203 mixture Substances 0.000 claims description 25
- 238000009472 formulation Methods 0.000 claims description 21
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 17
- 238000010438 heat treatment Methods 0.000 claims description 17
- 229910052717 sulfur Inorganic materials 0.000 claims description 17
- 239000011593 sulfur Substances 0.000 claims description 17
- 238000001514 detection method Methods 0.000 claims description 5
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- 239000000446 fuel Substances 0.000 description 2
- 238000007605 air drying Methods 0.000 description 1
- 238000004380 ashing Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000571 coke Substances 0.000 description 1
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- 125000001741 organic sulfur group Chemical group 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
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Abstract
The invention discloses an intelligent self-growing coal blending system based on a dynamic data model rule, which relates to the technical field of intelligent coal blending, and the invention obtains the coal blending demand information of a user through a data acquisition module, and rapidly searches formula information by establishing a formula entry library so as to reduce the calculation amount of a prefabricated formula generated in the later period and reduce the operation cost; generating a prefabricated formula through a formula generation module, performing combustion characteristic test, constructing a preliminary recommended set of the generated formula through screening, quantifying selling prices corresponding to the prefabricated formula in the set, constructing a generated formula entry set by combining energy efficiency of the selling prices, and transmitting the formula entry set to a user for selection; the automatic production module generates a coal sample, sends the coal sample to a client for confirmation, and automatically produces the coal sample when the user confirms the coal sample; the entry collection module collects the corresponding recipe entry solidification of the user confirmation and stores the corresponding recipe entry solidification to the recipe recommendation module so as to update in real time and increase the number of recipes in the recipe recommendation module, and the growth of the intelligent coal blending system is greatly increased.
Description
Technical Field
The invention relates to the technical field of intelligent coal blending, in particular to an intelligent self-growing coal blending system based on a dynamic data model rule.
Background
Blending coal refers to mixing different kinds of raw coal in proper proportion for producing coke, electric coal and boiler coal meeting quality requirements, which is one way adopted by many power plants to provide consistent fuel raw materials for power generation or meet different requirements, such as solving transportation problems, fuel cost, reducing slagging and SO x And (5) discharging.
The traditional coal blending is mainly carried out manually, a coal blending worker blends coal according to own experience, the workload is large and is difficult to inherit, along with the development of computer software, people write a calculation method of coal blending into the software, intelligent coal blending is realized by using a computer technology, the labor cost is reduced, the coal blending thought is expanded, certain limitation still exists, and the problem of lower growth of a system is caused due to lack of a solidification and integration process of a coal blending scheme;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims at: the data acquisition module is used for acquiring the coal blending requirement information of a user, and the formula entry library is established to quickly search the formula information, so that the calculation amount of the prefabricated formula generated in the later period is reduced, and the operation cost is reduced; generating a prefabricated formula through a formula generation module, performing combustion characteristic test, constructing a preliminary recommended set of the generated formula through screening, quantifying selling prices corresponding to the prefabricated formula in the preliminary recommended set of the formula, constructing a generated formula entry set by combining energy efficiency of the selling prices, and transmitting the formula entry set to a user for selection; the automatic production module generates a coal sample, sends the coal sample to a client for confirmation, and automatically produces the coal sample when the user confirms the coal sample; the entry collection module collects the corresponding recipe entry solidification of the user confirmation and stores the corresponding recipe entry solidification to the recipe recommendation module so as to update in real time and increase the number of recipes in the recipe recommendation module, and the growth of the intelligent coal blending system is greatly increased.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an intelligent self-growing coal blending system based on a dynamic data model rule comprises a data acquisition module which is respectively connected with a formula recommendation module, a formula generation module, a formula detection module, a user confirmation module, an automatic batching module, an automatic production module and an entry collection module in a signal manner,
the data acquisition module acquires the requirement information of the user and sends the requirement information to the formula recommendation module, the formula recommendation module acquires the requirement information of the user and searches in a formula entry library, and when the formula entry meeting the requirement of the user is searched, the formula entry meeting the requirement of the user is sent to a user display screen; if the formula entry library does not contain the formula entries meeting the requirements of the user, the formula recommendation module sends the requirement information of the user to the formula generation module;
the formula generating module receives the demand information of a user to generate a prefabricated formula, sends the prefabricated formula to the formula detecting module for combustion characteristic test, screens the formula passing through the combustion characteristic test, generates a formula preliminary recommended set according to the heat value sequence from high to low, quantifies the selling price corresponding to the prefabricated formula in the formula preliminary recommended set, and combines the selling price corresponding to the formula to construct and generate a formula entry set, wherein the formula entry is the selling price and the energy efficiency corresponding to the formula; then the formula entry set is sent to a user display screen for display so as to be selected by a user; marking the pre-prepared formulas in the formula entry set as the selected formulas after the user selects the pre-prepared formulas;
when the selected formula is generated, the automatic batching module receives the information and generates a corresponding coal sample through the automatic production module, user confirmation is carried out through the coal sample, and when the user confirmation is carried out, the automatic production module is controlled to carry out automatic production; the entry collection module collects the corresponding recipe entry solidification of the user confirmation and stores the corresponding recipe entry solidification to the recipe recommendation module so as to update in real time and increase the number of recipes in the recipe recommendation module.
Further, the specific mode of the data acquisition module for acquiring the requirement information of the user is as follows:
the user inputs the coal blending requirement according to the established format of the system, and the method specifically comprises the following steps: inputting an ordered triplet (x, y, z) at the user interface, wherein x is the heat released by the complete combustion of a unit mass of coal; y is the time for which the coal per unit mass can be completely burned; z is the total amount of blended coal the user needs to produce.
Further, the generation process of the pre-formulation is as follows:
the formula generation module is connected with a coal blending database, and after receiving the user coal blending requirement, the formula generation module invokes coal quality information and a basic coal blending model in the database to generate a prefabricated formula meeting the user coal blending requirement; the method comprises the following steps: the coal quality information in the database comprises moisture, ash content, sulfur content, volatile matters and heating value of raw coal, and the formula generation module firstly retrieves the coal quality information of the required raw coal by collecting the coal blending requirement of a user, and then inputs the coal blending requirement into a basic coal blending model to solve to obtain a prefabricated formula.
Further, the process of screening the pre-formulation is as follows:
the formula generating module is connected with a warehouse management system and used for monitoring the residual storage quantity of various raw coals in a warehouse in real time; the raw coal types used in the pre-formulation are respectively marked as P 1 ,P 2 ,P 3 ,Λ，P n N is the number of raw coal types, and the number of the raw coal types is correspondingly recorded as p 1 ,p 2 ,p 3 ,Λ,p n The method comprises the steps of carrying out a first treatment on the surface of the The residual quantity of various raw coals existing in the warehouse and needed to be used in the prefabricated formula is correspondingly recorded as q 1 ,q 2 ,q 3 ,Λ,q n The method comprises the steps of carrying out a first treatment on the surface of the Preliminary screening is carried out on the preformed formulas to check the residual amount of raw coal one by one, specifically: if the number of any raw coal in the prefabricated formula meets p i ≤q i I=1, 2,3, Λ, n, then marking the pre-formulation as a pre-selected formulation; otherwise, discarding the pre-formed formulation; the method comprises the steps of producing coal samples through an automatic production module according to a preselected formula to carry out combustion characteristic test, calculating cost prices corresponding to the preselected formula passing through the combustion characteristic test one by one, and matching n formulations with the lowest cost pricesThe m formulas with highest formulas and cost price are ranked from high to low according to the heat value to generate a preliminary recommendation set of the formulas.
Further, the calculation process of the different formulation costs is as follows:
the raw coal types used in a certain formula are respectively marked as P1, P2, P3, Λ, pn and n as the number of the raw coal types, and the use proportion of each raw coal in the formula is respectively marked as X 1 ,X 2 ,X 3 ,Λ,X n The method comprises the steps of carrying out a first treatment on the surface of the The cost price of P1, P2, P3, Λ, pn is respectively marked as C 1 ,C 2 ,C 3 ,Λ,C n The total cost of the recipe is calculated as:
further, the specific process of quantifying the preliminary recommended set of formulas is as follows:
and calculating the selling price corresponding to each formula in the formula preliminary recommended set through the cost price and the profit margin, sorting the formulas in the formula preliminary recommended set according to the selling price from high to low, and performing secondary sorting on the formulas with the same selling price according to the energy efficiency of the formulas to construct and generate a formula entry set.
Further, the selling price calculation mode of each formula in the preliminary recommendation set of the formula is as follows:
the profit margin of the blended coal is marked as epsilon, wherein epsilon is more than 0, the cost price of a certain formula is marked as C, and the selling price calculation formula of the formula is as follows:
S＝C(1+9)。
further, the multi-constraint conditions are respectively as follows:
moisture constraint conditions:wherein X is i For the mixture ratio of the ith raw coal, M i Is the moisture of the ith raw coal, M max N is the number of raw coal types required by the formula for the highest moisture limit of the blended coal;
ash constraint:wherein A is i Ash of the ith raw coal, A max Is the highest ash limit of the blended coal;
sulfur constraint conditions:wherein S is i Is the sulfur content of the ith raw coal, S max Is the highest sulfur limit of the blended coal;
volatile constraint conditions:wherein V is i Is the volatile component of the ith raw coal, V min Is the lowest volatile limit of the blended coal;
heating value constraint conditions:wherein Q is i Heating value Q of the ith raw coal min The lowest heating value of the blended coal is the lowest heating value;
constraint conditions of coal blending proportion:
the lowest cost objective function:wherein C is the total cost of the formulation, C i Is the cost price of the ith raw coal.
Further, the specific working steps of the entry collection module are as follows:
the entry collection module collects and temporarily stores the recipe entries confirmed by the user, and when the number of the temporarily stored recipe entries reaches a preset value, the entry collection module uploads the stored recipe entries to the recipe recommendation module so as to update and increase the number of the recipes in the recipe recommendation module in real time.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
according to the invention, the data acquisition module is used for acquiring the coal blending requirement information of the user, and the formula entry library is established for quickly searching the formula information, so that the calculation amount of the prefabricated formula generated in the later period is reduced, and the operation cost is reduced; generating a prefabricated formula through a formula generation module, performing combustion characteristic test, constructing a preliminary recommended set of the generated formula through screening, quantifying selling prices corresponding to the prefabricated formula in the preliminary recommended set of the formula, constructing a generated formula entry set by combining energy efficiency of the selling prices, and transmitting the formula entry set to a user for selection; the automatic production module generates a coal sample, sends the coal sample to a client for confirmation, and automatically produces the coal sample when the user confirms the coal sample; the entry collection module collects the corresponding recipe entry solidification of the user confirmation and stores the corresponding recipe entry solidification to the recipe recommendation module so as to update in real time and increase the number of recipes in the recipe recommendation module, and the growth of the intelligent coal blending system is greatly increased.
Drawings
FIG. 1 illustrates a flow chart of the operation of the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
an intelligent self-growing coal blending system based on a dynamic data model rule comprises a data acquisition module which is respectively connected with a formula recommendation module, a formula generation module, a formula detection module, a user confirmation module, an automatic batching module, an automatic production module and an entry collection module in a signal manner,
the specific working principle is as follows:
step one, a data acquisition module acquires demand information of a user and sends the demand information to a formula recommendation module to conduct preliminary recommendation, wherein the specific recommendation process is as follows:
the user inputs the coal blending requirement according to the established format of the system, and the method specifically comprises the following steps: inputting an ordered triplet (x, y, z) at the user interface, wherein x is the heat released by the complete combustion of a unit mass of coal in joules; y is the time in hours for which the coal can be completely burned per unit mass; z is the total amount of the blended coal required to be produced by a user, and the unit is ton (t), wherein the coal mass per unit mass is 1kg; the data acquisition module acquires the requirement information of the user and sends the requirement information to the formula recommendation module, and the formula recommendation module acquires the requirement information of the user and searches in a formula entry library; if the formula entry meeting the user requirement is searched, the formula entry is sent to a user display screen for the user to select, otherwise, the requirement information of the user is sent to a formula generation module to generate a new formula; the user receives the formula entry set displayed by the display screen, if confirming that a certain prefabricated formula in the set is selected, the formula entry set is marked as a selected formula, otherwise, the formula recommendation module sends the requirement information of the user to the formula generation module to generate a new formula;
the formula entry library is a database which is generated by acquiring coal blending information through big data and integrating and constructing a basic formula through feasibility analysis and comprises the basic formula and corresponding selling price and energy efficiency thereof; wherein the feasibility analysis comprises combustion characteristic analysis and constraint condition analysis; the combustion characteristic analysis is realized through a combustion characteristic test, and the constraint condition analysis is realized through a basic coal blending model.
Step two, a formula generating module receives the demand information of a user to generate a prefabricated formula, wherein the generation process of the prefabricated formula is as follows:
the formula generation module is connected with a coal blending database, and after receiving the user coal blending requirement, the formula generation module invokes coal quality information and a basic coal blending model in the database to generate a prefabricated formula meeting the user coal blending requirement; the method comprises the following steps: the coal quality information in the database comprises moisture, ash content, sulfur content, volatile matters and heating value of raw coal, and a formula generation module firstly retrieves the coal quality information of the raw coal by collecting the coal blending requirement of a user, and then inputs the coal blending requirement into a basic coal blending model to solve to obtain a prefabricated formula;
wherein the moisture refers to the total moisture of the coal, namely the sum of external moisture and internal moisture, can be measured by a nitrogen-passing drying method, the presence of the moisture is extremely unfavorable for the utilization of the coal, and the calorific value is reduced by 100kcal/kg (large calorie/kg) when the moisture is increased by 2%;
ash refers to residues left after the coal is thoroughly combusted, is a harmful substance, can be measured by a slow ashing method by using a muffle furnace, and generally the heating value is reduced by about 100kcal/kg when the ash is increased by 2%;
the sulfur content refers to total sulfur content of coal, is harmful elements in the coal, including organic sulfur and inorganic sulfur, and can be measured by a sulfur measuring instrument;
volatile refers to the product of the discharged gas and liquid state when the coal is heated under the conditions of high temperature and air isolation, is one of important indexes for identifying the type and quality of the coal, can be measured by using a muffle furnace, and generally, reduces as the deterioration degree of the coal increases;
the calorific value refers to the heat generated when the coal with unit mass is completely combusted, and is mainly divided into high calorific value and low calorific value, and the international trade general calorific value standard is air drying base high calorific value (Qgr, ad), which can accurately reflect the real quality of the coal and can be measured by using a constant temperature calorimeter;
the basic coal blending model adopts an objective function coal blending model with multiple constraint conditions, and specifically comprises the following steps: the multi-constraint conditions comprise moisture, ash, sulfur, volatile matters, heating value and coal blending proportion; the objective function is the lowest cost objective function; and inputting the multiple constraint conditions and the objective function into the coal blending model, and outputting the prefabricated formula.
Wherein the multi-constraint conditions are respectively as follows:
moisture constraint conditions:wherein X is i For the mixture ratio of the ith raw coal, M i Is the moisture of the ith raw coal, M max N is the number of raw coal types required by the formula for the highest moisture limit of the blended coal;
ash constraint:wherein A is i Ash of the ith raw coal, A max Is the highest ash limit of the blended coal;
sulfur constraint conditions:wherein S is i Is the sulfur content of the ith raw coal, S max Is the highest sulfur limit of the blended coal;
volatile constraint conditions:wherein V is i Is the volatile component of the ith raw coal, V min Is the lowest volatile limit of the blended coal;
heating value constraint conditions:wherein Q is i Heating value Q of the ith raw coal min The lowest heating value of the blended coal is the lowest heating value;
constraint conditions of coal blending proportion:
the lowest cost objective function:wherein C is the total cost of the formulation, C i Is the cost price of the ith raw coal.
Step three, sending the prefabricated formula to a formula detection module for combustion characteristic test, screening the formula passing through the combustion characteristic test, and generating a formula preliminary recommended set according to the heat value of the formula from high to low;
the combustion characteristic test process is as follows: the formula detection module receives the prefabricated formula generated by the formula generation module, sends the prefabricated formula to the automatic batching module, receives the information of the automatic batching module, generates a corresponding coal sample through the automatic production module, and measures the combustion characteristic of the coal sample by using the constant temperature calorimeter, wherein the combustion characteristic refers to the heat value of the coal sample and the time required by complete combustion of a unit coal sample; the test of the combustion characteristics means that the measured calorific value of the coal sample and the time required for complete combustion of the unit coal sample meet the requirements of users;
wherein the screening process is as follows: the formula generating module is connected with a warehouse management system and used for monitoring the residual storage quantity of various raw coals in a warehouse in real time; the raw coal types used in the pre-formulation are respectively marked as P 1 ,P 2 ,P 3 ,Λ，P n N is the number of raw coal types, and the number of the raw coal types is correspondingly recorded as p 1 ,p 2 ,p 3 ,Λ,p n The method comprises the steps of carrying out a first treatment on the surface of the The residual quantity of various raw coals existing in the warehouse and needed to be used in the prefabricated formula is correspondingly recorded as q 1 ,q 2 ,q 3 ,Λ,q n The method comprises the steps of carrying out a first treatment on the surface of the Preliminary screening is carried out on the preformed formulas to check the residual amount of raw coal one by one, specifically: if the number of any raw coal in the prefabricated formula meets p i ≤q i I=1, 2,3, Λ, n, then marking the pre-formulation as a pre-selected formulation; otherwise, discarding the pre-formed formulation; and then calculating the cost prices corresponding to the preselected formulas one by one, and generating a preliminary recommended set of the formulas according to the heat value of the n formulas with the lowest cost price and the m formulas with the highest cost price from high to low.
Step four, quantifying selling prices corresponding to the prefabricated formulas in the preliminary recommended set of the formulas and constructing and generating a formula entry set by combining energy efficiency of the selling prices;
the specific process of quantifying the preliminary recommended set of formulas is as follows: calculating the selling price of each formula in the formula preliminary recommended set through the cost price and the profit margin, sorting the formulas in the formula preliminary recommended set from high to low according to the selling price, and performing secondary sorting on the formulas with the same selling price from high to low according to the energy efficiency of the formulas to construct a formula entry set;
the formula selling price is calculated as follows: the profit margin of the blended coal is marked as epsilon (epsilon > 0), the cost price of a certain formula is marked as C (unit: yuan), and the selling price calculation formula of the formula is as follows:
s=c (1+epsilon), (unit: element);
wherein the formula entry is the selling price and energy efficiency corresponding to the formula.
Step five, sending the formula entry set to a user display screen for display so as to be selected by a user; if the user selects a pre-prepared recipe in the recipe entry set, marking the pre-prepared recipe as the selected recipe; if the user does not select any one of the formulas in the formula entry set, a back-off signal is generated, and at the moment, the formula generation module regenerates a new formula according to the user demand information.
Step six, when the selected formula is generated, the automatic batching module receives the information and generates a corresponding coal sample through the automatic production module, and then the user is confirmed through the coal sample;
if the user confirms that the coal sample formula is selected, the automatic production module starts automatic production; otherwise, the user re-selects the pre-prepared formulas in the formula entry set;
wherein, automatic batching module is connected with warehouse management system, realizes automatic batching through automatic valve and conveyer belt among automatic actuator control raw and other materials storage.
And seventhly, collecting and temporarily storing the recipe entries confirmed by the user by the entry collecting module, and uploading the stored recipe entries to the recipe recommending module by the entry collecting module when the number of the temporarily stored recipe entries reaches a preset value so as to update in real time and increase the number of the recipes in the recipe recommending module.
According to the technical scheme, the data acquisition module is used for acquiring the coal blending requirement information of the user, and the formula entry library is established to quickly search the formula information, so that the calculation amount of the prefabricated formula generated in the later period is reduced, and the operation cost is reduced; generating a prefabricated formula through a formula generation module, performing combustion characteristic test, constructing a preliminary recommended set of the generated formula through screening, quantifying selling prices corresponding to the prefabricated formula in the preliminary recommended set of the formula, constructing a generated formula entry set by combining energy efficiency of the selling prices, and transmitting the formula entry set to a user for selection; the automatic production module generates a coal sample, sends the coal sample to a client for confirmation, and automatically produces the coal sample when the user confirms the coal sample; the entry collection module collects the corresponding recipe entry solidification of the user confirmation and stores the corresponding recipe entry solidification to the recipe recommendation module so as to update in real time and increase the number of recipes in the recipe recommendation module, and the growth of the intelligent coal blending system is greatly increased.
The formulas are all formulas with dimensions removed and numerical calculation, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by a person skilled in the art according to the actual situation;
the foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (9)
1. An intelligent self-growing coal blending system based on a dynamic data model rule is characterized by comprising a data acquisition module which is respectively connected with a formula recommendation module, a formula generation module, a formula detection module, a user confirmation module, an automatic batching module, an automatic production module and an entry collection module in a signal manner,
the data acquisition module acquires the requirement information of the user and sends the requirement information to the formula recommendation module, the formula recommendation module acquires the requirement information of the user and searches in a formula entry library, and when the formula entry meeting the requirement of the user is searched, the formula entry meeting the requirement of the user is sent to a user display screen; if the formula entry library does not contain the formula entries meeting the requirements of the user, the formula recommendation module sends the requirement information of the user to the formula generation module;
the formula generating module receives the demand information of a user to generate a prefabricated formula, sends the prefabricated formula to the formula detecting module for combustion characteristic test, screens the formula passing through the combustion characteristic test, generates a formula preliminary recommended set according to the heat value sequence from high to low, quantifies the selling price corresponding to the prefabricated formula in the formula preliminary recommended set, and combines the selling price corresponding to the formula to construct and generate a formula entry set, wherein the formula entry is the selling price and the energy efficiency corresponding to the formula; then the formula entry set is sent to a user display screen for display so as to be selected by a user; marking the pre-prepared formulas in the formula entry set as the selected formulas after the user selects the pre-prepared formulas;
when the selected formula is generated, the automatic batching module receives the information and generates a corresponding coal sample through the automatic production module, user confirmation is carried out through the coal sample, and when the user confirmation is carried out, the automatic production module is controlled to carry out automatic production; the entry collection module collects the corresponding recipe entry solidification of the user confirmation and stores the corresponding recipe entry solidification to the recipe recommendation module so as to update in real time and increase the number of recipes in the recipe recommendation module.
2. The intelligent self-growing coal blending system based on the dynamic data model rule according to claim 1, wherein the specific mode of acquiring the demand information of the user by the data acquisition module is as follows:
the user inputs the coal blending requirement according to the established format of the system, and the method specifically comprises the following steps: inputting an ordered triplet (x, y, z) at the user interface, wherein x is the heat released by the complete combustion of a unit mass of coal; y is the time for which the coal per unit mass can be completely burned; z is the total amount of blended coal the user needs to produce.
3. The intelligent self-growing coal blending system based on dynamic data model rules of claim 1, wherein the generation process of the pre-formed formula is as follows:
the formula generation module is connected with a coal blending database, and after receiving the user coal blending requirement, the formula generation module invokes coal quality information and a basic coal blending model in the database to generate a prefabricated formula meeting the user coal blending requirement; the method comprises the following steps: the coal quality information in the database comprises moisture, ash content, sulfur content, volatile matters and heating value of raw coal, and the formula generation module firstly retrieves the coal quality information of the required raw coal by collecting the coal blending requirement of a user, and then inputs the coal blending requirement into a basic coal blending model to solve to obtain a prefabricated formula.
4. The intelligent self-growing coal blending system based on dynamic data model rules of claim 1, wherein the process of screening the pre-formulation is as follows:
the formula generating module is connected with a warehouse management system and used for monitoring the residual storage quantity of various raw coals in a warehouse in real time; the raw coal types used in the pre-formulation are respectively marked as P 1 ,P 2 ,P 3 ,Λ，P n N is the number of raw coal types, and the number of the raw coal types is correspondingly recorded as p 1 ,p 2 ,p 3 ,Λ,p n The method comprises the steps of carrying out a first treatment on the surface of the The residual quantity of various raw coals existing in the warehouse and needed to be used in the prefabricated formula is correspondingly recorded as q 1 ,q 2 ,q 3 ,Λ,q n The method comprises the steps of carrying out a first treatment on the surface of the Preliminary screening is carried out on the preformed formulas to check the residual amount of raw coal one by one, specifically: if the number of any raw coal in the prefabricated formula meets p i ≤q i I=1, 2,3, Λ, n, then marking the pre-formulation as a pre-selected formulation; otherwise, discarding the pre-formed formulation; and (3) producing coal samples through an automatic production module according to the preselected formulas to carry out combustion characteristic test, then calculating cost prices corresponding to the preselected formulas passing through the combustion characteristic test one by one, and generating a preliminary recommended set of formulas according to the heat value of the n formulas with the lowest cost price and the m formulas with the highest cost price in a sequence from high to low.
5. The intelligent self-growing coal blending system based on dynamic data model rules of claim 4, wherein the calculation process of different formulation costs is as follows:
the raw coal types used in a certain formula are respectively marked as P1, P2, P3, Λ, pn and n as the number of the raw coal types, and the use proportion of each raw coal in the formula is respectively marked as X 1 ,X 2 ,X 3 ,Λ,X n The method comprises the steps of carrying out a first treatment on the surface of the The cost price of P1, P2, P3, Λ, pn is respectively marked as C 1 ,C 2 ,C 3 ,Λ,C n The total cost of the recipe is calculated as:
6. the intelligent self-growing coal blending system based on dynamic data model rules of claim 4, wherein the specific process of quantifying the preliminary recommended set of formulas is as follows:
and calculating the selling price corresponding to each formula in the formula preliminary recommended set through the cost price and the profit margin, sorting the formulas in the formula preliminary recommended set according to the selling price from high to low, and performing secondary sorting on the formulas with the same selling price according to the energy efficiency of the formulas to construct and generate a formula entry set.
7. The intelligent self-growing coal blending system based on dynamic data model rules of claim 6, wherein the selling price calculation mode of each formula in the preliminary recommendation set of the formula is as follows:
the profit margin of the blended coal is marked as epsilon, wherein epsilon is more than 0, the cost price of a certain formula is marked as C, and the selling price calculation formula of the formula is as follows:
S＝C(1+ε)。
8. the intelligent self-growing coal blending system based on dynamic data model rules of claim 7, wherein the multi-constraint conditions are respectively:
moisture constraint conditions:wherein X is i For the mixture ratio of the ith raw coal, M i Is the moisture of the ith raw coal, M max N is the number of raw coal types required by the formula for the highest moisture limit of the blended coal;
ash constraint:wherein A is i Ash of the ith raw coal, A max Is the highest ash limit of the blended coal;
sulfur constraint conditions:wherein S is i Is the sulfur content of the ith raw coal, S max Is the highest sulfur limit of the blended coal;
volatile constraint conditions:wherein V is i Is the volatile component of the ith raw coal, V min Is the lowest volatile limit of the blended coal;
heating value constraint conditions:wherein Q is i Heating value Q of the ith raw coal min The lowest heating value of the blended coal is the lowest heating value;
constraint conditions of coal blending proportion:
the lowest cost objective function:wherein C is the total cost of the formulation, C i Is the cost price of the ith raw coal.
9. The intelligent self-growing coal blending system based on dynamic data model rules of claim 1, wherein the specific working steps of the entry collection module are as follows:
the entry collection module collects and temporarily stores the recipe entries confirmed by the user, and when the number of the temporarily stored recipe entries reaches a preset value, the entry collection module uploads the stored recipe entries to the recipe recommendation module so as to update and increase the number of the recipes in the recipe recommendation module in real time.
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| CN102592249A (en) * | 2012-02-10 | 2012-07-18 | 华北电力大学 | Fire coal blending method of thermal power plants |
| CN106203801A (en) * | 2016-06-30 | 2016-12-07 | 华润电力登封有限公司 | A kind of environmental protection blending method |
| CN110232497A (en) * | 2019-04-25 | 2019-09-13 | 华电国际电力股份有限公司技术服务分公司 | A kind of coal mixing combustion intelligent management and system |
| CN115456385A (en) * | 2022-09-01 | 2022-12-09 | 华润电力技术研究院有限公司 | Automatic coal blending method, system, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102592249A (en) * | 2012-02-10 | 2012-07-18 | 华北电力大学 | Fire coal blending method of thermal power plants |
| CN106203801A (en) * | 2016-06-30 | 2016-12-07 | 华润电力登封有限公司 | A kind of environmental protection blending method |
| CN110232497A (en) * | 2019-04-25 | 2019-09-13 | 华电国际电力股份有限公司技术服务分公司 | A kind of coal mixing combustion intelligent management and system |
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