CN115238971A - Intelligent brain analysis and processing system for coal preparation plant - Google Patents
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Abstract
The invention provides an intelligent brain analysis and processing system for a coal preparation plant, which comprises: the intelligent sorting prediction theoretical model is used for carrying out format yield correction through screening floating and sinking test data of raw coal, clean coal and gangue, drawing a selectivity curve, establishing a relation model of theoretical sorting density and clean coal ash content and raw coal ash content, establishing a relation model of theoretical sorting density and clean coal yield, and predicting the theoretical sorting density and clean coal yield through the clean coal ash content and the raw coal ash content; drawing a distribution curve, and calculating the sorting precision value and sorting density of equipment by combining with an on-site actual sorting process; and predicting the separation effect of the selected raw coal after entering the separation system through the separation precision and the separation density. By applying the intelligent brain analysis and processing system of the coal preparation plant, an intensive, safe, efficient and green modern benchmarking coal preparation plant can be built.
Description
Technical Field
The invention relates to the technical field of coal washing, in particular to an intelligent brain analysis and processing system for a coal preparation plant.
Background
The rapid development of the coal industry makes a great contribution to the economic development, and meanwhile, the safety management pressure of the coal industry is increased day by day. In order to improve the safety management level, reduce the safety risk and realize the zero death goal of safety production of more enterprises, related departments encourage coal mines and other high-risk industries to vigorously develop scientific and technological innovation, and scientific and technological strong safety actions of mechanical person changing and automatic person reduction are mainly carried out. In order to ensure the production of clean power raw coal and meet different requirements of users, the quality, classification, gradient processing and sale of the raw coal are realized, the construction of coal preparation plants is more and more emphasized, and domestic coal preparation plants continuously provide new development requirements from the original requirement of basic particle size classification to the control of ash content and water content and then to the current deep processing of the final coal.
However, the construction of the intelligent coal preparation plant at present has the problems that research and development are lagged behind the development requirements of enterprises, the technical standards and specifications of intelligent construction are lost and the like.
Disclosure of Invention
Therefore, the invention aims to overcome the defects that the development of the intelligent coal preparation plant is lagged behind the development requirements of enterprises and the technical standards and specifications of the intelligent construction are lost in the prior art, and provides the intelligent brain analysis and processing system for the coal preparation plant.
In order to achieve the purpose, the invention provides the following technical scheme:
the embodiment of the invention provides an intelligent brain analysis and processing system for a coal preparation plant, which comprises: the intelligent sorting prediction theoretical model is used for carrying out format yield correction through screening floating and sinking test data of raw coal, clean coal and gangue, drawing a selectivity curve, establishing a relation model of theoretical sorting density and clean coal ash content and raw coal ash content, establishing a relation model of theoretical sorting density and clean coal yield, and predicting the theoretical sorting density and clean coal yield through the clean coal ash content and the raw coal ash content; drawing a distribution curve, and calculating the sorting precision value and sorting density of equipment by combining with an on-site actual sorting process; and predicting the separation effect of the selected raw coal after entering the separation system through the separation precision and the separation density.
Optionally, the relationship model of the sorting density with the clean coal ash content and the raw coal ash content is as follows:
Q＝a 1 +a 2 H J +a 3 H Y +a 4 H J H Y +a 5 H J 2 +a 6 H Y 2
wherein Q is sorting density, H J Is clean coal ash content, H Y As raw coal ash content, a 1 、a 2 、a 3 、a 4 、a 5 、a 6 Is the model coefficient;
the relation model of the sorting density and the yield of the clean coal is as follows:
Q＝b 1 +b 2 D+b 3 D 2
wherein, b 1 、b 2 、b 3 For model coefficients, D is the clean coal yield.
Optionally, the intelligent brain analysis processing system of the coal preparation plant further includes: the intelligent separation prediction field model is used for analyzing the relationship between the ash content and the separation density of raw coal on a training field and the ash content and the yield of clean coal by adopting a deep reinforcement learning algorithm, and performing data modeling to obtain the training separation density of the ash content of the raw coal and the ash content of the clean coal in the actual production process.
Optionally, the intelligent brain analysis processing system of the coal preparation plant further includes: the intelligent heavy medium sorting model is used for controlling a detection instrument, an execution mechanism and a fuzzy controller through an intelligent decision model and carrying out online adaptive adjustment on process parameters; and analyzing and training a large amount of field data by adopting a deep reinforcement learning algorithm, establishing a model of the relationship among the opening of the shunt valve, the opening of the water replenishing valve of the qualified medium pipe, the medium adding time, the separation density of the suspension, the bucket position of the qualified medium bucket and the content of the qualified medium magnetic substances, and performing optimization control.
Optionally, the model of the relationship between the opening of the shunt valve, the opening of the water replenishing valve of the qualified medium pipe, the medium adding time, the separation density of the suspension, the bucket position of the qualified medium bucket, and the content of the qualified medium magnetic substance is as follows:
Y 1 ＝K 0 +K 1 A+K 2 B+K 3 C+K 4 AB+K 5 BC+K 6 AC+K 7 A 2 +K 8 B 2 +K 9 C 2 ；
Y 2 ＝K 00 +K 10 A+K 11 B+K 13 AB+K 14 A 2 +K 15 B 2 ；
Y 3 ＝K+K 16 A+K 17 B+K 18 AB+K 19 A 2 +K 20 B 2 ；
C＝K 21 +K 22 E P +K 23 E P 2 ；
wherein, Y 1 Is the opening degree of the flow divider, Y 2 Opening of water supply valve for qualified medium pipe, Y 3 For the medium adding time, A is the sorting density of the suspension, B is the barrel position of the qualified medium barrel, C is the content of the qualified medium magnetic substance, and K 0 、K 1 、K 2 、K 3 、K 4 、K 5 、K 6 、K 7 、K 8 、K 9 、K 00 、K 10 、K 11 、K 13 、K 14 、K 15 、K、K 16 、K 17 、K 18 、K 19 、K 20 、K 21 、K 22 、K 23 Are all model coefficients, E P The sorting accuracy.
Optionally, the intelligent brain analysis processing system of the coal preparation plant further includes: and the intelligent medium adding module is used for automatically responding to the medium adding application when the intelligent heavy medium sorting model sends the medium adding application.
Optionally, the intelligent brain analysis processing system of the coal preparation plant further includes: and the flexible production module is used for analyzing the relation between the ash content required by the market of the coal preparation plant and the clean coal ash content, the clean coal yield, the slack coal ash content and the slack coal yield, establishing an intelligent decision-making model of production organization, calculating the maximum value of the clean coal yield under the condition that the ash content required by the market, the slack coal ash content and the slack coal yield are constant, and further calculating the clean coal ash content, so that the separation density is adjusted, and the coal products required by the market are selected.
Optionally, the intelligent brain analysis processing system of the coal preparation plant further includes: the intelligent coal blending module is used for calculating the clean coal yield and the slack coal yield according to the coal product ash content required by the market, calculating the clean coal hourly yield and the slack coal hourly yield, automatically adjusting the frequency of a coal feeder for clean coal and slack coal products by combining the clean coal belt weigher data and the slack coal belt weigher data, and keeping the clean coal and slack coal belt coal quantity within a set interval by combining the fine adjustment of the frequency of the coal feeder and the coarse adjustment of the number of the coal feeder, so that the coal with the calorific value required by the market is blended.
Optionally, the intelligent brain analysis processing system of the coal preparation plant further includes: and the intelligent filter pressing module is used for automatically overall feeding based on an intelligent filter pressing control algorithm, automatically finishing feeding of the filter press and automatically queuing and discharging of the filter press.
Optionally, the intelligent brain analysis processing system of the coal preparation plant further includes: and the sorting granularity prediction module is used for comprehensively calculating the floating and sinking data of different size fractions in the grading and sorting process, and determining the optimal sorting granularity under different processes based on a distribution surface mathematical model with double variables of granularity and density.
The technical scheme of the invention has the following advantages:
the invention provides an intelligent brain analysis and processing system for a coal preparation plant, which comprises: the intelligent sorting prediction theoretical model is used for carrying out format yield correction through screening floating and sinking test data of raw coal, clean coal and gangue, drawing a selectivity curve, establishing a relation model of theoretical sorting density and clean coal ash content and raw coal ash content, establishing a relation model of theoretical sorting density and clean coal yield, and predicting the theoretical sorting density and clean coal yield through the clean coal ash content and the raw coal ash content; drawing a distribution curve, and calculating the sorting precision value and sorting density of equipment by combining with an on-site actual sorting process; and predicting the separation effect of the selected raw coal after entering the separation system through the separation precision and the separation density. By applying the intelligent brain analysis processing system of the coal preparation plant, an intensive, safe, efficient and green modern benchmarking coal preparation plant can be built, and closed-loop optimization management of a whole set of production business processes such as equipment operation monitoring, equipment fault early warning, operation state diagnosis, product quality analysis, safe production management, key target tracking and the like is realized; the production and operation of the coal preparation plant have the capabilities of real-time comprehensive perception, deep optimization and cooperation, accurate prediction and early warning and quick scientific decision making, and can also quickly respond to the fluctuation of the coal market, automatically adjust the key production parameters and realize flexible production; finally, the operation targets of dynamic balance, optimal production and optimal benefit are achieved, and the deep revolution of the production mode and the control mode of the coal preparation plant is promoted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic block diagram of an example of an intelligent brain analysis processing system of a coal preparation plant according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
Furthermore, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, an embodiment of the invention provides an intelligent brain analysis processing system for a coal preparation plant, including: and (3) intelligently sorting and predicting theoretical models.
In one embodiment, the intelligent brain analysis processing system of the coal preparation plant needs to establish an expert knowledge base mainly based on expert experiences of a coal preparation algorithm, a curve drawing method and an analysis and evaluation method, so that when a production problem occurs on site, the site problem can be rapidly solved according to the past expert experiences. Then, an intelligent separation prediction theoretical model in an intelligent brain analysis processing system of a coal preparation plant performs format yield correction through screening floating and sinking test data of raw coal, clean coal and gangue, draws a selectivity curve, and establishes a theoretical separation density Q and clean coal ash H J Raw coal ash H Y Relation model Q = a 1 +a 2 H J +a 3 H Y +a 4 H J H Y +a 5 H J 2 +a 6 H Y 2 Establishing a relation model Q = b of theoretical separation density Q and clean coal yield D 1 +b 2 D+b 3 D 2 The theoretical separation density Q and the clean coal yield D can be predicted through clean coal ash and raw coal ash; plotting distribution curves, combiningActual on-site sorting Process, calculating sorting accuracy E of the apparatus P Value (half of the difference between the two specific gravities corresponding to a distribution ratio of 75% and 25% on the distribution curve) and sorting density (specific gravity corresponding to a distribution ratio of 50% on the distribution curve), E P The smaller the value, the higher the sorting precision; through the sorting precision and the sorting density, the sorting effect of the selected raw coal after entering the sorting system can be predicted. In the examples of the present invention, a 1 、a 2 、a 3 、a 4 、a 5 、a 6 、b 1 、b 2 、b 3 Are the model coefficients.
In an embodiment, as shown in fig. 1, the intelligent brain analysis processing system of a coal preparation plant further includes: and (4) intelligently sorting and predicting a field model.
In a specific embodiment, the intelligent sorting prediction field model is used for analyzing the relationship between the ash content and the sorting density of raw coal and the ash content and the yield of clean coal on a training field by adopting a deep reinforcement learning algorithm, and performing data modeling to obtain the training sorting density Q under the ash content and the ash content of raw coal in the actual production process X And D X (ii) a Actual sorting Density Q S ＝(Q+Q X ) /2, actual clean coal yield D S ＝(D+D X )/2。
In an embodiment, as shown in fig. 1, the intelligent brain analysis processing system of a coal preparation plant further includes: and (4) an intelligent dense medium sorting model.
In a specific embodiment, the intelligent dense medium sorting model is used for controlling a detection instrument, an execution mechanism and a fuzzy controller through a production organization intelligent decision module to realize online adaptive adjustment of process parameters. Specifically, the method comprises the following steps: establishing the opening degree Y of the flow divider 1 Qualified medium pipe water replenishing valve opening Y 2 And a time of addition Y 3 A relation model of the separation density A of the suspension, the bucket position B of the qualified medium bucket and the content C of the qualified medium magnetic substance:
Y 1 ＝K 0 +K 1 A+K 2 B+K 3 C+K 4 AB+K 5 BC+K 6 AC+K 7 A 2 +K 8 B 2 +K 9 C 2 ，
Y 2 ＝K 00 +K 10 A+K 11 B+K 13 AB+K 14 A 2 +K 15 B 2 ，
Y 3 ＝K+K 16 A+K 17 B+K 18 AB+K 19 A 2 +K 20 B 2 ，
C＝K 21 +K 22 E P +K 23 E P 2 ，
wherein, K 0 、K 1 、K 2 、K 3 、K 4 、K 5 、K 6 、K 7 、K 8 、K 9 、K 00 、K 10 、K 11 、K 13 、K 14 、K 15 、K、K 16 、K 17 、K 18 、K 19 、K 20 、K 21 、K 22 、K 23 Are all model coefficients.
And after the dense medium sorting density is determined according to the market condition, relevant parameters are automatically adjusted, and the sorting density is adjusted to a target value.
A deep reinforcement learning algorithm can be adopted to analyze and train a large amount of field data, establish the relationship between the opening of a shunt valve, the opening of a water replenishing valve of a qualified medium pipe, the medium adding time and the sorting density of the suspension, the bucket position of a qualified medium bucket and the content of magnetic substances of the qualified medium, and perform optimization control; the specific control logic is as follows:
1. when the separation density is reduced to a set value of 1, increasing the opening of a shunt valve; if the sorting density returns to a reasonable interval, reducing the opening of the shunt valve; and if the separation density is continuously reduced to the set value 2, adding media, and stopping adding media after the separation density returns to a reasonable interval after adding the media.
2. After the sorting density exceeds a reasonable interval, supplementing water; and stopping water replenishing after the separation density returns to the reasonable interval.
3. Performing a float-sink test on raw coal, clean coal and gangue under different working conditions to obtain the yield of each density grade of different products, and calculating E under different working conditions P Analyzing the sorting effect of the equipment under different magnetic substance contentsAnd obtaining a reasonable magnetic substance content interval. Reducing the opening of the shunt valve after the content of the magnetic substance exceeds a reasonable interval; and increasing the opening of the shunt valve after the content of the magnetic substance is lower than the reasonable interval.
4. After the position of the medium mixing barrel is lower than a reasonable interval, water is supplemented and medium is added, and the flow distribution is reduced; after the mixing barrel is higher than the reasonable interval, the flow dividing quantity is increased.
In an embodiment, as shown in fig. 1, the intelligent brain analysis processing system of a coal preparation plant further includes: intelligent interpolation module.
In an embodiment, the intelligent medium adding module is used for automatically responding to the medium adding request when the intelligent heavy medium sorting model sends the medium adding request. When the intelligent heavy medium separation model sends a medium adding application, the intelligent medium adding module can automatically respond to the medium adding application to realize automatic action of the blast valve and the flushing robot during medium adding, automatic starting of the medium adding pump and automatic operation of the medium adding magnetic separator; after the medium is prepared, the medium adding instruction is automatically responded, the automatic air blowing and medium adding valve is automatically opened, the pump is automatically started, and medium adding is completed.
In an embodiment, as shown in fig. 1, the intelligent brain analysis processing system of a coal preparation plant further includes: a flexible production module.
In a specific embodiment, the flexible production module is used to study the market demand ash H (ash is linear to calorific value, F = cH) and clean coal ash H of a coal preparation plant J Yield of clean coal D J Slack coal ash H M Yield of slack coal D M And establishing an intelligent decision model of production organization, H = H J D J +H M D M In the market, ash content H and slack coal ash content H are required M Yield of slack coal D M Under relatively constant conditions, the yield D of clean coal is calculated J Maximum value of (H) J Limited in value range, between 1.3 and 1.8), and then calculating the clean coal ash content H J Therefore, the separation density is adjusted, and coal products required by the market are selected to realize the maximization of economic benefits and flexible production of the coal preparation plant (the lower the separation density is, the lower the yield of clean coal is, the lower the ash content of the clean coal is, and the higher the calorific value of the clean coal is).
In an embodiment, as shown in fig. 1, the intelligent brain analysis processing system of a coal preparation plant further includes: intelligent coal blending module.
In a specific embodiment, the intelligent coal blending module is used for calculating the clean coal yield and the slack coal yield according to the coal product ash content required by the market, calculating the clean coal hourly yield and the slack coal hourly yield, automatically adjusting the frequency of a coal feeder for clean coal and slack coal products by combining the clean coal belt scale data and the slack coal belt scale data, and keeping the coal carrying amount of the clean coal and the slack coal within a set interval by combining the fine adjustment of the frequency of the coal feeder and the coarse adjustment of the number of the coal feeders, so that the coal with the required calorific value in the market is blended.
In an embodiment, as shown in fig. 1, the intelligent brain analysis processing system of a coal preparation plant further includes: intelligent filter pressing module.
In one embodiment, the intelligent filter press module is used for automatic overall material supplement, automatic material feeding ending of the filter press and automatic queuing and discharging of the filter press based on an intelligent filter press control algorithm.
In the embodiment of the invention, automatic overall feeding is realized based on an intelligent filter-pressing control algorithm (upper and lower limits of the liquid level of a filter-pressing feeding barrel are set, characteristic parameters such as concentration of a bottom flow of a concentration tank and height of a sludge interface are combined, barrel position and concentration are monitored in real time, feeding is carried out when the barrel position reaches the lower limit, feeding is stopped when the barrel position is higher than the upper limit, feeding is carried out when the concentration is higher than a set value, a relation model is built through depth reinforcement learning), the filter press automatically finishes feeding (according to characteristic parameters such as feeding time, current and filtrate water flow, the filter press feeding finishing state is comprehensively judged by combining historical data, feeding is finished when the filtrate flow is lower than the set value, the relation model is built through depth reinforcement learning), automatic queuing and discharging of the filter presses (according to transfer load calculation, the downstream scraper loads of the filter presses can bear several filter presses, a first-in first-out principle is adopted to realize automatic overall feeding and queuing and discharging of a plurality of filter presses can be automatically stopped according to set time when all the filter presses are in a non-discharging state, and the relation model is built through depth reinforcement learning, and automatic overall production of a filter-pressing system is realized.
In an embodiment, as shown in fig. 1, the intelligent brain analysis processing system of a coal preparation plant further includes: and a sorting granularity prediction module.
In one embodiment, the sorting particle size prediction module is used for comprehensively calculating floating and sinking data of different particle sizes in the sorting process, and determining the optimal sorting particle size under different processes based on a distribution surface mathematical model with double variables of particle size and density.
The invention provides an intelligent brain analysis and processing system for a coal preparation plant, which comprises: the intelligent sorting prediction theoretical model is used for carrying out format yield correction through screening floating and sinking test data of raw coal, clean coal and gangue, drawing a selectivity curve, establishing a relation model of theoretical sorting density and clean coal ash content and raw coal ash content, establishing a relation model of theoretical sorting density and clean coal yield, and predicting the theoretical sorting density and clean coal yield through the clean coal ash content and the raw coal ash content; drawing a distribution curve, and calculating the sorting precision value and sorting density of equipment by combining with an on-site actual sorting process; and predicting the separation effect of the selected raw coal after entering the separation system through the separation precision and the separation density. By applying the intelligent brain analysis processing system of the coal preparation plant, an intensive, safe, efficient and green modern benchmarking coal preparation plant can be built, and closed-loop optimization management of a whole set of production business processes such as equipment operation monitoring, equipment fault early warning, operation state diagnosis, product quality analysis, safe production management, key target tracking and the like is realized; the production and operation of the coal preparation plant have the capabilities of real-time comprehensive perception, deep optimization and cooperation, accurate prediction and early warning and quick scientific decision making, and can also quickly respond to the fluctuation of the coal market, automatically adjust the key production parameters and realize flexible production; finally, the operation targets of dynamic balance, optimal production and optimal benefit are achieved, and the deep revolution of the production mode and the control mode of the coal preparation plant is promoted.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the scope of the invention.
Claims (10)
1. An intelligent brain analysis and processing system for a coal preparation plant, comprising: the intelligent sorting prediction theoretical model is used for carrying out format yield correction through screening floating and sinking test data of raw coal, clean coal and gangue, drawing a selectivity curve, establishing a relation model of theoretical sorting density and clean coal ash content and raw coal ash content, establishing a relation model of theoretical sorting density and clean coal yield, and predicting the theoretical sorting density and clean coal yield through the clean coal ash content and the raw coal ash content; drawing a distribution curve, and calculating the sorting precision value and sorting density of equipment by combining with an on-site actual sorting process; and predicting the separation effect of the selected raw coal after entering the separation system through the separation precision and the separation density.
2. The intelligent brain analysis and processing system of a coal preparation plant according to claim 1,
the relation model of the separation density, the clean coal ash content and the raw coal ash content is as follows:
Q＝a 1 +a 2 H J +a 3 H Y +a 4 H J H Y +a 5 H J 2 +a 6 H Y 2
wherein Q is sorting density, H J Is clean coal ash content, H Y As raw coal ash content, a 1 、a 2 、a 3 、a 4 、a 5 、a 6 Is the model coefficient;
the relation model of the sorting density and the yield of the clean coal is as follows:
Q＝b 1 +b 2 D+b 3 D 2
wherein, b 1 、b 2 、b 3 For model coefficients, D is the clean coal yield.
3. The intelligent brain analysis processing system of a coal preparation plant of claim 1, further comprising: the intelligent separation prediction field model is used for analyzing the relationship between the raw coal ash content and the separation density of a training field and the clean coal ash content and the clean coal yield by adopting a deep reinforcement learning algorithm, and carrying out data modeling to obtain the training separation density of the raw coal ash content and the clean coal ash content in the actual production process.
4. The intelligent brain analysis processing system of a coal preparation plant of claim 1, further comprising: the intelligent heavy medium sorting model is used for controlling a detection instrument, an execution mechanism and a fuzzy controller through an intelligent decision model and carrying out online adaptive adjustment on process parameters; and analyzing and training a large amount of field data by adopting a deep reinforcement learning algorithm, establishing a model of the relationship among the opening of the shunt valve, the opening of the water replenishing valve of the qualified medium pipe, the medium adding time, the separation density of the suspension, the bucket position of the qualified medium bucket and the content of the qualified medium magnetic substances, and performing optimization control.
5. The intelligent brain analysis processing system of the coal preparation plant according to claim 4, wherein the model of the relationship among the opening of the shunt valve, the opening of the water replenishing valve of the qualified medium pipe, the medium replenishing time, the separation density of the suspension, the bucket position of the qualified medium bucket and the content of the qualified medium magnetic substance is as follows:
Y 1 ＝K 0 +K 1 A+K 2 B+K 3 C+K 4 AB+K 5 BC+K 6 AC+K 7 A 2 +K 8 B 2 +K 9 C 2 ；
Y 2 ＝K 00 +K 10 A+K 11 B+K 13 AB+K 14 A 2 +K 15 B 2 ；
Y 3 ＝K+K 16 A+K 17 B+K 18 AB+K 19 A 2 +K 20 B 2 ；
C＝K 21 +K 22 E P +K 23 E P 2 ；
wherein, Y 1 Is the opening degree of the flow divider, Y 2 Opening of water supply valve for qualified medium pipe, Y 3 For medium addition time, A is in suspensionThe liquid separation density, B is the qualified medium barrel position, C is the qualified medium magnetic substance content, K 0 、K 1 、K 2 、K 3 、K 4 、K 5 、K 6 、K 7 、K 8 、K 9 、K 00 、K 10 、K 11 、K 13 、K 14 、K 15 、K、K 16 、K 17 、K 18 、K 19 、K 20 、K 21 、K 22 、K 23 Are all model coefficients, E P The sorting accuracy.
6. The intelligent brain analysis processing system of the coal preparation plant of claim 4, further comprising: and the intelligent medium adding module is used for automatically responding to the medium distribution when the intelligent heavy medium sorting model sends a medium adding application.
7. The intelligent brain analysis processing system of a coal preparation plant of claim 1, further comprising: and the flexible production module is used for analyzing the relation between the ash content required by the market of the coal preparation plant and the clean coal ash content, the clean coal yield, the slack coal ash content and the slack coal yield, establishing an intelligent decision-making model of production organization, calculating the maximum value of the clean coal yield under the condition that the ash content required by the market, the slack coal ash content and the slack coal yield are constant, and further calculating the clean coal ash content, so that the separation density is adjusted, and the coal products required by the market are selected.
8. The intelligent brain analysis processing system of a coal preparation plant of claim 1, further comprising: the intelligent coal blending module is used for calculating the clean coal yield and the end coal yield according to the coal product ash content required by the market, calculating the clean coal hourly yield and the end coal hourly yield, automatically adjusting the frequency of a coal feeder for clean coal and end coal products by combining the clean coal belt scale data and the end coal belt scale data, and keeping the coal carrying amount of the clean coal and the end coal within a set interval by combining the fine adjustment of the frequency of the coal feeder and the coarse adjustment of the number of the coal feeder, so that the coal with the heat required by the market is blended.
9. The intelligent brain analysis processing system of a coal preparation plant of claim 1, further comprising: and the intelligent filter pressing module is used for automatically overall feeding based on an intelligent filter pressing control algorithm, automatically finishing feeding of the filter press and automatically queuing and unloading the filter press.
10. The intelligent brain analysis processing system of a coal preparation plant of claim 1, further comprising: and the sorting granularity prediction module is used for comprehensively calculating the floating and sinking data of different size fractions in the grading and sorting process, and determining the optimal sorting granularity under different processes based on a distribution surface mathematical model with double variables of granularity and density.
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| CN117032110A (en) * | 2023-08-09 | 2023-11-10 | 北京中煤煤炭洗选技术有限公司 | Intelligent management system of coal preparation plant |
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| CN116168113A (en) * | 2023-02-10 | 2023-05-26 | 煤炭科学研究总院有限公司 | Method and system for drawing raw coal selectivity curve and gravity distribution curve |
| CN117195469A (en) * | 2023-07-24 | 2023-12-08 | 国能经济技术研究院有限责任公司 | Method, equipment and medium for determining whole process of coal preparation process flow |
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| CN117772407A (en) * | 2024-01-02 | 2024-03-29 | 北京龙软科技股份有限公司 | Dense medium sorting density adjustment self-adaptive control system |
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| CN119237311A (en) * | 2024-10-21 | 2025-01-03 | 中国矿业大学 | A predictive control method for intelligent process of dry heavy medium coal preparation |
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