CN110605178A - Intelligent control system and method for heavy medium sorting process - Google Patents
Intelligent control system and method for heavy medium sorting process Download PDFInfo
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- B03B5/00—Washing granular, powdered or lumpy materials; Wet separating
- B03B5/28—Washing granular, powdered or lumpy materials; Wet separating by sink-float separation
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Abstract
The invention relates to an intelligent control system and method for a dense-medium separation process, belongs to the technical field of separation processing, and solves the problems that the existing separation density setting is large in degree depending on experience, and other factors influencing the separation density, the product quality control is delayed and a control system is manually intervened. The system comprises: a first ash content measuring device for measuring the ash content of the raw coal; the second ash content measuring device is used for measuring the clean coal ash content obtained after the raw coal is separated by the circulating medium in real time; the production index prediction module is used for processing the raw coal ash and the clean coal ash to obtain a circulating medium density set value; and the circulating medium density adjusting module is used for adjusting the density of the circulating medium based on the deviation between the set value of the density of the circulating medium and the measured value of the density of the circulating medium. The system effectively reduces the dependence of the sorting density setting process on experience, can fully utilize historical data, and improves the timeliness of product quality control.
Description
Technical Field
The invention relates to the technical field of sorting processing, in particular to an intelligent control system and method for a heavy medium sorting process.
Background
The dense medium coal separation technology is an important separation technology in the existing coal separation industry, is widely applied due to good separation effect, and the density of the dense medium determines the quality of the separation effect, so that the separation density of the dense medium can be controlled in real time. The density control means of the existing coal preparation plant is to adopt a circulating medium density value (hereinafter referred to as set density) of a manual setting dense medium system and adopt a PID algorithm to track the set density to carry out density control of the system; the following disadvantages are mainly present: (1) the set density is set by an operator, depends on experience to a great extent and is not necessarily optimal; (2) the basis for setting the sorting density is only the ash content of the product after inspection, and the adjustment lag of setting the density of other factors (3) influencing the sorting density, such as the condition of raw coal and the like, is not comprehensively considered, because the product quality is that manual detection is carried out once at set time intervals, and the value of online detection is not relied on; product quality control is therefore also in practice delayed; (4) the density tracking control needs manual remote adjustment of various valves, which affects the control quality.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide an intelligent control system and method for a dense medium sorting process, so as to solve the problems that the existing sorting density setting depends on a large degree of experience, and other factors affecting the sorting density, the product quality control is delayed, and the control system is not manually intervened.
The purpose of the invention is mainly realized by the following technical scheme:
a dense media sorting process intelligent control system, the system comprising:
a first ash content measuring device for measuring the ash content of the raw coal;
the second ash content measuring device is used for measuring the clean coal ash content obtained after the raw coal is separated by the circulating medium in real time;
the production index prediction module is used for processing the raw coal ash and the clean coal ash to obtain a circulating medium density set value;
and the circulating medium density adjusting module is used for adjusting the density of the circulating medium based on the deviation between the set value of the density of the circulating medium and the measured value of the density of the circulating medium.
On the basis of the scheme, the invention is further improved as follows:
further, the production index prediction module obtains a circulating medium density set value by performing the following operations:
based on the real-time detected raw coal ash content, clean coal ash content, circulating medium density value and historical float and sink information, correcting the historical float and sink information to obtain float and sink information at the current moment; fitting to obtain a selectable curve based on the sinking and floating information at the current moment;
obtaining the theoretical separation density at the current moment according to the measured circulating medium density value at the current moment, the clean coal ash content and the selectivity curve;
and obtaining a circulating medium density set value at the current moment corresponding to the theoretical sorting density at the current moment according to a relation curve of the theoretical sorting density and the circulating medium density.
Further, the relation curve of the theoretical sorting density and the circulating medium density is obtained by the following method:
extracting a plurality of actual circulating medium density values, searching corresponding theoretical separation density by corresponding to the clean coal ash at that time, and fitting to obtain a relation curve between the theoretical separation density and the circulating medium density.
Further, in the production indicator prediction module,
when the change value of the clean coal ash output by the second ash content measuring device relative to the reference ash content is within a preset ash content change range, updating a circulating medium density set value according to the relation curve of the theoretical separation density and the circulating medium density;
and when the change value of the clean coal ash output by the second ash content measuring device relative to the reference ash content is not within the preset ash content change range, updating the relation curve of the theoretical separation density and the circulating medium density, and obtaining an updated circulating medium density set value.
Further, in the suspension density adjusting module, according to a deviation between the circulating medium density set value and the circulating medium density measured value, water supplementing, shunting or concentrated medium supplementing is performed, so that the circulating medium density measured value tracks the circulating medium density set value in real time.
Further, in the suspension density adjusting module,
if the deviation delta between the set value of the density of the circulating medium and the measured value of the density of the circulating medium is less than or equal to 0, supplementing water to a feeding pipeline, wherein the water supplementing quantity delta Q is delta/C;
if the deviation delta between the set value of the density of the circulating medium and the measured value of the density of the circulating medium is larger than 0, the enriched medium or the shunt is optimally determined by adopting a fuzzy control method according to the liquid level of each medium barrel and the content of the magnetic substance in the circulating medium which are detected on line.
And the system further comprises an online evaluation module which is used for calculating indexes including theoretical yield, actual yield and quantity efficiency in real time according to the feed material and product data detected on line and the material properties provided by the production index prediction module, and carrying out online evaluation according to a coal preparation effect evaluation method.
The invention also provides an intelligent control method for the dense medium separation process, which comprises the following steps:
detecting the density values of the raw coal ash, the clean coal ash and the circulating medium at the current moment in real time, and fitting to obtain a selectable curve at the current moment;
obtaining the theoretical separation density at the current moment according to the measured circulating medium density value at the current moment, the clean coal ash content and the selectivity curve;
obtaining a circulating medium density set value at the current moment corresponding to the theoretical sorting density at the current moment according to a relation curve of the theoretical sorting density and the circulating medium density;
and adjusting the density of the circulating medium based on the deviation between the set value of the density of the circulating medium and the measured value of the density of the circulating medium.
On the basis of the scheme, the invention is further improved as follows:
further, real-time detection the raw coal ash content, clean coal ash content and circulating medium density value of present moment, the optional curve of present moment is obtained in the fitting, include:
based on the real-time detected raw coal ash content, clean coal ash content, circulating medium density value and historical float and sink information, correcting the historical float and sink information to obtain float and sink information at the current moment; and fitting to obtain a selectable curve at the current moment based on the sinking and floating information at the current moment.
Further, the relation curve of the theoretical sorting density and the circulating medium density is obtained by the following method: and extracting a plurality of actual circulating medium density values and corresponding theoretical sorting densities, and fitting to obtain a relation curve between the theoretical sorting densities and the circulating medium densities.
The invention has the following beneficial effects: the dense medium sorting process intelligent control system and the dense medium sorting process intelligent control method can obtain the corresponding relation between the corresponding theoretical sorting density and the circulating medium density according to the feeding property of the dense medium sorting system, predict and give the circulating medium density set value of the dense medium sorting process intelligent control system, and adjust the circulating medium density based on the circulating medium density set value. Effectively reduces the dependence degree of the dense medium separation process on experience and realizes the real-time control of the product quality.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a schematic structural diagram of an intelligent control system for a dense medium separation process disclosed in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of an alternative curve in embodiment 1 of the present invention;
fig. 3 is a flowchart of an intelligent control method for the dense medium sorting process disclosed in embodiment 2 of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
One embodiment of the present invention discloses an intelligent control system for a dense medium sorting process, a schematic structural diagram of which is shown in fig. 1, and the system comprises: a first ash content measuring device (e.g., an ash meter 1#) for measuring the ash content of the raw coal; the second ash content measuring device (such as an ash meter 2#) is used for measuring the ash content of clean coal obtained after the raw coal is subjected to medium separation in real time; the production index prediction module is used for processing the raw coal ash and the clean coal ash to obtain a circulating medium density set value; and the suspension density adjusting module is used for adjusting the density of the circulating medium based on the deviation between the set value of the density of the circulating medium and the measured value of the density of the circulating medium (which can be measured by a density meter).
Preferably, the production index prediction module obtains the set value of the density of the circulating medium by performing the following operations:
(1) based on the real-time detected raw coal ash content and historical float and sink data, correcting the historical float and sink data to obtain float and sink data at the current moment; fitting to obtain a selectable curve based on the sinking and floating data at the current moment; the embodiment also provides a specific implementation manner for implementing the process, as follows:
in the practical application process, according to real-time detection data (ash content and weight of the fed raw coal), historical data (raw coal float and sink data (also called float and sink information), product yield, yield and ash content, historical circulating medium density and the like) and rapid production inspection data (quick ash and quick floating) of the dense medium system, the historical float and sink data are corrected according to online detection of the ash content, so that changed raw coal property data (mainly real-time raw coal float and sink data, including yield and ash content of different density levels) are predicted in real time; examples of historical sink and float data are shown in table 1.
TABLE 1 historical sink and float data sample
The method comprises the following specific steps:
1) comparing the ash content Ai of the raw coal obtained by real-time detection with the ash content Ay of the raw coal in historical float and sink data (see the historical float and sink data sample in the table 1), and calculating delta Ai to be Ai-Ay;
2) if the absolute value of the delta Ai is less than 0.2, correcting the ash content of the historical float and sink data by using an ash content correction method; if the absolute value of the delta Ai is more than 0.2, the historical floating and sinking data is corrected by a quantity correction method. Thus, new floating and sinking data at the ith time can be obtained, and the data can also be historical floating and sinking data at the next time.
And (4) performing mathematical fitting on the floating and sinking data at the ith moment to obtain a mathematical model (8 models can be available), so as to obtain the selectivity curve.
In the mode, various indexes can be calculated on line in real time by using the on-line detection data and the properties of the raw materials obtained by prediction optimization calculation without long-time experiments. The formulation of the calculation is common, but the connotation is the real-time result of the data calculation that changes in real-time.
The theoretical yield is obtained from an alternative curve, which can be expressed by 8 mathematical models (all described in the literature) according to the current research results. The present claim is characterized in that the parameters in the formula are real-time parameters after on-line real-time data fitting, rather than constants in general expressions.
For example, the arctan model:
y＝100(t2-arctan(k(x-c)))/(t2-t1)
wherein t is1、t2K and c are calculated by the ash content detection data of the feeding material at the ith moment. Then, when x1 expresses the required fly ash, the yield y1 is calculated, and the calculated yield is taken as x1 by using the formula, which corresponds to the required fly ashAnd calculating the theoretical separation density.
The actual yield is calculated as: γ 1 ═ Q2 × 100%/Q1
Wherein Q2 is the cleaned coal weight detected by the cleaned coal belt scale, and Q1 is the raw coal weight detected by the raw coal belt scale.
The calculation formula of the quantity efficiency is as follows:
where γ 1 is the calculated actual yield and γ 10 is the calculated theoretical yield.
The selectivity curves are a set of 5 curves, the two basic ones being the float yield-ash curve β and the float yield-density curve δ, from which are derived the float yield-elementary ash curve λ, the sediment yield-ash curve θ, and the delta ± 0.1 content-density curve ε. As shown in fig. 2.
(2) Obtaining the theoretical separation density at the current moment according to the actual circulating medium density, the clean coal ash content and the selectivity curve at the current moment; in particular, the amount of the solvent to be used,
detecting the measured circulating medium density value and the corresponding clean coal ash, obtaining a theoretical separation density (as shown in fig. 2) from a float yield-ash curve beta and a float yield-density curve delta according to the actually detected clean coal ash, and obtaining a current-time circulating medium density set value corresponding to the current-time theoretical separation density according to a relation curve of the theoretical separation density and the circulating medium density.
In the practical application process, through experiments, the circulating medium density measured values are changed for many times, so that a plurality of corresponding theoretical sorting densities (or similar data are sorted out by using the historical data) can be obtained, and a relation curve (generally a linear equation, in the form of y ═ a + bx,) between the theoretical sorting densities and the circulating medium density can be fitted by using a series of circulating medium density measured values to correspond to the theoretical sorting density data;
therefore, to change the ash content of the product, the corresponding theoretical sorting density is obtained by searching the optional line curve, so that the set density (namely the density of the circulating medium) of the suspension of the dense medium system is predicted according to the relation, and the suspension density adjusting system has an adjusting basis.
Preferably, in the production index prediction module, when a change value of the clean coal ash output by the second ash measurement device relative to a reference ash value is within a preset ash change range, a circulating medium density set value is updated according to a relation curve between the theoretical separation density and the circulating medium density; when the change value of the clean coal ash output by the second ash content measuring device relative to the reference ash content is not within the preset ash content change range, the change of the material property is large, and at the moment, the relation curve of the theoretical separation density and the density of the circulating medium needs to be updated to ensure the consistency of the corresponding relation and the raw coal property. And after updating the relation curve, obtaining an updated set value of the density of the circulating medium. The specific method comprises the following steps: if the change value delta A of the ash content of the online product belongs to { x1, x2}, directly predicting and calculating a new set value of the density of the circulating medium according to the relation curve of the theoretical sorting density and the density of the circulating medium; if the change value delta A of the ash content of the online product exceeds the value of x1 and x2, the relation curve of the theoretical sorting density and the density of the circulating medium is recalculated. Wherein { x1, x2} is an ash variation range, which is determined by the actual conditions of the enterprise.
The feedforward control and the feedback control are combined, and the relation is as follows: the feedforward control is mainly used for determining the correlation between the density composition of raw coal and ash content or determining the variation range of the density of a circulating medium according to the property of the raw material when a production system starts to run or the property of the raw material varies greatly, preliminarily giving the set density of the circulating medium and assisting in carrying out the feedback control; and the feedback control is used in the production process, and when the property of the raw material is not changed greatly, the set density is slightly adjusted within the adjusting range set by the feedforward control according to the ash content of the product.
Preferably, in the suspension density adjusting module, according to a deviation between the circulating medium density set value and the circulating medium density measured value, a water supplementing, shunting or concentrated medium supplementing operation is performed, so that the circulating medium density measured value tracks the circulating medium density set value in real time. The specific process is as follows: and if the density set value delta 0-the actual density measured value delta i is equal to or less than 0, supplementing water to the feeding pipeline, wherein the water supplementing quantity delta Q is determined according to the relation of delta Q and delta/C, and experiments are specifically needed. When Δ δ is greater than 0, the fuzzy control method is adopted to determine whether to supplement the enriched medium or split the enriched medium according to the liquid levels (measured by a level meter) of the medium barrels (the qualified medium barrel and the dilute medium barrel in fig. 1) and the magnetic content (measured by a magnetic content meter) in the circulating medium, which are detected on line, for example, the barrel positions of the qualified medium barrels are divided into 5 fuzzy sets, the valve opening of the feeding pipe is divided into 9 grades, and the membership degree of the fuzzy sets of the examples is shown in table 2. When the grade value is a negative value, the materials are discharged into the barrel, and when the grade value is a positive value, the shunt is opened, namely the materials are discharged into the dilute medium barrel. Thereby ensuring that the density of the circulating medium tracks the set value of the density in real time; the suspension density adjusting system consists of various actuators and driving adjusting strategy software thereof.
Table 2 control quantity variation division table in fuzzy control method
The automatic adjustment of each valve, bucket position realizes following function through optimizing control package: according to the set value of the density of the circulating medium, the measured value of the density of the circulating medium and the actual barrel position of the medium barrel, an intelligent optimization control algorithm-fuzzy control is adopted, fuzzy membership tables of qualified medium barrels and dilute medium barrels as shown in an example attached table 2 are specifically set, a control rule under each fuzzy membership condition is set by matching with expert knowledge, the optimal barrel position and the valve opening degree are calculated by using a fuzzy control algorithm, a suspension density adjusting system is instructed to automatically adjust the valve opening degree to an appropriate value, and the conditions that the liquid level of each barrel is appropriate and the difference value between the medium density and the set density is smaller than a user specified value are ensured.
In order to track and supervise the operation condition of the system, the system is also provided with an online evaluation module. The online evaluation module can calculate indexes such as theoretical yield, actual yield, quantity efficiency and the like in real time according to the data of the feeding materials and the products detected on line and the material properties provided by the production index prediction system, and perform online evaluation according to the coal dressing effect evaluation method. The evaluation of the sorting result is common in the field of coal sorting, and the evaluation method and the formula are shown in the management teaching materials of coal sorting plants. However, there is no precedent for online evaluation in the past, and the difference between the form of the evaluation formula of the invention and the original formula is that the time dimension is introduced, and the data which can be obtained originally through experiments and calculations of several days or even dozens of days is obtained in real time by adopting online detection or predictive calculation at present, and the data with different time frequencies can be selected, so that the online evaluation can be realized. The online evaluation module can acquire corresponding data by means of the instruments marked in fig. 1 to realize calculation of the relevant indexes. The measurement elements selected in the present embodiment are all measurement elements commonly used in the art, and the functions of several main elements are only exemplified here. Such as: taking an ash meter 1# as a first ash measuring device for measuring the ash content of the raw coal, and measuring the weight of the raw coal by using a belt scale 1 #; taking an ash meter 2# as a second ash content measuring device for measuring the ash content of the cleaned coal obtained after the raw coal is sorted in real time, and measuring the weight of the cleaned coal by using a belt scale 2 #; the weight of the middling coal is also measured by using a belt scale 3 #; the system can also be specially provided with a data acquisition module, and raw coal data, historical data, production inspection data and raw coal floating and sinking data are sent to the data acquisition module in a unified way and then are processed by the production index prediction module.
In the execution process of the system, various required data are collected in a centralized way through the data collection system. When the raw coal enters the process system, the data (namely real-time detection data) measured by the on-line detection instrument enters the data acquisition system, meanwhile, production inspection data (production inspection is a process of manual sampling and testing in the production process) is input and is transmitted to the production index prediction system in real time together with original historical data, raw coal floating and sinking data and the like; the production index prediction system calculates a reasonable set density (namely a density set value) of the circulating medium, and feeds the reasonable set density (namely the density set value) of the circulating medium into the suspension density adjusting system in a feedforward mode; the suspension density adjusting system simultaneously receives an instruction of the optimization control packet, adjusts various barrel positions, valves, flow distribution boxes and the like, tracks a density set value and ensures that the suspension density meets the set density. When the evaluation system indicates that the quality of the clean coal product does not meet the requirements or other evaluation indexes are unreasonable, the production index prediction system finely adjusts the set density again according to the real-time detection data, the historical data and the quality change of the raw coal, feeds the set density back to the suspension density adjustment system, and starts a new round of density adjustment.
Compared with the prior art, the dense medium separation process intelligent control system disclosed in the embodiment can obtain the corresponding relation between the corresponding theoretical separation density and the circulating medium density according to the feeding property of the dense medium system, predict and give the circulating medium density set value of the dense medium separation process intelligent control system, and adjust the circulating medium density based on the circulating medium density set value. Effectively reduces the dependence degree of the dense medium separation process on experience and realizes the real-time control of the product quality.
Example 2
In embodiment 2 of the present invention, an intelligent control method for a dense medium sorting process is disclosed, and a flowchart is shown in fig. 3, and includes the following steps:
step S1: detecting the density values of the raw coal ash, the clean coal ash and the circulating medium at the current moment in real time, and fitting to obtain a selectable curve at the current moment;
step S2: obtaining the theoretical separation density at the current moment according to the measured circulating medium density value at the current moment, the clean coal ash content and the selectivity curve;
step S3: obtaining a circulating medium density set value at the current moment corresponding to the theoretical sorting density at the current moment according to a relation curve of the theoretical sorting density and the circulating medium density;
step S4: and adjusting the density of the circulating medium based on the deviation between the set value of the density of the circulating medium and the measured value of the density of the circulating medium.
Preferably, the real-time detection of the values of the density of the raw coal ash, the clean coal ash and the circulating medium at the current moment, and the fitting to obtain the selectable curve at the current moment comprise:
based on the real-time detected raw coal ash content, clean coal ash content, circulating medium density value and historical float and sink information, correcting the historical float and sink information to obtain float and sink information at the current moment; and fitting to obtain a selectable curve at the current moment based on the sinking and floating information at the current moment.
Preferably, the theoretical sort density versus circulating media density curve is obtained by: and extracting a plurality of actual circulating medium density values and corresponding theoretical sorting densities, and fitting to obtain a relation curve between the theoretical sorting densities and the circulating medium densities.
The specific implementation process of the method embodiment of the present invention may refer to the system embodiment described above, and this embodiment is not described herein again. Since the principle of the present embodiment is the same as that of the above method embodiment, the present system also has the corresponding technical effects of the above method embodiment.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A dense media sorting process intelligent control system, the system comprising:
a first ash content measuring device for measuring the ash content of the raw coal;
the second ash content measuring device is used for measuring the clean coal ash content obtained after the raw coal is separated by the circulating medium in real time;
the production index prediction module is used for processing the raw coal ash and the clean coal ash to obtain a circulating medium density set value;
and the circulating medium density adjusting module is used for adjusting the density of the circulating medium based on the deviation between the set value of the density of the circulating medium and the measured value of the density of the circulating medium.
2. The dense media separation process intelligent control system of claim 1, wherein the production index prediction module obtains a circulating media density setpoint by performing the following operations:
based on the real-time detected raw coal ash content, clean coal ash content, circulating medium density value and historical float and sink information, correcting the historical float and sink information to obtain float and sink information at the current moment; fitting to obtain a selectable curve based on the sinking and floating information at the current moment;
obtaining the theoretical separation density at the current moment according to the measured circulating medium density value at the current moment, the clean coal ash content and the selectivity curve;
and obtaining a circulating medium density set value at the current moment corresponding to the theoretical sorting density at the current moment according to a relation curve of the theoretical sorting density and the circulating medium density.
3. The dense media sorting process intelligence control system of claim 2, wherein the theoretical sorting density versus circulating media density curve is obtained by:
extracting a plurality of actual circulating medium density values, searching corresponding theoretical separation density by corresponding to the clean coal ash at that time, and fitting to obtain a relation curve between the theoretical separation density and the circulating medium density.
4. The intelligent heavy media sorting process control system according to claim 2 or 3, wherein, in the production indicator prediction module,
when the change value of the clean coal ash output by the second ash content measuring device relative to the reference ash content is within a preset ash content change range, updating a circulating medium density set value according to the relation curve of the theoretical separation density and the circulating medium density;
and when the change value of the clean coal ash output by the second ash content measuring device relative to the reference ash content is not within the preset ash content change range, updating the relation curve of the theoretical separation density and the circulating medium density, and obtaining an updated circulating medium density set value.
5. The intelligent dense-media separation process control system according to claim 1, wherein in the suspension density adjustment module, according to a deviation between the circulating medium density set value and a circulating medium density measured value, a water supplementing, a shunting, or a concentrated medium supplementing operation is performed, so that the circulating medium density measured value tracks the circulating medium density set value in real time.
6. The dense-media sorting process intelligence control system of claim 5, wherein, in the suspension density adjustment module,
if the deviation delta between the set value of the density of the circulating medium and the measured value of the density of the circulating medium is less than or equal to 0, supplementing water to a feeding pipeline, wherein the water supplementing quantity delta Q is delta/C;
if the deviation delta between the set value of the density of the circulating medium and the measured value of the density of the circulating medium is larger than 0, the enriched medium or the shunt is optimally determined by adopting a fuzzy control method according to the liquid level of each medium barrel and the content of the magnetic substance in the circulating medium which are detected on line.
7. The intelligent control system according to claim 1, further comprising an online evaluation module for calculating in real time indicators including theoretical yield, actual yield, and quantity efficiency according to the online detected feed and product data and the material properties provided by the production indicator prediction module, and performing online evaluation according to a coal dressing effect evaluation method.
8. An intelligent control method for a dense medium sorting process is characterized by comprising the following steps:
detecting the density values of the raw coal ash, the clean coal ash and the circulating medium at the current moment in real time, and fitting to obtain a selectable curve at the current moment;
obtaining the theoretical separation density at the current moment according to the measured circulating medium density value at the current moment, the clean coal ash content and the selectivity curve;
obtaining a circulating medium density set value at the current moment corresponding to the theoretical sorting density at the current moment according to a relation curve of the theoretical sorting density and the circulating medium density;
and adjusting the density of the circulating medium based on the deviation between the set value of the density of the circulating medium and the measured value of the density of the circulating medium.
9. The intelligent control method for the dense-medium separation process according to claim 8, wherein the real-time detection of the values of the density of the raw coal ash, the clean coal ash and the circulating medium at the current moment is performed to obtain a selectable curve at the current moment by fitting, and the method comprises the following steps:
based on the real-time detected raw coal ash content, clean coal ash content, circulating medium density value and historical float and sink information, correcting the historical float and sink information to obtain float and sink information at the current moment; and fitting to obtain a selectable curve at the current moment based on the sinking and floating information at the current moment.
10. The intelligent heavy-media sorting process control method according to claim 8, wherein the theoretical sorting density versus circulating medium density curve is obtained by:
and extracting a plurality of actual circulating medium density values and corresponding theoretical sorting densities, and fitting to obtain a relation curve between the theoretical sorting densities and the circulating medium densities.
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| WO2021057349A1 (en) * | 2019-09-23 | 2021-04-01 | 中国矿业大学 | Intelligent control system and method for heavy medium separation process |
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