WO2021057349A1 - 一种重介分选过程智能控制系统及方法 - Google Patents

一种重介分选过程智能控制系统及方法 Download PDF

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WO2021057349A1
WO2021057349A1 PCT/CN2020/110366 CN2020110366W WO2021057349A1 WO 2021057349 A1 WO2021057349 A1 WO 2021057349A1 CN 2020110366 W CN2020110366 W CN 2020110366W WO 2021057349 A1 WO2021057349 A1 WO 2021057349A1
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density
circulating medium
ash content
medium density
sorting
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PCT/CN2020/110366
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English (en)
French (fr)
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匡亚莉
王光辉
王章国
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中国矿业大学
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
    • B03B—SEPARATING SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS
    • B03B5/00—Washing granular, powdered or lumpy materials; Wet separating
    • B03B5/28—Washing granular, powdered or lumpy materials; Wet separating by sink-float separation
    • B03B5/30—Washing granular, powdered or lumpy materials; Wet separating by sink-float separation using heavy liquids or suspensions
    • B03B5/44—Application of particular media therefor

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  • This application relates to the technical field of sorting and processing, and in particular to an intelligent control system and method for a heavy-medium sorting process.
  • Dense medium coal preparation technology is an important separation technology in the coal preparation industry. It is widely used because of its good separation effect.
  • the density of the heavy medium determines the quality of the separation effect, so it can control the separation of the heavy medium in real time. Density is very necessary.
  • the density control method of the existing coal preparation plant is to manually set the density value of the circulating medium of the heavy medium system (hereinafter referred to as the set density), and use the PID algorithm to track the set density for system density control; the main defects are as follows: (1) The level of the set density is set by the operator, which depends to a large extent on experience, and is not necessarily the best; (2) The basis for the setting of the sorting density is only the ash content of the product after inspection, without comprehensive consideration such as raw coal.
  • this application aims to provide an intelligent control system and method for the heavy-medium sorting process, which is used to solve the problem that the existing sorting density setting relies on experience to a large extent, without comprehensive consideration of other factors affecting the sorting density, The product quality control is lagging behind, and the problem of manual intervention control system.
  • An intelligent control system for a heavy-medium sorting process includes:
  • the first ash content measuring device is used to measure the ash content of raw coal
  • the second ash content measuring device is used for real-time measurement of the ash content of the clean coal obtained after the raw coal is separated by the circulating medium;
  • the production index prediction module is used to process the ash content of the raw coal and the ash content of the clean coal to obtain a set value of the density of the circulating medium;
  • the circulating medium density adjustment module adjusts the circulating medium density based on the deviation between the set value of the circulating medium density and the actual measured value of the circulating medium density.
  • the production index prediction module obtains the set value of the circulating medium density by executing the following operations:
  • the current setting value of the circulating medium density corresponding to the theoretical sorting density at the current moment is obtained.
  • a plurality of actual circulating medium density values are extracted, and the corresponding theoretical sorting density is found by corresponding to the ash content of the clean coal at that time, and the relationship curve between the theoretical sorting density and the circulating medium density is obtained by fitting.
  • the circulating medium is updated according to the relationship curve between the theoretical sorting density and the circulating medium density Density setting value;
  • the suspension density adjustment module according to the deviation between the set value of the circulating medium density and the actual measured value of the circulating medium density, the action of replenishing water, diverting or replenishing the concentrated medium is performed, so that the density of the circulating medium is actually measured The value tracks the set value of the circulating medium density in real time.
  • the fuzzy control method is adopted to optimize the liquid level of each medium barrel and the magnetic content in the circulating medium detected online. Make sure to add concentrated media or divert.
  • an online evaluation module for real-time calculation of indicators including theoretical yield, actual yield, and quantity efficiency based on the incoming material and product data detected online, and the properties of the raw materials provided by the production index prediction module , And conduct online evaluation according to the evaluation method of coal preparation effect.
  • This application also provides an intelligent control method for the heavy-media sorting process, which includes the following steps:
  • the density of the circulating medium is adjusted based on the deviation between the set value of the circulating medium density and the actual measured value of the circulating medium density.
  • the real-time detection of the raw coal ash content, the clean coal ash content, and the density value of the circulating medium at the current moment, and fitting the selectability curve at the current moment includes:
  • the relationship curve between the theoretical sorting density and the circulating medium density is obtained by the following method: extracting a plurality of actual circulating medium density values and corresponding theoretical sorting densities, and fitting to obtain the theoretical sorting density and the circulating medium density Relationship lines.
  • the beneficial effects of this application are as follows:
  • the intelligent control system and method for the heavy-medium sorting process provided in this application can obtain the corresponding theoretical sorting density and the corresponding relationship between the density of the circulating medium according to the feeding properties of the heavy-medium system, and predict and give
  • the density of the circulating medium is set by the intelligent control system of the heavy-medium sorting process, and the density of the circulating medium is adjusted based on the set value of the circulating medium density.
  • the reliance on experience of the heavy-medium sorting process is effectively reduced, and the real-time control of product quality is realized.
  • FIG. 1 is a schematic diagram of the structure of the intelligent control system for the heavy-medium sorting process disclosed in Embodiment 1 of the application;
  • FIG. 2 is a schematic diagram of the optional curve in Embodiment 1 of the application.
  • FIG. 3 is a flowchart of the intelligent control method for the heavy-medium sorting process disclosed in Embodiment 2 of the application.
  • a specific embodiment of the present application discloses an intelligent control system for the heavy-medium sorting process.
  • the schematic diagram of the structure is shown in Fig. 1.
  • the system includes: a first ash measuring device (for example, ash measuring instrument 1#) for measuring The ash content of raw coal; the second ash content measuring device (such as ash measuring instrument 2#) is used to measure the ash content of the clean coal obtained after the raw coal is separated by the medium in real time; the production index prediction module is used to process the ash content of the raw coal and the clean coal The ash content obtains the set value of the circulating medium density; the suspension density adjustment module adjusts the density of the circulating medium based on the deviation between the set value of the circulating medium density and the actual measured value of the circulating medium density (measured by a densitometer).
  • a first ash measuring device for example, ash measuring instrument 1#
  • the second ash content measuring device such as ash measuring instrument 2#
  • the production index prediction module is used to process the ash content of
  • the production index prediction module obtains the set value of the circulating medium density by executing the following operations:
  • the actual application process it can be based on the real-time detection data of the heavy medium system (incoming raw coal ash, weight), historical data (raw coal floating and sinking information (also known as floating and sinking information), product output, yield and ash, historical circulating medium density, etc. ) And fast production inspection data (fast ash, fast float), based on online detection of ash content, revise historical float and sink data, so as to predict the changed raw coal property data in real time (mainly real-time raw coal float and sink data, including yields of different density levels And ash); the sample of historical ups and downs data is shown in Table 1.
  • Mathematical fitting is performed on the floating and sinking data at the i-th moment to obtain a mathematical model (there can be 8 models), thereby obtaining a selectability curve.
  • the selectivity curve can be expressed by 8 mathematical models (all described in the literature).
  • the claim is characterized in that the parameters in the formula are real-time parameters after online real-time data fitting, rather than constants in general expressions.
  • the arctangent model is characterized in that the parameters in the formula are real-time parameters after online real-time data fitting, rather than constants in general expressions.
  • t 1 , t 2 , k, and c are all calculated from the ash detection data of the input material at the i-th time. Then, when x1 expresses the requirement of clean coal ash content, calculate the yield y1, and then use this formula to use the calculated yield as x1 to calculate the theoretical sorting density correspondingly.
  • Q2 is the weight of clean coal detected by the clean coal belt scale
  • Q1 is the weight of raw coal detected by the belt scale of raw coal.
  • the selectability curve is a set of 5 curves, the basic two are the float yield-ash curve ⁇ and the float yield-density curve ⁇ , from which the float yield-primitive ash curve ⁇ , sink yield- Ash content curve ⁇ , and ⁇ 0.1 content-density curve ⁇ . as shown in picture 2.
  • the production index prediction module when the change value of the ash content of the clean coal output by the second ash content measurement device with respect to the reference ash content value is within a preset ash content change range, sorting according to the theory The relationship curve between density and the density of the circulating medium, and the setting value of the circulating medium density is updated; when the change value of the ash content of the clean coal output by the second ash content measuring device with respect to the reference ash value is not within the preset ash content change range, it indicates The properties of the raw materials have changed greatly. At this time, the relationship curve between the theoretical separation density and the density of the circulating medium needs to be updated to ensure the consistency of the corresponding relationship with the properties of the raw coal.
  • the updated setting value of the circulating medium density is obtained.
  • the specific method is: if the change value of the ash content of the online product ⁇ A ⁇ ⁇ x1, x2 ⁇ , according to the relationship curve between the theoretical sorting density and the density of the circulating medium described above, directly predict and calculate the new set value of the circulating medium density; if online The product ash change value ⁇ A exceeds the ⁇ x1, x2 ⁇ value, and the relationship curve between the theoretical separation density and the density of the circulating medium is recalculated.
  • ⁇ x1, x2 ⁇ is a range of ash content, which is determined by the actual situation of the enterprise.
  • the control strategy that combines the feedforward control and feedback control has the relationship: the feedforward control is mainly used to determine the density of raw coal according to the nature of the raw material when the production system starts to start, or when the nature of the raw material changes greatly The relationship between the composition and ash content, or determine the range of density of the circulating medium, preliminarily set the set density of the circulating medium, and assist in the feedback control; and the feedback control is used in the production process when the raw material properties do not change. When it is large, according to the ash content of the product, the set density is fine-tuned within the adjustment range set by the feedforward control.
  • the action of replenishing water, diverting or replenishing the concentrated medium is performed to make the circulating medium density
  • the measured value tracks the set value of the circulating medium density in real time.
  • the fuzzy control method is used to determine the additional concentrated medium or the split flow.
  • the barrel position of the qualified medium barrel is divided into 5 fuzzy sets, and the valve opening of the feed pipe is divided into 9 levels.
  • the example is fuzzy The set membership degree is shown in Table 2.
  • the automatic adjustment of each valve and barrel position can be realized by the optimization control package as follows: According to the set value of the circulating medium density, the measured value of the circulating medium density and the actual barrel position of the medium barrel, the intelligent optimization control algorithm-fuzzy control can be adopted. , Specifically set the fuzzy membership table of qualified medium barrels and thin medium barrels in the example attached table 2, and cooperate with expert knowledge to set the control rules for each fuzzy membership degree, and use the fuzzy control algorithm to calculate the optimal barrel position and The valve opening, instructs the suspension density adjustment system to automatically adjust the valve opening to an appropriate value to ensure that the liquid level of each barrel is appropriate and the difference between the medium density and the set density is less than the user's specified value.
  • the system has also set up an online evaluation module.
  • the online evaluation module can calculate the theoretical yield, actual yield, quantity efficiency and other indicators in real time according to the data of the incoming materials and products detected online, and the properties of the raw materials provided by the production index prediction system, and according to the coal preparation effect evaluation method Conduct an online evaluation.
  • the evaluation of sorting results is common in the field of coal preparation, and the evaluation methods and formulas can be found in the coal preparation plant management textbook. However, there is no precedent for online evaluation in the past.
  • the difference between the form of the evaluation formula of this application and the original formula is the introduction of time dimension. Data that originally took several days or even dozens of days of experiment and calculation can be obtained. Now online detection or prediction is used.
  • the online evaluation module can obtain the corresponding data with the aid of the instruments marked in Figure 1 to realize the calculation of relevant indicators.
  • the measurement elements selected in this embodiment are all commonly used measurement elements in the field, and the functions of a few main elements are only listed here as an example.
  • the ash measuring instrument 1# is used as the first ash measuring device to measure the ash content of raw coal, and the belt scale 1# is used to measure the weight of the raw coal; the ash measuring instrument 2# is used as the second ash measuring device to measure the raw coal ash content in real time After separation, the ash content of the clean coal is obtained, and the weight of the clean coal is measured by the belt scale 2#; the weight of the medium coal is also measured by the belt scale 3#; the data collection module can also be set up to collect the raw coal data, historical data, production inspection data, and raw coal floating and sinking After the data are uniformly sent to the data collection module, they are then handed over to the production index prediction module for processing.
  • the data collection system collects all the required data first.
  • the data measured by the online detection instrument ie real-time detection data
  • the production inspection data is the process of manual sampling and testing in the production process
  • the historical data, raw coal floating and sinking data, etc. are sent to the production index forecasting system in real time; the production index forecasting system calculates a reasonable set density of the circulating medium (that is, the set density value), and feeds it forward to the suspension density adjustment system;
  • the liquid density adjustment system also receives instructions from the optimization control package, adjusts various barrel positions, valves, shunt boxes, etc., and tracks the density setting value to ensure that the suspension density meets the set density.
  • the production index prediction system again fine-tunes the set density based on real-time detection data and historical data, and changes in raw coal quality, and feeds it back to the suspension density adjustment system. Start a new round of density adjustment.
  • the intelligent control system for the heavy-medium sorting process disclosed in this embodiment can obtain the corresponding theoretical sorting density and the corresponding relationship between the circulating medium density according to the feeding properties of the heavy-medium system, and predict and give
  • the density of the circulating medium is set by the intelligent control system of the heavy-medium sorting process, and the density of the circulating medium is adjusted based on the set value of the circulating medium density.
  • the reliance on experience of the heavy-medium sorting process is effectively reduced, and the real-time control of product quality is realized.
  • Embodiment 2 of the present application an intelligent control method for the heavy-medium sorting process is disclosed.
  • the flowchart is shown in Fig. 3 and includes the following steps:
  • Step S1 real-time detection of the raw coal ash content, the clean coal ash content and the density value of the circulating medium at the current moment, and fitting the selectability curve at the current moment;
  • Step S2 Obtain the theoretical sorting density at the current time according to the actual measured value of the circulating medium density at the current time, the ash content of the clean coal, and the selectability curve;
  • Step S3 According to the relationship curve between the theoretical sorting density and the circulating medium density, obtain the current circulating medium density setting value corresponding to the theoretical sorting density at the current moment;
  • Step S4 Adjust the density of the circulating medium based on the deviation between the set value of the circulating medium density and the actual measured value of the circulating medium density.
  • the real-time detection of the raw coal ash content, the clean coal ash content, and the density value of the circulating medium at the current time, and fitting the selectability curve at the current time includes:
  • the relationship curve between the theoretical sorting density and the circulating medium density is obtained by: extracting a plurality of actual circulating medium density values and corresponding theoretical sorting densities, and fitting to obtain the theoretical sorting density and the circulating medium density The relationship curve.
  • the process of implementing the methods in the above-mentioned embodiments can be completed by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium.
  • the computer-readable storage medium is a magnetic disk, an optical disk, a read-only storage memory or a random storage memory, etc.

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Abstract

一种重介分选过程智能控制系统，该系统包括：第一灰分测量装置，用于测量原煤灰分；第二灰分测量装置，用于实时测量原煤经循环介质分选后得到的精煤灰分；生产指标预测模块，用于处理所述原煤灰分和所述精煤灰分，得到循环介质密度设定值；循环介质密度调节模块，基于所述循环介质密度设定值与循环介质密度实测值之间的偏差，调节循环介质密度。该系统有效降低了分选密度设定过程对于经验的依赖，能够充分利用历史数据，提高了产品质量控制的时效性。还包括一种重介分选过程智能控制方法。

Description

一种重介分选过程智能控制系统及方法 技术领域
本申请涉及分选加工技术领域，尤其涉及一种重介分选过程智能控制系统及方法。
背景技术
重介质选煤技术是现在选煤行业中重要的分选技术，因其分选效果好而被广泛应用，重介质的密度决定了分选效果的好坏，所以能够实时控制重介质的分选密度十分必要。现有的选煤厂的密度控制手段是，采用人工设定重介系统的循环介质密度值(以下简称设定密度)，采用PID算法跟踪设定密度进行系统的密度控制；主要存在以下缺陷：(1)设定密度的高低由操作人员设定，很大程度上依赖经验，并不一定最佳；(2)设定分选密度的依据只是检验后的产品灰分，没有综合考虑如原煤情况等其他影响分选密度因素(3)设定密度的调节滞后，因为产品质量是设定时间间隔进行一次人工检测，而在线检测的值不足为凭；因此实际上产品质量控制也是滞后的；(4)密度跟踪控制需要人工远程调节各种阀门，影响控制质量。
发明内容
鉴于上述的分析，本申请旨在提供一种重介分选过程智能控制系统及方法，用以解决现有分选密度设定依赖经验的程度较大，没有综合考虑其他影响分选密度因素、产品质量控制滞后、人工干预控制系统的问题。
本申请的目的主要是通过以下技术方案实现的：
一种重介分选过程智能控制系统，所述系统包括：
第一灰分测量装置，用于测量原煤灰分；
第二灰分测量装置，用于实时测量原煤经循环介质分选后得到的精煤灰分；
生产指标预测模块，用于处理所述原煤灰分和所述精煤灰分，得到循环介质密度设定值；
循环介质密度调节模块，基于所述循环介质密度设定值与循环介质密度实测值之间的偏差，调节循环介质密度。
在上述方案的基础上，本申请还做了如下改进：
进一步，所述生产指标预测模块，通过执行以下操作，得到循环介质密度设定值：
基于实时检测到的所述原煤灰分、精煤灰分、循环介质密度值以及历史浮沉信息，修正所述历史浮沉信息，得到当前时刻的浮沉信息；基于所述当前时刻的浮沉信息，拟合得到可选性曲线；
根据当前时刻的循环介质密度实测值、精煤灰分以及所述可选性曲线，得到当前时刻的理论分选密度；
根据理论分选密度与循环介质密度的关系曲线，得到与当前时刻的理论分选密度对应的当前时刻的循环介质密度设定值。
进一步，通过以下方式获得所述理论分选密度与循环介质密度的关系曲线：
提取多个实际循环介质密度值，通过对应当时的精煤灰分，查找到相应的理论分选密度，拟合得到所述理论分选密度与循环介质密度的关系曲线。
进一步，在所述生产指标预测模块中，
当所述第二灰分测量装置输出的精煤灰分相对于基准灰分值的变化值在预设的灰分变化范围内时，根据所述理论分选密度与循环介质密度的关系曲线，更新循环介质密度设定值；
当所述第二灰分测量装置输出的精煤灰分相对于基准灰分值的变化值不在预设的灰分变化范围内时，更新所述理论分选密度与循环介质密度的关系曲线，并得到更新后的循环介质密度设定值。
进一步，在所述悬浮液密度调节模块中，根据所述循环介质密度设定值与循环介质密度实测值之间的偏差，执行补水、分流或补加浓介质动作，使得所述循环介质密度实测值实时跟踪所述循环介质密度设定值。
进一步，在所述悬浮液密度调节模块中，
若所述循环介质密度设定值与循环介质密度实测值之间的偏差Δδ≤0，则向送料管道补水，补水量ΔQ＝Δδ/C；
若所述循环介质密度设定值与循环介质密度实测值之间的偏差Δδ>0，则根据在线检测的各介质桶的液位、以及循环介质中的磁性物含量，采用模糊控制方法，优化确定补加浓介或者分流。
进一步，还包括在线评价模块，用于根据在线检测的入料与产品数据，以及所述生产指标预测模块所提供的原料性质，实时计算包括理论产率、实际产率、数量效率在内的指标，并根据选煤效果评价方法进行在线评价。
本申请还提供了一种重介分选过程智能控制方法，包括以下步骤：
实时检测当前时刻的原煤灰分、精煤灰分以及循环介质密度值，拟合得到当前时刻的可选性曲线；
根据当前时刻的循环介质密度实测值、精煤灰分以及所述可选性曲线，得到当前时刻的理论分选密度；
根据理论分选密度与循环介质密度的关系曲线，得到与当前时刻的 理论分选密度对应的当前时刻的循环介质密度设定值；
基于所述循环介质密度设定值与所述循环介质密度实测值之间的偏差，调节循环介质密度。
在上述方案的基础上，本申请还做了如下改进：
进一步，所述实时检测当前时刻的原煤灰分、精煤灰分以及循环介质密度值，拟合得到当前时刻的可选性曲线，包括：
基于实时检测到的所述原煤灰分、精煤灰分、循环介质密度值以及历史浮沉信息，修正所述历史浮沉信息，得到当前时刻的浮沉信息；基于所述当前时刻的浮沉信息，拟合得到当前时刻的可选性曲线。
进一步，通过以下方式获得所述理论分选密度与循环介质密度的关系曲线：提取多个实际循环介质密度值以及相应的理论分选密度，拟合得到所述理论分选密度与循环介质密度的关系曲线。
本申请有益效果如下：本申请提供的重介分选过程智能控制系统及方法，能够根据重介系统的入料性质，得到相应的理论分选密度与循环介质密度的对应关系，预测并给定重介分选过程智能控制系统的循环介质密度设定值，并基于该循环介质密度设定值，调节循环介质密度。有效降低了重介分选过程对经验的依赖程度，实现了产品质量的实时控制。
本申请中，上述各技术方案之间还可以相互组合，以实现更多的优选组合方案。本申请的其他特征和优点将在随后的说明书中阐述，并且，部分优点可从说明书中变得显而易见，或者通过实施本申请而了解。本申请的目的和其他优点可通过说明书、权利要求书以及附图中所特别指出的内容中来实现和获得。
附图说明
附图仅用于示出具体实施例的目的，而并不认为是对本申请的限制， 在整个附图中，相同的参考符号表示相同的部件。
图1为本申请实施例1中公开的重介分选过程智能控制系统结构示意图；
图2为本申请实施例1中可选性曲线示意图；
图3为本申请实施例2中公开的重介分选过程智能控制方法流程图。
具体实施方式
下面结合附图来具体描述本申请的优选实施例，其中，附图构成本申请一部分，并与本申请的实施例一起用于阐释本申请的原理，并非用于限定本申请的范围。
本申请的一个具体实施例，公开了一种重介分选过程智能控制系统，结构示意图如图1所示，该系统包括：第一灰分测量装置(例如测灰仪1#)，用于测量原煤灰分；第二灰分测量装置(例如测灰仪2#)，用于实时测量原煤经介质分选后得到的精煤灰分；生产指标预测模块，用于处理所述原煤灰分和所述精煤灰分，得到循环介质密度设定值；悬浮液密度调节模块，基于所述循环介质密度设定值与循环介质密度实测值(可通过密度计测量测到)之间的偏差，调节循环介质密度。
优选地，所述生产指标预测模块，通过执行以下操作，得到循环介质密度设定值：
(1)基于实时检测到的所述原煤灰分以及历史浮沉资料，修正所述历史浮沉资料，得到当前时刻的浮沉资料；基于所述当前时刻的浮沉资料，拟合得到可选性曲线；本实施例还给出了实现这一过程的具体实施方式，如下所示：
在实际应用过程中，可以根据重介系统的实时检测数据(入料原煤灰分、重量)、历史数据(原煤浮沉资料(又称浮沉信息)、产品产量、产率与灰分、历史循环介质密度等)以及快速生产检查数据(快灰、快 浮)，根据在线检测灰分，修正历史浮沉资料，从而实时预测出变化的原料煤性质资料(主要是实时的原煤浮沉资料，包括不同密度级的产率与灰分)；历史浮沉资料的样例如表1所示。
表1历史浮沉资料样例
Figure PCTCN2020110366-appb-000001
具体步骤：
1)用实时检测得到的原煤灰分Ai，与历史的原煤浮沉资料中的原煤灰分Ay对比(见表1中历史浮沉资料样例)，计算ΔAi＝Ai-Ay；
2)如果ΔAi的绝对值小于0.2，用灰分校正法对历史浮沉资料的灰分进行校正；如果ΔAi的绝对值大于0.2，用出量校正法对历史浮沉资料进行校正。如此便得到新的、第i时刻的浮沉资料，此资料在下一时刻也可以是历史浮沉资料。
对第i时刻的浮沉资料进行数学拟合得到数学模型(可以有8种模型)，从而获得可选性曲线。
上述方式中，利用在线检测数据，并通过预测优化计算得到的原料性质，各种指标可以在线实时进行计算，不需要经过长时间的实验。计 算的公式形式是常见的，但内涵是实时变化的数据计算的实时结果。
所述理论产率从可选性曲线得到，按目前研究结果可选性曲线可以用8种数学模型表达(均有文献描述)。本权利要求的特征在于，公式中的参数为在线实时数据拟合后的实时参数，而不是一般表达式中的常量。例如反正切模型：
y＝100(t 2-arctan(k(x-c)))/(t 2-t 1)
其中的t 1、t 2、k、c，都是通过第i时刻入料灰分检测数据计算出来的。然后，当x1表达要求精煤灰分时，计算出产率y1，再利用此公式，将计算出的产率作为x1，对应计算出理论分选密度。
实际产率的计算公式为：γ1＝Q2*100％/Q1
其中Q2为精煤皮带称检测的精煤重量，Q1原煤皮带称检测的原煤重量。
所述数量效率的计算公式为：
Figure PCTCN2020110366-appb-000002
其中γ1为计算的实际产率，γ10为计算的理论产率。
可选性曲线是一组5条曲线，基本的两条是浮物产率－灰分曲线β与浮物产率－密度曲线δ，由此派生出浮物产率－基元灰分曲线λ、沉物产率－灰分曲线θ、以及δ±0.1含量－密度曲线ε。如图2所示。
(2)根据当前时刻的实际循环介质密度、精煤灰分以及所述可选性曲线，得到当前时刻的理论分选密度；具体地，
检测循环介质密度实测值以及对应的精煤灰分，根据实际检测的精煤灰分，从浮物产率－灰分曲线β与浮物产率－密度曲线δ，得到理论分选密度(如图2所示)，根据理论分选密度与循环介质密度的关系曲线，得到与当前时刻的理论分选密度对应的当前时刻的循环介质密度设定值。
在实际应用过程中，通过实验，多次改变循环介质密度实测值，便可以得到多个与之对应的理论分选密度(或用前述历史数据整理出类似数据)，用一系列循环介质密度实测值对应理论分选密度数据，便可拟合出理论分选密度与循环介质密度的关系曲线(一般是线性方程,形如y＝a+bx，)；
如此，欲改变产品灰分，就先由可选行曲线查得对应得理论分选密度，从而根据上述关系预测出重介系统悬浮液的设定密度(即循环介质密度)，使得悬浮液密度调节系统有调节依据。
优选地，在所述生产指标预测模块中，当所述第二灰分测量装置输出的精煤灰分相对于基准灰分值的变化值在预设的灰分变化范围内时，根据所述理论分选密度与循环介质密度的关系曲线，更新循环介质密度设定值；当所述第二灰分测量装置输出的精煤灰分相对于基准灰分值的变化值不在预设的灰分变化范围内时，表明原料性质变化较大，此时，需要更新所述理论分选密度与循环介质密度的关系曲线，以保证该对应关系与原煤性质的一致性。并在更新上述关系曲线后，得到更新后的循环介质密度设定值。具体方法为：如果在线产品灰分的变化值ΔA∈{x1,x2}，则根据前面所述理论分选密度与循环介质密度的关系曲线，直接预测计算新的循环介质密度设定值；如果在线产品灰分的变化值ΔA超出{x1,x2}值，则重新计算理论分选密度与循环介质密度的关系曲线。其中{x1,x2}为一个灰分变化范围，由企业实际情况确定。
所述前馈控制与反馈控制相结合的控制策略，其关系为：所述前馈控制主要用于当生产系统开始启车，或原料性质变动较大时，根据原料的性质，确定原煤的密度组成与灰分的相关关系，或确定循环介质密度的变动范围，初步给定循环介质的设定密度，并辅助进行所述反馈控制；而所述反馈控制用于生产过程中，当原料性质变化不大时，根据产品灰 分的情况，在所述前馈控制设定的调节范围内，对设定密度进行小幅微调。
优选地，在所述悬浮液密度调节模块中，根据所述循环介质密度设定值与循环介质密度实测值之间的偏差，执行补水、分流或补加浓介质动作，使得所述循环介质密度实测值实时跟踪所述循环介质密度设定值。具体过程为：如果密度设定值δ0-实际密度测量值δi＝Δδ≤0，则向送料管道补水，补水量ΔQ根据ΔQ＝Δδ/C的关系确定，具体需进行实验。当Δδ>0，则需要根据在线检测的各介质桶(图1中的合格介质桶、稀介桶)的液位(通过液位计测量得到)、以及循环介质中的磁性物含量(通过磁性物含量计测量得到)，采用模糊控制方法决定补加浓介或者分流，例如，合格介质桶的桶位划分为5个模糊集，入料管的阀门开度划分为9个等级，实例的模糊集隶属度见表2。当等级值为负值，向桶内放料，当等级值为正值则打开分流，即向稀介桶放料。从而保证循环介质密度实时跟踪密度设定值；悬浮液密度调节系统是由各种执行机构及其驱动调节策略软件组成。
表2模糊控制方法中控制量变化划分表
Figure PCTCN2020110366-appb-000003
各阀门、桶位的自动调节，通过优化控制包实现如下功能：可根据 所述循环介质密度设定值、循环介质密度实测值和实际的介质桶的桶位，采用智能优化控制算法-模糊控制，具体设定如示例附表2的合格介质桶与稀介桶的模糊隶属度表，配合专家知识，设定每个模糊隶属度情况下的控制规则，用模糊控制算法计算最佳桶位与阀门开度，指挥悬浮液密度调节系统将阀门开度自动调节至适当值，保证各桶液位适当、介质密度与设定密度差值小于用户规定值。
为能够对系统的运行情况进行跟踪、监督，该系统还设置了在线评价模块。在线评价模块可以根据在线检测的入料与产品的数据，以及所述生产指标预测系统所提供的原料性质，实时计算理论产率、实际产率、数量效率等指标，并根据选煤效果评价方法进行在线评价。分选结果的评价，在选煤领域是常见的，其评价方法及公式见选煤厂管理教材。但是以往没有在线评价的先例，本申请评价公式的形式与原有公式的差别在于引入了时间维，原本需要经过几天甚至几十天实验和计算才能够得到的数据，现在采用在线检测或预测计算实时得到，可以选择不同时间频度的数据，从而得以实现在线评价。在线评价模块可借助于图1中标出的各仪器获取相应的数据，用以实现相关指标的计算。本实施例中选取的测量元件均为本领域中常用的测量元件，此处仅示例性地列举几个主要元件的功能。如：将测灰仪1#作为第一灰分测量装置，用于测量原煤灰分，利用皮带称1#测量原煤重量；将测灰仪2#作为第二灰分测量装置，用于实时测量原煤经分选后得到精煤的灰分，利用皮带称2#测量精煤重量；还利用皮带秤3#测量中煤重量；还可以专门设置数据采集模块，将原煤数据、历史数据、生产检查数据、原煤浮沉资料统一发送至数据采集模块后，再交由所述生产指标预测模块进行处理。
上述系统在执行过程中，首先通过数据采集系统集中采集所需要的各种数据。当入料原煤开始进入工艺系统，在线检测仪表测得的数据(即 实时检测数据)进入数据采集系统，同时，输入生产检查数据(生产检查是生产过程中人工采样化验的过程)，与原有的历史数据、原煤浮沉资料等一起，实时地输送给生产指标预测系统；生产指标预测系统计算出合理的循环介质设定密度(即密度设定值)，前馈送入悬浮液密度调节系统；悬浮液密度调节系统同时接收优化控制包的指令，调节各种桶位、阀门、分流箱等，跟踪密度设定值，保证悬浮液密度符合设定密度。当评价系统指出精煤产品质量不满足要求，或其他评价指标不合理时，生产指标预测系统再次根据实时检测数据和历史数据、原煤质量变化，微调设定密度，反馈给悬浮液密度调节系统，开始新一轮密度调节。
与现有技术相比，本实施例公开的重介分选过程智能控制系统，能够根据重介系统的入料性质，得到相应的理论分选密度与循环介质密度的对应关系，预测并给定重介分选过程智能控制系统的循环介质密度设定值，并基于该循环介质密度设定值，调节循环介质密度。有效降低了重介分选过程对经验的依赖程度，实现了产品质量的实时控制。
实施例2
在本申请的实施例2中，公开了一种重介分选过程智能控制方法，流程图如图3所示，包括以下步骤：
步骤S1：实时检测当前时刻的原煤灰分、精煤灰分以及循环介质密度值，拟合得到当前时刻的可选性曲线；
步骤S2：根据当前时刻的循环介质密度实测值、精煤灰分以及所述可选性曲线，得到当前时刻的理论分选密度；
步骤S3：根据理论分选密度与循环介质密度的关系曲线，得到与当前时刻的理论分选密度对应的当前时刻的循环介质密度设定值；
步骤S4：基于所述循环介质密度设定值与所述循环介质密度实测值 之间的偏差，调节循环介质密度。
优选地，所述实时检测当前时刻的原煤灰分、精煤灰分以及循环介质密度值，拟合得到当前时刻的可选性曲线，包括：
基于实时检测到的所述原煤灰分、精煤灰分、循环介质密度值以及历史浮沉信息，修正所述历史浮沉信息，得到当前时刻的浮沉信息；基于所述当前时刻的浮沉信息，拟合得到当前时刻的可选性曲线。
优选地，通过以下方式获得所述理论分选密度与循环介质密度的关系曲线：提取多个实际循环介质密度值以及相应的理论分选密度，拟合得到所述理论分选密度与循环介质密度的关系曲线。
本申请方法实施例的具体实施过程参见上述系统实施例即可，本实施例在此不再赘述。由于本实施例与上述方法实施例原理相同，所以本系统也具有上述方法实施例相应的技术效果。
本领域技术人员可以理解，实现上述实施例方法的全部或部分流程，可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于计算机可读存储介质中。其中，所述计算机可读存储介质为磁盘、光盘、只读存储记忆体或随机存储记忆体等。
以上所述，仅为本申请较佳的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本申请的保护范围之内。

Claims (10)

  1. 一种重介分选过程智能控制系统，其特征在于，所述系统包括：
    第一灰分测量装置，用于测量原煤灰分；
    第二灰分测量装置，用于实时测量原煤经循环介质分选后得到的精煤灰分；
    生产指标预测模块，用于处理所述原煤灰分和所述精煤灰分，得到循环介质密度设定值；
    循环介质密度调节模块，基于所述循环介质密度设定值与循环介质密度实测值之间的偏差，调节循环介质密度。
  2. 根据权利要求1所述的重介分选过程智能控制系统，其特征在于，所述生产指标预测模块，通过执行以下操作，得到循环介质密度设定值：
    基于实时检测到的所述原煤灰分、精煤灰分、循环介质密度值以及历史浮沉信息，修正所述历史浮沉信息，得到当前时刻的浮沉信息；基于所述当前时刻的浮沉信息，拟合得到可选性曲线；
    根据当前时刻的循环介质密度实测值、精煤灰分以及所述可选性曲线，得到当前时刻的理论分选密度；
    根据理论分选密度与循环介质密度的关系曲线，得到与当前时刻的理论分选密度对应的当前时刻的循环介质密度设定值。
  3. 根据权利要求2所述的重介分选过程智能控制系统，其特征在于，通过以下方式获得所述理论分选密度与循环介质密度的关系曲线：
    提取多个实际循环介质密度值，通过对应当时的精煤灰分，查找到相应的理论分选密度，拟合得到所述理论分选密度与循环介质密度的关系曲线。
  4. 根据权利要求2或3所述的重介分选过程智能控制系统，其特征在于，在所述生产指标预测模块中，
    当所述第二灰分测量装置输出的精煤灰分相对于基准灰分值的变化值在预设的灰分变化范围内时，根据所述理论分选密度与循环介质密度的关系曲线，更新循环介质密度设定值；
    当所述第二灰分测量装置输出的精煤灰分相对于基准灰分值的变化值不在预设的灰分变化范围内时，更新所述理论分选密度与循环介质密度的关系曲线，并得到更新后的循环介质密度设定值。
  5. 根据权利要求1所述的重介分选过程智能控制系统，其特征在于，在所述悬浮液密度调节模块中，根据所述循环介质密度设定值与循环介质密度实测值之间的偏差，执行补水、分流或补加浓介质动作，使得所述循环介质密度实测值实时跟踪所述循环介质密度设定值。
  6. 根据权利要求5所述的重介分选过程智能控制系统，其特征在于，在所述悬浮液密度调节模块中，
    若所述循环介质密度设定值与循环介质密度实测值之间的偏差Δδ≤0，则向送料管道补水，补水量ΔQ＝Δδ/C；
    若所述循环介质密度设定值与循环介质密度实测值之间的偏差Δδ>0，则根据在线检测的各介质桶的液位、以及循环介质中的磁性物含量，采用模糊控制方法，优化确定补加浓介或者分流。
  7. 根据权利要求1所述的所述智能控制系统，其特征在于，还包括在线评价模块，用于根据在线检测的入料与产品数据，以及所述生产指标预测模块所提供的原料性质，实时计算包括理论产率、实际产率、数量效率在内的指标，并根据选煤效果评价方法进行在线评价。
  8. 一种重介分选过程智能控制方法，其特征在于，包括以下步骤：
    实时检测当前时刻的原煤灰分、精煤灰分以及循环介质密度值，拟合得到当前时刻的可选性曲线；
    根据当前时刻的循环介质密度实测值、精煤灰分以及所述可选性曲 线，得到当前时刻的理论分选密度；
    根据理论分选密度与循环介质密度的关系曲线，得到与当前时刻的理论分选密度对应的当前时刻的循环介质密度设定值；
    基于所述循环介质密度设定值与所述循环介质密度实测值之间的偏差，调节循环介质密度。
  9. 根据权利要求8所述的重介分选过程智能控制方法，其特征在于，所述实时检测当前时刻的原煤灰分、精煤灰分以及循环介质密度值，拟合得到当前时刻的可选性曲线，包括：
    基于实时检测到的所述原煤灰分、精煤灰分、循环介质密度值以及历史浮沉信息，修正所述历史浮沉信息，得到当前时刻的浮沉信息；基于所述当前时刻的浮沉信息，拟合得到当前时刻的可选性曲线。
  10. 根据权利要求8所述的重介分选过程智能控制方法，其特征在于，通过以下方式获得所述理论分选密度与循环介质密度的关系曲线：
    提取多个实际循环介质密度值以及相应的理论分选密度，拟合得到所述理论分选密度与循环介质密度的关系曲线。
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