WO2023179344A1 - 基于浮选尾煤矿浆检测的浮选智能加药系统及加药方法 - Google Patents

基于浮选尾煤矿浆检测的浮选智能加药系统及加药方法 Download PDF

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WO2023179344A1
WO2023179344A1 PCT/CN2023/079776 CN2023079776W WO2023179344A1 WO 2023179344 A1 WO2023179344 A1 WO 2023179344A1 CN 2023079776 W CN2023079776 W CN 2023079776W WO 2023179344 A1 WO2023179344 A1 WO 2023179344A1
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flotation
slurry
intelligent
dosing
unit
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PCT/CN2023/079776
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English (en)
French (fr)
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邢耀文
桂夏辉
刘秦杉
曹亦俊
王兰豪
刘炯天
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中国矿业大学
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Priority to AU2023240839A priority Critical patent/AU2023240839A1/en
Publication of WO2023179344A1 publication Critical patent/WO2023179344A1/zh

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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
    • B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
    • B03D1/00—Flotation
    • B03D1/14—Flotation machines
    • 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
    • B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
    • B03D1/00—Flotation
    • B03D1/14—Flotation machines
    • B03D1/1443—Feed or discharge mechanisms for flotation tanks
    • B03D1/145—Feed mechanisms for reagents
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to the technical field of flotation dosing, and in particular to a flotation intelligent dosing system and dosing method based on flotation tail coal slurry detection.
  • Flotation technology is the most economical and effective method for separating fine coal slime, and is also an important method for deep coal separation. It plays a vital role in treating coal slime water in coal preparation plants and realizing closed-circuit circulation of coal slime water. Precise control of the dosage during the flotation process is an important means to ensure the ash content of flotation clean coal, recovery rate of flotation clean coal, and reduce reagent consumption. If the dosage is too high, the selectivity of the flotation process will be poor, which will lead to increased reagent consumption. At the same time, increasing the ash content of clean coal cannot guarantee the quality of clean coal. If the dosage is too low and the selectivity is too high, the yield of clean coal will be reduced.
  • the current level of intelligence of the flotation system in the coal preparation plant is low, and the flotation dosing judgment is mainly done manually.
  • the flotation effect depends on the production experience and careful management of the flotation driver.
  • the flotation is judged based on the color of the slurry and the touch of the hand. Whether there are sensory results such as "coarse running" of the tailings, manual operation cannot achieve precise quantification, and has hysteresis and personal subjectivity, resulting in unstable production conditions.
  • the location of the flotation dosing device is generally high. In addition to the high physical exertion caused by frequent adjustments, the pungent odor generated by the volatilization of the drug will also affect the health of the flotation driver, and the flotation working environment needs to be improved.
  • the current application is mainly to use the pulp ash analyzer to detect the ash content of the concentrate slurry, but the lack of detection of the tailings slurry reduces the control of the recovery rate, and the investment cost of the slurry ash analyzer is high, and the measurement
  • the results have a certain hysteresis and cannot achieve real-time monitoring effects, making it impossible to adjust the dosage in time to ensure flotation efficiency.
  • embodiments of the present invention aim to provide a flotation intelligent dosing system and dosing method based on flotation tailings slurry detection, so as to solve the problems of untimely and manual adjustment of existing flotation dosing. Dosage inaccuracy issues.
  • the present invention provides a flotation intelligent dosing system based on flotation tailings slurry detection, including a flotation information collection unit, an ash intelligent prediction unit and a distributed control dosing unit.
  • the flotation information collection unit Obtain the flow rate, concentration, coarse particle content and image information of the slurry.
  • the ash content intelligent prediction unit processes the flow rate, concentration, coarse particle content and image information of the slurry to obtain the ash content of the slurry.
  • the distributed control dosing unit determines the ash content of the slurry according to the flow rate, concentration, coarse particle content and image information of the slurry.
  • the flow rate, concentration and coarse particle content of ash and slurry regulate the dosage.
  • the flotation intelligent dosing system includes a slurry preparer and flotation equipment.
  • the slurry preparer preprocesses the slurry and then carries out flotation through the flotation equipment.
  • the slurry preparer includes a barrel body, the upper end of the barrel body is provided with a water inlet pipe and a feed pipe, and the lower end of the barrel body is provided with a discharge pipe.
  • the flotation information collection unit includes a flow meter and a concentration meter, and the flow meter and the concentration meter are arranged downstream of the slurry preparer.
  • the flow meter and the concentration meter are respectively used to obtain the flow rate and concentration of the slurry flowing from the slurry preparer to the flotation equipment.
  • the flotation information collection unit also includes a force sensor and an industrial camera, and the force sensor and the industrial camera are both located at the tailings outlet of the flotation equipment.
  • the force sensor is used to obtain vibration data of the slurry impacting the baffle
  • the industrial camera is used to obtain images of the slurry.
  • the ash intelligent prediction unit includes an image ash prediction unit and a vibration pattern recognition unit.
  • the image ash prediction unit calculates the slurry grayscale based on the acquired slurry image.
  • the vibration mode recognition unit calculates the slurry grayscale based on the slurry acquired by the force sensor.
  • the impact baffle vibration data is used to calculate the coarse particle content of the slurry.
  • the distributed control dosing unit includes a centralized control center unit and a flotation control center unit.
  • the centralized control center unit sends out signals to the flotation control center unit according to the ash content, coarse particle content, slurry volume and slurry concentration of the slurry.
  • Dosing instruction the flotation control center unit analyzes the dosing instruction and issues a dosing dosage instruction to the automatic dosing room.
  • the present invention provides a flotation intelligent dosing method based on flotation tailings slurry detection, which is used in the above-mentioned flotation intelligent dosing system based on flotation tailings slurry detection.
  • the steps include:
  • the force sensor feeds back the collected information to the vibration pattern recognition unit based on the pattern recognition algorithm.
  • the pattern recognition algorithm based on SVM support vector machine regression uses a model trained with past data to analyze the coarse particles in the tailings slurry. content;
  • the industrial camera feeds back the collected information to the image ash content prediction unit based on the neural network.
  • the tailings ash content is predicted in real time;
  • S3 Pass the obtained ash content, coarse particle content, pulp volume and pulp concentration information to the centralized control center unit.
  • the decision-making system of the centralized control center unit issues dosing instructions to the flotation control center unit based on the above data to regulate the tailings ash content. , to achieve closed-loop control.
  • the present invention can achieve at least one of the following beneficial effects:
  • the present invention can realize 24-hour uninterrupted real-time monitoring of flotation parameters, timely adjustment of the dosage, and has strong timeliness.
  • the present invention introduces a pattern recognition algorithm to use a force sensor to detect the coarse particle content in the tailing coal slurry, and timely feedback the "coarse" situation in the tailing coal slurry, which reduces costs while ensuring data reliability, and can improve flotation efficiency. .
  • the intelligent dosing process of the present invention introduces PAC principal component analysis and BP neural network regression analysis database, as well as the quantitative dosing strategy of machine learning training, and uses mechanization to accurately control the dosing amount, which can avoid errors in worker operation and judgment.
  • the resulting undershoot or overshoot improves the recovery rate of clean coal.
  • the intelligent dosing process proposed by the present invention has simple process flow, low investment, low operating cost, significant economic benefits, no large equipment investment, and is easy to be modified on the basis of the original factory.
  • This invention combines the daily mining quality data of the factory to perform prediction regression correction and adjust the algorithm to prevent the deviation of the prediction results due to coal sample fluctuations, coal type changes, etc.
  • Figure 1 is a schematic structural diagram of a flotation intelligent dosing system according to a specific embodiment
  • Figure 2 is a control flow chart of the flotation intelligent dosing system of a specific embodiment.
  • 1-slurry preparation device 11-barrel; 12-water inlet pipe; 13-feed pipe; 14-discharge pipe; 2-flotation equipment; 31-flow meter; 32-concentration meter; 33-force sensor; 34 -Industrial camera; 35-Light source.
  • connection should be understood in a broad sense.
  • it can be a fixed connection, a detachable connection, or an integral connection.
  • it can be a mechanical connection or an electrical connection, it can be a direct connection, or it can be an indirect connection through an intermediate medium.
  • the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
  • top, bottom, “above,” “lower” and “on” are used throughout the description to refer to relative positions with respect to components of the device, such as the top and bottom substrates within the device relative position. It is understood that the installations are multi-functional, regardless of their orientation in space.
  • a specific embodiment of the present invention discloses a flotation intelligent dosing system based on flotation tail coal slurry detection (hereinafter referred to as "flotation intelligent dosing system"), including a flotation dosing system.
  • Flotation intelligent dosing system including a flotation dosing system.
  • the flotation information collection unit obtains the flow, concentration, coarse particle content and image information of the slurry
  • the ash intelligent prediction unit obtains the flow, concentration, coarse particle content of the slurry.
  • image information processing to obtain the ash content of the slurry
  • the distributed control dosing unit regulates the amount of dosing based on the ash content of the slurry and the flow, concentration and coarse particle content of the slurry.
  • the flotation intelligent dosing system includes a slurry preparer 1 and a flotation equipment 2.
  • the slurry preparer 1 pretreats the slurry and then floats through the flotation equipment 2.
  • the slurry preparer 1 includes a barrel 11.
  • the upper end of the barrel 11 is provided with a water inlet pipe 12 and a feed pipe 13.
  • the lower end of the barrel 11 is provided with a discharge pipe 14.
  • the flotation information collection unit includes a flow meter 31 and a concentration meter 32.
  • the flow meter 31 and the concentration meter 32 are located downstream of the slurry preparer 1 and are used to obtain the flow rate and concentration of the slurry flowing from the slurry preparer 1 to the flotation equipment 2. .
  • the flotation information collection unit also includes a force sensor 33 and an industrial camera 34.
  • the force sensor 33 and the industrial camera 34 are both located at the tailings outlet of the flotation equipment 2.
  • the force sensor 33 is used to obtain vibration data of the slurry impact baffle, and the industrial camera 34 is used to obtain images of the slurry.
  • the industrial camera 34 is equipped with a light source 35, a defogger and a light hood.
  • the ash content intelligent prediction unit includes an image ash content prediction unit and a vibration pattern recognition unit.
  • the image ash content prediction unit calculates the slurry grayscale according to the obtained slurry image, and the vibration mode recognition unit calculates the coarse particles of the slurry based on the slurry impact baffle vibration data obtained by the force sensor 33 content.
  • the distributed control dosing unit includes a centralized control center unit and a flotation control center unit.
  • the centralized control center unit issues dosing instructions to the flotation control center unit based on the ash content, coarse particle content, slurry volume and slurry concentration of the slurry. Select the control center unit to interpret the dosing instructions. After analysis, a dosing dosage instruction is sent to the automatic dosing room.
  • the automatic dosing room regulates the dosing amount of the flotation system by controlling the main and auxiliary solenoid valves, thereby regulating the tailings ash content, thus achieving a closed loop control.
  • the automatic dosing room controls the dosing amount of the slurry preparer 1 by controlling the main solenoid valve, and controls the dosing amount of the flotation equipment 2 by controlling the auxiliary solenoid valve.
  • the ash content prediction calculation module will combine the daily mining quality data of the factory to perform prediction regression correction and adjust the algorithm to prevent deviations in the prediction results due to coal sample fluctuations, coal type changes, etc.
  • FIG. 1 and 2 disclose a flotation intelligent dosing method based on flotation tailings slurry detection, using the flotation tailings slurry detection method of Embodiment 1.
  • Flotation intelligent dosing system the steps include:
  • the slurry preparer 1 includes a barrel 11.
  • the barrel 11 is provided with a water inlet pipe 11, a feed pipe 12 and a discharge pipe 14.
  • the water inlet pipe 12 and the feed pipe 13 are located at the upper end of the barrel 11.
  • the material tube 14 is located at the lower end of the barrel 11 .
  • the flow meter 31 and the concentration meter 32 are provided downstream of the slurry preparer 1 and are used to obtain the flow rate and concentration of the slurry flowing from the slurry preparer 1 to the flotation device 2 .
  • the force sensor 33 feeds back the collected information to the vibration pattern recognition unit based on the pattern recognition algorithm.
  • the pattern recognition algorithm based on SVM support vector machine regression uses the model trained by past data to analyze the coarse particles in the tailings slurry. content.
  • a force sensor 33 is set up at a specific position of the tailings discharge port, and a pattern recognition algorithm based on SVM support vector machine regression is used to imitate the "tactile feel" of the flotation driver to detect the coarse particle content, eliminate the influence of the coarse particle content on the ash content prediction, and Real-time monitoring of rough running conditions.
  • the industrial camera 34 feeds back the collected information to an image ash content prediction unit based on a neural network, for example, based on the YOLOV5 network, which predicts the tailings ash content in real time by analyzing the image grayscale distribution and integrating factors such as coarse particle content.
  • a neural network for example, based on the YOLOV5 network, which predicts the tailings ash content in real time by analyzing the image grayscale distribution and integrating factors such as coarse particle content.
  • the industrial camera 34, light source 35, defogger and other equipment are placed at the upper end of the flotation tailings outlet to monitor the image of the flotation tailings slurry.
  • the independent computing unit module will combine the feature values extracted from the grayscale of the slurry image captured by the industrial camera 34, the flow meter and concentration meter parameters set at the tailings outlet, as well as the intensity of the given light source, coarse particle content and other information through sample training
  • a good CNN neural network model calculates the corresponding gray score.
  • the system regularly performs ash prediction regression correction.
  • the ash prediction calculation unit will combine the real data of ash collected regularly from the processing plant to correct the prediction results by adjusting the principal component relationship of the operation parameters and the network weight, thereby preventing coal sample fluctuations. Deviations in prediction results caused by changes in coal type, etc.
  • the force sensor 33 and the industrial camera 34 are both located at the tailings outlet of the flotation equipment 2.
  • the force sensor 33 is used to obtain vibration data of the slurry hitting the baffle, and the industrial camera 34 is used to obtain images of the slurry.
  • the industrial camera 34 is equipped with a light source 35, a defogger and a light hood.
  • S3 The obtained information such as ash content, coarse particle content, slurry volume and slurry concentration is transmitted to the centralized control center unit.
  • the decision-making system of the centralized control center unit will issue dosing instructions to the flotation control center unit according to specific circumstances.
  • the flotation control center unit analyzes the dosing instructions and issues dosing instructions to the automatic dosing room.
  • the automatic dosing room controls the dosing amount of the flotation system by controlling the main and auxiliary solenoid valves, thereby regulating the tailings ash content, thereby achieving Control closed loop.
  • the centralized control center unit combines the predicted ash content, coarse particle content, slurry flow rate, slurry concentration and other information through the PAC principal component analysis algorithm and database information to control the flotation dosing process.
  • the centralized control center unit collects information such as real-time images of industrial cameras and combines them with predictions
  • the ash content, coarse particle content, slurry flow rate, slurry concentration, etc. are used to judge the flotation working conditions.
  • the PAC principal component analysis algorithm is combined with database information to control the flotation dosing process, send the dosing information to the dosing station, and accurately control the flotation through the mechanical dosing mode of the frequency converter and the mechanical diaphragm metering pump.
  • choose to add medicine When the change in the added medicine exceeds the set warning threshold, the system will alarm and require manual confirmation of the change.
  • the frequency converter is Siemens G120C 0.75KWLO (0.55KWHO) Class C, which is widely used in frequency conversion of pumps and fans, and supports bus control and analog input control to facilitate the flotation industrial control system to control it.
  • the mechanical diaphragm metering pump is made of Milton Roy GM0090PQ9MNN pump head PVC material, and the diaphragm is made of PTFE, which can accurately and effectively transport relatively viscous flotation chemicals. It is equipped with a variable frequency motor to facilitate flow control using a frequency converter.
  • the dosing tube flow meter is the NKGF-06F1I1/SLZ circular gear flowmeter. Its high precision, small range, and organic corrosion resistance are suitable for real-time monitoring of the dosage of flotation chemicals added.
  • the PLC uses Siemens S7 200 smart ST-20 transistor output, cooperates with the EAM03 analog input and output module, and cooperates with the information fed back by the flow meter to perform real-time closed-loop control of the dosage amount by controlling the frequency converter.
  • the flotation intelligent dosing system regulates the dosing amount based on the data information of the flotation tailings slurry (slurry flow, concentration, coarse particle content and ash content), and can realize 24-hour uninterrupted real-time monitoring of flotation parameters. Timely adjustment of dosage is highly time-effective.
  • the flotation intelligent dosing system introduces a pattern recognition algorithm and uses a force sensor to detect the coarse particle content in the tailing coal slurry, and promptly feeds back the "coarse" situation in the tailing coal slurry, which ensures data reliability while reducing the cost and can improve flotation efficiency.
  • the intelligent dosing process introduces PAC principal component analysis and BP neural network regression analysis database, and a quantitative dosing strategy trained by machine learning.
  • the strategy of using mechanization to accurately control the dosing amount can avoid undershooting or overshooting due to errors in worker operation and judgment, and improve the recovery rate of clean coal.
  • the flotation intelligent dosing system provided in this embodiment has a simple intelligent dosing process, low investment, low operating costs, significant economic benefits, and no large equipment investment. It is easy to transform on the basis of the original factory; combined with the daily procurement data of the factory Carry out prediction regression correction and adjust the algorithm to prevent deviations in prediction results due to fluctuations in coal samples, changes in coal type, etc.
  • This invention imitates the current flotation driver's reliance on visual and tactile dosing, and establishes a machine vision and tactile dosing decision-making mechanism based on artificial neural networks; it reduces the labor intensity of dosing for flotation workers and solves the problem of untimely manual adjustment. , the problem of inaccurate dosage can be realized in a timely and long-term manner to control the amount of reagent added to the flotation machine, while ensuring the ash content of the product, reducing roughness and increasing the recovery rate.

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Abstract

本发明涉及一种基于浮选尾煤矿浆检测的浮选智能加药系统及加药方法，属于浮选加药技术领域，解决了现有技术中浮选加药人工调节带来的不及时、剂量不准确的问题。本发明提供了一种基于浮选尾煤矿浆检测的浮选智能加药系统，包括浮选信息采集单元、灰分智能预测单元和分布式控制加药单元，所述浮选信息采集单元获取矿浆的流量、浓度、粗颗粒含量和图像信息，所述灰分智能预测单元对矿浆的流量、浓度、粗颗粒含量和图像信息处理获得矿浆的灰分，所述分布式控制加药单元根据矿浆的灰分及矿浆的流量、浓度和粗颗粒含量调控加药量。本发明可实现不间断实时监控浮选参数，及时对加药量进行调整。

Description

基于浮选尾煤矿浆检测的浮选智能加药系统及加药方法 技术领域
本发明涉及浮选加药技术领域，尤其涉及一种基于浮选尾煤矿浆检测的浮选智能加药系统及加药方法。
背景技术
浮选技术是最为经济有效的微细煤泥分离方法，也是煤炭深度分选的重要方法，在选煤厂的煤泥水处理、实现煤泥水闭路循环中起到至关重要的作用。浮选过程中加药量的精准控制是保证浮选精煤灰分、浮选精煤回收率和降低药剂消耗的重要手段，加药量过高，浮选过程选择性差，会导致增加药剂消耗的同时提高精煤灰分，无法保证精煤品质，加药量过低，选择性过高，则会导致精煤产率降低。
当前选煤厂的浮选系统的智能化水平低，浮选加药判断主要由人工进行，其浮选效果取决于浮选司机的生产经验以及精心管理程度，根据眼看矿浆颜色、手摸判断浮选尾矿是否存在“跑粗”问题等感官结果，手动进行操作，不能达到精确定量，具有滞后性和个人主观性，导致生产情况不稳定。此外，浮选加药装置所处位置一般较高，除了经常调节带来的体力消耗大外，药剂挥发所产生的刺激性气味也会影响浮选司机的健康，浮选工作环境有待改进。
传统的自动化加药系统多为开环控制，是在浮选司机确定数值后系统按照固定数值进行加药，缺少了根据工况而对加药量自动调整的反馈环节。随着市场对浮选的品质要求愈加严格以及工业过程智能化水平的不断提升，煤泥浮选智能化越来越受到人们的重视。浮选过程智能控制 的关键环节之一是实现浮选过程中产品指标的实时检测，当前对浮选矿浆灰分的预测主要有图像法和直接检测法两种检测方法，传统的图像灰分预测是通过对精矿泡沫、灰度等的分析得出精矿灰分，而由于精矿灰分浮动区间较小，预测精确度要求高，很难做到较为精确的灰分预测，精矿灰分预测的偏差也将扩大对回收率的影响。对于直接检测法，当前应用较多的主要是利用矿浆灰分仪对精矿矿浆灰分进行检测，而缺乏对尾矿矿浆的检测，减少了对回收率的控制，且矿浆灰分仪投资成本高，测定结果有一定的滞后性，不能达到实时的监测效果，无法及时调整加药量，保证浮选效率。
发明内容
鉴于上述的分析，本发明实施例旨在提供一种基于浮选尾煤矿浆检测的浮选智能加药系统及加药方法，用以解决现有浮选加药人工调节带来的不及时、剂量不准确的问题。
一方面，本发明提供了一种基于浮选尾煤矿浆检测的浮选智能加药系统，包括浮选信息采集单元、灰分智能预测单元和分布式控制加药单元，所述浮选信息采集单元获取矿浆的流量、浓度、粗颗粒含量和图像信息，所述灰分智能预测单元对矿浆的流量、浓度、粗颗粒含量和图像信息处理获得矿浆的灰分，所述分布式控制加药单元根据矿浆的灰分及矿浆的流量、浓度和粗颗粒含量调控加药量。
进一步地，所述浮选智能加药系统包括矿浆准备器和浮选设备，所述矿浆准备器对矿浆进行预处理后经所述浮选设备进行浮选。
进一步地，所述矿浆准备器包括桶体，所述桶体的上端设有进水管和进料管，所述桶体的下端设有出料管。
进一步地，所述浮选信息采集单元包括流量计和浓度计，所述流量计和所述浓度计设于所述矿浆准备器的下游。
进一步地，所述流量计和所述浓度计分别用于获取由所述矿浆准备器向所述浮选设备流入的矿浆的流量和浓度。
进一步地，所述浮选信息采集单元还包括力传感器和工业相机，所述力传感器和所述工业相机均设于所述浮选设备的尾矿出口处。
进一步地，所述力传感器用于获取矿浆撞击挡板震动数据，所述工业相机用于获取矿浆的图像。
进一步地，所述灰分智能预测单元包括图像灰分预测单元和振动模式识别单元，所述图像灰分预测单元根据获取的矿浆图像计算矿浆灰度，所述振动模式识别单元根据所述力传感器获取的矿浆撞击挡板震动数据计算矿浆的粗颗粒含量。
进一步地，所述分布式控制加药单元包括集控中心单元和浮选控制中心单元，所述集控中心单元根据矿浆的灰分、粗颗粒含量、矿浆量和矿浆浓度向浮选控制中心单元发出加药指令，所述浮选控制中心单元对加药指令进行解析，向自动加药室发出加药剂量指令。
另一方面，本发明提供了一种基于浮选尾煤矿浆检测的浮选智能加药方法，用于上述基于浮选尾煤矿浆检测的浮选智能加药系统，步骤包括：
S1：浮选入料经过矿浆准备器处理后，先经由流量计、浓度计收集流量、浓度信息，然后进入浮选设备分选，分选得到的尾矿进入信息采集区域；
S2：力传感器将采集到的信息反馈给基于模式识别算法的振动模式识别单元，基于SVM支持向量机回归的模式识别算法，通过以往数据训练出的模型，从而分析出尾矿矿浆中粗颗粒的含量；
工业相机将采集到的信息反馈给基于神经网络的图像灰分预测单元，通过对图像灰度分布分析，综合粗颗粒含量因素，实时进行尾矿灰分预测；
S3：将得到的灰分、粗颗粒含量、矿浆量和矿浆浓度信息传入集控中心单元，集控中心单元的决策系统根据以上数据向浮选控制中心单元发出加药指令，从而调控尾矿灰分，实现控制闭环。
与现有技术相比，本发明至少可实现如下有益效果之一：
(1)本发明可实现24小时不间断实时监控浮选参数，及时对加药量进行调整，具有较强的时效性。
(2)本发明引入模式识别算法利用力传感器探测尾煤矿浆中的粗颗粒含量，及时反馈尾煤矿浆中“跑粗”情况，在保证数据可靠性的同时降低了成本，能够提高浮选效率。
(3)本发明智能加药工艺引入了PAC主成分分析及BP神经网络回归分析数据库，及机器学习训练的定量加药策略，采用机械化精准控制加药量，能够避免由于工人操作和判断失误，产生的欠调或超调的情况，提高了精煤的回收率。
(4)本发明提出的智能加药工艺流程简单、投资少、运行成本低，经济效益显著，且无大型设备投入，易于在原厂基础上改造。
(5)本发明结合工厂日常采质化数据进行预测回归矫正，对算法进行调整，从而防止由于煤样波动，煤种变化等导致的预测结果的偏离。
本发明中，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。本发明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，或者通过实施本发明而了解。本发明的目的和其他优点可通过说明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，而并不认为是对本发明的限制，在整个附图中，相同的参考符号表示相同的部件。
图1为具体实施例的浮选智能加药系统结构示意图；
图2为具体实施例的浮选智能加药系统控制流程图。
附图标记：
1-矿浆准备器；11-桶体；12-进水管；13-进料管；14-出料管；2-浮选设备；31-流量计；32-浓度计；33-力传感器；34-工业相机；35-光源。
具体实施方式
下面结合附图来具体描述本发明的优选实施例，其中，附图构成本发明一部分，并与本发明的实施例一起用于阐释本发明的原理，并非用于限定本发明的范围。
在本发明实施例的描述中，需要说明的是，除非另有明确的规定和限定，术语“相连”应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或一体地连接，可以是机械连接，也可以是电连接，可以是直接相连，也可以通过中间媒介间接相连。对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本发明中的具体含义。
全文中描述使用的术语“顶部”、“底部”、“在……上方”、“下”和“在……上”是相对于装置的部件的相对位置，例如装置内部的顶部和底部衬底的相对位置。可以理解的是装置是多功能的，与它们在空间中的方位无关。
实施例1
本发明的一个具体实施例，如图1-图2所示，公开了一种基于浮选尾煤矿浆检测的浮选智能加药系统(以下简称“浮选智能加药系统”)，包括浮选信息采集单元、灰分智能预测单元和分布式控制加药单元，浮选信息采集单元获取矿浆的流量、浓度、粗颗粒含量和图像信息，灰分智能预测单元对矿浆的流量、浓度、粗颗粒含量和图像信息处理获得矿浆的灰分，分布式控制加药单元根据矿浆的灰分及矿浆的流量、浓度和粗颗粒含量调控加药量。
浮选智能加药系统包括矿浆准备器1和浮选设备2，矿浆准备器1对矿浆进行预处理后经浮选设备2进行浮选。
矿浆准备器1包括桶体11，桶体11的上端设有进水管12和进料管13，桶体11的下端设有出料管14。
浮选信息采集单元包括流量计31和浓度计32，流量计31和浓度计32设在矿浆准备器1的下游，用于获取由矿浆准备器1向浮选设备2流入的矿浆的流量和浓度。
浮选信息采集单元还包括力传感器33和工业相机34，力传感器33和工业相机34均设于浮选设备2的尾矿出口处，力传感器33用于获取矿浆撞击挡板震动数据，工业相机34用于获取矿浆的图像。
值得注意的是，工业相机34配有光源35、除雾器和遮光罩。
灰分智能预测单元包括图像灰分预测单元和振动模式识别单元，图像灰分预测单元根据获取的矿浆图像计算矿浆灰度，振动模式识别单元根据力传感器33获取的矿浆撞击挡板震动数据计算矿浆的粗颗粒含量。
分布式控制加药单元包括集控中心单元和浮选控制中心单元，集控中心单元根据矿浆的灰分、粗颗粒含量、矿浆量和矿浆浓度具体情况向浮选控制中心单元发出加药指令，浮选控制中心单元对加药指令进行解 析，向自动加药室发出加药剂量指令，自动加药室通过控制主副电磁阀调控浮选系统加药量，从而调控尾矿灰分，从而实现控制闭环。
具体地，自动加药室通过控制主电磁阀调控矿浆准备器1的加药量，通过控制副电磁阀调控浮选设备2的加药量。
灰分预测计算模块将结合工厂日常采质化数据进行预测回归矫正，对算法进行调整，从而防止由于煤样波动，煤种变化等导致的预测结果的偏离。
实施例2
本发明的另一个具体实施例，如图1-图2所示，公开了一种基于浮选尾煤矿浆检测的浮选智能加药方法，采用实施例1的基于浮选尾煤矿浆检测的浮选智能加药系统，步骤包括：
S1：浮选入料经过矿浆准备器1处理后，先经由流量计31、浓度计32收集流量、浓度信息，然后进入浮选设备2分选，分选得到的尾矿进入信息采集区域。
本实施例中，矿浆准备器1包括桶体11，桶体11设有进水管11、进料管12和出料管14，进水管12和进料管13设于桶体11的上端，出料管14设于桶体11的下端。
流量计31和浓度计32设在矿浆准备器1的下游，用于获取由矿浆准备器1向浮选设备2流入的矿浆的流量和浓度。
S2：力传感器33将采集到的信息反馈给基于模式识别算法的振动模式识别单元，基于SVM支持向量机回归的模式识别算法，通过以往数据训练出的模型，从而分析出尾矿矿浆中粗颗粒的含量。
在尾矿排料口的特定位置设置力传感器33，通过基于SVM支持向量机回归的模式识别算法模仿浮选司机的“触觉”检测粗颗粒含量，排除粗颗粒含量对灰分预测的影响，并且对跑粗情况进行实时监控。
工业相机34将采集到的信息反馈给基于神经网络，示例性地，如基于YOLOV5网络的图像灰分预测单元，通过对图像灰度分布分析，综合粗颗粒含量等因素，实时进行尾矿灰分预测。
工业相机34及光源35和去雾器等设备放置在浮选尾矿出口上端，对浮选尾矿浆的图像进行监测。独立计算单元模块将结合工业相机34捕捉到的矿浆图像灰度所提取特征值、尾矿出料口设置的流量计和浓度计参数，以及所给光源的强度、粗颗粒含量等信息通过样本训练好的CNN神经网络模型计算出所对应的灰分。
系统定期进行灰分预测回归矫正，灰分预测计算单元将结合选厂定期采质化的灰分的真实数据，通过调整运算参数的主成分关系以及网络权重对预测结果进行矫正，从而防止由于煤样波动，煤种变化等导致的预测结果的偏离。
本实施例中，力传感器33和工业相机34均设于浮选设备2的尾矿出口处，力传感器33用于获取矿浆撞击挡板震动数据，工业相机34用于获取矿浆的图像。工业相机34配有光源35、除雾器和遮光罩。
S3：得到的灰分、粗颗粒含量、矿浆量和矿浆浓度等信息传入集控中心单元，集控中心单元的决策系统将根据具体情况向浮选控制中心单元发出加药指令。
浮选控制中心单元对加药指令进行解析，向自动加药室发出所加药剂量指令，自动加药室通过控制主副电磁阀调控浮选系统加药量，从而调控尾矿灰分，从而实现控制闭环。
集控中心单元结合预测的灰分、粗颗粒含量、矿浆流量、矿浆浓度等信息经过PAC主成分分析算法结合数据库信息，对浮选加药环节进行控制。
具体地，集控中心单元采集到工业相机实时图像等信息，结合预测 的灰分、粗颗粒含量、矿浆流量、矿浆浓度等，对浮选工况进行判断。通过决策系统，经过PAC主成分分析算法结合数据库信息，对浮选加药环节进行控制，将加药信息发送至加药站，通过变频器加机械隔膜式计量泵的机械加药模式精准调控浮选加药，当所加药剂变动量超出所设定的警戒阈值时，系统报警，需要人工确认更改。
优选的，变频器选用西门子G120C 0.75KWLO(0.55KWHO)C类，其广泛应用与泵与风机的变频，并支持总线控制，模拟量输入控制，方便浮选工控系统对其进行控制。
优选的，机械隔膜计量泵选用米顿罗GM0090PQ9MNN泵头PVC材质，隔膜PTFE材质其可以准确有效的运输较为粘稠的浮选药剂，搭配变频电机方便利用变频器对其进行流量控制。
优选的，加药管流量计选用NKGF-06F1I1/SLZ圆齿轮流量计，其高精度、小量程、耐有机腐蚀的特性，适用于对加入浮选药剂量的实时监控。
优选的，PLC选用西门子S7 200 smart ST-20晶体管输出，配合EAM03模拟量输入输出模块，配合流量计反馈的信息，通过控制变频器对加药量进行实时闭环控制。
本实施例提供的浮选智能加药系统，根据浮选尾煤矿浆的数据信息(矿浆流量、浓度、粗颗粒含量和灰分)调控加药量，可实现24小时不间断实时监控浮选参数，及时对加药量进行调整，具有较强的时效性。
本实施例提供的浮选智能加药系统，引入模式识别算法利用力传感器探测尾煤矿浆中的粗颗粒含量，及时反馈尾煤矿浆中“跑粗”情况，在保证数据可靠性的同时降低了成本，能够提高浮选效率。
本实施例提供的浮选智能加药系统，智能加药工艺引入了PAC主成分分析及BP神经网络回归分析数据库，及机器学习训练的定量加药策 略，采用机械化精准控制加药量，能够避免由于工人操作和判断失误，产生的欠调或超调的情况，提高了精煤的回收率。
本实施例提供的浮选智能加药系统，智能加药工艺流程简单、投资少、运行成本低，经济效益显著，且无大型设备投入，易于在原厂基础上改造；结合工厂日常采质化数据进行预测回归矫正，对算法进行调整，从而防止由于煤样波动，煤种变化等导致的预测结果的偏离。
本发明模仿现阶段浮选司机依靠视觉和触觉加药，建立基于人工神经网络的机器视觉、触觉加药决策机制；减轻了浮选工人加药的劳动强度，解决了人工调节带来的不及时、剂量不准确的问题，能够实现及时和长时的对浮选机的药剂添加量进行控制，在保证产品灰分同时，减少跑粗提高回收率。
以上所述，仅为本发明较佳的具体实施方式，但本发明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发明揭露的技术范围内，可轻易想到的变化或替换，都应涵盖在本发明的保护范围之内。

Claims (10)

  1. 一种基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，包括浮选信息采集单元、灰分智能预测单元和分布式控制加药单元，所述浮选信息采集单元获取矿浆的流量、浓度、粗颗粒含量和图像信息，所述灰分智能预测单元对矿浆的流量、浓度、粗颗粒含量和图像信息处理获得矿浆的灰分，所述分布式控制加药单元根据矿浆的灰分及矿浆的流量、浓度和粗颗粒含量调控加药量。
  2. 根据权利要求1所述的基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，所述浮选智能加药系统包括矿浆准备器(1)和浮选设备(2)，所述矿浆准备器(1)对矿浆进行预处理后经所述浮选设备(2)进行浮选。
  3. 根据权利要求2所述的基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，所述矿浆准备器(1)包括桶体(11)，所述桶体(11)的上端设有进水管(12)和进料管(13)，所述桶体(11)的下端设有出料管(14)。
  4. 根据权利要求2所述的基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，所述浮选信息采集单元包括流量计(31)和浓度计(32)，所述流量计(31)和所述浓度计(32)设于所述矿浆准备器(1)的下游。
  5. 根据权利要求4所述的基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，所述流量计(31)和所述浓度计(32)分别用于获取由所述矿浆准备器(1)向所述浮选设备(2)流入的矿浆的流量和浓度。
  6. 根据权利要求4所述的基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，所述浮选信息采集单元还包括力传感器(33)和工业相机(34)，所述力传感器(33)和所述工业相机(34)均设于所述浮选设备(2)的尾矿出口处。
  7. 根据权利要求6所述的基于浮选尾煤矿浆检测的浮选智能加药系 统，其特征在于，所述力传感器(33)用于获取矿浆撞击挡板震动数据，所述工业相机(34)用于获取矿浆的图像。
  8. 根据权利要求6所述的基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，所述灰分智能预测单元包括图像灰分预测单元和振动模式识别单元，所述图像灰分预测单元根据获取的矿浆图像计算矿浆灰度，所述振动模式识别单元根据所述力传感器(33)获取的矿浆撞击挡板震动数据计算矿浆的粗颗粒含量。
  9. 根据权利要求1-8所述的基于浮选尾煤矿浆检测的浮选智能加药系统，其特征在于，所述分布式控制加药单元包括集控中心单元和浮选控制中心单元，所述集控中心单元根据矿浆的灰分、粗颗粒含量、矿浆量和矿浆浓度向浮选控制中心单元发出加药指令，所述浮选控制中心单元对加药指令进行解析，向自动加药室发出加药剂量指令。
  10. 一种基于浮选尾煤矿浆检测的浮选智能加药方法，其特征在于，用于权利要求1-9所述的基于浮选尾煤矿浆检测的浮选智能加药系统，步骤包括：
    S1：浮选入料经过矿浆准备器(1)处理后，先经由流量计(31)、浓度计(32)收集流量、浓度信息，然后进入浮选设备(2)分选，分选得到的尾矿进入信息采集区域；
    S2：力传感器(33)将采集到的信息反馈给基于模式识别算法的振动模式识别单元，基于SVM支持向量机回归的模式识别算法，通过以往数据训练出的模型，从而分析出尾矿矿浆中粗颗粒的含量；
    工业相机(34)将采集到的信息反馈给基于神经网络的图像灰分预测单元，通过对图像灰度分布分析，综合粗颗粒含量因素，实时进行尾矿灰分预测；
    S3：将得到的灰分、粗颗粒含量、矿浆量和矿浆浓度信息传入集控 中心单元，集控中心单元的决策系统根据以上数据向浮选控制中心单元发出加药指令，从而调控尾矿灰分，实现控制闭环。
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