Whole-fraction dry coal preparation system based on digital twinning

Abstract

The invention discloses a digital twinning-based full-fraction dry coal preparation system, comprising: a raw coal preparation module configured to crush raw coal and control the moisture of the raw coal; the sorting and upgrading module is configured to perform sorting and size grading on the crushed raw coal; the medium circulation module is configured to separate the clean coal product from the dense medium so as to recycle the dense medium in the separation process; the computer module is configured to establish a prediction model according to the historical data, load the working data of each module into the prediction model for coal quality prediction, and output the optimal equipment parameters of the coal preparation system under the current working condition to guide the production process of the coal preparation system; the digital twinning module is used for receiving state detection data of all the modules, establishing a three-dimensional model of the coal preparation system and achieving visualization, and the computer module displays the working state of the coal preparation system in real time based on the digital twinning module and controls adjustment of operation parameters of all the modules according to the working state.

Description

Technical Field

[0001] The present invention relates to the technical field of coal preparation, and in particular to a full-size dry coal preparation system based on digital twins. Background Art

[0002] my country is a coal-producing country. Coal accounts for more than 60% of China's energy consumption and is still the main fossil fuel in my country. Therefore, the efficient and clean use of coal and the promotion of green development of coal have become important directions for the current development of coal. In addition, with the rapid development of new energy, the country's dependence on coal has gradually decreased, and overcapacity has also become a major problem. Therefore, in today's era, we should accelerate the supply-side structural reform in the coal field, focus on promoting clean, efficient and low-carbon development, strengthen coal technology innovation, and improve the efficient use of coal. The world today is facing a severe water shortage problem, so the efficient dry coal preparation process can help improve the existing separation technology problems. With the rapid development of industrial technology and a new generation of information technology, the level of equipment intelligence in industrial manufacturing and other fields is increasing. Online operation status monitoring, abnormal alarm, fault diagnosis, life prediction, etc. for complex equipment have become hot directions and fields.

[0003] In the related art, during the coal preparation process, the operating parameters of each coal preparation equipment in the coal preparation system are difficult to adjust accurately, and there is no direct interaction between the coal preparation system and the user, so that the user cannot directly understand the operating conditions of each equipment, resulting in low process efficiency and high process cost. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first purpose of the present invention is to propose a full-size dry coal preparation system based on digital twins, which solves the limitation that the operating parameters in the coal preparation process are difficult to accurately adjust through the intelligent control of the coal preparation equipment by the computer module, and realizes the immersive human-computer interaction function between the user and the physical entity through the three-dimensional model established by the digital twin module, which has the advantages of equipment health detection, process effect prediction and analysis, optimization of process data, improvement of process efficiency, and reduction of process cost.

[0005] To achieve the above-mentioned objectives, the first embodiment of the present invention proposes a full-grade dry coal preparation system based on digital twin, including: a raw coal preparation module, configured to crush the raw coal and control the moisture content of the raw coal; a sorting and quality improvement module, configured to sort and grade the crushed raw coal; a medium circulation module, configured to separate the clean coal product and the heavy medium, and to separate the heavy medium in the coal powder to recycle the heavy medium in the sorting process; a computer module, which is respectively coupled to the raw coal preparation module, the sorting and quality improvement module and the medium circulation module, and is configured to establish a prediction model based on historical data, and to calculate the raw coal preparation module, the sorting and quality improvement module and the medium circulation module. The working data is loaded into the prediction model for coal quality prediction, and the optimal equipment parameters of the coal preparation system under the current working conditions are output to guide the production process of the coal preparation system; a digital twin module, one end of the digital twin module is respectively coupled with the raw coal preparation module, the sorting and upgrading module and the medium circulation module, for receiving the status detection data of each module, and establishing a three-dimensional model of the coal preparation system and realizing visualization; the other end of the digital twin module is coupled with the computer module, and the computer module displays the working status of the coal preparation system in real time based on the digital twin module, and controls the adjustment of the operating parameters of the raw coal preparation module, the sorting and upgrading module and the medium circulation module according to the working status.

[0006] According to the full-grade dry coal preparation system based on digital twin of the embodiment of the present invention, the raw coal preparation module is used to crush the raw coal and control the moisture content of the raw coal, the sorting and quality improvement module is used to sort and grade the crushed raw coal, the medium circulation module is used to separate the clean coal product and the heavy medium, and the heavy medium in the coal powder is separated to recycle the heavy medium in the sorting process, the computer module establishes a prediction model according to historical data, loads the working data of the raw coal preparation module, the sorting and quality improvement module and the medium circulation module into the prediction model to predict the coal quality, and outputs the optimal equipment parameters of the coal preparation system under the current working conditions to guide the production process of the coal preparation system; the digital twin module receives the status detection data of each module, and establishes a three-dimensional model of the coal preparation system and realizes visualization, the computer module displays the working status of the coal preparation system in real time based on the digital twin module, and controls the adjustment of the operating parameters of the raw coal preparation module, the sorting and quality improvement module and the medium circulation module according to the working status. Therefore, the system uses computer modules to intelligently control coal preparation equipment, thereby solving the limitation that operating parameters in the coal preparation process are difficult to accurately adjust. The three-dimensional model established by the digital twin module realizes immersive human-computer interaction between users and physical entities. It has the advantages of equipment health detection, process effect prediction and analysis, optimization of process data, improvement of process efficiency, and reduction of process costs.

[0007] In addition, the full-size dry coal preparation system based on digital twin according to the above embodiment of the present invention may also have the following additional technical features:

[0008] According to one embodiment of the present invention, the computer module includes: a PC computer, configured to process and save the working data of the coal preparation system, establish the prediction model based on the intelligent algorithm, and input the working data of the raw coal preparation module, the sorting and quality upgrading module and the medium circulation module into the prediction model to obtain the optimal equipment parameters of the coal preparation system; a PLC control unit, coupled to the PC computer, configured to receive the optimal equipment parameters obtained by the PC computer and input them into the raw coal preparation module, the sorting and quality upgrading module and the medium circulation module to control the production process of the coal preparation system.

[0009] According to one embodiment of the present invention, the PC computer includes: a MySQL database unit, configured to store and call the working parameter data of the raw coal preparation module, the sorting and quality upgrading module and the medium circulation module; a python environment programming unit, configured to perform data preprocessing and parameter optimization on the data of the raw coal preparation module, the sorting and quality upgrading module and the medium circulation module by calling a machine learning algorithm model based on the python compilation language environment.

[0010] According to one embodiment of the present invention, the digital twin module includes: a three-dimensional modeling unit, configured to establish a three-dimensional model of the coal preparation system based on the operating parameters, production data and dimensional data of the coal preparation system under the current operating conditions; and a data visualization unit, configured to visualize the three-dimensional model of the coal preparation system to visualize the production process of the coal preparation system.

[0011] According to one embodiment of the present invention, the digital twin module also includes: a feedback unit, configured to receive operating instructions and feed back the operating instructions to the computer module, so that the computer module controls the coal selection work of the raw coal preparation module, the sorting and upgrading module and the medium circulation module according to the operating instructions.

[0012] According to one embodiment of the present invention, the raw coal preparation module includes: a pre-screening device configured to classify the particle size of the raw coal; a crushing device configured to crush the raw coal; and a drying device configured to control the moisture in the raw coal.

[0013] According to one embodiment of the present invention, the raw coal preparation module further includes: a state detection unit, and the state detection unit includes: a vibration sensor and a temperature sensor, wherein the vibration sensor is used to detect the vibration amplitude and vibration frequency of the crushing device, and the temperature sensor is used to detect the temperature of the drying device.

[0014] According to one embodiment of the present invention, the sorting and quality improvement module includes: a sorting machine, configured to sort the crushed raw coal, wherein the sorting machine includes a photoelectric sorting machine, a composite dry sorting machine and a heavy medium fluidized bed sorting machine; a grading screen, configured to perform particle size classification on the crushed raw coal; a coal quality detection unit, wherein the coal quality detection unit includes: an online ash detection device and a parameter sensor, wherein the online ash detection device is used to detect the ash content and calorific value of each raw coal sorting product, and the parameter sensor is used to detect the vibration frequency, bed surface inclination, wind chamber air volume, inner bed gas velocity, bed pressure and bed height of the sorting machine.

[0015] According to one embodiment of the present invention, the medium circulation module includes: a de-mediating screen configured to separate clean coal products and heavy medium; a magnetic separator configured to separate heavy medium from coal powder; and a dust collector configured to absorb dust generated during the sorting process.

[0016] According to one embodiment of the present invention, the medium circulation module also includes: a bed detection unit configured to detect the bed density of the sorting machine; a quantitative feeding device configured to replenish the medium of the sorting machine based on the bed density of the sorting machine; and a medium flow detection unit configured to collect the medium and coal powder flow of the quantitative feeding device.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A block diagram of a full-size dry coal preparation system based on digital twin according to an embodiment of the present invention;

[0019] Figure 2 It is a block diagram of a full-size dry coal preparation system based on digital twin according to an embodiment of the present invention.

[0020] Reference numerals:

[0021] 100. Coal preparation system; 110. Raw coal preparation module; 111. Pre-screening device; 112. Crushing device; 113. Drying device; 114. Status detection unit; 120. Sorting and quality improvement module; 121. Photoelectric sorter; 122. Composite sorter; 123. Heavy medium fluidized bed sorter; 124. Classifying screen; 125. Coal quality detection unit; 1251. Online ash detection device; 1252. Parameter sensor; 130. Medium circulation module; 131. De-medium screen ; 132. Diversion device; 133. Magnetic separator; 134. Quantitative feeding device; 135. Dust collector; 136. Bed detection unit; 137. Flow detection unit; 140. Computer module; 141. PC computer; 1411. MySQL database unit; 1412. Python environment programming unit; 142. PLC control unit; 150. Digital twin module; 151. Three-dimensional modeling unit; 152. Data visualization unit; 153. Feedback unit. DETAILED DESCRIPTION

[0022] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0023] The following describes a full-size dry coal preparation system based on digital twins proposed in an embodiment of the present invention with reference to the accompanying drawings.

[0024] Figure 1 It is a block diagram of a full-size dry coal preparation system based on digital twin according to an embodiment of the present invention.

[0025] like Figure 1 As shown, the full-size dry coal preparation system 100 based on digital twin according to an embodiment of the present invention may include: a raw coal preparation module 110, a sorting and upgrading module 120, a medium circulation module 130, a computer module 140 and a digital twin module 150.

[0026] Among them, the raw coal preparation module 110 is configured to crush the raw coal and control the moisture content of the raw coal; the sorting and quality improvement module 120 is configured to sort and grade the crushed raw coal; the medium circulation module 130 is configured to separate the clean coal product and the heavy medium, and separate the heavy medium in the coal powder to recycle the heavy medium in the sorting process; the computer module 140 is respectively coupled to the raw coal preparation module 110, the sorting and quality improvement module 120 and the medium circulation module 130, and is configured to establish a prediction model based on historical data, load the working data of the raw coal preparation module 110, the sorting and quality improvement module 120 and the medium circulation module 130 into the prediction model to predict coal quality, and The optimal equipment parameters of the coal preparation system 100 under the current working conditions are output to guide the production process of the coal preparation system 100; one end of the digital twin module 150 is coupled to the raw coal preparation module 110, the sorting and quality upgrading module 120 and the medium circulation module 130 respectively, for receiving the status detection data of each module, and establishing a three-dimensional model of the coal preparation system 100 and realizing visualization; the other end of the digital twin module 150 is coupled to the computer module 140, and the computer module 140 displays the working status of the coal preparation system 100 in real time based on the digital twin module 150, and controls the adjustment of the operating parameters of the raw coal preparation module 110, the sorting and quality upgrading module 120 and the medium circulation module 130 according to the working status.

[0027] Specifically, when sorting the raw coal, the raw coal preparation module 110 is first used to crush the large pieces of raw coal and control the moisture content of the raw coal. Then, the crushed raw coal is sorted and graded by the sorting and quality improvement module 120 to obtain clean coal products and coal powder. The medium circulation module 130 then separates the clean coal products and heavy medium, and separates the heavy medium in the coal powder, so as to recycle the heavy medium in the sorting process. The computer module 140 obtains the working parameters of the coal preparation system 100 from the raw coal preparation module 110, the sorting and quality improvement module 120, and the medium circulation module 130, and performs preprocessing work such as data reorganization classification, reorganization, alignment, and sorting in the machine learning library of the computer module 140. The preprocessed data set is divided into a raw coal preparation data set, a sorting and quality improvement data set, and a medium circulation data set and stored in the computer module 140; based on the intelligent algorithm of the computer module 140, a prediction model is established using a large amount of historical data, and the optimal hyperparameters of the model are automatically saved through the threshold comparison method to generate the best prediction model, and the collected and preprocessed data set is loaded into the saved machine learning prediction model, and the optimal hyperparameters stored in the database in the computer module 140 are called to predict coal quality and output the optimal equipment parameters under the current working conditions; the optimal equipment parameters for the current working conditions output by the computer module 140 are fed back to the physical entities of the raw coal preparation module 110, the sorting and quality improvement module 120, and the medium circulation module 130 to adjust the equipment operating parameters, thereby guiding actual production.

[0028] Furthermore, the digital twin module 150 receives the status detection data of the corresponding module, and establishes a three-dimensional model of the coal preparation system 100 according to the status detection data and the relevant parameter data of each module, and visualizes the three-dimensional model, so as to display the working status of the coal preparation system 100 in real time. The digital twin module 150 transmits the working status of the coal preparation system 100 to the computer module 140, so that the computer module 140 controls the adjustment of the operating parameters of the raw coal preparation module 110, the sorting and upgrading module 120, and the medium circulation module 130 according to the working status, thereby guiding the actual production, thereby making the working process of the coal preparation system 100 more intuitive.

[0029] According to one embodiment of the present invention, Figure 2 As shown, the computer module 140 includes: a PC computer 141, configured to process and save the working data of the coal preparation system 100, establish a prediction model based on an intelligent algorithm, and input the working data of the raw coal preparation module 110, the sorting and quality upgrading module 120 and the medium circulation module 130 into the prediction model to obtain the optimal equipment parameters of the coal preparation system 100; a PLC control unit 142, coupled to the PC computer 141, configured to receive the optimal equipment parameters obtained by the PC computer 141 and input them into the raw coal preparation module 110, the sorting and quality upgrading module 120 and the medium circulation module 130 to control the production process of the coal preparation system 100.

[0030] Further, according to one embodiment of the present invention, Figure 2 As shown, the PC computer 141 includes: a MySQL database unit 1411, configured to store and call the working parameter data of the raw coal preparation module 110, the sorting and quality upgrading module 120 and the medium circulation module 130; a python environment programming unit 1412, configured to perform data preprocessing and parameter optimization on the data of the raw coal preparation module 110, the sorting and quality upgrading module 120 and the medium circulation module 130 based on the python compiled language environment by calling the machine learning algorithm model.

[0031] Specifically, the PC computer 141 can analyze and predict the working conditions of various equipment in the coal preparation system 100. Specifically, the MySQL database unit 1411 stores and calls the parameter data collected by the sensor, the density control model of the heavy medium fluidized bed, and the quality improvement control model of each coal preparation equipment, and inputs them into the python environment programming unit 1412 through the configured TCP / IP communication protocol. The python environment programming unit 1412 is based on the python compilation language environment, and performs corresponding data preprocessing and parameter optimization on the data from different modules by calling the machine learning algorithm model; the PLC control unit 142 controls the physical entities of the coal preparation system 100, such as the input interface, central processing unit CPU, I / O converter and output interface.

[0032] Specifically, the input interface connects the PC computer 141 and the PLC control unit 142, and is used to receive the data signal processed by the PC computer 141 and transmit it to the central processing unit CPU. The central processing unit CPU interprets the input signal and performs the specified task according to the instructions. The generated control instructions are transmitted to each physical module of the coal preparation system 100 via the I / O converter and the output interface to complete the operating condition adjustment.

[0033] That is to say, firstly, the PC computer 141 obtains the working parameters of the coal preparation system 100 from the detection units such as the state detection unit 114, the coal quality detection unit 125, the bed detection unit 136 and the medium flow detection unit 137, and performs data reorganization classification, reorganization, alignment and sorting preprocessing in the scikit-learn machine learning library based on the python environment programming language. The preprocessed data set is divided into a raw coal preparation data set, a sorting and quality improvement data set and a medium circulation data set and stored in the MySQL database unit 1411, wherein the TCP / IP communication protocol is configured between the MySQL database unit 1411 and the python environment programming unit 1412 to complete the input of the data set to the python environment programming unit 1412; then, the python environment programming unit 1412 uses the random forest intelligent algorithm based on the PSO optimization improvement to A large amount of historical data is used to establish a prediction model, and the optimal hyperparameters of the model are automatically saved through the threshold comparison method to generate the best prediction model, which is stored in the MySQL database unit 1411 in the comma separated value file CSV format; the python environment programming unit 1412 loads the collected and preprocessed data set CSV file into the saved machine learning prediction model, calls the optimal hyperparameters stored in the MySQL database, predicts the coal quality and outputs the optimal equipment parameters under the current working conditions; finally, the optimal equipment parameters for the current working conditions output by the python environment programming unit 1412 are transmitted to the PLC control unit 142, and the PLC control unit 142 reads and interprets the commands transmitted to the PLC control unit 142, and feeds back to the physical entities of the raw coal preparation module 110, the sorting and quality improvement module 120 and the medium circulation module 130 to adjust the equipment operating parameters, thereby guiding actual production.

[0034] Therefore, the present invention combines the full-size efficient dry coal preparation process and creatively proposes a full-size efficient dry coal preparation system 100 based on the digital twin framework. The intelligent control of the coal preparation equipment by the python environment programming unit 1412 based on the Python language solves the limitation that the operating parameters in the coal preparation process are difficult to accurately adjust. The three-dimensional model established by the digital twin module 150 realizes the immersive human-computer interaction function between the user and the physical entity, which has the advantages of equipment health detection, process effect prediction and analysis, optimization of process data, improvement of process efficiency, and reduction of process cost.

[0035] According to one embodiment of the present invention, Figure 2As shown, the digital twin module 150 includes: a three-dimensional modeling unit 151, configured to establish a three-dimensional model of the coal preparation system 100 according to the operating parameters, production data and dimensional data under the current operating conditions of the coal preparation system 100; a data visualization unit 152, configured to visualize the three-dimensional model of the coal preparation system 100 so as to visualize the production process of the coal preparation system 100.

[0036] Further, according to one embodiment of the present invention, Figure 2 As shown, the digital twin module 150 also includes: a feedback unit 153, configured to receive operation instructions and feed back the operation instructions to the computer module 140, so that the computer module 140 controls the coal selection work of the raw coal preparation module 110, the sorting and upgrading module 120 and the medium circulation module 130 according to the operation instructions.

[0037] Specifically, the three-dimensional modeling unit 151 can use its embedded three-dimensional modeling software to perform all-round, high-fidelity three-dimensional rendering and modeling of the coal preparation system 100 at a certain ratio according to the operating parameters, production data, and size data of the coal preparation system 100 under the current working conditions, so as to obtain a three-dimensional model of the coal preparation system 100. The data visualization unit 152 visualizes the three-dimensional model according to HTML5+U3D technology, which may specifically include data collection, data graphic display, data storage, data export, etc. Therefore, the operator can input instructions into the computer module 140 in real time according to the visualized three-dimensional model, so as to control the coal preparation work of the raw coal preparation module 110, the sorting and upgrading module 120, and the medium circulation module 130.

[0038] Furthermore, the feedback unit 153 can digitally process the operation instructions fed back by the operator, and transmit the result instructions to the PLC control unit 142 wirelessly, so as to guide the actual production and form an immersive interaction between the user and the physical entity module.

[0039] Specifically, first, the sensors distributed in the raw coal preparation module 110, the sorting and upgrading module 120, and the medium circulation module 130 collect the operating parameters, production data and dimensional data of the dry coal preparation system 100 and the equipment under the current working conditions, and transmit the converted digital signals to the 3D modeling unit 151 through the RS-232 interface; the 3D modeling unit 151 relies on 3Dmax / Creo5.0 / MODO and other 3D modeling and rendering engines, and adopts the unified modeling language UML to perform all-round, high-fidelity 3D modeling and rendering work on the entire coal preparation system 100 according to a certain proportion; then the data visualization unit 152 adopts web visualization, adopts B / S architecture, and is developed based on java+HTML technology to visualize the 3D model generated by the 3D modeling unit 151 on the web, and supports mainstream browsers including Googlechrome, 360 Speed, Firefox, IE, etc.; it is divided into three parts on the web display page, namely, display The three-dimensional model is displayed in the interactive display area, and the user can drag and enlarge the three-dimensional model. The working status and operating parameters of the equipment under the current working conditions are displayed in the parameter display area. Two thresholds are set for the operating status of each equipment in the system, one is the highest critical value and the other is the lowest critical value. When the equipment operating parameter is higher than the highest critical value or lower than the lowest critical value, the coal preparation system 100 will issue an alarm and automatically send a termination command to the PLC control unit 142 to request to stop the entire coal preparation process. The equipment with problems will be displayed in red in the interactive display area and the operating parameters with problems will be displayed in red in the parameter display area; the command sent by the user in the human-computer interaction area is input into the PLC control unit 142 as a digital signal through the RS232 interface, and is fed back to the physical entities of the raw coal preparation module 110, the sorting and quality improvement module 120, and the medium circulation module 130 after being processed by the central processing unit CPU, the I / O converter and the output interface, so as to realize the human-computer interaction of the system.

[0040] According to one embodiment of the present invention, Figure 2 As shown, the raw coal preparation module 110 includes: a pre-screening device 111, configured to classify the particle size of the raw coal; a crushing device 112, configured to crush the raw coal; and a drying device 113, configured to control the moisture in the raw coal.

[0041] According to one embodiment of the present invention, Figure 2 As shown, the raw coal preparation module 110 further includes: a state detection unit 114 , and the state detection unit 114 includes: a vibration sensor and a temperature sensor, wherein the vibration sensor is used to detect the vibration amplitude and vibration frequency of the crushing device 112 , and the temperature sensor is used to detect the temperature of the drying device 113 .

[0042] Specifically, first, the raw coal is classified into +100mm particle size and -100mm particle size raw coal by the pre-screening device 111, and the +100mm particle size raw coal is then crushed to -100mm particle size by the crushing device 112 and mixed with the raw coal before crushing. The mixed raw coal enters the drying device 113 to control the moisture content below 8%; then, the state detection unit 114 collects and detects the working state parameters of each device through sensors distributed on the equipment, including the belt gear speed, the temperature of the drying device 113, and the moisture content of the raw coal, and converts the collected analog signals into digital signals through an A / D converter, and transmits them to the PC computer 141 through a 5g network communication method to prepare for the preprocessing of the original data.

[0043] Specifically, the state detection unit 114 is composed of high-precision sensors such as vibration sensors and temperature sensors, which collect operating parameters during the actual operation of the equipment. The operating parameters include the vibration amplitude and vibration frequency of the pre-screening device 111 and the crushing device 112, the temperature of the drying device 113, etc.

[0044] According to one embodiment of the present invention, Figure 2 As shown, the sorting and quality improvement module 120 includes: a sorting machine, configured to sort the crushed raw coal, wherein the sorting machine includes a photoelectric sorting machine 121, a composite dry sorting machine and a heavy medium fluidized bed sorting machine 123; a grading screen 124, configured to perform particle size classification on the crushed raw coal; a coal quality detection unit 125, the coal quality detection unit 125 includes: an online ash detection device 1251 and a parameter sensor 1252, wherein the online ash detection device 1251 is used to detect the ash content and calorific value of each raw coal sorting product, and the parameter sensor 1252 is used to detect the vibration frequency, bed surface inclination, wind chamber air volume, inner bed gas velocity, bed pressure and bed height of the sorting machine.

[0045] Specifically, the raw coal processed by the raw coal preparation module 110 is firstly separated into oversize and undersize according to the 25mm particle size by the grading screen 124. The oversize is the 100-25mm raw coal and enters the photoelectric separator 121 for sorting to obtain two products, coarse clean coal and gangue. The undersize is further separated into 25-6mm and -6mm particle sizes by the 6mm particle size by the grading screen 124. The 25-6mm particle size raw coal enters the composite separator 122 for sorting to obtain two products, lump clean coal and gangue, while the -6mm particle size raw coal enters the heavy medium fluidized bed separator 123 for sorting to obtain two products, fine clean coal and gangue.

[0046] Furthermore, parameter sensors 1252 are distributed in the composite separator 122 and the heavy medium fluidized bed separator 123 to collect equipment operating parameters, including the bed surface inclination angle, vibration frequency and air volume of the composite separator 122, and the bed layer gas velocity, bed layer pressure and bed layer height of the heavy medium fluidized bed separator 123. The online ash detection device 1251 detects the ash content of the corresponding products of the photoelectric separator 121, the composite separator 122 and the heavy medium fluidized bed separator 123. The analog signals collected by the online ash detection device 1251 and the parameter sensor 1252 are converted into digital signals by an A / D converter and transmitted to the PC computer 141141 in the form of 5g wireless transmission.

[0047] According to one embodiment of the present invention, Figure 2 As shown, the medium circulation module 130 includes: a de-mediating screen 131, configured to separate the clean coal product and the heavy medium; a magnetic separator 133, configured to separate the heavy medium from the coal powder; and a dust collector 135, configured to absorb the dust generated during the sorting process.

[0048] Further, according to one embodiment of the present invention, Figure 2 As shown, the medium circulation module 130 also includes: a bed detection unit 136, configured to detect the bed density of the classifier; a quantitative feeding device 134, configured to replenish the medium of the classifier based on the bed density of the classifier; and a medium flow detection unit 137, configured to collect the medium and coal powder flow of the quantitative feeding device 134.

[0049] Specifically, the product sorted by the heavy medium fluidized bed separator 123 passes through the de-mediating screen 131 to separate the heavy medium and coal powder mixture to under the screen, and then is divided into two parts by the diversion device 132. One part directly enters the heavy medium fluidized bed separator 123 to form a medium circulation, and the other part enters the magnetic separator 133 to separate the heavy medium magnetic material from the mixture to form two products, magnetic material and coal powder, and enters the heavy medium fluidized bed separator 123 through the quantitative feeding device 134 to complete the bed density control work; the dust generated in the heavy medium fluidized bed separator 123 and the de-mediating screen 131 passes through the dust collector 135 to form exhaust air and mixed medium, and the mixed medium is transmitted by a belt to the quantitative feeding device 134 and re-enters the heavy medium fluidized bed separator 123 to form a medium circulation.

[0050] The flow detection unit 137 detects the valve opening of the diversion device, the real-time flow and the feeding speed of the quantitative feeding device 134 through sensors distributed on the diversion device 132 and the quantitative feeding device 134; the bed layer detection unit 136 is responsible for detecting the bed layer information of the heavy medium fluidized bed separator 123, including the bed layer gas velocity, bed layer pressure and bed layer height; the analog signal is converted by the A / D conversion device and sent to the PC computer 141.

[0051] The specific coal preparation process of the full-size dry coal preparation system 100 based on digital twin in the embodiment of the present invention is described in detail below, which may include the following steps:

[0052] S10: The PC computer 141 obtains the working parameters of the coal preparation system 100 from the state detection unit 114, the coal quality detection unit 125, the bed detection unit 136 and the medium flow detection unit 137, and performs preprocessing work such as data reorganization classification, reorganization, alignment and sorting in the scikit-learn machine learning library based on the python environment programming language. The preprocessed data set is divided into a raw coal preparation data set, a sorting and quality improvement data set and a medium circulation data set and stored in the MySQL database unit 1411, wherein the TCP / IP communication protocol is configured between the MySQL database unit 1411 and the python environment programming unit 1412 to complete the input of the data set to the python environment programming unit 1412;

[0053] S20: The python environment programming unit 1412 establishes a prediction model using a large amount of historical data through a random forest intelligent algorithm based on PSO optimization and improvement, and automatically saves the optimal hyperparameters of the model through a threshold comparison method, generates an optimal prediction model, and stores it in a comma-separated value file CSV format in the MySQL database unit 1411; the python environment programming unit 1412 loads the collected and preprocessed data set CSV file into the saved machine learning prediction model, calls the optimal hyperparameters stored in the MySQL database, predicts coal quality, and outputs the optimal equipment parameters under the current working conditions;

[0054] S30: The optimal equipment parameters of the current working conditions output by the python environment programming unit 1412 are transmitted to the PLC control unit 142. The PLC control unit 142 reads and interprets the commands transmitted to the PLC control unit 142, and feeds back to the physical entities of the raw coal preparation module 110, the sorting and upgrading module 120, and the medium circulation module 130, so as to adjust the equipment operating parameters and guide the actual production.

[0055] S40: The sensors distributed on the raw coal preparation module 110, the sorting and upgrading module 120, and the medium circulation module 130 collect the operating parameters, production data and dimensional data of the dry coal preparation system 100 and the equipment under the current working conditions, and transmit the converted digital signals to the 3D modeling unit 151 through the RS-232 interface; the 3D modeling unit 151 relies on 3Dmax / Creo5.0 / MODO and other 3D modeling and rendering engines, and adopts the unified modeling language UML to perform all-round, high-fidelity 3D modeling and rendering work on the entire coal preparation system 100 according to a certain proportion; then the data visualization unit 152 adopts web visualization, adopts B / S architecture, and is developed based on java+HTML technology to visualize the 3D model generated by the 3D modeling unit 151 on the web; the web display page is divided into three parts, namely, the display interaction area, the parameter display area and the human-computer interaction area, and the 3D model It is displayed in the interactive display area. Users can drag and enlarge the three-dimensional model by themselves. The working conditions and operating parameters of the equipment under the current working conditions are displayed in the parameter display area. Two thresholds are set for the operating status of each equipment in the system, one is the highest critical value and the other is the lowest critical value. When the equipment operating parameters are higher than the highest critical value or lower than the lowest critical value, the coal preparation system 100 will issue an alarm and automatically send a termination command to the PLC control unit 142 to request to stop the entire coal preparation process. The equipment with problems will be displayed in red in the interactive display area and the operating parameters with problems will be displayed in red in the parameter display area; the commands sent by the user in the human-computer interaction area are input into the PLC control unit 142 as digital signals through the RS232 interface, and are fed back to the physical entities of the raw coal preparation module 110, the sorting and quality improvement module 120, and the medium circulation module 130 after processing by the central processing unit CPU, I / O converter and output interface, so as to realize the human-computer interaction of the system.

[0056] Therefore, the coal preparation system of the present invention solves the limitation that the operating parameters in the coal preparation process are difficult to accurately adjust through the intelligent control of the coal preparation equipment by the Python environment programming unit under the Python language. The three-dimensional model established by the digital twin module realizes the immersive human-computer interaction function between the user and the physical entity, and has the advantages of equipment health detection, process effect prediction and analysis, optimization of process data, improvement of process efficiency and reduction of process cost.

[0057] In summary, according to the full-grade dry coal preparation system based on digital twin of the embodiment of the present invention, the raw coal preparation module is used to crush the raw coal and control the moisture content of the raw coal, the sorting and quality improvement module is used to sort and grade the crushed raw coal, the medium circulation module is used to separate the clean coal product and the heavy medium, and the heavy medium in the coal powder is separated to recycle the heavy medium in the sorting process, the computer module establishes a prediction model according to historical data, loads the working data of the raw coal preparation module, the sorting and quality improvement module and the medium circulation module into the prediction model to predict the coal quality, and outputs the optimal equipment parameters of the coal preparation system under the current working conditions to guide the production process of the coal preparation system; the digital twin module receives the status detection data of each module, and establishes a three-dimensional model of the coal preparation system and realizes visualization, the computer module displays the working status of the coal preparation system in real time based on the digital twin module, and controls the adjustment of the operating parameters of the raw coal preparation module, the sorting and quality improvement module and the medium circulation module according to the working status. Therefore, the system uses computer modules to intelligently control coal preparation equipment, thereby solving the limitation that operating parameters in the coal preparation process are difficult to accurately adjust. The three-dimensional model established by the digital twin module realizes immersive human-computer interaction between users and physical entities. It has the advantages of equipment health detection, process effect prediction and analysis, optimization of process data, improvement of process efficiency, and reduction of process costs.

[0058] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0059] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0060] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, 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 an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A full-size dry coal preparation system based on digital twin, characterized in that: include: A raw coal preparation module configured to crush the raw coal and control the moisture content of the raw coal; A sorting and upgrading module, configured to sort and grade the crushed raw coal; a medium circulation module configured to separate the clean coal product from the heavy medium and to separate the heavy medium from the coal powder so as to recycle the heavy medium during the separation process; A computer module, the computer module is coupled to the raw coal preparation module, the sorting and upgrading module and the medium circulation module respectively, and is configured to establish a prediction model according to historical data, load the working data of the raw coal preparation module, the sorting and upgrading module and the medium circulation module into the prediction model to predict coal quality, and output the optimal equipment parameters of the coal preparation system under the current working conditions to guide the production process of the coal preparation system; A digital twin module, one end of the digital twin module is respectively coupled to the raw coal preparation module, the sorting and upgrading module and the medium circulation module, and is used to receive status detection data of each module, and establish a three-dimensional model of the coal preparation system and realize visualization; the other end of the digital twin module is coupled to the computer module, and the computer module displays the working status of the coal preparation system in real time based on the digital twin module, and controls the adjustment of operating parameters of the raw coal preparation module, the sorting and upgrading module and the medium circulation module according to the working status.

2. The full-size dry coal preparation system based on digital twin according to claim 1 is characterized in that: The computer module comprises: A PC computer is configured to process and save the working data of the coal preparation system, establish the prediction model based on an intelligent algorithm, and input the working data of the raw coal preparation module, the sorting and upgrading module, and the medium circulation module into the prediction model to obtain the optimal equipment parameters of the coal preparation system; A PLC control unit is coupled to the PC computer and is configured to receive the optimal equipment parameters obtained by the PC computer and input them into the raw coal preparation module, the sorting and upgrading module and the medium circulation module to control the production process of the coal preparation system.

3. The full-size dry coal preparation system based on digital twin according to claim 2 is characterized in that: The PC computer comprises: A MySQL database unit configured to store and call working parameter data of the raw coal preparation module, the sorting and upgrading module, and the medium circulation module; The python environment programming unit is configured to perform data preprocessing and parameter optimization on the data of the raw coal preparation module, the sorting and upgrading module, and the medium circulation module based on the python compiled language environment by calling the machine learning algorithm model.

4. The full-size dry coal preparation system based on digital twin according to claim 1 is characterized in that: The digital twin module includes: a three-dimensional modeling unit configured to establish a three-dimensional model of the coal preparation system according to operating parameters, production data and dimension data of the coal preparation system under current working conditions; A data visualization unit is configured to visualize the three-dimensional model of the coal preparation system to visualize the production process of the coal preparation system.

5. The full-size dry coal preparation system based on digital twin according to claim 4 is characterized in that: The digital twin module also includes: The feedback unit is configured to receive operation instructions and feed back the operation instructions to the computer module, so that the computer module controls the coal selection work of the raw coal preparation module, the sorting and upgrading module and the medium circulation module according to the operation instructions.

6. The full-size dry coal preparation system based on digital twin according to claim 1 is characterized in that: The raw coal preparation module comprises: a pre-screening device configured to classify the particle size of the raw coal; A crushing device configured to crush raw coal; A drying device is configured to control the moisture content of the raw coal.

7. The full-size dry coal preparation system based on digital twin according to claim 6 is characterized in that: The raw coal preparation module also includes: A state detection unit, the state detection unit comprises: a vibration sensor and a temperature sensor, wherein the vibration sensor is used to detect the vibration amplitude and vibration frequency of the crushing device, and the temperature sensor is used to detect the temperature of the drying device.

8. The full-size dry coal preparation system based on digital twin according to claim 1 is characterized in that: The sorting and quality improvement module comprises: A sorting machine configured to sort the crushed raw coal, wherein the sorting machine includes a photoelectric sorting machine, a composite dry sorting machine and a heavy medium fluidized bed sorting machine; A grading screen configured to grade the crushed raw coal into particle sizes; A coal quality detection unit, the coal quality detection unit comprising: an online ash detection device and a parameter sensor, wherein the online ash detection device is used to detect the ash content and calorific value of each raw coal sorting product, and the parameter sensor is used to detect the vibration frequency, bed surface inclination, wind chamber air volume, inner bed gas velocity, bed pressure and bed height of the sorting machine.

9. The full-size dry coal preparation system based on digital twin according to claim 1, characterized in that: The medium circulation module comprises: A de-mediating screen configured to separate the clean coal product from the heavy medium; a magnetic separator configured to separate heavy medium from the pulverized coal; A dust collector is configured to absorb dust generated during the sorting process.

10. The full-size dry coal preparation system based on digital twin according to claim 9, characterized in that: The medium circulation module also includes: a bed layer detection unit configured to detect bed layer density of the sorting machine; a dosing device configured to replenish the separator with media based on a bed density of the separator; The medium flow detection unit is configured to collect the medium and coal powder flow of the quantitative feeding device.

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