An intelligent control system for flotation process based on neural network

Abstract

The present invention discloses a flotation process intelligent control system based on a neural network, which relates to the field of intelligent control technology and includes an intelligent control module for controlling data transmission between various modules. The present invention first uses an image capture device to capture images of a flotation cell, determines a video of the cell bottom and a video of foam rising in the cell, then performs feature classification and rate calculation on the cell bottom and foam rising videos to determine the foam generation rate and foam rise rate. Secondly, the foam generation rate and foam rise rate are judged and analyzed to determine whether the foam meets the standard. The real-time liquid level data of the flotation cell is then analyzed and processed to determine the critical liquid level arrival time of the flotation cell. Finally, a flotation foam collection manual is used to standardize the collection method of the staff. The above method can not only determine whether the foam meets the standard, but also standardize the collection method of the staff, thereby improving the separation efficiency of minerals.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, in particular to a flotation process intelligent control system based on a neural network. Background Art

[0002] The principle process of froth flotation involves bubbles rising through the slurry, carrying selective mineral particles that are loosely bound at the gas-liquid interface. The froth formed on the surface of the slurry is then scraped off. This process is simple in concept but complex in detail. Its adaptability and effectiveness have made froth flotation the most widely used method for separating complex, low-grade ores. Over 90% of copper, lead, zinc, molybdenum, antimony, and nickel are recovered worldwide using froth flotation. Although the first patents for froth flotation were issued in the 19th century, the equipment, technology, and understanding of surface chemistry used in flotation are still evolving.

[0003] In the existing mineral separation process through foam, the foam generation rate, the foam rising rate and the liquid level of the flotation tank are not analyzed, resulting in the generated foam not meeting the standards, thereby reducing the separation efficiency of the minerals. Summary of the Invention

[0004] In order to solve the above technical problems, an intelligent control system for flotation process based on neural network is provided. This technical solution solves the problem proposed in the above background technology that in the existing mineral separation process through foam, the foam generation rate, the foam rising rate and the liquid level of the flotation tank are not analyzed, resulting in the generated foam not meeting the standards, thereby reducing the separation efficiency of the minerals.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A flotation process intelligent control system based on a neural network, comprising:

[0007] An intelligent control terminal, which is used to control each module to perform rate and height analysis on the foam and liquid level inside the flotation cell, determine whether the foam meets the standards, and when the critical liquid level of the flotation cell is reached. The intelligent control terminal is used to control data transmission and information exchange between each module;

[0008] A database system for storing initial design parameters of a froth flotation process;

[0009] An image capture device, which is used to capture and process images of the interior of the flotation cell, obtaining a video of the cell bottom and a video of foam rising in the cell;

[0010] a foam generation rate determination module, the foam generation rate determination module being used to perform rate analysis processing on the generated foam to determine the foam generation rate;

[0011] a foam rising rate determining module, the foam rising rate determining module being used to calculate and process the rising path of the foam to determine the foam rising rate;

[0012] A foam rate analysis module, which is used to analyze and process the foam generation rate and the foam rise rate to determine whether the foam meets the standard;

[0013] a flotation tank liquid level change determination module, the flotation tank liquid level change determination module being used to analyze and process the liquid level of the flotation tank and determine the rate of change of the liquid level of the flotation tank;

[0014] A critical liquid level arrival time determination module is used to predict and analyze the rate of change of the liquid level of the flotation tank to determine the critical liquid level arrival time of the flotation tank;

[0015] A flotation tank liquid level warning module, which performs warning settings based on the arrival time of the flotation tank critical liquid level;

[0016] A foam collection design module is used to standardize the collection method of staff.

[0017] Preferably, the foam generation rate determination module is used to perform rate analysis on the generated foam, and determining the foam generation rate specifically includes the following steps:

[0018] Based on the intelligent control terminal, the image capture device is controlled to collect and process images of the bottom of the flotation tank to obtain a video of the bottom of the flotation tank;

[0019] Based on the spot detection algorithm, feature extraction is performed on the bottom video of the flotation tank to obtain the amount of foam generated;

[0020] Based on the intelligent control terminal, the bottom video of the flotation tank is processed to obtain the duration of the bottom video;

[0021] Based on the intelligent control terminal, the amount of foam generated and the duration of the trough bottom video are calculated and processed to determine the foam generation rate.

[0022] Preferably, the foam rising rate determination module is used to calculate the rising path of the foam, and determining the foam rising rate specifically includes the following steps:

[0023] Based on the intelligent control terminal, the image capture device is controlled to collect and process images of the interior of the flotation cell, and obtain a video of the rising foam in the flotation cell;

[0024] Based on the intelligent control terminal, the foam in the video of the foam rising in the flotation tank is marked to obtain the position of the marked foam, wherein the marked foam is specifically the foam just generated at the bottom of the flotation tank;

[0025] Based on the intelligent control terminal, the path analysis of the foam rising video in the flotation tank is performed with the marked foam position as the feature to obtain the rising path of the marked foam;

[0026] Based on the intelligent control terminal, the rising path of the marked foam is analyzed and processed to determine the rising rate of the foam.

[0027] Preferably, the analyzing and processing of the rising path of the marked foam based on the intelligent control terminal to determine the rising rate of the foam specifically includes the following steps:

[0028] Based on the intelligent control terminal, the rising foam video in the flotation tank is intercepted and processed with the marked foam position and the rising path of the marked foam as the characteristics to obtain the rising duration of the marked foam;

[0029] Based on the intelligent control terminal, the length of the rising path of the marked foam is measured to obtain the scaled length of the rising path;

[0030] Based on the intelligent control terminal, the image capture device is analyzed and processed to determine the image scaling ratio;

[0031] Based on the intelligent control terminal, the image scaling ratio and the scaling length of the ascending path are calculated and processed to determine the actual length of the ascending path;

[0032] Based on the intelligent control terminal, the actual length of the rising path and the rising time of the marked foam are calculated and processed to determine the foam rising rate.

[0033] Preferably, the foam rate analysis module is used to analyze and process the foam generation rate and the foam rise rate, and determining whether the foam meets the standard specifically includes the following steps:

[0034] Based on the intelligent control terminal, the foam generation rate and foam rising rate are judged and processed;

[0035] If the foam generation rate is less than the set foam generation rate threshold, the foam does not meet the standard, and the flotation reagent ratio and pulp concentration are readjusted;

[0036] If the foam generation rate is greater than or equal to the set foam generation rate threshold, and the foam rise rate is greater than or equal to the set foam rise rate threshold, the foam meets the standard, and the flotation tank level is analyzed based on the intelligent control terminal;

[0037] If the foam rising rate is less than the set foam rising rate threshold, the foam does not meet the standard, and the flotation reagent ratio and pulp concentration are readjusted.

[0038] Preferably, the flotation tank liquid level change determination module is used to analyze and process the liquid level of the flotation tank, and determining the rate of change of the liquid level of the flotation tank specifically includes the following steps:

[0039] Based on the intelligent control terminal, the database system is read and processed to obtain the initial design parameters of the froth flotation process;

[0040] Based on the intelligent control terminal, the initial design parameters of the foam flotation process are read and processed to obtain the initial liquid level data of the flotation cell;

[0041] Based on the intelligent control terminal, the initial liquid level data of the flotation tank is analyzed and processed to determine the liquid level change rate of the flotation tank.

[0042] Preferably, the analyzing and processing of the initial liquid level data of the flotation tank based on the intelligent control terminal to determine the liquid level change rate of the flotation tank specifically includes the following steps:

[0043] Based on the intelligent control terminal, the liquid level sensor data is read and processed to determine the real-time liquid level data of the flotation tank;

[0044] Based on the intelligent control terminal, the difference between the initial liquid level data of the flotation tank and the real-time liquid level data of the flotation tank is calculated to obtain the liquid level change data of the flotation tank;

[0045] Based on the intelligent control terminal, the duration of the foam flotation process is read and processed to obtain the foam flotation duration information;

[0046] Based on the intelligent control terminal, the liquid level change data of the flotation tank and the foam flotation duration information are calculated and processed to determine the liquid level change rate of the flotation tank.

[0047] Preferably, the critical liquid level arrival time determination module is used to predict and analyze the liquid level change rate of the flotation tank, and determining the critical liquid level arrival time of the flotation tank specifically includes the following steps:

[0048] Based on the intelligent control terminal, the database system is read and processed to obtain the critical liquid level data of the flotation tank;

[0049] Based on the intelligent control terminal, the real-time liquid level data of the flotation tank and the critical liquid level data of the flotation tank are subjected to difference calculation processing to obtain the liquid level data to be changed of the flotation tank;

[0050] Based on the intelligent control terminal, the data of the liquid level to be changed and the liquid level change rate of the flotation tank are calculated and processed to determine the time when the critical liquid level of the flotation tank is reached.

[0051] Preferably, the flotation tank liquid level warning module performs warning setting according to the flotation tank critical liquid level arrival time, specifically comprising the following steps:

[0052] Based on the intelligent control terminal, a communication connection is established with the staff's electronic equipment through the communication module;

[0053] Based on the intelligent control terminal, the critical liquid level arrival time of the flotation tank is transmitted to the staff's electronic equipment;

[0054] The staff adds water to the inside of the flotation tank according to the time when the critical liquid level of the flotation tank is reached, so that the liquid level data of the flotation tank is higher than the critical liquid level data of the flotation tank.

[0055] Preferably, the foam collection design module is used to standardize the collection method of the staff and specifically includes the following steps:

[0056] Based on the intelligent control terminal, the database system is read and processed to obtain the flotation foam collection manual;

[0057] Based on the intelligent control terminal, the flotation foam collection manual is processed to extract information and determine the standard collection method for staff;

[0058] The intelligent control terminal standardizes the staff's collection method through the staff's standard collection method.

[0059] Furthermore, a method for intelligent control of a flotation process based on a neural network is proposed, which is used to adopt the above-mentioned intelligent control system of a flotation process based on a neural network, including:

[0060] S1. Based on the intelligent control terminal, feature analysis and rate calculation are performed on the bottom video of the flotation tank to determine the foam generation rate;

[0061] S2. Based on the intelligent control terminal, the path analysis and rate calculation of the foam rising video in the flotation tank are performed to determine the foam rising rate;

[0062] S3. Based on the intelligent control terminal, the foam rising rate and the foam generation rate are compared and judged to determine whether the foam meets the standard;

[0063] S4. Based on the intelligent control terminal, perform difference calculation and rate calculation on the liquid level of the flotation tank to determine the rate of change of the liquid level of the flotation tank;

[0064] S5. Based on the intelligent control terminal, the liquid level change rate of the flotation tank and the critical liquid level data of the flotation tank are calculated and processed to determine the critical liquid level arrival time of the flotation tank;

[0065] S6. Based on the intelligent control terminal, set up early warning according to the critical liquid level arrival time of the flotation tank;

[0066] S7. Based on the intelligent control terminal, the collection method of the staff is standardized through the flotation foam collection manual.

[0067] Furthermore, a storage medium is proposed, on which a computer program is stored. When the computer program is called and run, it executes the above-mentioned intelligent control method for flotation process based on neural network.

[0068] Compared with the existing technology, the present invention provides a flotation process intelligent control system based on neural network, which has the following beneficial effects:

[0069] The present invention firstly collects images of a flotation tank through an image capture device to determine the bottom video of the flotation tank and the video of foam rising in the flotation tank, and then performs feature classification and rate calculation on the bottom video of the flotation tank and the video of foam rising in the flotation tank to determine the foam generation rate and the foam rising rate. Secondly, the foam generation rate and the foam rising rate are judged and analyzed to determine whether the foam meets the standard. Then, the real-time liquid level data of the flotation tank is analyzed and processed to determine the critical liquid level arrival time of the flotation tank. Finally, the collection method of the staff is standardized through the flotation foam collection manual. The above method can not only judge whether the foam meets the standard, but also standardize the collection method of the staff, thereby improving the separation efficiency of minerals. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a structural diagram of a flotation process intelligent control system based on neural network proposed by the present invention;

[0071] Figure 2 Schematic diagram of the process for determining the foam generation rate in the present invention;

[0072] Figure 3 A schematic diagram of the process of obtaining the rising path of the marked foam in the present invention;

[0073] Figure 4 Schematic diagram of the process for determining the foam rising rate in the present invention;

[0074] Figure 5 A schematic diagram of the process for determining whether foam meets the standards in the present invention;

[0075] Figure 6 A schematic diagram of the process of obtaining initial liquid level data of a flotation tank in the present invention;

[0076] Figure 7 Schematic diagram of the process for determining the rate of change of the liquid level of the flotation tank in the present invention;

[0077] Figure 8 Schematic diagram of the process for determining the critical liquid level arrival time of the flotation tank in the present invention;

[0078] Figure 9 This is a flow chart of the flotation tank liquid level warning module in the present invention performing warning setting based on the critical liquid level arrival time of the flotation tank;

[0079] Figure 10 This is a flow chart of the foam collection design module in the present invention for standardizing the collection method of staff. DETAILED DESCRIPTION

[0080] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0081] Reference Figure 1 As shown, a flotation process intelligent control system based on a neural network includes:

[0082] An intelligent control terminal, which is used to control each module to perform rate and height analysis on the foam and liquid level inside the flotation cell, determine whether the foam meets the standards, and when the critical liquid level of the flotation cell is reached. The intelligent control terminal is used to control data transmission and information exchange between each module;

[0083] A database system for storing initial design parameters of a froth flotation process;

[0084] An image capture device, which is used to capture and process images of the interior of the flotation cell, obtaining a video of the cell bottom and a video of foam rising in the cell;

[0085] a foam generation rate determination module, the foam generation rate determination module being used to perform rate analysis processing on the generated foam to determine the foam generation rate;

[0086] a foam rising rate determining module, the foam rising rate determining module being used to calculate and process the rising path of the foam to determine the foam rising rate;

[0087] A foam rate analysis module, which is used to analyze and process the foam generation rate and the foam rise rate to determine whether the foam meets the standard;

[0088] a flotation tank liquid level change determination module, the flotation tank liquid level change determination module being used to analyze and process the liquid level of the flotation tank and determine the rate of change of the liquid level of the flotation tank;

[0089] A critical liquid level arrival time determination module is used to predict and analyze the rate of change of the liquid level of the flotation tank to determine the critical liquid level arrival time of the flotation tank;

[0090] A flotation tank liquid level warning module, which performs warning settings based on the arrival time of the flotation tank critical liquid level;

[0091] A foam collection design module, which is used to standardize the collection method of staff;

[0092] It will be understood by those skilled in the art that when minerals are separated in a flotation tank, the minerals are attached to the foam to achieve separation. When the foam generation rate is slow, it will affect the separation efficiency of the minerals. In addition, the foam rising rate will also affect the separation efficiency of the minerals, because the existence time of the foam is limited. If the foam rising rate is too slow, the foam will break during the rising process, and the liquid level of the flotation tank will also affect the foam generation rate, because when the liquid level of the flotation tank decreases, the concentration of the ore pulp inside the flotation tank will increase, thereby causing the foam generation rate to decrease. Therefore, by monitoring the foam generation rate, foam rising rate and flotation tank liquid level data during the foam flotation process, and designing corresponding adjustment plans, the separation efficiency of the minerals can be improved.

[0093] Reference Figure 2 As shown, the foam generation rate determination module is used to perform rate analysis on the generated foam, and determining the foam generation rate specifically includes the following steps:

[0094] Based on the intelligent control terminal, the image capture device is controlled to collect and process images of the bottom of the flotation tank to obtain a video of the bottom of the flotation tank;

[0095] Based on the spot detection algorithm, feature extraction is performed on the bottom video of the flotation tank to obtain the amount of foam generated;

[0096] Based on the intelligent control terminal, the bottom video of the flotation tank is processed to obtain the duration of the bottom video;

[0097] Based on the intelligent control terminal, the amount of foam generated and the duration of the tank bottom video are calculated and processed to determine the foam generation rate;

[0098] In this embodiment, in order to improve the adhesion of minerals on the foam and make the foam generated from the bottom of the flotation tank, the bottom of the flotation tank is imaged by an image capture device to obtain a bottom video of the flotation tank. Then, the bottom video of the flotation tank is feature extracted by a spot detection algorithm to determine the amount of foam generated in the bottom video of the flotation tank. Finally, the foam generation rate is determined by calculating the amount with the duration of the bottom video.

[0099] Reference Figure 3 As shown, the foam rising rate determination module is used to calculate the rising path of the foam. Determining the foam rising rate specifically includes the following steps:

[0100] Based on the intelligent control terminal, the image capture device is controlled to collect and process images of the interior of the flotation cell, and obtain a video of the rising foam in the flotation cell;

[0101] Based on the intelligent control terminal, the foam in the video of the foam rising in the flotation tank is marked to obtain the position of the marked foam, wherein the marked foam is specifically the foam just generated at the bottom of the flotation tank;

[0102] Based on the intelligent control terminal, the path analysis of the foam rising video in the flotation tank is performed with the marked foam position as the feature to obtain the rising path of the marked foam;

[0103] It is understandable that in order to obtain the rising path of the marked bubble, a video must be collected. The video is a dynamic scene. If the rising path of the marked bubble is determined by the naked eye, misjudgment may occur because multiple bubbles are generated at a time, not just one. Therefore, the rising path of the marked bubble is extracted through a neural network algorithm to determine the rising path of the marked bubble.

[0104] Based on the intelligent control terminal, the rising path of the marked foam is analyzed and processed to determine the rising rate of the foam;

[0105] In this embodiment, since the foam is generated at the bottom of the flotation tank and more than one foam is generated, the generated foam is marked, and then the video of the foam rising in the flotation tank is analyzed and processed to determine the rising path of the marked foam. It can be understood that since the foam is generated in the same area, the rising path of the foam is roughly the same. Therefore, only one foam needs to be marked to determine the rising path of the foam.

[0106] Reference Figure 4 As shown, based on the intelligent control terminal, analyzing and processing the rising path of the marked foam and determining the rising rate of the foam specifically include the following steps:

[0107] Based on the intelligent control terminal, the rising foam video in the flotation tank is intercepted and processed with the marked foam position and the rising path of the marked foam as the characteristics to obtain the rising duration of the marked foam;

[0108] Based on the intelligent control terminal, the length of the rising path of the marked foam is measured to obtain the scaled length of the rising path;

[0109] Based on the intelligent control terminal, the image capture device is analyzed and processed to determine the image scaling ratio;

[0110] Based on the intelligent control terminal, the image scaling ratio and the scaling length of the ascending path are calculated and processed to determine the actual length of the ascending path;

[0111] Based on the intelligent control terminal, the actual length of the rising path and the rising time of the marked foam are calculated and processed to determine the rising rate of the foam;

[0112] In this embodiment, the rising path of the foam is from the bottom of the flotation tank to the top of the liquid surface of the flotation tank, but the video of the rising foam in the flotation tank captured by image shooting is the rising process of multiple bubbles. Therefore, it is necessary to capture the duration of the rising video of the foam in the flotation tank to obtain the rising duration of the marked foam. The image is a zoom of the scene. In order to determine the rising rate of the foam, the image scaling ratio is required to obtain the actual length of the rising path, and then the rising rate of the foam is obtained.

[0113] Reference Figure 5 As shown, the foam rate analysis module is used to analyze and process the foam generation rate and the foam rise rate, and determine whether the foam meets the standard. Specifically, the steps include:

[0114] Based on the intelligent control terminal, the foam generation rate and foam rising rate are judged and processed;

[0115] If the foam generation rate is less than the set foam generation rate threshold, the foam does not meet the standard, and the flotation reagent ratio and pulp concentration are readjusted;

[0116] If the foam generation rate is greater than or equal to the set foam generation rate threshold, and the foam rise rate is greater than or equal to the set foam rise rate threshold, the foam meets the standard, and the flotation tank level is analyzed based on the intelligent control terminal;

[0117] If the foam rising rate is less than the set foam rising rate threshold, the foam does not meet the standard, and the flotation reagent ratio and pulp concentration are readjusted;

[0118] In this embodiment, when the foam generation rate or the foam rise rate is too slow, the mineral separation efficiency will be reduced. The foam generation rate and the foam rise rate are related to the pulp concentration and the flotation reagent ratio in the flotation tank. Therefore, when the foam generation rate and the foam rise rate do not meet the standards, the flotation reagent ratio and the pulp concentration are readjusted.

[0119] Reference Figure 6 As shown, the flotation tank liquid level change determination module is used to analyze and process the liquid level of the flotation tank. Determining the liquid level change rate of the flotation tank specifically includes the following steps:

[0120] Based on the intelligent control terminal, the database system is read and processed to obtain the initial design parameters of the froth flotation process;

[0121] Based on the intelligent control terminal, the initial design parameters of the foam flotation process are read and processed to obtain the initial liquid level data of the flotation cell;

[0122] Based on the intelligent control terminal, the initial liquid level data of the flotation tank is analyzed and processed to determine the liquid level change rate of the flotation tank;

[0123] In this embodiment, when mineral separation is performed in the flotation cell, various data of the flotation cell before mineral separation are recorded to facilitate subsequent adjustments, namely, the initial design parameters of the froth flotation process. In order to accurately obtain the liquid level change rate of the flotation cell, it is necessary to determine the initial liquid level data of the flotation cell, and the initial liquid level data of the flotation cell is recorded in the initial design parameters of the froth flotation process.

[0124] Reference Figure 7 As shown, based on the intelligent control terminal, the initial liquid level data of the flotation tank is analyzed and processed to determine the liquid level change rate of the flotation tank, which specifically includes the following steps:

[0125] Based on the intelligent control terminal, the liquid level sensor data is read and processed to determine the real-time liquid level data of the flotation tank;

[0126] Based on the intelligent control terminal, the difference between the initial liquid level data of the flotation tank and the real-time liquid level data of the flotation tank is calculated to obtain the liquid level change data of the flotation tank;

[0127] Based on the intelligent control terminal, the duration of the foam flotation process is read and processed to obtain the foam flotation duration information;

[0128] Based on the intelligent control terminal, the liquid level change data of the flotation tank and the foam flotation duration information are calculated and processed to determine the liquid level change rate of the flotation tank;

[0129] In this embodiment, after the initial liquid level data of the flotation cell is determined, it is only necessary to determine the real-time liquid level data and foam flotation duration information of the flotation cell to obtain the liquid level change rate of the flotation cell. Therefore, the liquid level of the flotation cell is monitored in real time by the liquid level sensor. Therefore, it is only necessary to read the data of the liquid level sensor to obtain the real-time liquid level data of the flotation cell.

[0130] Reference Figure 8 As shown, the critical liquid level arrival time determination module is used to predict and analyze the liquid level change rate of the flotation tank. Determining the critical liquid level arrival time of the flotation tank specifically includes the following steps:

[0131] Based on the intelligent control terminal, the database system is read and processed to obtain the critical liquid level data of the flotation tank;

[0132] Based on the intelligent control terminal, the real-time liquid level data of the flotation tank and the critical liquid level data of the flotation tank are subjected to difference calculation processing to obtain the liquid level data to be changed of the flotation tank;

[0133] Based on the intelligent control terminal, the data of the liquid level to be changed and the liquid level change rate of the flotation tank are calculated and processed to determine the time when the critical liquid level of the flotation tank is reached;

[0134] In this embodiment, when the liquid level of the flotation tank drops to a critical value, the concentration of waste materials in the flotation tank will increase, such as gravel. The increase in the concentration of waste materials will affect the foam generation rate and the foam rise rate. Therefore, in order to avoid reducing the separation efficiency of minerals, the liquid level of the flotation tank is monitored in real time.

[0135] Reference Figure 9 As shown, the flotation tank liquid level warning module performs warning setting based on the arrival time of the flotation tank critical liquid level, which specifically includes the following steps:

[0136] Based on the intelligent control terminal, a communication connection is established with the staff's electronic equipment through the communication module;

[0137] Based on the intelligent control terminal, the critical liquid level arrival time of the flotation tank is transmitted to the staff's electronic equipment;

[0138] The staff adds water to the inside of the flotation tank according to the time when the critical liquid level of the flotation tank reaches, so that the liquid level data of the flotation tank is higher than the critical liquid level data of the flotation tank;

[0139] In this embodiment, in order for the staff to accurately grasp the time when the liquid level of the flotation tank reaches the critical value, the critical liquid level arrival time of the flotation tank is sent to the staff's electronic device, so that the staff can accurately grasp the liquid level changes of the flotation tank. Then, the staff adds water to the inside of the flotation tank to reduce the concentration of waste materials, so as not to reduce the foam generation rate and the foam rising rate.

[0140] Reference Figure 10 As shown, the foam collection design module is used to standardize the collection method of the staff and specifically includes the following steps:

[0141] Based on the intelligent control terminal, the database system is read and processed to obtain the flotation foam collection manual;

[0142] Based on the intelligent control terminal, the flotation foam collection manual is processed to extract information and determine the standard collection method for staff;

[0143] The intelligent control terminal standardizes the staff’s data collection method through the staff’s standard collection method;

[0144] In this embodiment, the collection method of the staff will also affect the separation efficiency of the minerals, because an irregular collection method may cause the foam to break prematurely, thereby causing the minerals attached to the foam surface to scatter back into the flotation tank. In order to avoid the above situation, the collection method of the staff is standardized through the flotation foam collection manual.

[0145] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A flotation process intelligent control system based on neural network, characterized in that: include: An intelligent control terminal, which is used to control each module to perform rate and height analysis on the foam and liquid level inside the flotation cell, determine whether the foam meets the standards, and when the critical liquid level of the flotation cell is reached. The intelligent control terminal is used to control data transmission and information exchange between each module; A database system for storing initial design parameters of a froth flotation process; An image capture device, which is used to capture and process images of the interior of the flotation cell, obtaining a video of the cell bottom and a video of foam rising in the cell; The foam generation rate determination module is used to perform rate analysis on the generated foam. Determining the foam generation rate specifically includes the following steps: Based on the intelligent control terminal, the image capture device is controlled to collect and process images of the bottom of the flotation tank to obtain a video of the bottom of the flotation tank; Based on the spot detection algorithm, feature extraction is performed on the bottom video of the flotation tank to obtain the amount of foam generated; Based on the intelligent control terminal, the bottom video of the flotation tank is processed to obtain the duration of the bottom video; Based on the intelligent control terminal, the amount of foam generated and the duration of the tank bottom video are calculated and processed to determine the foam generation rate; The foam rising rate determination module is used to calculate the rising path of the foam. The foam rising rate determination module specifically includes the following steps: Based on the intelligent control terminal, the image capture device is controlled to collect and process images of the interior of the flotation cell, and obtain a video of the rising foam in the flotation cell; Based on the intelligent control terminal, the foam in the video of the foam rising in the flotation tank is marked to obtain the position of the marked foam, wherein the marked foam is specifically the foam just generated at the bottom of the flotation tank; Based on the intelligent control terminal, the path analysis of the foam rising video in the flotation tank is performed with the marked foam position as the feature to obtain the rising path of the marked foam; Based on the intelligent control terminal, the rising path of the marked foam is analyzed and processed to determine the rising rate of the foam; A foam rate analysis module, which is used to analyze and process the foam generation rate and the foam rise rate to determine whether the foam meets the standard; a flotation tank liquid level change determination module, the flotation tank liquid level change determination module being used to analyze and process the liquid level of the flotation tank and determine the rate of change of the liquid level of the flotation tank; A critical liquid level arrival time determination module is used to predict and analyze the rate of change of the liquid level of the flotation tank to determine the critical liquid level arrival time of the flotation tank; A flotation tank liquid level warning module, which performs warning settings based on the arrival time of the flotation tank critical liquid level; A foam collection design module is used to standardize the collection method of staff.

2. The intelligent control system for flotation process based on neural network according to claim 1, characterized in that: The intelligent control terminal is used to analyze and process the rising path of the marked foam to determine the rising rate of the foam, which specifically includes the following steps: Based on the intelligent control terminal, the rising foam video in the flotation tank is intercepted and processed with the marked foam position and the rising path of the marked foam as the characteristics to obtain the rising duration of the marked foam; Based on the intelligent control terminal, the length of the rising path of the marked foam is measured to obtain the scaled length of the rising path; Based on the intelligent control terminal, the image capture device is analyzed and processed to determine the image scaling ratio; Based on the intelligent control terminal, the image scaling ratio and the scaling length of the ascending path are calculated and processed to determine the actual length of the ascending path; Based on the intelligent control terminal, the actual length of the rising path and the rising time of the marked foam are calculated and processed to determine the foam rising rate.

3. The intelligent control system for flotation process based on neural network according to claim 1, characterized in that: The foam rate analysis module is used to analyze and process the foam generation rate and the foam rise rate to determine whether the foam meets the standard. Specifically, the steps include: Based on the intelligent control terminal, the foam generation rate and foam rising rate are judged and processed; If the foam generation rate is less than the set foam generation rate threshold, the foam does not meet the standard, and the flotation reagent ratio and pulp concentration are readjusted; If the foam generation rate is greater than or equal to the set foam generation rate threshold, and the foam rise rate is greater than or equal to the set foam rise rate threshold, the foam meets the standard, and the flotation tank level is analyzed based on the intelligent control terminal; If the foam rising rate is less than the set foam rising rate threshold, the foam does not meet the standard, and the flotation reagent ratio and pulp concentration are readjusted.

4. The intelligent control system for flotation process based on neural network according to claim 1, characterized in that: The flotation tank liquid level change determination module is used to analyze and process the liquid level of the flotation tank. Determining the liquid level change rate of the flotation tank specifically includes the following steps: Based on the intelligent control terminal, the database system is read and processed to obtain the initial design parameters of the froth flotation process; Based on the intelligent control terminal, the initial design parameters of the foam flotation process are read and processed to obtain the initial liquid level data of the flotation cell; Based on the intelligent control terminal, the initial liquid level data of the flotation tank is analyzed and processed to determine the liquid level change rate of the flotation tank.

5. The intelligent control system for flotation process based on neural network according to claim 4, characterized in that: The method of analyzing and processing the initial liquid level data of the flotation tank based on the intelligent control terminal to determine the liquid level change rate of the flotation tank specifically includes the following steps: Based on the intelligent control terminal, the liquid level sensor data is read and processed to determine the real-time liquid level data of the flotation tank; Based on the intelligent control terminal, the difference between the initial liquid level data of the flotation tank and the real-time liquid level data of the flotation tank is calculated to obtain the liquid level change data of the flotation tank; Based on the intelligent control terminal, the duration of the foam flotation process is read and processed to obtain the foam flotation duration information; Based on the intelligent control terminal, the liquid level change data of the flotation tank and the foam flotation duration information are calculated and processed to determine the liquid level change rate of the flotation tank.

6. The intelligent control system for flotation process based on neural network according to claim 1, characterized in that: The critical liquid level arrival time determination module is used to predict and analyze the liquid level change rate of the flotation tank. Determining the critical liquid level arrival time of the flotation tank specifically includes the following steps: Based on the intelligent control terminal, the database system is read and processed to obtain the critical liquid level data of the flotation tank; Based on the intelligent control terminal, the real-time liquid level data of the flotation tank and the critical liquid level data of the flotation tank are subjected to difference calculation processing to obtain the liquid level data to be changed of the flotation tank; Based on the intelligent control terminal, the data of the liquid level to be changed and the liquid level change rate of the flotation tank are calculated and processed to determine the time when the critical liquid level of the flotation tank is reached.

7. The intelligent control system for flotation process based on neural network according to claim 1, characterized in that: The flotation tank liquid level warning module performs warning setting based on the flotation tank critical liquid level arrival time, which specifically includes the following steps: Based on the intelligent control terminal, a communication connection is established with the staff's electronic equipment through the communication module; Based on the intelligent control terminal, the critical liquid level arrival time of the flotation tank is transmitted to the staff's electronic equipment; The staff adds water to the inside of the flotation tank according to the time when the critical liquid level of the flotation tank is reached, so that the liquid level data of the flotation tank is higher than the critical liquid level data of the flotation tank.

8. The intelligent control system for flotation process based on neural network according to claim 1, characterized in that: The foam collection design module is used to standardize the collection method of the staff and specifically includes the following steps: Based on the intelligent control terminal, the database system is read and processed to obtain the flotation foam collection manual; Based on the intelligent control terminal, the flotation foam collection manual is processed to extract information and determine the standard collection method for staff; The intelligent control terminal standardizes the staff's collection method through the staff's standard collection method.

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