Method for grading coarse and fine red mud

Abstract

The invention provides a coarse and fine red mud grading method, which relates to the technical field of bauxite, and comprises the following steps: inputting red mud slurry into a tank with a stirring system to disperse red mud particles in the slurry, and synchronously adding water to control the rheological property; the red mud slurry is input into gravity separation equipment to be separated, so that coarse red mud is settled to the bottom to form underflow concentrate, and fine red mud and water are discharged as overflow tailings; the overflow tailings obtained through primary separation are diluted with water again and input into another set of gravity separation equipment again, secondary underflow concentrate is formed through separation, and the remaining fine particles and water serve as final overflow tailings to be discharged; according to the method, multi-stage gravity classification is used as initial classification, fineness classification of overflow tailings is improved through ultrasonic classification, size distribution classification is unified on the basis of the classification according to the sound wave reflection principle, the classification benchmark is more perfect, grade adaptation is more reasonable, and the reasonability of follow-up recycling is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of bauxite, and in particular to a method for classifying coarse and fine red mud. Background Art

[0002] A large amount of waste red mud is generated in the process of alumina production. Red mud is a red, powdery, high-water-content, strong alkaline solid waste left after the aluminum industry extracts aluminum from alumina. The composition of red mud is complex and contains alkali and a small amount of radioactive substances. The percentage of the main chemical components is shown in the table below. The red mud particles are too fine. At present, most domestic red mud yards use the wet storage method and long-term stacking after dehydration and drying. The former filters water and seeps into the ground to pollute the groundwater quality, and the latter is easy to cause dust flying after long-term stacking and drying, which seriously pollutes the environment and endangers human health. On the one hand, red mud is an industrial waste that causes environmental pollution, and on the other hand, it also has its resource nature. Laboratory experiments on red mud have proved that valuable metals such as Ca, Te, Ti, etc. can be recovered from red mud. Based on the principle of reducing the amount and harm of solid waste, making full and reasonable use of solid waste and disposing of it harmlessly, the development and utilization of red mud can effectively promote environmental cleaning, energy conservation and emission reduction, and the development of circular economy. In the development and utilization of red mud, its classification is very necessary; Traditional red mud classification is mainly carried out in the following manner: adding red mud slurry into a dilution tank, and then sending the diluted slurry into a hydrocyclone group for coarse and fine red mud classification, the bottom flow of the hydrocyclone enters the coarse red mud slurry tank and is sent to the coarse red mud treatment system of the sedimentation process, and the overflow of the hydrocyclone enters the fine red mud slurry tank and is sent to the fine red mud treatment system of the sedimentation process; however, in the above-mentioned method, gravity sedimentation classification is mainly used, and the different components in the red mud are complex, and gravity does not necessarily match the shape and size. In addition to gravity classification, the quality of red mud also needs to consider the size, shape and distribution factors. The size, shape and distribution of red mud particles obtained by different gravity classifications may not be adapted to the level of gravity classification, which affects the rationality of subsequent recycling and use. Therefore, the present invention proposes a coarse and fine red mud classification method to solve the problems existing in the prior art. Summary of the invention

[0003] In view of the above problems, the present invention proposes a method for classifying coarse and fine red mud. The method uses multi-stage gravity classification as the initial classification, ultrasonic classification to improve the fineness of the overflow tailings, and uses the principle of sound wave reflection on the basis of the above classification to unify the size distribution classification. The classification standard is more perfect and the grade adaptation is more reasonable, thereby ensuring the rationality of subsequent recycling and use.

[0004] To achieve the purpose of the present invention, the present invention is implemented by the following technical scheme: a method for classifying coarse and fine red mud, comprising the following steps: S1: The red mud slurry is fed into a tank with a stirring system so that the red mud particles are dispersed in the slurry, and water is added simultaneously to control the rheology; S2: The red mud slurry is input into the gravity separation equipment for separation, so that the coarse red mud settles to the bottom to form underflow concentrate, and the fine red mud and water are discharged as overflow tailings; S3: The overflow tailings obtained from the first separation are diluted with water again and input into another set of gravity separation equipment to separate into secondary underflow concentrates. The remaining fine particles and water are discharged as the final overflow tailings; S4: The final overflow tailings are input into the ultrasonic classification equipment, and the cavitation effect, micro-jet effect and acoustic streaming effect of ultrasonic waves in the liquid are used for further classification; S5: collecting red mud particle slurry of different particle sizes after further classification at different locations in the ultrasonic classification device; S6: Based on the above steps, the red mud slurry after N-level classification is collected, and the sound wave transmitter emits the sound wave of the same frequency to the red mud slurry of N levels, and the receiver receives the reflected sound wave; S7: Processing and analyzing the received reflected sound wave signal, extracting the signal characteristic parameters, and inferring the size and distribution of red mud slurries of different grades according to the signal characteristic parameters; S8: Setting a division threshold, dividing and combining the N-level red mud slurry again according to size and distribution, and obtaining a maximum of four levels of graded products.

[0005] A further improvement is that: S1 comprises the following steps: The red mud slurry is added into a tank with a stirring system, and the red mud slurry is stirred by a stirring blade with variable frequency speed regulation to control the maximum dispersion of the red mud particles in the slurry; During the stirring process, 20-30% of clean water is added compared to the red mud slurry, and the stirring time is controlled to 30-70 minutes to make the red mud slurry achieve optimal rheology.

[0006] A further improvement is that: S2 comprises the following steps: The red mud slurry is input into the gravity separation equipment for separation, and the pressure during separation is controlled to be ≥0.1Mpa; The coarse red mud settles to the bottom to form underflow concentrate, and the fine red mud and water are discharged as overflow tailings. In this process, a mixed slurry of solid and liquid is retained.

[0007] A further improvement is that: S3 comprises the following steps: Add water to dilute the overflow tailings obtained from the first separation, and control the proportion of water added to 20-40% of the overflow tailings; Input the diluted material into another set of gravity separation equipment, and control the pressure during separation to be ≥0.12Mpa; A secondary underflow concentrate and a final overflow tailings are obtained, during which a mixed slurry of solids and liquids is retained.

[0008] A further improvement is that in S4, during the ultrasonic grading process, the frequency of the ultrasonic wave is controlled to be between 50-100 kHz, the power of the ultrasonic wave is controlled to be between 60-150 W, and the action time is controlled to be between 100-350 s.

[0009] A further improvement is that in S5, the collection of different parts includes the following steps: Collecting the mixed red mud slurry of particle sizes in the final overflow tailings at the inlet area of ​​the ultrasonic classification equipment; Collect the red mud slurry after ultrasonic particle size classification of the final overflow tailings in the ultrasonic action area; In the bottom settling area, the coarsest red mud particle slurry in the final overflow tailings is collected; In the top overflow area, the suspension formed by the finest red mud particle slurry and water in the final overflow tailings is collected.

[0010] Further improvement is that in S7, the signal processing and analysis includes filtering, amplification, and digitization, specifically: according to the characteristics of the signal and the type of noise, a bandpass filter and a band-stop filter are used to receive the reflected sound wave signal to obtain a filtered signal, a programmable gain amplifier is used to make the amplitude of the reflected sound wave signal reach a level suitable for digitization, an analog-to-digital converter is used to convert the amplified analog signal into a digital signal, the digitized signal is stored in a computer, and a time stamp is added.

[0011] A further improvement is that in S7, an algorithm is used to extract signal characteristic parameters, which specifically includes the following steps: Calculate the mean, variance, and peak time domain characteristic parameters of the signal; Use the Fast Fourier Transform (FFT) algorithm to convert the signal from the time domain to the frequency domain and analyze the spectral characteristics of the signal; Use wavelet transform to perform multi-scale analysis on the signal and extract the characteristics of the signal at different scales; The envelope of the signal is extracted through the envelope detection algorithm to analyze the amplitude change of the signal; Based on the extracted characteristic parameters, the reflected acoustic wave characteristics of red mud particles are inferred; By comparing the corresponding relationship between different characteristic parameters and known particle sizes, a mapping relationship between characteristic parameters and particle sizes is established.

[0012] A further improvement is that in S7, the size and distribution of red mud particles are inferred according to the characteristic parameters of the acoustic wave signal, which specifically includes the following steps: A machine learning method combining support vector machine (SVM) and neural network was used to establish a model between the characteristic parameters of acoustic wave signals and the size and distribution of red mud particles. The model is trained using sample data with known particle size and distribution to enable the model to predict the particle size and distribution of unknown samples; The extracted acoustic signal characteristic parameters are input into the trained model to obtain the prediction results of the size and distribution of red mud particles.

[0013] A further improvement is that in S8, based on the inferred overall range of size and distribution parameters of red mud slurries of different grades, the numerical thresholds are divided into at most four sub-ranges with arithmetic difference as the standard, and the size and distribution parameters of the N-grade red mud slurry are brought into the numerical thresholds of the four sub-ranges, and then divided and combined again to obtain a classification product of at most four grades.

[0014] The beneficial effects of the present invention are: 1. The present invention first uses gravity separation equipment to perform the first separation on the red mud slurry to obtain underflow concentrate and overflow tailings, and then gravity separation is performed on the overflow tailings again to obtain secondary underflow concentrate and final overflow tailings. The gravity classification is more refined, and then the final overflow tailings are further classified by ultrasonic classification. The effect of ultrasonic waves on shape classification is used to improve the classification fineness of the final overflow tailings. Finally, the size and distribution of N-level red mud slurry are obtained by the effect of sound waves and emission, combined with signal feature parameter extraction and algorithm calculation. This is used as the final classification standard for combined classification. In summary, multi-level gravity classification is used as the initial classification, ultrasonic classification is used to improve the fineness of the overflow tailings classification, and the principle of sound wave reflection is used on the basis of the above classification to unify the size distribution classification. The classification benchmark is more perfect, the level adaptation is more reasonable, and the rationality of subsequent recycling and use is guaranteed.

[0015] 2. The present invention divides the overall range of the size and distribution parameters of red mud slurries of different grades into four sub-ranges at most based on the inferred arithmetic difference values, and the red mud slurry in each sub-range has similar performance. The N-level red mud slurry of the above-mentioned process is further divided and combined according to the size and distribution parameters of the four sub-ranges, and a maximum of four-level division is performed to simplify the division. When referring to it, you can refer to the aforementioned detailed N-level division, or you can use the final four-level division as the standard, providing a variety of division options, which is more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0017] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0018] Embodiment 1 according to Figure 1 As shown, this embodiment proposes a method for classifying coarse and fine red mud, comprising the following steps: S1: The red mud slurry is fed into a tank with a stirring system so that the red mud particles are dispersed in the slurry, and water is added simultaneously to control the rheology; S2: The red mud slurry is input into the gravity separation equipment for separation, so that the coarse red mud settles to the bottom to form underflow concentrate, and the fine red mud and water are discharged as overflow tailings; S3: The overflow tailings obtained from the first separation are diluted with water again and input into another set of gravity separation equipment to separate into secondary underflow concentrates. The remaining fine particles and water are discharged as the final overflow tailings; S4: The final overflow tailings are input into the ultrasonic classification equipment, and the cavitation effect, micro-jet effect and acoustic streaming effect of ultrasonic waves in the liquid are used for further classification; S5: collecting red mud particle slurry of different particle sizes after further classification at different locations in the ultrasonic classification device; S6: Based on the above steps, the red mud slurry after N-level classification is collected, and the sound wave transmitter emits the sound wave of the same frequency to the red mud slurry of N levels, and the receiver receives the reflected sound wave; S7: Processing and analyzing the received reflected sound wave signal, extracting the signal characteristic parameters, and inferring the size and distribution of red mud slurries of different grades according to the signal characteristic parameters; S8: Setting a division threshold, dividing and combining the N-level red mud slurry again according to size and distribution, and obtaining a maximum of four levels of graded products.

[0019] The red mud components and contents of Fe2O3-55%, Al2O3-18%, and Na2O-2.5% are dispersed to the maximum extent in the tank by adding water and stirring. First, through the separation equipment, the number of groups is 4, the pressure is 0.1Mpa, 30% of the high-grade iron ore concentrate is recovered, and the remaining 70% of the tailings are scavenged through the separation equipment, the number of groups is 2, the pressure is 0.15Mpa, 20% of the low-grade iron ore concentrate is recovered, and the remaining 50% of the tailings, the output rate of red mud can reach 50%, and the recovery rate of iron can reach more than 55%. Multi-level gravity classification is used as the initial classification, and ultrasonic classification is used to improve the fineness of the overflow tailings. Based on the above classification, the principle of sound wave reflection is unified to the size distribution classification, the classification benchmark is more perfect, and the level adaptation is more reasonable, ensuring the rationality of subsequent recycling and use.

[0020] Embodiment 2 according to Figure 1 As shown, this embodiment proposes a method for classifying coarse and fine red mud, comprising the following steps: The red mud slurry is fed into a tank with a stirring system, so that the red mud particles are dispersed in the slurry, and water is added simultaneously to control the rheology; specifically, the following steps are included: the red mud slurry is added into a tank with a stirring system, and the red mud slurry is stirred by a stirring blade with variable frequency speed regulation to control the maximum dispersion of the red mud particles in the slurry; during the stirring process, 20-30% of clean water is added compared to the red mud slurry, and the stirring time is controlled to 30-70 minutes, so that the red mud slurry achieves the best rheology; through the unique stirring blade design and variable frequency speed regulation technology, the efficient dispersion of red mud particles in the slurry is achieved, laying a solid foundation for subsequent classification; The red mud slurry is input into the gravity separation equipment for separation, so that the coarse red mud settles to the bottom to form an underflow concentrate, and the fine red mud and water are discharged as overflow tailings; specifically, the following steps are included: the red mud slurry is input into the gravity separation equipment for separation, and the pressure during separation is controlled to be ≥0.1Mpa; the coarse red mud settles to the bottom to form an underflow concentrate, and the fine red mud and water are discharged as overflow tailings, and in this process, a mixed slurry of solid and liquid is retained; The overflow tailings obtained by the first separation are diluted again with water, and then input into another set of gravity separation equipment again, separated to form a secondary underflow concentrate, and the remaining fine particles and water are discharged as the final overflow tailings; specifically, the following steps are included: adding water to dilute the overflow tailings obtained by the first separation, and controlling the water addition ratio to 20-40% of the overflow tailings; inputting the diluted material into another set of gravity separation equipment, and controlling the pressure during separation to be ≥0.12Mpa; obtaining the secondary underflow concentrate and the final overflow tailings, and in this process, retaining the mixed slurry of solid and liquid; through the re-dilution and selection of the tailings, the valuable components in the fine particles are effectively screened out, reducing the waste of resources; The combination of gravity separation and ultrasonic classification technology achieves efficient and accurate classification of red mud particles. It improves the efficiency and accuracy of classification, reduces energy consumption while ensuring classification accuracy. No chemical reagents need to be added during the entire classification process, which is environmentally friendly and in line with the concept of green and sustainable development. The final overflow tailings are input into the ultrasonic classification equipment, and further classified by using the cavitation effect, microjet effect and acoustic streaming effect of ultrasound in the liquid; during the ultrasonic classification process, the frequency of the ultrasound is controlled to be between 50-100kHz, the power of the ultrasound is between 60-150W, and the action time is 100-350s; At different parts of the ultrasonic grading equipment, red mud particle slurries of different particle sizes after further classification are collected; the collection at different parts includes the following steps: collecting the mixed red mud slurry of particle sizes in the final overflow tailings in the inlet area of ​​the ultrasonic grading equipment; collecting the red mud slurry after ultrasonic particle size grading of the final overflow tailings in the ultrasonic action area; collecting the coarsest red mud particle slurry in the final overflow tailings in the bottom sedimentation area; collecting the suspension formed by the finest red mud particle slurry and water in the final overflow tailings in the top overflow area; in the ultrasonic grading, the sound waves will interact with the red mud particles during the propagation process. This interaction will cause the particles to be subjected to the pressure and vibration of the sound waves, thereby affecting their motion state. By adjusting the parameters such as the frequency, intensity and action time of the sound waves, the effective separation of red mud particles of different sizes, shapes and densities can be achieved. The ultrasonic generator is used to generate sound waves of a certain frequency and intensity and transmit them to the red mud suspension. When the sound waves propagate in the suspension, they will interact with the red mud particles, including scattering, reflection and transmission. These interactions cause the particles to be subjected to pressure and vibration from the sound waves, resulting in changes in displacement and velocity. Since particles of different sizes, shapes, and densities respond differently to sound waves, effective separation of particles can be achieved by adjusting the parameters of the sound waves. Generally, larger particles are subjected to greater sound wave pressure, making them easier to separate. Ultrasonic classification technology has higher efficiency and accuracy. It can achieve effective separation of particles in a shorter period of time, and can more accurately control the characteristics of particles such as size, shape, and density. In addition, ultrasonic classification technology is also highly adaptable and can meet the classification needs of red mud particles of different types and properties.

[0021] Based on the above steps, red mud slurry after N-level classification is collected, and sound waves of the same frequency are emitted to the red mud slurry of N levels through the sound wave transmitter, and the reflected sound waves are received by the receiver; when the sound waves encounter different medium interfaces during propagation, reflection and transmission will occur. The size, shape and density of red mud particles will affect the propagation path and reflection characteristics of the sound waves. Therefore, by emitting sound waves into the red mud slurry and receiving the reflected sound wave signals, the relevant information of the red mud particles can be analyzed and then classified. To achieve sound wave reflection classification, corresponding equipment is required. These equipment include sound wave transmitters, receivers, signal processing units and control systems. The sound wave transmitter is used to emit sound waves into the red mud slurry, the receiver is used to receive the reflected sound wave signals, the signal processing unit is used to process and analyze the received signals, and the control system is used to control the entire classification process. The sound wave reflection classification method does not require direct contact with the red mud particles, avoiding the wear and pollution problems that may exist in the traditional classification method. By accurately measuring parameters such as the propagation time and amplitude of the sound wave signal, high-precision classification of red mud particles can be achieved. The acoustic reflection classification method can monitor the particle distribution in red mud slurry in real time, providing an important basis for quality control in the production process; Process and analyze the received reflected sound wave signals, extract the signal characteristic parameters, and infer the size and distribution of red mud slurries of different grades based on the signal characteristic parameters; The signal processing and analysis includes filtering, amplification, and digitization, specifically: according to the characteristics of the signal and the type of noise, a bandpass filter and a bandstop filter are used to receive the reflected sound wave signal to obtain a filtered signal, a programmable gain amplifier is used to make the amplitude of the reflected sound wave signal reach a level suitable for digitization, an analog-to-digital converter is used to convert the amplified analog signal into a digital signal, and the digitized signal is stored in a computer and time-stamped; The algorithm is used to extract the characteristic parameters of the signal, which specifically includes the following steps: calculating the mean, variance, and peak time domain characteristic parameters of the signal; using the fast Fourier transform (FFT) algorithm to convert the signal from the time domain to the frequency domain and analyze the spectral characteristics of the signal; using the wavelet transform to perform multi-scale analysis on the signal and extract the characteristics of the signal at different scales; extracting the envelope of the signal through the envelope detection algorithm to analyze the amplitude change of the signal; inferring the reflected acoustic wave characteristics of the red mud particles based on the extracted characteristic parameters; establishing a mapping relationship between the characteristic parameters and the particle size by comparing the corresponding relationship between different characteristic parameters and the known particle size; The size and distribution of red mud particles are inferred based on the characteristic parameters of the acoustic wave signal, specifically comprising the following steps: using a machine learning method combining a support vector machine (SVM) and a neural network to establish a model between the characteristic parameters of the acoustic wave signal and the size and distribution of red mud particles; using sample data of known particle size and distribution to train the model so that the model predicts the particle size and distribution of unknown samples; inputting the extracted characteristic parameters of the acoustic wave signal into the trained model to obtain the prediction results of the size and distribution of the red mud particles; when establishing the model, considering the influence of factors such as the shape, density, and concentration of the red mud particles on the reflection of the acoustic wave; when applying the model, verifying and calibrating the prediction results to ensure their accuracy and reliability; The division threshold is set, and the N-level red mud slurry is divided and combined again according to size and distribution to obtain a classification product of at most four levels. Specifically, according to the inferred overall range of size and distribution parameters of red mud slurry of different levels, the numerical thresholds are divided into at most four sub-ranges with arithmetic difference as the standard, and the size and distribution parameters of the N-level red mud slurry are brought into the numerical thresholds of the four sub-ranges, and divided and combined again to obtain a classification product of at most four levels.

[0022] The coarse and fine red mud classification method first uses gravity separation equipment to perform the first separation on the red mud slurry to obtain underflow concentrate and overflow tailings, and then gravity separation is performed on the overflow tailings again to obtain secondary underflow concentrate and final overflow tailings, and the gravity classification is more refined. Then, the final overflow tailings are further classified by ultrasonic classification, and the classification fineness of the final overflow tailings is improved by the effect of ultrasonic on shape classification. Finally, the size and distribution of N-level red mud slurry are obtained by the effect of sound waves and emission, combined with the extraction of signal characteristic parameters and the calculation of algorithms, and combined classification is performed based on this as the final classification standard. In summary, multi-level gravity classification is used as the initial classification, ultrasonic classification is used to improve the fineness of the overflow tailings classification, and the principle of sound wave reflection is used on the basis of the above classification to unify the size distribution classification, the classification benchmark is more perfect, the level adaptation is more reasonable, and the rationality of subsequent recycling and use is guaranteed. In addition, according to the inferred overall range of size and distribution parameters of red mud slurries of different grades, the present invention uses arithmetic difference as the standard and divides the numerical thresholds into four sub-ranges at most. The red mud slurry in each sub-range has similar performance. The N-grade red mud slurry of the aforementioned process is further divided and combined according to the size and distribution parameters of the four sub-ranges, and a maximum of four-grade division is performed, so that the division is simplified. When referring to it, you can refer to the aforementioned detailed N-grade division, or you can use the final four-grade division as the standard, providing a variety of division options, which is more scientific and reasonable.

[0023] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for classifying coarse and fine red mud, characterized in that: The following steps are involved: S1: The red mud slurry is fed into a tank with a stirring system so that the red mud particles are dispersed in the slurry, and water is added simultaneously to control the rheology; S2: The red mud slurry is input into the gravity separation equipment for separation, so that the coarse red mud settles to the bottom to form underflow concentrate, and the fine red mud and water are discharged as overflow tailings; S3: The overflow tailings obtained from the first separation are diluted with water again and input into another set of gravity separation equipment to separate into secondary underflow concentrates. The remaining fine particles and water are discharged as the final overflow tailings; S4: The final overflow tailings are input into the ultrasonic classification equipment, and the cavitation effect, micro-jet effect and acoustic streaming effect of ultrasonic waves in the liquid are used for further classification; S5: collecting red mud particle slurry of different particle sizes after further classification at different locations in the ultrasonic classification device; S6: Based on the above steps, the red mud slurry after N-level classification is collected, and the sound wave transmitter emits the sound wave of the same frequency to the red mud slurry of N levels, and the receiver receives the reflected sound wave; S7: Processing and analyzing the received reflected sound wave signal, extracting the signal characteristic parameters, and inferring the size and distribution of red mud slurries of different grades according to the signal characteristic parameters; S8: Setting a division threshold, dividing and combining the N-level red mud slurry again according to size and distribution, and obtaining a maximum of four levels of graded products.

2. A method for classifying coarse and fine red mud according to claim 1, characterized in that: The S1 comprises the following steps: The red mud slurry is added into a tank with a stirring system, and the red mud slurry is stirred by a stirring blade with variable frequency speed regulation to control the maximum dispersion of the red mud particles in the slurry; During the stirring process, 20-30% of clean water is added compared to the red mud slurry, and the stirring time is controlled to 30-70 minutes to make the red mud slurry achieve optimal rheology.

3. A method for classifying coarse and fine red mud according to claim 1, characterized in that: The S2 comprises the following steps: The red mud slurry is input into the gravity separation equipment for separation, and the pressure during separation is controlled to be ≥0.1Mpa; The coarse red mud settles to the bottom to form underflow concentrate, and the fine red mud and water are discharged as overflow tailings. In this process, a mixed slurry of solid and liquid is retained.

4. A method for classifying coarse and fine red mud according to claim 1, characterized in that: The S3 comprises the following steps: Add water to dilute the overflow tailings obtained from the first separation, and control the proportion of water added to 20-40% of the overflow tailings; Input the diluted material into another set of gravity separation equipment, and control the pressure during separation to be ≥0.12Mpa; A secondary underflow concentrate and a final overflow tailings are obtained, during which a mixed slurry of solids and liquids is retained.

5. A method for classifying coarse and fine red mud according to claim 1, characterized in that: In S4, during the ultrasonic classification process, the frequency of the ultrasonic wave is controlled to be between 50-100 kHz, the power of the ultrasonic wave is controlled to be between 60-150 W, and the action time is controlled to be between 100-350 s.

6. A method for classifying coarse and fine red mud according to claim 1, characterized in that: In S5, the collection of different parts includes the following steps: Collecting the mixed red mud slurry of particle sizes in the final overflow tailings at the inlet area of ​​the ultrasonic classification equipment; Collect the red mud slurry after ultrasonic particle size classification of the final overflow tailings in the ultrasonic action area; In the bottom settling area, the coarsest red mud particle slurry in the final overflow tailings is collected; In the top overflow area, the suspension formed by the finest red mud particle slurry and water in the final overflow tailings is collected.

7. A method for classifying coarse and fine red mud according to claim 1, characterized in that: In S7, the signal processing and analysis includes filtering, amplification, and digitization, specifically: according to the characteristics of the signal and the type of noise, a bandpass filter and a band-stop filter are used to receive the reflected sound wave signal to obtain a filtered signal, a programmable gain amplifier is used to make the amplitude of the reflected sound wave signal reach a level suitable for digitization, an analog-to-digital converter is used to convert the amplified analog signal into a digital signal, the digitized signal is stored in a computer, and a time stamp is added.

8. A method for classifying coarse and fine red mud according to claim 7, characterized in that: In S7, an algorithm is used to extract signal characteristic parameters, which specifically includes the following steps: Calculate the mean, variance, and peak time domain characteristic parameters of the signal; Use the Fast Fourier Transform (FFT) algorithm to convert the signal from the time domain to the frequency domain and analyze the spectral characteristics of the signal; Use wavelet transform to perform multi-scale analysis on the signal and extract the characteristics of the signal at different scales; The envelope of the signal is extracted through the envelope detection algorithm to analyze the amplitude change of the signal; Based on the extracted characteristic parameters, the reflected acoustic wave characteristics of red mud particles are inferred; By comparing the corresponding relationship between different characteristic parameters and known particle sizes, a mapping relationship between characteristic parameters and particle sizes is established.

9. A method for classifying coarse and fine red mud according to claim 8, characterized in that: In S7, the size and distribution of red mud particles are inferred according to the characteristic parameters of the acoustic wave signal, which specifically includes the following steps: A machine learning method combining support vector machine (SVM) and neural network was used to establish a model between the characteristic parameters of acoustic wave signals and the size and distribution of red mud particles. The model is trained using sample data with known particle size and distribution to enable the model to predict the particle size and distribution of unknown samples; The extracted acoustic signal characteristic parameters are input into the trained model to obtain the prediction results of the size and distribution of red mud particles.

10. A method for classifying coarse and fine red mud according to claim 1, characterized in that: In S8, based on the inferred overall range of size and distribution parameters of red mud slurries of different grades, the numerical thresholds are divided into at most four sub-ranges with arithmetic difference as the standard, and the size and distribution parameters of the N-grade red mud slurry are brought into the numerical thresholds of the four sub-ranges, and divided and combined again to obtain a graded product of at most four grades.

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