Intelligent control method and system for concentration pressure filtration

Abstract

The application provides a concentrated pressure filtration intelligent control method and system, and relates to the technical field of environmental protection. The method comprises the following steps: obtaining the moisture value and ash value of raw coal after crushing and screening, determining the raw coal wetting value according to the moisture value and ash value, and determining the washing water quantity according to the raw coal wetting value; washing the raw coal after crushing and screening according to the washing water quantity to obtain washed coal slime, and sending the washed coal slime into a concentration tank for concentration to obtain first concentrated coal slime; obtaining the concentration particle size, concentration ash value and concentration pH value of the concentrated coal slime, determining the pressure filtration conditioner category according to the concentration particle size, concentration ash value and concentration pH value; and performing conditioning on the concentrated coal slime according to the pressure filtration conditioner category to obtain second concentrated coal slime, and performing pressure filtration and drying on the concentrated coal slime to obtain dried coal cake, so as to improve the coal production efficiency and the coal slime product quality, and reduce the water consumption during coal washing.

Description

Technical Field

[0001] This application relates to the field of environmental protection technology, specifically to a method and system for intelligent control of concentration and filtration. Background Technology

[0002] Coal preparation is a crucial production stage in the coal industry. Its purpose is to separate mined raw coal, removing impurities such as gangue, to obtain a high-quality clean coal product. Traditional coal preparation processes typically include steps such as raw coal humidification, coal slime concentration, and filter press dewatering. Raw coal humidification involves adding an appropriate amount of water to the screened and crushed raw coal to achieve optimal conditions for subsequent wet separation. Coal slime concentration utilizes chemical flocculation to clarify and separate the low-concentration coal slime water obtained after wet separation, increasing the coal slime concentration. Filter press dewatering uses mechanical pressing to further reduce the moisture content of the coal slime, yielding a filter cake product.

[0003] Current coal preparation processes rely heavily on manual control of process parameters based on experience. For example, the determination of water injection volume during raw coal wetting lacks quantitative basis and is often subjectively adjusted by operators based on visual assessment of coal particle wetness; the dosage of reagents during concentration is also largely estimated based on experience, making it difficult to accurately determine the optimal dosage; and the settings for parameters such as pressure and time during filter press dewatering are not precise enough, often based on the control levels determined by coal type. This extensive process control method easily leads to problems such as uneven raw coal wetting, poor concentration and flocculation effects, and insufficient filter press dewatering, thus restricting coal preparation production efficiency and coal slime product quality. Summary of the Invention

[0004] This application provides a method and system for intelligent control of coal concentration and filtration, which can improve coal preparation efficiency and coal slime product quality, and reduce water consumption during coal washing.

[0005] In the first aspect, this application provides a method for intelligent control of concentration and filtration, the method comprising: obtaining the moisture value and ash value of the raw coal after crushing and screening, determining the raw coal moisture content value based on the moisture value and ash value, and determining the amount of washing water based on the raw coal moisture content value;

[0006] The crushed and screened raw coal is washed with water according to the amount of washing water to obtain washed coal slurry, and the washed coal slurry is sent to a thickening tank for thickening to obtain the first thickened coal slurry.

[0007] Obtain the concentrated particle size, concentrated ash content, and concentrated pH value of the concentrated coal slime, and determine the type of filter press conditioner based on the concentrated particle size, the concentrated ash content, and the concentrated pH value;

[0008] According to the type of filter press conditioner and the conditioning of the concentrated coal slime, a second concentrated coal slime is obtained, and the concentrated coal slime is then filtered and dried to obtain a dried coal cake.

[0009] By adopting the above technical solution, the moisture and ash values ​​of raw coal after crushing and screening are obtained, and the optimal raw coal moisture content is determined based on these property parameters. Then, the precise amount of washing water is calculated, realizing the quantitative intelligent control of the raw coal moisture content process. This effectively avoids problems such as uneven coal particle moisture and water waste caused by conventional empirical water spraying, creating favorable conditions for subsequent water washing and separation.

[0010] After obtaining washed coal slime through water washing, this method sends it to a thickening tank for concentration. Simultaneously, key characteristic parameters such as particle size, ash content, and pH of the concentrated coal slime are obtained. An intelligent decision-making model determines the appropriate type of filter press conditioner, and then the concentrated coal slime undergoes targeted conditioning treatment to ensure its properties meet filter press requirements. The innovation of this step lies in establishing a quantitative correlation between the properties of concentrated coal slime and the type of filter press conditioner. This overcomes the limitations of traditional conditioner selection relying on experience, achieving dynamic optimization of agent type and dosage, and significantly improving the coal slime conditioning effect.

[0011] The conditioned coal slime finally enters the filter press drying process. Based on the coal slime property data obtained in the previous stages, the optimal process parameters for filter press drying, including pressure, time, and temperature, are determined through intelligent optimization algorithms. This ensures that the coal slime dewatering and drying process is always carried out under optimal conditions, guaranteeing low moisture content and high quality of the filter cake product.

[0012] A second aspect of this application provides a concentration and filtration intelligent control system, comprising:

[0013] The data acquisition module is used to acquire the moisture and ash content of the raw coal after crushing and screening, determine the raw coal moisture content value based on the moisture and ash content value, and determine the amount of washing water based on the raw coal moisture content value.

[0014] The first processing module is used to wash the crushed and screened raw coal with water according to the amount of washing water to obtain washed coal slurry, and send the washed coal slurry into a thickening tank for thickening to obtain the first thickened coal slurry.

[0015] The second processing module is used to obtain the concentrated particle size, concentrated ash content, and concentrated pH value of the concentrated coal slime, and to determine the type of filter press conditioner based on the concentrated particle size, the concentrated ash content, and the concentrated pH value.

[0016] The third processing module is used to condition the concentrated coal slime according to the type of filter press conditioner to obtain a second concentrated coal slime, and to filter and dry the concentrated coal slime to obtain a dried coal cake.

[0017] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0018] 1. Based on big data analysis of raw coal properties and production processes, this application implements precise intelligent control over key processes such as crushing and screening, washing, concentration, conditioning, and pressure drying. This enables the entire coal preparation process to be known, controllable, and optimized, forming a comprehensive innovative solution of "intelligent control at the source, process optimization, and end-of-pipe assurance." Compared with existing technologies, this invention reduces water consumption at the source, improves production efficiency and automation levels, stabilizes product quality, and provides strong technical support for achieving water conservation, efficiency improvement, and clean production.

[0019] 2. The intelligent thickening control method of this application fully utilizes multi-source information such as coal slime properties, reagent types and characteristics, and process monitoring data to construct a closed-loop control strategy covering the entire process of "coal slime properties - reagent selection - process optimization - endpoint criteria," achieving intelligent and refined management of the thickening process. Compared with traditional experience-based operations, this method can significantly improve the thickening potential and rate of coal slime, shorten the thickening time, reduce reagent consumption costs, and decrease labor input, resulting in significant technical and economic benefits. Simultaneously, the quantitative monitoring data of the thickening process provides data support for process diagnosis, equipment management, and production scheduling, promoting the intelligent transformation and upgrading of coal preparation plants. With the widespread application of intelligent thickening control technology, the traditional extensive coal slime water separation process will be replaced by refined, digitalized, and intelligent modern thickening and dewatering technologies, making a significant contribution to achieving clean and efficient utilization of coal and the cascade development of resources.

[0020] 3. The intelligent pressure filter conditioning control method of this application fully utilizes multi-source information such as the type of pressure filter conditioner, reagent formulation, and coal slurry property detection data to construct a closed-loop control strategy covering the entire process: "conditioner type - reagent formulation - process optimization - endpoint criterion," achieving intelligent and refined management of the pressure filter conditioning process. Compared with traditional experience-based operations, this method can significantly improve the filterability of coal slime, improve the forming quality and moisture content of the filter cake, shorten the dewatering time, reduce reagent consumption costs, and reduce labor input, resulting in significant technical and economic benefits. Simultaneously, the quantitative monitoring data of the conditioning process provides data support for process diagnosis, equipment management, and production scheduling, promoting the intelligent transformation and upgrading of coal preparation plants. With the widespread application of intelligent pressure filter conditioning control technology, the traditional extensive coal slime dewatering process will be replaced by refined, digitalized, and intelligent modern flocculation conditioning technology, making a significant contribution to achieving clean and efficient utilization of coal and the cascade development of resources. Attached Figure Description

[0021] Figure 1 An architecture diagram of a concentration and filtration intelligent control method provided in an embodiment of this application;

[0022] Figure 2 This is an architecture diagram of a concentration and filtration intelligent control system provided in an embodiment of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0024] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple devices refer to two or more devices, and multiple screen terminals refer to two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0026] To facilitate understanding of the methods and apparatus provided in the embodiments of this application, the background of the embodiments of this application will be introduced before introducing the embodiments of this application.

[0027] Coal preparation is a crucial production stage in the coal industry. Its purpose is to separate and process mined raw coal, removing impurities such as gangue, to obtain a clean coal product that meets quality requirements. Traditional coal preparation processes typically include main steps such as raw coal humidification, coal slime thickening, and filter press dewatering. First, raw coal humidification involves adding an appropriate amount of water to the screened and crushed raw coal to achieve the optimal state for subsequent wet separation. Second, coal slime thickening utilizes the flocculation effect of reagents to clarify and separate the low-concentration coal slime water obtained after wet separation, increasing the concentration of the coal slime. Finally, filter press dewatering uses mechanical pressing to further reduce the moisture content of the coal slime, obtaining a filter cake product.

[0028] However, existing coal preparation processes rely heavily on manual experience to control process parameters at each stage, which has many shortcomings. For example, in the raw coal wetting stage, the determination of the water spray volume often lacks quantitative basis and is mainly adjusted based on the operator's subjective judgment of the degree of coal particle wetting. In the coal slime thickening process, the dosage of flocculant is also mostly estimated based on experience, making it difficult to accurately determine the optimal dosage. In the pressure filtration and dewatering stage, the setting of key parameters such as pressure and time is not precise enough, often controlled based on a rough classification of coal types. This extensive process control method easily leads to a series of problems, such as uneven raw coal wetting, unsatisfactory coal slime thickening and flocculation effects, and insufficient pressure filtration and dewatering, which seriously restricts the improvement of coal preparation production efficiency and the assurance of coal slime product quality.

[0029] After the background introduction above, those skilled in the art can understand the problems existing in the prior art. The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0030] Based on the aforementioned background technology, further please refer to... Figure 1 , Figure 1 This application provides an architecture diagram of a concentration and filtration intelligent control method. This device can be implemented using a computer program or run as an independent utility application. Specifically, in this application embodiment, the method can be applied to a controller, but it can also be applied to electronic devices such as servers. A concentration and filtration intelligent control method includes the following steps:

[0031] S101, obtain the moisture value and ash value of the raw coal after crushing and screening, determine the raw coal moisture content value based on the moisture value and ash value, and determine the amount of washing water based on the raw coal moisture content value;

[0032] In one embodiment of the present invention, the moisture and ash values ​​of the raw coal after crushing and screening are first obtained as the basis for determining the optimal moisture content of the raw coal. Through experimental analysis of a large number of raw coal samples with different qualities, a quantitative relationship model between the moisture and ash content of the raw coal and the optimal moisture content is established. When new raw coal enters the coal preparation system, its moisture and ash values ​​are detected in real time. The detection data is input into the aforementioned model, and the optimal moisture content of that batch of raw coal can be accurately calculated. This intelligent moisture content method based on the properties of raw coal overcomes the blindness and uncertainty of traditional manual adjustment of water spray volume, achieving precise control of the raw coal moisture content process.

[0033] Based on the calculated moisture content of the raw coal and parameters such as the amount of raw coal, the amount of washing water can be further determined. Specifically, by multiplying the moisture content by the amount of raw coal and considering an appropriate surplus coefficient, a precise setpoint for the washing water volume can be obtained. This setpoint is transmitted to the automatic water supply system, which dynamically adjusts the water flow rate of the spray heads in real time by regulating valve opening and pump frequency to match the setpoint, thereby ensuring the accuracy and continuity of water supply during the raw coal moisture content adjustment process. Compared with conventional extensive water spraying, this method can adaptively adjust the washing water volume according to changes in the properties of raw coal and production load, meeting moisture content adjustment requirements while avoiding excessive waste of water resources.

[0034] Based on the above embodiments, as an optional embodiment, determining the raw coal moisture content value based on the moisture value and ash content value, and determining the amount of washing water based on the raw coal moisture content value, includes:

[0035] Based on the moisture content and ash content, the moisture content of the raw coal is estimated using an empirical formula, wherein the empirical formula is:

[0036] Rw = a - b·Mad + c·Aad

[0037] In the formula, a, b, and c are empirical coefficients related to the coal preparation process, Rw is the moisture content of the raw coal, Mad is the moisture content, and Aad is the ash content.

[0038] Based on the raw coal moisture content, the required amount of washing water is calculated using the washing water volume formula: W = Rw·Q / (1 - Rw), where Q is the amount of raw coal within a given time period, Rw is the raw coal moisture content, and W is the moisture content.

[0039] In one embodiment of the present invention, in order to achieve precise control of the raw coal moisture blending process, the raw coal moisture blending value is estimated using empirical formulas based on the moisture and ash content of the raw coal, and the amount of washing water is further calculated.

[0040] First, the moisture content (Mad) and ash content (Aad) of the raw coal are measured in real time using online moisture and ash analyzers, and the data is input into the intelligent moisture blending control system. The system incorporates an empirical formula based on extensive production practice: Rw = a - b·Mad + c·Aad, where Rw is the optimal moisture blending value for the raw coal, and a, b, and c are empirical coefficients related to the coal preparation process. The values ​​of these empirical coefficients need to be determined through repeated experiments and statistical analysis based on the actual process conditions of the coal preparation plant and the characteristics of the raw coal to ensure the reliability of the formula's estimation results. By substituting the measured moisture content (Mad) and ash content (Aad) of the raw coal into this empirical formula, the optimal moisture blending value (Rw) for the raw coal can be calculated quickly and accurately.

[0041] After obtaining the raw coal moisture content Rw, the system calculates the moisture content W according to the washing water volume formula W = Rw·Q / (1 - Rw). Here, Q is the amount of raw coal within a given time period, which can be collected in real time by equipment such as belt scales. Substituting the raw coal moisture content Rw and the raw coal volume Q into the formula yields the precise washing water volume setpoint W. The system transmits this setpoint to the automatic water supply control unit, which dynamically matches the actual water supply flow rate with the setpoint W by adjusting the opening of the water supply valve and the pump frequency, thereby achieving precise control of the raw coal moisture content process.

[0042] S102, the raw coal after crushing and screening is washed with water according to the amount of washing water to obtain washed coal slurry, and the washed coal slurry is sent to a thickening tank for thickening to obtain the first thickened coal slurry.

[0043] In one embodiment of the present invention, the raw coal after crushing and screening is washed with water according to the washing water volume determined in the aforementioned steps to obtain washed coal slime. Specifically, the calculated washing water volume setpoint is transmitted to the raw coal humidification system, and the water flow rate of the spray heads is automatically controlled to ensure the raw coal reaches optimal moisture content before entering the washing process. The moistened raw coal is then fed into the washing equipment. Under the action of the water flow, the coal and impurities such as gangue are separated due to their difference in specific gravity. Lighter coal particles float with the water flow, while heavier gangue sinks, thus obtaining preliminarily purified washed coal slime. Compared with raw coal that has not been adequately humidified, the coal slime obtained by this method exhibits better coal and gangue separation, significantly improving the recovery rate and quality of clean coal.

[0044] To further increase the concentration of washed coal slime, this embodiment sends it to a thickener for concentration treatment, ultimately obtaining the first concentrated coal slime. In the thickener, coal particles in the washed coal slime continuously sink under gravity, while gangue impurities float to the surface, achieving secondary separation of the coal slime. Simultaneously, the supernatant continuously overflows and is discharged, while the high-concentration coal slime at the bottom of the tank is discharged via horizontal screw conveyor, completing the conversion from low-concentration washed coal slime to high-concentration coal slime. Compared to direct subsequent dewatering, the concentration treatment effectively reduces the load on the filter press equipment, lowers the moisture content of the filter cake, and shortens the dewatering time, thereby significantly improving coal preparation production efficiency.

[0045] Based on the above embodiments, as an optional embodiment, the step of feeding the washed coal slime into a thickening tank for thickening to obtain a first thickened coal slime includes:

[0046] S201, obtain the washed particle size, washed ash content and washed pH value of the washed coal slime, and determine the type of concentrated agent based on the washed particle size, the washed ash content and the washed pH value;

[0047] In one embodiment of the present invention, in order to optimize the concentration process and improve the concentration efficiency and effect, before sending the washed coal slime into the thickening tank, key property parameters such as the washed particle size, washed ash content and washed pH value of the washed coal slime are obtained, and the optimal type of thickening agent is determined accordingly.

[0048] In practice, specialized sensors such as online particle size analyzers, ash content analyzers, and pH meters are used to sample and analyze the washed coal slime from the washing and screening system in real time, continuously monitoring changes in particle size distribution, ash content, and pH. This monitoring data is transmitted in real time to the intelligent control system, serving as the basis for selecting the optimal concentrate. The system has a built-in database of numerous concentrate application cases, covering records of the effectiveness of various concentrates under different coal slime properties. By mining and analyzing this historical data, a quantitative correlation model is established between washed particle size, washed ash content, washed pH value, and the type of concentrate.

[0049] Once the measured property data of washed coal slime is input into the system, pattern recognition and matching algorithms automatically search through massive amounts of historical data for samples most similar to the current coal slime properties. Referring to the types of reagents successfully applied in those samples, the optimal concentration reagent is quickly determined. Compared to the traditional method of selecting reagents based on human experience, this intelligent optimization method overcomes the blindness and limitations of experience-based judgment. It fully considers multiple property parameters of washed coal slime and utilizes data analysis and knowledge mining techniques to find the optimal formulation in complex and ever-changing production environments, exhibiting higher scientific rigor and reliability.

[0050] S202, according to the type of concentrated agent, a concentrated agent corresponding to the type of concentrated agent is added to the thickening tank, and the real-time pH value and tank surface image of the concentrated coal slime after the concentrated agent is added are obtained in real time, and the floc characteristics are determined according to the tank surface image.

[0051] In one embodiment of the present invention, according to a preferred type of concentrate, an automatic dosing system precisely adds the corresponding concentrate to the thickening tank. To monitor the effect of the concentrate on the thickening process in real time, and to simultaneously acquire the real-time pH value of the thickened coal slime and an image of the tank surface after dosing, the characteristic parameters of the coal slime flocs under the flocculation effect of the concentrate are determined by analyzing the tank surface image.

[0052] In practice, once the intelligent control system determines the optimal type of concentrate, it sends a formula instruction to the automatic reagent preparation unit. Based on the reagent type, the unit matches the corresponding solution ratio and concentration parameters, and a solution pump delivers the reagent solution to the thickening tank. Simultaneously, an online pH meter installed in the thickening tank continuously monitors the real-time pH value of the concentrated coal sludge after reagent dosing and transmits the data to the control system. The system dynamically adjusts the dosage and frequency of reagent dosing according to the optimal working pH range, ensuring that the coal sludge pH value remains within the optimal activity range of the reagent, thus guaranteeing optimal flocculation results.

[0053] Working synchronously with the pH meter is a high-definition imaging device installed above the thickening tank, which captures real-time images of the tank surface to observe the dynamic changes in the flocculation process. These continuously acquired images are uploaded in real-time to an intelligent analysis unit via an image transmission system. This unit has a built-in image recognition algorithm specifically designed for the thickening process, capable of extracting key information such as the size, density, and distribution of flocs from the color, texture, and shape features of the tank surface images. For example, by comparing and analyzing the differences in tank surface images before and after chemical dosing, the impact of the chemical on floc formation can be determined; the size and quantity of flocs can be used to assess the flocculation effect; and by tracking the movement trajectory of the flocs, the changes in their aggregation and settling rates can be understood.

[0054] Based on the above embodiments, as an optional embodiment, determining the floc characteristics based on the pool surface image includes:

[0055] The threshold segmentation method is used to extract the flocculent region from the pool surface image, and geometric measurements are performed based on the flocculent region to determine the geometric data of the flocculent.

[0056] The distribution state of flocs in the pool surface image is obtained by marking and counting them using connected component analysis.

[0057] Morphological analysis was performed on the floc region to extract quantitative indicators reflecting the morphology of the floc.

[0058] The floc region is scanned pixel by pixel, the gray value of each pixel is extracted to obtain the gray histogram of the floc, and the transparency of the floc region is determined based on the gray histogram.

[0059] The geometric data, the distribution state, the floc transparency, and the quantitative index are used as floc characteristics.

[0060] In one embodiment of the present invention, the intelligent control system analyzes and processes images of the sedimentation tank surface to extract key characteristic parameters reflecting the properties of the flocs, providing a basis for optimizing coagulant dosing. The system employs a series of image analysis algorithms to accurately segment the floc region from the tank surface image and quantitatively characterizes its geometric dimensions, distribution, morphological features, and transparency, ultimately obtaining a floc feature vector that comprehensively reflects the characteristics of the flocs.

[0061] In practice, the first step is to extract the flocculent region from the original pool surface image using a threshold segmentation method. Based on the difference in grayscale between the flocculents and the water, an appropriate grayscale threshold is set to binarize the image, resulting in a binary image containing only flocculents. On this basis, geometric measurements of the flocculent region are performed to obtain geometric data such as the area, perimeter, and circumscribed rectangle dimensions of the flocculents, which are used to characterize the size of the flocculents. Next, connected component analysis is used to mark and count the flocculents in the pool surface image. By scanning the binary image, interconnected flocculent pixels are divided into connected components and marked. The number of connected components is counted to obtain the number of flocculents. Simultaneously, the distribution of the flocculents on the pool surface is determined based on the location distribution of each connected component—whether it is uniformly dispersed or unevenly aggregated. Then, morphological analysis is performed on the flocculent region to extract quantitative indicators reflecting the shape of the flocculents. By calculating morphological parameters such as the shape factor, roundness, and density of the flocculent region, the shape characteristics of the flocculents are quantitatively described, such as whether the flocculents are dense or loose, and whether the edges are smooth or irregular. Next, the flocculent region is scanned pixel by pixel to extract the grayscale value of each pixel, resulting in a grayscale histogram of the flocculent region. Based on the morphological characteristics of the grayscale histogram, such as the grayscale distribution range, average grayscale, and most frequent grayscale, the transparency of the flocculent is determined. Generally, higher flocculent transparency indicates better flocculation, signifying stronger complexing ability of the coagulant and higher density of the generated flocculent.

[0062] Finally, the geometric data, distribution state, floc transparency, and morphological quantitative indicators obtained from the above analysis are combined into a multidimensional feature vector as a floc characteristic describing the floc properties, which is then used for subsequent optimization decisions on coagulant dosage.

[0063] By extracting floc features using the aforementioned image analysis methods, key properties such as floc size, quantity, distribution, morphology, and density can be comprehensively, accurately, and quantitatively evaluated, overcoming the subjectivity and uncertainty of manual visual judgment. These quantitative indicators with clear physical meaning can sensitively reflect changes in coagulant dosing conditions, laying the foundation for intelligent optimization control of the coagulation process.

[0064] For example, when the coagulant dosage is insufficient, the resulting flocs are small in size, few in number, sparsely distributed, and loosely shaped, and the value of the floc feature vector will be significantly lower. Conversely, when the coagulant dosage is excessive, the resulting flocs are excessively large, irregular in shape, and have reduced transparency, and the value of the floc feature vector will be significantly higher. Therefore, by analyzing and comparing the changing trends of floc characteristics under different operating conditions, the optimal coagulant dosage can be accurately determined, achieving optimal control of the coagulation process.

[0065] S203, determine the dosage adjustment of concentrated agent based on the real-time pH value and the characteristics of the flocs;

[0066] In one embodiment of the present invention, the intelligent control system determines the dynamic adjustment value of the dosage of the concentration agent by analyzing and optimizing the concentrated coal slime pH value and floc characteristic parameters acquired in real time through intelligent algorithms, thereby achieving precise control of the concentration process and optimization of agent addition.

[0067] In practice, online pH meters and surface imaging equipment continuously monitor the concentration process, obtaining real-time pH values ​​of the concentrated coal slime and characteristic parameters of the flocs, such as size, density, and settling rate. This data is then transmitted to the intelligent control system in real time. The system incorporates an intelligent optimization model based on big data analysis and machine learning technologies. This model, through learning and training on a large amount of historical production data and expert experience, has grasped the inherent correlation between key parameters and reagent dosage during the concentration process.

[0068] Once new real-time monitoring data is input into the system, the intelligent optimization model first determines whether the current pH value is within the optimal operating range of the target agent. If it deviates from the range, it calculates the deviation between the pH value and the center value of the optimal range, using this deviation as one of the bases for adjusting the agent dosage. Simultaneously, the model comprehensively evaluates the floc characteristic parameters obtained from the pool surface image analysis. Based on indicators such as floc size, quantity, density, and settling rate, it determines whether the current flocculation effect has reached the optimal state. If not, it further analyzes the difference between the floc characteristic parameters and the optimal state, using this difference as another basis for adjusting the agent dosage.

[0069] S204, when the characteristics of the flocculent and the real-time pH value meet the preset concentrated coal slime standard, the first concentrated coal slime is obtained.

[0070] In one embodiment of the present invention, the intelligent control system determines the endpoint of the thickening process by continuously monitoring and dynamically analyzing the characteristics of flocs and the real-time pH value of the thickened coal slurry in the thickening tank. When the monitoring parameters stably reach the preset thickened coal slurry standard, it can be considered that the first thickened coal slurry that meets the requirements has been obtained.

[0071] In practice, the intelligent control system pre-sets a series of endpoint criteria for the concentration process based on the target product quality requirements of the concentrated coal slime, such as solids content, ash content, and particle size, as well as production experience, forming a complete standard database of concentrated coal slime. These criteria cover multiple dimensions, including the quantitative range of floc characteristic parameters and pH value, as well as duration. For example, the endpoint criteria for the concentration process can be set as follows: the average size of the flocs is greater than a certain critical value, the clarity of the suspension is higher than a certain value, the pH value is stable within the optimal working range of the reagent, and the above conditions are maintained for more than 5 minutes.

[0072] During the thickening process, the intelligent control system receives real-time images of the pool surface and pH data transmitted from online monitoring equipment. Through image recognition and data processing algorithms, it extracts characteristic parameters such as floc size, density, settling rate, and pool liquid clarity. These parameters, combined with real-time pH values, are then compared and analyzed against preset standards for thickened coal slime. Once all monitored parameters reach or exceed the standard values ​​and remain stable for a certain period, the system determines that the thickening process has reached its optimal endpoint. At this point, the high-concentration coal slime accumulated at the bottom of the thickening tank is considered the first thickened coal slime that meets the requirements.

[0073] S103, obtain the concentrated particle size, concentrated ash content and concentrated pH value of the concentrated coal slime, and determine the type of filter press conditioner based on the concentrated particle size, the concentrated ash content and the concentrated pH value;

[0074] In one embodiment of the present invention, to optimize the type of filter press conditioner, key property parameters such as the concentrated particle size, concentrated ash content, and concentrated pH value of the concentrated coal slime are first obtained. Specifically, dedicated sensors such as online particle size analyzers, ash content analyzers, and pH meters are used to sample and analyze the coal slime in the thickening tank in real time, continuously monitoring changes in its particle size distribution, ash content, and pH. This monitoring data is transmitted in real time to an intelligent decision-making system as input for determining the type of filter press conditioner.

[0075] The intelligent decision-making system has a built-in database of numerous application cases of filter press conditioners, covering the effectiveness records of various conditioners under different coal slime properties. By mining and analyzing this historical data, a quantitative correlation model is established between concentrated particle size, concentrated ash content, concentrated pH value, and filter press conditioner types. When new concentrated coal slime monitoring data is input into the system, a matching algorithm automatically finds the most similar historical coal sample and references the successfully applied conditioner types to quickly determine the optimal conditioner for the current coal slime.

[0076] S104, according to the type of filter press conditioner and the conditioning of the concentrated coal slime, a second concentrated coal slime is obtained, and the concentrated coal slime is filtered and dried to obtain a dried coal cake.

[0077] In one embodiment of the present invention, the concentrated coal slime is flocculated and conditioned according to the filter press conditioning agent category determined in the aforementioned steps to obtain a second concentrated coal slime with better properties, creating favorable conditions for subsequent filter press. Specifically, the conditioning agent category information output by the intelligent decision-making system is transmitted to the reagent preparation unit, which has a built-in formula library for various conditioning agents and a process parameter setting module. Based on the selected reagent category, the corresponding formula composition and optimal process parameters, including solution concentration, pH value, temperature, etc., are automatically matched, and a suitable conditioning agent solution is prepared by the solution preparation device as required.

[0078] The prepared conditioning agent solution is pumped into the concentrated coal slime tank via a metering pump and thoroughly mixed with the coal slime. The flocculating components in the conditioning agent, through mechanisms such as adsorption and charge neutralization, destabilize and aggregate the coal slime particles, forming a dense floc structure, thereby significantly improving the settling and filterability of the coal slime. The entire conditioning process is controlled in real time by an automatic dosing system. Based on changes in parameters such as the liquid level, flow rate, and concentration in the coal slime tank, the system dynamically adjusts the agent addition rate and dosage to ensure optimal conditioning results. Compared to manual dosing, this intelligent control system achieves precise quantitative dosing and synchronous optimization of the agent, reducing the problem of flocculation failure caused by excessive or insufficient agent dosage.

[0079] The second concentrated coal slime, after conditioning, exhibits a significantly increased flocculent particle size and a more compact internal structure, which facilitates the smooth progress of the filter press dewatering process. The second concentrated coal slime is pumped to a plate and frame filter press for pressing and dewatering. Maintaining constant pressure for a certain period allows the water in the coal slime to be continuously squeezed out, ultimately yielding a high-quality coal slime filter cake. To further reduce the moisture content of the filter cake and improve the economic value of the coal slime, this embodiment introduces a drying process after filtration. Hot air from the drying equipment evaporates the residual moisture in the filter cake, resulting in a dried coal cake product with extremely low moisture content.

[0080] Based on the above embodiments, as an optional embodiment, the step of conditioning the concentrated coal slime according to the type of filter press conditioner to obtain the second concentrated coal slime includes:

[0081] S301, Configure the corresponding filter press conditioner according to the type of filter press conditioner, and add the filter press conditioner into the concentrated coal slime;

[0082] In one embodiment of the present invention, according to the filter press conditioner category preferably determined by the intelligent control system, the automatic dosing system precisely prepares the filter press conditioner solution according to the formula and concentration corresponding to the category, and the prepared filter press conditioner is added to the first concentrated coal slime through a metering pump and pipeline system for thorough mixing and conditioning reaction, and finally obtains the second concentrated coal slime that is suitable for the filter press dewatering process.

[0083] In practice, the intelligent control system comprehensively analyzes the properties of the first concentrated coal slime, such as particle size distribution, ash content, and surface chemical characteristics, as well as production experience and experimental data. It then matches and selects the optimal type of filter press conditioner from the filter press conditioner database, such as cationic polyacrylamide or nonionic polyacrylamide. The system sends the selected filter press conditioner type information to the automatic dosing unit. The dosing unit then searches the reagent formulation database for the corresponding optimal concentration and solution ratio parameters, controlling the metering pump, solvent pump, and mixer to automatically prepare the filter press conditioner solution with the optimal concentration and composition. The prepared filter press conditioner solution is then pumped into the first concentrated coal slime storage tank via pipeline, where it is thoroughly mixed with the concentrated coal slime. The storage tank is equipped with a mechanical stirring device that continuously stirs the tank while the filter press conditioner is being added, promoting full contact and adsorption between the reagent molecules and the coal slime particles, thus enhancing the flocculation and agglomeration effect. After a certain period of mixing, the filter press conditioner reacts fully with the concentrated coal slime, improving the surface properties of the coal slime particles. Hydrophilic groups are shielded, while hydrophobic groups are exposed. This reduces electrostatic repulsion between particles and enhances van der Waals forces, forming flocs with a certain strength and size. The dewatering effect of the coal slime particles is significantly improved. The resulting chemically conditioned coal slime is the second concentrated coal slime, characterized by high floc strength, high solids content, and good filter cake plasticity, which is highly beneficial for the subsequent dewatering process in the filter press.

[0084] S302, after adding the filter press conditioner, obtain the coal slurry property data of the concentrated coal slime, and adjust the dosage of the filter press conditioner according to the coal slurry property data and the preset property threshold. When the coal slurry property data meets the preset coal slurry property standard, the second concentrated coal slime is obtained.

[0085] In one embodiment of the present invention, after the initial addition of the filter press conditioner, the intelligent control system continuously acquires real-time coal slurry property data of the concentrated coal slime through online monitoring equipment, and dynamically compares and analyzes the monitoring data with preset property thresholds. Based on the analysis results, the amount of filter press conditioner added is adjusted in real time until the coal slurry property data stably reaches the preset standard, thus obtaining the second concentrated coal slime that meets the requirements of the filter press process.

[0086] In practice, after the filter press conditioner is initially added and thoroughly mixed with the first concentrated coal slurry, the intelligent control system initiates the online coal slurry property monitoring program. The online coal slurry analyzer continuously extracts samples of the conditioned concentrated coal slurry through sampling pipelines. Using optical, acoustic, and electrical sensors, it measures the particle size distribution, zeta potential, viscosity, flocculation degree, and other property parameters of the coal slurry in real time, obtaining property data reflecting the coal slurry conditioning effect, and transmitting the data to the intelligent control system in real time.

[0087] The intelligent control system pre-sets a series of threshold ranges and optimal values ​​for coal slurry properties based on the requirements of the pressure filtration process, forming a complete standard database of coal slurry properties. These thresholds and standard values ​​were obtained through extensive production practice and experimental optimization, representing the optimal coal slurry state for pressure filtration performance. For example, the optimal flocculation degree range, maximum particle size, and minimum Zeta potential value of the coal slurry can be set as criteria.

[0088] The intelligent control system compares real-time coal slurry property data with preset thresholds and analyzes and evaluates the deviation between the current coal slurry conditioning effect and the optimal state through expert optimization algorithms. If the deviation exceeds the allowable range, the system will determine whether the amount of filter press conditioner added is appropriate and calculate the adjustment value. When the overall coal slurry property data is below the lower limit of the threshold, it indicates that the conditioner addition is insufficient and the addition amount needs to be increased; when the overall property data is above the upper limit of the threshold, it indicates that the conditioner addition is excessive and the addition amount needs to be reduced.

[0089] Based on the calculated adjustment value, the intelligent control system issues instructions, and the automatic dosing and injection device precisely executes the increase or decrease of the filter press conditioner dosage. After the dosage is adjusted, the system continues to monitor the changes in coal slurry properties and performs the next round of dosage optimization calculation accordingly. This process is repeated iteratively until all coal slurry property data are stable within the optimal range of the preset threshold and maintained for a certain period of time. At this point, the coal slurry is determined to be in the optimal conditioning state, meeting the performance requirements of the filter press process, thus yielding the second concentrated coal slime.

[0090] Based on the above embodiments, as an optional embodiment, the coal slurry property data includes: coal slurry concentration data, coal slurry particle size distribution, and coal slurry pH value.

[0091] In one embodiment of this application, the intelligent control system comprehensively understands the physicochemical properties of the coal slurry by real-time acquisition of key parameters reflecting its characteristics, providing data support for optimizing the flocculation and dewatering process. The coal slurry property data mainly include indicators such as coal slurry concentration, viscosity, particle size distribution, and pH value. These indicators reflect the flocculation and dewatering properties of the coal slurry from different perspectives and are key factors affecting the flocculation and dewatering effect.

[0092] In practice, online concentration meters are installed on the coal slurry conveying pipeline. Using principles such as gamma rays, ultrasound, and microwaves, the real-time concentration of the coal slurry is continuously measured to obtain accurate concentration data. The coal slurry concentration directly affects the dosage of flocculant and the flocculation effect. If the concentration is too low, the distance between coal particles is large, which is not conducive to flocculation; if the concentration is too high, the coal slurry viscosity is high, and the flocculant is not evenly dispersed, which is also not conducive to flocculation. Therefore, understanding the changes in coal slurry concentration can provide a basis for calculating the dosage of coagulant.

[0093] Secondly, an online viscometer was used to measure the flow characteristics of the coal slurry, obtaining rheological parameters reflecting the slurry viscosity, such as plastic viscosity and yield stress. The viscosity of the coal slurry affects the collision frequency and flocculation rate between coal particles; the higher the viscosity, the stronger the interaction between coal particles, and the faster the flocculation. However, excessive viscosity can lead to uneven flocculant dispersion and decreased floc strength. Viscosity monitoring can optimize the mixer speed and improve the mixing effect between the flocculant and the coal slurry.

[0094] Secondly, online particle size analysis instruments such as laser particle size analyzers are used to measure the particle size distribution of coal particles in the coal slurry, obtaining coal slurry particle size distribution data. Particle size distribution reflects the size composition of coal particles and directly affects the specific surface area of ​​coal particles and the adsorption capacity of flocculants. Generally, the smaller the particle size, the larger the specific surface area, and the better the flocculation effect. However, excessively small particle size can lead to problems such as excessive flocculant addition and loose flocs. By monitoring particle size distribution, the operating conditions of the crusher can be adjusted to control the particle size distribution of the coal particles fed into the slurry, creating favorable particle composition conditions for flocculation.

[0095] Finally, an online pH meter was used to continuously monitor the acidity and alkalinity of the coal slurry, obtaining real-time pH data. The flocculation process is highly sensitive to pH; only within a suitable pH range can the flocculant fully hydrolyze and ionize, neutralizing the charged groups on the surface of coal particles to form flocs. Excessively high or low pH values ​​in the coal slurry will inhibit the flocculation reaction and reduce the flocculation effect. pH monitoring allows for timely adjustment of the alkali dosage to maintain the optimal pH environment for flocculation.

[0096] The aforementioned coal slurry concentration, viscosity, particle size distribution, and pH value are transmitted in real time to the intelligent control system, forming a complete coal slurry property database. This allows for multi-faceted and comprehensive evaluation of the flocculation potential of the coal slurry. These quantitative property indicators can sensitively reflect the dynamic changes in the physicochemical properties of the coal slurry, helping to accurately determine the optimal timing and dosage of flocculant addition, and providing reliable data support for process optimization in the flocculation process. Simultaneously, statistical analysis of the coal slurry property data can also detect abnormal fluctuations in the production process, provide early warnings of deteriorating coal slurry quality trends, and offer a reference for production scheduling decisions.

[0097] Based on the above embodiments, as an optional embodiment, the step of pressing and drying the concentrated coal slime to obtain dried coal cake includes:

[0098] S402, Obtain the coal slurry properties of the concentrated coal slime, and determine the filter press pressure and pressure time based on the coal slurry properties;

[0099] Specifically, in one embodiment of the present invention, before the second concentrated coal slime enters the filter press, the intelligent control system obtains the real-time property parameters of the concentrated coal slime through an online coal slurry property analyzer, and dynamically determines the optimal filtration pressure and holding time based on the property parameters and the filtration process optimization model, thereby guiding the process operation of the filter press and realizing intelligent optimization control of the coal slime filtration and dewatering process.

[0100] In practice, the online coal slurry property analyzer samples and analyzes the second thickened coal slurry entering the filter press in real time, obtaining key property parameters such as particle size distribution, ash content, concentration, viscosity, and pH value. The measured data is then transmitted to the intelligent control system in real time. Using these coal slurry property parameters, combined with historical production data and a process optimization model, the system calculates the optimal combination of process parameters for the current coal slurry properties, namely the optimal filter pressure and holding time.

[0101] The pressure filtration process optimization model was trained using extensive production practice data and experimental results, establishing a quantitative correlation between coal slurry properties, pressure filtration process parameters, and pressure filtration dewatering performance. The model comprehensively considers the differences in dewatering effects of coal slurries with different properties under various pressure and time combinations. A multi-objective optimization algorithm is used to find the optimal balance point for indicators such as pressure filtration efficiency, filter cake moisture content, and energy consumption cost, deriving the preferred values ​​for pressure filtration and holding time. For example, when the coal slurry particle size is fine and the concentration is high, the model tends to select a higher pressure filtration and a longer holding time to overcome the high resistance of the filter cake and ensure sufficient dewatering; when the coal slurry particle size is coarse and the concentration is low, the model tends to select a lower pressure and a shorter time to avoid over-pressing and reduce energy consumption costs.

[0102] S401, the concentrated coal slime is filtered according to the filter pressure and the filter time to obtain a preliminary coal cake;

[0103] In one embodiment of the present invention, the intelligent control system controls the filter press to perform dewatering treatment on the second concentrated coal slime according to the optimized filter pressure and holding time, ultimately obtaining a preliminary coal cake with reduced moisture content. The filter pressing process is carried out efficiently under the precise control of optimized parameters, and the coal slime is fully dewatered and consolidated to form a preliminary coal cake with good filter cake quality.

[0104] In practice, the intelligent control system sends the optimal filter pressure and holding time settings, calculated from coal slurry property analysis and process optimization models, to the filter press control unit. The control unit automatically executes the filter press operation strictly according to the optimized parameters. First, the filter press feed pump uniformly delivers the second concentrated coal slurry to the filter press inlet. Under the pressure of the feed pump, the coal slurry enters the filter chamber and forms a filter cake layer under the constraint of the filter plates and filter cloth. Subsequently, the press pump applies pressure to the filter cake in the filter chamber according to the set filter pressure curve, further compacting and dewatering the filter cake. During the holding pressure stage, the press pump maintains a constant pressure output for a duration consistent with the optimized holding time. Throughout the filter press process, the filtrate is continuously squeezed out from the filter cake and discharged to the filtrate collection system through channels in the filter cloth and filter plates.

[0105] The quality of the filter press process directly affects the dewatering effect and subsequent drying performance of the filter cake. The control unit strictly executes the optimized pressure curve to ensure that a reasonable low pressure is applied during the filter cake formation stage, allowing the coal slime particles to spread evenly and preventing surface clogging; an appropriate high pressure is applied during the dewatering and consolidation stage to deeply compact the filter cake and promote pore water drainage; and a constant pressure is maintained during the pressure holding stage to suppress filter cake rebound and maintain filter cake structural stability. The pressure changes are stable throughout the pressing process, matching the properties of the coal slime and dewatering characteristics, which is conducive to continuous and homogeneous dewatering of the filter cake and prevents adverse phenomena such as filter cake cracking and filter cloth clogging.

[0106] S403, Obtain the humidity of the coal cake, and determine the drying time and drying temperature based on the humidity of the coal cake and the properties of the coal slurry;

[0107] In one embodiment of the present invention, before the preliminary coal cake enters the drying equipment, the intelligent control system obtains the real-time moisture content data of the preliminary coal cake through an online humidity detector, and combines it with the concentrated coal slime and coal slurry property parameters obtained in the previous stage, and uses a drying process optimization model to dynamically determine the optimal drying time and drying temperature parameters, guide the process operation of the drying equipment, and realize intelligent optimization control of the coal slime drying process.

[0108] In practice, an online humidity detector performs rapid, non-contact measurement on the initial coal cake entering the dryer, obtaining a value reflecting the overall moisture content of the coal cake. Simultaneously, the intelligent control system receives coal slurry property data from the preceding filtration stage, primarily including parameters such as particle size distribution, ash content, and initial coal slurry concentration. The system uses the coal cake moisture content and coal slurry property parameters as input, substituting them into the drying process optimization model for calculation and analysis, to derive the optimal combination of process parameters suitable for the current drying process, namely, the preferred values ​​for drying time and drying temperature.

[0109] The drying process optimization model, established based on extensive production operation data and experimental results, reveals the inherent relationship between coal cake properties, drying process parameters, and drying performance. The model considers the differences in drying characteristics of coal cakes with varying moisture content, particle size, and ash content under different drying conditions. Through mechanistic analysis and data mining, it characterizes the kinetic features of processes such as moisture evaporation, heat transfer, and mass transfer in the coal cake. Intelligent algorithms are used to optimize and solve for the balance point of multiple objectives, including drying time, energy consumption, and product quality, ultimately obtaining the optimal values ​​for drying time and temperature parameters. For example, when the coal cake has a high moisture content and fine particle size, the model will appropriately extend the drying time and increase the drying temperature to ensure sufficient moisture evaporation; when the coal cake has a low moisture content and coarse particle size, the model will appropriately shorten the time and decrease the temperature to prevent drying cracks and energy waste.

[0110] S404, the preliminary coal cake is dried according to the drying time and drying temperature to obtain a dried coal cake.

[0111] In one embodiment of the present invention, the intelligent control system controls the drying equipment to precisely dry the initial coal cake according to the optimized drying time and temperature parameters, ultimately obtaining a qualified dried coal cake product with the required moisture content. The drying process is carried out efficiently under the precise control of optimized parameters, the moisture in the coal cake is fully evaporated and removed, and the quality of the dried coal slime reaches its optimal state.

[0112] In practice, the intelligent control system sends the optimal drying time and temperature setpoints calculated by the coal cake moisture analysis and drying process optimization model to the drying equipment control unit. The control unit strictly executes the optimized parameters and automatically carries out the drying operation. First, the feeding device quantitatively feeds the initial coal cake into the dryer cylinder. The cylinder is arranged at a slight inclination, and the coal cake spirals forward inside the cylinder, contacting the hot air in a counter-current flow. Surface moisture and bound water in the micropores are continuously evaporated and carried away. The hot air system dynamically adjusts the burner firepower and airflow according to the optimized drying temperature, introducing high-temperature hot air from the rear of the cylinder, flowing counter-currently upwards along the direction of coal cake movement, and finally exiting from the front of the cylinder. The residence time of the coal cake in the cylinder is controlled by the cylinder rotation speed through a frequency converter, based on the optimized drying time.

[0113] Throughout the drying process, the moisture in the coal slime continuously evaporates under the heat of the hot air. Capillary water on the surface of fine particles evaporates first, followed by capillary water and adhering water inside the particles, and finally, deep-seated adsorbed water. The drying process follows an optimized temperature curve: initially, the temperature is appropriately increased to accelerate surface moisture evaporation; in the middle stage, a suitable temperature is maintained to ensure low-stress evaporation of internal bound water and prevent over-baking of the coal cake surface; and in the final stage, the temperature is appropriately reduced to consolidate the drying effect and prevent re-moistening. Under optimized control, the drying hot air temperature changes smoothly, and the heat supply is precisely matched to the heat absorption requirements of the coal cake, resulting in uniform and thorough drying of the material.

[0114] The drying time of the coal cake inside the drying drum is strictly controlled within the optimized drying time range. The drum rotation speed is adjusted in real time according to the dynamic changes in the online moisture monitoring value of the coal cake to ensure that the material has sufficient residence time in the drum to complete the drying process, while avoiding energy waste and coal cake quality degradation caused by excessive residence. The precision of drying time control greatly improves the drying uniformity of coal slime and overcomes the drawbacks of conventional drying processes, such as large fluctuations in material residence time, insufficient drying of coal cake, or over-baking.

[0115] Please see Figure 2 , Figure 2 This application provides an embodiment of an intelligent control system architecture for a concentration and filtration system, which may include:

[0116] Data acquisition module 1 is used to acquire the moisture value and ash value of the raw coal after crushing and screening, determine the raw coal moisture content value based on the moisture value and ash value, and determine the amount of washing water based on the raw coal moisture content value.

[0117] The first processing module 2 is used to wash the crushed and screened raw coal according to the amount of washing water to obtain washed coal slurry, and send the washed coal slurry into a thickening tank for thickening to obtain the first thickened coal slurry.

[0118] The second processing module 3 is used to obtain the concentrated particle size, concentrated ash content and concentrated pH value of the concentrated coal slime, and to determine the type of filter press conditioner based on the concentrated particle size, the concentrated ash content and the concentrated pH value.

[0119] The third processing module 4 is used to condition the concentrated coal slime according to the type of filter press conditioner to obtain a second concentrated coal slime, and to filter and dry the concentrated coal slime to obtain a dried coal cake.

[0120] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0121] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will readily conceive of those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0122] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for intelligent control of concentration and filtration, characterized in that, The method includes: Obtain the moisture and ash content of the raw coal after crushing and screening, determine the raw coal moisture content ratio based on the moisture and ash content, and determine the amount of washing water based on the raw coal moisture content ratio; the process of determining the raw coal moisture content ratio and the amount of washing water based on the raw coal moisture content ratio includes: Based on the moisture content and ash content, the moisture content of the raw coal is estimated using an empirical formula, wherein the empirical formula is: Rw = a - b·Mad + c·Aad In the formula, a, b, and c are empirical coefficients related to the coal preparation process, Rw is the moisture content of the raw coal, Mad is the moisture content, and Aad is the ash content. Based on the raw coal moisture content, the required amount of washing water is calculated using the washing water volume formula: W = Rw·Q / (1 - Rw), where Q is the amount of raw coal within a given time period, Rw is the raw coal moisture content, and W is the moisture content. The crushed and screened raw coal is washed with water according to the amount of washing water to obtain washed coal slurry, and the washed coal slurry is sent to a thickening tank for thickening to obtain the first thickened coal slurry. Obtain the concentrated particle size, concentrated ash content, and concentrated pH value of the concentrated coal slime, and determine the type of filter press conditioner based on the concentrated particle size, the concentrated ash content, and the concentrated pH value; According to the type of filter press conditioner and the conditioning of the concentrated coal slime, a second concentrated coal slime is obtained, and the concentrated coal slime is then filtered and dried to obtain a dried coal cake.

2. The method according to claim 1, characterized in that, The step of feeding the washed coal slime into a thickening tank for thickening to obtain the first thickened coal slime includes: The washing particle size, washing ash content, and washing pH value of the washed coal slime are obtained, and the type of concentrated reagent is determined based on the washing particle size, the washing ash content, and the washing pH value. According to the type of concentrated agent, the concentrated agent corresponding to the concentrated agent is added to the concentrated tank, and the real-time pH value and tank surface image of the concentrated coal slime after the concentrated agent is added are obtained in real time, and the floc characteristics are determined based on the tank surface image. The dosage adjustment of the concentrated agent is determined based on the real-time pH value and the characteristics of the flocs. When the characteristics of the flocs and the real-time pH value meet the preset concentrated coal slime standard, the first concentrated coal slime is obtained.

3. The method according to claim 1, characterized in that, The step of conditioning the concentrated coal slime according to the type of filter press conditioner to obtain a second concentrated coal slime includes: Configure the corresponding filter press conditioner according to the type of filter press conditioner, and add the filter press conditioner to the concentrated coal slime; After adding the filter press conditioner, the coal slurry properties data of the concentrated coal slime are obtained, and the dosage of the filter press conditioner is adjusted according to the coal slurry properties data and the preset properties threshold. When the coal slurry properties data meet the preset coal slurry properties standard, the second concentrated coal slime is obtained.

4. The method according to claim 1, characterized in that, The process of pressing and drying the concentrated coal slime to obtain dried coal cake includes: Obtain the coal slurry properties of the concentrated coal slime, and determine the filtration pressure and pressure time based on the coal slurry properties; The concentrated coal slime is filtered according to the specified filtration pressure and filtration time to obtain a preliminary coal cake; The humidity of the coal cake is obtained, and the drying time and drying temperature are determined based on the humidity of the coal cake and the properties of the coal slurry. The preliminary coal cake is dried according to the drying time and drying temperature to obtain a dried coal cake.

5. The method according to claim 2, characterized in that, Determining the floc characteristics based on the pool surface image includes: The threshold segmentation method is used to extract the flocculent region from the pool surface image, and geometric measurements are performed based on the flocculent region to determine the geometric data of the flocculent. The distribution state of flocs in the pool surface image is obtained by marking and counting them using connected component analysis. Morphological analysis was performed on the floc region to extract quantitative indicators reflecting the morphology of the floc. The floc region is scanned pixel by pixel, the gray value of each pixel is extracted to obtain the gray histogram of the floc, and the transparency of the floc region is determined based on the gray histogram. The geometric data, the distribution state, the floc transparency, and the quantitative index are used as floc characteristics.

6. The method according to claim 3, characterized in that, The coal slurry properties data include: coal slurry concentration data, coal slurry particle size distribution, and coal slurry pH value.

7. A concentration and filtration device, characterized in that, include: The data acquisition module is used to acquire the moisture and ash content of the raw coal after crushing and screening, determine the raw coal moisture content value based on the moisture and ash content value, and determine the amount of washing water based on the raw coal moisture content value. The step of determining the raw coal moisture content value based on the moisture and ash content values, and determining the amount of washing water based on the raw coal moisture content value, includes: Based on the moisture content and ash content, the moisture content of the raw coal is estimated using an empirical formula, wherein the empirical formula is: Rw = a - b·Mad + c·Aad In the formula, a, b, and c are empirical coefficients related to the coal preparation process, Rw is the moisture content of the raw coal, Mad is the moisture content, and Aad is the ash content. Based on the raw coal moisture content, the required amount of washing water is calculated using the washing water volume formula: W = Rw·Q / (1 - Rw), where Q is the amount of raw coal within a given time period, Rw is the raw coal moisture content, and W is the moisture content. The first processing module is used to wash the crushed and screened raw coal with water according to the amount of washing water to obtain washed coal slurry, and send the washed coal slurry into a thickening tank for thickening to obtain the first thickened coal slurry. The second processing module is used to obtain the concentrated particle size, concentrated ash content, and concentrated pH value of the concentrated coal slime, and to determine the type of filter press conditioner based on the concentrated particle size, the concentrated ash content, and the concentrated pH value. The third processing module is used to condition the concentrated coal slime according to the type of filter press conditioner to obtain a second concentrated coal slime, and to filter and dry the concentrated coal slime to obtain a dried coal cake.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores multiple instructions. The instructions are applicable to being loaded by a processor and executed as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, The device includes a processor, a memory, and a transceiver, wherein the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 6.

Images