A method and system for reducing oil sludge volume based on high-pressure diaphragm filtration

Abstract

This invention relates to the fields of high-pressure filtration and low-temperature sludge reduction technology. Specifically, it relates to a method and system for low-temperature sludge reduction based on high-pressure diaphragm filtration. The invention involves injecting surfactants and compound dehydrating agents into a conditioning tank to deconstruct the oil-sludge-water emulsion system and reconstruct the solid-phase coalescing network, outputting pretreated material. A high-pressure plunger pump dynamically matches the feed pressure according to the material's rheological characteristics, and feed is terminated based on filter plate deformation feedback. The high-pressure diaphragm filter press dehydrates and unclogs pores under low pressure, reconstructs the filter cake skeleton under medium pressure, and removes bound water under high pressure, outputting a filter cake with low moisture content. The oil-water mixture produced by filtration is demulsified by a demulsifier, and then separated into sludge and wastewater by an air flotation machine, achieving sludge recovery and wastewater purification. This efficiently reduces sludge volume and recovers resources, balancing environmental protection and economic efficiency.

Description

Technical Field

[0001] This invention relates to the field of high-pressure filtration and low-temperature sludge reduction technology, specifically, to a method and system for low-temperature sludge reduction based on high-pressure diaphragm filtration. Background Technology

[0002] High-pressure filtration combined with low-temperature sludge reduction technology is an important technology specifically applied to the reduction of oily sludge. Its core principle is to efficiently separate the oil-sludge-water three phases and deeply remove moisture from the filter cake, thereby reducing the volume of oily sludge and enabling resource recovery. This aligns with the core environmental protection requirements for oily sludge reduction and resource utilization. In oily sludge, the oil-sludge-water system forms a stable emulsion, and the filter cake, after forming, has a loose internal pore structure, with bound water easily adsorbed onto the particle surface or trapped in tiny pores. These characteristics lead to incomplete demulsification and low free liquid release efficiency in traditional treatments, making it difficult to efficiently remove bound water and achieve deep sludge reduction. This also results in waste of oily resources and increased pressure on wastewater purification. To address this technical problem, we provide a low-temperature oily sludge reduction method and system based on high-pressure diaphragm filtration. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for reducing oil sludge volume based on high-pressure diaphragm filtration, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, one objective of this invention is to provide a low-temperature sludge reduction method based on high-pressure diaphragm filtration, comprising the following steps:

[0005] S1. Receive the initial physical property parameter set of oily sludge through the conditioning tank, inject the surfactant action coefficient and dehydrating agent action coefficient into the initial physical property parameter set of oily sludge, generate an oil-sludge-water three-phase separation optimization data package, where the surfactant action coefficient is used to deconstruct the interfacial tension data matrix of the emulsion system, the dehydrating agent action coefficient is used to reconstruct the solid phase coalescence topology network, and output the low-temperature pretreatment material data stream.

[0006] S2. Receive the low-temperature pretreatment material data stream, construct a pressure gradient model using a high-pressure plunger pump, and match the feed pressure decision tree based on the material's rheological properties. When a node in the feed pressure decision tree triggers the chamber filling threshold, a high-pressure diaphragm filter press feed termination command is generated.

[0007] S3. Establish a pore reconstruction algorithm for diaphragm periodic extrusion. Execute a low-pressure dehydration command based on the initial filter cake impedance data to release the free liquid data stream. Then, activate a multi-stage pressing protocol based on real-time liquid content feedback. The first-round pressing protocol generates a medium-pressure command to cause the filter cake skeleton to collapse and reorganize. The second-round pressing protocol generates a high-pressure command to directionally remove bound water data packets, and finally outputs the filter cake entity.

[0008] S4. Extract phase separation features from the oil-water mixture released by pressure filtration, break the emulsion data packet structure through the demulsifier action function, and then separate the sludge oil data cluster and wastewater data cluster through the density field analysis module of the air flotation machine, and output sludge oil recovery instructions and wastewater purification instructions.

[0009] The second objective of this invention is to provide a system for implementing a low-temperature sludge reduction method based on high-pressure diaphragm filtration, comprising:

[0010] The low-temperature chemical conditioning unit integrates a temperature maintenance module and a two-stage dosing subsystem. The surfactant dosing pump is connected to the anionic surfactant storage tank, the dehydrating agent dosing pump is connected to the compound chemical storage tank, and the stirring mechanism adjusts the speed in real time according to the oil droplet size feedback.

[0011] The high-pressure feed control unit and the high-pressure plunger pump are equipped with a real-time pressure sensor array. Its rheological characteristic analysis module is linked with the feed pressure decision tree. When the filter plate deformation monitor detects that the deformation data exceeds the safety margin, it triggers a feed termination command.

[0012] The multi-stage pressing execution unit of the high-pressure diaphragm filter press has a built-in periodic telescopic diaphragm mechanism. After the diaphragm extrusion controller activates the low-pressure dewatering program based on the filter cake impedance data, it executes the skeleton recombination command and the bound water directional removal command in stages through the progressive pressurization module.

[0013] The oil-water deep separation unit integrates an online potential analyzer and an automatic demulsifier dosing device in the wastewater tank. When the Zeta potential peak migrates to the critical region, the air flotation machine is activated. The density field control module of the air flotation machine generates a waste-oil enrichment command through a probability model of bubble and oil droplet combination.

[0014] The intelligent product management unit's waste oil storage tank is equipped with a calorific value monitor that is linked to the oil refining valve. The wastewater treatment plant interface is equipped with a turbidity sensor that is linked to the water purification agent dosing pump in a closed loop. When the second derivative of the turbidity decay curve approaches zero, the drainage valve is closed.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0016] This invention utilizes a low-temperature chemical conditioning unit to inject surfactants and dehydrating agents, precisely deconstructing the emulsion system and reconstructing the solid-phase coalescing topology network. Combined with a floc strength prediction algorithm, it ensures thorough separation of the oil-sludge-water three phases, laying a high-quality material foundation for subsequent pressure filtration. The high-pressure feed control unit employs a dynamic rheological response mechanism to match the feed pressure in real time and dynamically correct the filling threshold, ensuring both feeding efficiency and preventing filter plate overload damage. The multi-stage pressing execution unit unblocks capillary channels through a pore reconstruction algorithm, forming a drainage network through medium-pressure skeleton reconstruction, and then precisely removes bound water with high-pressure directional dehydration, significantly reducing the filter cake moisture content and achieving deep reduction of oil sludge. The deep oil-water separation unit combines Zeta potential monitoring and density field analysis to efficiently break down emulsions and separate sludge from waste oil. The intelligent product management unit achieves sludge recovery and wastewater discharge meeting standards. The process is adapted to the characteristics of oily sludge, improving reduction efficiency, achieving resource recovery and environmental compliance, balancing equipment safety and treatment economy, and meeting the core requirements of low-temperature oil sludge treatment. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the overall workflow of the present invention;

[0018] Figure 2 This is a schematic diagram of the overall structure of the present invention;

[0019] The meanings of the labels in the diagram are as follows:

[0020] 1. Low-temperature chemical conditioning unit; 2. High-pressure feeding control unit; 3. Multi-stage pressing execution unit; 4. Deep oil-water separation unit; 5. Intelligent product management unit. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, one of the objectives of this embodiment is to provide a low-temperature sludge reduction method based on high-pressure diaphragm filtration, comprising the following steps:

[0023] S1. Receive the initial physical property parameter set of oily sludge through the conditioning tank, inject the surfactant action coefficient and dehydrating agent action coefficient into the initial physical property parameter set of oily sludge, generate an oil-sludge-water three-phase separation optimization data package, where the surfactant action coefficient is used to deconstruct the interfacial tension data matrix of the emulsion system, the dehydrating agent action coefficient is used to reconstruct the solid phase coalescence topology network, and output the low-temperature pretreatment material data stream.

[0024] S2. Receive the low-temperature pretreatment material data stream, construct a pressure gradient model by the high-pressure plunger pump, and match the feed pressure decision tree based on the material rheological characteristics. When the feed pressure decision tree node triggers the chamber filling threshold, generate a high-pressure diaphragm filter press feed termination command.

[0025] S3. Establish a pore reconstruction algorithm for diaphragm periodic extrusion. Execute low-pressure dehydration command based on initial filter cake impedance data to release free liquid data stream. Then activate multi-stage pressing protocol based on real-time liquid content feedback. The first pressing protocol generates medium-pressure command to cause filter cake skeleton to collapse and reorganize. The second pressing protocol generates high-pressure command to directionally remove bound water data packets and finally output filter cake entity.

[0026] S4. Extract phase separation features from the oil-water mixture released by pressure filtration, break the emulsion data packet structure through the demulsifier action function, and then separate the sludge oil data cluster and wastewater data cluster through the density field analysis module of the air flotation machine, and output sludge oil recovery instructions and wastewater purification instructions.

[0027] The surfactant action coefficient is demulsified by deconstructing the interfacial tension data matrix of the emulsion system. Specifically, an oil phase dispersion evaluation model is established in the conditioning tank, and the amount of anionic surfactant added is converted into the interfacial tension decay gradient. When the oil droplet size data packet is detected to drop to a preset proportion of the initial value, it is determined that the oil-sludge-water stable system has been destroyed, and an oil-sludge-water three-phase separation optimization data packet is generated.

[0028] When the dehydrating agent action coefficient reconstructs the solid-phase coalescence topology network, the floc strength prediction algorithm is activated simultaneously: the charge neutralization efficiency parameter is generated based on the compound ratio of polyaluminum chloride and polyacrylamide, and the floc growth trajectory is simulated by combining the stirring speed data stream. When the floc density data reaches the solid-liquid separation threshold, the low-temperature pretreatment material data stream output command is triggered.

[0029] The feed pressure decision tree adopts a dynamic rheological response mechanism: the material rheological characteristic curve is collected by the real-time pressure sensor of the high-pressure plunger pump. When the curve slope change value reaches the preset tolerance range, the optimal feed pressure node is automatically matched. The chamber filling threshold is dynamically corrected according to the filter plate deformation data feedback. When the deformation monitoring data exceeds the safety margin, a feed termination command is generated.

[0030] When the pore reconstruction algorithm executes the low-pressure dehydration command, it establishes a free liquid release efficiency evaluation model: calculates the pore connectivity index based on the initial filter cake impedance data, and generates a diaphragm pre-compression command when the index is lower than the critical threshold, and clears the capillary channels through low-frequency vibration mode.

[0031] The first medium-pressure command of the multi-stage pressing protocol activates the filter cake skeleton recombination program. Based on the real-time liquid content feedback, the skeleton collapse risk coefficient is calculated. When the coefficient is within the safe range, the progressive pressurization module is activated to rearrange the filter cake particles to form a drainage channel network.

[0032] The next round of high-pressure command-directed removal of bound water data packets includes:

[0033] Scan the microstructure topology of the filter cake, identify the coordinates of the bound water enrichment area, and implement directional dehydration by adjusting the direction and angle of the diaphragm extrusion. After dehydration, a verification code for filter cake moisture content meeting the standard is generated.

[0034] When the demulsifier action function decrypts the emulsion data packet, it performs oil-water interface energy level analysis:

[0035] The Zeta potential distribution spectrum of the emulsion is collected. When the amount of demulsifier added causes the peak value of the spectrum to shift to the demulsification critical region, a phase separation start signal is triggered. The density field analysis module generates a sludge-oil enrichment command by monitoring the probability of bubble and oil droplet combination.

[0036] The logic for generating wastewater purification instructions includes:

[0037] Extract the turbidity decay curve of the aqueous phase. When the second derivative of the curve approaches zero, the separation is considered complete. The waste oil recovery command triggers the waste oil calorific value assessment model. When the calorific value data is higher than the reuse threshold, an oil refining command is generated.

[0038] Further explanation is needed regarding the initial physical property parameters of the oily sludge received in the conditioning tank. A stable emulsion system is formed between the oil, sludge, and water. This system consists of oil droplets uniformly dispersed in the aqueous phase and sludge particles adsorbing and encapsulating the oil droplets. High interfacial tension makes separation of the three phases difficult. The core function of the surfactant's interaction coefficient is to demulsify the interfacial tension data matrix of this system, laying the foundation for the subsequent reconstruction of the solid-phase coalescing topology network by the dewatering agent. The specific implementation method is as follows:

[0039] First, an oil phase dispersion assessment model is established in the conditioning tank. The oil phase dispersion assessment model is a mathematical model that quantifies the uniformity of oil droplet dispersion in the oil-sludge-water system. Its input parameters include the oil content, water content, and particle size distribution of the initial physical properties of the oily sludge. The output is the oil phase dispersion index (with a value of 0 to 1. The closer the index is to 1, the more uniform the oil droplet dispersion and the more stable the emulsion system). The model is trained and optimized through historical demulsification data to ensure that it can accurately reflect the correlation between the oil droplet dispersion state and the demulsification effect.

[0040] Next, the dosage of anionic surfactant is converted into an interfacial tension decay gradient. Anionic surfactants are agents with demulsifying functions; their molecules can adsorb at the oil-water interface, reducing interfacial tension to disrupt the emulsion equilibrium. The type selected needs to be calibrated according to the properties of the oily sludge. The interfacial tension decay gradient is an indicator describing the rate of change of interfacial tension with the amount of surfactant added. The more surfactant added, the faster the interfacial tension decreases, and the larger the absolute value of the gradient. During the conversion process, anionic surfactant is injected into the conditioning tank according to the preset addition gradient. At the same time, the oil-water interfacial tension data is collected in real time through an interfacial tension sensor. Each addition amount is correlated with the corresponding change in interfacial tension to generate a continuous interfacial tension decay gradient, which intuitively reflects the destructive effect of the agent on interfacial tension.

[0041] Subsequently, the changes in the median value of the oil droplet size data package were continuously monitored. The oil droplet size data package is a collection of oil droplet size distribution data collected in real time by a laser particle size analyzer. It includes the proportion of oil droplets in different size ranges. The median value is the middle value of the size distribution, that is, 50% of the oil droplets are larger than this value and 50% are smaller than this value. Its change directly reflects the aggregation state of the oil droplets. When the emulsion system is disrupted, the oil droplets will aggregate with each other, and the median value of the size will decrease. During the monitoring process, the laser particle size analyzer collects data at a frequency of 2 minutes / time. The system automatically extracts the median value of the size from each data package and compares it with the initial median value of the size (the median value of the size before the addition of surfactant) to calculate the decrease ratio.

[0042] When the median oil droplet size in the data packet drops to a preset percentage of the initial value, the oil-sludge-water stable system is deemed to have been completely destroyed. This preset percentage needs to be calibrated experimentally, taking into account the emulsification strength of different types of oily sludge, and is typically set to 50% to 70% (e.g., if the initial median particle size is 10 micrometers, the determination is triggered when it drops to 5 to 7 micrometers). This percentage ensures complete destruction of the emulsion system while avoiding waste due to excessive surfactant addition. After the determination, the system integrates current interfacial tension data, oil droplet size distribution data, and surfactant dosage data to generate an optimized data packet for the oil-sludge-water three-phase separation. This data packet contains key parameters for three-phase separation (such as optimal surfactant dosage, final interfacial tension value, and oil droplet aggregation state), providing precise preliminary conditions for reconstructing the solid-phase coalescing topology network using the dewatering agent's action coefficient, ensuring that the low-temperature pretreated material data stream output meets the requirements of subsequent pressure filtration.

[0043] Throughout the process, the oil phase dispersion assessment model provides a quantitative basis for the demulsification effect, the interfacial tension decay gradient enables a precise correlation between the amount of reagent added and the demulsification effect, and the median particle size monitoring provides an intuitive judgment standard for system damage. The three work together to ensure that the demulsification process is efficient and controllable, avoiding the impact of incomplete destruction of the emulsion system on the subsequent separation effect, and preventing excessive reagent addition from increasing the processing cost, ultimately achieving the initial optimization of oil-sludge-water three-phase separation.

[0044] After the surfactant completes the demulsification of the oil-sludge-water emulsion system and generates a three-phase separation optimization data package, the dispersed sludge particles still lack a stable aggregate structure, making it difficult to achieve efficient solid-liquid separation through subsequent pressure filtration. At this point, it is necessary to reconstruct the solid-phase aggregate topology network with the help of the dewatering agent's action coefficient, and simultaneously activate the floc strength prediction algorithm to ensure that the formed flocs are both dense and have sufficient strength to meet the process requirements of high-pressure diaphragm pressure filtration. The specific implementation method is as follows:

[0045] The activation of the dewatering agent action coefficient reconstruction solid-phase coalescence topology network and floc strength prediction algorithm is carried out simultaneously. The core logic is: compound ratio calibration—charge neutralization quantification—growth trajectory simulation—density determination. First, it is determined that the dewatering agent adopts a compound system of polyaluminum chloride and polyacrylamide. Polyaluminum chloride is an inorganic flocculant that can generate positively charged ions through hydrolysis to neutralize the negative charge on the surface of sludge particles. Polyacrylamide is an organic coagulant aid whose long molecular chains can be adsorbed on the surface of neutralized sludge particles, forming a bridging effect to promote particle aggregation. The compound ratio, i.e., the mass ratio of the two added, needs to be calibrated based on the initial physical property parameters of oily sludge, such as sludge concentration, pH value, zeta potential, etc. For example, for high-concentration acidic oily sludge, the compound ratio is set to 20:1 (polyaluminum chloride: polyacrylamide), and for low-concentration neutral sludge, it is set to 10:1 to ensure that the compound system is adapted to the sludge characteristics and improves the aggregation effect. Based on this mixing ratio, a charge neutralization efficiency parameter is generated. This parameter quantifies the degree to which the surface charge of sludge particles is neutralized, ranging from 0 to 1. A value closer to 1 indicates more thorough neutralization, weaker electrostatic repulsion between sludge particles, and easier aggregation. During the generation process, the zeta potential of the sludge mixture is collected in real time using an online potential monitor. Combined with a correlation model between the mixing ratio and potential changes, the charge neutralization efficiency parameter is calculated. For example, when the mixing ratio is 15:1 and the zeta potential changes from -25mV to -5mV, the charge neutralization efficiency parameter is 0.8, directly reflecting the charge neutralization effect. Subsequently, the floc growth trajectory was simulated using the stirring speed data stream. The stirring speed data stream is the real-time rotation speed data of the stirring mechanism inside the conditioning tank. The stirring mechanism dynamically adjusts the rotation speed based on the previously mentioned oil droplet particle size feedback and the current charge neutralization efficiency parameter (e.g., the rotation speed is set to 150 rpm in the initial neutralization stage to promote uniform mixing of the agent and sludge; the rotation speed is reduced to 60 rpm in the aggregation stage to avoid breaking the generated flocs). The rotation speed data is collected at a frequency of 10 seconds per data stream. The floc growth trajectory refers to the simulation of the process from the dispersed state of sludge particles to the aggregation and formation of flocs, including the dynamic changes in floc particle size growth and structural densification. During the simulation, the floc strength prediction algorithm takes the compounding ratio, charge neutralization efficiency parameter, and stirring speed data stream as inputs. By replicating the historical floc growth data to establish a model, it outputs dynamic parameters such as floc particle size, looseness, and internal binding force in real time, clearly presenting the floc growth state. Meanwhile, floc density data is collected in real time through online monitoring equipment. Floc density data is an indicator that quantifies the compactness of floc particles. The floc particle size distribution is collected by a laser particle size analyzer, and the turbidity of the mixed liquor is collected by a turbidity analyzer. The results are calculated by combining the data (the higher the density, the larger the particle size and the lower the turbidity). This data directly reflects the solid-liquid separation potential of the floc. The higher the density, the easier it is for water to be removed during subsequent pressure filtration.When the floc density data reaches the solid-liquid separation threshold, a low-temperature pretreatment material data stream output command is triggered. The solid-liquid separation threshold is a preset floc density critical value, experimentally calibrated to 0.7 (range 0 to 1). When the density data is ≥0.7, it indicates that the flocs have formed a compact and stable structure, capable of withstanding the pressure of subsequent high-pressure filtration without breaking, while also providing channels for water permeation, thus meeting the solid-liquid separation requirements. After the command is triggered, the system integrates current charge neutralization efficiency parameters, floc growth trajectory data, floc density data, dehydrating agent compounding ratio, and other information to generate a low-temperature pretreatment material data stream. This data stream contains key information such as the material's aggregation state and solid-liquid separation potential, providing accurate material characteristic basis for constructing a pressure gradient model for the subsequent high-pressure plunger pump, ensuring the compatibility between the feed and the filtration process. Throughout the process, precise calibration of the compounding ratio provides the basis for charge neutralization, dynamic adjustment of stirring speed ensures stable floc growth, floc strength prediction algorithm enables visual monitoring of the growth process, and finally, the density threshold judgment ensures that the material meets the standards. The three work together to achieve efficient reconstruction of the solid-phase coalescing topology network, laying a solid foundation for subsequent steps of low-temperature sludge reduction.

[0046] After receiving the low-temperature pretreated material data stream, to ensure that the feeding process of the high-pressure diaphragm filter press adapts to the dynamic characteristics of the material while avoiding overload damage to the filter plates, the feed pressure decision tree employs a dynamic rheological response mechanism. This mechanism adjusts the feed pressure by capturing real-time changes in the material's rheological properties, and simultaneously dynamically corrects the filling threshold based on filter plate deformation feedback, ensuring efficient and safe feeding. The specific implementation method is as follows:

[0047] The dynamic rheological response mechanism of the feed pressure decision tree revolves around the logic of characteristic acquisition, pressure matching, threshold correction, and safety control. First, it's important to clarify that the dynamic rheological response mechanism is an adaptive mechanism that dynamically adjusts the feed pressure based on the real-time rheological characteristics of the material (i.e., the resistance change pattern during material flow). This ensures that the feed pressure always matches the material flow state, avoiding feed blockage or inefficiency caused by fixed pressure. The core first step of this mechanism is to acquire the material rheological characteristic curve through a real-time pressure sensor on the high-pressure plunger pump. This high-precision sensor, installed at the outlet of the high-pressure plunger pump, has a sampling frequency set to 10 times per second, continuously capturing pressure fluctuation data during the feeding process. The material rheological characteristic curve is a continuous curve plotted with time on the horizontal axis and pressure on the vertical axis. The curve's trend directly reflects changes in the material's flow resistance; for example, an upward curve indicates increased material resistance, while a stable curve indicates a stable material flow state. After acquiring the rheological characteristic curve, the system calculates the slope change in real time. The slope change is the ratio of pressure change to time change between two adjacent sampling periods, accurately capturing sudden or gradual changes in material resistance. For example, a sudden increase in slope indicates a rapid rise in material resistance, which may indicate that the filter chamber is about to fill or the material flowability is deteriorating. When the slope change reaches a preset tolerance range, the feed pressure decision tree automatically matches the optimal feed pressure node. The preset tolerance range is a threshold interval (e.g., ±5%) calibrated based on the process requirements and material characteristics of the high-pressure diaphragm filter press, used to determine whether the slope change is a normal fluctuation. The optimal feed pressure node is a set of pressure parameters preset in the decision tree, with each node corresponding to a specific rheological characteristic state. For example, when the slope change exceeds the tolerance range and shows an upward trend, a node with a 10% pressure increase is matched; when the slope change is below the tolerance range and shows a downward trend, a node with an 8% pressure decrease is matched, ensuring that the feed pressure can adapt to changes in material resistance in a timely manner and maintain feeding efficiency. Meanwhile, the chamber filling threshold is dynamically corrected based on the filter plate deformation data. The chamber filling threshold is the critical pressure value used to determine whether the filter chamber of the high-pressure diaphragm filter press is full of material. The initial value is calibrated based on the filter chamber volume and material density. The filter plate deformation data is collected in real time by a deformation sensor installed on the edge of the filter plate. This sensor can capture the minute deformation of the filter plate under the action of the feed pressure (accuracy up to 0.01 mm), and the data sampling frequency is consistent with that of the pressure sensor. The correction logic is as follows: when the filter plate deformation data is within the safe range and the initial filling threshold is reached multiple times, if there is still room for the subsequent feed pressure to rise, the system will finely adjust and increase the filling threshold (e.g., increase it by 3% each time) to avoid prematurely determining that the filling is complete and resulting in insufficient filter chamber utilization. If the deformation data is close to the safety margin, the filling threshold will be appropriately reduced (e.g., decrease it by 5% each time) to prevent the filter chamber from being overfilled.When deformation monitoring data exceeds the safety margin, the system immediately generates a feed termination command for the high-pressure diaphragm filter press. The safety margin is the maximum deformation limit that the filter plate can withstand (determined by the strength of the filter plate material and equipment design standards, usually set to 0.5 mm). Exceeding this value means that the filter plate faces the risk of damage. After the termination command is generated, the high-pressure plunger pump immediately stops feeding. At the same time, the system records the current rheological characteristic data, filter plate deformation data, and feed pressure parameters, providing a reference for optimizing the feed parameters of similar materials in the future. This ensures that the entire feeding process efficiently utilizes the filter chamber space while strictly protecting equipment safety, laying a stable material foundation for the subsequent diaphragm extrusion dewatering process. The entire process achieves dynamic capture of material characteristics through real-time pressure sensing, precise pressure adjustment through decision tree node matching, and adaptive correction of the filling threshold through filter plate deformation feedback. Finally, the safety margin serves as the bottom line guarantee, forming a closed-loop control of perception-adjustment-correction-protection, allowing the feeding process to fully adapt to the dynamic changes of the material and the safety requirements of the equipment.

[0048] After the high-pressure diaphragm filter press completes feeding, an initial filter cake is formed in the filter chamber. However, the internal pore structure of the filter cake is loose and may have insufficient connectivity, making it difficult for free liquid to be released quickly. At this time, the pore reconstruction algorithm needs to execute a low-pressure dewatering command and dynamically optimize the dewatering process by establishing a free liquid release efficiency evaluation model to ensure efficient discharge of free liquid and lay the foundation for subsequent multi-stage pressing. The specific implementation method is as follows:

[0049] The core logic of the pore reconstruction algorithm for executing low-pressure dehydration commands is efficiency assessment, connectivity determination, and pre-pressure unblocking. First, a free liquid release efficiency assessment model is established. This model is a mathematical model that quantifies the discharge rate and total amount of free liquid (i.e., liquid that is not adsorbed by filter cake particles and can flow through the pores) in the filter cake. Its core input parameters are the initial filter cake impedance data and the real-time discharge flow rate. The output is the free liquid release efficiency value (ranging from 0 to 1, with the value closer to 1 indicating higher release efficiency). The model is trained and optimized using historical low-pressure dehydration data and can accurately reflect the correlation between the filter cake pore state and the release efficiency. The key input to this model is the initial filter cake impedance data. This data refers to the quantified resistance of the filter cake to liquid flow after feeding, collected by a pressure sensor built into the high-pressure diaphragm filter press. At the initial stage of low-pressure dewatering (pressure set at 0.3-0.5 MPa), the sensor captures the pressure loss of liquid penetrating the filter cake in real time. Combined with parameters such as filter cake thickness and filter cloth permeability, the initial filter cake impedance data is calculated. This data directly reflects the flow resistance within the pores of the filter cake; a higher impedance indicates a greater pore clogging tendency. Based on the initial filter cake impedance data, a pore connectivity index is calculated. This index describes the degree of interconnectivity between pores within the filter cake; a lower impedance indicates lower pore flow resistance and better connectivity, with the index closer to 1; conversely, a higher impedance indicates a higher index closer to 0. During the calculation, the model transforms abstract impedance values ​​into intuitive connectivity indices through a correlation formula (experimentally calibrated) between impedance data and pore connectivity. For example, when the initial filter cake impedance data reaches a certain critical value, the pore connectivity index is 0.7, indicating that most pores are connected. When the pore connectivity index falls below the critical threshold, the system generates a diaphragm pre-compression command. The critical threshold is the standard for judging whether pore connectivity meets the criteria for rapid release of free liquid. Considering the characteristics of filter cakes formed by different oily sludge types, it was experimentally calibrated to be 0.6 (range 0 to 1). When the index is below 0.6, it indicates poor pore connectivity in the filter cake, with some capillary channels blocked by fine particles, resulting in a significant decrease in free liquid release efficiency. The diaphragm pre-compression command is a control signal that triggers the diaphragm of the high-pressure diaphragm filter press to perform slight pre-compression and vibration. Its pressure parameter is set to 0.6-0.8 MPa, which neither damages the initial filter cake structure nor obstructs blocked channels through slight compression and vibration. Subsequently, the capillary channels are cleared by low-frequency vibration mode. Low-frequency vibration mode refers to the diaphragm reciprocating at a low frequency of 5-10 Hz. This frequency can generate enough vibration force to break up the fine particles that block the pores, while avoiding the collapse of the filter cake skeleton caused by high-frequency vibration. The capillary channels are the liquid flow channels with tiny diameters inside the filter cake and are the main path for the discharge of free liquid. Their blockage is the core reason for the low efficiency of free liquid release.During vibration, the slight reciprocating motion of the diaphragm is transmitted to the filter cake, loosening and dislodging particles blocking the capillary channels, thus reopening the pore connectivity pathways. At this point, the pore connectivity index gradually increases, and the free liquid release efficiency improves simultaneously. When the pore connectivity index rises above the critical threshold and the free liquid release efficiency value stabilizes above 0.8, the low-frequency vibration mode stops, and the low-pressure dehydration command continues until the free liquid release rate drops to a preset lower limit (e.g., less than 5 liters per minute), at which point the free liquid data stream release is complete. Throughout the process, the dehydration status is monitored in real time using a free liquid release efficiency evaluation model. With the pore connectivity index as the core criterion, the low-frequency vibration mode precisely unblocks the capillary channels, ensuring both efficient free liquid release and protecting the integrity of the filter cake structure, providing a good pore foundation for the smooth execution of subsequent multi-stage pressing protocols.

[0050] After the low-pressure dehydration release of free liquid data stream is completed, although most of the free liquid has been discharged from the filter cake, its internal skeleton structure is still relatively loose, with a disordered pore distribution and some channels easily blocked, making it difficult to meet the requirements of subsequent high-pressure removal of bound water. At this time, the first round of medium-pressure command of the multi-stage pressing protocol needs to activate the filter cake skeleton recombination program. Through precise control of pressure and risk prediction, the filter cake particles are rearranged to form a regular drainage channel network, laying the structural foundation for the second round of high-pressure dehydration. The specific implementation method is as follows:

[0051] The core logic of the first round of medium-pressure command activation of the filter cake skeleton reorganization program is liquid content monitoring—risk prediction—gradual pressurization—structural reorganization. First, it is clarified that the medium-pressure command is a medium-pressure control signal output by the high-pressure diaphragm filter press. The pressure value is calibrated to 1-2 MPa, providing sufficient force to move the filter cake particles without causing the filter cake skeleton to collapse due to excessive pressure. The filter cake skeleton reorganization program is the core program controlling the diaphragm extrusion rhythm and pressure gradient. Its goal is to break the initial loose particle accumulation state of the filter cake, guiding the particles to re-aggregate in a direction conducive to drainage, forming a continuous and stable skeleton structure. After the program is activated, the skeleton collapse risk coefficient is calculated based on real-time liquid content feedback. Real-time liquid content refers to the mass percentage of remaining liquid in the filter cake after low-pressure dehydration, collected in real-time by a microwave humidity sensor installed inside the filter plate, with a sampling frequency set to 5 seconds / time. The data directly reflects the moisture content and structural stability of the filter cake (the higher the liquid content, the softer the filter cake, and the greater the risk of skeleton collapse). The filter cake collapse risk coefficient is a quantitative indicator of the probability of structural damage during pressurization. Its value ranges from 0 to 1; the closer the coefficient is to 1, the higher the collapse risk. The calculation uses real-time liquid content as the core input, combined with auxiliary parameters such as initial filter cake impedance data and filter cake thickness change rate, and is derived through a preset risk assessment model. For example, when the liquid content is 40%, the calculated risk coefficient based on impedance data is 0.3, indicating a low pressurization risk under the current condition. When the filter cake collapse risk coefficient is within the safe range, the system initiates a gradual pressurization module. The safe range is the risk coefficient range calibrated through numerous experiments (typically 0.2-0.6). Within this range, the filter cake has sufficient plasticity to support particle rearrangement and can withstand the pressurization force without collapsing. If the coefficient is below 0.2, it indicates that the filter cake is too dry and the structure is too rigid, and the pressurization rate can be appropriately increased. If the coefficient is above 0.6, pressurization is paused, and the current medium pressure is maintained until the liquid content decreases and the risk coefficient falls back to the safe range. The progressive pressurization module is the actuator that controls the gradual increase of diaphragm pressure. Its core function is to prevent sudden pressure changes that could cause the filter cake skeleton to collapse instantly. The specific pressurization logic is as follows: based on the initial medium pressure, the pressure is steadily increased in increments of 0.1-0.2 MPa every 30 seconds, while continuously monitoring changes in liquid content and risk factor. If the risk factor shows an upward trend, pressurization is immediately paused and the current pressure is maintained for 30 seconds until the filter cake structure adapts before resuming pressurization. During the progressive pressurization process, the filter cake particles gradually rearrange under pressure to form a drainage channel network. This drainage channel network is a continuous porous system formed by the orderly accumulation of particles within the filter cake. The channels are distributed in a mesh pattern and are interconnected, providing a smooth path for the discharge of bound water during subsequent high-pressure dehydration.During the specific arrangement process, the gradual increase in pressure pushes the loose particles in the filter cake to gather in the direction of balanced force, and the originally messy pores are sorted into regular channels. At the same time, the gaps between particles are compacted and reduced, which not only improves the density of the filter cake, but also ensures the connectivity of the channels. When the pressure is increased to the preset upper limit of medium pressure (2 MPa) and the liquid content is stably reduced to below 30%, the filter cake skeleton is reorganized and the drainage channel network is formed. At this time, the first round of medium-pressure pressing ends, and the system is ready to activate the second round of high-pressure command. The entire process realizes dynamic risk prediction through real-time liquid content feedback, and ensures the safety of skeleton reorganization with gradual pressure increase. The final drainage channel network not only improves the structural stability of the filter cake, but also provides a key guarantee for the efficient removal of bound water in the subsequent process, so that the upstream and downstream links of the multi-stage pressing protocol are efficiently connected, and the moisture content of the filter cake is steadily reduced.

[0052] After the first round of medium-pressure commands completes the reorganization of the filter cake skeleton and forms a regular drainage channel network, the remaining liquid in the filter cake is mostly bound water. This type of water is adsorbed on the surface of the filter cake particles or trapped in tiny pores, and it is difficult to remove efficiently by conventional high-pressure extrusion alone. Therefore, the second round of high-pressure commands needs to use a directional dehydration strategy to accurately remove the bound water data packets, ensuring that the moisture content of the filter cake is reduced to the target requirement. The specific implementation method is as follows:

[0053] The core logic of the second round of high-pressure command-directed removal of bound water is microscopic scanning—regional positioning—directional extrusion—compliance verification. First, a scanning operation of the filter cake microstructure topology map is performed. The filter cake microstructure topology map is a visual map reflecting the arrangement of particles, the distribution of pores, and the location of liquid accumulation inside the filter cake. It can intuitively show the areas where bound water accumulates. The scanning is achieved through the low-field nuclear magnetic resonance imaging module built into the high-pressure diaphragm filter press. This module penetrates the filter cake in a non-invasive manner and collects microscopic data inside the filter cake at a frequency of 10 seconds / time. After system processing, a real-time updated topology map is generated to ensure accurate capture of the dynamic distribution of bound water. Based on the topology map generated by scanning, the system automatically identifies the coordinates of bound water enrichment areas. These areas refer to regions where bound water is concentrated within the filter cake, typically corresponding to areas with dense pores and small pore sizes. Removing bound water from these areas is far more difficult than from other areas. The coordinates are based on three-dimensional spatial positioning parameters established by the filter chamber where the filter cake is located. By mapping the topology map to the physical space of the filter chamber, the specific location of the enrichment area (such as the upper left part of the filter cake, the middle part of the middle layer, etc.) is clearly defined, with coordinate accuracy controlled at the centimeter level to ensure that subsequent extrusion operations can accurately target this area. Subsequently, directional dewatering is implemented by adjusting the diaphragm extrusion direction angle. The pressure value of the second round of high-pressure commands is calibrated to 2-4 MPa. This pressure provides sufficient force to break the adsorption force between the bound water and the filter cake particles without damaging the established drainage channel network due to excessive pressure. The diaphragm extrusion direction angle refers to the angle between the direction of the high-pressure diaphragm's extrusion force and the filter cake plane, precisely controlled by the servo drive module of the high-pressure diaphragm filter press, with an adjustment range of 0-30 degrees. During specific adjustments, the system calculates the optimal extrusion angle based on the coordinates of the bound water enrichment area. For example, for the enrichment area in the middle layer of the filter cake, the extrusion angle is adjusted to 15 degrees, concentrating the extrusion force on this area to push the bound water away from the particle surface, quickly flowing into the surrounding drainage channels and being discharged. Simultaneously, the diaphragm extrudes the filter cake in a periodic expansion and contraction pattern (expansion frequency 5 Hz) to avoid continuous high pressure clogging the filter cake pores, ensuring the continuity of bound water removal. During directional dewatering, the system continuously tracks the filter cake moisture content changes through a real-time liquid content monitoring module. When the monitoring data shows that the overall moisture content of the filter cake drops to a preset threshold (experimentally calibrated to below 60%, the specific value can be adjusted according to the requirements of sludge treatment), the directional removal of bound water is considered complete. At this point, the system automatically generates a filter cake moisture content compliance verification code. The filter cake moisture content compliance verification code is a unique digital identifier that contains key information such as dehydration time, final moisture content, and treatment effect of enrichment area. It is used to confirm that the batch of filter cake has met the dehydration standard and serves as a process trigger credential for subsequent filter cake output and oil-water mixture treatment, ensuring the traceability and standardization of the entire low-temperature sludge reduction process.The entire process achieves precise positioning of bound water through microscopic scanning, targeted removal through adjustable high-pressure extrusion, and final verification of effectiveness using a compliance verification code. This ensures the high efficiency of bound water removal while avoiding energy waste and filter cake structure damage caused by blind high-pressure extrusion. It perfectly connects the first round of skeleton reconstruction with the subsequent product separation process, promoting the achievement of the filter cake reduction target.

[0054] After the high-pressure diaphragm filter press completes the dewatering of the filter cake and outputs the filter cake solid, the oil-water mixture released during the filtration process remains in a stable emulsion state. The oil droplets are encapsulated by the emulsion membrane and evenly dispersed in the aqueous phase, forming emulsion data packets that are difficult to separate naturally. Direct treatment would result in a waste of oily resources and increase the difficulty of wastewater purification. Therefore, it is necessary to break down the emulsion structure through the action function of a demulsifier, and then separate the oily and wastewater using an air flotation machine. The specific implementation method is as follows:

[0055] The core logic of the demulsifier action function for deciphering emulsion data packages involves interface energy level analysis, potential monitoring, demulsification triggering, and oil enrichment. First, the oil-water interface energy level analysis is performed. The demulsifier action function is a mathematical model that quantifies the relationship between demulsifier dosage and emulsion demulsification effect, dynamically outputting the optimal dosage strategy based on emulsion characteristics. The emulsion data package contains key parameters such as oil droplet size distribution, emulsion film strength, and Zeta potential, reflecting the stable state of the emulsion. The oil-water interface energy level analysis analyzes the energy state of molecular interactions at the oil-water interface, determining emulsion stability by calculating the interface free energy, providing a theoretical basis for demulsifier dosage. The analysis process uses parameters such as emulsion film strength and oil droplet spacing from the emulsion data package as input, outputting interface energy level values. Higher energy level values ​​indicate a more stable emulsion, requiring a larger demulsifier dosage. After analysis, the Zeta potential distribution spectrum of the emulsion was collected using an online potential analyzer integrated into the wastewater tank. Zeta potential is a key indicator characterizing the surface charge of oil droplets in an emulsion. Oil droplets typically carry a negative charge; the stronger the charge, the greater the electrostatic repulsion between droplets, and the more stable the emulsion. The Zeta potential distribution spectrum is a curve with the potential value on the horizontal axis and the percentage of oil droplets at the corresponding potential on the vertical axis, visually presenting the distribution of surface charge on the oil droplets. For example, when the peak value is concentrated around -30mV, it indicates that most oil droplets carry a strong negative charge, and the emulsion has high stability. During the data collection process, the online potential analyzer continuously monitored at a frequency of 5 seconds per measurement to ensure real-time capture of the dynamic changes in Zeta potential during demulsification, providing data support for judging the demulsification effect. Simultaneously, a demulsifier was added to the wastewater tank via an automatic demulsifier dosing device. Cationic demulsifiers (such as polyquaternium salts and cationic polyacrylamide) were selected, whose positively charged groups in their molecular structure can neutralize the negative charge on the surface of oil droplets, disrupting the stability of the emulsion film and promoting oil droplet aggregation. The initial dosage is set at 0.1%-0.3% of the emulsion volume, and then gradually adjusted according to changes in the Zeta potential distribution spectrum, with each adjustment not exceeding 0.05% to avoid excessive dosage increasing treatment costs or causing secondary pollution of wastewater. When the demulsifier dosage causes the spectrum peak to migrate to the demulsification critical zone, the system immediately triggers the phase separation start signal. The demulsification critical zone is the experimentally calibrated Zeta potential range (usually -10mV to 10mV). At this point, the surface charge of the oil droplets is close to neutral, the electrostatic repulsion force is greatly reduced, the emulsion film ruptures, and the oil droplets begin to aggregate to form larger oil droplets. The system tracks the peak position of the spectrum in real time. Once the peak enters the critical zone, the demulsifier dosage is immediately stopped, and the phase separation start signal is triggered simultaneously, instructing the air flotation unit to start operation, ensuring seamless connection between the demulsification and phase separation processes. After the air flotation machine is started, its density field analysis module begins operation. This module is the core detection component of the air flotation machine. Through multiple built-in density sensors, it collects density data from different areas within the machine, generating a density field distribution map to determine the bonding state between oil droplets and bubbles. The oil droplet density is approximately 0.85 g / cm³. 3Less than water (1g / cm³) 3 Bubble density (close to 0 g / cm³) 3 The density field analysis module calculates the density differences between different regions to quantify the probability of oil droplet and bubble combination. A higher probability indicates that the oil droplets are more easily carried to the surface by the bubbles, resulting in better oil-sludge separation. When the probability of oil droplet and bubble combination reaches a preset threshold (experimentally calibrated to be above 85%), the density field analysis module generates an oil-sludge enrichment command. This command triggers the oil skimming device of the flotation machine to scrape the oil that has risen to the surface into a dedicated oil-sludge storage tank. Simultaneously, the volume and distribution of bubbles generated by the flotation machine are adjusted (controlling the bubble diameter to 50-100 micrometers) to ensure that uncombined oil droplets can continue to combine with the bubbles, thus improving the oil-sludge recovery rate. The entire process uses oil-water interface energy level analysis to precisely guide the addition of demulsifier, Zeta potential monitoring to control the timing of demulsification, and density field analysis to achieve efficient enrichment of sludge and oil. This ensures the complete dissolution of the emulsion and maximizes the recovery of sludge and oil resources, providing a high-quality pretreatment foundation for subsequent sludge and oil recycling and wastewater purification.

[0056] Please see Figure 2 As shown, a second objective of this invention is to provide a system for implementing a low-temperature sludge reduction method based on high-pressure diaphragm filtration, comprising any of the above-mentioned methods, including:

[0057] The low-temperature chemical conditioning unit 1 conditioning tank integrates a temperature maintenance module and a two-stage dosing subsystem. The surfactant dosing pump is connected to the anionic surfactant storage tank, the dehydrating agent dosing pump is connected to the compound chemical storage tank, and the stirring mechanism adjusts the speed in real time according to the oil droplet particle size feedback.

[0058] The high-pressure feed control unit 2 high-pressure plunger pump is equipped with a real-time pressure sensor array. Its rheological characteristic analysis module is linked with the feed pressure decision tree. When the filter plate deformation monitor detects that the deformation data exceeds the safety margin, it triggers a feed termination command.

[0059] The multi-stage pressing execution unit 3 high-pressure diaphragm filter press has a built-in periodic telescopic diaphragm mechanism. After the diaphragm extrusion controller activates the low-pressure dewatering program based on the filter cake impedance data, it executes the skeleton recombination command and the bound water directional removal command in stages through the progressive pressurization module.

[0060] The oil-water deep separation unit 4 integrates an online potential analyzer and an automatic demulsifier dosing device for the wastewater tank. When the Zeta potential peak migrates to the critical zone, the air flotation machine is started. The density field control module of the air flotation machine generates a waste-oil enrichment command through the probability model of bubble and oil droplet combination.

[0061] The intelligent product management unit 5 is equipped with a calorific value monitor that is linked to the oil refining valve. The sewage treatment plant interface is equipped with a turbidity sensor that is linked to the water purification agent dosing pump in a closed loop. When the second derivative of the turbidity decay curve approaches zero, the drainage valve is closed.

[0062] This invention injects surfactants and compound dehydrating agents into a conditioning tank to deconstruct the oil-sludge-water emulsion system and reconstruct the solid-phase coalescing network, outputting pretreated materials. A high-pressure plunger pump dynamically matches the feed pressure according to the material's rheological characteristics, and terminates the feed based on filter plate deformation feedback. A high-pressure diaphragm filter press dehydrates and opens pores under low pressure, reconstructs the filter cake skeleton under medium pressure, and removes bound water under high pressure, outputting a filter cake with low moisture content. The oil-water mixture produced by the filter press is demulsified by a demulsifier, and then the sludge and wastewater are separated by an air flotation machine, achieving sludge recovery and wastewater purification. This efficiently reduces oil sludge volume and recovers resources, taking into account both environmental protection and economic efficiency.

[0063] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for reducing oil sludge volume based on high-pressure diaphragm filtration, characterized in that: Includes the following steps: S1. Receive the initial physical property parameter set of oily sludge through the conditioning tank, inject the surfactant action coefficient and dehydrating agent action coefficient into the initial physical property parameter set of oily sludge, generate an oil-sludge-water three-phase separation optimization data package, where the surfactant action coefficient is used to deconstruct the interfacial tension data matrix of the emulsion system, the dehydrating agent action coefficient is used to reconstruct the solid phase coalescence topology network, and output the low temperature pretreatment material data stream. The surfactant action coefficient is demulsified by deconstructing the interfacial tension data matrix of the emulsion system. Specifically, an oil phase dispersion evaluation model is established in the conditioning tank, and the amount of anionic surfactant added is converted into the interfacial tension decay gradient. When the value of the oil droplet size data packet drops to a preset proportion of the initial value, it is determined that the oil-sludge-water stable system has been destroyed, and an oil-sludge-water three-phase separation optimization data packet is generated. When the dehydrating agent action coefficient reconstructs the solid-phase coalescence topology network, the floc strength prediction algorithm is activated simultaneously: the charge neutralization efficiency parameter is generated based on the compounding ratio of polyaluminum chloride and polyacrylamide, and the floc growth trajectory is simulated by combining the stirring speed data stream. When the floc density data reaches the solid-liquid separation threshold, the low-temperature pretreatment material data stream output command is triggered. S2. Receive the low-temperature pretreatment material data stream, construct a pressure gradient model by the high-pressure plunger pump, and match the feed pressure decision tree based on the material rheological characteristics. When the feed pressure decision tree node triggers the chamber filling threshold, generate a high-pressure diaphragm filter press feed termination command. The feed pressure decision tree adopts a dynamic rheological response mechanism: the material rheological characteristic curve is collected by the real-time pressure sensor of the high-pressure plunger pump. When the curve slope change value touches the preset tolerance range, the optimal feed pressure node is automatically matched. The chamber filling threshold is dynamically corrected according to the filter plate deformation data feedback. When the deformation monitoring data exceeds the safety margin, a feed termination command is generated. S3. Establish a pore reconstruction algorithm for diaphragm periodic extrusion. Execute low-pressure dehydration command based on initial filter cake impedance data to release free liquid data stream. Then activate multi-stage pressing protocol based on real-time liquid content feedback. The first pressing protocol generates medium-pressure command to cause filter cake skeleton to collapse and reorganize. The second pressing protocol generates high-pressure command to directionally remove bound water data packets and finally output filter cake entity. When the pore reconstruction algorithm executes the low-pressure dehydration command, a free liquid release efficiency evaluation model is established: the pore connectivity index is calculated based on the initial filter cake impedance data, and when the index is lower than the critical threshold, a diaphragm pre-compression command is generated to clear the capillary channels through a low-frequency vibration mode. S4. Extract phase separation features from the oil-water mixture released by pressure filtration, break the emulsion data packet structure through the demulsifier action function, and then separate the sludge oil data cluster and wastewater data cluster through the density field analysis module of the air flotation machine, and output sludge oil recovery instructions and wastewater purification instructions. When the demulsifier action function decrypts the emulsion data packet, it performs oil-water interface energy level analysis: The Zeta potential distribution spectrum of the emulsion is collected. When the amount of demulsifier added causes the peak value of the spectrum to shift to the demulsification critical region, a phase separation start signal is triggered. The density field analysis module generates a sludge-oil enrichment command by monitoring the probability of bubble-oil droplet combination.

2. The method for reducing oil sludge volume based on high-pressure diaphragm filtration according to claim 1, characterized in that: The first round of medium-pressure command in the multi-stage pressing protocol activates the filter cake skeleton recombination program. Based on the real-time liquid content feedback, the skeleton collapse risk coefficient is calculated. When the coefficient is within the safe range, the progressive pressurization module is activated to rearrange the filter cake particles to form a drainage channel network.

3. The method for reducing oil sludge volume based on high-pressure diaphragm filtration according to claim 2, characterized in that: The next round of high-pressure command-directed removal of bound water data packets includes: Scan the microstructure topology of the filter cake, identify the coordinates of the bound water enrichment area, and implement directional dehydration by adjusting the direction and angle of the diaphragm extrusion. After dehydration, a verification code for filter cake moisture content meeting the standard is generated.

4. The method for reducing oil sludge volume based on high-pressure diaphragm filtration according to claim 1, characterized in that: The logic for generating wastewater purification instructions includes: Extract the turbidity decay curve of the aqueous phase. When the second derivative of the curve approaches zero, the separation is considered complete. The waste oil recovery command triggers the waste oil calorific value assessment model. When the calorific value data is higher than the reuse threshold, an oil refining command is generated.

5. A system for implementing a low-temperature sludge reduction method based on high-pressure diaphragm filtration as described in any one of claims 1-4, characterized in that, include: Low-temperature chemical conditioning unit (1) The conditioning tank integrates a temperature maintenance module and a two-stage dosing subsystem. The surfactant dosing pump is connected to the anionic surfactant storage tank, the dehydrating agent dosing pump is connected to the compound chemical storage tank, and the stirring mechanism adjusts the speed in real time according to the oil droplet size feedback. High pressure feed control unit (2) The high pressure plunger pump is equipped with a real-time pressure sensor array. Its rheological characteristic analysis module is linked with the feed pressure decision tree. When the filter plate deformation monitor detects that the deformation data exceeds the safety margin, it triggers the feed termination command. Multi-stage pressing execution unit (3) The high-pressure diaphragm filter press has a built-in periodic telescopic diaphragm mechanism. After the diaphragm extrusion controller activates the low-pressure dewatering program based on the filter cake impedance data, it executes the skeleton reorganization command and the bound water directional removal command in stages through the progressive pressurization module. The oil-water deep separation unit (4) integrates an online potential analyzer and an automatic demulsifier dosing device for the sewage tank. When the peak value of the Zeta potential migrates to the critical zone, the air flotation machine is started. The density field control module of the air flotation machine generates a sludge-oil enrichment command through the probability model of the combination of bubbles and oil droplets. The product intelligent management unit (5) is equipped with a calorific value monitoring instrument and linked oil refining valves. The sewage treatment plant interface is equipped with a turbidity sensor and a closed-loop linkage water cleaning agent dosing pump. When the second derivative of the turbidity decay curve approaches zero, the drainage valve is closed.

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