Energy efficiency optimization method and system for slag discharge in coal gas debenzene process

Abstract

The application relates to the technical field of coal chemical engineering, and discloses an energy efficiency optimization method and system for slag discharge in a coal gas debenzene process. The method uses a reaction kinetics model to calculate a wash oil slag generation rate and integrates the cumulative slag amount. When the slag amount reaches a threshold value, a mapping relationship between the slag discharge time length and the target quality is established through a fluid mechanics model to realize quantitative slag discharge. In the material conveying process, a comprehensive emulsification risk index is constructed based on the shear and turbulent flow characteristics of the pipeline pressure signal, and the flow control or hot lean oil dilution mode is dynamically switched according to the index. The risk judgment threshold value is adaptively corrected by using the separation quality feedback of the mechanized clarifying tank. Through the coupling of the multi-physical field model and the closed-loop feedback, the quantitative control of the slag discharge process and the emulsification inhibition of the conveying process are realized, and the separation efficiency and the operation stability of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of coal chemical process technology, specifically to an energy efficiency optimization method and system for slag discharge during the benzene removal process of coal gas. Background Technology

[0002] In the crude benzene recovery section of the coal gas purification process, wash oil is recycled as an absorbent. With increasing operating time, high-boiling-point components and absorbed impurities in the wash oil undergo thermal condensation and polymerization reactions in the high-temperature environment of the regenerator, forming high-viscosity polymer residue. To maintain the absorption performance of the wash oil and prevent system blockage, the residue-containing material at the bottom of the regenerator needs to be periodically discharged and transported via a sludge tank to a mechanized clarification tank for oil, water, and residue separation.

[0003] Existing slag discharge control mainly relies on manual experience or fixed time intervals. However, the slag formation rate during wash oil regeneration is not constant; it is nonlinearly affected by various variables such as crude benzene production load, coke production, and regeneration temperature. Using a fixed-cycle slag discharge method often fails to match the actual slag formation rate, leading to two extreme situations: first, delayed slag discharge causes slag and coking at the bottom of the regenerator, reducing heat exchange efficiency and even clogging the tower; second, excessively frequent slag discharge results in a large amount of effective wash oil being discharged with the residue, increasing material consumption and the load on subsequent separation processes.

[0004] Furthermore, in the conveying of slag-containing materials, due to the high viscosity and high solids content of the materials, forced conveying using high-pressure steam or pumps is commonly employed in industrial settings. Existing conveying control systems lack real-time monitoring of the fluid state within the pipes, often prioritizing high flow rates or pressure differentials to prevent blockages. This blind, high-intensity conveying generates severe shearing and turbulence within the pipeline, easily leading to deep emulsification of the oil-water-slag three-phase mixture. Once a stable emulsion forms, its gravity settling separation effect in downstream mechanized clarification tanks will significantly decrease, resulting in excessive water content in the separated clarified liquid or excessively high oil content in the discharged waste residue. Current control schemes cannot automatically adjust the conveying strategy based on changes in rheological characteristics during the conveying process, making it difficult to achieve a balance between preventing blockages and preventing emulsification. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an energy efficiency optimization method and system for slag discharge during the benzene removal process of coal gas. This solves the problems of slag accumulation or waste of wash oil caused by the mismatch between slag discharge volume and slag production rate in the wash oil regeneration system, as well as the problem of deep emulsification caused by uncontrolled shearing during the transportation of high-viscosity slag-containing materials, which reduces separation efficiency.

[0006] To achieve the above objectives, the first aspect of the present invention provides an energy efficiency optimization method for slag discharge during the benzene removal process of coal gas, in order to solve the technical problems of lagging control of slag discharge volume in the washing oil regeneration system, easy blockage of the conveying pipeline, and low separation efficiency due to emulsification.

[0007] The energy efficiency optimization method for slag discharge during the benzene removal process of coal gas includes the following steps:

[0008] The operating parameters of the coal gas benzene removal system are collected, the instantaneous slag formation rate of the circulating wash oil is calculated using a reaction kinetic model, and the current cumulative slag amount is obtained by integrating the instantaneous slag formation rate over time.

[0009] When the accumulated slag reaches the preset slag discharge threshold, the target discharge quality for this slag discharge is determined. A mapping relationship between the opening time of the slag discharge valve and the target discharge quality is established based on the fluid dynamics model. The target discharge quality is converted into the target opening time of the slag discharge valve using the mapping relationship. The slag discharge valve is then controlled to open for the time calculated from the target opening time to quantitatively discharge the slag-containing material into the dilute slag tank.

[0010] During the transportation of slag-containing materials, the pipeline pressure signal of the inlet conveying pipeline of the slag tank is collected in real time, and the characteristic components representing shear and turbulence are extracted to construct a comprehensive emulsification risk index. Based on the comprehensive emulsification risk index, a flow regulation strategy or a dilution decoupling strategy is executed for the slag-containing materials.

[0011] Obtain the separation quality data of the clarified liquid at the outlet of the mechanized clarifier, and adaptively adjust the risk judgment threshold of the comprehensive emulsification risk index for the next batch control based on the deviation between the separation quality data of the clarified liquid at the outlet and the target value.

[0012] In a further embodiment, the deviation between the separation quality data and the target value includes the deviation between the measured water content or slag content of the outlet clear liquid and the preset process allowable upper limit value.

[0013] In a further embodiment, the calculation of the instantaneous slag formation rate of the circulating wash oil using a reaction kinetic model specifically includes: collecting the crude benzene production rate to characterize the production load of the coal gas debenzene system, and collecting the wash oil regeneration temperature to characterize the reaction kinetic activity; inputting the crude benzene production rate and the wash oil regeneration temperature into a pre-constructed Arrhenius-form reaction kinetic equation to calculate the instantaneous slag formation rate of the circulating wash oil.

[0014] In a further embodiment, the mapping relationship is constructed based on the flow characteristics of the slag discharge valve, specifically including: determining the instantaneous mass flow rate through the slag discharge valve based on the real-time pressure difference before and after the slag discharge valve and the density parameters of the slag-containing material; and determining the operating duration of the slag discharge valve based on the ratio of the target discharge mass to the instantaneous mass flow rate.

[0015] In a further embodiment, the method further includes a model parameter correction step based on liquid level feedback:

[0016] After the slag discharge valve is closed and the liquid level in the slag tank is stable, the actual slag discharge mass is calculated by using the geometric dimensions of the slag tank and the liquid level increment, and the actual slag discharge mass is compared with the theoretical target mass.

[0017] When there is a deviation between the two, the comprehensive flow coefficient in the fluid dynamics model is adjusted to compensate for the change in the flow capacity of the slag discharge valve caused by wear or changes in the viscosity of the medium.

[0018] In a further embodiment, the comprehensive emulsification risk index is a dimensionless indicator used to quantify the emulsification tendency of fluids within pipelines.

[0019] The extraction and construction of the feature components include: separating the DC component in the pipeline pressure signal as a static pressure difference component to characterize the average shear stress experienced by the fluid in overcoming pipe wall friction; and separating the AC component in the pipeline pressure signal as a dynamic turbulence component to characterize the intensity of turbulence fluctuations inside the fluid.

[0020] The comprehensive emulsification risk index is a weighted superposition of the static pressure difference component and the dynamic turbulence component to comprehensively evaluate the breakage risk of the fluid during transportation.

[0021] In a further embodiment, the flow regulation strategy or dilution decoupling strategy specifically includes the following hierarchical control strategy:

[0022] Set safety thresholds and critical thresholds to divide risks into low-risk, medium-risk, and high-risk ranges.

[0023] In the low-risk zone, a flow regulation strategy targeting maximum flow rate is implemented;

[0024] In the medium-risk range, a pressure stabilization and suppression strategy aimed at suppressing pressure fluctuations is implemented, which reduces turbulence intensity by limiting the opening and rate of change of the steam regulating valve.

[0025] In the high-risk range, the dilution decoupling strategy is executed.

[0026] In a further embodiment, the dilution decoupling strategy serves as a proactive intervention to address severe emulsification risks, and its specific steps include:

[0027] Cut off or lock the steam source for conveying, and open the hot lean oil bypass valve connected to the thin slag tank to inject hot lean oil;

[0028] The dilution effect of hot lean oil is used to reduce the dynamic viscosity and solid content of the mixture in the tank, thereby reducing the driving pressure difference and shear force required for fluid transportation until the comprehensive emulsification risk index returns to the safe range.

[0029] In a further embodiment, the adaptive correction employs a batch control strategy to address the control deviation caused by the delayed feedback of the separation effect.

[0030] The batch control strategy specifically includes using the water content or slag content of the clarified liquid at the outlet of the mechanized clarification tank as a key quality characteristic.

[0031] If the key quality characteristics of the current batch are worse than the process target value, the risk judgment threshold for the next batch will be lowered to trigger the voltage stabilization and suppression strategy or the dilution and decoupling strategy in advance.

[0032] If the key quality characteristics are better than the process target values, the risk assessment threshold for the next batch will be maintained or relaxed to optimize delivery efficiency while ensuring quality.

[0033] In a further embodiment, when adaptively correcting the risk determination threshold, a filtering mechanism with dead zone characteristics and a boundary constraint mechanism are introduced.

[0034] The filtering mechanism is used to ignore quality fluctuations with amplitude within the dead zone range and only responds when the deviation exceeds the dead zone range.

[0035] The boundary constraint mechanism is used to limit the modified risk assessment threshold to a preset maximum and minimum value.

[0036] This method achieves slag discharge control based on the coupled calculation of reaction kinetics and fluid dynamics models. During the slag quantity monitoring stage, by collecting parameters such as crude benzene production rate, coke production rate, and bottom temperature of the wash oil regenerator, a functional relationship between raw material load, thermal condensation reaction temperature, and slag formation rate is established. The instantaneous slag formation rate is then integrated over time to obtain the cumulative slag quantity characterizing the degree of wash oil aging.

[0037] When the accumulated slag reaches a preset threshold, the quantitative slag discharge logic is triggered. For the condition of pressure fluctuation at the bottom of the wash oil regenerator, a fluid dynamics model is used to calculate the mass flow rate of the slag discharge valve in real time. A mapping relationship between the valve opening duration and the target discharge mass is established based on real-time differential pressure data. By dynamically adjusting the opening duration to compensate for flow rate deviations caused by differential pressure changes, quantitative discharge of slag-containing materials is achieved.

[0038] During the material transport to the mechanized clarification tank, a risk control strategy based on pressure signal characteristic analysis is adopted. Pressure data of the transport pipeline is collected in real time, and the static pressure difference component characterizing the fluid overcoming friction resistance and the dynamic turbulence component characterizing the intensity of internal fluid pulsation are extracted. A comprehensive emulsification risk index is constructed through weighted calculation to quantify the degree of shearing and fragmentation and emulsification tendency of the fluid during the transport process.

[0039] The system implements tiered control based on the range of the comprehensive emulsification risk index: maintaining the set flow rate in the low-risk range; suppressing pressure fluctuations by limiting the opening of the steam regulating valve in the medium-risk range; and implementing a dilution decoupling mode in the high-risk range. The dilution decoupling mode cuts off or locks the conventional conveying power source, opens the hot lean oil bypass valve to inject high-temperature hot lean oil, and utilizes the hot lean oil to reduce the dynamic viscosity and solids content of the mixture, improving material flowability and reducing conveying resistance.

[0040] The method also includes a closed-loop correction mechanism based on batch separation performance. Separation quality data from the mechanized clarifier is acquired, and the deviation between the actual value and the process target value is calculated. This deviation is then used to correct the comprehensive emulsification risk index threshold in the next batch control logic, thereby offsetting the impact of equipment characteristic drift or changes in raw material composition on control accuracy.

[0041] A second aspect of the present invention provides an energy efficiency optimization system for slag discharge during benzene removal from coal gas, the system executing the energy efficiency optimization method for slag discharge during benzene removal from coal gas, the system comprising:

[0042] The field equipment components include a wash oil regenerator, a thin sludge tank, a mechanized clarification tank, and actuators on the connecting pipelines; the actuators include a sludge discharge valve located at the bottom of the wash oil regenerator, a steam regulating valve at the inlet of the thin sludge tank, and a hot lean oil bypass valve;

[0043] The central control unit, which is communicatively connected to the field device components, is configured with:

[0044] The data acquisition module is used to obtain the production load and temperature of the crude benzene section as input variables, and to acquire the real-time pipeline pressure signal of the delivery pipeline.

[0045] The triggering module is used to run the reaction kinetics model to monitor the slag accumulation state and control the slag discharge valve to perform quantitative slag discharge based on the fluid dynamics model.

[0046] The delivery module extracts feature components based on the pipeline pressure signal and calculates a comprehensive emulsification risk index. Based on the comprehensive emulsification risk index, it coordinates the adjustment of the steam regulating valve and the hot lean oil bypass valve to achieve graded delivery under different risk levels.

[0047] The calibration module is used to iteratively correct the risk assessment threshold in the conveying module based on the separation quality data of the mechanized clarification tank.

[0048] This invention provides an energy efficiency optimization method and system for slag discharge during benzene removal from coal gas, which has the following beneficial effects:

[0049] This invention calculates the accumulated slag volume in real time using a reaction kinetic model and dynamically compensates for the opening duration of the slag discharge valve using a fluid dynamics model. This control method uses real-time differential pressure data to correct the flow rate calculation, solving the problem of slag volume deviation caused by pressure fluctuations at the bottom of the wash oil regenerator. It prevents slag accumulation and coking in the regenerator due to delayed slag discharge and avoids waste of wash oil due to excessive slag discharge, thus achieving quantitative control of the slag discharge process.

[0050] This invention utilizes the static shear component and dynamic turbulence component of the pressure signal from the delivery pipeline to construct an emulsification risk index, and introduces a hot lean oil dilution decoupling mechanism based on this index. This mechanism can identify emulsification tendencies during the delivery process online, and reduce the solids content and flow resistance of high-viscosity materials by injecting high-temperature hot lean oil. This prevents blockage of the delivery pipeline and reduces the degree of emulsification after the material enters the mechanized clarification tank, thereby improving the efficiency of subsequent oil-water separation and the quality of the clarified liquid.

[0051] This invention establishes a threshold adaptive correction logic based on downstream separation quality feedback. The system automatically adjusts the emulsification risk assessment parameters for the next batch based on the actual separation effect of the mechanized clarifier. This mechanism effectively eliminates control model drift caused by equipment wear, raw material composition fluctuations, or accumulated detection errors, ensuring that the system maintains stable control accuracy under long-term operating conditions. Attached Figure Description

[0052] Figure 1 This is one of the schematic diagrams of the coal gas benzene removal and slag discharge energy efficiency optimization system according to an embodiment of the present invention;

[0053] Figure 2 This is a flowchart of the energy efficiency optimization method for benzene removal and slag discharge from coal gas according to an embodiment of the present invention;

[0054] Figure 3 This is a flowchart illustrating the specific processing steps of step S100 in an embodiment of the present invention.

[0055] Figure 4 The diagram shows the dynamic response waveform of the collaborative conveying process in an embodiment of the present invention; wherein, (a) is a dynamic change diagram of the comprehensive emulsification risk index (ERI) during the conveying process; and (b) is a diagram of the collaborative response action of the actuator.

[0056] Figure 5 This is a schematic diagram of the adaptive threshold convergence process according to an embodiment of the present invention;

[0057] Figure 6 This is a statistical distribution comparison chart of the oil content in the residue and steam consumption in the conventional scheme of this invention embodiment;

[0058] Figure 7 This is the second schematic diagram of the energy efficiency optimization system for benzene removal and slag discharge from coal gas according to an embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of the connection topology between the signal acquisition module and the field detection component in an embodiment of the present invention. Detailed Implementation

[0060] 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.

[0061] Figure 1 This is a schematic diagram of a coal gas benzene removal and slag discharge energy efficiency optimization system according to an embodiment of the present invention. Figure 7 This is a second schematic diagram of the energy efficiency optimization system for benzene removal and slag discharge from coal gas, according to an embodiment of the present invention. Figure 1 and Figure 7 As shown, this embodiment of the invention provides a coal gas benzene removal and slag discharge energy efficiency optimization system, including field equipment components and a central control unit.

[0062] See Figure 7 The on-site equipment components include a wash oil regenerator, a thin sludge tank, and a mechanized clarifier. The bottom outlet of the wash oil regenerator is connected to the inlet of the thin sludge tank via a sludge discharge valve. The thin sludge tank is equipped with a level gauge and an internal pressure transmitter. The top medium inlet of the thin sludge tank is connected to two pipelines: the first pipeline is a steam delivery pipeline, equipped with a steam regulating valve and an inlet pressure transmitter; the second pipeline is a hot lean oil dilution pipeline, equipped with a hot lean oil bypass valve. The bottom outlet of the thin sludge tank is connected to the mechanized clarifier via a delivery pipeline. The separated liquid outlet of the mechanized clarifier is connected to the cold drum section recovery system.

[0063] The central control unit is connected to the field instruments and actuators, and is configured to perform logic control for slag discharge and conveying. Internally, the central control unit is divided into a data acquisition module, a triggering module, a conveying module, and a calibration module.

[0064] Figure 8 This is a schematic diagram of the connection topology between the signal acquisition module and the field detection component in an embodiment of the present invention. Figure 8As shown, the acquisition module is configured to obtain production process status parameters. The acquisition module connects to a crude benzene flow meter, a coke metering scale, and a wash oil regenerator temperature sensor to acquire crude benzene production rate, coke production rate, and wash oil regeneration temperature data. The acquisition module also connects to a level gauge, an in-tank pressure transmitter, and an inlet pressure transmitter to acquire the liquid level signal in the sludge tank and the pressure fluctuation signal during the conveying process.

[0065] The trigger module receives data on the crude benzene production rate and the wash oil regeneration temperature. Internally, the trigger module stores a pre-built reaction kinetic model. This model, employing the Arrhenius equation, describes the rate at which the wash oil undergoes thermal polymerization to generate slag during high-temperature regeneration.

[0066] The specific calculation formula is as follows:

[0067] ;

[0068] in, The instantaneous sludge formation rate of the circulating wash oil is calculated; The collected crude benzene production rate (characterizing system load); This refers to the temperature at which the wash oil is regenerated. Pre-exponential factor, For activation energy, The gas constant is... This is the load impact index. These parameters are preset through regression analysis of historical operating data.

[0069] The trigger module inputs the collected data in real time. and Calculate the current And perform time integration on it ( ), thus obtaining the current accumulated slag amount.

[0070] The conveying module is configured to regulate the parameters of the conveying medium. When the liquid level in the thin slag tank reaches the conveying condition, the conveying module receives the pressure data sequence collected by the inlet pressure transmitter and calculates the comprehensive emulsification risk index. The conveying module compares this comprehensive emulsification risk index with a preset critical threshold: when the comprehensive emulsification risk index is lower than the safety threshold, the steam regulating valve is adjusted to operate in maximum flow rate mode; when the comprehensive emulsification risk index is in the critical range, the opening of the steam regulating valve is reduced; when the comprehensive emulsification risk index exceeds the critical threshold, the steam regulating valve is locked and the hot lean oil bypass valve is opened to inject hot oil into the thin slag tank.

[0071] The calibration module is configured to optimize control parameters. It acquires data on the water content or slag content of the clarified liquid after separation in the mechanized clarification tank and calculates the deviation between the separation effect and the target value. Based on this deviation, the calibration module updates the critical threshold used in the conveying module.

[0072] The mechanized clarification tank used in this embodiment can adopt a gravity settling structure commonly used in the coking industry, and is equipped with a low-speed slag scraper inside. However, unlike the prior art, this system does not treat it as an isolated physical separation unit, but rather as a quality verification node in the energy efficiency optimization closed loop.

[0073] Specifically, this embodiment of the invention actively creates optimal feeding conditions with zero emulsification for the mechanized clarifier by controlling the fluid morphology upstream, solving the industry problem of sedimentation failure caused by feed emulsification in traditional processes. Simultaneously, the quality data of the clarified liquid at the clarifier outlet is transmitted in real time to the correction module of the central control unit, giving the clarifier the functional attributes of a system feedback sensor, thereby supporting an adaptive batch control strategy.

[0074] The field equipment components described in this embodiment not only perform the physical function of material transfer, but also serve as the sensing endpoints and execution components of this energy efficiency optimization system. (See also...) Figure 7 Specifically, it includes the following key units:

[0075] The actuator employs a slag discharge valve (located at the bottom of the wash oil regenerator), preferably a high-temperature resistant and wear-resistant pneumatic shut-off ball valve or a V-type regulating valve. It receives commands from the trigger module and, through precise control of the opening duration (e.g., millisecond or second-level control), converts the theoretically calculated target discharge mass into the actual physical slag discharge volume, thus achieving quantitative transfer of the slag.

[0076] The steam regulating valve (located at the steam inlet of the slag tank) is preferably a pneumatic diaphragm regulating valve with a high regulation ratio. This valve is not only used to open / close steam delivery, but its core function is as the actuator for a pressure stabilization and suppression strategy. Under the control of the delivery module, this valve limits the steam velocity and pressure rise rate (dL / dt) entering the pipeline by frequently fine-tuning its opening, thereby suppressing turbulent pulsations within the pipe.

[0077] The hot lean oil bypass valve (located in the hot lean oil dilution pipeline) connects to the hot lean oil circulation pipeline at the bottom of the crude benzene stripping tower. As the actuator of the dilution decoupling strategy, this valve opens rapidly when the system detects a high risk of blockage, introducing high-temperature (170℃-190℃) and low-viscosity lean oil. This hot lean oil bypass valve is designed as a quick-opening shut-off valve to ensure rapid reheating and dilution to the thin sludge tank in emergency situations.

[0078] The field instrument refers to the inlet pressure transmitter (located after the steam regulating valve and before the sludge tank), used to acquire pipeline pressure signals in real time. This inlet pressure transmitter has high-frequency sampling capability (e.g., sampling frequency ≥10Hz), enabling it to capture minute pressure fluctuations during fluid transport. The acquisition module uses the data from this inlet pressure transmitter to separate the DC component representing shear force and the AC component representing turbulence intensity, thereby constructing a comprehensive emulsification risk index.

[0079] The pressure transmitter inside the tank (located at the top of the thin slag tank) is used to monitor the back pressure inside the thin slag tank, preventing overpressure inside the tank due to blockage of the downstream mechanized clarification tank, and playing a safety interlocking role.

[0080] Level gauge (located on the side wall of the thin slag tank), such as Figure 7 The preferred method is to use a radar level gauge or a differential pressure level gauge to monitor the cumulative height of slag in the tank in real time. This cumulative height data is used to provide feedback on the actual slag discharge volume of the slag discharge valve (model correction) and also serves as a trigger signal to start the conveying process (i.e., to notify the conveying module to intervene when the liquid level reaches the conveying threshold).

[0081] The crude benzene flow meter and the wash oil regeneration temperature sensor (located upstream of the system) are used to collect the production load and wash oil regeneration temperature of the crude benzene section, respectively, providing input variables for the reaction kinetic model to trigger the module to calculate the instantaneous slag generation rate.

[0082] like Figure 2 As shown, Figure 2 This is a flowchart of a method for optimizing the energy efficiency of benzene removal and slag discharge from coal gas according to an embodiment of the present invention. The embodiment of the present invention provides a method for optimizing the energy efficiency of benzene removal and slag discharge from coal gas, comprising the following steps:

[0083] Step S100: Calculate the degree of washing oil aging and integrate the accumulated slag amount using the material balance model; collect crude benzene production rate, coke production rate and regeneration temperature, calculate the instantaneous slag generation rate and perform time integration.

[0084] Step S200: Dynamically trigger the slag discharge action based on the accumulated slag amount; when the accumulated slag amount reaches the preset threshold, open the slag discharge valve to discharge the slag-containing material into the dilute slag tank, and calculate the opening time of the slag discharge valve.

[0085] Step S300: Perform coordinated conveying control based on the comprehensive emulsification risk index; during the conveying of thin slag, collect data in real time through the high-frequency pressure transmitter installed on the conveying pipeline at the inlet of the thin slag tank and calculate the comprehensive emulsification risk index, which includes static differential pressure components and dynamic turbulence components. Based on the comprehensive emulsification risk index, switch the control strategy between high-efficiency conveying mode, pressure stabilization and suppression mode and dilution decoupling mode.

[0086] Step S400: Adaptively correct control parameters based on separation effect; obtain the quality data of the clarified liquid after separation in the mechanized clarification tank, calculate the deviation from the target value, and use the deviation to update the critical threshold in the next batch delivery control.

[0087] The specific logic for calculating the deviation from the target value is as follows:

[0088] The maximum water content of the outlet clarified liquid allowed by the process is set to be... (For example .

[0089] The data acquisition module obtains the actual water content of the clarified liquid at the outlet of the mechanized clarification tank after the current batch of slag discharge is completed.

[0090] Calculate the deviation .

[0091] like (i.e., positive deviation) indicates that the actual moisture content exceeds the standard, which means that the slag discharge speed of the previous batch was too fast, resulting in emulsification. The system will lower the risk judgment threshold (tighten control) in the next batch.

[0092] like (i.e., negative deviation) indicates that the separation effect is better than the standard and there is room for energy efficiency optimization. The system will maintain or appropriately increase the risk judgment threshold (relax control) in the next batch to shorten working time and reduce steam consumption.

[0093] See Figure 2 Step S100 involves calculating the degree of aging of the wash oil and integrating the accumulated slag amount using a material balance model. This step specifically includes: collecting data on the crude benzene production rate, coke production rate, and regeneration temperature; calculating the instantaneous slag formation rate; and performing time integration. In the wash oil regeneration process, slag formation mainly originates from the high-boiling-point polymers absorbed in the wash oil and the thermal condensation reaction of the wash oil itself at high temperatures. This reaction rate is influenced by both the material throughput load and the reaction temperature. This embodiment uses a semi-empirical kinetic mathematical model to characterize this process, with process detection values ​​as input and the slag formation rate as output. Figure 3 As shown, step S100 may specifically include the following steps:

[0094] S101, real-time acquisition of multi-source data. The acquisition module operates according to a preset sampling period. (For example, every 1 minute) Synchronously read the crude benzene mass flow rate count value of the crude benzene section. Values ​​of coke weighing scales at the coking end and the temperature value of the thermocouple at the bottom of the wash oil regenerator .in, and All data have undergone moving average filtering to eliminate transient interference.

[0095] S102, Instantaneous slag formation rate calculation. The system calls a pre-calibrated slag formation rate model for calculation. This slag formation rate model is based on the Arrhenius reaction kinetic equation, and the specific calculation formula is as follows:

[0096] ;

[0097] In the formula, This represents the theoretical slag formation rate at time t, expressed in kg / h. This represents the standard reference temperature for regenerator design, with a value of 170℃. Indicates the baseline aging factor. This indicates the temperature-sensitive factor.

[0098] The above model parameters and It was determined through regression analysis of historical production data. The specific calibration (training) steps are as follows:

[0099] In the sample collection step, the most recent N cleaning cycles (e.g., 5 cycles) are selected as the training sample set. Each sample contains a sequence of process record data within that cycle. , , ) and the total weight of slag manually removed at the end of the cycle (as a label value). ).

[0100] In the parameter fitting step, an objective function is constructed to ensure that the theoretical integral total slag quantity calculated based on the slag formation rate model is consistent with the actual total slag weight. The mean square error between the data is minimized. The least squares method is used to iteratively solve historical data to obtain the optimal solution that best suits the current operating conditions. and value.

[0101] S103, Cumulative Integral and State Locking. In this step, the system uses a discrete integral algorithm to calculate the instantaneous slag formation rate. By accumulating the time, the cumulative slag volume within the current slag discharge cycle is obtained. The calculation formula is as follows:

[0102] ;

[0103] In the formula, n represents the number of sampling cycles from the end of the last slag discharge to the current time; This represents the k-th sampling time. This indicates the current cumulative theoretical slag volume, in kg.

[0104] The system sets a quality threshold for slag discharge batches. This quality threshold The method for determining it is as follows: ,in The geometric volume of the dead zone below the bottom slag discharge port of the wash oil regenerator. The average bulk density of the waste residue. The preset safety fill factor (range 0.6-0.8) is used. At the end of each calculation cycle, the system will adjust the current... and Perform numerical comparison. When a determination is made... At this time, the system keeps the current integral value unchanged and generates a high-level slag discharge request signal to be transmitted to the trigger module, and then proceeds to step S200.

[0105] Regarding the data acquisition interface communication protocol and the configuration of the basic computing modules inside the DCS (Distributed Control System) system involved in the above steps, those skilled in the art can perform conventional configuration settings according to the specific brand and model of the field control system. The specific implementation method is a well-known technology in this field and will not be elaborated here.

[0106] Step S200 involves dynamically triggering the slag discharge action based on the accumulated slag volume. Specifically, this step includes: when the accumulated slag volume reaches a preset threshold, opening the slag discharge valve to discharge the slag-containing material into the dilute slag tank, and calculating the valve opening duration. The slag discharge control model in this embodiment is based on the orifice outflow principle in fluid mechanics. This model treats the slag discharge valve as a throttling element under varying pressure differences, establishing a deterministic relationship between the discharge duration and the target discharge mass through the law of conservation of energy (Bernoulli's equation), thereby achieving quantitative control of high-temperature, high-viscosity non-Newtonian fluids. Step S200 may specifically include the following steps:

[0107] S201, Slag discharge action triggered. The trigger module receives status signals from previous steps in real time. When the slag discharge request signal is detected (i.e., accumulated slag amount), the trigger module will initiate the action. When the threshold is exceeded, the trigger module sends an opening command to the slag discharge valve at the bottom of the wash oil regenerator. The slag discharge valve is a pneumatic O-type shut-off ball valve or a wear-resistant ceramic valve, which has full open and full close characteristics to adapt to high-temperature media containing solid particles.

[0108] S202, Valve opening duration calculation. Simultaneously with issuing the valve opening command, the system calls the slag discharge duration prediction model to calculate the target duration of this discharge process. The calculation formula is as follows:

[0109] ;

[0110] In the formula, This indicates the target duration for which the slag discharge valve will remain open, in seconds (s). The cumulative slag amount locked in step S103 is expressed in kilograms (kg). This indicates the flow cross-sectional area when the slag discharge valve is fully open, in square meters. This value is determined by the valve's nominal diameter. Calculation ; This represents the pressure difference driving the slag discharge, which is the difference between the pressure at the bottom of the regenerator and the back pressure inside the slag tank, and is expressed in Pascals (Pa). The corrected density of the slag mixture is expressed by the formula: ,in The density of the hot wash oil, For the density of solid slag, The preset empirical value for solid content (ranging from 0.05 to 0.15). This represents the overall flow coefficient of the slag discharge valve, which is a dimensionless parameter.

[0111] For key model parameters The system uses the following on-site calibration (training) steps for initialization and correction:

[0112] In the initial setup steps, when the system is run for the first time, configure... The theoretical initial value is (0.65);

[0113] In the sample acquisition step, after performing one slag discharge action, the actual opening time of the slag discharge valve is recorded. And utilize the increase in liquid level in the sludge tank and tank cross-sectional area Back-inferring actual slag discharge quality ;

[0114] In the parameter correction step, the following formula is used:

[0115] ;

[0116] Calculate the correction value and update it in the model parameter library for the next calculation.

[0117] Regarding the parameters in the above formula The specific method for obtaining this information is as follows: read the real-time value from the regenerator tower bottom pressure transmitter. Real-time value of pressure transmitter of thin slag tank Calculate the difference between the two. If a high-precision differential pressure transmitter is not available on site, a preset constant can be used instead. This constant is determined based on the process design pressure.

[0118] S203, Execution and Reset. The system starts a countdown timer; when the slag discharge valve has been open for the calculated time... Immediately afterwards, a valve-closing command is output. After the system detects the feedback signal that the slag discharge valve is fully closed, a reset operation is performed, which includes: resetting the accumulated slag integrator. The system is reset to zero, and the integral calculation for the next cycle is restarted. The liquid level monitoring program for the sludge tank is started, and the liquid level in the tank is continuously monitored through the liquid level gauge. When the detected liquid level is greater than the preset starting liquid level for delivery (e.g., 15% of the tank capacity) and the liquid level fluctuation rate is less than the preset stable threshold (e.g., ±2%), a delivery ready signal is generated, triggering the subsequent step S300.

[0119] The valve drive logic and safety interlock protection involved in the above steps can be set by those skilled in the art according to the specific characteristics of the field actuator. The specific implementation method is a well-known technology in the field and will not be described in detail here.

[0120] Step S300 involves implementing coordinated transport control based on a comprehensive emulsification risk index. This step specifically includes: during the transport of thin slag, real-time acquisition of pressure signals and calculation of a risk index containing both static differential pressure and dynamic turbulence components; and switching control strategies between high-efficiency transport mode, pressure stabilization and suppression mode, and dilution decoupling mode based on this index.

[0121] The physical principle of this step is based on the droplet breakup theory in fluid mechanics. In a gas-liquid-solid three-phase transport process, when the turbulent pulsating kinetic energy or viscous shear stress of the fluid exceeds the surface tension binding of the droplets (i.e., the Weber number We exceeds a critical value), the dispersed phase droplets will undergo irreversible breakup, forming a stable emulsion that is difficult to settle. This embodiment indirectly characterizes the shear and turbulence intensity within the pipe by monitoring the statistical characteristics of the pressure signal, thereby constructing a quantitative index of emulsification risk. Step S300 may specifically include the following steps:

[0122] S301, High-frequency signal acquisition and feature extraction. After the transmission module starts, it inputs signals at a sampling frequency through the analog input channel. (Preferred to be 10Hz to 50Hz) Continuously acquire the instantaneous pressure value of the steam inlet pressure transmitter. Using a length of Time sliding window covers the past The data sequence within seconds (e.g., 5 seconds). Within each control cycle, two statistical characteristics of the data within the window are calculated: first, the static pressure differential component. First, the average driving force required for the fluid to overcome the frictional resistance of the pipe wall; second, the dynamic turbulent component. It characterizes the amplitude of pressure pulsation in a gas-liquid two-phase flow.

[0123] S302, Comprehensive Emulsification Risk Index Model Calculation. A multivariate weighted model is used to map the above physical characteristics into a dimensionless comprehensive emulsification risk index (ERI). The calculation model is as follows:

[0124] ;

[0125] In the formula: express The comprehensive emulsification risk index calculated at all times;

[0126] This represents the difference between the average inlet pressure and the back pressure of the thin slag tank within the current window, expressed in Pascals (Pa). Indicates the inner diameter of the conveying pipeline, in meters (m);

[0127] This indicates the equivalent length of the pipeline, including the total equivalent length of straight pipes, bends, and valves, and is expressed in meters (m).

[0128] This represents the normalization coefficient for shear stress weight, in units of...

[0129] This represents the normalization coefficient for turbulence disturbance weights, in units of...

[0130] The first term in the formula represents the average shear stress at the pipe wall, and the second term represents the intensity of turbulent fluctuations.

[0131] Model parameters and The training (calibration) steps are as follows:

[0132] During the commissioning phase, multiple batches of conveying experiments were conducted at different steam conveying pressures, and the pressure waveform data for each batch was recorded. After each batch was conveyed, samples were taken and analyzed in a mechanized clarifying tank to determine the oil-water interface separation time or the oil content of the clarified liquid as a quantitative label for the degree of emulsification. Linear regression analysis was used to determine the influence weights of static shear force and dynamic pulsation on the emulsification label. Generally, shear stress contributes more to the emulsification of high-viscosity fluids, therefore… The weight of the value is usually higher than (For example (Normalized proportion).

[0133] S303, three-level state machine collaborative control.

[0134] The system presets two control thresholds, namely the safety threshold. and critical threshold The conveying module adjusts according to real-time data. Values ​​are subject to hierarchical control:

[0135] High-efficiency conveying mode ( At this point, the flow is stable and the risk of emulsification is low. The PID controller is set to a flow regulation strategy, and the steam regulating valve opening is set to the maximum allowable value to shorten the delivery cycle.

[0136] Voltage regulation suppression mode (VPS) At this point, precursors to slug flow appear in the pipeline. The control logic switches to pressure control mode, lowers the steam inlet pressure setpoint (SP), and limits the rate of change of the steam regulating valve opening, using the throttling effect to suppress dynamic turbulent components. .

[0137] Dilution decoupling mode ( At this point, there is a serious risk of emulsification. The system performs the following actions: First, it locks the current opening of the steam regulating valve or forces it to close; second, it opens the hot lean oil bypass valve and injects high-temperature hot lean oil into the thin slag tank.

[0138] The physical mechanism of this mode lies in the fact that the injected hot lean oil reduces the dynamic viscosity of the mixture through dilution. And solids content. According to the Hagen-Poiseuille law, a decrease in viscosity significantly reduces the pressure gradient required to maintain flow, thus causing both the static and dynamic components of the ERI exponent to decrease simultaneously. When monitored... After the temperature drops back to a safe range, close the bypass valve to resume normal delivery.

[0139] Regarding thresholds The determination method employs a stepped pressure increase test. During the system commissioning period, the delivery pressure is gradually increased until a sudden change in the turbidity of the separated liquid at the clarifier outlet is observed. At this point, the calculated ERI value is recorded, and 80%-90% of this value is set as the critical threshold. .

[0140] For the PID controller parameter tuning involved in the above steps, those skilled in the art can use the Ziegler-Nichols method for on-site tuning. The specific implementation method is a well-known technology in this field and will not be described in detail here.

[0141] Step S400 involves adaptively correcting control parameters based on the separation effect. This step specifically includes: acquiring the quality data of the clarified liquid after separation in the mechanized clarifier, calculating the deviation from the target value, and using this deviation to update the critical threshold in the next batch transport control. This step employs a run-to-run (R2R) batch control strategy, whose physical principle is based on Stokes's settling law. The separation efficiency of the mechanized clarifier mainly depends on the particle size distribution of the dispersed phase droplets. When the emulsification risk index (ERI) threshold is set too high during transport, the strong shear force within the pipeline will break the droplets to the micrometer level, causing the settling velocity to decrease quadratically. This step monitors the downstream lagging separation quality and reversely corrects the upstream ERI threshold, thereby finding a dynamic balance between transport efficiency and separation quality. Step S400 may specifically include the following steps:

[0142] S401, Separate quality data acquisition and quantification.

[0143] After the mechanized clarifier completes the settling and separation of the current batch of materials, the calibration module obtains quantitative indicators of the separation effect. The data comes from an online moisture analyzer on the clarifier outlet pipe or from laboratory test data. The system selects the moisture content from the supernatant (regenerated wash oil). or slag content As a key quality characteristic (CQC).

[0144] The system uses a normalization formula to convert physical concentration values ​​into a dimensionless separation performance index. The calculation is as follows:

[0145] ;

[0146] In the formula:

[0147] This indicates the current slag discharge batch serial number;

[0148] Indicates the first The actual measured impurity concentration value of the batch;

[0149] This indicates the maximum permissible impurity concentration specified in the process standard (for example, according to GB / T24217 "Technical Conditions for Coking Wash Oil", the upper limit of the water content in circulating wash oil is usually set at 1.0%).

[0150] To separate the performance index, the value range is usually within... Between these values, a value of 1 indicates complete purity, while a value less than 0 indicates serious exceedance of the standard.

[0151] S402, Threshold Correction Model Calculation.

[0152] The system employs an exponentially weighted moving average control algorithm with dead zone to control the critical thresholds in the preceding steps. The model is iteratively updated. Its structure includes a deviation calculation unit, a nonlinear gain unit, and an integral execution unit.

[0153] The iterative formula for the corrected model is as follows:

[0154] ;

[0155] In the formula:

[0156] This indicates the deviation between current performance and target performance;

[0157] This indicates the critical threshold used for controlling the next batch of transport;

[0158] This indicates the critical threshold used in the current batch;

[0159] This indicates the set target quality performance index (usually set as a safety margin of 0.2 to 0.3, meaning the target concentration is controlled at 70%-80% of the standard upper limit).

[0160] This represents the control gain coefficient (learning rate).

[0161] This represents the nonlinear sensitivity index.

[0162] Model parameters and The offline optimization (training) steps are as follows:

[0163] Export operational data from no fewer than 100 batches from the DCS historical database, including the usage thresholds for each batch. With final separation moisture content ;

[0164] Construct the evaluation function The evaluation function consists of a weighted sum of the settling time (i.e., the number of batches that recover to steady state after operating condition fluctuations) and the steady-state error variance.

[0165] Using the grid search method, in and A step-by-step search is performed within the specified range, and sample data is substituted into the model for simulation calculation to select the cost function. The minimum parameter combination is used as the system default parameter.

[0166] S403, Safety Boundary Constraint. To prevent control divergence due to instrument drift or extreme operating conditions, the system performs calculations on the safety boundary constraints. Apply amplitude limiting constraints:

[0167] ;

[0168] In the formula:

[0169] The theoretical value calculated by the formula;

[0170] The minimum allowable threshold (set to 60% of the baseline value) is set to prevent the system from excessively reducing the flow rate, which could lead to blockage of the delivery pipeline.

[0171] The maximum allowable threshold (set to 120% of the baseline value) is set to prevent the emulsion from entering subsequent processes and disrupting the balance of the ammonium sulfate system.

[0172] For the data interaction involved in the above steps, the calibration module reads the analysis data of the LIMS (Laboratory Information Management System) system through the OPC (OLE for Process Control) protocol, and stores the calculation results in the PLC's power-off holding register for the next batch of logic calls.

[0173] It should be noted that the hot lean oil mentioned in this embodiment refers to lean oil directly drawn from the bottom of the crude benzene stripping tower (or regeneration tower) and not yet cooled by the lean-rich oil heat exchanger. In this embodiment, the temperature of the injected hot lean oil is controlled between 170℃ and 190℃. Selecting hot lean oil in this temperature range has a dual technical effect: on the one hand, this temperature is much higher than the softening point of the wash oil residue, which can provide sufficient enthalpy to prevent the residue from condensing at the pipe wall due to temperature drop; on the other hand, high-temperature lean oil has lower viscosity and higher solubility, which can quickly break the interfacial film of the emulsion, thereby playing a role in demulsification and reducing the overall viscosity of the mixture.

[0174] To illustrate the implementation process of the present invention under actual working conditions more intuitively, the following example is taken from the crude benzene section of a coking plant with an annual coke production of 2 million tons.

[0175] The basic parameters are set as follows:

[0176] The target equipment is a wash oil regenerator (diameter) Bottom slag discharge dead zone volume .

[0177] The slag discharge valve in the actuator adopts a DN50 pneumatic ceramic ball valve (fully open cross-sectional area). ), Flow coefficient calibration value .

[0178] The delivery pipeline uses a diameter of DN65 and an equivalent length of... Back pressure .

[0179] Set the bulk density of the slag Hot wash oil density Preset solids content .

[0180] In the control objectives, the quality threshold for slag discharge batches is set as follows: .

[0181] Single batch execution process:

[0182] Phase 1, Slag Accumulation (S100)

[0183] The system monitored the regenerator bottom temperature in real time, which remained stable at 182℃, and the crude benzene flow rate was 18.5 t / h. Based on the Arrhenius model integral calculation, after approximately 4.5 hours of operation, the cumulative slag volume integral value... Reaching 395kg, exceeding the set threshold The system triggered a slag discharge request.

[0184] Phase Two, Calculation of Slag Discharge Time (S200)

[0185] The system reads the bottom pressure of the regenerator as 0.18 MPa and calculates the driving pressure difference. .

[0186] Mixed density corrected to Substitute the values ​​into the formula to calculate the target on-time:

[0187] ;

[0188] The system controls the slag discharge valve to open for 16.3 seconds and then automatically closes to complete the slag discharge.

[0189] Phase 3, Coordinated Conveying (S300)

[0190] After the slag discharge is completed, the system opens the steam valve for conveying.

[0191] T=0~15s: During the initial stage of steam purging, the ERI index is 0.3, which is lower than the safety threshold. (Set to 0.4), the system maintains full-speed delivery (high-efficiency mode).

[0192] T=16~20s: Monitoring the standard deviation of inlet pressure The ERI index surged to 0.75, exceeding the critical threshold. (Currently set to 0.7).

[0193] Response action: The system determines that it has entered the dilution decoupling mode, immediately freezes the steam valve opening, and opens the hot lean oil bypass valve to inject high-temperature lean oil.

[0194] T=25s: After dilution, the ERI index drops back to 0.35. The system closes the bypass valve and resumes the pressure-stabilized delivery mode until the end.

[0195] Phase 4, Adaptive Correction (S400)

[0196] After the transfer was completed, the online analyzer in the downstream mechanized clarification tank measured the water content of the supernatant of this batch to be 0.8% (better than the target value of 1.0%).

[0197] Calculate performance deviation This indicates that the current control is too conservative. The system uses the EWMA (Exponentially Weighted Moving Average) algorithm to adjust the critical threshold for the next batch. The value was increased from 0.70 to 0.72 to further improve conveying efficiency while ensuring separation meets requirements.

[0198] To verify the effectiveness of the embodiments of the present invention, a comparative test was conducted at the aforementioned production site for 30 days. For the first 15 days, the traditional timed slag discharge and manual purging control logic was used (control group). For the following 15 days, the system was switched to the dynamic slag discharge and ERI coordinated conveying control logic of the present invention (experimental group).

[0199] The test results are shown in Table 1, and the corresponding comparison charts are shown below. Figures 4 to 6 As shown.

[0200] Figure 4 This demonstrates the coordinated action of the steam valve and the hot lean oil valve when the ERI index exceeds the threshold. For example... Figure 4 The diagram shown is a dynamic response waveform of the collaborative conveying process in an embodiment of the present invention. Figure 4(a) illustrates the dynamic changes in the comprehensive emulsification risk index (ERI) during the conveying process. The horizontal axis represents time, and the vertical axis represents the ERI value. The dashed line in the figure represents the system's preset emulsification risk control threshold (the first threshold is approximately 0.7), and the dotted line represents the target stable risk level (corresponding to the second threshold of approximately 0.4). In the initial stage of conveying, the ERI remains at a low level and is within a stable fluctuation range. When the system detects a change in the accumulation state of the slag and enters the conveying trigger stage, the ERI rises rapidly in a short period of time and once exceeds the emulsification risk control threshold, indicating that the emulsification risk has significantly increased under the current operating conditions. Subsequently, under the action of the collaborative control strategy, the ERI gradually falls back and tends to the target stable range (below the second threshold), eventually returning to a safe and controllable level. Figure 4 (b) illustrates the coordinated response of the actuators corresponding to the ERI change, where the left ordinate represents the steam valve opening, the right ordinate represents the hot lean oil injection flow rate, and the shaded area represents the effective duration of the hot lean oil injection. It can be seen that after the ERI rises and exceeds the control threshold, the system triggers a coordinated control mechanism, adjusting the steam valve opening accordingly and simultaneously initiating hot lean oil injection to change the physical properties and flow state of the transported medium, thereby suppressing further expansion of emulsification risk. As the ERI falls back below the second threshold, the hot lean oil injection gradually ends, and the steam valve opening returns to a stable operating state. Therefore, this embodiment of the invention achieves dynamic regulation and stable control of emulsification risk during transport by real-time monitoring of ERI and driving coordinated responses from multiple actuators.

[0201] Figure 5 This demonstrates the critical threshold as the batch size increases. (Dotted solid line) and separation performance The convergence process of (solid square lines). For example... Figure 4 The figure shown is a schematic diagram of the batch-to-batch convergence process of the adaptive threshold in an embodiment of the present invention. The horizontal axis represents the slag discharge batch sequence k, and the left vertical axis represents the critical threshold Ω. crit The right vertical axis represents the separation performance index J(k). Where Ω crit J(k) represents the critical threshold for emulsification risk used to trigger coordinated regulation under the current control strategy, J(k) represents the separation performance evaluation index for the corresponding slag discharge batch, and the dashed line represents the preset target performance level J. target It can be seen that, in the initial stage, the critical threshold Ω crit The system is set at a relatively high threshold, resulting in a conservative control strategy and a low separation performance J(k). As the slag discharge batches progress, the system adaptively updates the threshold based on historical operating data and performance feedback. crit The performance gradually decreases and stabilizes, making the control strategy more sensitive. Meanwhile, the separation performance J(k) generally shows an upward trend and revolves around the target performance J in subsequent batches. targetFluctuations indicate that the system performance is gradually optimizing and reaching the desired level. When Ω crit Once both J(k) and J(k) enter the stable interval, the adaptive update process converges, and the threshold remains within a reasonable range and no longer changes significantly.

[0202] Figure 6 It displays the median, quartiles, and outliers, providing a clear comparison of the data distribution differences between the control and experimental groups. For example... Figure 6 The figure shown is a statistical distribution comparison chart of the experimental group and the control group in terms of residue oil content and steam consumption in an embodiment of the present invention. Among them, Figure 6 The left side shows a box plot comparison of the washing oil loss rate. The vertical axis represents the percentage of oil content in the residue, and the horizontal axis corresponds to the control group using the traditional control scheme and the experimental group using the collaborative control scheme of this invention, respectively. Figure 6 The right side shows a box plot comparison of steam consumption, with the vertical axis representing steam consumption per unit time and the horizontal axis corresponding to the control group and the experimental group. It can be seen that, compared to the control group, the experimental group showed a significant downward shift in the overall oil content of the residue, with a significantly lower median value and reduced data dispersion. This indicates that the oil content of the residue was effectively suppressed and operational stability was improved after adopting the scheme of this invention. Simultaneously, the steam consumption level of the experimental group was generally lower than that of the control group, and its distribution range was more concentrated, demonstrating that this invention effectively reduced steam consumption while ensuring the conveying and separation effects. Therefore, this invention, by introducing a synergistic control mechanism based on emulsification risk assessment, statistically reduces both washing oil loss and steam consumption, demonstrating significant energy-saving, consumption-reducing, and operational optimization effects.

[0203] The evaluation indicators are compared as follows:

[0204] Evaluation indicators Control group (traditional regimen) Experimental group (Solution of this invention) Optimization effect Remark Average slag discharge deviation rate ±45% ±8% Accuracy improved by 37% Traditional solutions often result in purging or blockage. Steam consumption per ton of slag 125kg / t 92kg / t Energy saving of 26.4% Reduced over-purging Oil content of residue 18.50% 6.20% Reduced by 66.5% Precise control of slag discharge valve duration downstream separation moisture content 2.4% (highly volatile) 0.9% (stable) Quality meets standards Effectively inhibits emulsification Frequency of slag discharge pipe blockage 4 times / 15 days 0 times / 15 days More stable operation Dilution decoupling mode takes effect

[0205] As shown in Table 1, compared with the commonly used timed and length-based coarse control strategy in the prior art, the solution of this invention demonstrates significant technical advantages in all key indicators. By reducing steam consumption and washing oil loss, the operating cost of the unit is significantly reduced; pipeline blockage accidents are completely eliminated, reducing the safety risks caused by manual unblocking; and the stable achievement of downstream moisture content effectively protects the water balance of the subsequent ammonium sulfate section, verifying the industrial applicability and advanced nature of the multi-level collaborative control and adaptive feedback mechanism in this invention.

[0206] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the energy efficiency of slag discharge during benzene removal from coal gas, characterized in that, include: The operating parameters of the coal gas benzene removal system are collected, the instantaneous slag formation rate of the circulating wash oil is calculated using a reaction kinetic model, and the current cumulative slag amount is obtained by integrating the instantaneous slag formation rate over time. When the accumulated slag reaches the preset slag discharge threshold, the target discharge quality for this slag discharge is determined. A mapping relationship between the opening time of the slag discharge valve and the target discharge quality is established based on the fluid dynamics model. The target discharge quality is converted into the target opening time of the slag discharge valve using the mapping relationship, and the slag discharge valve is controlled to open for the target opening time to quantitatively discharge the slag-containing material into the dilute slag tank. During the transportation of slag-containing materials, the pressure signal of the inlet pipeline of the slag tank is collected in real time, and the characteristic components representing shear and turbulence are extracted to construct a comprehensive emulsification risk index. Based on the comprehensive emulsification risk index, a flow regulation strategy or a dilution decoupling strategy is executed for the slag-containing materials. Obtain the separation quality data of the clarified liquid at the clarifier outlet, and adaptively adjust the risk judgment threshold of the comprehensive emulsification risk index used for the next batch control based on the deviation between the separation quality data and the target value. The extraction and construction of the feature components include: separating the DC component in the pipeline pressure signal as a static pressure difference component to characterize the average shear stress experienced by the fluid in overcoming pipe wall friction; separating the AC component in the pipeline pressure signal as a dynamic turbulence component to characterize the intensity of turbulence fluctuations inside the fluid; and the comprehensive emulsification risk index is formed by weighted superposition of the static pressure difference component and the dynamic turbulence component. Specifically, the flow regulation strategy or dilution decoupling strategy includes: setting safety thresholds and critical thresholds to divide the risk into low-risk, medium-risk, and high-risk ranges; in the low-risk range, implementing a flow regulation strategy targeting maximum flow velocity; in the medium-risk range, implementing a pressure stabilization and suppression strategy targeting pressure fluctuations by limiting the opening and rate of change of the steam regulating valve to reduce turbulence intensity; and in the high-risk range, implementing the dilution decoupling strategy.

2. The energy efficiency optimization method for slag discharge during benzene removal from coal gas according to claim 1, characterized in that, The calculation of the instantaneous slag formation rate of the circulating wash oil using a reaction kinetic model specifically includes: collecting the crude benzene production rate to characterize the production load of the coal gas debenzene system, and collecting the wash oil regeneration temperature to characterize the reaction kinetic activity; inputting the crude benzene production rate and the wash oil regeneration temperature into a pre-constructed Arrhenius-form reaction kinetic equation to calculate the instantaneous slag formation rate of the circulating wash oil.

3. The energy efficiency optimization method for slag discharge during benzene removal from coal gas according to claim 1, characterized in that, The mapping relationship is constructed based on the flow characteristics of the slag discharge valve, specifically including: determining the instantaneous mass flow rate through the slag discharge valve according to the real-time pressure difference before and after the slag discharge valve and the density parameters of the slag-containing material; and determining the operating time of the slag discharge valve by the ratio of the target discharge mass to the instantaneous mass flow rate.

4. The energy efficiency optimization method for slag discharge during benzene removal from coal gas according to claim 3, characterized in that, The method further includes: After the slag discharge valve is closed and the liquid level in the slag tank is stable, the actual slag discharge mass is calculated by using the geometric dimensions of the slag tank and the liquid level increment, and the actual slag discharge mass is compared with the theoretical target mass. When there is a deviation between the two, the comprehensive flow coefficient in the fluid dynamics model is adjusted to compensate for the change in the flow capacity of the slag discharge valve.

5. The energy efficiency optimization method for slag discharge during benzene removal from coal gas according to claim 1, characterized in that, The dilution decoupling strategy specifically includes: Cut off or lock the steam source for conveying, and open the hot lean oil bypass valve connected to the thin slag tank to inject hot lean oil; The dilution effect of hot lean oil is used to reduce the dynamic viscosity and solid content of the mixture in the tank, thereby reducing the driving pressure difference and shear force required for fluid transportation until the comprehensive emulsification risk index returns to the safe range.

6. The energy efficiency optimization method for slag discharge during benzene removal from coal gas according to claim 1, characterized in that, The adaptive correction employs a batch control strategy, which specifically includes: The water content or slag content of the clarified liquid at the clarification tank outlet is taken as a key quality characteristic. If the key quality characteristics of the current batch are worse than the process target value, the risk judgment threshold for the next batch will be lowered to trigger the voltage stabilization and suppression strategy or the dilution and decoupling strategy in advance. If the key quality characteristics are better than the process target values, then the risk assessment threshold for the next batch shall be maintained or relaxed.

7. The energy efficiency optimization method for slag discharge during benzene removal from coal gas according to claim 6, characterized in that, When adaptively correcting the risk assessment threshold, a filtering mechanism with dead zone characteristics and a boundary constraint mechanism are introduced. The filtering mechanism is used to ignore quality fluctuations in amplitude within the dead zone range and only responds when the deviation exceeds the dead zone range. The boundary constraint mechanism is used to limit the modified risk assessment threshold to a preset maximum and minimum value.

8. An energy efficiency optimization system for slag discharge during benzene removal from coal gas, characterized in that, The system executes the energy efficiency optimization method for slag discharge during benzene removal from coal gas according to any one of claims 1-7, and the system comprises: The field equipment components include a wash oil regenerator, a thin sludge tank, a clarification tank, and actuators on the connecting pipelines; the actuators include a sludge discharge valve located at the bottom of the wash oil regenerator, a steam regulating valve at the inlet of the thin sludge tank, and a hot lean oil bypass valve; The central control unit, which is communicatively connected to the field device components, is configured with: The data acquisition module is used to obtain the production load and temperature of the crude benzene section as input variables, and to acquire the real-time pipeline pressure signal of the delivery pipeline. The triggering module is used to run the reaction kinetics model to monitor the slag accumulation state and control the slag discharge valve to perform quantitative slag discharge based on the fluid dynamics model. The delivery module extracts feature components based on the pipeline pressure signal and calculates a comprehensive emulsification risk index. Based on the comprehensive emulsification risk index, it coordinates the adjustment of the steam regulating valve and the hot lean oil bypass valve to achieve graded delivery under different risk levels. The calibration module is used to iteratively correct the risk assessment threshold in the conveying module based on the separation quality data of the clarification tank.

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