Crude oil electro-desalting system and use method thereof

Abstract

The invention discloses a crude oil electro-desalting system and a use method thereof, and belongs to the technical field of crude oil electro-desalting. The method comprises the following steps: acquiring an initial emulsion state grid of crude oil to be treated at an inlet of electric desalting equipment, wherein the grid represents a spatial distribution state of water drops in the crude oil; obtaining inlet macroscopic process parameters of the crude oil; determining an interface barrier index for quantifying the emulsion stability based on the statistical characteristics and macroscopic parameters of the grid; on the basis of a preset system target coalescence rate and the interface barrier index, a preset coalescence kinetic equation is solved, so that the optimal electric field intensity and the optimal demulsifying agent dosage which give consideration to the treatment effect and the operation cost are obtained; and finally, controlling the operation of the electro-desalting system based on the optimal parameter combination. According to the method, the intrinsic characteristics of the emulsion are represented, optimization control is performed based on the intrinsic characteristics, and the technical problems that in the prior art, a control strategy is rigid and cannot adapt to dynamic changes of crude oil properties are solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of crude oil electric desalination technology, and in particular to a crude oil electric desalination system and a method for using the same. Background Art

[0002] During extraction and transportation, crude oil typically mixes with formation water or process water, forming a stable water-in-oil (W / O) emulsion. This water contains large amounts of salts, such as chlorides. If not removed, these salts can cause severe corrosion to equipment during the subsequent refining process and potentially poison and deactivate catalysts. Therefore, crude oil electrical desalination is an essential pre-treatment unit in modern refineries.

[0003] The conventional crude oil electro-desalting process consists of three key steps: demulsifier injection, mixing and heating, and high-voltage electric field coalescence separation. First, a chemical demulsifier is injected into the crude oil to break down the interfacial film formed by natural emulsifiers (such as asphaltenes and colloids) coating the surface of water droplets. The crude oil is then heated to reduce viscosity and thoroughly mixed with wash water through a mixing valve to dilute and extract salts from the crude oil. Finally, this mixture is fed into a large horizontal settling tank, known as the electro-desalting tank. Pairs of electrodes are installed within the tank, and high-voltage AC or DC current is applied to create a strong electric field. Under the influence of the electric field, the polar water droplets are stretched and deformed, attracting and colliding with each other. Eventually, they coalesce into sufficiently large droplets that settle by gravity to the bottom of the tank and are discharged, achieving oil-water separation.

[0004] In existing technologies, the operating parameters of electrical desalination systems, such as the applied electric field strength, demulsifier injection dosage, and operating temperature, are often set based on static empirical values ​​or regular offline crude oil analysis. For example, operators may, based on experience, set a fixed electric field strength and demulsifier dosage for crude oil from a specific oil field. The fundamental flaw of this control method is its inability to adapt to the dynamic changes in crude oil properties. Crude oil from different oil fields, and even from the same oil field at different production stages, can vary significantly in water content, salt content, viscosity, density, and the type and content of natural emulsifiers, resulting in a wide range of stability in the emulsions formed.

[0005] The use of fixed, non-adaptive control strategies can lead to a series of problems. When the emulsion being processed is more stable than expected, the fixed parameter settings may not be sufficient to effectively demulsify, resulting in water and salt content of the outlet crude oil exceeding the standard, affecting the normal operation of subsequent processing units and the quality of the final product. Conversely, when the stability of the emulsion being processed is low, the fixed parameter settings may result in excessive consumption of electrical energy and demulsifier, not only increasing production costs, but also causing unnecessary waste of resources and environmental burden. Although some improved schemes introduce simple feedback control based on outlet water content, this control method has a lag in response and cannot distinguish between the root causes of efficiency changes (whether the emulsion itself has changed or the electric field is insufficient), making it difficult to achieve truly optimal control. SUMMARY

[0006] The present application aims to improve the technical problem that the existing crude oil electric desalting control method cannot adapt to the dynamic changes of crude oil properties, leading to unstable treatment effect and waste of resources, and provides a crude oil electric desalting system and a use method thereof, achieving adaptive and optimal control of the electric desalting process.

[0007] In a first aspect, the present application provides a use method of a crude oil electric desalting system, comprising: obtaining an initial emulsion state grid of crude oil to be processed at the inlet of an electric desalting device, the initial emulsion state grid representing the spatial distribution state of water droplets in the crude oil to be processed; obtaining an inlet macroscopic process parameter of the crude oil to be processed; determining an interfacial barrier index of the crude oil to be processed based on the statistical characteristics of the initial emulsion state grid and the inlet macroscopic process parameter; solving a preset coalescence kinetics equation based on a preset system target coalescence rate, the interfacial barrier index and the inlet macroscopic process parameter to obtain an optimal electric field strength and an optimal demulsifier dosage; and controlling the crude oil electric desalting system to operate based on the optimal electric field strength and the optimal demulsifier dosage.

[0008] In a possible implementation manner of the first aspect, the statistical characteristics of the initial emulsion state grid include a standard deviation of the numerical values of each grid cell in the initial emulsion state grid.

[0009] In a possible implementation manner of the first aspect, the step of determining the interfacial barrier index of the crude oil to be processed specifically comprises: obtaining the interfacial barrier index by a preset index construction model based on the standard deviation of the numerical values of each grid cell in the initial emulsion state grid, the inlet temperature of the crude oil to be processed and the inlet component proxy value.

[0010] In a possible implementation manner of the first aspect, the preset coalescence kinetics equation represents the comprehensive action relationship between the electric field strength and the demulsifier dosage on the predicted coalescence rate of the emulsion, and the predicted coalescence rate is negatively correlated with the interfacial barrier index.

[0011] In a possible implementation manner of the first aspect, the step of solving the preset coalescence kinetics equation specifically includes: constructing an operation cost function, the operation cost function being positively correlated with the square of the electric field intensity and the demulsifier dosage; and solving a minimum value of the operation cost function under a constraint condition that the predicted coalescence rate is equal to the system target coalescence rate, to obtain the optimal electric field intensity and the optimal demulsifier dosage.

[0012] In a possible implementation manner of the first aspect, the step of obtaining the initial emulsion state grid specifically includes: collecting cross-section electrical or acoustic characteristic data of the crude oil to be processed by a tomographic imaging sensor array arranged at an inlet of the electric desalting device, and reconstructing into the initial emulsion state grid.

[0013] In a possible implementation manner of the first aspect, after the step of controlling the operation of the crude oil electric desalting system, the method further includes: obtaining an actual post-dewatering water content at an outlet of the crude oil electric desalting system; obtaining a predicted post-dewatering water content by the coalescence kinetics equation based on the inlet macroscopic process parameters and the optimal electric field intensity and the optimal demulsifier dosage currently adopted; and correcting the interfacial barrier index for a next control cycle based on an efficiency deviation between the actual post-dewatering water content and the predicted post-dewatering water content.

[0014] In a possible implementation manner of the first aspect, the step of correcting the interfacial barrier index for a next control cycle specifically includes: combining the efficiency deviation with a preset correction gain to obtain an index correction amount; and combining the interfacial barrier index calculated in a current control cycle with the index correction amount to obtain a corrected interfacial barrier index for the next control cycle.

[0015] In a second aspect, the present application provides a crude oil electric desalting system, including: a state acquisition module configured to obtain an initial emulsion state grid of crude oil to be processed at an inlet of an electric desalting device, and obtain inlet macroscopic process parameters of the crude oil to be processed, the initial emulsion state grid representing a spatial distribution state of water droplets in the crude oil to be processed; a parameter determination module configured to determine an interfacial barrier index of the crude oil to be processed based on statistical characteristics of the initial emulsion state grid and the inlet macroscopic process parameters; an operation solving module configured to solve a preset coalescence kinetics equation based on a preset system target coalescence rate, the interfacial barrier index, and the inlet macroscopic process parameters, to obtain an optimal electric field intensity and an optimal demulsifier dosage; and a control execution module configured to control operation of the crude oil electric desalting system based on the optimal electric field intensity and the optimal demulsifier dosage.

[0016] In a possible implementation of the second aspect, the feedback correction module is configured to: acquire an actual water content after dehydration at an outlet of the crude oil electric desalting system; obtain a predicted water content after dehydration based on the inlet macroscopic process parameters and the optimal electric field intensity and optimal demulsifier dosage currently adopted by using the coalescence kinetics equation; and correct the interfacial barrier index for a next control cycle based on an efficiency deviation between the actual water content after dehydration and the predicted water content after dehydration. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a crude oil electric desalting system and a method of using the same is provided for some embodiments of the present application; Figure 2 A flowchart of a crude oil electric desalting system and a method of using the same is provided for some embodiments of the present application; DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application.

[0019] Hereinafter, the terms "first", "second", and the like are used only for the purpose of description, and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0020] In the present application, unless otherwise explicitly specified and limited, the term "connection" should be understood broadly, for example, "connection" can be fixed connection, or detachable connection, or integral; can be directly connected, or indirectly connected through an intermediate medium. In addition, the term "electrical connection" can be a way of achieving electrical connection for signal transmission.

[0021] As used herein, "about", "approximately", or "nearly" includes the stated value and a reference value within an acceptable range of deviation from the specific value, characterized in that the acceptable range of deviation is determined by the ordinary skill in the art considering the measurement being discussed and the error related to the measurement of the specific quantity (i.e., the limitation of the measurement method).

[0022] Crude oil electric desalting is a key process in the field of petroleum chemical industry. The core objective is to use the synergistic effect of high-voltage electric field and chemical demulsifier to promote the coalescence and sedimentation of dispersed salt water droplets in crude oil, so as to realize oil-water separation. In the prior art, the operating parameters of the electric desalting system, such as electric field strength, demulsifier injection amount, operating temperature, etc., are usually set by the experience of the operator or based on the offline test data of the crude oil. This static or semi-static control strategy faces the following problems: the physicochemical properties of the crude oil, including water content, salt content, density, viscosity, and natural emulsifier (such as asphaltene, colloid) content, will dynamically change with the oil source, batch, and production time. Using fixed operating parameters cannot match the actual state of the current crude oil emulsion in real time, which may lead to incomplete demulsification, excessive water and salt content in the outlet crude oil, or excessive consumption of electric energy and chemical agents, increasing the production cost and causing potential environmental problems. The prior art has failed to effectively solve the problem of how to accurately and adaptively optimize the control of the electric desalting process according to the real-time dynamic changes of the microstructure and properties of the emulsion.

[0023] The present application aims to improve the technical problem in the prior art that the crude oil electric desalting control method cannot adapt to the dynamic changes of the properties of the crude oil, leading to unstable treatment effect and resource waste, and provides a crude oil electric desalting system and a use method thereof, realizing adaptive optimization control of the electric desalting process.

[0024] As shown in Figure 1 , in one embodiment, the method comprises: S100, obtaining an initial emulsion state grid of the crude oil to be treated at the inlet of the electric desalting equipment, the initial emulsion state grid representing the spatial distribution state of water droplets in the crude oil to be treated.

[0025] In this step, in order to realize deep characterization of the state of the crude oil emulsion, the present application is no longer limited to traditional single-point, macroscopic physical parameter measurement (such as average water content), but obtains spatial distribution data reflecting the microstructure information of the emulsion. The initial emulsion state grid ( ) is a two-dimensional or three-dimensional data matrix, and the value of each element (or grid cell) of the grid represents the local phase state information at the corresponding position in the physical space, such as the local water content, conductivity or dielectric constant of the position. This grid-based data structure can intuitively present the size, morphology and dispersion degree of the water droplets in the crude oil.

[0026] Exemplarily, the initial emulsion state grid is obtained by The way can be through installing non-invasive tomography sensor array on the inlet pipeline of the electric desalter. Alternatively, electrical resistance tomography (ERT) technology can be adopted. The ERT system arranges a circle of electrodes around the pipeline cross section, injects weak excitation current to different electrode pairs in turn, and measures the voltage response on other electrode pairs. Due to the huge difference in conductivity between oil and water, by collecting a large number of voltage measurement data and using specific image reconstruction algorithms (such as linear back projection algorithm, Tikhonov regularization algorithm, etc.), the conductivity distribution map in the pipeline cross section can be inverted. The conductivity distribution map can be used as the initial emulsion state grid described in the present application. For example, the cross section can be divided into a pixel grid, and the value of each pixel represents the average conductivity of the region, which can be converted into local water cut. High-resolution can clearly reveal whether the emulsion is in the form of "water-in-oil" or "oil-in-water", and whether the water droplets are dispersed or have appeared preliminary aggregation.

[0027] S200, acquiring the inlet macroscopic process parameters of the crude oil to be treated.

[0028] In this step, in addition to the microscopic emulsion state grid, a series of traditional macroscopic process parameters representing the overall state of the crude oil need to be collected. These parameters can be obtained by installing conventional industrial sensors on the pipeline.

[0029] The inlet macroscopic process parameters can at least include inlet temperature , inlet pressure , inlet flow rate , inlet total water cut , inlet component proxy value . The inlet temperature can be measured by a thermocouple or a thermal resistance thermometer. The inlet pressure can be measured by a pressure transmitter. The inlet flow rate can be measured by a flowmeter (such as a turbine flowmeter or a Coriolis mass flowmeter). The inlet total water cut can be measured by an online water cut analyzer (such as microwave method or capacitance method). The inlet component proxy value For indirect characterization of the key chemical components in crude oil that affect the stability of emulsion, especially the content of natural surfactants such as asphaltene and resin. It can be chosen to directly measure these components on-line, or a correlation model between spectral characteristics and the content of these components can be established through near-infrared (NIR) or Raman spectroscopy analysis technology, so as to output a dimensionless proxy value. For example, the relative abundance of asphaltene can be characterized by using the combination of absorbance of a specific waveband, and the value of . These macroscopic parameters together constitute a description of the state of the current crude oil to be treated.

[0030] S300, based on the statistical characteristics of the initial emulsion state grid and the inlet macroscopic process parameters, determining the interface barrier index of the crude oil to be treated.

[0031] The interface barrier index ( ) is used to measure the inherent stability of the current crude oil emulsion against water droplet coalescence. A high value means that the emulsion is very stable, and the water droplets are wrapped in a solid interface film, making it difficult to coalesce, and requiring more external energy (higher electric field or more demulsifier) to break the stability. Conversely, a low value means that the emulsion itself is unstable and prone to demulsification.

[0032] In a preferred embodiment, the step of determining the interface barrier index is specifically obtained by constructing a model through the following index:

[0033] wherein, is the standard deviation of all grid cell values in the initial emulsion state grid . The size of the standard deviation reflects the uniformity of the internal phase distribution of the emulsion. If the water droplets are small and uniformly dispersed in the oil phase, the values of each point in will be close (all close to 0), and the standard deviation will be small. Conversely, if the water droplets are large and unevenly distributed, resulting in local high water content areas and pure oil areas in , the range of fluctuation of its value is large, and the standard deviation will be large.

[0034] An emulsion with the same macroscopic water content but microscopically composed of a large number of small and dispersed water droplets (corresponding to a smaller value), has a larger total oil-water interface area and a more stable interface film, and is therefore more difficult to demulsify, corresponding to a higher interface barrier index .

[0035] is the inlet temperature, ​is the reference temperature (e.g. ), is the temperature sensitivity coefficient. This term characterizes the effect of temperature on emulsion stability. Generally, increasing temperature will decrease oil phase viscosity and interfacial tension, thus reducing emulsion stability, i.e. will decrease.

[0036] is the aforementioned inlet composition proxy value, which directly reflects the relative content of natural emulsifiers. Substances such as asphaltenes will adsorb at the oil-water interface, forming a tough interfacial film that greatly increases emulsion stability. Therefore, is positively correlated with .

[0037] , , are preset weight coefficients. These coefficients are dimensionless and are used to balance the contribution of different factors to . Their values can be calibrated by a large number of experimental tests and data regression analysis on crude oil from a specific oilfield or refinery, and once calibrated, they remain fixed for a period of time.

[0038] Exemplarily, assuming that the of the emulsion is obtained by ERT, the standard deviation of its grid cell values is calculated. The currently measured inlet temperature (°C), the inlet composition proxy value . Assuming that the preset calibration parameters of the system are: , , , , , . Then the interfacial barrier index is calculated as follows:

[0039] This value quantifies the intrinsic stability of the current incoming emulsion.

[0040] S400, based on the preset system target coalescence rate, the interfacial barrier index, and the inlet macroscopic process parameters, solving a preset coalescence kinetics equation to obtain the optimal electric field strength and the optimal demulsifier dosage.

[0041] The coalescence kinetics equation describes how the actual coalescence rate of the emulsion in the electric desalting equipment is affected by the externally applied operating parameters (electric field strength and demulsifier dosage

[0042] ) and the intrinsic properties of the emulsion (represented by the interfacial barrier index and the inlet macroscopic process parameters ). and macroscopic parameters) jointly affect the coalescence kinetics.

[0043] The intrinsic logic of this coalescence kinetics equation is that the coalescence rate of water droplets is determined by the balance between the "driving force" and "resistance". The electric field and demulsifier provide the driving force, while the interfacial barrier and viscosity of the emulsion provide the resistance.

[0044] In a preferred embodiment, the preset coalescence kinetics equation has the following specific form:

[0045] wherein, is the model-predicted characteristic coalescence rate (unit, such as ), which represents the speed of water droplets effectively settling due to coalescence. is a system proportionality constant with the dimension of velocity, whose value is calibrated by experiments or historical data. is the coalescence driving force term, which is composed of the electric field effect and chemical demulsification effect. is the applied electric field intensity (unit, such as ). The dipole interaction force of the electric field on water droplets is proportional to the square of the electric field intensity . is the electric field effect coefficient, which is a system calibration parameter.

[0046] is the effective demulsifier effect term, which is a function of the demulsifier dosage (unit, such as ). The effect of the demulsifier does not increase linearly without limit, but there is a saturation effect. The saturation effect can be characterized by the following model:

[0047] wherein, represents the maximum theoretical effect that the demulsifier can achieve, is the characteristic concentration, which represents the dosage at which about 63% of the maximum effect is achieved. is the demulsifier effect coefficient. are system calibration parameters. is the coalescence resistance term, which is composed of the interfacial stability and macroscopic viscosity. is the interfacial barrier index calculated in the previous step, which is directly used as a core component of the resistance.

[0048] is the viscosity (unit, such as ) of the crude oil at temperature , which hinders the Brownian motion and collision coalescence of water droplets. The relationship between viscosity and temperature is usually described by an Arrhenius-type formula: wherein is the pre-factor, is the flow activation energy, is the ideal gas constant. is the viscous drag coefficient. These are all system calibration parameters.

[0049] All calibration parameters ( ) are obtained through offline experiments or historical data fitting. It is understandable that they are fixed for a specific electric desalination system and the main type of oil processed.

[0050] After defining the coalescence kinetics equation, the optimization goal is to find a set of operating parameters , so that the predicted coalescence rate Exactly equal to a preset system target aggregation rate . It is an engineering target value set according to production requirements (such as required outlet water content standard, processing load) and equipment capacity. For example, you can set .

[0051] At this point, a constraint equation is obtained:

[0052] This equation contains two unknowns: and This means that there are infinitely many sets of In order to obtain the only optimal solution, the present invention introduces economic considerations, that is, to find the combination with the lowest operating cost while meeting the technical requirements.

[0053] To do this, construct an operating cost function , which is mainly composed of consumed electricity and demulsifier costs:

[0054] in, is the electric power, which is proportional to the square of the electric field strength Approximately proportional; is the mass flow rate of the demulsifier, and and crude oil flow is proportional to the product of . is the cost per unit of electricity, is the cost per unit mass of demulsifier. After simplification, the cost function can be expressed as:

[0055] in and It is a cost coefficient that combines electricity prices, chemical prices, flow rate and equipment efficiency.

[0056] Finally, the optimization problem is transformed into a constrained nonlinear programming problem: Minimize:

[0057] Subject to:

[0058] And the operation constraints: , .

[0059] This optimization problem can be solved by standard numerical optimization algorithms (such as Lagrange multiplier method, sequential quadratic programming (SQP) method, etc.), so as to obtain a unique and deterministic optimal electric field strength. and optimal demulsifier dosage .

[0060] For example, following the above calculation, it is known that Assuming macro parameters , calculated The system calibration parameters are: , , , , , Target coalescence rate Cost coefficient , .

[0061] The constraint equation is: The optimization goal is: Solving this problem with a numerical solver yields a unique set of solutions, such as , .

[0062] To verify the solution, substitute the right side of the constraint equation:

[0063] The result and target value Very close, the slight difference is due to the numerical precision and The value of is rounded, that is, the solution is reasonable.

[0064] S500: Control the operation of the crude oil electric desalting system based on the optimal electric field strength and the optimal demulsifier dosage.

[0065] In this step, the optimal parameters calculated in the previous step Sent to the control execution module of the system.

[0066] The control execution module will Converted into a control signal for the high voltage power supply system of the electric desalination equipment, such as adjusting the output voltage of the transformer so that the electric field strength between the electrode plates reaches .

[0067] At the same time, Convert it into a control signal for the demulsifier injection pump, such as adjusting the stroke or frequency of the metering pump, so that the demulsifier concentration in the injected crude oil can be accurately achieved. .

[0068] This method forms a complete control cycle. The system will repeat all steps from S100 to S500 at a preset frequency (for example, every 5 minutes), thereby achieving continuous, dynamic, and adaptive optimization control of the electrical desalination process.

[0069] To further improve the long-term accuracy of the model and its adaptability to unmodeled factors, the present invention also provides a feedback correction mechanism, which complements the aforementioned feed-forward control and forms a feed-forward-feedback composite control system.

[0070] The feedback correction mechanism is executed after one control cycle ends, such as Figure 2 As shown, the following steps are included: S610: Obtain the actual water content after dehydration at the outlet of the crude oil electric desalting system.

[0071] An online water content analyzer is also installed on the outlet pipe of the system to measure the remaining water content in the crude oil after the electrical desalting treatment, which is recorded as .

[0072] S620. Based on the inlet macro-process parameters and the currently used optimal electric field strength and optimal demulsifier dosage, a predicted moisture content after dehydration is obtained through the agglomeration kinetic equation.

[0073] The model can not only be used to solve the optimal parameters in the forward direction, but also to predict the treatment effect in the reverse direction. and coalescence rate (Here it is equal to ), can be calculated based on the residence time in the device ( =Effective volume of the equipment) to estimate the effect of coagulation and sedimentation. A simplified sedimentation model can be established, for example, the amount of water removed is related to Directly proportional.

[0074] From this, a predicted outlet moisture content can be calculated .

[0075] Exemplarily, assume the dewatering efficiency With there is a relationship: wherein is the equipment efficiency coefficient.

[0076] The predicted outlet moisture content .

[0077] At the same time, the actual dewatering efficiency .

[0078] S630, based on the efficiency deviation between the actual post-dewatering moisture content and the predicted post-dewatering moisture content, correct the interfacial barrier index for the next control cycle.

[0079] Comparing and , the prediction deviation of the model .

[0080] If , it means that the actual efficiency is lower than the predicted efficiency. This indicates that the actual stability of the emulsion is higher than the current estimate of the model, i.e. the current value is underestimated.

[0081] If , it means that the actual efficiency is higher than the predicted efficiency, indicating that the value is overestimated.

[0082] If , it means that the model is very accurate.

[0083] This deviation is feedback information, which captures the comprehensive influence of all factors not explicitly expressed by the model (such as the influence of trace unknown chemical substances, sensor drift, etc.) on the stability of the system. The present invention uses this deviation to dynamically "calibrate" .

[0084] The correction step is specifically:

[0085]

[0086] Wherein: is the corrected interfacial barrier index that will be used for the next control cycle.

[0087] is the original value calculated by the S300 step in the current cycle.

[0088] It is the index correction calculated based on the deviation.

[0089] It is a preset correction gain (or feedback gain) that controls the amplitude of the correction. The sign of should be set so that when hour, , thereby increasing the index value for the next cycle.

[0090] At the beginning of the next control cycle, this modified value will be used when solving the aggregation kinetic equation in step S400. Value, not directly calculated by S300 In this way, the system has the ability of self-learning and error correction, and can continuously adapt to the slow changes in crude oil properties and the inaccuracy of the model, ensuring the long-term stability and optimization of the control effect.

[0091] Optionally, modify the gain It can also be adaptive. For example, when the deviation When the duration is large, you can increase the To speed up convergence; when the deviation is stable in a small range, reduce To prevent overshoot and oscillation.

[0092] In order to implement the above method, the present application also provides a crude oil electric desalination system. The system may include: The state acquisition module is composed of a series of sensors and instruments such as a tomographic imaging sensor array (such as an ERT electrode array and its data acquisition unit), a temperature sensor, a pressure sensor, a flow meter, an online water content analyzer, an online spectrum analyzer (such as a NIR probe), etc. installed on the inlet pipe of the electric desalination equipment. This module is responsible for real-time acquisition and various macro-process parameters.

[0093] The functions of these three logic modules are usually performed by a processor in an industrial control computer (IPC) or an embedded controller. The processor runs a specific software program that implements the method of the present invention. It receives data from the state acquisition module and performs the calculation of S300 to obtain , execute the optimization solution of S400 to obtain , and execute the correction logic of S610-S630 when feedback is enabled.

[0094] The control execution module includes a controller connected to a high voltage power supply and an emulsifier injection pump, such as an output card of a PLC (Programmable Logic Controller) or a DCS (Distributed Control System). Instructions, and convert them into specific voltage or frequency signals to accurately control the actions of the actuator.

[0095] The system also includes necessary input and output interfaces, communication buses (such as Modbus, Profibus) and human-machine interface (HMI) for system monitoring, parameter setting and alarm.

[0096] Those skilled in the art can understand that the above description of each embodiment is exemplary rather than exhaustive. Various modifications and changes can be made without departing from the spirit and scope of the application. For example, the parameters used to characterize In addition to the standard deviation, the parameters used to characterize the statistical properties can also be skewness, kurtosis, or frequency domain features based on Fourier transform / wavelet transform. The specific mathematical form of the coalescence kinetics equation and the cost function can also take other reasonable forms according to a deeper understanding of the specific physical and chemical process. These modifications and changes should fall within the scope of protection of the present application.

[0097] In summary, the technical scheme provided by the present application realizes precise and adaptive optimization control of the electric desalting process by characterizing the microspatial state of the emulsion and constructing a coalescence kinetics model with the internal stability index as the core, and improves the technical problems of rigid control strategy and inability to adapt to dynamic changes in the properties of crude oil in the prior art.

[0098] Through the description of the above embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, i.e. the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0099] In the several embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative, and the division of the modules or units is only a logical function division, and there can be another division way in actual implementation, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual ones can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0100] The units described as separate components may or may not be physically separate, and the components displayed as units may be a physical unit or multiple physical units, that is, may be located in one place, or also can be distributed to multiple different places. According to the actual needs, part or all of the units can be selected to achieve the purpose of the embodiment.

[0101] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware.

[0102] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for using a crude oil electric desalting system, characterized in that: include: Obtaining an initial emulsion state grid of the crude oil to be processed at an inlet of an electric desalter, wherein the initial emulsion state grid represents a spatial distribution state of water droplets in the crude oil to be processed; Obtaining inlet macro-process parameters of the crude oil to be processed; determining an interfacial barrier index of the crude oil to be processed based on the statistical characteristics of the initial emulsion state grid and the inlet macro-process parameters; Solving a preset coalescence kinetics equation based on a preset system target coalescence rate, the interface barrier index, and the inlet macro-process parameters to obtain an optimal electric field strength and an optimal demulsifier dosage; Based on the optimal electric field strength and the optimal demulsifier dosage, the operation of the crude oil electric desalting system is controlled.

2. The method according to claim 1, characterized in that The statistical characteristics of the initial emulsion state grid include the standard deviation of the values ​​of each grid cell in the initial emulsion state grid.

3. The method according to claim 2, characterized in that The step of determining the interface barrier index of the crude oil to be processed is specifically as follows: The interface barrier index is obtained by constructing a model through a preset index based on the standard deviation of the values ​​of each grid cell in the initial emulsion state grid, the inlet temperature of the crude oil to be processed, and the inlet component proxy value.

4. The method according to claim 1, wherein The preset coalescence kinetic equation characterizes the comprehensive effect of electric field intensity and demulsifier dosage on the predicted coalescence rate of the emulsion, and the predicted coalescence rate is negatively correlated with the interface barrier index.

5. The method according to claim 1 or 4, characterized in that The steps of solving a preset coalescence kinetic equation are specifically as follows: Constructing an operation cost function, wherein the operation cost function is positively correlated with the square of the electric field intensity and the demulsifier dosage; Under the constraint condition that the predicted coalescence rate is equal to the system target coalescence rate, the minimum value of the operation cost function is solved to obtain the optimal electric field intensity and the optimal demulsifier dosage.

6. The method according to claim 1, characterized in that The step of obtaining the initial emulsion state grid is specifically as follows: The cross-sectional electrical or acoustic characteristic data of the crude oil to be processed are collected by a tomographic imaging sensor array arranged at the inlet of the electrical desalting device, and reconstructed into the initial emulsion state grid.

7. The method according to any one of claims 1 to 6, characterized in that After controlling the operation of the crude oil electric desalination system, the method further includes: Obtaining the actual dehydrated water content at the outlet of the crude oil electric desalting system; Based on the inlet macro-process parameters and the currently used optimal electric field strength and optimal demulsifier dosage, a predicted water content after dehydration is obtained through the coalescence kinetic equation; The interface barrier index for the next control cycle is corrected based on the efficiency deviation between the actual moisture content after dehydration and the predicted moisture content after dehydration.

8. The method according to claim 7, characterized in that The step of correcting the interface barrier index for the next control cycle is specifically as follows: Combining the efficiency deviation with a preset correction gain to obtain an exponential correction amount; The interface barrier index calculated in the current control cycle is combined with the index correction amount to obtain a corrected interface barrier index for the next control cycle.

9. A crude oil electric desalination system, characterized in that: include: a state acquisition module configured to obtain an initial emulsion state grid of the crude oil to be processed at the inlet of the electric desalter, and to obtain inlet macroscopic process parameters of the crude oil to be processed, wherein the initial emulsion state grid represents the spatial distribution state of water droplets in the crude oil to be processed; a parameter determination module configured to determine an interfacial barrier index of the crude oil to be processed based on the statistical characteristics of the initial emulsion state grid and the inlet macro-process parameters; Running a solution module configured to solve a preset coalescence kinetics equation based on a preset system target coalescence rate, the interface barrier index, and the inlet macro-process parameters to obtain an optimal electric field strength and an optimal demulsifier dosage; The control execution module is configured to control the operation of the crude oil electric desalting system based on the optimal electric field strength and the optimal demulsifier dosage.

10. The system according to claim 9, characterized in that Also includes: The feedback correction module is configured to: Obtaining the actual dehydrated water content at the outlet of the crude oil electric desalting system; Based on the inlet macro-process parameters and the currently used optimal electric field strength and optimal demulsifier dosage, a predicted water content after dehydration is obtained through the coalescence kinetic equation; The interface barrier index for the next control cycle is corrected based on the efficiency deviation between the actual moisture content after dehydration and the predicted moisture content after dehydration.

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