A method, medium and system for determining pickling parameters in a pipeline dead zone

Abstract

The present invention provides a method, medium and system for determining pickling parameters in a pipeline dead zone, belonging to the technical field of pickling in a pipeline dead zone, including: First, establish a geometric model of the pipeline dead zone to determine characteristics such as the length, diameter and shape of the dead zone. Then define pickling parameters, including acid concentration, temperature, pickling time and the power of relevant pumps. Next, construct pickling effect evaluation indexes, such as pickling uniformity, residual dirt amount and pipe wall corrosion degree. Use computational fluid dynamics software to simulate the acid liquid flow and reaction process under different pickling parameters, and establish a mathematical relationship model between pickling parameters and pickling effect evaluation indexes. Based on this, design a multi-objective optimization model, with the goal of maximizing pickling uniformity, minimizing residual dirt amount and pipe wall corrosion degree, establish constraint conditions, and solve to obtain multiple optimal solutions. Finally, verify through small-scale experiments, select the optimal solution with the best pickling effect as the target solution, and output the corresponding best pickling parameters.

Description

Technical Field

[0001] The invention belongs to the technical field of pipeline dead zone pickling, and in particular relates to a pipeline dead zone pickling parameter determination method, medium and system. Background Art

[0002] Pipeline systems are widely used in many industrial fields such as chemical industry, petroleum, and natural gas. In these pipeline systems, there are often some dead corners that are difficult to clean, namely pipeline dead zones. These dead zones are usually caused by factors such as the complexity of the pipeline structure and the non-uniformity of the flow field. They are prone to accumulate a large amount of dirt and sediment, which seriously affects the operation status and service life of the pipeline. In order to solve the dirt problem in the pipeline dead zone, chemical pickling is usually used for cleaning. However, there are some problems with the existing pipeline pickling technology. First, the geometric structure of the pipeline dead zone is complex and diverse, and it is difficult to accurately predict the flow and reaction process of the acid in the dead zone. Secondly, there are many parameters that affect the pickling effect, including acid concentration, temperature, time, and pump power, which are difficult to fully optimize and control. Furthermore, there are certain safety hazards in the pickling process, such as corrosion of the pipe wall caused by acid leakage. Therefore, how to effectively determine the optimal process parameters for pickling of pipeline dead zones has become a key issue that needs to be solved in the current pipeline cleaning technology. Summary of the invention

[0003] In view of this, the present invention provides a method, medium and system for determining pipeline dead zone pickling parameters, which can solve the technical problem that it is difficult to effectively determine the optimal process parameters for pipeline dead zone pickling in the prior art.

[0004] The present invention is achieved in that:

[0005] A first aspect of the present invention provides a method for determining a pipeline dead zone pickling parameter, wherein the method is used to determine the pickling parameters of a pipeline dead zone pickling device, and comprises the following steps:

[0006] S10, establishing a geometric model of the pipeline dead zone, including the length, diameter and shape of the dead zone;

[0007] S20, define pickling parameters, including acid concentration, temperature, pickling time, acid pump power, and air pump power;

[0008] S30, constructing pickling effect evaluation indicators, including pickling uniformity, residual dirt amount and pipe wall corrosion degree;

[0009] S40, using computational fluid dynamics software to simulate the acid flow and reaction process under different pickling parameters;

[0010] S50, establishing a mathematical relationship model between pickling parameters and pickling effect evaluation indicators according to the simulation results;

[0011] S60. Design a multi-objective optimization model with the objectives of maximizing pickling uniformity, minimizing the amount of residual dirt, and the degree of pipe wall corrosion, and establish the constraints of the multi-objective optimization model;

[0012] S70. Solve the multi-objective optimization model to obtain multiple optimal solutions;

[0013] S80. Conduct small-scale experimental verification on the multiple optimal solutions, use the optimal solution with the best pickling effect as the target solution, and output the corresponding pickling parameters according to the target solution.

[0014] Specifically, the step S10 specifically includes: establishing a three-dimensional digital geometric model of the pipe dead zone. First, obtain the actual geometric dimension information of the pipe dead zone through means such as on-site investigation or laser scanning, including the dead zone length, diameter, and shape. Then, use computer-aided design software or computational fluid dynamics software to establish a three-dimensional digital model that can accurately reflect the actual structural characteristics of the pipe dead zone according to the collected geometric data. This geometric model provides a necessary basis for subsequent flow simulation and parameter optimization. Of course, the model can also be directly established according to the drawing documents of the pipe design.

[0015] Among them, the specific steps of the step S20 include: defining the pickling parameters to be optimized. According to the actual process requirements and experience, determine the main optimization parameters, including acid concentration, temperature, pickling time, and the power of the acid pump and air pump. Among them, the acid concentration is generally set between 5% and 20%; the temperature is controlled between 20 °C and 80 °C; the pickling time is set to 1 hour to 8 hours; the upper limit of the pump power is determined according to the on-site conditions. The specific value ranges of these parameters need to be appropriately adjusted in combination with the actual situation.

[0016] Among them, the step S30 includes: constructing a multi-index system for evaluating the pickling effect. First, define the evaluation indexes from three aspects: pickling uniformity, pickling dirt removal rate, and pipe wall corrosion degree. Among them, the pickling uniformity index reflects the distribution of the acid solution in the pipe dead zone; the pickling dirt removal rate index quantifies the cleaning effect of the dirt before and after pickling; the pipe wall corrosion degree index evaluates the corrosion degree of the pickling on the pipe material. These three indexes together constitute a comprehensive multi-objective evaluation system, which can objectively reflect the overall pickling effect.

[0017] Among them, the step S40 includes: simulating the acid solution flow and reaction process under different pickling parameters by using computational fluid dynamics software. First, based on the geometric model established in step S10, corresponding computational grids are constructed in the CFD software. Then, combining the pickling parameters determined in step S20, boundary conditions and appropriate physical models are set, such as turbulence models, chemical reaction kinetics models, etc. Through numerical simulation, key information such as the velocity field, concentration field, and temperature field in the pipe dead zone can be obtained, providing a basis for subsequent parameter optimization.

[0018] Among them, the step S50 includes: establishing a mathematical relationship model between pickling parameters and pickling effect evaluation indicators. Using the nonlinear regression analysis method, an equation for pickling uniformity, an equation for pickling decontamination rate, and an equation for pipe wall corrosion degree are established respectively. These three mathematical models can accurately describe the quantitative relationship between the optimized parameters and each evaluation indicator, providing support for multi-objective optimization.

[0019] Among them, the step S60 includes: designing a multi-objective optimization model. The objective functions include: maximizing pickling uniformity, maximizing pickling decontamination rate, and minimizing pipe wall corrosion degree. The decision variables cover acid solution concentration, temperature, pickling time, acid solution pump power, air pump power, etc. The constraint conditions mainly include: temperature range, concentration range, time range, total pump power limit, and maximum corrosion degree requirement, etc. This multi-objective optimization problem can be solved by using evolutionary algorithms such as NSGA-II or MOEA / D to obtain a set of Pareto optimal solutions.

[0020] Among them, the step S70 includes: solving the multi-objective optimization model. Using the aforementioned evolutionary algorithm, iterative calculations are performed on the optimization model. The algorithm will search for feasible solutions that meet the constraint conditions and gradually approach the Pareto optimal front, and finally obtain a set of Pareto optimal solutions. The pickling parameter combinations corresponding to these solutions are all optimized feasible solutions.

[0021] Among them, the step S80 includes: small-scale experimental verification. First, select the solution with the best pickling effect from the Pareto optimal solution set as the target solution. Then, conduct small-scale pickling experiments in the laboratory or on-site to test the performance of the pickling parameters corresponding to this target solution in actual operation. By comparing the experimental results with the model prediction, the accuracy and reliability of the established mathematical relationship model are further verified. Finally, output the verified optimal pickling parameter combination as the final process parameters for pickling the pipe dead zone.

[0022] Among them, the pipeline dead zone pickling device includes a clamp-type conduit blind plate, a first stainless steel pipe, a second stainless steel pipe, a first conduit, a second conduit, an acid solution pump, an air pump, and an acid solution tank; the clamp-type conduit blind plate is used to be installed at the end of the pipeline dead zone, so that the pipeline dead zone communicates with the outside; the first stainless steel pipe and the second stainless steel pipe are juxtaposed and embedded and welded in the notch of the clamp-type conduit blind plate; one end of the first stainless steel pipe outside the clamp-type conduit blind plate is connected to the acid solution pump through the first conduit, and one end of the second stainless steel pipe outside the clamp-type conduit blind plate is connected to the air pump through the first conduit, the acid solution pump is connected to the acid solution tank through a pipeline, and the acid solution tank is filled with pickling solution.

[0023] Furthermore, when the pipeline dead zone pickling device is in use, the acid solution pump pumps the acid solution into the dead zone, and the air pump pumps in air, so that the acid solution in the dead zone is stirred by the air and flows faster, improving the contact between the acid solution and the surface of the dead zone.

[0024] Among them, the mathematical relationship model includes a pickling uniformity equation, a pickling decontamination rate equation, and a pipe wall corrosion degree equation.

[0025] The mathematical relationship model includes a pickling uniformity equation, a pickling decontamination rate equation, and a pipe wall corrosion degree equation.

[0026] The pickling uniformity equation is specifically expressed as follows:

[0027]

[0028] In the formula, U is the pickling uniformity (a value between 0 and 1, the closer to 1, the more uniform); n is the number of sampling points in the pipeline dead zone; T i is the acid solution concentration at the i-th sampling point; is the average acid solution concentration of all sampling points.

[0029] Parameter acquisition method:

[0030] T i is obtained by an experimental method, including the following steps:

[0031] Step 1: Uniformly select n sampling points in the pipeline dead zone;

[0032] Step 2: Measure the acid solution concentration at each sampling point using a portable acidimeter;

[0033] Step 3: Record the acid solution concentration value T i .

[0034] The calculation method of

[0035]

[0036] The pickling decontamination rate equation is specifically expressed as follows:

[0037]

[0038] In the formula, R is the pickling decontamination rate (percentage); W i is the initial weight of the dirt in the pipeline dead zone before pickling; W f is the weight of the remaining dirt in the pipeline dead zone after pickling; t is the actual pickling time; t0 is the reference pickling time (usually taken as 1 hour); k is the time influence coefficient.

[0039] Method for obtaining parameters:

[0040] W i and W f are obtained through experiments, including the following steps:

[0041] Step 1: Before pickling, use an endoscope and a laser thickness gauge to measure the thickness and area of the dirt in the pipeline dead zone;

[0042] Step 2: According to the density of the dirt and the measurement results, calculate the initial dirt weight W i ;

[0043] Step 3: After pickling, repeat Step 1 and Step 2 to calculate the weight of the remaining dirt W f .

[0044] The k value is determined through regression analysis, and pickling experiments at multiple different times are required and fitted to obtain.

[0045] The pipe wall corrosion degree equation is specifically expressed as follows:

[0046]

[0047] In the formula, C is the pipe wall corrosion degree (mm / year); C0 is the reference corrosion rate; E a is the activation energy of the corrosion reaction; R is the gas constant; T is the actual pickling temperature (K); T0 is the reference temperature (usually taken as 298K); H + is the actual hydrogen ion concentration; is the reference hydrogen ion concentration; n is the reaction order; i is the imaginary unit; ω is the fluid disturbance frequency.

[0048] Method for obtaining parameters:

[0049] C0, E a and n are obtained through experiments, including the following steps:

[0050] Step 1: Conduct corrosion experiments at different temperatures and acid concentrations;

[0051] Step 2: Measure the corrosion rate under different conditions;

[0052] Step 3: Use the non - linear regression method to fit and obtain the values of C0, E a and n.

[0053] H + Obtained by measuring the pH value of the acid solution: H + = 10 -pH

[0054] ω is obtained through hydrodynamic analysis and experimental measurement. The fluid disturbance frequency can be measured using a hot - wire anemometer or particle image velocimetry (PIV).

[0055] Furthermore, the constraint conditions of the multi - objective optimization model at least include: temperature constraint, acid solution concentration constraint, pickling time constraint, and acid solution pump power constraint.

[0056] The multi - objective optimization model can be expressed as:

[0057] maxF(x)=[U(x), R(x), - C(x)];

[0058] s.t.g j (x)≤0, j = 1, 2,..., m;

[0059] h k (x)=0, k = 1, 2,..., p;

[0060] x l ≤x≤x u ;

[0061] Wherein, F(x) is the objective function vector, including maximizing the pickling uniformity U(x), maximizing the pickling decontamination rate R(x), and minimizing the pipe wall corrosion degree C(x) (taking a negative value to convert it into a maximization problem); x is the decision variable vector, including acid solution concentration, temperature, pickling time, acid solution pump power, and air pump power; g j (x) is the inequality constraint condition; h k (x) is the equality constraint condition; x l and x u are respectively the lower limit and upper limit of the decision variable.

[0062] The constraint conditions are as follows:

[0063] 1. Temperature constraint: 20°C ≤ T ≤ 80°C;

[0064] 2. Acid solution concentration constraint: 5% ≤ c ≤ 20%;

[0065] 3. Pickling time constraint: 1h ≤ t ≤ 8h;

[0066] 4. Acid solution pump power constraint: P pump + P air ≤ P max ;

[0067] 5. Safety constraint: C(x) ≤ C max ;

[0068] wherein, P pump is the power of the acid solution pump, P air is the power of the air pump, P max is the total power limit, and C max is the maximum allowable corrosion degree.

[0069] This multi-objective optimization problem can be solved using a multi-objective evolutionary algorithm (such as NSGA-II or MOEA / D) to obtain a set of Pareto optimal solutions. Then, according to the actual requirements and weights, the most suitable solution is selected from the Pareto optimal solutions as the final pickling parameter combination.

[0070] The second aspect of the present invention provides a computer-readable storage medium, wherein program instructions are stored in the computer-readable storage medium, and when the program instructions run on a computer, they are used to execute the above-mentioned method for determining pickling parameters in a pipeline dead zone.

[0071] The third aspect of the present invention provides a system for determining pickling parameters in a pipeline dead zone, which includes the above-mentioned computer-readable storage medium.

[0072] Compared with the prior art, the beneficial effects of the method, medium, and system for determining pickling parameters in a pipeline dead zone provided by the present invention are as follows:

[0073] Firstly, through on-site investigation and CAD / CFD modeling, a digital model that can accurately reflect the geometric characteristics of the pipeline dead zone is established. This lays the foundation for subsequent flow simulation and parameter optimization. Secondly, a multi-objective evaluation index system including pickling uniformity, decontamination rate, and corrosion degree is defined, comprehensively reflecting the overall effect of pickling. On this basis, the acid solution flow and reaction process in the pipeline dead zone under different process parameters are simulated using CFD software, and a corresponding mathematical relationship model is established.

[0074] Finally, the present invention designs a multi-objective optimization model with the goal of maximizing pickling uniformity and decontamination rate and minimizing the wall corrosion degree, and optimizes and solves each process parameter. Through iterative calculation of the evolutionary algorithm, a set of Pareto optimal solutions are obtained, that is, a feasible solution that achieves the best trade-off between different objectives. Small-scale experimental verification further confirms the accuracy of the established mathematical model and outputs a verified optimal pickling parameter combination.

[0075] In summary, the present invention solves the technical problem that it is difficult to effectively determine the optimal process parameters for pickling the dead zone of pipelines in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a flowchart of the method provided by the present invention;

[0077] Figure 2 is a schematic diagram of a pickling device for the dead zone of a pipeline;

[0078] In the drawings, the list of components represented by each reference numeral is as follows:

[0079] 10. Clamp-on type pipeline blind plate; 11. Notch; 21. First stainless steel pipe; 22. Second stainless steel pipe; 23. First conduit; 24. Second conduit; 25. Acid solution pump; 26. Air pump; 27. Acid solution tank. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.

[0081] As Figure 1 shown, it is a flowchart of a method for determining pickling parameters for the dead zone of a pipeline provided by the first aspect of the present invention. This method is used to determine the pickling parameters of a pickling device for the dead zone of a pipeline and includes the following steps:

[0082] S10. Establish a geometric model of the pipeline dead zone, including the dead zone length, diameter, and shape;

[0083] S20. Define pickling parameters, including acid solution concentration, temperature, pickling time, as well as the power of the acid solution pump and the power of the air pump;

[0084] S30. Construct evaluation indexes for pickling effects, including pickling uniformity, residual dirt amount, and pipe wall corrosion degree;

[0085] S40. Use computational fluid dynamics software to simulate the acid solution flow and reaction process under different pickling parameters;

[0086] S50. According to the simulation results, establish a mathematical relationship model between pickling parameters and pickling effect evaluation indexes;

[0087] S60. Design a multi-objective optimization model with the goal of maximizing pickling uniformity, minimizing the residual dirt amount, and the pipe wall corrosion degree, and establish the constraint conditions of the multi-objective optimization model;

[0088] S70. Solve the multi-objective optimization model to obtain multiple optimal solutions;

[0089] S80. Conduct small-scale experimental verification on multiple optimized solutions, use the optimized solution with the best pickling effect as the target solution, and output the corresponding pickling parameters according to the target solution.

[0090] As Figure 2 shown, it is a schematic diagram of a pipe dead zone pickling device, including a clamp-on conduit blind plate 10, a first stainless steel pipe 21, a second stainless steel pipe 22, a first conduit 23, a second conduit 24, an acid liquid pump 25, an air pump 26, and an acid liquid tank 27; the clamp-on conduit blind plate is used to be installed at the end of the pipe dead zone to make the pipe dead zone communicate with the outside; the first stainless steel pipe and the second stainless steel pipe are juxtaposed and welded in the notch 11 of the clamp-on conduit blind plate; one end of the first stainless steel pipe outside the clamp-on conduit blind plate is connected to the acid liquid pump through the first conduit, one end of the second stainless steel pipe outside the clamp-on conduit blind plate is connected to the air pump through the first conduit, the acid liquid pump is connected to the acid liquid tank through a pipe, and the acid liquid tank is filled with pickling solution.

[0091] The following is a detailed description of the specific implementation manners of the above steps:

[0092] The specific implementation manner of step S10 is to establish a geometric model of the pipe dead zone. First, it is necessary to obtain the actual geometric dimension information of the pipe dead zone, including the dead zone length, diameter, and shape, etc., through on-site investigation or advanced technical means such as laser scanning. Then, using computer-aided design (CAD) software or computational fluid dynamics (CFD) software, according to the obtained geometric information, establish a three-dimensional digital model of the pipe dead zone. This geometric model can accurately reflect the actual structural characteristics of the pipe dead zone and provide a basis for subsequent flow simulation and parameter optimization.

[0093] The specific implementation manner of step S20 is to define pickling parameters. According to the actual process requirements and experience, determine the pickling parameters to be optimized, mainly including: acid liquid concentration, temperature, pickling time, and the power of the acid liquid pump and the air pump. Among them, the acid liquid concentration is generally between 5% and 20%; the temperature is recommended to be controlled between 20 degrees Celsius and 80 degrees Celsius; the pickling time can be set from 1 hour to 8 hours; the upper limit of the pump power needs to be determined according to the on-site conditions. The value ranges of these parameters need to be appropriately adjusted in combination with the actual process situation.

[0094] The specific implementation manner of step S30 is to construct an evaluation index for pickling effect. First, define the evaluation index for pickling effect from three aspects: pickling uniformity, pickling dirt removal rate, and pipe wall corrosion degree. Among them, the pickling uniformity index can reflect whether the acid liquid is evenly distributed in the pipe dead zone; the pickling dirt removal rate index can quantitatively evaluate the cleaning effect of dirt before and after pickling; the pipe wall corrosion degree index can evaluate the corrosion degree of the pickling on the pipe material. These three indexes together constitute a multi-objective evaluation system, which can comprehensively reflect the overall pickling effect.

[0095] The specific implementation of step S40 is to use CFD software to simulate the acid solution flow and reaction process under different pickling parameters. First, according to the geometric model established in step S10, the corresponding computational grid is constructed in the CFD software. Then, combined with the pickling parameters defined in step S20, the boundary conditions and physical models are set, such as the turbulence model, chemical reaction kinetics model, etc. Through numerical simulation, key information such as the velocity field, concentration field, and temperature field in the pipe dead zone under different parameter combinations can be obtained. These simulation results provide a scientific basis for subsequent parameter optimization.

[0096] The specific implementation of step S50 is to establish a mathematical relationship model between pickling parameters and pickling effect evaluation indicators. According to the simulation results of step S40, the method of nonlinear regression analysis is used to establish the pickling uniformity equation, pickling decontamination rate equation, and pipe wall corrosion degree equation respectively. Among them, the pickling uniformity equation can be expressed as where U is the pickling uniformity, n is the number of sampling points, T i is the acid solution concentration at the i-th sampling point, is the average acid solution concentration. The pickling decontamination rate equation can be expressed as where R is the decontamination rate, W i and W f are the dirt weights before and after pickling respectively, t is the pickling time, t0 is the reference time, and k is the time influence coefficient. The pipe wall corrosion degree equation can be expressed as where C is the corrosion degree, C0 is the reference corrosion rate, E a is the activation energy, R is the gas constant, T is the actual temperature, T0 is the reference temperature, H + is the actual hydrogen ion concentration, is the reference concentration, n is the reaction order, i is the imaginary unit, and ω is the fluid disturbance frequency. These three mathematical models can accurately describe the quantitative relationship between pickling parameters and evaluation indicators.

[0097] The specific implementation of step S60 is to design a multi-objective optimization model. The objective functions include: maximizing the pickling uniformity U(x), maximizing the pickling decontamination rate R(x), and minimizing the pipe wall corrosion degree -C(x). The decision variables x include: acid solution concentration, temperature, pickling time, acid solution pump power, and air pump power. The constraint conditions mainly include: the temperature is between 20 degrees Celsius and 80 degrees Celsius, the concentration is between 5% and 20%, the pickling time is between 1 hour and 8 hours, the total pump power does not exceed P max , and the pipe wall corrosion degree does not exceed C max , etc. This multi-objective optimization problem can be solved using evolutionary algorithms (such as NSGA-II or MOEA / D) to obtain a set of Pareto optimal solutions.

[0098] The specific implementation of step S70 is to solve the multi-objective optimization model. Using the aforementioned evolutionary algorithm, the optimization model is solved and calculated. The algorithm will iteratively search for solutions that satisfy the constraints and gradually approach the Pareto optimal front. Eventually, a set of Pareto optimal solutions will be obtained, which are solutions that achieve the best trade-off between different objectives. The pickling parameter combinations corresponding to these solutions are all feasible and optimized.

[0099] The specific implementation of step S80 is small-scale experimental verification. First, select the pickling solution with the best pickling effect from the Pareto optimal solution set as the target solution. Then, conduct small-scale pickling experiments in the laboratory or on-site to test the performance of the pickling parameters corresponding to this target solution in actual operation. By comparing the experimental results with the model predictions, the accuracy and reliability of the established mathematical relationship model are further verified. Finally, output the verified optimal pickling parameter combination as the final process parameters for pipeline dead zone pickling.

[0100] Generally speaking, this method based on geometric modeling, flow simulation, mathematical modeling, and multi-objective optimization can systematically determine the optimal parameters for pipeline dead zone pickling.

[0101] To better understand and implement the present invention, a specific embodiment 1 of the method of the present invention is provided below. The method of this embodiment 1 is specifically described as follows: First, through advanced technical means such as on-site investigation or laser scanning, key geometric parameters such as the length L, diameter D, and shape of the pipeline dead zone are obtained. Then, using computer-aided design (CAD) software or computational fluid dynamics (CFD) software, a three-dimensional digital model of the pipeline dead zone is established based on the collected geometric data. This geometric model can be described by the following mathematical expression: G = {L, D, S}; where G represents the geometric model of the pipeline dead zone, and S represents the shape characteristics of the pipeline dead zone, which can be described mathematically in a parametric or meshed manner. By establishing this geometric model, the actual structural characteristics of the pipeline dead zone can be accurately reflected, providing the necessary basic data for subsequent flow simulation and parameter optimization.

[0102] Next is step S20, which is to define the pickling parameters to be optimized. The purpose of this step is to determine the key optimization parameters that affect the pickling effect. mainly including:

[0103] 1. Acid concentration c, whose value range is generally 5% ≤ c ≤ 20%;

[0104] 2. Pickling temperature T, controlled between 20°C ≤ T ≤ 80°C;

[0105] 3. Pickling time t, set to 1h ≤ t ≤ 8h;

[0106] 4. Acid pump power P pump ;

[0107] 5. Air pump power P air 。

[0108] The specific value ranges of these parameters need to be appropriately adjusted in combination with the actual process requirements and on-site conditions.

[0109] In step S30, a multi-index system for evaluating the pickling effect needs to be constructed. It mainly includes the following three indicators:

[0110] 1. Pickling uniformity U:

[0111]

[0112] where n is the number of sampling points in the pipeline dead zone, T i is the acid concentration at the i-th sampling point, is the average acid concentration of all sampling points. This indicator reflects whether the acid is evenly distributed in the pipeline dead zone, and its value range is 0 ≤ U ≤ 1. The closer the value is to 1, the more uniform it is.

[0113] 2. Pickling decontamination rate R:

[0114]

[0115] where W i and W f are the weights of the dirt in the pipeline dead zone before and after pickling respectively, t is the actual pickling time, t0 is the reference pickling time (usually taken as 1 hour), and k is the time influence coefficient. This indicator quantifies the cleaning effect of the dirt before and after pickling.

[0116] 3. Pipe wall corrosion degree C:

[0117]

[0118] where C0 is the reference corrosion rate, E a is the activation energy of the corrosion reaction, R is the gas constant, T0 is the reference temperature (usually taken as 298K), H + is the actual hydrogen ion concentration, is the reference hydrogen ion concentration, n is the reaction order, i is the imaginary unit, and ω is the fluid perturbation frequency. This indicator evaluates the corrosion degree of the pickling on the pipeline material.

[0119] These three indicators together constitute a comprehensive multi-objective evaluation system, which can objectively reflect the overall pickling effect.

[0120] In step S40, it is necessary to use CFD software to simulate the acid liquid flow and reaction process under different pickling parameters. The specific implementation method is as follows:

[0121] First, based on the geometric model established in step S10, construct the corresponding computational grid in the CFD software. The quality of the grid has a great impact on the accuracy of the simulation results, and grid independence verification is required to ensure that the grid size is fine enough.

[0122] Then, input the pickling parameters determined in step S20 into the CFD model and set appropriate boundary conditions and physical models. It mainly includes:

[0123] 1. Inlet boundary condition: u| inlet = u0, c| inlet = c0, T| inlet = T0;

[0124] 2. Outlet boundary condition:

[0125] 3. Wall boundary condition: u = 0,

[0126] 4. Turbulence model: Adopt the k-ε model or the k-ω SST model;

[0127] 5. Chemical reaction kinetics model: Use the Arrhenius equation to describe the corrosion reaction between the acid solution and the dirt;

[0128] Through numerical simulation, key information such as the velocity field u = (u, v, w), concentration field c, and temperature field T in the pipeline dead zone can be obtained, providing a basis for subsequent parameter optimization.

[0129] In step S50, it is necessary to establish a mathematical relationship model between the pickling parameters and the pickling effect evaluation index. Specifically as follows:

[0130] 1. Pickling uniformity equation:

[0131]

[0132] Among them, is the average acid solution concentration. This equation reflects the quantitative relationship between the pickling parameters x = (c, T, t, P pump , P air ) and the pickling uniformity U.

[0133] 2. Pickling decontamination rate equation:

[0134]

[0135] Among them, k is the time influence coefficient, which needs to be obtained by regression fitting through pickling experiments at multiple different times. This equation describes the relationship between the pickling parameters x and the pickling decontamination rate R.

[0136] 3. Pipe wall corrosion degree equation:

[0137]

[0138] Among them, C0, E a and n are obtained through experiments, H + = 10 -pH , and ω is obtained through hydrodynamic analysis and experimental measurement. This equation quantitatively describes the relationship between the pickling parameter x and the pipe wall corrosion degree C.

[0139] These three mathematical models can accurately reflect the quantitative relationship between the optimization parameters and each evaluation index, providing support for subsequent multi-objective optimization.

[0140] In step S60, it is necessary to design a multi-objective optimization model. The objective function can be expressed as:

[0141] maxF(x) = [U(x), R(x), -C(x)];

[0142] where x = (c, T, t, P pump , P air ) is the decision variable vector. The goal is to maximize the pickling uniformity U(x) and the pickling decontamination rate R(x), while minimizing the pipe wall corrosion degree C(x) (taking a negative value to convert it into a maximization problem).

[0143] The constraint conditions include:

[0144] 1. Temperature constraint: 20°C ≤ T ≤ 80°C;

[0145] 2. Concentration constraint: 5% ≤ c ≤ 20%;

[0146] 3. Time constraint: 1h ≤ t ≤ 8h;

[0147] 4. Pump power constraint: P pump + P air ≤ P max ;

[0148] 5. Corrosion degree constraint: C(x) ≤ C max ;

[0149] This multi-objective optimization problem can be solved using an evolutionary algorithm (such as NSGA-II or MOEA / D) to obtain a set of Pareto optimal solutions.

[0150] In step S70, it is necessary to solve the aforementioned multi-objective optimization model. The specific implementation is as follows:

[0151] 1. Initialization: Generate an initial population X = {x1, x2,..., x N}, where N is the population size. Each individual x iThey are all a set of feasible decision variables.

[0152] 2. Objective function evaluation: For each individual x in the population i , calculate its objective function value F(x i ) = [U(x i ), R(x i ), -C(x i )].

[0153] 3. Selection: Use binary tournament selection to select the individuals with better performance in the population as the parents.

[0154] 4. Evolutionary operations: Perform crossover and mutation operations on the parents to generate new offspring individuals.

[0155] 5. Environmental selection: Combine the parents and offspring, and select suitable individuals to form a new generation of population according to non-dominated sorting and crowding degree calculation.

[0156] 6. Termination condition check: If the termination condition is met (such as the iteration number reaches the upper limit), output the Pareto optimal solution set; otherwise, return to step 2 to continue the iteration.

[0157] Through the above evolutionary calculation, a set of Pareto optimal solutions can be obtained, that is, the solutions that achieve the best trade-off between different objectives. The pickling parameter combinations corresponding to these solutions are all optimized and feasible schemes.

[0158] Finally, small-scale experiments need to be carried out for verification in step S80. The specific implementation methods are as follows:

[0159] 1. Select the pickling effect optimal scheme from the Pareto optimal solution set as the target solution.

[0160] 2. Conduct small-scale pickling experiments in the laboratory or on-site to test the performance of the pickling parameters corresponding to the target solution in actual operation. Key parameters such as flow velocity field, concentration field, and temperature field during the experiment can be measured, and indicators such as pickling uniformity, decontamination rate, and corrosion degree can be evaluated.

[0161] 3. Compare and analyze the experimental results with the predictions of the previous mathematical model to verify the accuracy and reliability of the established relationship model. If there are large deviations, the mathematical model needs to be further optimized and corrected.

[0162] 4. Output the verified optimal pickling parameter combination as the final process parameters for pickling the pipeline dead zone.

[0163] Generally speaking, this method based on geometric modeling, flow simulation, mathematical modeling, and multi-objective optimization can systematically determine the optimal parameters for pickling the pipeline dead zone.

[0164] The second aspect of the present invention provides a computer-readable storage medium, wherein program instructions are stored in the computer-readable storage medium, and when the program instructions run on a computer, they are used to execute the above-mentioned method for determining pickling parameters in a pipeline dead zone.

[0165] The third aspect of the present invention provides a system for determining pickling parameters in a pipeline dead zone, which includes the above-mentioned computer-readable storage medium.

[0166] Specifically, the principle of the present invention is as follows:

[0167] First of all, establishing a geometric model of the pipeline dead zone is the key. The pipeline dead zone usually has a relatively complex three-dimensional structure, and accurately obtaining its geometric information is crucial for subsequent flow simulation and parameter optimization. By means of on-site investigation or laser scanning, etc., to collect the actual size data of the pipeline, and using CAD / CFD software to construct a digital model, the structural characteristics of the pipeline dead zone can be fully reflected; in addition, for pipelines designed by means of BIM, etc., the designed three-dimensional structure diagram can be directly used.

[0168] Secondly, defining multi-objective evaluation indicators for pickling effects is also one of the core innovations of the present invention. Optimizing a single indicator is difficult to comprehensively reflect the actual pickling effect. Therefore, the present invention proposes a three-index system including pickling uniformity, decontamination rate, and corrosion degree. These three indicators evaluate the pickling effect from different angles, paying attention to both the cleaning effect and safety, and providing a basis for subsequent multi-objective optimization.

[0169] Next, applying CFD software to simulate the acid liquid flow and reaction process under different process parameters is the basis for determining the optimal parameters. This simulation process takes into account many factors such as pipeline geometry, boundary conditions, turbulence characteristics, and chemical reaction kinetics, and can more accurately predict key parameters such as flow field distribution, concentration distribution, and temperature distribution in the pipeline dead zone. By comparing the simulation results under different parameter combinations, a mathematical relationship model between process parameters and evaluation indicators can be established.

[0170] Finally, the present invention designs a multi-objective optimization model, aiming to maximize pickling uniformity and decontamination rate and minimize the corrosion degree of the pipe wall, and systematically optimizes the process parameters. This multi-objective optimization method can find the best balance between different objectives and obtain a set of Pareto optimal solutions. Then, through small-scale experiments for verification, the best parameter combination is selected as the final result output. This methodology based on simulation-optimization-verification makes full use of the advantages of computer-aided simulation and intelligent algorithms, and can more scientifically determine the optimal process parameters for pickling the pipeline dead zone.

[0171] In summary, the core principles of the method of the present invention include: 1) establishing a pipeline geometric model to lay the foundation for flow simulation; 2) defining a comprehensive multi-objective evaluation index system; 3) using CFD simulation and mathematical modeling to describe the relationship between process parameters and evaluation indexes; 4) applying an evolutionary algorithm for multi-objective optimization solution; 5) verifying the optimization results through small-scale experiments.

[0172] To further better understand and implement the present invention, the following provides Example 2 of a specific application scenario of the present invention: There are some dead zones in the pipeline system of a chemical plant, which are prone to accumulating a large amount of dirt and affecting the operation of the system. To solve this problem, the chemical plant decides to use the method of the present invention to pickling the pipeline dead zones. The specific implementation process is as follows:

[0173] 1. Establishment of the geometric model of the pipeline dead zone

[0174] First, obtain the pipeline construction drawing file. The length L of the pipeline dead zone is 3.5 m, the diameter D is 0.6 m, and the structure is relatively complex, showing a certain bending characteristic.

[0175] Then, establish a three-dimensional CAD model of the pipeline dead zone in AutoCAD software, as shown in Table 1. This model can more accurately reflect the actual geometric characteristics of the pipeline dead zone.

[0176] Table 1 Geometric model of the pipeline dead zone

[0177] parameter numerical value length L 3.5m diameter D 0.6m structural feature bending

[0178] 2. Definition of pickling parameters

[0179] According to the actual process requirements and experience, the main pickling parameters to be optimized are determined as follows:

[0180] 1) Acid concentration c: 5% - 20%;

[0181] 2) Pickling temperature T: 20°C - 80°C;

[0182] 3) Pickling time t: 1 h - 8 h;

[0183] 4) Power P of the acid pump pump : maximum 50 kW;

[0184] 5) Power P of the air pump air : maximum 30 kW;

[0185] The specific value ranges of these parameters need to be appropriately adjusted in combination with on-site conditions.

[0186] 3. Construction of pickling effect evaluation indexes

[0187] To comprehensively evaluate the effect of pickling, the present invention defines the following three evaluation indicators:

[0188] 1) Pickling uniformity U: It reflects the distribution of acid solution in the dead zone of the pipeline.

[0189] 2) Pickling decontamination rate R: It quantifies the cleaning effect of dirt before and after pickling.

[0190] 3) Pipe wall corrosion degree C: It evaluates the corrosion degree of the pipe material by pickling.

[0191] These three indicators together constitute a multi-objective evaluation system, which can objectively reflect the overall effect of pickling.

[0192] 4. CFD simulation of acid solution flow and reaction process

[0193] Based on the pipeline geometric model established in Step 1, the corresponding computational grid was constructed in the FLUENT software. The grid quality was controlled with y+ < 5 to ensure better boundary layer resolution accuracy.

[0194] Then, the pickling parameters determined in Step 2 were input into the CFD model, and the following boundary conditions and physical models were set:

[0195] 1) Inlet boundary condition:

[0196] Velocity u0 = 1.5 m / s;

[0197] Concentration c0 = 10%;

[0198] Temperature T0 = 40 °C;

[0199] 2) Outlet boundary condition: Free outflow;

[0200] 3) Wall boundary condition: No-slip, concentration gradient is 0, convective heat transfer;

[0201] 4) Turbulence model: The k-ω SST model is adopted;

[0202] 5) Chemical reaction kinetics: The Arrhenius equation is used to describe the reaction between the acid solution and the dirt;

[0203] Through numerical simulation, the velocity field, concentration field and temperature field distributions in the dead zone of the pipeline were obtained, providing basic data for subsequent parameter optimization. Some key simulation results are shown in Table 2.

[0204] Table 2 CFD simulation results

[0205]

[0206]

[0207] 5. Establish a mathematical relationship model

[0208] Based on the CFD simulation results, the pickling uniformity equation, pickling decontamination rate equation, and pipe wall corrosion degree equation were established respectively by using the method of nonlinear regression analysis.

[0209] 1) Pickling uniformity equation:

[0210]

[0211] Among them, n = 20 is the number of sampling points in the dead zone of the pipeline, T i is the acid solution concentration at the i-th sampling point, and T is the average concentration. This equation represents the relationship between the pickling parameters x = (c, T, t, P pump , P air ) and the pickling uniformity U.

[0212] 2) Pickling decontamination rate equation:

[0213]

[0214] Among them, W i = 2.5 kg and W f = 0.4 kg are the weights of the dirt before and after pickling respectively, and t is the pickling time (in hours). This equation describes the relationship between the pickling parameters x and the pickling decontamination rate R.

[0215] 3) Pipe wall corrosion degree equation:

[0216]

[0217] Among them, E a = 42000 J / mol is the activation energy of the corrosion reaction, R = 8.314 J / (mol·K) is the gas constant, T0 = 298 K is the reference temperature, pH = 1.8 is the pH value of the acid solution, and f = 5 Hz is the fluid disturbance frequency. This equation describes the relationship between the pickling parameters x and the pipe wall corrosion degree C.

[0218] These three mathematical models provide support for subsequent multi-objective optimization.

[0219] 6. Construction and solution of the multi-objective optimization model

[0220] According to the above mathematical models, the present invention designs the following multi-objective optimization problem:

[0221] maxF(x) = [U(x), R(x), -C(x)];

[0222] s.t. 20°C ≤ T ≤ 80°C;

[0223] 5% ≤ c ≤ 20%;

[0224] 1 h ≤ t ≤ 8 h;

[0225] P pump + F air ≤ 80 kW;

[0226] C(x) ≤ 0.12 mm / year;

[0227] where x = (c, T, t, P pump , P air ) is the decision variable vector.

[0228] The NSGA-II algorithm was used to solve and calculate this multi-objective optimization problem, and a set of Pareto optimal solutions was obtained. Among them, a typical Pareto optimal solution is shown in Table 3.

[0229] Table 3 Pareto Optimal Solution

[0230] parameter numerical value concentration c 12％ temperature T 55℃ time t 4h <![CDATA[Pump power P pump > 40kW <![CDATA[Pump power P air > 25kW uniformity U 0.92 decontamination rate R 92％ corrosion degree C 0.10mm / year

[0231] It can be seen that this Pareto optimal solution achieves a good balance among the three objective indicators: a relatively high pickling uniformity, a relatively large decontamination rate, and the pipe wall corrosion degree is also within a controllable range. This provides an ideal parameter combination for subsequent experimental verification.

[0232] 7. Small-Scale Experimental Verification

[0233] According to the aforementioned Pareto optimal solution, corresponding pickling experiments were carried out in the laboratory environment.

[0234] The specific steps are as follows:

[0235] 1) Twenty sampling points were evenly arranged in the dead zone of the pipeline, and the acid concentration at each point was measured using a portable pH meter, and the average concentration was calculated.

[0236] 2) Through the measurement of an endoscope and a laser thickness gauge, the initial dirt weight W i = 2.4 kg, and the remaining dirt weight W f = 0.3 kg after the experiment.

[0237] 3) The corrosion rate of the pipeline material was measured to be 0.09 mm / year, which was in good agreement with the model prediction results.

[0238] 4) During the experiment, it was observed that the acid solution flowed relatively evenly in the dead zone of the pipeline, and there was no obvious dead angle accumulation. The experimental results showed that the pickling uniformity U = 0.90, the decontamination rate R = 87.5%, and the corrosion degree C = 0.09 mm / year, which were relatively close to the prediction results of the Pareto optimal solution.

[0239] Through the verification of this small-scale experiment, the accuracy and reliability of the established mathematical model were further confirmed. Therefore, the pickling parameter combination corresponding to this Pareto optimal solution can be used as the final process parameter output.

[0240] In summary, the specific application of the method of the present invention in the pickling of the pipeline dead zone in this chemical plant fully reflects its systematicness, scientificity and reliability. By means of geometric modeling, flow simulation, mathematical modeling and multi-objective optimization, the optimal pickling parameters were determined, and the accuracy of the results was further confirmed through experimental verification.

[0241] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention.

Claims

1. A method for determining the pickling parameters of a pipeline dead zone, characterized in that: The method is used to determine the pickling parameters of a pipeline dead zone pickling device, comprising the following steps: S10, establishing a geometric model of the pipeline dead zone, including the length, diameter and shape of the dead zone; S20, define pickling parameters, including acid concentration, temperature, pickling time, acid pump power, and air pump power; S30, constructing pickling effect evaluation indicators, including pickling uniformity, residual dirt amount and pipe wall corrosion degree; S40, using computational fluid dynamics software to simulate the acid flow and reaction process under different pickling parameters; S50. According to the simulation results, a mathematical relationship model between the pickling parameters and the pickling effect evaluation index is established, including a pickling uniformity equation, a pickling decontamination rate equation, and a pipe wall corrosion degree equation; the pickling uniformity equation is specifically expressed as follows: ; In the formula, is the pickling uniformity; is the number of sampling points in the pipeline dead zone; For the Acid concentration at each sampling point; is the average acid concentration of all sampling points; The pickling decontamination rate equation is specifically expressed as follows: ; In the formula, is the pickling decontamination rate; is the initial weight of dirt in the dead zone of the pipeline before pickling; It is the weight of the dirt remaining in the dead zone of the pipe after pickling; is the actual pickling time; is the benchmark pickling time; is the time influence coefficient; The pipe wall corrosion degree equation is specifically expressed as follows: ; In the formula, is the degree of corrosion of the pipe wall; is the baseline corrosion rate; is the activation energy of the corrosion reaction; is the gas constant; is the actual pickling temperature; is the reference temperature; is the actual hydrogen ion concentration; is the baseline hydrogen ion concentration; is the reaction order; is an imaginary unit; is the fluid disturbance frequency; S60, designing a multi-objective optimization model with the objectives of maximizing pickling uniformity and minimizing residual dirt and pipe wall corrosion, and establishing constraints of the multi-objective optimization model; S70, solving the multi-objective optimization model to obtain multiple optimization solutions; S80, performing small-scale experimental verification on the multiple optimization solutions, taking the optimization solution with the best pickling effect as the target solution, and outputting corresponding pickling parameters according to the target solution.

2. A method for determining pipeline dead zone pickling parameters according to claim 1, characterized in that: The pipeline dead zone pickling device includes a clamp-type conduit blind plate, a first stainless steel pipe, a second stainless steel pipe, a first conduit, a second conduit, an acid pump, an air pump, and an acid tank; the clamp-type conduit blind plate is used to be installed at the end of the pipeline dead zone, so that the pipeline dead zone is connected to the outside; the first stainless steel pipe and the second stainless steel pipe are embedded in the notch of the clamp-type conduit blind plate in parallel and welded; the first stainless steel pipe is connected to the acid pump through the first conduit at one end outside the clamp-type conduit blind plate, the second stainless steel pipe is connected to the air pump through the first conduit at one end outside the clamp-type conduit blind plate, the acid pump is connected to the acid tank through a pipeline, and the acid tank is filled with pickling liquid.

3. A method for determining pipeline dead zone pickling parameters according to claim 2, characterized in that: When the pipeline dead zone pickling device is in use, the acid pump pumps acid into the dead zone, and the air pump pumps air, so that the acid in the dead zone is stirred by the air to accelerate the flow, thereby improving the contact between the acid and the surface of the dead zone.

4. A method for determining pipeline dead zone pickling parameters according to claim 1, characterized in that: The constraints of the multi-objective optimization model include at least: temperature constraints, acid concentration constraints, pickling time constraints and acid pump power constraints.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, the method for determining the dead zone pickling parameters of a pipeline according to any one of claims 1 to 4 is used to execute.

6. A pipeline dead zone pickling parameter determination system, characterized in that: Contains the computer-readable storage medium of claim 5.

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