Pipeline dead zone pickling parameter determination method, medium and system

Abstract

The invention provides a pipeline dead zone pickling parameter determination method, medium and system, and belongs to the technical field of pipeline dead zone pickling, and the method comprises the steps: firstly building a pipeline dead zone geometric model, and determining the length, diameter, shape and other characteristics of a dead zone; then defining pickling parameters including acid liquor concentration, temperature, pickling time and related pump power; then, acid pickling effect evaluation indexes are constructed, such as acid pickling uniformity, residual dirt amount and pipe wall corrosion degree; the method comprises the following steps: simulating acid liquid flowing and reaction processes under different pickling parameters by utilizing computational fluid dynamics software, and establishing a mathematical relationship model between the pickling parameters and pickling effect evaluation indexes; on the basis, a multi-objective optimization model is designed, constraint conditions are established by taking maximization of pickling uniformity and minimization of residual dirt amount and pipe wall corrosion degree as objectives, and a plurality of optimal solutions are solved. And finally, through small-scale experiment verification, selecting an optimal solution with an optimal pickling effect as a target solution, and outputting a corresponding optimal pickling parameter.

Description

Technical Field

[0001] This invention belongs to the field of pipeline dead zone pickling technology, specifically, it relates to a method, medium and system for determining pipeline dead zone pickling parameters. Background Art

[0002] Pipeline systems are widely used in many industrial fields such as chemical, petroleum, and natural gas. These systems often contain hard-to-clean dead zones, or "dead areas." These dead zones are typically caused by the complexity of the pipeline structure and the non-uniformity of the flow field, easily accumulating large amounts of dirt and deposits, severely affecting the pipeline's operating condition and service life. To solve the problem of dirt accumulation in these dead zones, chemical acid washing is commonly used. However, existing pipeline acid washing technologies have several problems. First, the geometric structure of pipeline dead zones is complex and diverse, making it difficult to accurately predict the flow and reaction process of acid within them. Second, numerous parameters affect the acid washing effect, including acid concentration, temperature, time, and pump power, making comprehensive optimization and control difficult. Furthermore, there are certain safety hazards during the acid washing process, such as pipe wall corrosion caused by acid leakage. Therefore, effectively determining the optimal process parameters for acid washing of pipeline dead zones has become a key problem that urgently needs to be solved in current pipeline cleaning technology. Summary of the Invention

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

[0004] This invention is implemented as follows:

[0005] A first aspect of the present invention provides a method for determining pickling parameters for a pipeline dead zone, wherein determining the pickling parameters of a pipeline dead zone pickling device includes the following steps:

[0006] S10. Establish a geometric model of the pipe dead zone, including the dead zone length, diameter, and shape;

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

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

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

[0010] S50. Based on the simulation results, establish a mathematical relationship model between pickling parameters and pickling effect evaluation indicators;

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

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

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

[0014] Specifically, step S10 includes: establishing a three-dimensional digital geometric model of the pipeline dead zone. First, the actual geometric dimensions of the pipeline dead zone, including its length, diameter, and shape, are obtained through on-site surveys or laser scanning. Then, using computer-aided design software or computational fluid dynamics software, a three-dimensional digital model that accurately reflects the actual structural characteristics of the pipeline dead zone is established based on the collected geometric data. This geometric model provides the necessary foundation for subsequent flow simulation and parameter optimization. Alternatively, the model can be directly established based on the pipeline design drawings.

[0015] The specific steps of step S20 include: defining the pickling parameters to be optimized. Based on actual process requirements and experience, the main optimization parameters are determined, including acid concentration, temperature, pickling time, and the power of the acid pump and the air pump. The acid concentration is generally set between 5% and 20%; the temperature is controlled between 20 degrees Celsius and 80 degrees Celsius; the pickling time is set between 1 hour and 8 hours; and the upper limit of the pump power is determined based on site conditions. The specific value ranges of these parameters need to be adjusted appropriately based on the actual situation.

[0016] Step S30 includes: constructing a multi-index system for evaluating the pickling effect. First, evaluation indicators are defined from three aspects: pickling uniformity, pickling decontamination rate, and pipe wall corrosion degree. The pickling uniformity index reflects the distribution of acid in the dead zone of the pipeline; the pickling decontamination rate index quantifies the cleaning effect of dirt before and after pickling; and the pipe wall corrosion degree index assesses the degree of corrosion of the pipe material by pickling. These three indicators together constitute a comprehensive multi-objective evaluation system, which can objectively reflect the overall effect of pickling.

[0017] Step S40 includes simulating the acid flow and reaction process under different pickling parameters using computational fluid dynamics (CFD) software. First, based on the geometric model established in step S10, a corresponding computational mesh is constructed in the CFD software. Then, combined with the pickling parameters determined in step S20, boundary conditions and suitable physical models, such as turbulence models and chemical reaction kinetic models, are set. Through numerical simulation, key information such as the velocity field, concentration field, and temperature field within the pipe dead zone can be obtained, providing a basis for subsequent parameter optimization.

[0018] Step S50 includes: establishing mathematical relationship models between pickling parameters and pickling effect evaluation indicators. Nonlinear regression analysis is used to establish equations for pickling uniformity, pickling decontamination rate, and pipe wall corrosion. These three mathematical models can accurately describe the quantitative relationship between optimization parameters and each evaluation indicator, providing support for multi-objective optimization.

[0019] 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. Decision variables include acid concentration, temperature, pickling time, acid pump power, and gas pump power. Constraints mainly include temperature range, concentration range, time range, total pump power limit, and maximum corrosion requirement. 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.

[0020] Step S70 includes solving a multi-objective optimization model. The optimization model is iteratively calculated using the aforementioned evolutionary algorithm. The algorithm searches for feasible solutions that satisfy the constraints and gradually approaches the Pareto optimal front, ultimately obtaining a set of Pareto optimal solutions. The acid washing parameter combinations corresponding to these solutions are all optimized feasible schemes.

[0021] Step S80 includes: small-scale experimental verification. First, the optimal pickling solution is selected from the Pareto optimal solution set as the target solution. Then, a small-scale pickling experiment is conducted in the laboratory or on-site to test the performance of the pickling parameters corresponding to the 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, the verified optimal pickling parameter combination is output as the final process parameters for pipeline dead zone pickling.

[0022] The pipeline dead zone pickling device includes a clamp-type conduit blind flange, 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 flange is installed at the end of the pipeline dead zone, allowing the pipeline dead zone to communicate with the outside. The first and second stainless steel pipes are embedded and welded side by side into the notch of the clamp-type conduit blind flange. The first stainless steel pipe is connected to the acid pump through the first conduit at one end outside the clamp-type conduit blind flange, and the second stainless steel pipe is connected to the air pump through the first conduit at one end outside the clamp-type conduit blind flange. The acid pump is connected to the acid tank through a pipe, and the acid tank contains pickling solution.

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

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

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

[0026] The specific expression of the pickling uniformity equation is as follows:

[0027]

[0028] In the formula, U represents the pickling uniformity (a value between 0 and 1, with values ​​closer to 1 indicating greater uniformity); n represents the number of sampling points within the pipe dead zone; T i Let be the acid concentration at the i-th sampling point; This represents the average acid concentration at all sampling points.

[0029] Parameter acquisition method:

[0030] T i The method of obtaining the sample experimentally includes the following steps:

[0031] Step 1: Select n sampling points evenly within the dead zone of the pipeline;

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

[0033] Step 3: Record the acid concentration value T at each sampling point. i .

[0034] The calculation method is as follows:

[0035]

[0036] The specific expression of the pickling and stain removal rate equation is as follows:

[0037]

[0038] In the formula, R is the pickling and stain removal rate (percentage); W i W represents the initial weight of the fouling within the dead zone of the pipeline before pickling. f t is the weight of residual dirt in the dead zone of the pipeline after pickling; t is the actual pickling time; t0 is the baseline pickling time (usually 1 hour); k is the time influence coefficient.

[0039] Parameter acquisition method:

[0040] W i and W f The method of obtaining the sample experimentally includes the following steps:

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

[0042] Step 2: Calculate the initial dirt weight W based on the dirt density and measurement results. i ;

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

[0044] The k value is determined through regression analysis, which requires multiple sets of pickling experiments at different times and fitting the results.

[0045] The corrosion rate equation for pipe walls is expressed as follows:

[0046]

[0047] In the formula, C is the pipe wall corrosion rate (mm / year); C0 is the baseline 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 + This represents the actual hydrogen ion concentration. ω is the baseline hydrogen ion concentration; n is the reaction order; i is the imaginary unit; ω is the fluid disturbance frequency.

[0048] Parameter acquisition method:

[0049] C0, E a And n were obtained experimentally, 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 a nonlinear regression method to fit and obtain C0 and E. a And the value of n.

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

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

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

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

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

[0058] stg j (x)≤0,j=1,2,...,m;

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

[0060] x l ≤x≤x u ;

[0061] In the formula, F(x) is the objective function vector, which includes maximizing the pickling uniformity U(x), maximizing the pickling decontamination rate R(x), and minimizing the pipe wall corrosion C(x) (negative values ​​are taken to transform it into a maximization problem); x is the decision variable vector, including acid concentration, temperature, pickling time, acid pump power, and air pump power; g j (x) represents the inequality constraints; h k (x) represents the equality constraint; x l and x u These are the lower and upper bounds of the decision variable, respectively.

[0062] The constraints are as follows:

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

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

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

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

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

[0068] Among them, P pump P is the power of the acid pump. air P is the power of the air pump. max For total power limitation, C max This represents the maximum permissible corrosion level.

[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, based on actual requirements and weights, the most suitable solution is selected from the Pareto optimal solutions as the final combination of pickling parameters.

[0070] A second aspect of the present invention provides a computer-readable storage medium storing program instructions that, when executed in a computer, perform the above-described method for determining pipeline dead zone pickling parameters.

[0071] A third aspect of the present invention provides a system for determining pickling parameters for pipeline dead zones, wherein the system includes the aforementioned computer-readable storage medium.

[0072] Compared with existing technologies, the beneficial effects of the method, medium, and system for determining pipeline dead zone pickling parameters provided by this invention are:

[0073] First, through on-site investigation and CAD / CFD modeling, a digital model accurately reflecting the geometric characteristics of the pipeline dead zone was established. This laid the foundation for subsequent flow simulation and parameter optimization. Second, a multi-objective evaluation index system, including pickling uniformity, decontamination rate, and corrosion degree, was defined to comprehensively reflect the overall pickling effect. Based on this, CFD software was used to simulate the acid flow and reaction process within the pipeline dead zone under different process parameters, and corresponding mathematical relationship models were established.

[0074] Finally, this invention designs a multi-objective optimization model to maximize pickling uniformity and decontamination rate while minimizing pipe wall corrosion, and optimizes various process parameters. Through iterative calculation using an evolutionary algorithm, a set of Pareto optimal solutions is obtained, representing a feasible solution that achieves the best trade-off among different objectives. Small-scale experiments further confirm the accuracy of the established mathematical model and output the validated optimal combination of pickling parameters.

[0075] In summary, this invention solves the technical problem that existing technologies struggle to effectively determine the optimal process parameters for pickling dead zones in pipelines. Attached Figure Description

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

[0077] Figure 2 Schematic diagram of the pickling device for the dead zone of the pipeline;

[0078] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0079] 10. Includes a clamp-on blind flange for the conduit; 11. Notch; 21. First stainless steel pipe; 22. Second stainless steel pipe; 23. First conduit; 24. Second conduit; 25. Acid pump; 26. Air pump; 27. Acid tank. Detailed Implementation

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

[0081] like Figure 1 The diagram shown is a flowchart of a method for determining pickling parameters in a pipeline dead zone according to the first aspect of this invention. This method is used to determine the pickling parameters of a pipeline dead zone pickling device and includes the following steps:

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

[0083] S20. Define pickling parameters, including acid concentration, temperature, pickling time, acid pump power, and air pump power.

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

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

[0086] S50. Based on the simulation results, establish a mathematical relationship model between pickling parameters and pickling effect evaluation indicators;

[0087] S60. Design a multi-objective optimization model with the objectives of maximizing pickling uniformity, minimizing residual fouling, and minimizing pipe wall corrosion, and establish the constraints of the multi-objective optimization model.

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

[0089] S80. Conduct small-scale experiments to verify multiple optimized solutions, take the optimized solution with the best pickling effect as the target solution, and output the corresponding pickling parameters according to the target solution.

[0090] like Figure 2 The diagram shows a schematic of a pipeline dead zone pickling device, including a clamp-type conduit blind flange 10, a first stainless steel pipe 21, a second stainless steel pipe 22, a first conduit 23, a second conduit 24, an acid pump 25, an air pump 26, and an acid tank 27. The clamp-type conduit blind flange is installed at the end of the pipeline dead zone, allowing the pipeline dead zone to communicate with the outside. The first and second stainless steel pipes are embedded side by side and welded into the notch 11 of the clamp-type conduit blind flange. The first stainless steel pipe is connected to the acid pump through the first conduit at one end outside the clamp-type conduit blind flange, and the second stainless steel pipe is connected to the air pump through the first conduit at one end outside the clamp-type conduit blind flange. The acid pump is connected to the acid tank through a pipe, and the acid tank contains pickling solution.

[0091] The specific implementation methods of the above steps are described in detail below:

[0092] The specific implementation of step S10 involves establishing a geometric model of the pipeline dead zone. First, the actual geometric dimensions of the pipeline dead zone, including its length, diameter, and shape, need to be obtained through on-site investigation or advanced technologies such as laser scanning. 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 obtained geometric information. This geometric model accurately reflects the actual structural characteristics of the pipeline dead zone, providing a foundation for subsequent flow simulation and parameter optimization.

[0093] The specific implementation of step S20 involves defining pickling parameters. Based on actual process requirements and experience, the pickling parameters that need optimization are determined, mainly including: acid concentration, temperature, pickling time, and the power of the acid pump and air pump. The acid 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 to 1 hour to 8 hours; and the upper limit of the pump power needs to be determined based on site conditions. The value range of these parameters needs to be adjusted appropriately based on the actual process conditions.

[0094] The specific implementation of step S30 involves constructing an evaluation index for pickling effectiveness. First, the evaluation index is defined from three aspects: pickling uniformity, pickling decontamination rate, and pipe wall corrosion degree. Specifically, the pickling uniformity index reflects the uniformity of acid distribution within the dead zone of the pipeline; the pickling decontamination rate index quantitatively assesses the cleaning effect of the dirt before and after pickling; and the pipe wall corrosion degree index assesses the degree of corrosion of the pipeline material by pickling. These three indexes together constitute a multi-objective evaluation system that comprehensively reflects the overall effect of pickling.

[0095] The specific implementation of step S40 involves using CFD software to simulate the acid flow and reaction process under different pickling parameters. First, based on the geometric model established in step S10, a corresponding computational grid is constructed in the CFD software. Then, combined with the pickling parameters defined in step S20, boundary conditions and physical models, such as turbulence models and chemical reaction kinetic models, are set. Through numerical simulation, key information such as the velocity field, concentration field, and temperature field within the dead zone of the pipeline 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 involves establishing a mathematical relationship model between pickling parameters and pickling effect evaluation indicators. Based on the simulation results of step S40, a nonlinear regression analysis method is used to establish equations for pickling uniformity, pickling decontamination rate, and pipe wall corrosion. The pickling uniformity equation can be expressed as follows: Where U is the pickling uniformity, n is the number of sampling points, and T i Let be the acid concentration at the i-th sampling point. Let be the average acid concentration. The acid pickling and detergency equation can be expressed as: Where R is the decontamination rate, W i and W f Here, t represents the weight of the fouling before and after pickling, t is the pickling time, t0 is the baseline time, and k is the time influence coefficient. The pipe wall corrosion equation can be expressed as: Where C is the corrosion degree, C0 is the reference corrosion rate, and E a Here, R is the activation energy, T is the gas constant, T is the actual temperature, T0 is the reference temperature, and H is the gas constant. + This represents the actual hydrogen ion concentration. Here, is the baseline 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 involves designing a multi-objective optimization model. The objective functions include: maximizing pickling uniformity U(x), maximizing pickling decontamination rate R(x), and minimizing pipe wall corrosion -C(x). The decision variables x include: acid concentration, temperature, pickling time, acid pump power, and air pump power. The main constraints are: temperature between 20°C and 80°C, concentration between 5% and 20%, pickling time between 1 hour and 8 hours, and total pump power not exceeding P. max Pipe wall corrosion degree does not exceed C max 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 involves solving a multi-objective optimization model. The aforementioned evolutionary algorithm is used to solve the optimization model. The algorithm iteratively searches for solutions that satisfy the constraints and gradually approaches the Pareto optimal front. Ultimately, a set of Pareto optimal solutions is obtained, representing solutions that achieve the best trade-off between different objectives. The acid washing parameter combinations corresponding to these solutions are feasible and optimized.

[0099] The specific implementation of step S80 involves small-scale experimental verification. First, the optimal pickling solution is selected from the Pareto optimal solution set as the target solution. Then, small-scale pickling experiments are conducted in the laboratory or on-site to test the performance of the pickling parameters corresponding to the 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, the verified optimal combination of pickling parameters is output as the final process parameters for pipeline dead zone pickling.

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

[0101] To better understand and implement this invention, a specific embodiment 1 of the method of this invention is provided below. The method of this embodiment 1 is specifically described as follows: First, key geometric parameters such as the length L, diameter D, and shape of the pipe dead zone are obtained through advanced technologies such as on-site survey or laser scanning. Then, computer-aided design (CAD) software or computational fluid dynamics (CFD) software is used to establish a three-dimensional digital model of the pipe dead zone 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 pipe dead zone, and S represents the shape characteristics of the pipe dead zone, which can be mathematically described using parametric or meshing methods. By establishing this geometric model, the actual structural characteristics of the pipe dead zone can be accurately reflected, providing the necessary basic data for subsequent flow simulation and parameter optimization.

[0102] The next step is S20, which defines the pickling parameters that need to be optimized. The purpose of this step is to identify the key optimization parameters that affect the pickling effect. These mainly include:

[0103] 1. The acid concentration c generally ranges from 5% to 20%.

[0104] 2. The pickling temperature T should be controlled between 20℃ ≤ T ≤ 80℃;

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

[0106] 4. Acid pump power P pump ;

[0107] 5. Air pump power P air .

[0108] The specific range of values ​​for these parameters needs to be adjusted appropriately based on actual process requirements and on-site conditions.

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

[0110] 1. Pickling uniformity U:

[0111]

[0112] Where n is the number of sampling points within the pipe dead zone, T i Let be the acid concentration at the i-th sampling point. U represents the average acid concentration at all sampling points. This indicator reflects the uniformity of acid distribution within the dead zone of the pipeline, with a value range of 0 ≤ U ≤ 1. The closer the value is to 1, the more uniform the distribution.

[0113] 2. Pickling and stain removal rate R:

[0114]

[0115] Among them, W i and W f The values ​​represent the weight of the fouling in the dead zone of the pipeline before and after pickling, respectively; t represents the actual pickling time; t0 represents the baseline pickling time (usually 1 hour); and k represents the time influence coefficient. This indicator quantifies the cleaning effect of the fouling before and after pickling.

[0116] 3. Pipe wall corrosion degree C:

[0117]

[0118] Where C0 is the baseline corrosion rate, E a Here, is the activation energy of the corrosion reaction, R is the gas constant, T0 is the reference temperature (usually taken as 298K), and H... + This represents the actual hydrogen ion concentration. ω represents the baseline hydrogen ion concentration, n is the reaction order, i is the imaginary unit, and ω is the fluid disturbance frequency. This index assesses the degree of corrosion caused by pickling to pipeline materials.

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

[0120] In step S40, CFD software is used to simulate the acid 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, the corresponding computational mesh is constructed in the CFD software. The quality of the mesh has a significant impact on the accuracy of the simulation results, and mesh independence verification is required to ensure that the mesh size is sufficiently fine.

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

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

[0124] 2. Export boundary conditions:

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

[0126] 4. Turbulence model: The k-∈ model or the k-ωSST model is adopted;

[0127] 5. Chemical reaction kinetic model: The Arrhenius equation is used to describe the corrosion reaction between acid and dirt;

[0128] Numerical simulation can obtain key information such as the velocity field u=(u,v,w), concentration field c, and temperature field T within the dead zone of the pipeline, providing a basis for subsequent parameter optimization.

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

[0130] 1. Equation for pickling uniformity:

[0131]

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

[0133] 2. Acid pickling and stain removal rate equation:

[0134]

[0135] Where k is the time influence coefficient, which needs to be obtained through regression fitting of multiple pickling experiments at different times. This equation describes the relationship between the pickling parameter x and the pickling detergency R.

[0136] 3. Pipe wall corrosion equation:

[0137]

[0138] Among them, C0 and E a And n were obtained experimentally, H + =10 -pH ω was obtained through fluid dynamics analysis and experimental measurements. 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 optimization parameters and various evaluation indicators, providing support for subsequent multi-objective optimization.

[0140] In step S60, a multi-objective optimization model needs to be designed. 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 Let be the decision variable vector. The objective is to maximize the pickling uniformity U(x) and the pickling decontamination rate R(x), while minimizing the pipe wall corrosion C(x) (taking negative values ​​to transform it into a maximization problem).

[0143] The constraints include:

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

[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 constraint: C(x) ≤ C max ;

[0149] 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.

[0150] In step S70, it is necessary to solve the aforementioned multi-objective optimization model. The specific implementation method 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: A binary tournament selection method was used to select the best-performing individuals in the population as parents.

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

[0155] 5. Environmental selection: The parent and offspring generations are merged, and suitable individuals are selected to form a new generation of population based on non-dominant ranking and crowding degree calculation.

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

[0157] Through the evolutionary calculations described above, a set of Pareto optimal solutions can be obtained, which represent solutions that achieve the best trade-offs among different objectives. The acid washing parameter combinations corresponding to these solutions are all optimized and feasible schemes.

[0158] Finally, a small-scale experimental verification is required in step S80. The specific implementation method is as follows:

[0159] 1. Select the solution with the best pickling effect from the Pareto optimal solution set as the objective 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 rate, concentration, and temperature fields can be measured during the experiment, and indicators such as pickling uniformity, decontamination rate, and corrosion rate can be evaluated.

[0161] 3. Compare and analyze the experimental results with the predictions of the aforementioned mathematical model to verify the accuracy and reliability of the established relational model. If there are significant deviations, the mathematical model needs further optimization and correction.

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

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

[0164] A second aspect of the present invention provides a computer-readable storage medium storing program instructions that, when executed in a computer, perform the above-described method for determining pipeline dead zone pickling parameters.

[0165] A third aspect of the present invention provides a system for determining pickling parameters for pipeline dead zones, wherein the system includes the aforementioned computer-readable storage medium.

[0166] Specifically, the principle of this invention is:

[0167] First, establishing a geometric model of the pipeline dead zone is crucial. Pipeline dead zones typically have relatively complex three-dimensional structures, and accurately obtaining their geometric information is essential for subsequent flow simulation and parameter optimization. Collecting actual pipeline dimensions through on-site surveys or laser scanning, and then constructing a digital model using CAD / CFD software, can fully reflect the structural characteristics of the pipeline dead zone. Additionally, for pipelines designed using BIM or similar methods, the design's three-dimensional structural drawings can be directly used.

[0168] Secondly, defining multi-objective evaluation indicators for pickling effectiveness is also one of the core innovations of this invention. Optimizing a single indicator is insufficient to fully reflect the actual effect of pickling; therefore, this invention proposes a three-indicator system including pickling uniformity, decontamination rate, and corrosion degree. These three indicators evaluate the pickling effect from different perspectives, considering both cleaning effectiveness and safety, providing a basis for subsequent multi-objective optimization.

[0169] Next, CFD software was used to simulate the acid flow and reaction process under different process parameters, which is the basis for determining the optimal parameters. This simulation process considers many factors such as pipe geometry, boundary conditions, turbulence characteristics, and chemical reaction kinetics, and can accurately predict key parameters such as flow field distribution, concentration distribution, and temperature distribution within the pipe 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, this invention designs a multi-objective optimization model to systematically optimize process parameters, aiming to maximize pickling uniformity and decontamination rate while minimizing pipe wall corrosion. This multi-objective optimization method can find the optimal balance among different objectives, obtaining a set of Pareto optimal solutions. Small-scale experiments are then used for verification, and the optimal parameter combination is selected as the final output. This simulation-optimization-verification methodology fully utilizes the advantages of computer-aided simulation and intelligent algorithms, enabling a more scientific determination of the optimal process parameters for pickling in pipe dead zones.

[0171] In summary, the core principles of the method of this 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 evolutionary algorithms to solve multi-objective optimization problems; and 5) verifying the optimization results through small-scale experiments.

[0172] To further understand and implement this invention, a specific application scenario, Example 2, is provided below: A chemical plant's pipeline system contains dead zones that easily accumulate large amounts of dirt, affecting system operation. To solve this problem, the chemical plant decided to use the method of this invention to acid-wash the dead zones in the pipeline. 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 drawings. The dead zone of the pipeline has a length L = 3.5m and a diameter D = 0.6m, and its structure is relatively complex, exhibiting certain bending characteristics.

[0175] Then, a 3D CAD model of the pipe dead zone was created in AutoCAD software, as shown in Table 1. This model can accurately reflect the actual geometric characteristics of the pipe dead zone.

[0176] Table 1 Geometric Model of Pipe Dead Zone

[0177] parameter Numerical Length L 3.5m Diameter D 0.6m Structural characteristics bending

[0178] 2. Definition of pickling parameters

[0179] Based on actual process requirements and experience, the main pickling parameters that need to be optimized are as follows:

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

[0181] 2) Pickling temperature T: 20℃~80℃;

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

[0183] 4) Acid pump power P pump Maximum 50kW;

[0184] 5) Air pump power P air Maximum 30kW;

[0185] The specific range of values ​​for these parameters needs to be adjusted appropriately based on the site conditions.

[0186] 3. Construction of pickling effect evaluation indicators

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

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

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

[0190] 3) Pipe wall corrosion degree C: The degree of corrosion of the pipe material by pickling was evaluated.

[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 flow and reaction process

[0193] Based on the pipeline geometric model established in step 1, a corresponding computational mesh was constructed in FLUENT software. The mesh quality was controlled to be y+<5 to ensure good boundary layer analytical accuracy.

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

[0195] 1) Import boundary conditions:

[0196] Velocity u0 = 1.5 m / s;

[0197] Concentration c0 = 10%;

[0198] Temperature T0 = 40℃;

[0199] 2) Export boundary conditions: Free outflow;

[0200] 3) Wall boundary conditions: no slip, concentration gradient of 0, convective heat transfer;

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

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

[0203] Numerical simulations yielded the velocity, concentration, and temperature field distributions within the dead zone of the pipeline, providing fundamental 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, nonlinear regression analysis was used to establish equations for pickling uniformity, pickling decontamination rate, and pipe wall corrosion.

[0209] 1) Equation for pickling uniformity:

[0210]

[0211] Where n = 20 is the number of sampling points within the pipe dead zone, T i Let be the acid concentration at the i-th sampling point, and T be the average concentration. This equation represents the acid washing parameters x = (c, T, t, P). pump ,P air The relationship between pickling uniformity U and pickling uniformity U.

[0212] 2) Acid pickling and stain removal rate equation:

[0213]

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

[0215] 3) Pipe wall corrosion 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 parameter x and the corrosion degree C of the pipe wall.

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

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

[0220] Based on the above mathematical model, this invention designs the following multi-objective optimization problem:

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

[0222] st 20℃≤T≤80℃;

[0223] 5% ≤ c ≤ 20%;

[0224] 1h≤t≤8h;

[0225] P pump +F air ≤80kW;

[0226] C(x)≤0.12mm / year;

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

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

[0229] Table 3 Pareto optimal solution

[0230] parameter Numerical 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 the Pareto optimal solution achieves a good balance among the three objective indicators: high pickling uniformity, high decontamination rate, and pipe wall corrosion within a controllable range. This provides a relatively ideal parameter combination for subsequent experimental verification.

[0232] 7. Small-scale experimental verification

[0233] Based on the aforementioned Pareto optimal solution, corresponding acid washing experiments were conducted in a laboratory environment to verify the results.

[0234] The specific steps are as follows:

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

[0236] 2) The initial dirt weight W was measured using an endoscope and laser thickness gauge. i =2.4kg, the weight of residual dirt after the experiment W f =0.3kg.

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

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

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

[0240] In summary, the application of the method of this invention in the pickling of dead zones in the pipelines of this chemical plant fully demonstrates its systematic nature, scientific rigor, and reliability. Optimal pickling parameters were determined through geometric modeling, flow simulation, mathematical modeling, and multi-objective optimization, and the accuracy of the results was further confirmed through experimental verification.

[0241] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining pipeline dead zone pickling parameters, 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, establishing a mathematical relationship model between pickling parameters and pickling effect evaluation indicators according to the simulation results; 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 mathematical relationship model includes a pickling uniformity equation, a pickling decontamination rate equation, and a pipe wall corrosion degree equation.

5. A method for determining pipeline dead zone pickling parameters according to claim 4, characterized in that: The pickling uniformity equation is specifically expressed as follows: Where U is the pickling uniformity; n is the number of sampling points in the dead zone of the pipeline; T i is the acid concentration at the i-th sampling point; is the average acid concentration of all sampling points.

6. A method for determining pipeline dead zone pickling parameters according to claim 5, characterized in that: The pickling decontamination rate equation is specifically expressed as follows: Where, R is the pickling decontamination rate; W i W is the initial weight of dirt in the dead zone of the pipeline before pickling; f is the weight of the residual dirt in the dead zone of the pipeline after pickling; t is the actual pickling time; t0 is the benchmark pickling time; k is the time influence coefficient.

7. A method for determining pipeline dead zone pickling parameters according to claim 1, characterized in that: The pipe wall corrosion degree equation is specifically expressed as follows: Where, C is the corrosion degree of the pipe wall; 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; T0 is the reference temperature; H + is the actual hydrogen ion concentration; is the base hydrogen ion concentration; n is the reaction order; i is the imaginary unit; ω is the fluid disturbance frequency.

8. A method for determining pipeline dead zone pickling parameters according to claim 7, 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.

9. 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 8 is used to execute.

10. A system for determining the dead zone pickling parameters of a pipeline, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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