A configuration method of oil and gas recovery facilities for multi-working-condition coastal product oil wharf

Abstract

This invention discloses a method for configuring oil and gas recovery facilities at coastal refined oil terminals adaptable to multiple operating conditions. It relates to the technical field of environmental pollution control, particularly air and water pollution control. Oil and gas pollution from terminals has a significant destructive impact on the environment. To protect the environment, terminals need to be equipped with oil and gas recovery facilities. This method first collects the basic operating parameters of the target terminal and the pipeline laying distance, and determines the spatial constraints. A multi-objective evaluation matrix is ​​constructed, and candidate scheme scores are calculated and ranked using a comprehensive evaluation model. Finally, the footprint of the optimal scheme is used, combined with the spatial constraints, to determine the physical boundaries. Based on the pipeline laying distance, selection parameters for the induced draft module used to overcome long-distance pressure drop are simultaneously output, generating spatial layout instructions that include physical arrangement and fluid compensation. This invention deeply couples process quantitative optimization with the physical space of the terminal, significantly reducing the overall operating energy consumption of the oil and gas recovery process and contributing to energy conservation and emission reduction in ports.

Description

Technical Field

[0001] This invention relates to the technical field of environmental pollution control, particularly to the field of air and water pollution control, and more specifically, to a method for configuring oil and gas recovery facilities for coastal refined oil terminals that are adaptable to various operating conditions. Background Technology

[0002] Coastal refined oil terminals require oil and gas recovery facilities to control volatile organic compound (VOC) emissions during loading and unloading operations. Due to the complex operating conditions faced by coastal terminals, such as varying cargo types and large fluctuations in flow rates, stringent engineering requirements are placed on the quantitative optimization and spatial configuration of oil and gas recovery processes. Currently, commonly used oil and gas recovery technologies mainly include condensation, adsorption, absorption, membrane separation, and their combinations. In existing oil and gas recovery facility engineering designs, the process determination logic primarily focuses on matching single chemical process parameters, i.e., directly specifying the recovery process based on data such as oil and gas inlet concentration, gas volume processed, and target recovery rate. However, existing facility configuration methods have the following technical shortcomings when facing the complex and variable engineering sites of coastal terminals:

[0003] The optimization criteria for the process are not effectively coupled with the physical space constraints of the site plan. Traditional configuration methods do not take the morphological characteristics of the hydraulic structures at the wharf's front (such as pier-type wharves or contiguous wharves) and the explosion-proof safety distance as prerequisites. This leads to a situation where combined processes with high comprehensive evaluations derived solely from process optimization (such as adsorption and absorption combined processes with large footprints) often face blind spots in the wharf's front where the operating platform size is limited, resulting in insufficient explosion-proof distances or overloading of hydraulic structures. This makes it impossible to safely implement the theoretically optimal solution on actual engineering sites.

[0004] The system lacks a fluid compensation mechanism to compensate for changes in spatial layout. When the oil and gas recovery facilities are forced to be moved to the land storage area behind the wharf due to the high spatial constraints of the front hydraulic structures, the existing static configuration method ignores the fluid pressure drop along the long-distance pipeline transportation. Because the dynamic matching logic between pipeline laying distance and draft module parameters is not established during the scheme determination stage, it is very easy to cause the failure of the front-end hull micro-positive pressure control or the surge in system energy consumption during actual operation.

[0005] Therefore, there is an urgent need for a configuration method that can deeply integrate the physical form of the wharf space with multi-dimensional evaluation indicators, in order to achieve adaptive and coordinated matching between the optimal configuration scheme and the wharf topography and pipeline pressure drop while objectively and quantitatively seeking the best. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing oil and gas recovery facility selection, which often only considers process parameters, deviates from the actual terminal site limitations, and ignores the fluid pressure drop caused by layout changes. This invention provides a method for configuring oil and gas recovery facilities that is adaptable to various operating conditions at coastal refined oil terminals.

[0007] The technical solution of this invention is: a method for configuring oil and gas recovery facilities at coastal refined oil terminals adaptable to multiple operating conditions, the method comprising:

[0008] S1. Collect basic operating parameters of the target terminal. The basic operating parameters include at least the physical and chemical properties of the cargo loaded, the characteristics of the loading flow, the concentration of oil and gas imports, the annual turnover, and the pipeline laying distance from the terminal to the land behind it. Determine the plan layout of the target terminal and determine whether the spatial constraint condition of the target terminal is a high spatial constraint condition or a low spatial constraint condition based on the plan layout.

[0009] S2. Identify the entities to be evaluated. Several candidate technologies for oil and gas recovery were proposed, and a system was constructed that includes... A multi-objective evaluation index system is established, comprising cost-based and benefit-based indicators. Cost-based indicators include system operating energy consumption, land area, environmental impact, and construction costs. Benefit-based indicators include processing efficiency, processing capacity, annual recovery volume, equipment lifespan, and system safety. Based on the basic operating parameters collected in step S1, the first evaluation index is calculated or obtained. The candidate solution is in the... Raw data under each indicator ,in, and All are positive integers greater than 1. The value ranges from 1 to positive integers, The value ranges from 1 to Positive integers, thus constructing a OK Original decision matrix of the column ;

[0010] S3. Regarding the original decision matrix The indicators in the data are normalized according to their cost-type or benefit-type classification to generate dimensionless data. OK Initialization matrix of columns And set the normalized ideal optimal value of each evaluation index to 1, thereby directly constructing a vector of relatively ideal solutions where each element is 1;

[0011] S4. Based on the spatial constraint conditions determined in step S1, construct a system for mapping spatial constraints using a pre-defined condition-weighting mapping logic. The judgment matrix of each evaluation index is used to establish a system of linear equations containing Lagrange multipliers based on the weighted least squares method. This system of linear equations is then solved using weight normalization constraints to obtain the objective weight vectors of each evaluation index reflecting the current physical boundary limitations of the wharf. ;

[0012] S5. Calculate the initialization matrix The absolute difference between the normalized data of each candidate technical solution and the vector of the relative ideal solution is used to calculate the correlation coefficient of each candidate technical solution under various evaluation indicators using the grey relational model; the correlation coefficients are then used in conjunction with the objective weight vector obtained in step S4. The weighted grey relational degree of each candidate technical solution is calculated; finally, based on the magnitude of the weighted grey relational degree, all candidate technical solutions are ranked and selected.

[0013] S6. Select the candidate technical solution with the largest weighted grey relational degree value as the optimal configuration solution; call the original land area data corresponding to the optimal configuration solution in step S2, and combine it with the spatial constraint conditions and pipeline laying distance determined in step S1 to generate the corresponding spatial layout instructions to instruct the oil and gas recovery facilities to be deployed at the dock according to the physical layout location of the optimal configuration solution.

[0014] In any of the above technical solutions, further, step S1, identifying the planar layout of the target wharf and establishing a spatial constraint model, specifically includes:

[0015] Obtain user interaction instructions regarding the wharf layout type, specifying the target wharf as a pier-type wharf or a contiguous wharf; if a user interaction instruction is received, directly establish the type specified in the instruction as the layout form of the target wharf.

[0016] If no user interaction command is received, automatic identification is triggered to obtain the general layout plan data of the target wharf and calculate the continuous working platform area and continuous front length of the outer edge of the hydraulic structure.

[0017] When the area of ​​the continuous operation platform is less than the preset area threshold and the hydraulic structure is discretely distributed, it is identified as a pier-type wharf and judged as a high spatial constraint condition.

[0018] When the area of ​​the continuous operation platform is greater than or equal to the preset area threshold, and the continuous length of the front edge is greater than or equal to the preset length threshold, it is identified as a continuous wharf and judged as a low spatial constraint condition.

[0019] In any of the above technical solutions, further, in step S3, an initialization matrix is ​​generated. The specific formula is:

[0020] Let the initialization matrix be... The elements are ;

[0021] For benefit-related indicators, the normalization formula is: ;

[0022] For cost-related indicators, the normalization formula is: ;

[0023] in, This indicates that all candidate technical solutions are in the... The maximum raw data under each evaluation indicator; This indicates that all candidate technical solutions are in the... The minimum raw data under each evaluation indicator;

[0024] The vector of the relative ideal solution is denoted as Its form of expression is .

[0025] In any of the above technical solutions, further, the step of calculating the objective weight vector in step S4 includes:

[0026] Constructing a judgment matrix for indicator evaluation ,in Indicates the first The evaluation index is relative to the first The relative importance evaluation value of each evaluation indicator and The values ​​are all up to Positive integers;

[0027] The objective weight column vector to be determined is: Introducing Lagrange multipliers Establish linear equations ;

[0028] For including Lagrange multipliers of 3D column vector, ; for OK The coefficient matrix of the column, its elements The calculation formula is:

[0029] ;

[0030] Linear equations By combining the weight normalization constraint with the equations, the weight values ​​of each evaluation index are calculated, and then the objective weight column vector is solved. ;

[0031] The weight normalization constraint is .

[0032] In any of the above technical solutions, further, in step S4, a judgment matrix is ​​constructed. The specific steps are as follows:

[0033] Obtain the spatial constraint conditions of the target wharf determined in step S1;

[0034] Call the preset working condition and scale mapping rule library. The mapping rule library pre-stores multiple sets of fixed quantization scale values ​​corresponding to low spatial constraint working conditions and high spatial constraint working conditions respectively.

[0035] Based on the obtained spatial constraint conditions, the first condition under the current working condition is retrieved and extracted from the mapping rule base. The evaluation index is relative to the first A fixed quantization scale value for each evaluation indicator is determined, and the extracted fixed quantization scale value is assigned to... To generate the judgment matrix ;

[0036] Wherein, the generated judgment matrix Satisfies the property of positive reciprocal algebra: , as well as .

[0037] In any of the above technical solutions, the specific steps for calculating the weighted grey relational degree in step S5 further include:

[0038] Calculate the initialization matrix elements in With the relative ideal solution vector absolute difference of corresponding terms :

[0039] ;

[0040] Get all absolute differences minimum value and maximum value Calculate the first The candidate technical solutions are in the first Correlation coefficients under each evaluation indicator :

[0041] ;

[0042] In the formula, The resolution coefficient has a range of values. ;

[0043] correlation coefficient The corresponding value in the objective weight vector calculated in step S4 Multiply and sum to get the first... Weighted grey relational degree of each candidate technical solution :

[0044] .

[0045] In any of the above technical solutions, the specific logic for generating the corresponding spatial layout instruction in step S6 is as follows:

[0046] If the spatial constraint condition determined in step S1 is a low spatial constraint condition, then a first layout instruction is generated, instructing that the oil and gas recovery facility corresponding to the optimal configuration scheme be arranged at the front of the wharf.

[0047] If the spatial constraint condition determined in step S1 is a high spatial constraint condition, then the original data of the land area is further compared with the preset threshold for the land area of ​​the dock platform:

[0048] When the original data of the land area is less than or equal to the land area threshold of the wharf platform, a second layout instruction is generated, which instructs the oil and gas recovery facilities to be compactly arranged on the wharf operation platform.

[0049] When the original data of the land area exceeds the land area threshold of the wharf platform, a third layout instruction is generated, which instructs the oil and gas recovery facilities to be arranged in the land storage area behind the wharf. At the same time, based on the pipeline laying distance collected in step S1, the preset distance and equipment mapping rule library is queried to match the selection parameters of the induced draft module to overcome the pressure drop of long-distance pipelines, and the selection parameters of the induced draft module are output together with the third layout instruction as the fluid dynamic compensation configuration.

[0050] The beneficial effects of this invention are:

[0051] This invention overcomes the challenges of applying theoretical process optimization to actual construction sites, avoiding the problem of insufficient space for equipment placement. Traditional methods often find that the optimal process selected on-site is too small to fit the equipment. In step S1, this invention pre-defines whether the wharf is spacious (contiguous) or congested (pier-type), and in step S6, it forcibly compares the original data of the calculated optimal solution's footprint with the actual allowable area threshold of the wharf platform. Through this physical boundary determination, the system clearly indicates whether the equipment should be placed directly at the wharf's front edge or must be moved to the rear. This eliminates serious safety accidents caused by overloading of hydraulic structures or insufficient explosion-proof spacing, ensuring 100% feasibility of the proposed solution.

[0052] The built-in pipeline resistance compensation scheme solves the operational hazard of insufficient fan suction power when facilities are relocated. When the wharf front is too small, and the system determines that the oil and gas recovery facilities must be placed in the rear land storage area hundreds or even thousands of meters away, the excessively long transport pipeline will generate huge gas resistance. This invention does not ignore this critical engineering detail. The system directly extracts the pre-collected pipeline laying distance, automatically looks up the table in the internal rule base, and matches the induced draft fan power and wind pressure parameters that can overcome the pipeline resistance. This directly eliminates the need for complicated on-site fluid dynamics calculations, ensuring that oil and gas in the front-end hull can be stably extracted no matter how far the equipment is placed, guaranteeing the normal operation of the entire system.

[0053] This invention provides an objective evaluation mechanism that eliminates human interference. Previous equipment configurations often relied on subjective expert scoring, which was prone to bias. This invention addresses multiple indicators with vastly different unit values, such as construction costs, energy consumption, and land occupation. First, it maps the optimal performance of each evaluation indicator to a perfect score of 1 (constructing a relatively ideal solution vector). Then, it uses the weighted least squares method to calculate objective weights based on actual data fluctuations. Finally, it uses a grey relational model to calculate the comprehensive score for each solution. This purely data-driven mathematical mechanism fairly compares cost and land occupation under the same standard, resulting in an objective, rigorous, and highly convincing optimal solution for engineering purposes. Attached Figure Description

[0054] The advantages of the above and additional aspects of the present invention will become apparent and readily understood in the description of the embodiments in conjunction with the following drawings, wherein:

[0055] Figure 1 This is a schematic flowchart of a method for configuring oil and gas recovery facilities at a coastal refined oil terminal under multiple operating conditions, according to an embodiment of the present invention. Detailed Implementation

[0056] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0057] In the following description, many specific details are set forth in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0058] like Figure 1 As shown in the figure, this embodiment provides a method for configuring oil and gas recovery facilities at coastal refined oil terminals that are adaptable to multiple operating conditions. The method includes:

[0059] S1. Collect basic operating condition parameters and determine spatial constraint conditions: First, collect basic operating condition parameters of the target terminal through a data interface or human-computer interaction interface. In order to comprehensively quantify the variable operating conditions of terminal operations, this embodiment constructs an operating condition feature dataset covering physicochemical properties and physical boundaries. The basic operating condition parameters include at least: physicochemical properties of the cargo being loaded (covering saturated vapor pressure and main components), loading flow characteristics (including average flow and maximum flow), oil and gas import concentration, annual turnover, and pipeline laying distance from the terminal to the rear land area.

[0060] Among them, the first four process parameters provide the original data support for the subsequent construction of the decision matrix to estimate indicators such as energy consumption and recovery volume; while the introduction of the pipeline laying distance parameter is to provide the physical basis for pipeline pressure drop when encountering high space constraints and needing to move the equipment back, so as to truly realize the engineering closed loop of process selection and overall layout.

[0061] Secondly, the planar layout of the target terminal is identified, and the spatial constraints of the terminal are determined accordingly. In existing technologies, coastal refined oil terminals are mainly divided into two layout types: pier-type and continuous-area type. To improve the automation and robustness of the system in complex engineering environments, this step employs a dual mechanism combining manual verification and algorithmic extraction.

[0062] First, the system listens for the engineer's input command regarding the type of dock layout. If the system successfully receives the command, it prioritizes the expert's experience and directly establishes the spatial constraints of the dock based on this.

[0063] Secondly, if the system does not receive a manual input instruction within the preset period, or receives an automatic parsing trigger signal, the following logic will be executed: the system imports the engineering files of the target wharf, extracts the closed outer edge of the front hydraulic structure, and calculates the continuous operating platform area of ​​the core operating area and the continuous berthing length along the shoreline.

[0064] The system has pre-set area and length thresholds to characterize the dimensional features of the wharf. After logical comparison: if the area of ​​the continuous operating platform is less than the area threshold, and the outer edge connectivity analysis determines that there are independent berthing piers and mooring piers on both sides (i.e., the structure is discontinuous), the system automatically identifies the target as a pier-type wharf; conversely, if the area of ​​the continuous operating platform is greater than or equal to the preset area threshold, and the continuous length of the front edge is greater than or equal to the preset length threshold, it indicates that the central loading and unloading area is continuous and spacious, and is therefore identified as a contiguous wharf.

[0065] If the wharf is arranged in a pier-type layout, consisting of a working platform, berthing piers, and mooring piers, the size of the working platform is limited. The system determines this as a high space constraint condition and the configuration logic locks the rear land area layout or compact process preferred. If the wharf is arranged in a continuous layout, with the loading and unloading operation area located in the center and ample length, the system determines this as a low space constraint condition and the configuration logic allows the wharf front-end layout.

[0066] The construction of the operating condition feature vector is the data foundation of the entire configuration method. In practical engineering applications, parameters... to The data source can be the wharf's basic design data or the historical operational data of the DCS system. By accurately identifying the wharf's spatial constraint model, this invention can pre-screen layout schemes that are not feasible from the physical boundaries, avoiding repeated design modifications caused by neglecting the load limits at the wharf's front edge or insufficient fire separation distances in traditional designs. This dual definition of space and working conditions provides boundary conditions with practical engineering physical significance for subsequent multi-objective decision-making.

[0067] S2. Constructing a multi-dimensional evaluation index system and decision matrix: In the actual selection of terminal oil and gas recovery projects, facing... Different candidate technical solutions require comprehensive consideration from multiple dimensions. Establishment This embodiment specifically establishes nine evaluation indicators: construction and installation cost, energy consumption, processing efficiency, processing capacity, annual recovery volume, service life, land area, safety, and environmental impact. These nine indicators are strictly divided into two categories: the first category is cost-based indicators (the smaller the value, the better), including construction and installation cost, system operating energy consumption, land area, and environmental impact; the second category is benefit-based indicators (the larger the value, the better), including processing efficiency, processing capacity, annual recovery volume, equipment service life, and system safety.

[0068] Based on the basic operating parameters collected in step S1, calculate or obtain the first... The candidate solution is in the... Raw data under each indicator ,in , This constructs a OK Original decision matrix of the column .

[0069] The selection of the nine evaluation indicators was not arbitrary, but rather took into full account the unique operating environment of coastal refined oil terminals. In particular, the introduction of environmental impact and safety as key dimensions is in response to the increasingly stringent HSE (Health, Safety, and Environment) management system requirements for petrochemical terminals. This indicator system breaks away from the limitations of previous approaches that solely focused on return on investment, constructing a holistic evaluation framework encompassing economic viability, technological maturity, environmental friendliness, and inherent safety, thereby ensuring the comprehensiveness and sustainability of decision-making results.

[0070] S3. Dimensionless Processing of the Decision Matrix: Due to the vast differences in the physical units and orders of magnitude of various indicators (for example, construction costs can reach millions of yuan, while energy consumption is typically only a few tenths of a kilowatt-hour), direct mathematical calculations would completely mask the effects of smaller indicators due to their large numerical values. Therefore, the decision matrix needs to be dimensionless. Initialization processing is performed to eliminate the influence of the indicator's dimensions.

[0071] Define the decision matrix Dimensionless transformation is used to initialize the matrix. .

[0072] For benefit-based indicators, the higher the value, the higher the score. The normalization formula is:

[0073] ;

[0074] For cost-related indicators, the smaller the value, the higher the score. The normalization formula is:

[0075] ;

[0076] In the formula, This indicates that all candidate technical solutions are in the... The maximum raw data under each evaluation indicator; This indicates that all candidate technical solutions are in the... The minimum raw data under each evaluation indicator. Not zero.

[0077] After initialization, independent relatively ideal solution vectors are constructed based on the optimal values ​​of each processed index. .

[0078] Implementing dimensionless processing is a crucial step in eliminating the physical unit barrier of data. The normalization algorithm used in this step, through mathematical mapping, compresses all indicator data into the range of 0 (excluding 0) to 1, effectively mapping discrete physical quantities with different dimensions to a numerical space of the same dimension. This ensures the fair expression of the weights of each indicator in the subsequent grey relational analysis; regardless of whether the original indicator was cost-based or benefit-based, after processing, the value of the indicator with the best performance will definitely become 1.

[0079] S4. Calculation of index weights based on the least squares method: The traditional analytic hierarchy process (AHP) relies heavily on the subjective experience of experts when determining weights and is prone to inconsistencies in the judgment matrix. In order to improve the scientific nature of decision-making, this invention adopts the least squares method based on weights to solve for objective weights.

[0080] First, based on the spatial constraints established in step S1 and the core principles of energy conservation and emission reduction, domain experts... Each evaluation indicator is compared pairwise for importance to construct a... OK Column judgment matrix Elements in the matrix No. The evaluation index is relative to the first The relative importance evaluation value of each evaluation indicator and The values ​​are all up to Positive integers.

[0081] Construct a judgment matrix The specific steps are as follows:

[0082] Obtain the spatial constraint conditions of the target wharf determined in step S1; call the preset condition and scale mapping rule library, which pre-stores multiple sets of fixed quantization scale values ​​corresponding to low and high spatial constraint conditions; based on the obtained spatial constraint conditions, retrieve and extract the first value under the current condition from the mapping rule library. The evaluation index is relative to the first A fixed quantization scale value for each evaluation indicator is determined, and the extracted fixed quantization scale value is assigned to... To generate the judgment matrix The generated judgment matrix Satisfies the property of positive reciprocal algebra: , as well as .

[0083] The core idea of ​​the least squares method is to find a set of optimal weights. This makes all as close as possible That is, minimizing the sum of squared errors. To solve this extremum problem, Lagrange multipliers are introduced. As an auxiliary variable, it is added with physical constraints whose total weight is 1. By taking the partial derivative of the objective function and setting it to zero, the complex optimization problem is cleverly transformed into solving a problem based on the coefficient matrix. Weight vector and constant vector The linear equations formed .

[0084] vector The expression is ,vector The expression is ,matrix for OK The coefficient matrix of the column, its elements The calculation formula is:

[0085] .

[0086] linear equations With constraints Solving the system of equations simultaneously yields the objective weight vector, which eliminates subjective bias. .

[0087] The above method of calculation makes the final weight vector more objectively reflect the actual preference of the terminal for specific indicators (such as energy conservation and emission reduction) under actual working conditions, providing solid algorithmic support for scientific decision-making.

[0088] S5. Calculate the weighted grey relational degree and select the optimal solution: In the multi-objective selection decision of oil and gas recovery facilities, each process often has different advantages and disadvantages. This embodiment introduces a grey relational degree model to objectively evaluate the comprehensive performance of the solution by quantifying the gap between each candidate technical solution and the absolute ideal state.

[0089] First, calculate the normalized data for each candidate solution. The absolute difference between the vector of the relatively ideal solution (where all elements are 1) ,Right now This difference directly reflects the score gap between a candidate process and the perfect state on a specific evaluation indicator.

[0090] Next, in all absolute differences Find the smallest gap inside and the largest gap Substitute the correlation coefficient Calculation formula:

[0091] ;

[0092] The similarity score for each individual indicator is calculated using this method, and a resolution coefficient is introduced into the formula. (The range of values ​​is) Its engineering significance lies in smoothing and weakening the interference of individual extreme differences on the overall evaluation system, thereby improving the sensitivity of comparison between the correlation coefficients of various schemes.

[0093] Finally, the calculated correlation coefficients The objective weight of this indicator obtained in step S4 Multiply the corresponding products and sum them to get the first product. Weighted grey relational degree of each candidate technical solution :

[0094] ;

[0095] This step couples the individual subject performance score with the subject importance weight. The calculated final weighted grey relational degree... The closer the value is to 1, the better the overall performance of the candidate technical solution under the current dock operating conditions. The system ultimately... The values ​​are sorted in descending order, and the technical solution corresponding to the maximum value is output as the optimal configuration for the current project.

[0096] S6. Output layout instructions based on spatial constraints and optimal configuration scheme: After completing the weighted grey relational degree sorting and selecting the optimal configuration scheme in step S5, step S6 aims to transform the quantitative decision result into a layout strategy for the engineering physical space.

[0097] In a specific embodiment, the system calls the original land area data corresponding to the optimal configuration scheme in step S2, and combines it with the spatial constraints and pipeline laying distance determined in step S1 to generate specific spatial layout instructions through preset logical decision branches. Specifically, the system executes the following decision steps:

[0098] When the spatial constraint condition determined in step S1 is a low spatial constraint condition (e.g., the target object is a continuous wharf with sufficient space leeway in the loading and unloading area), the system generates a first layout instruction. This first layout instruction instructs that the oil and gas recovery facilities corresponding to the optimal configuration be directly arranged at the wharf's front edge. By arranging them nearby at the front edge, the laying length of the oil and gas collection pipeline network is shortened to the greatest extent, thereby reducing the system's frictional resistance and operating energy consumption.

[0099] When the spatial constraint condition determined in step S1 is a high spatial constraint condition (e.g., the target object is a pier-type wharf with severely limited platform size), the system needs to further determine whether the physical dimensions of the optimal solution meet the bearing capacity and area requirements of the upstream hydraulic structure. At this time, the system compares the extracted raw land area data with the preset wharf platform land area threshold:

[0100] If the original footprint data is less than or equal to the wharf platform footprint threshold, it indicates that the optimal configuration (such as partially integrated membrane separation process equipment) can be accommodated by the limited operating platform. Based on this, the system generates a second layout instruction. This second layout instruction instructs that the oil and gas recovery facilities be compactly arranged on the wharf operating platform.

[0101] Conversely, if the original land area exceeds the threshold for the wharf platform, indicating that the facility (such as a large-scale adsorption + absorption combined process equipment) cannot be safely deployed at the wharf's front edge, the system generates a third layout instruction, directing the oil and gas recovery facility to be located in the land storage area behind the wharf. Furthermore, addressing the pipeline extension and fluid pressure drop issues caused by facility relocation, the system, while generating the third layout instruction, retrieves the pipeline laying distance collected in step S1 and, based on an internally preset distance-equipment mapping rule library (i.e., a pre-stored database of the correspondence between different pipeline length ranges and the required rated wind pressure and power of the induced draft fan), matches and determines the selection parameters of the induced draft module that can overcome the pressure drop over long pipeline distances. The determined induced draft module selection parameters are output as a fluid dynamic compensation configuration along with the third layout instruction, thus ensuring the normal operation of the system under long-distance transportation conditions without the need for real-time fluid dynamics calculations.

[0102] In another embodiment of the present invention, to further verify the accuracy and engineering applicability of the configuration method described in the present invention, this embodiment takes a real project of a 30,000-ton refined oil terminal built in a coastal port as the verification object and executes the complete oil and gas recovery facility configuration process:

[0103] The system collects basic operating parameters of the target terminal through a data interface, specifically: the cargo being loaded is 92# gasoline; the loading flow rate is a maximum flow rate of 2500 m³ / h. 3 / h; the oil and gas import concentration is 35% Vol; the annual turnover is 2.5 million tons; the pipeline laying distance from the wharf operation platform to the rear land tank area is 1500 meters.

[0104] Simultaneously, the system retrieved the hydraulic CAD drawings of the wharf and, through an algorithm, determined that the area of ​​its continuous operation platform was 850m². 2 The internally preset area threshold is 1200m². 2 Furthermore, there are independent mooring piers on both sides, and the system identifies it as a pier-type wharf, and determines that the current target wharf is in a high spatial constraint condition.

[0105] The system identified four candidate technical solutions: Solution 1 (adsorption + absorption), Solution 2 (condensation + adsorption), Solution 3 (membrane separation + condensation), and Solution 4 (pure condensation).

[0106] Option 1 (Adsorption + Absorption): System operating energy consumption 120kW (cost-effective), floor space 220m² 2 (Cost-based), annual recycling volume 1150 tons (profit-based), construction and installation cost 3.8 million yuan (cost-based).

[0107] Option 2 (Condensation + Adsorption): System operating energy consumption 180kW, floor space 160m² 2 The annual recycling volume is 1,300 tons, and the construction and installation cost is 4.5 million yuan.

[0108] Option 3 (Membrane Separation + Condensation): System operating energy consumption 150kW, floor space 110m² 2 The annual recycling volume is 1,200 tons, and the construction and installation cost is 5.2 million yuan.

[0109] Option 4 (Pure Condensation): System operating energy consumption 240kW, floor space 140m² 2 The annual recycling volume is 1,100 tons, and the construction and installation cost is 3.1 million yuan.

[0110] The above data together form a 4*9 dimensional original decision matrix.

[0111] The system processes the original decision matrix according to different normalization formulas for cost-type and benefit-type indicators to generate an initialization matrix. At this point, the optimal performance of each indicator among all options is mapped to 1, and the system directly constructs a vector of relatively ideal solutions where all elements are 1.

[0112] Given that the wharf is identified as operating under high spatial constraints, domain experts significantly increased the relative importance of land area and safety performance when constructing the judgment matrix. The system solves the linear equations using the weighted least squares method to derive an objective weight vector. The calculations show that land area has a weight of 0.28, processing efficiency has a weight of 0.18, construction cost has a weight of 0.12, and the remaining indicators share the remaining proportions. This weight allocation accurately reflects the physical reality of the scarcity of land for pier-type wharves.

[0113] The system calculates the absolute difference between the data of each scheme and the ideal scheme vector in the initialization matrix, with the resolution coefficient set to 0.5, to obtain the correlation coefficient matrix of each scheme; then it multiplies the corresponding values ​​of the weight vector and sums them.

[0114] The final calculated weighted grey relational degrees for each scheme are as follows: Scheme 1 is 0.62, Scheme 2 is 0.81, Scheme 3 is 0.76, and Scheme 4 is 0.58. The system sorts the schemes in descending order of their values. Since Scheme 2 is the most suitable, the system selects Scheme 2 (condensation + adsorption) as the optimal configuration for the current terminal.

[0115] The system calls the optimal configuration scheme (Scheme 2) with a land area of ​​160m². 2 .

[0116] Due to the high spatial constraints of the work environment, the system compares the area data with the preset threshold for the footprint of the dock platform (in this embodiment, the maximum allowable explosion-proof safety equipment area for the waterworks platform is 120m²). 2 Compare them.

[0117] Due to 160m 2 >120m 2 The system generates a third layout instruction, which forcibly instructs the oil and gas recovery facility to be located in the land storage area behind the wharf.

[0118] Simultaneously, the system extracts the pipeline laying distance (1500 meters), calls the internal distance-equipment mapping rule library, and matches and determines the pipeline pressure drop of 4500Pa that needs to be overcome at this distance. Along with the command, the system outputs the matching exhaust module selection parameters: rated air volume 3000m³ / h. 3 / h, rated wind pressure 5000Pa, explosion-proof motor power 37kW.

[0119] This example demonstrates that if traditional manual selection is used, the site size may be mistakenly selected as large as 220m² due to neglecting the limitations of hydraulic structures. 2 Option 1 resulted in severe overloading or violation of fire safety distances at the wharf front. The method of this invention not only objectively selected Option 2, which has the best overall performance, but also automatically completed equipment compensation for long-distance transportation through a cross-judgment mechanism, overcoming engineering challenges from process calculations to overall layout.

[0120] In summary, this invention proposes a method for configuring oil and gas recovery facilities at coastal refined oil terminals that is adaptable to multiple operating conditions, including:

[0121] S1. Collect basic operating parameters of the target terminal. The basic operating parameters include at least the physical and chemical properties of the cargo loaded, the characteristics of the loading flow, the concentration of oil and gas imports, the annual turnover, and the pipeline laying distance from the terminal to the land area behind it. Determine the plan layout of the target terminal and determine whether the spatial constraint condition of the target terminal is a high spatial constraint condition or a low spatial constraint condition based on the plan layout.

[0122] S2. Identify the entities to be evaluated. Several candidate technologies for oil and gas recovery were proposed, and a system was constructed that includes... A multi-objective evaluation index system is established, comprising cost-based and benefit-based indicators. Cost-based indicators include system operating energy consumption, land area, environmental impact, and construction costs. Benefit-based indicators include processing efficiency, processing capacity, annual recovery volume, equipment lifespan, and system safety. Based on the basic operating parameters collected in step S1, the first evaluation index is calculated or obtained. The candidate solution is in the... Raw data under each indicator ,in, and All are positive integers greater than 1. The value ranges from 1 to positive integers, The value ranges from 1 to Positive integers, thus constructing a OK Original decision matrix of the column .

[0123] S3. Regarding the original decision matrix The indicators in the data are normalized according to their cost-type or benefit-type classification to generate dimensionless data. OK Initialization matrix of columns Furthermore, the normalized ideal optimal value of each evaluation index is set to 1, thereby directly constructing a vector of relatively ideal solutions where each element is 1.

[0124] S4. Based on the spatial constraints determined in step S1, construct a framework for... The judgment matrix of each evaluation indicator is used to establish a system of linear equations containing Lagrange multipliers based on the least squares method of weights. The system of linear equations is then solved in conjunction with weight normalization constraints to obtain the objective weight vector of each evaluation indicator. .

[0125] S5. Calculate the initialization matrix The absolute difference between the normalized data of each candidate technical solution and the vector of the relative ideal solution is used to calculate the correlation coefficient of each candidate technical solution under various evaluation indicators using the grey relational model; the correlation coefficients are then used in conjunction with the objective weight vector obtained in step S4. The weighted grey relational degree of each candidate technical solution is calculated; finally, based on the magnitude of the weighted grey relational degree, all candidate technical solutions are ranked and selected.

[0126] S6. Select the candidate technical solution with the largest weighted grey relational degree value as the optimal configuration solution; call the original land area data corresponding to the optimal configuration solution in step S2, and combine it with the spatial constraint conditions determined in step S1 to generate the corresponding spatial layout instructions to indicate the physical layout location and supporting selection of the optimal configuration solution in the front or rear land area of ​​the wharf.

[0127] The steps in this invention can be adjusted, combined, or deleted according to actual needs.

[0128] The units in the device of the present invention can be merged, divided, or reduced according to actual needs.

[0129] In this invention, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; "linking" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of these terms in this invention according to the specific circumstances.

[0130] The shapes of the components in the accompanying drawings are schematic and may differ from their actual shapes. The drawings are only used to illustrate the principles of the present invention and are not intended to limit the present invention.

[0131] Although the invention has been disclosed in detail with reference to the accompanying drawings, it should be understood that these descriptions are merely exemplary and not intended to limit the application of the invention. The scope of protection of the invention is defined by the appended claims and may include various modifications, alterations, and equivalents made to the invention without departing from the scope and spirit of the invention.

Claims

1. A method for configuring oil and gas recovery facilities at coastal refined oil terminals adaptable to multiple operating conditions, characterized in that, The method includes: S1. Collect basic operating parameters of the target terminal. The basic operating parameters include at least the physical and chemical properties of the cargo loaded, the characteristics of the loading flow, the concentration of oil and gas imports, the annual turnover, and the pipeline laying distance from the terminal to the land behind it. Determine the plan layout of the target terminal and determine whether the spatial constraint condition of the target terminal is a high spatial constraint condition or a low spatial constraint condition based on the plan layout. S2. Identify the entities to be evaluated. Several candidate technologies for oil and gas recovery were proposed, and a system was constructed that includes... A multi-objective evaluation index system is established, comprising cost-based and benefit-based indicators. Cost-based indicators include system operating energy consumption, land area, environmental impact, and construction costs. Benefit-based indicators include processing efficiency, processing capacity, annual recovery volume, equipment lifespan, and system safety. Based on the basic operating parameters collected in step S1, the first evaluation index is calculated or obtained. The candidate solution is in the... Raw data under each indicator ,in, and All are positive integers greater than 1. The value ranges from 1 to positive integers, The value ranges from 1 to Positive integers, thus constructing a OK Original decision matrix of the column ; S3. Regarding the original decision matrix The indicators in the data are normalized according to their cost-type or benefit-type classification to generate dimensionless data. OK Initialization matrix of columns And set the normalized ideal optimal value of each evaluation index to 1, thereby directly constructing a vector of relatively ideal solutions where each element is 1; S4. Based on the spatial constraint conditions determined in step S1, construct a system for mapping spatial constraints using a pre-defined condition-weighting mapping logic. The judgment matrix of each evaluation index is used to establish a system of linear equations containing Lagrange multipliers based on the weighted least squares method. This system of linear equations is then solved using weight normalization constraints to obtain the objective weight vectors of each evaluation index reflecting the current physical boundary limitations of the wharf. ; S5. Calculate the initialization matrix The absolute difference between the normalized data of each candidate technical solution and the vector of the relative ideal solution is used to calculate the correlation coefficient of each candidate technical solution under various evaluation indicators using the grey relational model; the correlation coefficients are then used in conjunction with the objective weight vector obtained in step S4. The weighted grey relational degree of each candidate technical solution is calculated; finally, based on the magnitude of the weighted grey relational degree, all candidate technical solutions are ranked and selected. S6. Select the candidate technical solution with the largest weighted grey relational degree value as the optimal configuration solution; call the original land area data corresponding to the optimal configuration solution in step S2, and combine it with the spatial constraint conditions and pipeline laying distance determined in step S1 to generate the corresponding spatial layout instructions to instruct the oil and gas recovery facilities to be deployed at the dock according to the physical layout location of the optimal configuration solution.

2. The method for configuring oil and gas recovery facilities for coastal refined oil terminals adaptable to multiple operating conditions as described in claim 1, characterized in that, Step S1 involves identifying the planar layout of the target wharf and establishing a spatial constraint model, specifically including: Obtain user interaction instructions regarding the wharf layout type, specifying the target wharf as a pier-type wharf or a contiguous wharf; if a user interaction instruction is received, directly establish the type specified in the instruction as the layout form of the target wharf. If no user interaction command is received, automatic identification is triggered to obtain the general layout plan data of the target wharf and calculate the continuous working platform area and continuous front length of the outer edge of the hydraulic structure. When the area of ​​the continuous operation platform is less than the preset area threshold and the hydraulic structure is discretely distributed, it is identified as a pier-type wharf and judged as a high spatial constraint condition. When the area of ​​the continuous operation platform is greater than or equal to the preset area threshold, and the continuous length of the front edge is greater than or equal to the preset length threshold, it is identified as a continuous wharf and judged as a low spatial constraint condition.

3. The method for configuring oil and gas recovery facilities for coastal refined oil terminals adaptable to multiple operating conditions as described in claim 1, characterized in that, In step S3, an initialization matrix is ​​generated. The specific formula is: Let the initialization matrix be... The elements are ; For benefit-related indicators, the normalization formula is: ; For cost-related indicators, the normalization formula is: ; in, This indicates that all candidate technical solutions are in the... The maximum raw data under each evaluation indicator; This indicates that all candidate technical solutions are in the... The minimum raw data under each evaluation indicator; The vector of the relative ideal solution is denoted as Its form of expression is .

4. The method for configuring oil and gas recovery facilities for coastal refined oil terminals adaptable to multiple operating conditions as described in claim 1, characterized in that, Step S4, which involves calculating the objective weight vector, includes: Constructing a judgment matrix for indicator evaluation ,in Indicates the first The evaluation index is relative to the first The relative importance evaluation value of each evaluation indicator and The values ​​are all up to Positive integers; The objective weight column vector to be determined is: Introducing Lagrange multipliers Establish linear equations ; For including Lagrange multipliers of 3D column vector, ; for OK The coefficient matrix of the column, its elements The calculation formula is: ； Linear equations By combining the weight normalization constraint with the equations, the weight values ​​of each evaluation index are calculated, and then the objective weight column vector is solved. ; The weight normalization constraint is .

5. The method for configuring oil and gas recovery facilities for coastal refined oil terminals adaptable to multiple operating conditions as described in claim 4, wherein a judgment matrix is ​​constructed in step S4. The specific steps are as follows: Obtain the spatial constraint conditions of the target wharf determined in step S1; Call the preset working condition and scale mapping rule library. The mapping rule library pre-stores multiple sets of fixed quantization scale values ​​corresponding to low spatial constraint working conditions and high spatial constraint working conditions respectively. Based on the obtained spatial constraint conditions, the first condition under the current working condition is retrieved and extracted from the mapping rule base. The evaluation index is relative to the first A fixed quantization scale value for each evaluation indicator is determined, and the extracted fixed quantization scale value is assigned to... To generate the judgment matrix ; in, The generated judgment matrix Satisfies the property of positive reciprocal algebra: , as well as .

6. The method for configuring oil and gas recovery facilities for coastal refined oil terminals adaptable to multiple operating conditions as described in claim 4, characterized in that, The specific steps for calculating the weighted grey relational degree in step S5 include: Calculate the initialization matrix elements in With the relative ideal solution vector absolute difference of corresponding terms : ； Get all absolute differences minimum value and maximum value Calculate the first The candidate technical solutions are in the first Correlation coefficients under each evaluation indicator : ； In the formula, The resolution coefficient has a range of values. ; correlation coefficient The corresponding value in the objective weight vector calculated in step S4 Multiply and sum to get the first... Weighted grey relational degree of each candidate technical solution : 。 7. The method for configuring oil and gas recovery facilities for coastal refined oil terminals adaptable to multiple operating conditions as described in claim 1, characterized in that, The specific logic for generating the corresponding spatial layout instruction in step S6 is as follows: If the spatial constraint condition determined in step S1 is a low spatial constraint condition, then a first layout instruction is generated, instructing that the oil and gas recovery facility corresponding to the optimal configuration scheme be arranged at the front of the wharf. If the spatial constraint condition determined in step S1 is a high spatial constraint condition, then the original data of the land area is further compared with the preset threshold for the land area of ​​the dock platform: When the original data of the land area is less than or equal to the land area threshold of the wharf platform, a second layout instruction is generated, which instructs the oil and gas recovery facilities to be compactly arranged on the wharf operation platform. When the original data of the land area exceeds the land area threshold of the wharf platform, a third layout instruction is generated, which instructs the oil and gas recovery facilities to be arranged in the land storage area behind the wharf. At the same time, based on the pipeline laying distance collected in step S1, the preset distance and equipment mapping rule library is queried to match the selection parameters of the induced draft module to overcome the pressure drop of long-distance pipelines, and the selection parameters of the induced draft module are output together with the third layout instruction as the fluid dynamic compensation configuration.

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