Method and system for optimizing economic benefits of triethylene glycol dehydration device

Abstract

The embodiment of the invention provides an optimization method and system for economic benefits of a triethylene glycol dehydration device, and the optimization method comprises the steps: determining a target function and to-be-optimized parameters based on the operation condition of a to-be-processed triethylene glycol dehydration device; performing single-variable sensitivity analysis on the to-be-optimized parameters, and determining the sensitivity of the to-be-optimized parameters; determining an initial solution meeting a constraint condition based on the sensitivity of the to-be-optimized parameter; and taking the initial solution as a starting point, and performing iterative optimization through a sequential quadratic programming method to obtain an optimization result of the economic benefits of the to-be-treated triethylene glycol dehydration device.

Description

Technical Field

[0001] The embodiments of the present application belong to the technical field of chemical process optimization, and in particular, relate to a method and system for optimizing the economic benefits of a triethylene glycol dehydration device. Background Art

[0002] Natural gas from underground usually contains water vapor, which easily forms hydrates when the pressure and temperature of natural gas change, and does not meet the requirements of natural gas gathering and deep processing. Triethylene glycol has become the most widely used solvent for natural gas dehydration due to its good hydrophilicity and thermal stability, as well as low viscosity and solubility in liquid hydrocarbons. In order to ensure that natural gas does not form hydrates during transportation, a dehydration process using triethylene glycol absorption is usually used. In the production process of triethylene glycol dehydration units, process optimization is a key link to improve production efficiency, reduce production costs, and reduce energy consumption. With the increasing complexity of the process, how to effectively optimize key variables is one of the hot topics in the chemical industry.

[0003] In the existing technology of triethylene glycol dehydration unit for natural gas processing, sequential quadratic programming (SQP) is usually used for parameter optimization. When SQP is used for optimization, there are problems such as too many iterative calculations, high calculation costs, large derivative calculation errors, and complex constraint processing. The optimization of the economic benefits of triethylene glycol dehydration unit belongs to a few-variable optimization problem, and the traditional derivative optimization method is prone to fall into the local optimal solution.

[0004] Therefore, how to quickly determine a globally optimal initial solution through sensitivity analysis under the condition of few variables and further optimize it on this basis has become an important issue in the field of chemical process optimization, especially for triethylene glycol dehydration units. Based on this, a method and system for optimizing the economic benefits of triethylene glycol dehydration units are needed. Summary of the invention

[0005] The embodiments of this specification provide a method and system for optimizing the economic benefits of a triethylene glycol dehydration device to solve some or all of the problems: In the prior art, in a triethylene glycol dehydration device for natural gas processing, sequential quadratic programming (SQP) is usually used to directly optimize the parameters during parameter optimization. However, since the optimization of the economic benefits of a triethylene glycol dehydration device belongs to a few-variable optimization problem, when using sequential quadratic programming for optimization, there are problems such as too many iterative calculations, high calculation costs, large derivative calculation errors, and complex constraint processing. The traditional derivative optimization method is prone to fall into a local optimal solution.

[0006] In order to solve the above technical problems and find the global optimal solution, the embodiments of this specification are implemented as follows:

[0007] The present specification provides a method for optimizing the economic benefits of a triethylene glycol dehydration device, the optimization method comprising:

[0008] Based on the operation status of the triethylene glycol dehydration unit to be treated, determine the objective function and parameters to be optimized;

[0009] Performing univariate sensitivity analysis on the parameter to be optimized to determine the sensitivity of the parameter to be optimized;

[0010] Based on the sensitivity of the parameter to be optimized, determining an initial solution that satisfies the constraint condition;

[0011] Taking the initial solution as a starting point, iterative optimization is performed through a sequential quadratic programming method to obtain an optimization result of the economic benefit of the triethylene glycol dehydration device to be treated.

[0012] The present specification also provides an optimization system for the economic benefits of a triethylene glycol dehydration device, the optimization system comprising:

[0013] An initialization module determines the objective function and parameters to be optimized based on the operation status of the triethylene glycol dehydration unit to be treated;

[0014] A sensitivity analysis module performs univariate sensitivity analysis on the parameter to be optimized to determine the sensitivity of the parameter to be optimized;

[0015] An initial solution determination module, which determines an initial solution that satisfies the constraint conditions based on the sensitivity of the parameter to be optimized;

[0016] The quadratic programming module uses the initial solution as a starting point and performs iterative optimization through a sequential quadratic programming method to obtain an optimization result of the economic benefit of the triethylene glycol dehydration device to be treated.

[0017] At least one of the above-mentioned technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects: using the method of univariate sensitivity analysis, an efficient and accurate initialization method is provided, which can quickly find the optimization solution that meets the constraints, and provide a good foundation for the subsequent iterative optimization of the sequential quadratic programming method, thereby effectively reducing the computational overhead, improving the optimization efficiency, and preventing falling into the local optimal solution. While ensuring the accuracy of the final result, the global optimal solution is obtained, which can avoid the problem of conventional sequential quadratic programming methods being trapped in the local optimal solution in the process of optimizing the economic benefits of triethylene glycol dehydration devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. Some specific embodiments of the present application will be described in detail in an illustrative and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings:

[0019] Figure 1 A schematic flow chart of a method for optimizing the economic benefits of a triethylene glycol dehydration device provided in an embodiment of this specification;

[0020] Figure 2 A schematic diagram of the sensitivity analysis results of lean TEG flow provided in the embodiments of this specification;

[0021] Figure 3 A schematic diagram of the sensitivity analysis results of the stripping gas process provided in the embodiments of this specification;

[0022] Figure 4 A schematic diagram of the sensitivity analysis results of the reboiler temperature provided in the embodiments of this specification;

[0023] Figure 5 A schematic diagram of the iterative process of the quadratic programming method combined with the single factor sensitivity analysis method provided in the embodiment of this specification;

[0024] Figure 6 A schematic diagram of the iterative process of the quadratic programming method provided in the embodiment of this specification without combining the single factor sensitivity analysis method;

[0025] Figure 7 A schematic diagram of a system for optimizing the economic benefits of a triethylene glycol dehydration device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0027] Current chemical process optimization techniques usually rely on numerical optimization algorithms, such as sequential quadratic programming (SQP), which performs well in multivariable optimization problems. However, when faced with optimization problems with fewer variables, these methods still have the following prominent problems: (1) High computational cost: Traditional optimization algorithms rely on repeated numerical simulations and a large number of iterative calculations. Especially when faced with complex chemical processes with multiple variables to be optimized, the algorithm needs to gradually adjust each variable, which results in huge computational overhead. Especially when high process simulation accuracy is required, the calculation time of a single iteration is long, and the efficiency of the entire optimization process is low. (2) Large derivative calculation errors: Due to the possible tolerance in process simulation, numerical optimization algorithms often have deviations when performing derivative calculations. The accumulation of such errors may make it difficult for the algorithm to find the global optimal solution, and it is easy to fall into the local optimal solution during the iteration process, affecting the globality and stability of the optimization results. (3) Complex constraint processing: The optimization of chemical processes often requires consideration of a large number of constraints, such as the operating range of the process, the physical limitations of the equipment, and product quality indicators. The existence of these constraints increases the complexity of the optimization problem. When dealing with these complex constraints, traditional optimization algorithms usually rely on the quality of the initial solution. If the initial solution is not ideal, the algorithm may repeatedly iterate near the boundary of the constraint, resulting in reduced optimization efficiency. (4) Traditional derivative optimization methods can easily fall into local optimal solutions.

[0028] In order to solve the above problems, the embodiments of this specification provide a method and system for optimizing the economic benefits of a triethylene glycol dehydration device. By using a univariate sensitivity analysis method, an efficient and accurate initialization method is provided, which can quickly find an optimization solution that satisfies the constraints, and provide a good foundation for subsequent iterative optimization of a sequential quadratic programming method, thereby effectively reducing computational overhead, improving optimization efficiency, and ensuring the accuracy of the final result while obtaining a global optimal solution. This can avoid the problem of conventional sequential quadratic programming methods falling into a local optimal solution during the optimization process of the economic benefits of a triethylene glycol dehydration device.

[0029] Figure 1 The following is a flow chart of a method for optimizing the economic benefits of a triethylene glycol dehydration device provided in the embodiments of this specification. From a program perspective, the execution subject of the method for optimizing the economic benefits of a triethylene glycol dehydration device is a program installed on an application server or an application terminal. It can be understood that the method can be executed by any device, equipment, platform, or device cluster with computing and processing capabilities. Figure 1 As shown, the optimization method includes:

[0030] Step S101: Determine the objective function and parameters to be optimized based on the operation status of the triethylene glycol dehydration device to be processed.

[0031] In the embodiments of this specification, the triethylene glycol dehydration device to be processed is a triethylene glycol dehydration device that needs to be optimized for economic benefits. The triethylene glycol dehydration device to be processed is built based on the triethylene glycol dehydration process. The construction method of the triethylene glycol dehydration device to be processed does not constitute a limitation on this application.

[0032] In the embodiment of this specification, the objective function is:

[0033] (stripping gas cost + reboiler power cost + circulating pump power cost + hydrocarbon loss) / product gas flow

[0034] in:

[0035] Stripping gas cost = stripping gas consumption * natural gas price;

[0036] Reboiler power consumption cost = reboiler power consumption * natural gas price * gas-to-electricity ratio / natural gas-to-electricity efficiency;

[0037] Circulation pump electricity consumption cost = circulation pump electricity consumption * natural gas price * gas-to-electricity ratio / natural gas-to-electricity conversion efficiency.

[0038] In the embodiment of this specification, the reboiler temperature is positively correlated with the reboiler power consumption, and the circulation pump power consumption is positively correlated with the lean TEG flow rate;

[0039] The parameters to be optimized include: lean TEG flow rate, stripping gas consumption, and reboiler temperature.

[0040] It should be noted that the reboiler is electrically heated, so the reboiler temperature is positively correlated with the reboiler power consumption; the lean TEG flow is powered by the circulating pump, so the circulating pump power consumption is positively correlated with the lean TEG flow, and the offshore platform uses natural gas for power generation.

[0041] In the objective function, the stripping gas consumption is the flow rate of the process simulation, the reboiler power consumption is the heat load of the reboiler in the process simulation, and the circulating pump power consumption is the power of the circulating pump in the process simulation. The three variables can be read by the process simulation software. Therefore, in the objective function, the parameters to be optimized are: lean TEG flow rate, stripping gas consumption, and reboiler temperature.

[0042] In the embodiments of this specification, the lean TEG (triethylene glycol) flow rate refers to the flow rate of the triethylene glycol solution after regeneration treatment during the natural gas dehydration process. Lean TEG refers to triethylene glycol from which absorbed water has been removed, that is, a relatively pure triethylene glycol solution obtained by removing water from water-rich TEG (rich TEG) by heating and other methods. In natural gas processing, the lean TEG flow rate affects the dehydration efficiency and energy consumption. Generally, the lean TEG flow rate needs to be determined based on the following factors: Raw gas flow rate: The amount of natural gas entering the system determines how much lean TEG is needed for effective dehydration. Design capacity: The design capacity and operating conditions of the dehydration device will also affect the flow setting of the lean TEG. Lean TEG concentration: The lower the water content in the lean TEG, the stronger its moisture absorption capacity, but at the same time it may require higher regeneration costs. Process requirements: In order to achieve a specific dryness standard, the flow rate of the lean TEG may need to be adjusted. Economic efficiency: Balance the relationship between the dehydration effect and energy consumption to achieve the best economic benefits. Therefore, it is of great significance to optimize the lean TEG flow rate.

[0043] Stripping gas consumption refers to the gas flow rate introduced into the system in order to separate dissolved or entrained gas from liquid in a chemical process, or to improve the quality of a certain process fluid (such as increasing the concentration of glycol lean liquid). The stripping gas can be steam, nitrogen or other suitable gases. The stripping gas of the device of the present invention is natural gas dry gas, and its main function is to help the volatile components transfer from the liquid phase to the gas phase by increasing the pressure of the gas phase or providing heat. In the glycol dehydration device in natural gas processing, the flow rate of the stripping gas is not the larger the better, but should be adjusted according to the actual effect. Excessive flow may not only lead to energy waste, but also affect the quality of the final product. Therefore, it is of great significance to optimize the stripping gas consumption.

[0044] The temperature of the reboiler refers to the temperature inside the equipment used to heat the bottom liquid of the distillation tower or other separation process to partially vaporize it. The temperature control of the reboiler is crucial to ensure the effectiveness of the distillation process because it directly affects the amount of liquid evaporated from the bottom of the tower and the purity of the final product. The operating temperature of the reboiler is usually determined by the following factors: Material properties: Different materials have different boiling points, so the reboiler needs to set the appropriate temperature according to the properties of the material. Process requirements: In order to achieve a specific separation effect, it may be necessary to adjust the temperature of the reboiler to control the vaporization rate and product quality. Heat source type: The reboiler can use steam, electric heating or other heat sources, and the characteristics and availability of these heat sources will also affect the choice of temperature. Safety and efficiency: Too high a temperature may cause the material to decompose or form by-products, while too low a temperature cannot effectively provide sufficient vapor flow. Therefore, it is necessary to choose a temperature that meets the operating requirements and is safe and effective.

[0045] Step S103: performing univariate sensitivity analysis on the parameter to be optimized to determine the sensitivity of the parameter to be optimized.

[0046] Since the conventional sequential quadratic programming method is easy to fall into a local optimal solution when used for the optimization of the economic benefits of the triethylene glycol dehydration device, the optimization result of the economic benefits of the triethylene glycol dehydration device is not ideal. Therefore, in order to avoid the problem of local optimal solution, the embodiment of this specification adopts the method of univariate sensitivity analysis to provide an effective initial value for the sequential quadratic programming method.

[0047] In the embodiment of this specification, the univariate sensitivity analysis is performed on the parameter to be optimized to determine the sensitivity of the parameter to be optimized, specifically including:

[0048] Selecting a number of preset points based on the upper and lower limits of each parameter to be optimized;

[0049] Determine the objective function value and constraint condition value of each parameter of the parameters to be optimized at each corresponding preset point;

[0050] Based on the objective function value and constraint condition value of each parameter of the parameters to be optimized at each corresponding preset point, the sensitivity of each parameter of the parameters to be optimized is determined.

[0051] Specifically in this embodiment, when performing a univariate sensitivity analysis on the parameters to be optimized, the parameters to be optimized are first set to initial values, specifically, the parameters to be optimized are set to initial values, that is, the initial values ​​of the lean TEG flow rate, the initial values ​​of the stripping gas consumption, and the initial values ​​of the reboiler temperature are set. Subsequently, when performing a univariate sensitivity analysis, two parameters to be optimized are fixed and the other parameter to be optimized is changed for analysis.

[0052] When setting the parameter to be optimized as the initial value, the selection of the initial value is not specifically limited, as long as the initial value is within the upper and lower limits of each parameter to be optimized. When performing univariate sensitivity analysis, the number of preset points is selected as a fixed value, and the number of the preset points can be selected in a pre-set manner. In one embodiment of the present specification, the number of preset points is selected as 6.

[0053] In the embodiments of this specification, the plurality of preset points are selected based on the upper and lower limits of each parameter of the parameter to be optimized, specifically including:

[0054] Based on the upper and lower limits of each parameter of the parameter to be optimized, a plurality of preset points are selected according to the number of preset points.

[0055] In a specific embodiment, the selection of a plurality of preset points may adopt an equal interval manner to set a plurality of preset points, or may adopt an unequal interval manner to select a plurality of preset points.

[0056] At the same time, when performing univariate sensitivity analysis, there is no restriction on the order of sensitivity analysis of the parameters to be optimized. For example, the sensitivity analysis of the lean TEG flow rate can be performed first, followed by the sensitivity analysis of the stripping gas consumption, and finally the sensitivity analysis of the reboiler temperature. Of course, the sensitivity analysis of the stripping gas consumption can also be performed first, followed by the sensitivity analysis of the reboiler, and finally the sensitivity analysis of the lean TEG flow rate. Of course, when performing univariate sensitivity analysis, other orders can also be used for analysis, and the order of sensitivity analysis of the parameters to be optimized does not constitute a limitation intended by this application.

[0057] This application takes the example of first performing a sensitivity analysis of lean TEG flow, then performing a sensitivity analysis of stripping gas consumption, and finally performing a sensitivity analysis of reboiler temperature.

[0058] First, the lean TEG flow rate, stripping gas consumption and reboiler temperature are all set to initial values. In one embodiment of the present specification, the initial value of the lean TEG flow rate is set to 7.1, the initial value of the stripping gas consumption is set to 150, and the initial value of the reboiler temperature is set to 180.4. When performing sensitivity analysis, the stripping gas flow rate and the reboiler temperature are kept at the initial values. At the same time, the lean TEG flow rate is set to several preset points, for example, the lean TEG flow rate is set to 2, 3.08, 4.16, 5.24, 6.32, 7.4, so as to analyze the sensitivity of the lean TEG flow rate (such as Figure 2 Then, the lean TEG flow rate and the reboiler temperature were kept at the initial values, and the stripping gas consumption was set to several preset points, for example, the stripping gas consumption was set to 115, 152, 189, 226, 263, and 300, so as to analyze the sensitivity of the stripping gas consumption (such as Figure 3 Finally, the lean TEG flow rate and stripping gas consumption were kept at the initial values, and the reboiler temperature was set to several preset points, for example, the reboiler temperature was set to 170, 174, 178, 182, 186 and 190, so as to analyze the sensitivity of the reboiler temperature (such as Figure 4 shown).

[0059] Step S105: Based on the sensitivity of the parameters to be optimized, determine an initial solution that meets the constraint conditions.

[0060] Based on the above univariate sensitivity analysis, the objective function value is determined, and then the initial solution that meets the constraints is determined.

[0061] In the embodiment of this specification, determining the initial solution satisfying the constraint condition based on the sensitivity of the parameter to be optimized specifically includes:

[0062] Determining a critical operating range of each parameter of the parameter to be optimized based on the sensitivity of the parameter to be optimized;

[0063] Based on the key operating range of each parameter of the parameter to be optimized,

[0064] Determine the objective function value of each parameter of the parameters to be optimized within the corresponding operating range;

[0065] Determine the optimal solution corresponding to each parameter of the parameters to be optimized based on the objective function value of each parameter of the parameters to be optimized within the corresponding operating range;

[0066] The combination of the optimal solutions corresponding to the parameters to be optimized is used as the initial solution that meets the constraint conditions.

[0067] In a specific embodiment, the sensitivity analysis is implemented by selecting a preset number of global points within the upper and lower limit intervals corresponding to each parameter to be optimized. The specific number of preset points can be determined according to the business scenario. The more preset numbers, the better. The specific preset number is not limited here.

[0068] In the embodiment of this specification, the constraint condition is: the moisture content of dry gas does not exceed 1.1 lb / mmscf.

[0069] In the embodiment of this specification, the method further comprises:

[0070] If the combination of the optimal solutions corresponding to the parameters to be optimized does not satisfy the constraint conditions, the best optimization parameters are selected through single variable sensitivity, and the parameters to be optimized are adjusted to obtain the optimal solution of the algorithm that satisfies the constraint conditions.

[0071] In the embodiments of the present specification, each parameter of the parameter to be optimized is adjusted by judging the sensitivity of a single variable, specifically, the adjustment is performed by increasing or decreasing the parameter of the parameter to be optimized.

[0072] Continuing with the previous example, the initial solution is determined as: lean TEG flow rate is 2, stripping gas consumption is 115, and reboiler temperature is 170. At this time, the objective function value is 1032.421, which is the global minimum. However, the preset constraints are not met at this time. Further, according to the univariate sensitivity analysis, the parameters to be optimized are adjusted so that the preset constraints are finally met. The final initial solution is: lean TEG flow rate is 3.17, stripping gas consumption is 115, and reboiler temperature is 174.333.

[0073] Step S107: taking the initial solution as a starting point, performing iterative optimization through sequential quadratic programming to obtain an optimization result of the economic benefit of the triethylene glycol dehydration device to be treated.

[0074] In the embodiment of this specification, the initial solution is used as the starting point, and iterative optimization is performed through the sequential quadratic programming method to obtain the optimization result of the economic benefit of the triethylene glycol dehydration device to be treated, which specifically includes:

[0075] Taking the initial solution as a starting point, determining the gradient change direction of each parameter of the parameters to be optimized;

[0076] Based on the gradient change direction of each parameter of the parameter to be optimized, searching and calculating each parameter of the parameter to be optimized according to a preset step size to obtain a new iteration variable;

[0077] Based on the new iterative variable, the gradient change direction is judged and search calculation is performed until the tolerance meets the preset value, the iteration is stopped, and the optimization result of the economic benefit of the triethylene glycol dehydration device to be treated is obtained.

[0078] In the embodiments of this specification, the tolerance includes the tolerance of the objective function or the tolerance of the optimization variable;

[0079] The preset value of the tolerance of the objective function is 0.01, and the preset value of the tolerance of the optimization variable is 0.01.

[0080] In the embodiment of this specification, taking the initial solution as the starting point, determining the gradient change direction of each parameter of the parameter to be optimized specifically includes:

[0081] Taking the initial solution as a starting point, each parameter of the parameter to be optimized is derived respectively to obtain the gradient change direction of each parameter of the parameter to be optimized.

[0082] In the embodiments of this specification, the tolerance of the objective function is used to determine the change of the objective function value. If the change of the objective function value in two consecutive iterations is less than this tolerance value, it is considered that the solution has converged; the tolerance of the optimization variable is used to determine the change of the optimization variable. If the change of the objective function value in two consecutive iterations is less than this tolerance value, it is considered that the solution has converged.

[0083] Continuing with the previous example, based on the initial solution, the iteration is performed and the final optimization is: lean TEG flow is 2.926, stripping gas consumption is 115, reboiler temperature is 174.512, the objective function value is 1157.801, and the dry gas moisture content is 1.099762<1.1, which meets the constraints. The specific iterative process diagram is shown in the figure below. Figure 5 shown.

[0084] The present specification further verifies the optimization of the sequential quadratic programming method without sensitivity analysis (e.g. Figure 6The experiment found that if the sequential quadratic programming method of sensitivity analysis is not used to optimize the economic benefits of the triethylene glycol dehydration unit, the optimal point is sequence number 90, the objective function value is 1327.436, and in the process of obtaining the optimal solution, the number of calculations is more, the time is longer, the calculation results are not good, and it is obviously trapped in the local optimal solution.

[0085] Will Figure 5 and Figure 6 The results are compared and analyzed. It can be seen that the sequential quadratic programming method is optimized by using univariate sensitivity analysis, which reduces the number of calculations, shortens the calculation time, and avoids falling into the problem of local optimal solution.

[0086] The method for optimizing the economic benefits of a triethylene glycol dehydration device provided in the embodiments of this specification uses a univariate sensitivity analysis method to provide effective initial values ​​for a sequential quadratic programming method, and provides an efficient and accurate initialization method that can quickly find an optimization solution that satisfies constraints, thereby providing a good foundation for subsequent iterative optimization of the sequential quadratic programming method, thereby effectively reducing computational overhead, improving optimization efficiency, and ensuring the accuracy of the final result while obtaining a global optimal solution, which can avoid the problem of a conventional sequential quadratic programming method falling into a local optimal solution during the process of optimizing the economic benefits of a triethylene glycol dehydration device.

[0087] The aforementioned embodiment of this specification provides a method for optimizing the economic benefits of a triethylene glycol dehydration device. Based on the same idea, the embodiment of this specification also provides a system for optimizing the economic benefits of a triethylene glycol dehydration device. Figure 7 This is a schematic diagram of an optimization system for the economic benefits of a triethylene glycol dehydration device provided in the embodiments of this specification. Figure 7 As shown, the optimization system includes:

[0088] Initialization module 701, based on the operation status of the triethylene glycol dehydration device to be processed, determines the objective function and the parameters to be optimized;

[0089] A sensitivity analysis module 703 performs univariate sensitivity analysis on the parameter to be optimized to determine the sensitivity of the parameter to be optimized;

[0090] An initial solution determination module 705 determines an initial solution that satisfies the constraint conditions based on the sensitivity of the parameter to be optimized;

[0091] The quadratic programming module 707 uses the initial solution as a starting point and performs iterative optimization through a sequential quadratic programming method to obtain an optimization result of the economic benefit of the triethylene glycol dehydration device to be treated.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the economic benefits of a triethylene glycol dehydration device, characterized in that: The optimization method comprises: Based on the operation status of the triethylene glycol dehydration unit to be treated, determine the objective function and parameters to be optimized; Performing univariate sensitivity analysis on the parameter to be optimized to determine the sensitivity of the parameter to be optimized; Based on the sensitivity of the parameter to be optimized, determining an initial solution that satisfies the constraint condition; Taking the initial solution as a starting point, iterative optimization is performed through a sequential quadratic programming method to obtain an optimization result of the economic benefit of the triethylene glycol dehydration device to be treated.

2. The optimization method according to claim 1, characterized in that: The objective function is: (stripping gas cost + reboiler power cost + circulating pump power cost + hydrocarbon loss) / product gas flow in: Stripping gas cost = stripping gas consumption * natural gas price; Reboiler power consumption cost = reboiler power consumption * natural gas price * gas-to-electricity ratio / natural gas-to-electricity efficiency; Circulation pump electricity consumption cost = circulation pump electricity consumption * natural gas price * gas-to-electricity ratio / natural gas-to-electricity conversion efficiency.

3. The optimization method according to claim 2, characterized in that: The reboiler temperature is positively correlated with the reboiler power consumption, and the circulation pump power consumption is positively correlated with the lean TEG flow rate; The parameters to be optimized include: lean TEG flow rate, stripping gas consumption, and reboiler temperature.

4. The optimization method according to claim 1, characterized in that: The performing univariate sensitivity analysis on the parameter to be optimized to determine the sensitivity of the parameter to be optimized specifically includes: Selecting a number of preset points based on the upper and lower limits of each parameter to be optimized; Determine the objective function value and constraint condition value of each parameter of the parameters to be optimized at each corresponding preset point; Based on the objective function value and constraint condition value of each parameter of the parameters to be optimized at each corresponding preset point, the sensitivity of each parameter of the parameters to be optimized is determined.

5. The optimization method according to claim 1, characterized in that: The step of determining an initial solution that satisfies the constraint conditions based on the sensitivity of the parameter to be optimized specifically includes: Determining a critical operating range of each parameter of the parameter to be optimized based on the sensitivity of the parameter to be optimized; The key operating ranges of the parameters to be optimized are respectively based on the key operating ranges of the parameters to be optimized Determine the objective function value of each parameter of the parameter to be optimized at each corresponding global point; Determine the optimal solution corresponding to each parameter of the parameter to be optimized based on the objective function value of each parameter of the parameter to be optimized at each corresponding global point; The combination of the optimal solutions corresponding to the parameters to be optimized is used as the initial solution that meets the constraint conditions.

6. The optimization method according to claim 5, characterized in that: The method further comprises: If the combination of the optimal solutions corresponding to the various parameters of the parameters to be optimized does not satisfy the constraints, the various parameters of the parameters to be optimized are adjusted by a univariate sensitivity analysis method to obtain an initial solution that satisfies the constraints.

7. The optimization method according to claim 1, characterized in that: The method uses the initial solution as a starting point and performs iterative optimization through a sequential quadratic programming method to obtain an optimization result of the economic benefit of the triethylene glycol dehydration device to be treated, specifically including: Taking the initial solution as a starting point, determining the gradient change direction of each parameter of the parameters to be optimized; Based on the gradient change direction of each parameter of the parameter to be optimized, searching and calculating each parameter of the parameter to be optimized according to a preset step size to obtain a new iteration variable; Based on the new iterative variable, the gradient change direction is judged and search calculation is performed until the tolerance meets the preset value, the iteration is stopped, and the optimization result of the economic benefit of the triethylene glycol dehydration device to be treated is obtained.

8. The optimization method according to claim 7, characterized in that: The tolerance includes the tolerance of the objective function or the tolerance of the optimization variable; The preset value of the tolerance of the objective function is 0.01, and the preset value of the tolerance of the optimization variable is 0.

01.

9. The optimization method according to claim 7, characterized in that: The step of taking the initial solution as a starting point and determining the gradient change direction of each parameter of the parameter to be optimized specifically includes: Taking the initial solution as a starting point, each parameter of the parameter to be optimized is derived respectively to obtain the gradient change direction of each parameter of the parameter to be optimized.

10. A system for optimizing the economic benefits of a triethylene glycol dehydration device, characterized in that: The optimization system comprises: An initialization module determines the objective function and parameters to be optimized based on the operation status of the triethylene glycol dehydration unit to be treated; A sensitivity analysis module performs univariate sensitivity analysis on the parameter to be optimized to determine the sensitivity of the parameter to be optimized; An initial solution determination module, which determines an initial solution that satisfies the constraint conditions based on the sensitivity of the parameter to be optimized; The quadratic programming module uses the initial solution as a starting point and performs iterative optimization through a sequential quadratic programming method to obtain an optimization result of the economic benefit of the triethylene glycol dehydration device to be treated.

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