A method for optimizing a quench system of an ethylene plant

Abstract

This invention discloses an optimization method for an ethylene plant quench system, comprising the following steps: establishing an ethylene plant quench system model based on the ethylene plant quench system and corresponding process design parameters; obtaining simulated cracked gas and determining the actual components and component fractions of the cracked gas; performing process simulation based on the calculated actual components and component fractions of the cracked gas using the ethylene plant quench system model to obtain operating parameters; performing multi-objective optimization on the operating parameters to obtain optimal operating parameters; and optimizing the ethylene plant quench system based on the obtained optimal operating parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of chemical engineering, in particular to an optimization method for a quench system of an ethylene plant. BACKGROUND

[0002] Ethylene is an important basic raw material in the petrochemical industry, and the energy utilization efficiency and environmental impact in its production process have always been the focus of the industry. In the entire ethylene production process, the ethylene plant is the core of production, and the quench system is one of the key links of the ethylene plant, connecting the cracking and compression systems. Its role is to separate the light and heavy components in the cracking gas to reduce the feed load entering the compression separation section and recover heat energy to improve the energy efficiency of the entire plant. The traditional ethylene plant quench system is mostly operated with standard operating parameters designed, and there is a large gap between the design value and the actual production operation value. This often fails to fully consider the complexity of the cracking gas and the dynamics of the process, resulting in low heat recovery efficiency, insufficient energy utilization, reduced product quality, and increased greenhouse gas emissions.

[0003] Current solutions to the problems of the quench system in China mainly focus on targeted process

[0004] adjustment, improvement of existing equipment, and use of more efficient heat exchangers, but these methods do not comprehensively optimize the quench process from the perspective of the overall system, especially when considering the complex cracking gas components and optimizing process parameters. There is a lack of an effective method to accurately control and optimize the quench process to ensure improved energy recovery efficiency while reducing environmental impact.

[0005] Existing quench system optimization methods often focus on a single objective, such as improving energy utilization, while ignoring other important factors such as environmental impact and long-term sustainability. Therefore, there is an urgent need for a multi-objective optimization method that considers energy utilization efficiency, energy consumption, profit, and environmental impact to achieve comprehensive optimization of the ethylene plant quench system. SUMMARY

[0006] The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

[0007] The present application aims to solve the above problems, and provides an optimization method of an ethylene device quenching system, which comprises the following steps:

[0008] The technical scheme of the present application is as follows:

[0009] The present application provides an optimization method of an ethylene device quenching system, which comprises the following steps:

[0010] Step S1: establishing an ethylene device quenching system model according to the ethylene device quenching system and corresponding process design parameters;

[0011] Step S2: obtaining simulated calculation of cracking gas, and determining the real components and component fractions of the cracking gas;

[0012] Step S3: running the ethylene device quenching system model based on the calculated real components and component fractions of the cracking gas to perform process simulation, and obtaining operation parameters;

[0013] Step S4: performing multi-objective optimization on the operation parameters to obtain optimal operation parameters;

[0014] Step S5: optimizing the ethylene device quenching system according to the obtained optimal operation parameters.

[0015] According to an embodiment of the optimization method of the ethylene device quenching system, the cracking gas comprises multiple fractions, and the optimization method of the ethylene device quenching system uses the real component method to simplify the fractions in the cracking gas, thereby obtaining the real components of the cracking gas; wherein when the optimization method of the ethylene device quenching system determines the real components of the cracking gas using the real component method, the cutting component width of each main fraction in the cracking gas is set according to the boiling point of the main fraction, then the main fraction in the cracking gas is cut into a series of narrow fractions according to the cutting component width of each main fraction, and finally the narrow fractions after cutting are selected to match the real components, thereby obtaining the cracking gas composed of a series of real components for subsequent process simulation.

[0016] According to an embodiment of the optimization method of the ethylene device quenching system, when the optimization method of the ethylene device quenching system selects the real components matching each narrow fraction, the real components matching the carbon number, boiling point and structure group of each narrow fraction are sequentially selected according to the increasing order of the carbon number of each narrow fraction to replace each narrow fraction, so as to determine the real components of the cracking gas for subsequent process simulation.

[0017] According to an embodiment of the optimization method of the quench system of the ethylene plant, after determining the real components of the cracked gas, the initial cracked gas component ratio is calculated based on the distillate molar fraction in the cracked gas, and then the enthalpy value correction method is used to iteratively correct the cracked gas component ratio, so as to obtain the accurate cracked gas component ratio.

[0018] According to an embodiment of the optimization method of the quench system of the ethylene plant, when the enthalpy value of the cracked gas is corrected, the continuous least square algorithm is used to iteratively correct the component ratio of the cracked gas, so as to obtain the cracked gas component ratio close to the actual measured value, including the following steps:

[0019] Step C1: calculating the initial value of the molar fraction of each real component to obtain the initial cracked gas component;

[0020] Step C2: defining the constraint condition and the iteration stop condition;

[0021] Step C3: constructing the objective function of the quadratic approximation model;

[0022] Step C4: iteratively correcting the enthalpy value of the cracked gas based on the constructed quadratic approximation model objective function and the corresponding constraint condition and iteration stop condition, so as to obtain the cracked gas component ratio close to the actual measured value.

[0023] According to an embodiment of the optimization method of the quench system of the ethylene plant, in step C1, the initial value of the molar fraction of each real component is estimated based on the real cracked gas distillate molar fraction, so as to complete the initialization of the cracked gas component.

[0024] According to an embodiment of the optimization method of the quench system of the ethylene plant, in step C3, the objective function of the quadratic approximation model is constructed based on the actual measured value, and then the enthalpy value of the cracked gas is approximated multiple times through the quadratic approximation model objective function, so as to obtain the enthalpy value of the cracked gas close to the actual measured value, and further obtain the final cracked gas component content.

[0025] According to an embodiment of the optimization method of the quench system of the ethylene plant, after the real components and component ratios of the cracked gas are determined, a plurality of target optimization models are established based on the quench system of the ethylene plant, a plurality of optimization targets are determined through the plurality of established target optimization models, and then multi-objective optimization is performed on the obtained based on the determined optimization targets, so as to obtain optimal operation parameters; wherein the target optimization model includes an energy utilization efficiency model, a profit model and a greenhouse gas emission model.

[0026] According to an embodiment of the optimization method of the quench system of the ethylene plant, the C-MOEA / D algorithm is used for multi-objective optimization, and the method includes the following steps:

[0027] Step D1: initialize a population and set a weight vector of each optimization target;

[0028] Step D2: decompose the multi-objective optimization problem into a plurality of sub-problems and assign the weight vector;

[0029] Step D3: evaluate the fitness of the population individuals and distribute the population individuals to obtain an initial solution set of each sub-problem;

[0030] Step D4: cross and mutate the population individuals to generate new population individuals;

[0031] Step D5: evaluate the existing population individuals and the new population individuals based on the weight vector corresponding to each sub-problem, and update the solution set of each sub-problem.

[0032] According to an embodiment of the optimization method of the quench system of the ethylene plant, in step D1, the objective function is defined according to the constructed energy utilization efficiency model, profit model and greenhouse gas emission model, then the profit maximization, energy utilization efficiency energy consumption and greenhouse gas emission minimization are set as optimization targets based on the defined objective function, and the corresponding weight vector is set.

[0033] According to an embodiment of the optimization method of the quench system of the ethylene plant, in step D5, the PBI method is used to calculate the distance between the existing solution and the candidate solution of each population individual and the corresponding weight vector, and then the performance of the existing solution and the candidate solution is evaluated according to the calculated distance, so as to determine whether to update the corresponding population individual; wherein,

[0034] If the distance between the existing solution and the weight vector is greater than the distance between the candidate solution and the weight vector, the corresponding population individual is not updated;

[0035] If the distance between the existing solution and the weight vector is greater than the distance between the candidate solution and the weight vector, the corresponding population individual is updated to the candidate solution.

[0036] The application also provides a computer readable medium storing computer program code which, when executed by a processor, implements the method as described above.

[0037] The application also provides an optimization device of a quench system of an ethylene plant, comprising:

[0038] a memory for storing instructions executable by the processor; and

[0039] a processor for executing the instructions to implement the method as described above.

[0040] The application has the following beneficial effects compared with the prior art: The application is directed to optimization of a quench system of an ethylene plant, an ethylene plant quench model simulating a process of the ethylene plant quench system is established with reference to the ethylene plant quench system, process simulation is performed through the established ethylene plant quench model, and thus operation parameters are obtained for optimization. When performing optimization of the operation parameters, energy utilization efficiency energy consumption, economic benefits and environmental impact are comprehensively considered, corresponding energy utilization efficiency models, profit models and greenhouse gas emission models are established to determine optimization targets, and operation parameters are optimized based on the determined optimization targets, and thus optimal operation parameters are obtained. Compared with the prior art, the application not only focuses on a single performance index, but also improves production efficiency, reduces operating costs, reduces environmental pollution and resource waste, and is conducive to enterprises in improving economic benefits while reducing negative impact on the environment, and achieving sustainable development, under the condition of comprehensively considering energy utilization efficiency, economic benefits and environmental protection. BRIEF DESCRIPTION OF DRAWINGS

[0041] The above features and advantages of the application can be better understood after reading the detailed description of embodiments of the present application in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale and components having similar related functions or features can have the same or similar reference labels.

[0042] Figure 1 is a flow chart illustrating an embodiment of the optimization method of the quench system of the ethylene plant of the application.

[0043] Figure 2 is a flow chart illustrating an embodiment of the determination method of the cracking gas component fraction of the application.

[0044] Figure 3 is a flow chart illustrating an embodiment of the multi-objective optimization based on the C-MOEA / D algorithm of the application. DETAILED DESCRIPTION

[0045] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0046] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0047] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0048] In detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure will be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0049] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0050] For ease of description, spatial relation terms such as “below,” “below,” “lower than,” “below,” “above,” “upper,” etc., may be used herein to describe the relationship of an element or feature shown in the accompanying drawings to other elements or features. It will be understood that these spatial relation terms are intended to include orientations of the device in use or operation other than those depicted in the accompanying drawings. For example, if the device in the accompanying drawings is flipped, the orientation of an element described as “below,” “below,” or “below” to other elements or features will change to “above” said other elements or features. Thus, the exemplary terms “below” and “below” can encompass both upward and downward directions. The device may also have other orientations (rotated 90 degrees or in other orientations), and therefore the spatial relation descriptors used herein should be interpreted accordingly. Furthermore, it will be understood that when a layer is referred to as being “between” two layers, it can be the only layer between the two layers, or there may be one or more layers in between.

[0051] In the context of this application, the structure described above the second feature may include embodiments in which the first and second features are formed in direct contact, or embodiments in which additional features are formed between the first and second features, such that the first and second features may not be in direct contact.

[0052] It should be understood that when a component is referred to as "on another component," "connected to another component," "coupled to another component," or "in contact with another component," it can be directly on, connected to, coupled to, or in contact with that other component, or there may be an intervening component. In contrast, when a component is referred to as "directly on another component," "directly connected to," "directly coupled to," or "directly in contact with" another component, there is no intervening component. Similarly, when a first component is referred to as "electrically contacting" or "electrically coupled to" a second component, there is an electrical path between the first and second components that allows current to flow. This electrical path may include capacitors, coupled inductors, and / or other components that allow current to flow, even if there is no direct contact between the conductive components.

[0053] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0054] An embodiment of an optimization method for a quenching system in an ethylene plant is disclosed herein. Figure 1 This is a flowchart illustrating an embodiment of an optimized method for the quenching system of an ethylene plant according to the present invention. Please refer to... Figure 1 The following is a detailed explanation of each step in the optimization method for the quenching system of an ethylene plant.

[0055] Step S1: Establish a model of the ethylene plant quench system based on the quench system and the corresponding process design parameters.

[0056] In this embodiment, in order to accurately optimize the operating parameters of the ethylene plant quench system, an ethylene plant quench system model is established by referring to the ethylene plant quench system and the actual plant process design parameters. The ethylene plant quench system model is used to simulate the operation process of the ethylene plant quench system, thereby optimizing the operating parameters of the ethylene plant quench system.

[0057] In one implementation, Aspen Plus V10.0 is used to build a quench system model for an ethylene plant, plant process design parameters are used as model building parameters, and SRK method is used as the property method.

[0058] Step S2: Obtain the simulated cracked gas and determine the actual components and component fractions of the cracked gas.

[0059] In actual production, the quench system of an ethylene plant is generally connected to the cracking system and the compression system. It is used to separate the light and heavy components from the cracked gas, thereby reducing the feed load entering the compression separation section and recovering heat energy. Therefore, in order to simulate the operating parameters of the quench system of an ethylene plant, it is necessary to select a cracked gas for subsequent process simulation based on actual needs.

[0060] Because the obtained cracked gas contains a complex composition with multiple fractions, its true components and mole fractions cannot be accurately determined. Therefore, before conducting process simulation, it is necessary to determine its true components and calculate its component fractions for use in subsequent process simulations, thereby obtaining the corresponding operating parameters.

[0061] Specifically, in this embodiment, the true component method is used to simplify and substitute the components in the cracked gas, thereby confirming the true components of the cracked gas. In determining the true components of the cracked gas, firstly, a corresponding cut-off component width needs to be set for each major fraction based on its boiling point. Then, based on the cut-off component width of each major fraction, the major fractions in the cracked gas are cut into a series of narrow fractions, and a matching true component is selected for each of the cut narrow fractions, ultimately obtaining the true components of the cracked gas.

[0062] In selecting matching real components for each narrow fraction, it is necessary to select real components that match the carbon number, boiling point and structural family of each narrow fraction in the order of increasing carbon number to replace them, and finally obtain a series of cracked gas composed of real components for subsequent process simulation.

[0063] In one embodiment, a heavy liquid is used as the pyrolysis feedstock, and the resulting pyrolysis gas is used to simulate the quench system process of an ethylene plant. The main fractions of this pyrolysis gas include pyrolysis gasoline, pyrolysis diesel, and pyrolysis fuel oil. This embodiment will be further described in detail below using this pyrolysis gas as an example.

[0064] In this embodiment, the boiling point ranges of the main fractions in the cracked gas are as follows: cracked gasoline is between C9 and 204°C, cracked diesel is between 204°C and 288°C, and cracked fuel oil has a boiling point exceeding 288°C. When segmenting the cracked gas, the segmentation width is first set to 10°C for cracked gasoline, 20°C for cracked diesel, and 30°C for cracked fuel oil, based on their boiling points. Then, the cracked gasoline, cracked diesel, and cracked fuel oil are segmented into a series of narrow fractions using the set segmentation widths, and a real component is selected to replace each of the segmented narrow fractions.

[0065] In selecting real components, starting with the C9 fraction of the cracked gasoline, real components matching the carbon number, boiling point, and structural family of each narrow fraction are selected in ascending order of carbon number to obtain a cracked gas mixture composed of a series of real components for subsequent process simulation.

[0066] Furthermore, in this embodiment, after determining the actual composition of the cracked gas, it is also necessary to calculate its component fraction. Considering the energy loss during the heat exchange process of the quench system in the ethylene unit, it is necessary to perform enthalpy correction on the component fraction of the cracked gas to obtain an accurate component fraction.

[0067] In the process of enthalpy correction of the cracked gas component fraction, a continuous least squares algorithm is used to iteratively compare and correct the cracked gas component fraction based on the actual measured values ​​of the plant, thereby obtaining a cracked gas component fraction that approximates the actual measured values. Figure 2 This is a flowchart illustrating an embodiment of the method for determining the fraction of pyrolysis gas components according to the present invention. Please refer to it. Figure 2 The following is a detailed explanation of each step in the method for determining the component fractions of cracked gas:

[0068] Step C1: Calculate the initial mole fraction of each real component to obtain the initial pyrolysis gas composition.

[0069] In this embodiment, when calculating the initial value of the mole fraction of each real component, it is necessary to use the mole fraction of the real cracked gas fraction provided by the process design package to estimate the initial value of the mole fraction of each real component in turn, complete the initialization of the cracked gas component fraction, and then perform enthalpy correction on the initialized cracked gas component fraction to obtain a more accurate cracked gas component fraction.

[0070] For example, assuming the cracked gas contains n real components, the mole fractions of each real component x1, x2, ..., x2 are estimated sequentially using the cracked gas fraction mole fractions provided by the process design package. n Finally, the initial fractions of the cracked gas components are obtained as x0 = [x1, x2, ..., x]. n ].

[0071] Step C2: Define constraints and iteration termination conditions.

[0072] In this embodiment, since the fractions contained in different cracked gases are not the same, before enthalpy correction of the cracked gas component fractions, in order to obtain more accurate cracked gas component fractions and prevent iterative divergence, corresponding constraints and iteration termination conditions need to be formulated. Among these, the following principles must be followed when formulating the constraints: 1. The sum of the mole fractions of each real component in the cracked gas is 1; 2. The sum of the mole fractions of the real components corresponding to each fraction after division is equal to the mole fraction of each fraction.

[0073] Taking the cracked gas from the cracking of the aforementioned heavy liquid as an example, the mole fractions of each actual component corresponding to each fraction in the cracked gas are set to be between (0, 0.23). Based on the constraint setting principles and the mole fractions of each fraction in the cracked gas, the following constraint conditions are set for subsequent iterative calculations:

[0074]

[0075] In this embodiment, when setting the iteration termination condition, two parameter values ​​need to be set: the tolerance between the enthalpy value of the cracked gas after each iteration and the actual measured value, and the maximum number of iterations. The set tolerance value and the maximum number of iterations are used to determine whether the iterative calculation of the cracked gas component fraction is complete. Specifically, if the solution after iterative correction is less than the set tolerance value, or the current number of iterations is greater than the maximum number of iterations, then the iterative correction ends, and the final cracked gas component fraction is obtained.

[0076] Step C3: Construct the objective function of the quadratic approximation model.

[0077] In this embodiment, when using the continuous least squares algorithm to iteratively calculate the component fractions of the cracked gas, the actual measured values ​​from the plant are used to compare and correct the enthalpy value of the cracked gas after each iteration. This requires constructing an objective function for a quadratic approximation model based on the actual measured values. Then, the enthalpy value of the cracked gas is approximated multiple times using the objective function of the quadratic approximation model to obtain a value approximately equal to the actual measured value, thus yielding the final component content of the cracked gas. Furthermore, in this embodiment, to simplify the complexity of the problem and ensure that the sum of the mole fractions of the actual components corresponding to each fraction in the cracked gas is equal to the process design value, a corresponding linear approximation model is constructed based on the constraints of the quadratic approximation model to improve the accuracy of the model.

[0078] Step C4: Based on the constructed objective function of the quadratic approximation model and the corresponding constraints and iteration termination conditions, the enthalpy value of the cracked gas is iteratively corrected to obtain the component fraction of the cracked gas that approximates the actual measured value.

[0079] In this embodiment, when iteratively correcting the enthalpy value of the cracked gas using the constructed quadratic approximation model objective function and the corresponding constraints and iteration termination conditions, a quadratic programming solver is used to solve for the direction of movement after each iteration. And step size, and determine the direction of movement. Optimal step size Then use the solved direction of movement and along the direction of movement Optimal step size To update the current solution.

[0080] For example, the component fraction of the gas from the kth cracking operation. The quadratic programming solver is used to obtain the current iteration optimization direction. and along the direction of movement Optimal step size And use it to update the current solution. The formula is as follows:

[0081]

[0082] After each update of the current solution, it is necessary to determine whether the current iteration meets the set iteration termination condition. Assuming the tolerance between the cracked gas enthalpy value and the actual measured value after each iteration is set to 1e-6, and the maximum number of iterations is 100, if the tolerance between the cracked gas enthalpy value and the actual measured value after the current iteration is less than 1e-6, or the number of iterations is greater than 100, then the iteration optimization is terminated, yielding the final cracked gas component content that approximates the actual measured value.

[0083] Step S3: Based on the calculated actual components and component fractions of the cracked gas, run the ethylene unit quench system model to perform process simulation and obtain operating parameters.

[0084] In this embodiment, after determining the actual components and component fractions of the cracked gas through the above step S2, the actual components and component fractions of the cracked gas are used as input values ​​to run the ethylene unit quench system model, thereby simulating the actual operation process of the ethylene unit quench system and obtaining the operating parameters.

[0085] Step S4: Perform multi-objective optimization of the operating parameters to obtain the optimal operating parameters.

[0086] In this embodiment, after obtaining the model parameters through step S3, multiple objective optimization models are established with reference to the quench system of the ethylene plant. Then, the operating parameters are optimized using these multiple objective optimization models to obtain the optimal operating parameters. The objective optimization models include an energy utilization efficiency model, a profit model, and a greenhouse gas emission model. Multiple objective functions are established for these models, and the combined C-MOEA / D algorithm is used for multi-objective optimization to obtain the optimal operating parameters.

[0087] Specifically, in this embodiment, a slack analysis method is used to establish an energy utilization efficiency model. This model is then used to calculate the energy utilization rate of the ethylene plant's quench system, which is then used as one of the optimization objectives. Before establishing the energy utilization efficiency model, a detailed analysis of the overall energy flow of the ethylene plant's quench system is conducted to determine all energy inputs and outputs. Then, the slack loss of the ethylene plant's quench system is calculated based on the slack balance equation. The slack efficiency of the ethylene plant's quench system is then calculated based on the calculated slack loss, serving as the energy utilization efficiency model. The formula for calculating the slack loss of the ethylene plant's quench system is as follows:

[0088]

[0089]

[0090] in, This indicates the total loss of the quench system in the ethylene plant. , These represent the inlet and outlet of the quench system of the ethylene plant, respectively. This indicates mass flow rate, and 'e' represents the unit mass flow rate (e.g., 㶲). Indicates heat exchange. , Enthalpy at the state point and reference point, respectively. , These represent the temperatures at the reference point and the boundary, respectively. Indicates system power. , These represent the specific enthalpy of the state point and the reference point, respectively. , Let represent the specific entropy at the state point and the reference point, respectively. Using this formula, the total enthalpy loss of the ethylene plant's quench system is calculated. Based on this calculated total enthalpy loss, the enthalpy efficiency of the ethylene plant's quench system is then calculated. That is, the energy utilization rate that the energy utilization efficiency model needs to calculate:

[0091]

[0092] in, This indicates the total loss of the quench system in the ethylene plant. This refers to the inlet of the quench system in an ethylene plant.

[0093] The formula for the profit model is as follows:

[0094]

[0095] The profit calculated by the above profit model formula is used as one of the optimization objectives to obtain better economic efficiency.

[0096] For greenhouse gas emission models, the emissions of CO2, CH4, and N2O during the rapid cooling process must first be calculated. Then, the total greenhouse gas emissions are calculated according to the proportions of CO2, CH4, and N2O in the greenhouse gases. The model is as follows:

[0097]

[0098] in, The values ​​represent the energy consumption during indirect emissions, qs represents the quenching process, dir represents direct emissions, ind represents indirect emissions, and j represents indirect emissions caused by different energy consumption processes. This indicates the CO2 emissions during the rapid cooling process. This indicates the amount of direct CO2 emissions. Indicates indirect CO2 emissions. This indicates the amount of CH4 emitted during the rapid cooling process. This indicates the amount of direct CH4 emissions. This indicates indirect CH4 emissions. This indicates the amount of N2O emitted during the rapid cooling process. This represents the direct emissions of N2O. This indicates indirect N2O emissions. , , , respectively represent , , Indirect emission coefficient, This represents the total greenhouse gas emissions.

[0099] After establishing the energy utilization efficiency model, profit model, and greenhouse gas emission model, the optimal operating parameters are obtained by using the C-MOEA / D algorithm to perform multi-objective optimization with the optimization objectives of maximizing profit, minimizing energy consumption and greenhouse gas emissions, and maximizing energy utilization efficiency. Figure 3 This is a flowchart illustrating an embodiment of the present invention for multi-objective optimization based on the C-MOEA / D algorithm. Please refer to it. Figure 3 The following is a detailed explanation of each step in multi-objective optimization based on the C-MOEA / D algorithm:

[0100] Step D1: Initialize the population and set the weight vectors for each optimization objective.

[0101] In this embodiment, before initializing the population, the population size needs to be set first. Then, based on the population size, a corresponding number of initial candidate solutions are generated to serve as population individuals, thereby completing the population initialization. For example, if the population size N=100, then 100 initial candidate solutions need to be generated as initial population individuals.

[0102] Meanwhile, since the problem to be optimized is a multi-objective optimization problem, after the population is initialized, the corresponding weight vectors need to be set according to the importance of each optimization objective in the multi-objective optimization problem, for subsequent evolution and updates.

[0103] For example, in this embodiment, the objective function is defined based on the constructed energy utilization efficiency model, profit model, and greenhouse gas emission model, including: energy utilization efficiency energy consumption function. Profit function and greenhouse gas emission function Then, based on the defined objective function, profit maximization, energy utilization efficiency, energy consumption, and greenhouse gas emissions minimization are set as optimization objectives, and corresponding weight vectors are set for these optimization objectives.

[0104] In addition, in order to limit the running time of the algorithm and avoid spending too much computing resources on complex problems, this embodiment also sets a maximum number of evaluations maxF as a constraint. For example, the maximum number of evaluations maxF=20000 means that there will be a maximum of 20000 evolutions.

[0105] Step D2: Decompose the multi-objective optimization problem into multiple sub-problems and assign weight vectors.

[0106] In this embodiment, after initializing the population through the above steps, the multi-objective optimization problem is decomposed into a set of multi-objective single-objective optimization sub-problems. Each sub-problem only considers a part of the objective function, and then these sub-problems are optimized simultaneously to obtain the optimal solution.

[0107] In one implementation, the sub-problems are decomposed with reference to the optimization objective. For example, the number of sub-problems is decomposed to be equal to the number of optimization objectives, where each sub-problem corresponds to an optimization objective, and its initial weight vector corresponds to the weight vector of the optimization objective.

[0108] Step D3: Evaluate the fitness of individuals in the population and assign them to other individuals to obtain the initial solution set for each subproblem.

[0109] In this embodiment, before synchronously optimizing the decomposed subproblems, the initialized population individuals need to be assigned to each subproblem to form an initial solution set for the subproblems. Then, optimization and updates are performed on the solution sets of the subproblems. Since the fitness of each individual is different for each subproblem, when assigning individuals, the fitness of each individual under each subproblem must first be evaluated, and then individuals adapted to the subproblems are assigned to the corresponding subproblems. Specifically, when evaluating the fitness of the population under a certain subproblem, it is usually calculated based on the objective function and constraints of that subproblem.

[0110] Specifically, in this embodiment, when evaluating fitness, it is necessary to define relevant decision variables and formulate corresponding constraints based on the actual process of the quench system of the ethylene unit and the cracked gas. Then, the fitness of individuals in the population under different sub-problems is evaluated through the defined decision variables and constraints.

[0111] Taking the pyrolysis gas obtained by using the aforementioned heavy liquid as pyrolysis feedstock as an example, the defined decision variables are: pyrolysis gasoline reflux flow rate F1, light fuel oil production rate F2, quench oil reflux flow rate F3, light fuel oil reflux temperature T1, second-stage quench water reflux temperature T2, and first-stage quench water reflux temperature T3. Then, based on the actual plant production environment, the following constraints are defined for the defined decision variables:

[0112]

[0113]

[0114] 000

[0115]

[0116] in, and These represent the top temperature and bottom temperature of the quench oil tower, respectively. and Let represent the top temperature and bottom temperature of the quench tower, respectively. The fitness of each individual in the population under different subproblems is evaluated by defining constraints and optimization objectives for each subproblem, and then assigned to obtain the initial solution set for each subproblem.

[0117] Step D4: Perform crossover and mutation on the individuals in the population to generate new individuals in the population.

[0118] In this embodiment, after the population individuals are allocated through the above steps, a genetic algorithm is used to perform crossover and mutation operations on the current population to generate new population individuals, i.e., new candidate solutions.

[0119] Step D5: Evaluate the existing population individuals and new population individuals based on the weight vectors corresponding to each subproblem, and update the solution set of each subproblem.

[0120] In this embodiment, after generating new population individuals through the above steps, the PBI method is used to calculate the distances between existing solutions and candidate solutions and their corresponding weight vectors, thereby evaluating their performance and updating the solution set of the corresponding subproblem. The distances calculated by the PBI method mainly consist of the vertical distance d1 and the radial distance d2, calculated using the following formula:

[0121]

[0122]

[0123]

[0124] Among them, z Let represent the vector consisting of the minimum values ​​of the corresponding optimization objective function, w represent the weight vector associated with individuals in the population, f(x) represent the objective function vector of individual x, and θ be a non-negative parameter used to adjust the contribution of vertical and radial distances to the overall evaluation of the individual. After calculating the distances between the existing solutions and candidate solutions of an individual in the population and their corresponding weight vectors using this formula, the performance of the existing and candidate solutions is evaluated based on the calculated distances to determine whether to update the individual in the population. Specifically, if the distance between the existing solution and the weight vector is greater than the distance between the candidate solution and the weight vector, the corresponding individual in the population is not updated. If the distance between the existing solution and the weight vector is greater than the distance between the candidate solution and the weight vector, the corresponding individual in the population is updated to a candidate solution.

[0125] Step D6: Determine whether the optimization termination condition has been met. If yes, stop the optimization and obtain a series of optimal solutions as the optimal operation parameters. If not, repeat steps D3 to D5 to evolve and update until the optimization termination condition is met.

[0126] In this embodiment, after updating the solution set of the subproblem through the above steps, it is necessary to determine whether the optimization termination condition has been met, i.e., whether the maximum number of evaluations has been reached, or whether the maximum running time has been reached. If so, the optimization ends, and the current series of optimal solutions are used as the optimal operating parameters. If not, steps D3 to D5 are repeated to evolve and update repeatedly until the optimization termination condition is met.

[0127] Step S5: Optimize the quench system of the ethylene unit based on the obtained optimal operating parameters.

[0128] In this embodiment, after obtaining the optimal operating parameters through the above steps, the obtained optimal operating parameters are used to optimize the quench system of the ethylene plant. This improves production efficiency, reduces operating costs, and also emphasizes reducing environmental pollution and resource waste. Improving production efficiency helps enterprises reduce negative environmental impacts while increasing economic benefits, thus achieving sustainable development.

[0129] This specification also provides a computer-readable medium storing computer program code that, when executed by a processor, implements the optimization method for the quench system of an ethylene plant as described above.

[0130] This specification also provides an optimization apparatus for an ethylene plant quenching system, comprising a processor-executable instruction memory and a processor for executing the instructions in the instruction memory to implement the optimization method for the ethylene plant quenching system as described above.

[0131] The prior description of this disclosure is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the examples and designs described herein, but should be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0132] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

[0133] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0134] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor such that the processor can read and write information to / from the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside as discrete components in the user terminal.

[0135] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functionality may be stored or transmitted as one or more instructions or code on or through a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, encompassing any medium that facilitates the transfer of a computer program from one location to another. A storage medium may be any available medium accessible to a computer. By way of example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Any connection is also legitimately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of a medium. As used in this article, disk and disc include compact discs (CDs), laser discs, optical discs, digital multi-purpose discs (DVDs), floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

Claims

1. An optimization method for a quenching system in an ethylene plant, characterized in that, Includes the following steps: Step S1: Establish a model of the ethylene plant quench system based on the quench system and corresponding process design parameters; Step S2: Obtain the cracked gas calculated by simulation, and determine the true composition and component fraction of the cracked gas; the cracked gas contains multiple fractions, and the optimization method of the ethylene unit quench system adopts the true composition method to simplify and replace the fractions in the cracked gas, thereby obtaining the true composition of the cracked gas; The initial component fraction of the cracked gas is calculated based on the molar fraction of the distillate in the cracked gas. Then, the enthalpy correction method is used to iteratively correct the component fraction of the cracked gas to obtain an accurate component fraction of the cracked gas. Step S3: Based on the calculated actual components and component fractions of the cracked gas, run the ethylene unit quench system model to perform process simulation and obtain operating parameters; Step S4: Perform multi-objective optimization on the operating parameters to obtain the optimal operating parameters; Step S5: Optimize the quench system of the ethylene unit based on the obtained optimal operating parameters.

2. The optimization method for the quenching system of an ethylene plant according to claim 1, characterized in that, The optimization method for the quench system of the ethylene unit uses the real component method to determine the real components of the cracked gas. The corresponding cutting component width is set according to the boiling point of each major fraction in the cracked gas. Then, the major fraction in the cracked gas is cut into a series of narrow fractions according to the cutting component width of each major fraction. Finally, the matching real components are selected for the cut narrow fractions, so as to obtain the cracked gas composed of a series of real components for subsequent process simulation.

3. The optimization method for the quenching system of an ethylene plant according to claim 2, characterized in that, The optimization method for the quench system of the ethylene unit involves selecting matching real components for each narrow fraction according to the increasing order of carbon number of each narrow fraction, and then replacing them with real components that match their carbon number, boiling point and structural group. This determines the real components of the cracked gas for subsequent process simulation.

4. The optimization method for the quenching system of an ethylene plant according to claim 3, characterized in that, The optimization method for the quenching system of the ethylene plant involves iteratively correcting the enthalpy of the cracked gas component fraction based on actual measured values ​​when correcting the enthalpy of the cracked gas component fraction, thereby obtaining the corrected cracked gas component fraction.

5. The optimization method for the quenching system of an ethylene plant according to claim 4, characterized in that, The optimization method for the quench system of the ethylene unit uses a continuous least squares algorithm to iteratively correct the enthalpy value of the cracked gas component fraction when performing enthalpy correction on the cracked gas component fraction, thereby obtaining the corrected cracked gas component fraction. The method includes the following steps: Step C1: Calculate the initial mole fraction of each real component to obtain the initial pyrolysis gas composition; Step C2: Define constraints and iteration termination conditions; Step C3: Construct the objective function of the quadratic approximation model; Step C4: Based on the constructed quadratic approximation model objective function and the corresponding constraints and iteration termination conditions, the enthalpy value of the cracked gas is iteratively corrected to obtain the corrected component fraction of the cracked gas.

6. The optimization method for the quenching system of an ethylene plant according to claim 5, characterized in that, In step C1, the initial values ​​of the mole fractions of each real component are estimated sequentially based on the actual mole fractions of the cracked gas fraction, thereby completing the initialization of the cracked gas components.

7. The optimization method for the quenching system of an ethylene plant according to claim 5, characterized in that, In step C3, the objective function of the quadratic approximation model is constructed based on the actual measured value. Then, the enthalpy value of the cracked gas is approximated multiple times through the objective function of the quadratic approximation model to obtain the corrected enthalpy value of the cracked gas, and thus the final content of cracked gas components is obtained.

8. The optimization method for the quench system of an ethylene plant according to claim 1, characterized in that, After determining the true composition and component fraction of the cracked gas, the optimization method for the quench system of the ethylene plant establishes multiple target optimization models based on the quench system. Multiple optimization objectives are determined through these models, and then the operating parameters are optimized based on these objectives to obtain the optimal operating parameters. The target optimization models include an energy utilization efficiency model, a profit model, and a greenhouse gas emission model.

9. The optimization method for the quenching system of an ethylene plant according to claim 4, characterized in that, The optimization method for the quench system of the ethylene unit employs the C-MOEA / D algorithm for multi-objective optimization, including the following steps: Step D1: Initialize the population and set the weight vectors for each optimization objective; Step D2: Decompose the multi-objective optimization problem into multiple sub-problems and assign weight vectors; Step D3: Evaluate the fitness of individuals in the population and assign individuals to obtain the initial solution set for each subproblem; Step D4: Perform crossover and mutation on the individuals in the population to generate new individuals in the population; Step D5: Evaluate the existing population individuals and new population individuals based on the weight vectors corresponding to each subproblem, and update the solution set of each subproblem.

10. The optimization method for the quench system of an ethylene plant according to claim 9, characterized in that, In step D1, an objective function is defined based on the constructed energy utilization efficiency model, profit model, and greenhouse gas emission model. Then, based on the defined objective function, profit maximization, energy utilization efficiency, energy consumption minimization, and greenhouse gas emission minimization are set as optimization objectives, and corresponding weight vectors are set.

11. A computer-readable medium storing computer program code, characterized in that, The computer program code, when executed by a processor, implements the method as described in any one of claims 1-10.

12. An optimized device for a quenching system in an ethylene plant, characterized in that, include: Memory is used to store instructions that can be executed by the processor; as well as A processor for executing the instructions to implement the method as described in any one of claims 1-10.

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