Multi-effect rectification processing technology of fluid raw material

Abstract

The invention relates to a multi-effect rectification processing technology of a fluid raw material, which comprises the following steps: M1, adding the fluid raw material into a preheater according to a preset flow, preheating the fluid raw material to 55-60 DEG C, and adding the fluid raw material into a rectification tower of the fluid raw material; m2, after the fluid raw material is rectified by the rectifying tower, light component steam leaving from the top of the rectifying tower sequentially passes through a first-stage condenser, a second-stage condenser, a third-stage condenser and a fourth-stage condenser which are preset on the top of the rectifying tower to be respectively cooled to 50-55 DEG C, 40-45 DEG C, 30-35 DEG C and 20-25 DEG C; condensate is conveyed to the first-stage collector, the second-stage collector, the third-stage collector and the fourth-stage collector through a conveying pipeline. According to the invention, not only can the flow of the fluid raw material be accurately controlled, but also the energy loss is reduced and the rectification effect is improved in the rectification process of the fluid raw material.

Description

Technical Field

[0001] The invention relates to the technical field of fluid raw material distillation, in particular to a multi-effect distillation processing technology for fluid raw materials. Background Art

[0002] Volatile fluid substances are an important part of modern industry, and distillation technology is an indispensable process in volatile fluid substances. Distillation technology separates substances with different boiling points through sublimation to achieve mixture decomposition and purification, and is widely used in many industrial processes such as petroleum refining, chemical production, and energy.

[0003] In the prior art, the distillation of volatile fluid substances is a method of extracting and purifying fluid raw materials by sublimating and separating different components in the substance to make it meet specific application requirements. Various components in the fluid substance have different boiling points. By heating the fluid raw materials in a distillation tower, evaporating and condensing these components, different grades of products can be separated. However, during the separation process, the flow rate of the fluid raw materials cannot be accurately controlled, resulting in excessive flow, uneven preheating, and reduced separation of components, or excessive preheating due to too small flow, resulting in energy loss. At the same time, the lack of effective control during the distillation process leads to insufficient distillation. Summary of the invention

[0004] In view of the above problems, the present invention provides a multi-effect distillation process for fluid raw materials, which can not only accurately control the flow rate of the fluid raw materials, but also reduce energy loss and improve the distillation effect during the distillation of the fluid raw materials.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0006] A multi-effect distillation process for a fluid raw material, the process comprising:

[0007] M1. The fluid raw material is added to the preheater according to the preset flow rate, and the fluid raw material is preheated to 55-60°C and then added to the fluid raw material distillation tower;

[0008] M2. After the fluid raw material is rectified by the distillation tower, the light component steam leaving the top of the distillation tower is sequentially cooled to 50-55°C, 40-45°C, 30-35°C and 20-25°C by the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower, respectively, and the condensate is transported to the first-stage collector, the second-stage collector, the third-stage collector and the fourth-stage collector through the transport pipeline to obtain the first-stage raw material collection liquid, the second-stage raw material collection liquid, the third-stage raw material collection liquid and the fourth-stage raw material collection liquid;

[0009] M3. Add the primary raw material collection liquid and the secondary raw material collection liquid to the heat exchanger to exchange heat to 120-125°C and then enter the first pressurized tower, cool through the reboiler in the first pressurized tower, part of which enters the fluid raw material primary distillation tank, and the other part and the tertiary raw material collection liquid and the quaternary raw material collection liquid enter the second pressurized tower;

[0010] M4. After being cooled in the reboiler in the second pressurized tower, a part enters the secondary distillation tank of the fluid raw material, and the other part enters the tertiary distillation tank of the fluid raw material to obtain the primary distillation liquid, the secondary distillation liquid and the tertiary distillation liquid of the fluid raw material.

[0011] Furthermore, the fluid raw material is added to the preheater at a preset flow rate. According to the volume of the preset device and the real-time temperature data information of the fluid raw material, a particle swarm algorithm based on an adaptive feedback adjustment factor is used to optimize the preset flow rate to obtain optimized fluid raw material data information.

[0012] Furthermore, the optimization of the preset flow rate using a particle swarm algorithm based on an adaptive feedback adjustment factor includes:

[0013] M11. Based on the volume of the preset device and the real-time temperature data information of the fluid raw material, the particle population is initialized, the maximum number of iterations L is determined, and the initialized particle population data information is obtained;

[0014] M12. Based on the initialized particle population data information, establish the fitness function G of the individual particle population,

[0015]

[0016] Among them, x is the data information of the particle population after initialization, α1, α2 and α3 are the fitness determining factors of the individual particle population, and the fitness values ​​of the individual particle population are calculated to obtain the fitness value data information of the individual particle population;

[0017] M13. Based on the fitness value data information of the individuals in the particle population, establish a target optimization function H,

[0018]

[0019] Among them, y is the fitness value data information of the individual particle population, β1, β2 and β3 are the target optimization factors of the individual particle population, and the flow rate of the fluid raw material is optimized to obtain the optimized flow rate data information of the fluid raw material.

[0020] Furthermore, the constraints of the fitness determining factors α1, α2 and α3 of the individual particle population are:

[0021]

[0022] Furthermore, the target optimization factors β1, β2 and β3 of the individual particle population are:

[0023] Among them, y is the fitness value data information of the individual particle population, and y0 is the mean value of the individual particle population.

[0024] Furthermore, the mean value y0 of the individuals in the particle population is,

[0025]

[0026] Where n is the sample size, y i is the fitness value data information of the i-th individual in the particle population, δ i is the weight coefficient of the i-th individual in the particle population.

[0027] Furthermore, the operating pressure of the preheater and the distillation tower is 0.06-0.08MPaG, the operating pressure of the first pressure tower is 1.2-1.5MPaG, the operating pressure of the second pressure tower is 1.5-1.8MPaG, the operating pressure of the first distillation tank of the fluid raw material is 0.02-0.05MPaG, the operating pressure of the second distillation tank of the fluid raw material is 0.06-0.08MPaG, and the operating pressure of the tertiary distillation tank of the fluid raw material is 0.04-0.06MPaG.

[0028] Furthermore, the first-stage condenser preset at the top of the distillation tower is a shell and tube condenser or a spiral plate condenser, the second-stage condenser preset at the top of the distillation tower is a sleeve condenser, the third-stage condenser preset at the top of the distillation tower is a natural convection air cooling condenser, and the fourth-stage condenser preset at the top of the distillation tower is a forced convection air cooling condenser.

[0029] Further, in step M2, the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower are cooled to 50-55° C., 40-45° C., 30-35° C. and 20-25° C. respectively, including:

[0030] M21. Based on the light component steam, obtain various component data information of the light component steam, and construct various component data sets of steam;

[0031] M22. Input various component data sets of the steam into the distillation tower top condenser processing network model for training and learning, and determine the condenser power optimization function Q,

[0032]

[0033] Among them, r is the data set of various components of steam, ω1, ω2 and ω3 are the target optimization factors of condenser power, and the trained distillation tower top condenser processing network model is obtained;

[0034] M23. Based on the trained distillation tower top condenser processing network model, various component data sets of steam are input, the condenser power is optimized, and the optimized condenser power data information is obtained.

[0035] Furthermore, the constraint function f of the target optimization factors ω1, ω2 and ω3 of the condenser power is,

[0036]

[0037] Among them, the value range of the constraint function f is (3,4).

[0038] The present invention has the following positive effects:

[0039] 1. The present invention optimizes the preset flow rate by adopting a particle swarm algorithm based on an adaptive feedback adjustment factor, and processes the fluid raw material in combination with the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower. It can not only accurately control the flow rate of the fluid raw material, but also reduce energy loss and improve the distillation effect during the distillation of the fluid raw material.

[0040] 2. The present invention optimizes the condenser power by constructing a distillation tower top condenser processing network model, and obtains optimized condenser power data information, thereby accurately controlling the distillation process of the fluid raw material, which not only ensures the purity and safety of the product, but also improves production efficiency and reduces energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the processing process of the present invention;

[0042] Figure 2 It is a schematic diagram of the process of the particle swarm algorithm based on the adaptive feedback adjustment factor of the present invention;

[0043] Figure 3 The present invention is a schematic diagram of the process of constructing a distillation tower top condenser processing network model. DETAILED DESCRIPTION

[0044] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0045] Example 1: Figure 1 As shown, a multi-effect distillation process for a fluid raw material, the process comprising:

[0046] M1. The fluid raw material is added to the preheater according to the preset flow rate, and the fluid raw material is preheated to 55-60°C and then added to the fluid raw material distillation tower;

[0047] M2. After the fluid raw material is rectified by the distillation tower, the light component steam leaving the top of the distillation tower is sequentially cooled to 50-55°C, 40-45°C, 30-35°C and 20-25°C by the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower, respectively, and the condensate is transported to the first-stage collector, the second-stage collector, the third-stage collector and the fourth-stage collector through the transport pipeline to obtain the first-stage raw material collection liquid, the second-stage raw material collection liquid, the third-stage raw material collection liquid and the fourth-stage raw material collection liquid;

[0048] M3. Add the primary raw material collection liquid and the secondary raw material collection liquid to the heat exchanger to exchange heat to 120-125°C and then enter the first pressurized tower, cool through the reboiler in the first pressurized tower, part of which enters the fluid raw material primary distillation tank, and the other part and the tertiary raw material collection liquid and the quaternary raw material collection liquid enter the second pressurized tower;

[0049] M4. After being cooled in the reboiler in the second pressurized tower, a part enters the secondary distillation tank of the fluid raw material, and the other part enters the tertiary distillation tank of the fluid raw material to obtain the primary distillation liquid, the secondary distillation liquid and the tertiary distillation liquid of the fluid raw material.

[0050] In this embodiment, the fluid raw material is added to the preheater at a preset flow rate. According to the volume of the preset device and the real-time temperature data information of the fluid raw material, a particle swarm algorithm based on an adaptive feedback adjustment factor is used to optimize the preset flow rate to obtain optimized fluid raw material data information.

[0051] In this embodiment, if Figure 2 As shown, the optimization of the preset flow rate using the particle swarm algorithm based on the adaptive feedback adjustment factor includes:

[0052] M11. Based on the volume of the preset device and the real-time temperature data information of the fluid raw material, the particle population is initialized, the maximum number of iterations L is determined, and the initialized particle population data information is obtained;

[0053] M12. Based on the initialized particle population data information, establish the fitness function G of the individual particle population,

[0054]

[0055] Among them, x is the data information of the particle population after initialization, α1, α2 and α3 are the fitness determining factors of the individual particle population, and the fitness values ​​of the individual particle population are calculated to obtain the fitness value data information of the individual particle population;

[0056] M13. Based on the fitness value data information of the individuals in the particle population, establish a target optimization function H,

[0057]

[0058] Among them, y is the fitness value data information of the individual particle population, β1, β2 and β3 are the target optimization factors of the individual particle population, and the flow rate of the fluid raw material is optimized to obtain the optimized flow rate data information of the fluid raw material.

[0059] In this embodiment, the constraints of the fitness determining factors α1, α2 and α3 of the individual particle population are:

[0060]

[0061] In this embodiment, the target optimization factors β1, β2 and β3 of the individual particle population are:

[0062]

[0063] Among them, y is the fitness value data information of the individual particle population, and y0 is the mean value of the individual particle population.

[0064] In this embodiment, the mean value y0 of the individuals in the particle population is,

[0065]

[0066] Where n is the sample size, y i is the fitness value data information of the i-th individual in the particle population, δ i is the weight coefficient of the i-th individual in the particle population.

[0067] In this embodiment, the operating pressure of the preheater and the distillation tower is 0.06-0.08MPaG, the operating pressure of the first pressure tower is 1.2-1.5MPaG, the operating pressure of the second pressure tower is 1.5-1.8MPaG, the operating pressure of the first distillation tank of the fluid raw material is 0.02-0.05MPaG, the operating pressure of the second distillation tank of the fluid raw material is 0.06-0.08MPaG, and the operating pressure of the tertiary distillation tank of the fluid raw material is 0.04-0.06MPaG.

[0068] Example 2: Based on the multi-effect distillation process of a fluid raw material in Example 1, the present invention is further illustrated and described below.

[0069] like Figure 1 As shown, a multi-effect distillation process for a fluid raw material, the process comprising:

[0070] M1. The fluid raw material is added to the preheater according to the preset flow rate, and the fluid raw material is preheated to 55-60°C and then added to the fluid raw material distillation tower;

[0071] M2. After the fluid raw material is rectified by the distillation tower, the light component steam leaving the top of the distillation tower is sequentially cooled to 50-55°C, 40-45°C, 30-35°C and 20-25°C by the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower, respectively, and the condensate is transported to the first-stage collector, the second-stage collector, the third-stage collector and the fourth-stage collector through the transport pipeline to obtain the first-stage raw material collection liquid, the second-stage raw material collection liquid, the third-stage raw material collection liquid and the fourth-stage raw material collection liquid;

[0072] M3. Add the primary raw material collection liquid and the secondary raw material collection liquid to the heat exchanger to exchange heat to 120-125°C and then enter the first pressurized tower, cool through the reboiler in the first pressurized tower, part of which enters the fluid raw material primary distillation tank, and the other part and the tertiary raw material collection liquid and the quaternary raw material collection liquid enter the second pressurized tower;

[0073] M4. After being cooled in the reboiler in the second pressurized tower, a part enters the secondary distillation tank of the fluid raw material, and the other part enters the tertiary distillation tank of the fluid raw material to obtain the primary distillation liquid, the secondary distillation liquid and the tertiary distillation liquid of the fluid raw material.

[0074] In this embodiment, the fluid raw materials include petroleum and its fractions, condensate, natural gas and refinery gas, the first-stage condenser preset at the top of the distillation tower is a shell and tube condenser or a spiral plate condenser, the second-stage condenser preset at the top of the distillation tower is a sleeve condenser, the third-stage condenser preset at the top of the distillation tower is a natural convection air cooling condenser, and the fourth-stage condenser preset at the top of the distillation tower is a forced convection air cooling condenser.

[0075] In this embodiment, if Figure 3 As shown, in step M2, the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower cool it to 50-55°C, 40-45°C, 30-35°C and 20-25°C respectively, including:

[0076] M21. Based on the light component steam, obtain various component data information of the light component steam, and construct various component data sets of steam;

[0077] M22. Input various component data sets of the steam into the distillation tower top condenser processing network model for training and learning, and determine the condenser power optimization function Q,

[0078]

[0079] Among them, r is the data set of various components of steam, ω1, ω2 and ω3 are the target optimization factors of condenser power, and the trained distillation tower top condenser processing network model is obtained;

[0080] M23. Based on the trained distillation tower top condenser processing network model, various component data sets of steam are input, the condenser power is optimized, and the optimized condenser power data information is obtained.

[0081] In this embodiment, the constraint function f of the target optimization factors ω1, ω2 and ω3 of the condenser power is,

[0082]

[0083] Among them, the value range of the constraint function f is (3,4).

[0084] In this embodiment, the present invention provides a multi-effect distillation processing system for a fluid raw material, including a computer device, which is programmed or configured to execute any one of the steps of the multi-effect distillation processing process for the fluid raw material.

[0085] In this embodiment, the present invention provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the multi-effect distillation processing techniques for fluid raw materials.

[0086] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0087] In summary, the present invention can not only accurately control the flow rate of the fluid raw material, but also reduce energy loss and improve the distillation effect during the distillation of the fluid raw material.

[0088] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A multi-effect distillation process for a fluid raw material, characterized in that: The process comprises: M1. The fluid raw material is added to the preheater according to the preset flow rate, and the fluid raw material is preheated to 55-60°C and then added to the fluid raw material distillation tower; M2. After the fluid raw material is rectified by the distillation tower, the light component steam leaving the top of the distillation tower is sequentially cooled to 50-55°C, 40-45°C, 30-35°C and 20-25°C by the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower, respectively, and the condensate is transported to the first-stage collector, the second-stage collector, the third-stage collector and the fourth-stage collector through the transport pipeline to obtain the first-stage raw material collection liquid, the second-stage raw material collection liquid, the third-stage raw material collection liquid and the fourth-stage raw material collection liquid; M3. Add the primary raw material collection liquid and the secondary raw material collection liquid to the heat exchanger to exchange heat to 120-125°C and then enter the first pressurized tower, cool through the reboiler in the first pressurized tower, part of which enters the fluid raw material primary distillation tank, and the other part and the tertiary raw material collection liquid and the quaternary raw material collection liquid enter the second pressurized tower; M4. After being cooled in the reboiler in the second pressurized tower, a part enters the secondary distillation tank of the fluid raw material, and the other part enters the tertiary distillation tank of the fluid raw material to obtain the primary distillation liquid, the secondary distillation liquid and the tertiary distillation liquid of the fluid raw material.

2. The multiple-effect distillation process for fluid raw materials according to claim 1, characterized in that: The fluid raw material is added to the preheater at a preset flow rate. According to the volume of the preset device and the real-time temperature data information of the fluid raw material, a particle swarm algorithm based on an adaptive feedback adjustment factor is used to optimize the preset flow rate to obtain optimized fluid raw material data information.

3. The multiple-effect distillation process for fluid raw materials according to claim 2, characterized in that: The optimization of the preset flow rate by using a particle swarm algorithm based on an adaptive feedback adjustment factor includes: M11. Based on the volume of the preset device and the real-time temperature data information of the fluid raw material, the particle population is initialized, the maximum number of iterations L is determined, and the initialized particle population data information is obtained; M12. Based on the initialized particle population data information, establish the fitness function G of the individual particle population, Among them, x is the data information of the particle population after initialization, α1, α2 and α3 are the fitness determining factors of the individual particle population, and the fitness values ​​of the individual particle population are calculated to obtain the fitness value data information of the individual particle population; M13. Based on the fitness value data information of the individuals in the particle population, establish a target optimization function H, Among them, y is the fitness value data information of the individual particle population, β1, β2 and β3 are the target optimization factors of the individual particle population, and the flow rate of the fluid raw material is optimized to obtain the optimized flow rate data information of the fluid raw material.

4. The multiple-effect distillation process for fluid raw materials according to claim 3, characterized in that: The constraints of the fitness determining factors α1, α2 and α3 of the individual particle population are:

5. The multiple-effect distillation process for fluid raw materials according to claim 3, characterized in that: The target optimization factors β1, β2 and β3 of the individual particle population are: Among them, y is the fitness value data information of the individual particle population, and y0 is the mean value of the individual particle population.

6. The multiple-effect distillation process for fluid raw materials according to claim 5, characterized in that: The mean y0 of the individuals in the particle population is, Where n is the sample size, y i is the fitness value data information of the i-th individual in the particle population, δ i is the weight coefficient of the i-th individual in the particle population.

7. The multiple-effect distillation process for fluid raw materials according to claim 1, characterized in that: The operating pressure of the preheater and the distillation tower is 0.06-0.08 MPaG, the operating pressure of the first pressure tower is 1.2-1.5 MPaG, the operating pressure of the second pressure tower is 1.5-1.8 MPaG, and the operating pressure of the primary distillation tank of the fluid raw material is 0.02 -0.05MPaG, the operating pressure of the secondary distillation tank of the fluid raw material is 0.06-0.08 MPaG, and the operating pressure of the three-stage distillation tank for the fluid raw material is 0.04-0.06MPaG.

8. The multiple-effect distillation process for fluid raw materials according to claim 1, characterized in that: The first-stage condenser preset at the top of the distillation tower is a shell and tube condenser or a spiral plate condenser, the second-stage condenser preset at the top of the distillation tower is a sleeve condenser, the third-stage condenser preset at the top of the distillation tower is a natural convection air cooling condenser, and the fourth-stage condenser preset at the top of the distillation tower is a forced convection air cooling condenser.

9. The multiple-effect distillation process for fluid raw materials according to claim 1, characterized in that: In step M2, the first-stage condenser, the second-stage condenser, the third-stage condenser and the fourth-stage condenser preset at the top of the distillation tower are cooled to 50-55° C., 40-45° C., 30-35° C. and 20-25° C. respectively, including: M21. Based on the light component steam, obtain various component data information of the light component steam, and construct various component data sets of steam; M22. Input various component data sets of the steam into the distillation tower top condenser processing network model for training and learning, and determine the condenser power optimization function Q, Among them, r is the data set of various components of steam, ω1, ω2 and ω3 are the target optimization factors of condenser power, and the trained distillation tower top condenser processing network model is obtained; M23. Based on the trained distillation tower top condenser processing network model, various component data sets of steam are input, the condenser power is optimized, and the optimized condenser power data information is obtained.

10. The multiple-effect distillation process for fluid raw materials according to claim 9, characterized in that: The constraint function f of the target optimization factors ω1, ω2 and ω3 of the condenser power is, Among them, the value range of the constraint function f is (3,4).

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