CN116924340B - Preparation method and equipment of chlorine pentafluoride - Google Patents
Preparation method and equipment of chlorine pentafluoride Download PDFInfo
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- CN116924340B CN116924340B CN202310983656.9A CN202310983656A CN116924340B CN 116924340 B CN116924340 B CN 116924340B CN 202310983656 A CN202310983656 A CN 202310983656A CN 116924340 B CN116924340 B CN 116924340B
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- KNSWNNXPAWSACI-UHFFFAOYSA-N chlorine pentafluoride Chemical compound FCl(F)(F)(F)F KNSWNNXPAWSACI-UHFFFAOYSA-N 0.000 title claims abstract description 72
- 238000002360 preparation method Methods 0.000 title claims abstract description 16
- YCKRFDGAMUMZLT-UHFFFAOYSA-N Fluorine atom Chemical compound [F] YCKRFDGAMUMZLT-UHFFFAOYSA-N 0.000 claims abstract description 75
- 239000011737 fluorine Substances 0.000 claims abstract description 75
- 229910052731 fluorine Inorganic materials 0.000 claims abstract description 75
- 238000001179 sorption measurement Methods 0.000 claims abstract description 36
- JOHWNGGYGAVMGU-UHFFFAOYSA-N trifluorochlorine Chemical compound FCl(F)F JOHWNGGYGAVMGU-UHFFFAOYSA-N 0.000 claims abstract description 34
- 238000001914 filtration Methods 0.000 claims abstract description 32
- 238000009833 condensation Methods 0.000 claims abstract description 22
- 230000005494 condensation Effects 0.000 claims abstract description 22
- 238000006243 chemical reaction Methods 0.000 claims abstract description 15
- 239000003054 catalyst Substances 0.000 claims abstract description 5
- 239000007789 gas Substances 0.000 claims description 74
- 238000000034 method Methods 0.000 claims description 72
- 230000008569 process Effects 0.000 claims description 57
- 239000002994 raw material Substances 0.000 claims description 38
- PUZPDOWCWNUUKD-UHFFFAOYSA-M sodium fluoride Chemical compound [F-].[Na+] PUZPDOWCWNUUKD-UHFFFAOYSA-M 0.000 claims description 26
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 claims description 24
- 239000000945 filler Substances 0.000 claims description 20
- 229920000642 polymer Polymers 0.000 claims description 19
- AFBPFSWMIHJQDM-UHFFFAOYSA-N N-methylaniline Chemical compound CNC1=CC=CC=C1 AFBPFSWMIHJQDM-UHFFFAOYSA-N 0.000 claims description 16
- WUKWITHWXAAZEY-UHFFFAOYSA-L calcium difluoride Chemical compound [F-].[F-].[Ca+2] WUKWITHWXAAZEY-UHFFFAOYSA-L 0.000 claims description 15
- 229910001634 calcium fluoride Inorganic materials 0.000 claims description 15
- 235000013024 sodium fluoride Nutrition 0.000 claims description 13
- 239000011775 sodium fluoride Substances 0.000 claims description 13
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 12
- 238000004519 manufacturing process Methods 0.000 claims description 12
- 238000003062 neural network model Methods 0.000 claims description 8
- XFNJYAKDBJUJAJ-UHFFFAOYSA-N 1,2-dibromopropane Chemical compound CC(Br)CBr XFNJYAKDBJUJAJ-UHFFFAOYSA-N 0.000 claims description 7
- BXGYYDRIMBPOMN-UHFFFAOYSA-N 2-(hydroxymethoxy)ethoxymethanol Chemical compound OCOCCOCO BXGYYDRIMBPOMN-UHFFFAOYSA-N 0.000 claims description 7
- VMPVEPPRYRXYNP-UHFFFAOYSA-I antimony(5+);pentachloride Chemical compound Cl[Sb](Cl)(Cl)(Cl)Cl VMPVEPPRYRXYNP-UHFFFAOYSA-I 0.000 claims description 7
- 229910052757 nitrogen Inorganic materials 0.000 claims description 7
- 239000012043 crude product Substances 0.000 claims description 6
- 238000001035 drying Methods 0.000 claims description 6
- 239000011148 porous material Substances 0.000 claims description 6
- 238000005406 washing Methods 0.000 claims description 6
- 230000035484 reaction time Effects 0.000 claims description 5
- DBJLJFTWODWSOF-UHFFFAOYSA-L nickel(ii) fluoride Chemical compound F[Ni]F DBJLJFTWODWSOF-UHFFFAOYSA-L 0.000 claims description 3
- 239000012535 impurity Substances 0.000 abstract description 6
- TXEYQDLBPFQVAA-UHFFFAOYSA-N tetrafluoromethane Chemical compound FC(F)(F)F TXEYQDLBPFQVAA-UHFFFAOYSA-N 0.000 abstract description 6
- 238000009776 industrial production Methods 0.000 abstract description 2
- 230000009471 action Effects 0.000 description 32
- 238000012549 training Methods 0.000 description 29
- 230000007613 environmental effect Effects 0.000 description 22
- 230000006870 function Effects 0.000 description 22
- 230000000052 comparative effect Effects 0.000 description 20
- 238000005457 optimization Methods 0.000 description 19
- 230000002787 reinforcement Effects 0.000 description 18
- 238000013528 artificial neural network Methods 0.000 description 17
- 238000002156 mixing Methods 0.000 description 10
- 230000004913 activation Effects 0.000 description 8
- 238000011478 gradient descent method Methods 0.000 description 8
- 238000012360 testing method Methods 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 7
- 238000012544 monitoring process Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 230000003993 interaction Effects 0.000 description 6
- KRHYYFGTRYWZRS-UHFFFAOYSA-N Fluorane Chemical compound F KRHYYFGTRYWZRS-UHFFFAOYSA-N 0.000 description 4
- 238000010438 heat treatment Methods 0.000 description 4
- 229910000040 hydrogen fluoride Inorganic materials 0.000 description 4
- 239000002245 particle Substances 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000013329 compounding Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 238000010298 pulverizing process Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 230000002194 synthesizing effect Effects 0.000 description 2
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 description 1
- 239000000460 chlorine Substances 0.000 description 1
- 229910052801 chlorine Inorganic materials 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000004132 cross linking Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000012847 fine chemical Substances 0.000 description 1
- 150000004820 halides Chemical class 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000007800 oxidant agent Substances 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C01—INORGANIC CHEMISTRY
- C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
- C01B7/00—Halogens; Halogen acids
- C01B7/24—Inter-halogen compounds
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D46/00—Filters or filtering processes specially modified for separating dispersed particles from gases or vapours
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D5/00—Condensation of vapours; Recovering volatile solvents by condensation
- B01D5/0057—Condensation of vapours; Recovering volatile solvents by condensation in combination with other processes
- B01D5/0072—Condensation of vapours; Recovering volatile solvents by condensation in combination with other processes with filtration
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D53/00—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols
- B01D53/02—Separation of gases or vapours; Recovering vapours of volatile solvents from gases; Chemical or biological purification of waste gases, e.g. engine exhaust gases, smoke, fumes, flue gases, aerosols by adsorption, e.g. preparative gas chromatography
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01J—CHEMICAL OR PHYSICAL PROCESSES, e.g. CATALYSIS OR COLLOID CHEMISTRY; THEIR RELEVANT APPARATUS
- B01J19/00—Chemical, physical or physico-chemical processes in general; Their relevant apparatus
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- Chemical & Material Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Organic Chemistry (AREA)
- Inorganic Chemistry (AREA)
- Engineering & Computer Science (AREA)
- Analytical Chemistry (AREA)
- General Chemical & Material Sciences (AREA)
- Oil, Petroleum & Natural Gas (AREA)
- Organic Low-Molecular-Weight Compounds And Preparation Thereof (AREA)
Abstract
The invention provides a preparation method of chlorine pentafluoride, which comprises the following steps: (1) Sequentially performing primary condensation, secondary condensation, adsorption and filtration on the fluorine-containing mixed gas to obtain purified fluorine gas; (2) Introducing chlorine trifluoride into a reactor containing a catalyst, and then introducing purified fluorine gas to react to obtain chlorine pentafluoride. The equipment disclosed by the invention can furthest remove impurities such as carbon tetrafluoride in fluorine gas after the fluorine gas is treated by combining the first condenser, the second condenser, the adsorption tower and the filtering tower. The invention provides a preparation method and equipment of chlorine pentafluoride, which can purify fluorine-containing mixed gas, react with chlorine trifluoride under reasonable reaction conditions, can obtain high-purity chlorine pentafluoride, and has great significance for industrial production of chlorine pentafluoride.
Description
Technical Field
The invention belongs to the field of fine chemical industry, and particularly relates to a preparation method and equipment of chlorine pentafluoride.
Background
The chemical formula of the chlorine pentafluoride is ClF 5 Is a compound of fluorine and chlorine, also a mutual halide, and was synthesized in 1963 for the first time. This colorless gas is a strong oxidizer, once a candidate for rocket use. One commonly used method for synthesizing chlorine pentafluoride is ClF 3 And F is equal to 2 The reaction is carried out under high temperature and high pressure: clF (ClF) 3 +F 2 →ClF 5 。
In general, the reaction for synthesizing chlorine pentafluoride is usually carried out in a general reactor, and the structure is relatively single, but the chlorine pentafluoride has a plurality of problems in the synthesis process, such as: the purity of the raw material gas, especially the main impurity components in the fluorine-containing mixed gas are HF and N 2 、O 2 、CF 4 、CO 2 And the like,in addition, factors such as the proportion of reaction raw materials, equipment and the like affect the synthesis effect. There is therefore a need for a new process and apparatus for the preparation of chlorine pentafluoride.
Disclosure of Invention
The invention aims to provide a preparation method and equipment of chlorine pentafluoride, which can purify fluorine-containing mixed gas, react with chlorine trifluoride under reasonable reaction conditions, can obtain high-purity chlorine pentafluoride, and has great significance for industrial production of the chlorine pentafluoride.
The first aspect of the invention provides a method for preparing chlorine pentafluoride, which comprises the following steps:
(1) Sequentially performing primary condensation, secondary condensation, adsorption and filtration on the fluorine-containing mixed gas to obtain purified fluorine gas;
(2) Introducing chlorine trifluoride into a reactor containing a catalyst, and then introducing purified fluorine gas to react to obtain chlorine pentafluoride.
In a preferred embodiment, the temperature of the primary condensation in the step (1) is-95 to-55 ℃, and the temperature of the secondary condensation is-185 to-135 ℃.
Preferably, the temperature of the primary condensation in the step (1) is-80 ℃, and the temperature of the secondary condensation is-160 ℃.
In a preferred embodiment, the filtering in the step (1) is to pass the fluorine-containing mixed gas through a polymer filler with the pore diameter of 0.5-3 μm at the temperature of-170 to-160 ℃.
In order to increase the adsorption amount of carbon tetrafluoride, the filtering in the step (1) is to pass the fluorine-containing mixed gas through a polymer filler with the pore diameter of 1-2 mu m at the temperature of-165 ℃.
In a preferred embodiment, the catalyst in step (2) is nickel fluoride. Purchased from aledine N105110.
In a preferred embodiment, the ratio of the amounts of fluorine gas and chlorine trifluoride in step (2) is from 5 to 10:1.
In order to improve the purity of the product, it is preferable that the ratio of the amounts of fluorine gas and chlorine trifluoride in the step (2) is 6:1.
In a preferred embodiment, the reaction temperature in step (2) is 200 to 220 ℃ and the reaction time is 1 to 2 hours.
In order to increase the yield of the product, it is preferable that the reaction temperature in the step (2) is 210℃and the reaction time is 1.5 hours.
In a preferred embodiment, the method for preparing the polymer filler comprises the following steps: the molar ratio was set to 1: 3-4 benzene and N-methylaniline are used as raw materials, 1, 2-dibromopropane, dimethanol formal and antimony pentachloride are added under the protection of nitrogen, and the raw materials are uniformly mixed and heated to 90-110 ℃, and the temperature is kept for 15-20 h; filtering to obtain a crude product, and washing and drying to obtain the polymer filler.
Preferably, the preparation method of the polymer filler comprises the following steps: the molar ratio was set to 1:3.5:2:5:4, mixing benzene, N-methylaniline, 1, 2-dibromopropane, dimethanol formal and antimony pentachloride, uniformly mixing and heating to 100 ℃ under the protection of nitrogen, and preserving heat for 18h; filtering to obtain a crude product, and washing and drying to obtain the polymer filler.
The invention obtains the polymer filler with special structure through a specific preparation method, and separates fluorine gas from carbon tetrafluoride, solid particles and other impurities with large particle diameters. The inventors found that the impurities such as carbon tetrafluoride in the fluorine gas can be removed to the maximum extent after the fluorine gas is treated by the combination of the first condenser, the second condenser, the adsorption tower and the filtration tower. Through a large number of experiments, the molar ratio is 1:3.5:2:5:4, benzene, N-methylaniline, 1, 2-dibromopropane, dimethanol formal and antimony pentachloride, and the obtained filler has the best filtering effect, and the hypothesis is that the pore diameter obtained by crosslinking benzene and N-methylaniline is high in compactness, more uniform in pore diameter and better in filtering effect.
In a preferred embodiment, the adsorption is carried out using calcium fluoride and sodium fluoride in a weight ratio of 1:3-5.
In order to enhance the effect of removing hydrogen fluoride, it is preferable that the adsorption is carried out using calcium fluoride and sodium fluoride in a weight ratio of 1:4.
In the invention, calcium fluoride and sodium fluoride are used for adsorption to further remove gases such as hydrogen fluoride; as a preferred embodiment, the adsorption is with a weight ratio of calcium fluoride to sodium fluoride of 1:4. Preferably, the calcium fluoride is purchased from aladine, C104250; sodium fluoride, available from aladine, S111586. The invention can strengthen the adsorption efficiency by mixing the two substances in a specific proportion, and the inventor discovers that the pulverization rate of calcium fluoride and sodium fluoride after hydrogen fluoride is adsorbed is reduced by compounding, the service life of the calcium fluoride is prolonged, and the sodium fluoride can be suspected to activate the calcium fluoride to generate an inner skeleton adsorption structure, so that the adsorption efficiency is enhanced.
In a second aspect, the present invention provides an apparatus for preparing chlorine pentafluoride, the apparatus comprising a fluorine gas raw material tank, a first condenser, a second condenser, an adsorption tower, a filtration tower, a reactor and a chlorine trifluoride raw material tank; the fluorine gas raw material tank is used for accessing fluorine-containing mixed gas, the pipeline of fluorine gas raw material tank is connected with first condenser, first condenser is connected with the second condenser, the second condenser is connected with the adsorption tower, the adsorption tower is connected with the filter tower, the filter tower is connected with the reactor, the reactor is connected with the chlorine trifluoride raw material tank simultaneously.
In order to improve the safety of the equipment and the intelligent control effect, in a preferred embodiment, a thermometer and a pressure gauge are arranged on the first condenser and the second condenser; and flowmeter valves are arranged on connecting pipelines among the fluorine gas raw material tank, the first condenser, the second condenser, the adsorption tower, the filtering tower, the reactor and the chlorine trifluoride raw material tank.
Preferably, the apparatus further comprises a control module comprising a temperature controller, a pressure controller and a flow controller; wherein the temperature controller is respectively connected with the first condenser, the second condenser and the reactor and is used for monitoring and controlling the temperatures of the connecting devices; the pressure controller is respectively connected with the fluorine gas raw material tank and the chlorine trifluoride raw material tank and is used for monitoring and controlling the pressure of the two raw material tanks; the flow controller is connected with a flowmeter valve on the connecting pipeline and is used for monitoring and controlling the flow of gas.
Preferably, the control module uses the following control formula to control:
(1) T1=80+0.05 (P1-P2), wherein T1 represents the temperature of the first condenser, P1 represents the pressure of the fluorine gas feed tank, and P2 represents the pressure of the first condenser;
(2) T2=160-0.03 x (F2-F1), where T2 represents the temperature of the second condenser, F2 represents the flow of fluorine gas after passing through the first condenser, and F1 represents the flow of fluorine gas through the second condenser.
Preferably, the device further comprises an intelligent control module, wherein the intelligent control module comprises a prediction sub-module and an optimization sub-module; the prediction submodule is a deep neural network model and is used for predicting the yield and purity of the chlorine pentafluoride according to the technological parameters; the optimization sub-module uses a gradient descent method algorithm to adjust process parameters according to the prediction result of the prediction sub-module so as to maximize the predicted yield and purity.
Preferably, the deep neural network model comprises three fully connected layers, each layer consisting of several neurons, each neuron having an activation function and parameterized by weights and biases.
Preferably, the activation function is a modified linear unit function.
Preferably, the training data of the prediction submodule is derived from historical operation data of equipment, and the training data comprises parameters such as temperature, pressure, flow rates of fluorine gas and chlorine trifluoride, and the like of a fluorine gas raw material tank, a first condenser, a second condenser, an adsorption tower, a filtering tower, a reactor and a chlorine trifluoride raw material tank, and yield and purity of corresponding chlorine pentafluoride.
Preferably, the formula of the gradient descent method adopted by the optimization submodule is as follows: wherein θ represents model parameters, η represents learning rate, +.>Representing the gradient of the loss function J (θ) with respect to θ.
Preferably, the optimizing submodule optimizes the yield and purity of the chlorine pentafluoride by adjusting the process parameters of the fluorine gas raw material tank, the first condenser, the second condenser, the adsorption tower, the filtering tower, the reactor and the chlorine trifluoride raw material tank according to the prediction result of the predicting submodule, including but not limited to temperature, pressure, flow rates of fluorine gas and chlorine trifluoride and the like.
Preferably, the apparatus further comprises a first intelligent control module comprising a reinforcement learning model for predicting yield and purity of chlorine pentafluoride based on the inputted process parameters and then adjusting the process parameters based on the prediction.
Preferably, the reinforcement learning model includes a state evaluation module for evaluating the current environmental state based on the current process parameters and the equipment state.
Preferably, the reinforcement learning model includes a policy decision module for selecting an action, i.e., modifying a process parameter, based on the current environmental conditions.
Preferably, the training of the reinforcement learning model includes an interactive process with the equipment in which the model changes the process parameters of the equipment by performing actions and obtains feedback based on the new equipment status and the yield and purity of chlorine pentafluoride.
Preferably, the training of the reinforcement learning model further includes updating the weight of the model according to an update formula of Q-learning, where the update formula of Q-learning is: where α is the learning rate, w is the weight of the model, Q (S, a) is the output of the model, i.e. the expected return, TD error = R + γ max_ a Q (S ', a) -Q (S, a), where γ is the discount factor, R is the reward, S' is the new environmental state, a is the possible movementAnd (3) doing so.
Preferably, the first intelligent control module of the apparatus further comprises a first optimization sub-module that uses a gradient descent algorithm to adjust process parameters based on the prediction results to maximize the predicted yield and purity.
Compared with the prior art, the invention has the advantages that:
the invention provides a preparation method and equipment of chlorine pentafluoride, which can purify fluorine-containing mixed gas, react with chlorine trifluoride through reasonable reaction conditions, and can obtain high-purity chlorine pentafluoride. The invention obtains the polymer filler with special structure through a specific preparation method, and separates fluorine gas from carbon tetrafluoride, solid particles and other impurities with large particle diameters. The equipment disclosed by the invention can furthest remove impurities such as carbon tetrafluoride in fluorine gas after the fluorine gas is treated by combining the first condenser, the second condenser, the adsorption tower and the filtering tower. The invention can strengthen the adsorption efficiency by mixing the calcium fluoride and the sodium fluoride according to a specific proportion, and the inventor discovers that the pulverization rate of the calcium fluoride and the sodium fluoride after adsorbing the hydrogen fluoride is reduced and the service life of the calcium fluoride and the sodium fluoride is prolonged by compounding the calcium fluoride and the sodium fluoride.
Drawings
Fig. 1 is a schematic diagram of an apparatus for preparing chlorine pentafluoride.
In the figure, 1, a fluorine gas raw material tank; 2. a first condenser; 3. a second condenser; 4. an adsorption tower; 5. a filtering tower; 6. chlorine trifluoride material tank; 7. a reactor; 8. a thermometer; 9. a pressure gauge; 10. a flowmeter valve.
Detailed Description
The following description of the technical solutions in the embodiments of the present invention will be clear and complete, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The embodiment provides a preparation method of chlorine pentafluoride, which comprises the following steps:
(1) Sequentially performing primary condensation, secondary condensation, adsorption and filtration on the fluorine-containing mixed gas to obtain purified fluorine gas; wherein the temperature of primary condensation is-80 ℃, and the temperature of secondary condensation is-160 ℃; the adsorption is carried out by using calcium fluoride and sodium fluoride in a weight ratio of 1:4. The filtering is to make the fluorine-containing mixed gas pass through a polymer filler with the aperture of 1-2 mu m at the temperature of-165 ℃; the preparation method of the polymer filler comprises the following steps: the molar ratio was set to 1:3.5:2:5:4, mixing benzene, N-methylaniline, 1, 2-dibromopropane, dimethanol formal and antimony pentachloride, uniformly mixing and heating to 100 ℃ under the protection of nitrogen, and preserving heat for 18h; filtering to obtain a crude product, and washing and drying to obtain the polymer filler.
(2) Introducing chlorine trifluoride into a reactor containing nickel fluoride, and then introducing purified fluorine gas to react to obtain chlorine pentafluoride. Wherein the ratio of the amounts of the substances of fluorine gas and chlorine trifluoride is 6:1; the reaction temperature was 210℃and the reaction time was 1.5h.
The present embodiment provides an apparatus for producing chlorine pentafluoride, which comprises a fluorine gas raw material tank 1, a first condenser 2, a second condenser 3, an adsorption tower 4, a filtration tower 5, a reactor 7, and a chlorine trifluoride raw material tank 6; the fluorine gas raw material tank 1 is used for accessing fluorine-containing mixed gas, the pipeline of fluorine gas raw material tank 1 is connected with first condenser 2, first condenser 2 is connected with second condenser 3, second condenser 3 is connected with adsorption tower 4, adsorption tower 4 is connected with filter tower 5, filter tower 5 is connected with reactor 7, reactor 7 is connected with chlorine trifluoride raw material tank 6 simultaneously. The first condenser 2 and the second condenser 3 are provided with a thermometer 8 and a manometer 9; and a flowmeter valve 10 is arranged on a connecting pipeline among the fluorine gas raw material tank 1, the first condenser 2, the second condenser 3, the adsorption tower 4, the filtering tower 5, the reactor 7 and the chlorine trifluoride raw material tank 6.
Example 2
The differences between this embodiment and embodiment 1 are: the preparation method of the polymer filler comprises the following steps: the molar ratio was set to 1:4:2:5:4, mixing benzene, N-methylaniline, 1, 2-dibromopropane, dimethanol formal and antimony pentachloride, uniformly mixing and heating to 110 ℃ under the protection of nitrogen, and preserving heat for 15h; filtering to obtain a crude product, and washing and drying to obtain the polymer filler.
Comparative example 1
The difference between this comparative example and example 1 is that: the temperature of the primary condensation in the step (1) is 45 ℃, and the temperature of the secondary condensation is-35 ℃. The filtering in the step (1) is to make the fluorine-containing mixed gas pass through a polymer filler with the pore diameter of 2-4 mu m at the temperature of-150 ℃.
Comparative example 2
The difference between this comparative example and example 1 is that: the ratio of the amounts of fluorine gas and chlorine trifluoride in the step (2) is 4:1. The reaction temperature in the step (2) is 180 ℃ and the reaction time is 1.5h.
Comparative example 3
The difference between this comparative example and example 1 is that: the preparation method of the polymer filler comprises the following steps: the molar ratio was set to 1:3.5:1:1:6, mixing benzene, N-methylaniline, 1, 2-dibromopropane, dimethanol formal and antimony pentachloride, uniformly mixing and heating to 100 ℃ under the protection of nitrogen, and preserving heat for 18h; filtering to obtain a crude product, and washing and drying to obtain the polymer filler.
Comparative example 4
The difference between this comparative example and example 1 is that: the adsorption is carried out by using calcium fluoride.
Comparative example 5
The difference between this comparative example and example 1 is that: the apparatus does not include a second condenser, an adsorption column.
Comparative example 6
The difference between this comparative example and example 1 is that: the device does not comprise an adsorption tower and a filtering tower.
Performance testing
The yields and purities of chlorine pentafluoride in the examples and comparative examples were measured, and the results are shown in Table 1.
TABLE 1 Performance test results
| Project | Yield% | Purity% |
| Example 1 | - | ＞99.9999 |
| Example 2 | - | ＞99.99 |
| Comparative example 1 | 85.5 | 98.1 |
| Comparative example 2 | 88.3 | 99.2 |
| Comparative example 3 | 81.2 | 96.7 |
| Comparative example 4 | 83.3 | 97.2 |
| Comparative example 5 | 86.3 | 98.4 |
| Comparative example 6 | 83.1 | 96.9 |
The purity of the chlorine pentafluoride prepared by the method is high as shown by examples, and the purity is obviously reduced by changing the reaction conditions and equipment as shown by comparative examples.
Example 3
A control module is added on the basis of embodiment 1. The control module includes a temperature controller 11, a pressure controller 12, and a flow controller 13.
The temperature controller 11 is connected to the first condenser 2, the second condenser 3 and the reactor 7, respectively, for monitoring and controlling the temperatures of these devices. The pressure controller 12 is connected to the fluorine gas raw material tank 1 and the chlorine trifluoride raw material tank 6, respectively, and is used for monitoring and controlling the pressures of the two raw material tanks. The flow controller 13 is connected with the flowmeter valve 10 on the connecting pipeline and is used for monitoring and controlling the flow of gas.
The control module controls using the following control formula:
(1) T1=80+0.05 (P1-P2), where T1 represents the temperature of the first condenser, P1 represents the pressure of the fluorine gas feed tank, and P2 represents the pressure of the first condenser. This formula is to adjust the temperature of the first condenser based on the pressure difference to ensure stability of the condensation process.
(2) T2=160-0.03 x (F2-F1), where T2 represents the temperature of the second condenser, F2 represents the flow of fluorine gas after passing through the first condenser, and F1 represents the flow of fluorine gas through the second condenser. This formula is to adjust the temperature of the second condenser based on the flow difference to ensure the efficiency of the condensation process.
Through the intelligent control module, the reaction conditions can be monitored and adjusted in real time so as to improve the yield and purity of the chlorine pentafluoride.
Example 4
On the basis of the embodiment 3, the control module is further improved, and an optimization algorithm based on machine learning is introduced. The intelligent control module 14 is a deep neural network model for predicting the yield and purity of chlorine pentafluoride based on input process parameters (e.g., temperature, pressure, and flow, etc.), and then adjusting the process parameters to maximize the predicted yield and purity.
The control module 14 includes the following sub-modules:
a data collection sub-module: this sub-module collects real-time data from thermometer 8, manometer 9 and flow meter valve 10, as well as yield and purity of chlorine pentafluoride.
And a prediction submodule: this sub-module is a deep neural network model for predicting the yield and purity of chlorine pentafluoride for a given process parameter.
And (3) an optimization sub-module: the sub-module uses optimization algorithms such as gradient descent method and the like to adjust the process parameters according to the prediction result so as to maximize the predicted yield and purity.
The prediction submodule uses the following control formula:
o1, o2=f (T1, T2, T3, P1, P2, F1, F2, F3), wherein O1 represents a predicted yield of chlorine pentafluoride, wherein O1 represents a predicted purity of chlorine pentafluoride, T1, T2, T3 represents temperatures of the first condenser, the second condenser and the reactor, respectively, P1, P2 represents pressures of the fluorine gas feed tank and the chlorine trifluoride feed tank, respectively, and F1, F2, F3 represents gas flows of the fluorine gas feed tank, the first condenser and the reactor, respectively. The function f is a neural network model of the prediction submodule.
The intelligent control module can automatically learn and adjust the technological parameters through a machine learning algorithm so as to optimize the production process of the chlorine pentafluoride and improve the yield and purity of the chlorine pentafluoride.
The deep neural network model in the prediction submodule may be a three-layer fully connected neural network. The neural network may be composed of an input layer, a hidden layer, and an output layer. For example, the neural network may be constructed in the following manner.
Input layer: the input layer has 8 nodes corresponding to the process parameters (T1, T2, T3, P1, P2, F1, F2, F3) respectively.
Hidden layer: the hidden layer has a design with an adjustable number of nodes, which number needs to be determined experimentally. Assuming we choose 16 nodes, each node receives a weighted input from the input layer and then generates an output through the processing of an activation function (e.g., reLU). The weight matrix W1 and the bias vector b1 are parameters learned during training, and the shapes are (16, 8) and (16,).
Output layer: the output layer has 2 nodes corresponding to the predicted yield and purity of chlorine pentafluoride, respectively. Each node receives a weighted input from the hidden layer and then generates a predicted value through processing of an activation function (here typically a linear activation function or Sigmoid function, depending on the range of the output is used). The weight matrix W2 and the bias vector b2 are also parameters learned during training, and the shapes are (2, 16) and (2,) respectively.
The flow of data in a network can be described as the following calculation process:
H1＝ReLU(W1*X+b1)
Y＝Sigmoid(W2*H1+b2)
where X is input data, Y is a predicted value, H1 is the output of the hidden layer, '+' represents matrix multiplication, '+' represents vector addition, and ReLU and Sigmoid represent ReLU activation function and Sigmoid activation function, respectively.
The neural network constructed is described below.
During training of the network, the weights and biases are updated using a back propagation (Backpropagation) algorithm and an optimization algorithm (e.g., random gradient descent SGD) to minimize the gap between the predicted and actual values of the network, i.e., the value of the loss function.
The training data of the prediction submodule mainly originate from various types of data collected in the production process of equipment, and comprises the following components:
technological parameters: such as reaction temperature, pressure, flow rates of fluorine gas and chlorine trifluoride, etc.
Production results: yield and purity of chlorine pentafluoride.
These data are obtained by real-time monitoring and recording during operation of the device. For each production process, the present embodiment may correspond the process parameters to the corresponding production results (i.e. yield and purity of chlorine pentafluoride) one by one, to form one piece of training data.
The training process is specifically as follows:
s1001 data preprocessing: first, preprocessing of the collected raw data is required. This step typically includes removing outliers, data normalization (e.g., adjusting all data to between 0-1), etc., in order to allow the neural network to better learn and understand the data.
S1002 divides the data set: the preprocessed data is divided into training and testing sets. In general, 80% of the data can be used as a training set for training a model; the remaining 20% of the data was used as a test set to evaluate the performance of the model.
S1003 training model: and training the neural network model by using the data in the training set. During the training process, the neural network can minimize the gap (i.e. the loss function) between the predicted result and the actual result of the model by continuously adjusting its weight and bias. This process is typically performed using gradient descent or a variant thereof.
S1004 test model: after model training is complete, we can use the data in the test set to test the performance of the model, such as the prediction accuracy.
S1005 adjusts the model: if the model performs poorly on the test set, performance of the model may be further improved by adjusting the structure of the neural network (e.g., increasing the number of nodes in the hidden layer, changing the activation function, etc.) or adjusting the training process (e.g., adjusting the learning rate, increasing the number of training rounds, etc.).
S1006, prediction and optimization: when the model training is completed and the test set is well performed, the method can be applied to the actual production process, and the process parameters are adjusted in real time according to the result of model prediction, so that the yield and purity of the chlorine pentafluoride are maximized.
In this embodiment, the optimization sub-module uses an optimization algorithm such as a gradient descent method, and adjusts the process parameters according to the prediction result, so as to maximize the predicted yield and purity.
The gradient descent method is an iterative optimization algorithm for finding local minima of the function. In the optimization sub-module, our objective function, i.e., predicted yield and purity of chlorine pentafluoride, is optimized using this method.
For the gradient descent method, the update rule is as follows:
wherein θ is a parameter to be optimized, such as the process parameters here, including reaction temperature, pressure, flow rates of fluorine gas and chlorine trifluoride, etc.; alpha is learning rate, controlling optimized step length;is the gradient of the objective function F with respect to θ, indicating the direction of change of the function F at θ.
The specific optimization process is as follows:
s2001 initialization: first, initialization process parameters are required, which may be random or empirically based.
S2002 gradient calculation: then, the current technological parameters are input by using a prediction submodule to obtain the predicted yield and purity of the chlorine pentafluoride. The two predicted values are compared with a target value (here, may be an experimentally obtained optimal value) to calculate the value of the loss function. Then, the gradient of the loss function with respect to each process parameter is calculated.
S2003 update parameters: then, each process parameter is updated according to the update rule of the gradient descent method.
S2004 check convergence: a stop criterion needs to be set. The iteration may be stopped when the value of the loss function is less than a set threshold, or the number of iterations exceeds a set maximum number. Otherwise, the iteration can be continued by returning to the step 2.
Through the optimization process, the process parameters which can maximize the yield and purity of the chlorine pentafluoride can be found. The actual production process can then be adjusted based on this result, thereby improving production efficiency and product quality.
Example 5
On the basis of the embodiment 3, the control module is further improved, and a reinforcement learning model is introduced. In this embodiment, the intelligent control module of the apparatus includes a reinforcement learning model for predicting the yield and purity of chlorine pentafluoride based on the inputted process parameters and then adjusting the process parameters based on the prediction.
The reinforcement learning model includes two main components: a state evaluation module and a policy decision module. The state evaluation module evaluates the current environmental state based on the current process parameters and the equipment state. This may include, for example, process parameters such as reactor temperature, pressure, and flow rate. The policy decision module then selects an action based on the current environmental state, which is the manner in which the process parameters are adjusted.
Training of the reinforcement learning model includes an interaction process with the device. In this process, the model changes the process parameters of the plant by performing actions and obtains feedback based on the new plant conditions and the yield and purity of chlorine pentafluoride. For example, if an action results in an increase in the yield and purity of chlorine pentafluoride, then the action will be positively fed back, and conversely, negatively fed back.
Training of the reinforcement learning model also includes updating the weights of the model according to the updated formula of Q-learning. The updated formula of Q-learning is: in this formula, α is the learning rate, w is the weight of the model, Q (S, a) is the output of the model, i.e. the expected return, TD error = r+γ x max_ a Q (S ', a) -Q (S, a), where γ is the discount factor, R is the reward, S' is the new environmental state, a is the possible action.
In this embodiment, the control module of the device further includes an optimizing sub-module. The optimization sub-module uses an optimization algorithm such as a gradient descent method and the like to adjust the process parameters according to the prediction result so as to maximize the predicted yield and purity. For example, the optimization submodule may find parameter values that maximize yield and purity by changing values of process parameters, such as temperature or pressure. For a specific implementation, reference may be made to similar parts in embodiment 4.
In this embodiment, the new control module 15 is a reinforcement learning model for optimizing process parameters to maximize yield and purity of chlorine pentafluoride. Unlike deep neural networks, reinforcement learning models learn through interactions with the environment and optimize their decisions through rewards or penalty mechanisms. In this case, the environment is the entire chlorine pentafluoride production apparatus, and the rewards are determined based on the yield and purity of the chlorine pentafluoride.
The basic workflow of the reinforcement learning model is as follows:
s3001 observes an environmental state S, where S includes various process parameters of the apparatus, such as temperature, pressure, flow rates of fluorine and chlorine trifluoride, and the like.
S3002 selects an action a based on the current environmental state S, where a represents the process parameters to be adjusted and the magnitude and direction of their adjustment. The selection of action a is based on a policy pi that is part of the reinforcement learning model and that can be updated over time to improve the selection of actions.
S3003 performs action a and observes the change in environmental state and the resulting prize R. The prize R is a number representing the effect of action a on the yield and purity of chlorine pentafluoride. If the yield and purity are increased, the reward R is positive; if the yield and purity decrease, the prize R is negative.
S3004 updates the policy pi based on the observed new environmental state S' and the prize R. This is done by updating a formula involving a parameter y called the benefit factor and a parameter a called the learning rate. The benefit factor gamma determines how much attention the model pays to the future rewards, while the learning rate alpha determines the speed of the model update strategy. One commonly used update formula is the update formula of the Q-learning algorithm:
Q(S,A)←Q(S,A)+α[R+γmaxA'Q(S',A')-Q(S,A)]
in this formula, Q (S, A) represents the expected return for taking action A in state S, and maxA 'Q (S', A ') represents the maximum expected return that would be possible in the new state S'. The meaning of this formula is that if an action results in a high reward and a better future state, then the expected return of that action in the corresponding state should be increased.
S3005 the above steps S3001-S3004 are repeated until a predetermined number of training cycles is reached or the yield and purity of chlorine pentafluoride reach a satisfactory level.
In this embodiment, deep Q Network (DQN) is described in detail below as an alternative reinforcement learning model. DQN is a method combining deep learning and Q learning, capable of handling complex input states, and is very suitable for controlling chlorine pentafluoride production equipment.
The following is the implementation of the DQN model:
1. defining environmental states and action spaces
The ambient State (State) is composed of various process parameters such as temperature, pressure, fluorine gas flow, amount of substance of chlorine trifluoride, etc.
An Action is an instruction to change a process parameter, such as increasing/decreasing temperature, pressure, or fluorine gas flow.
2. Constructing a network
Two identically structured deep neural networks are used, one being the Q-network and the other being the target Q-network. Both networks receive as input the environmental status and output the Q value for each possible action. The architecture of the network may include a plurality of fully connected or convolutional layers depending on the particular form of the environmental state.
3. Initialization of
The weights of the two neural networks are initialized randomly, and other super parameters such as discount factors gamma, learning rate alpha and the like are set.
4. Interaction with environment
The model learns during interactions with the environment. The specific process is as follows:
selecting: and selecting one action to execute according to the output of the current Q-network and the epsilon-greedy strategy.
The actions are performed: the selected action is applied to the apparatus, changing the process parameters.
Observing feedback: new environmental status and rewards are acquired. The reward may be the yield and purity of chlorine pentafluoride.
5. Updating Q-network
Calculating a target value: and calculating the maximum Q value of the new environment state by using the target Q-network, and calculating the target value according to the rewards and the discount factor gamma.
Update Q-network: gradient descent optimization is performed using the mean square error as a loss function.
6. Updating target Q-network
The weights of the Q-network are copied to the target Q-network periodically to stabilize the learning process.
The above is a basic implementation process of the DQN model, and can be properly adjusted and optimized according to practical situations. This exemplary reinforcement learning model includes a state evaluation module and a policy decision module.
A state evaluation module: this is the neural network portion of the DQN that receives as input the environmental status and then outputs the predicted Q value for each possible action. These Q values are actually an estimate of the individual actions in the current state. This network learns and improves these evaluations through interactions and feedback with the environment.
Policy decision module: this is the action selection part of the DQN. It uses an epsilon-greedy policy to select actions based on the Q value. Initially, the model may choose more randomly to act in order to better explore the environment; but as learning proceeds it will increasingly choose actions depending on the Q value, i.e. the output of the state evaluation module.
The training data of the reinforcement learning model comes mainly from the interaction process with the environment. In our scenario, the environment is a production facility of chlorine pentafluoride, the model interacts with the environment by performing different actions (changing the process parameters of the facility) and observes the resulting new environmental conditions (new process parameters of the facility) and rewards (yield and purity of chlorine pentafluoride).
The specific training steps are as follows:
s4001 initializes: an initial weight is set for the neural network, an initial policy (e.g., epsilon-greedy policy) is defined, and an initial environmental state is set.
S4002 performs the following operations for each step of training:
an action is selected based on the current environmental state and policy.
This action is performed, observing the new environmental status and rewards.
The environmental status, actions, rewards and new environmental status of this step are stored.
A portion is randomly selected from the stored experiences, and the weights of the neural network are updated with an update formula of Q-learning. Specifically, for each step of the selected experience, we calculate the difference between the actual return (the reward plus the expected return for the next step) and the predicted return for the neural network (i.e., the TD error), and then use this error for back propagation and gradient descent to update the weights of the neural network.
The specific formula is as follows:where α is the learning rate, w is the weight of the neural network, Q (S, a) is the output of the neural network, i.e. the expected return, TD error = r+γ x max_ a Q (S ', a) -Q (S, a), where γ is the discount factor, R is the reward, S' is the new environmental state, a is the possible action.
Step S4002 is repeated until a stop condition is satisfied, such as a predetermined number of training cycles is reached, or the yield and purity of chlorine pentafluoride reach a satisfactory level.
It should be noted that reinforcement learning training typically requires a significant amount of time and computational resources, as the model requires a significant amount of trial and error to learn how to select the optimal action. In addition, in order to improve the stability and efficiency of Learning, some advanced technologies such as Target Network (Target Network), double Q Learning (Double Q-Learning), etc. may be adopted.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (8)
1. A method for preparing chlorine pentafluoride, said method being implemented on the basis of an apparatus for preparing chlorine pentafluoride, characterized in that it comprises the steps of:
(1) Sequentially performing primary condensation, secondary condensation, adsorption and filtration on the fluorine-containing mixed gas to obtain purified fluorine gas;
(2) Introducing chlorine trifluoride into a reactor containing a catalyst, and then introducing purified fluorine gas to react to obtain chlorine pentafluoride;
the preparation method of the polymer filler comprises the following steps: the molar ratio was set to 1: 3-4 benzene and N-methylaniline are used as raw materials, 1, 2-dibromopropane, dimethanol formal and antimony pentachloride are added under the protection of nitrogen, and the raw materials are uniformly mixed and heated to 90-110 ℃, and the temperature is kept for 15-20 h; filtering to obtain a crude product, and washing and drying to obtain the polymer filler;
the equipment comprises a fluorine gas raw material tank, a first condenser, a second condenser, an adsorption tower, a filtering tower, a reactor and a chlorine trifluoride raw material tank; the fluorine gas raw material tank is used for being connected with fluorine-containing mixed gas, a pipeline of the fluorine gas raw material tank is connected with a first condenser, the first condenser is connected with a second condenser, the second condenser is connected with an adsorption tower, the adsorption tower is connected with a filtering tower, the filtering tower is connected with a reactor, and the reactor is simultaneously connected with a chlorine trifluoride raw material tank;
the apparatus further comprises a control module; the control module comprises a temperature controller, a pressure controller and a flow controller;
the control module controls using the following control formula:
(1) T1=80+0.05 (P1-P2), where T1 represents the temperature of the first condenser, P1 represents the pressure of the fluorine gas feed tank, and P2 represents the pressure of the first condenser; the formula is to adjust the temperature of the first condenser based on the pressure difference to ensure the stability of the condensation process;
(2) T2=160-0.03 x (F2-F1), wherein T2 represents the temperature of the second condenser, F2 represents the flow of fluorine gas after passing through the first condenser, and F1 represents the flow of fluorine gas through the second condenser; the formula is to adjust the temperature of the second condenser based on the flow difference to ensure the efficiency of the condensation process;
the control module further includes a prediction sub-module that uses the following control formula:
o1, o2=f (T1, T2, T3, P1, P2, F1, F2, F3), wherein O1 represents a predicted yield of chlorine pentafluoride, wherein O2 represents a predicted purity of chlorine pentafluoride, T1, T2, T3 represents temperatures of the first condenser, the second condenser and the reactor, respectively, P1, P2 represents pressures of the fluorine gas feed tank and the chlorine trifluoride feed tank, respectively, and F1, F2, F3 represents gas flows of the fluorine gas feed tank, the first condenser and the reactor, respectively; the function f is a neural network model of the prediction submodule.
2. The method for preparing chlorine pentafluoride according to claim 1, wherein the temperature of the primary condensation in the step (1) is-95 to-55 ℃, and the temperature of the secondary condensation is-185 to-135 ℃.
3. The method for preparing chlorine pentafluoride according to claim 1, wherein the filtering in the step (1) is to pass a fluorine-containing mixed gas through a polymer filler with a pore diameter of 0.5-3 μm at-170 to-160 ℃.
4. A method for producing chlorine pentafluoride according to claim 3, wherein said catalyst in said step (2) is nickel fluoride.
5. The method for preparing chlorine pentafluoride according to claim 1, wherein the ratio of the amounts of fluorine gas and chlorine trifluoride in the step (2) is 5-10:1.
6. The method for preparing chlorine pentafluoride according to claim 5, wherein the reaction temperature in the step (2) is 200-220 ℃ and the reaction time is 1-2 hours.
7. The method for preparing chlorine pentafluoride according to claim 1, wherein the adsorption is carried out by using calcium fluoride and sodium fluoride in a weight ratio of 1:3-5.
8. The method for preparing chlorine pentafluoride according to claim 7, wherein the first condenser and the second condenser are provided with a thermometer and a manometer; and flowmeter valves are arranged on connecting pipelines among the fluorine gas raw material tank, the first condenser, the second condenser, the adsorption tower, the filtering tower, the reactor and the chlorine trifluoride raw material tank.
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