A method for regenerating a tetrafluoroethane catalyst

Abstract

This invention belongs to the field of catalyst regeneration technology, specifically a regeneration method for a tetrafluoroethane catalyst, comprising the following steps: S1: Real-time monitoring and data acquisition: During normal operation of the reaction system, key process parameters are continuously monitored and acquired in real time; S2: Intelligent diagnosis of deactivation mode and regeneration decision-making: The real-time data acquired in S1 is input into a preset catalyst deactivation diagnosis model. The diagnosis model outputs specific deactivation type, severity, and bed location information. When the degree of deactivation reaches a preset threshold, this invention accurately determines the main cause of deactivation through the intelligent diagnosis model and matches different regeneration media and microwave parameters accordingly, achieving "targeted treatment." The phased targeted treatment avoids the secondary damage that may be caused by traditional "one-size-fits-all" regeneration, significantly improving the efficiency and stability of activity recovery and helping to extend the overall service life of the catalyst.

Description

Technical Field

[0001] This invention relates to the field of catalyst regeneration technology, and more particularly to a method for regenerating a tetrafluoroethane catalyst. Background Technology

[0002] In the industrial production of tetrafluoroethane, trichloroethylene and hydrogen fluoride are typically used as raw materials, and a gas-phase fluorination reaction is carried out in the presence of aluminum fluoride or chromium fluoride-based catalysts. After long-term operation, the catalyst will gradually deactivate due to carbon buildup, metal oxide impurity deposition, local over-fluorination or sintering of the fluorinated crystal structure, resulting in a decrease in reaction conversion rate and selectivity, requiring regeneration or replacement.

[0003] Traditional regeneration methods are mostly offline, which involves removing the deactivated catalyst from the reactor and restoring its activity through high-temperature calcination, acid washing, or hydrogen fluoride gas retreatment. This method has the following significant drawbacks:

[0004] Long production interruption time: Loading and unloading catalysts causes the unit to shut down, affecting continuous production.

[0005] Inaccurate regeneration: It is impossible to accurately determine the specific cause of catalyst deactivation (whether it is mainly due to carbon deposition or structural sintering). Often, a "one-size-fits-all" regeneration condition is used, which results in unstable effects and may cause secondary damage to the catalyst structure.

[0006] High energy consumption: Offline processing involves additional energy consumption for heating, cooling, and transportation.

[0007] Not linked to process control: Regeneration is an independent event, disconnected from the real-time operating status of the production system and changes in process parameters.

[0008] Therefore, we propose a method for regenerating tetrafluoroethane catalysts to solve the above problems. Summary of the Invention

[0009] The purpose of this invention is to address the shortcomings of the prior art by proposing a method for regenerating tetrafluoroethane catalysts.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] A method for regenerating a tetrafluoroethane catalyst includes the following steps:

[0012] S1: Real-time monitoring and data acquisition: During the normal operation of the reaction system, key process parameters are continuously monitored and acquired in real time;

[0013] S2: Intelligent diagnosis and regeneration decision of deactivation mode: The real-time data collected in S1 is input into the preset catalyst deactivation diagnosis model. The diagnosis model outputs the specific deactivation type, severity and bed location information. When the degree of deactivation reaches the preset threshold, the system automatically or by operator confirmation triggers the regeneration program.

[0014] S3: Reaction system pretreatment: purge residual reactants to create a stable initial environment for regeneration;

[0015] S4: In-situ microwave-assisted staged regeneration: Based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in-situ in the reactor, and different regeneration media are introduced to carry out staged targeted treatment.

[0016] S5: Post-regeneration treatment and stabilization: Stop microwave radiation, switch back to nitrogen purging to remove residual hydrogen fluoride and regeneration byproducts in the reactor, and at the same time, slowly adjust the system temperature to close to the normal reaction start temperature.

[0017] S6: Regeneration effect verification and process restart;

[0018] S7: Catalyst health record update and residual performance assessment;

[0019] S8: Adaptive adjustment of the system's operating strategy;

[0020] S9: Knowledge base iteration and strategy optimization.

[0021] Preferably, in step S1, during normal operation of the reaction system, key process parameters are continuously monitored and collected in real time. These key process parameters include the temperature distribution curves of each bed in the reactor, the pressure difference between the system inlet and outlet, the conversion rate of the raw material trichloroethylene, the selectivity of the product tetrafluoroethane, and the concentration change rate of impurities in the tail gas, including dichloroethylene monofluoroethylene and dichloroethylene difluoroethylene.

[0022] Preferably, in step S2, the real-time data collected in step S1 is input into a preset catalyst deactivation diagnostic model. This model is established based on historical data, catalyst characteristics, and reaction kinetics, and can correlate parameter change patterns with the primary cause of deactivation.

[0023] If the bed hotspot shifts downward and is accompanied by a significant increase in pressure drop, it is diagnosed as "carbon deposition-dominated" deactivation.

[0024] If the conversion rate and selectivity decrease slowly in tandem, while the pressure drop does not change significantly, the diagnosis is "structural sintering or over-fluorination-dominated" deactivation.

[0025] If the concentration of a specific intermediate in the exhaust gas rises abnormally sharply, it indicates that the acidic sites on the catalyst surface are locally deactivated.

[0026] The diagnostic model outputs specific inactivation type, severity, and bed location information. When the inactivation level reaches a preset threshold, the system automatically or with operator confirmation triggers the regeneration procedure.

[0027] Preferably, in step S3, the feeding of trichloroethylene raw material into the reactor is stopped, while nitrogen and / or diluted hydrogen fluoride gas are continued to be fed in, gradually reducing the system load, and adjusting the reactor temperature to the pre-regeneration base temperature, which is 200-250°C.

[0028] Preferably, in step S4, based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in situ within the reactor, and different regeneration media are introduced to perform phased targeted treatment:

[0029] Phase A: If carbon deposits are diagnosed, a nitrogen mixture containing a low concentration of oxygen is introduced into the reactor; under microwave action, the carbon deposits in the catalyst are selectively heated and undergo a mild oxidation reaction with oxygen to generate carbon dioxide, which is then discharged; the microwave power and the duration of this phase are adjusted according to the severity of carbon deposits.

[0030] Phase B: Subsequently, the process switches to introducing pure hydrogen fluoride gas or a high-concentration hydrogen fluoride / nitrogen mixture. With microwave assistance, hydrogen fluoride molecules are efficiently activated, enabling them to penetrate into the interior of the catalyst's microcrystalline structure, repairing active sites damaged by sintering or over-reaction, and replenishing surface fluorine species to restore its fluorination capacity. The temperature in this phase is precisely controlled by microwave energy.

[0031] Preferably, in step S6, a low-flow-rate trichloroethylene feedstock is reintroduced for a trial reaction. Preliminary conversion rate and selectivity data are monitored online. The data are compared with the baseline data in step S1. If the predetermined recovery index is reached, the regeneration is considered successful. Subsequently, the system gradually increases the load according to the optimization procedure until it returns to full-load normal production. The key parameters of the entire process are recorded and fed back to the inactivation diagnosis model in step S2 for model self-learning and optimization.

[0032] Preferably, in step S7, after each completion of the regeneration effect verification in step S6, the health record of this regeneration is entered into the exclusive "digital twin" of the catalyst batch. Using machine learning algorithms, the historical regeneration cycle, activity recovery decay trend, and deactivation mode evolution information are analyzed to predict the possible lifetime of the catalyst batch in the next operating cycle and the upper limit of activity that can be reached after the next regeneration, and to evaluate its "residual efficiency index". The complete record includes the deactivation type at the time of triggering, the specific parameters of each stage of regeneration, the final percentage of activity recovered, and the physicochemical characterization sampling data of the catalyst after regeneration.

[0033] Preferably, in step S8, the adaptive adjustment of the reaction system operation strategy specifically involves:

[0034] Based on the "residual efficiency index" calculated by S7, the optimal operating strategy is automatically generated and executed:

[0035] Strategy A: If the remaining efficiency is predicted to be high, the catalyst will continue to be used in the main bed of the original reactor. The process parameters will be fine-tuned, the space velocity will be slightly reduced or the feed ratio will be optimized to match the current optimal performance range of the catalyst, so as to extend its stable operation time while ensuring the conversion rate.

[0036] Strategy B: If the predicted remaining efficiency is moderate and the diagnostic model shows that its deactivation mode tends to be structural aging, the system automatically performs "internal rotation" of the catalyst bed, transferring some of the catalyst originally located in the high-load zone to the later stage of the reactor or the reactor with a lower load, while replenishing the core position with fresh or deeply regenerated catalyst with higher efficiency. This realizes the cascade utilization of catalyst assets and maximizes their economic value.

[0037] Strategy C: If the predicted remaining efficiency is close to the economic tipping point, and the "digital twin" model determines that the cost of the next regeneration will be higher than the value it generates, an early warning will be issued, a catalyst replacement plan will be generated, and the optimal replacement window will be accurately calculated to guide the ordering of new catalysts.

[0038] Preferably, the actual operating effect data after the execution of strategy S8 is compared with the prediction of S7. The deviation data is used to continuously calibrate and optimize the "digital twin" health model and strategy generation algorithm, forming a complete intelligent closed loop of "data acquisition - diagnosis and regeneration - evaluation and prediction - strategy execution - feedback learning", so that the decision-making of the entire catalyst management system becomes more and more accurate over time.

[0039] Preferably, in step S4, sensors are deployed inside the reactor to monitor regeneration-specific parameters in real time, including:

[0040] Microwave energy absorption spectrum: Analyze the reflection and absorption characteristics of microwaves by the catalyst bed to indirectly determine the real-time progress of carbon removal or fluorination reaction.

[0041] Online composition analysis of regenerated exhaust gas: Using rapid mass spectrometry or Fourier transform infrared spectroscopy, the concentrations of CO2, CO, unreacted O2 or HF, and trace by-products that may be generated in the exhaust gas are analyzed in real time.

[0042] Bed micro-area temperature and hot spot monitoring: Using a distributed fiber optic temperature measurement system, the bed temperature field is mapped in real time to accurately detect local overheating that may be caused by the exothermic reaction of oxidation.

[0043] Compared with the prior art, the advantages of the present invention are as follows:

[0044] By accurately identifying the main cause of deactivation through an intelligent diagnostic model and matching different regeneration media and microwave parameters accordingly, a "targeted approach" is achieved. The phased targeted treatment avoids the secondary damage that may be caused by the traditional "one-size-fits-all" regeneration, significantly improves the efficiency and stability of activity recovery, and helps to extend the overall service life of the catalyst.

[0045] The regeneration process takes place in situ within the reactor, eliminating the need to disassemble and transport the catalyst. This significantly shortens or even eliminates long-term shutdowns of the production unit caused by catalyst replacement, thereby greatly improving the effective operating time of the unit, reducing the huge economic losses caused by production interruptions, and lowering the additional logistics and energy costs involved in offline regeneration.

[0046] By utilizing the selective and bulk heating characteristics of microwaves on catalysts or carbon deposits, rapid and uniform heating can be achieved with high energy efficiency. Combined with distributed temperature monitoring, the regeneration temperature can be precisely controlled, effectively preventing damage to the catalyst structure from localized overheating. Compared with traditional external heating methods, this method offers faster heating, lower energy consumption, and more precise temperature control.

[0047] By establishing a "digital twin" health record for catalysts and combining it with residual performance assessment and prediction, the system can adaptively adjust its operating strategy. This enables the tiered utilization and refined management of catalyst assets, and allows for scientific decision-making regarding their optimal use location, regeneration timing, and even replacement window, thereby maximizing their economic value throughout their entire life cycle.

[0048] By integrating data acquisition, diagnosis, regeneration, verification, prediction, strategy execution, and feedback learning, data from each regeneration and subsequent operation is used to optimize the inactivation diagnosis model and health prediction model. This makes the system's decisions increasingly accurate over time, forming an intelligent closed loop that continuously accumulates knowledge and improves itself, providing support for the long-term optimization and stable operation of the process. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for regenerating a tetrafluoroethane catalyst proposed in this invention. Detailed Implementation

[0050] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this embodiment, and not all embodiments.

[0051] Example 1

[0052] Reference Figure 1 A method for regenerating a tetrafluoroethane catalyst includes the following steps:

[0053] S1: Real-time monitoring and data acquisition: During the normal operation of the reaction system, key process parameters are continuously monitored and acquired in real time;

[0054] S2: Intelligent diagnosis and regeneration decision of deactivation mode: The real-time data collected in S1 is input into the preset catalyst deactivation diagnosis model. The diagnosis model outputs the specific deactivation type, severity and bed location information. When the degree of deactivation reaches the preset threshold, the system automatically or by operator confirmation triggers the regeneration program.

[0055] S3: Reaction system pretreatment: purge residual reactants to create a stable initial environment for regeneration;

[0056] S4: In-situ microwave-assisted staged regeneration: Based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in-situ in the reactor, and different regeneration media are introduced to carry out staged targeted treatment.

[0057] S5: Post-regeneration treatment and stabilization: Stop microwave radiation, switch back to nitrogen purging to remove residual hydrogen fluoride and regeneration byproducts in the reactor, and at the same time, slowly adjust the system temperature to close to the normal reaction start temperature.

[0058] S6: Regeneration effect verification and process restart;

[0059] S7: Catalyst health record update and residual performance assessment;

[0060] S8: Adaptive adjustment of the system's operating strategy;

[0061] S9: Knowledge base iteration and strategy optimization.

[0062] In this embodiment, during S1, key process parameters are continuously monitored and collected in real time during normal operation of the reaction system. The key process parameters include the temperature distribution curves of each bed in the reactor, the pressure difference between the inlet and outlet of the system, the conversion rate of the raw material trichloroethylene, the selectivity of the product tetrafluoroethane, and the concentration change rate of impurities in the tail gas, including dichloroethylene monofluoroethylene and dichloroethylene difluoroethylene.

[0063] In this embodiment, in S2, the real-time data collected in S1 is input into a preset catalyst deactivation diagnostic model. The catalyst deactivation diagnostic model is established based on historical data, catalyst characteristics, and reaction kinetics, and can correlate parameter change patterns with the main causes of deactivation.

[0064] If the bed hotspot shifts downward and is accompanied by a significant increase in pressure drop, it is diagnosed as "carbon deposition-dominated" deactivation.

[0065] If the conversion rate and selectivity decrease slowly in tandem, while the pressure drop does not change significantly, the diagnosis is "structural sintering or over-fluorination-dominated" deactivation.

[0066] If the concentration of a specific intermediate in the exhaust gas rises abnormally sharply, it indicates that the acidic sites on the catalyst surface are locally deactivated.

[0067] The diagnostic model outputs specific inactivation type, severity, and bed location information. When the inactivation level reaches a preset threshold, the system automatically or with operator confirmation triggers the regeneration procedure.

[0068] In this embodiment, in step S3, the feeding of trichloroethylene raw material into the reactor is stopped, while nitrogen and / or diluted hydrogen fluoride gas are continued to be fed in, gradually reducing the system load, and adjusting the reactor temperature to the pre-regeneration base temperature, which is 200°C.

[0069] In this embodiment, in S4, based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in situ within the reactor, and different regeneration media are introduced to perform phased targeted treatment:

[0070] Phase A: If carbon deposits are diagnosed, a nitrogen mixture containing a low concentration of oxygen (2% by volume) is introduced into the reactor. Under microwave irradiation, the carbon deposits in the catalyst are selectively heated and undergo a mild oxidation reaction with oxygen to generate carbon dioxide, which is then discharged. The microwave power and the duration of this phase are adjusted according to the severity of the carbon deposits.

[0071] Phase B: Subsequently, the process switches to introducing pure hydrogen fluoride gas or a high-concentration hydrogen fluoride / nitrogen mixture. With microwave assistance, hydrogen fluoride molecules are efficiently activated, enabling them to penetrate into the interior of the catalyst's microcrystalline structure, repairing active sites damaged by sintering or over-reaction, and replenishing surface fluorine species to restore its fluorination capacity. The temperature in this phase is precisely controlled by microwave energy.

[0072] In this embodiment, in S6, a low-flow-rate trichloroethylene feedstock is reintroduced for a trial reaction. The initial conversion rate and selectivity data are monitored online. The data are compared with the baseline data in S1. If the predetermined recovery index is reached, the regeneration is determined to be successful. Subsequently, the system gradually increases the load according to the optimization procedure until it is restored to the full-load normal production state. The key parameters of the entire process are recorded and fed back to the inactivation diagnosis model in S2 for the model's self-learning and optimization.

[0073] In this embodiment, in S7, after each completion of the regeneration effect verification in S6, the health record of this regeneration is entered into the exclusive "digital twin" of the catalyst batch. Using machine learning algorithms, the historical regeneration cycle, activity recovery decay trend, and deactivation mode evolution information are analyzed to predict the possible lifetime of the catalyst batch in the next operating cycle and the upper limit of activity that can be reached after the next regeneration, and to evaluate its "residual efficiency index". The complete record includes the deactivation type at the time of triggering, the specific parameters of each stage of regeneration, the final percentage of activity recovered, and the physicochemical characterization sampling data of the catalyst after regeneration.

[0074] In this embodiment, in S8, the adaptive adjustment of the reaction system operation strategy specifically involves:

[0075] Based on the "residual efficiency index" calculated by S7, the optimal operating strategy is automatically generated and executed:

[0076] Strategy A: If the remaining efficiency is predicted to be high, the catalyst will continue to be used in the main bed of the original reactor. The process parameters will be fine-tuned, the space velocity will be slightly reduced or the feed ratio will be optimized to match the current optimal performance range of the catalyst, so as to extend its stable operation time while ensuring the conversion rate.

[0077] Strategy B: If the predicted remaining efficiency is moderate and the diagnostic model shows that its deactivation mode tends to be structural aging, the system automatically performs "internal rotation" of the catalyst bed, transferring some of the catalyst originally located in the high-load zone to the later stage of the reactor or the reactor with a lower load, while replenishing the core position with fresh or deeply regenerated catalyst with higher efficiency. This realizes the cascade utilization of catalyst assets and maximizes their economic value.

[0078] Strategy C: If the predicted remaining efficiency is close to the economic tipping point, and the "digital twin" model determines that the cost of the next regeneration will be higher than the value it generates, an early warning will be issued, a catalyst replacement plan will be generated, and the optimal replacement window will be accurately calculated to guide the ordering of new catalysts.

[0079] In this embodiment, the actual operating effect data after the execution of strategy S8 is compared with the prediction of S7. The deviation data is used to continuously calibrate and optimize the "digital twin" health model and strategy generation algorithm, forming a complete intelligent closed loop of "data acquisition - diagnosis and regeneration - evaluation and prediction - strategy execution - feedback learning", so that the decision-making of the entire catalyst management system becomes more and more accurate over time.

[0080] In this embodiment, in step S4, sensors are deployed inside the reactor to monitor regeneration-specific parameters in real time, including:

[0081] Microwave energy absorption spectrum: Analyze the reflection and absorption characteristics of microwaves by the catalyst bed to indirectly determine the real-time progress of carbon removal or fluorination reaction.

[0082] Online composition analysis of regenerated exhaust gas: Using rapid mass spectrometry or Fourier transform infrared spectroscopy, the concentrations of CO2, CO, unreacted O2 or HF, and trace by-products that may be generated in the exhaust gas are analyzed in real time.

[0083] Bed micro-area temperature and hot spot monitoring: Using a distributed fiber optic temperature measurement system, the bed temperature field is mapped in real time to accurately detect local overheating that may be caused by the exothermic reaction of oxidation.

[0084] Example 2

[0085] Reference Figure 1 A method for regenerating a tetrafluoroethane catalyst includes the following steps:

[0086] S1: Real-time monitoring and data acquisition: During the normal operation of the reaction system, key process parameters are continuously monitored and acquired in real time;

[0087] S2: Intelligent diagnosis and regeneration decision of deactivation mode: The real-time data collected in S1 is input into the preset catalyst deactivation diagnosis model. The diagnosis model outputs the specific deactivation type, severity and bed location information. When the degree of deactivation reaches the preset threshold, the system automatically or by operator confirmation triggers the regeneration program.

[0088] S3: Reaction system pretreatment: purge residual reactants to create a stable initial environment for regeneration;

[0089] S4: In-situ microwave-assisted staged regeneration: Based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in-situ in the reactor, and different regeneration media are introduced to carry out staged targeted treatment.

[0090] S5: Post-regeneration treatment and stabilization: Stop microwave radiation, switch back to nitrogen purging to remove residual hydrogen fluoride and regeneration byproducts in the reactor, and at the same time, slowly adjust the system temperature to close to the normal reaction start temperature.

[0091] S6: Regeneration effect verification and process restart;

[0092] S7: Catalyst health record update and residual performance assessment;

[0093] S8: Adaptive adjustment of the system's operating strategy;

[0094] S9: Knowledge base iteration and strategy optimization.

[0095] In this embodiment, during S1, key process parameters are continuously monitored and collected in real time during normal operation of the reaction system. The key process parameters include the temperature distribution curves of each bed in the reactor, the pressure difference between the inlet and outlet of the system, the conversion rate of the raw material trichloroethylene, the selectivity of the product tetrafluoroethane, and the concentration change rate of impurities in the tail gas, including dichloroethylene monofluoroethylene and dichloroethylene difluoroethylene.

[0096] In this embodiment, in S2, the real-time data collected in S1 is input into a preset catalyst deactivation diagnostic model. The catalyst deactivation diagnostic model is established based on historical data, catalyst characteristics, and reaction kinetics, and can correlate parameter change patterns with the main causes of deactivation.

[0097] If the bed hotspot shifts downward and is accompanied by a significant increase in pressure drop, it is diagnosed as "carbon deposition-dominated" deactivation.

[0098] If the conversion rate and selectivity decrease slowly in tandem, while the pressure drop does not change significantly, the diagnosis is "structural sintering or over-fluorination-dominated" deactivation.

[0099] If the concentration of a specific intermediate in the exhaust gas rises abnormally sharply, it indicates that the acidic sites on the catalyst surface are locally deactivated.

[0100] The diagnostic model outputs specific inactivation type, severity, and bed location information. When the inactivation level reaches a preset threshold, the system automatically or with operator confirmation triggers the regeneration procedure.

[0101] In this embodiment, in step S3, the feeding of trichloroethylene raw material into the reactor is stopped, while nitrogen and / or diluted hydrogen fluoride gas are continued to be fed in, gradually reducing the system load, and adjusting the reactor temperature to the pre-regeneration base temperature, which is 230°C.

[0102] In this embodiment, in S4, based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in situ within the reactor, and different regeneration media are introduced to perform phased targeted treatment:

[0103] Phase A: If carbon deposits are diagnosed, a nitrogen mixture containing a low concentration of oxygen (3% by volume) is introduced into the reactor. Under microwave irradiation, the carbon deposits in the catalyst are selectively heated and undergo a mild oxidation reaction with oxygen to generate carbon dioxide, which is then discharged. The microwave power and the duration of this phase are adjusted according to the severity of the carbon deposits.

[0104] Phase B: Subsequently, the process switches to introducing pure hydrogen fluoride gas or a high-concentration hydrogen fluoride / nitrogen mixture. With microwave assistance, hydrogen fluoride molecules are efficiently activated, enabling them to penetrate into the interior of the catalyst's microcrystalline structure, repairing active sites damaged by sintering or over-reaction, and replenishing surface fluorine species to restore its fluorination capacity. The temperature in this phase is precisely controlled by microwave energy.

[0105] In this embodiment, in S6, a low-flow-rate trichloroethylene feedstock is reintroduced for a trial reaction. The initial conversion rate and selectivity data are monitored online. The data are compared with the baseline data in S1. If the predetermined recovery index is reached, the regeneration is determined to be successful. Subsequently, the system gradually increases the load according to the optimization procedure until it is restored to the full-load normal production state. The key parameters of the entire process are recorded and fed back to the inactivation diagnosis model in S2 for the model's self-learning and optimization.

[0106] In this embodiment, in S7, after each completion of the regeneration effect verification in S6, the health record of this regeneration is entered into the exclusive "digital twin" of the catalyst batch. Using machine learning algorithms, the historical regeneration cycle, activity recovery decay trend, and deactivation mode evolution information are analyzed to predict the possible lifetime of the catalyst batch in the next operating cycle and the upper limit of activity that can be reached after the next regeneration, and to evaluate its "residual efficiency index". The complete record includes the deactivation type at the time of triggering, the specific parameters of each stage of regeneration, the final percentage of activity recovered, and the physicochemical characterization sampling data of the catalyst after regeneration.

[0107] In this embodiment, in S8, the adaptive adjustment of the reaction system operation strategy specifically involves:

[0108] Based on the "residual efficiency index" calculated by S7, the optimal operating strategy is automatically generated and executed:

[0109] Strategy A: If the remaining efficiency is predicted to be high, the catalyst will continue to be used in the main bed of the original reactor. The process parameters will be fine-tuned, the space velocity will be slightly reduced or the feed ratio will be optimized to match the current optimal performance range of the catalyst, so as to extend its stable operation time while ensuring the conversion rate.

[0110] Strategy B: If the predicted remaining efficiency is moderate and the diagnostic model shows that its deactivation mode tends to be structural aging, the system automatically performs "internal rotation" of the catalyst bed, transferring some of the catalyst originally located in the high-load zone to the later stage of the reactor or the reactor with a lower load, while replenishing the core position with fresh or deeply regenerated catalyst with higher efficiency. This realizes the cascade utilization of catalyst assets and maximizes their economic value.

[0111] Strategy C: If the predicted remaining efficiency is close to the economic tipping point, and the "digital twin" model determines that the cost of the next regeneration will be higher than the value it generates, an early warning will be issued, a catalyst replacement plan will be generated, and the optimal replacement window will be accurately calculated to guide the ordering of new catalysts.

[0112] In this embodiment, the actual operating effect data after the execution of strategy S8 is compared with the prediction of S7. The deviation data is used to continuously calibrate and optimize the "digital twin" health model and strategy generation algorithm, forming a complete intelligent closed loop of "data acquisition - diagnosis and regeneration - evaluation and prediction - strategy execution - feedback learning", so that the decision-making of the entire catalyst management system becomes more and more accurate over time.

[0113] In this embodiment, in step S4, sensors are deployed inside the reactor to monitor regeneration-specific parameters in real time, including:

[0114] Microwave energy absorption spectrum: Analyze the reflection and absorption characteristics of microwaves by the catalyst bed to indirectly determine the real-time progress of carbon removal or fluorination reaction.

[0115] Online composition analysis of regenerated exhaust gas: Using rapid mass spectrometry or Fourier transform infrared spectroscopy, the concentrations of CO2, CO, unreacted O2 or HF, and trace by-products that may be generated in the exhaust gas are analyzed in real time.

[0116] Bed micro-area temperature and hot spot monitoring: Using a distributed fiber optic temperature measurement system, the bed temperature field is mapped in real time to accurately detect local overheating that may be caused by the exothermic reaction of oxidation.

[0117] Example 3

[0118] Reference Figure 1 A method for regenerating a tetrafluoroethane catalyst includes the following steps:

[0119] S1: Real-time monitoring and data acquisition: During the normal operation of the reaction system, key process parameters are continuously monitored and acquired in real time;

[0120] S2: Intelligent diagnosis and regeneration decision of deactivation mode: The real-time data collected in S1 is input into the preset catalyst deactivation diagnosis model. The diagnosis model outputs the specific deactivation type, severity and bed location information. When the degree of deactivation reaches the preset threshold, the system automatically or by operator confirmation triggers the regeneration program.

[0121] S3: Reaction system pretreatment: purge residual reactants to create a stable initial environment for regeneration;

[0122] S4: In-situ microwave-assisted staged regeneration: Based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in-situ in the reactor, and different regeneration media are introduced to carry out staged targeted treatment.

[0123] S5: Post-regeneration treatment and stabilization: Stop microwave radiation, switch back to nitrogen purging to remove residual hydrogen fluoride and regeneration byproducts in the reactor, and at the same time, slowly adjust the system temperature to close to the normal reaction start temperature.

[0124] S6: Regeneration effect verification and process restart;

[0125] S7: Catalyst health record update and residual performance assessment;

[0126] S8: Adaptive adjustment of the system's operating strategy;

[0127] S9: Knowledge base iteration and strategy optimization.

[0128] In this embodiment, during S1, key process parameters are continuously monitored and collected in real time during normal operation of the reaction system. The key process parameters include the temperature distribution curves of each bed in the reactor, the pressure difference between the inlet and outlet of the system, the conversion rate of the raw material trichloroethylene, the selectivity of the product tetrafluoroethane, and the concentration change rate of impurities in the tail gas, including dichloroethylene monofluoroethylene and dichloroethylene difluoroethylene.

[0129] In this embodiment, in S2, the real-time data collected in S1 is input into a preset catalyst deactivation diagnostic model. The catalyst deactivation diagnostic model is established based on historical data, catalyst characteristics, and reaction kinetics, and can correlate parameter change patterns with the main causes of deactivation.

[0130] If the bed hotspot shifts downward and is accompanied by a significant increase in pressure drop, it is diagnosed as "carbon deposition-dominated" deactivation.

[0131] If the conversion rate and selectivity decrease slowly in tandem, while the pressure drop does not change significantly, the diagnosis is "structural sintering or over-fluorination-dominated" deactivation.

[0132] If the concentration of a specific intermediate in the exhaust gas rises abnormally sharply, it indicates that the acidic sites on the catalyst surface are locally deactivated.

[0133] The diagnostic model outputs specific inactivation type, severity, and bed location information. When the inactivation level reaches a preset threshold, the system automatically or with operator confirmation triggers the regeneration procedure.

[0134] In this embodiment, in step S3, the feeding of trichloroethylene raw material into the reactor is stopped, while nitrogen and / or diluted hydrogen fluoride gas are continued to be fed in, gradually reducing the system load, and adjusting the reactor temperature to the pre-regeneration base temperature, which is 250°C.

[0135] In this embodiment, in S4, based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in situ within the reactor, and different regeneration media are introduced to perform phased targeted treatment:

[0136] Phase A: If carbon deposits are diagnosed, a nitrogen mixture containing a low concentration of oxygen (5% by volume) is introduced into the reactor. Under microwave irradiation, the carbon deposits in the catalyst are selectively heated and undergo a mild oxidation reaction with oxygen to generate carbon dioxide, which is then discharged. The microwave power and the duration of this phase are adjusted according to the severity of the carbon deposits.

[0137] Phase B: Subsequently, the process switches to introducing pure hydrogen fluoride gas or a high-concentration hydrogen fluoride / nitrogen mixture. With microwave assistance, hydrogen fluoride molecules are efficiently activated, enabling them to penetrate into the interior of the catalyst's microcrystalline structure, repairing active sites damaged by sintering or over-reaction, and replenishing surface fluorine species to restore its fluorination capacity. The temperature in this phase is precisely controlled by microwave energy.

[0138] In this embodiment, in S6, a low-flow-rate trichloroethylene feedstock is reintroduced for a trial reaction. The initial conversion rate and selectivity data are monitored online. The data are compared with the baseline data in S1. If the predetermined recovery index is reached, the regeneration is determined to be successful. Subsequently, the system gradually increases the load according to the optimization procedure until it is restored to the full-load normal production state. The key parameters of the entire process are recorded and fed back to the inactivation diagnosis model in S2 for the model's self-learning and optimization.

[0139] In this embodiment, in S7, after each completion of the regeneration effect verification in S6, the health record of this regeneration is entered into the exclusive "digital twin" of the catalyst batch. Using machine learning algorithms, the historical regeneration cycle, activity recovery decay trend, and deactivation mode evolution information are analyzed to predict the possible lifetime of the catalyst batch in the next operating cycle and the upper limit of activity that can be reached after the next regeneration, and to evaluate its "residual efficiency index". The complete record includes the deactivation type at the time of triggering, the specific parameters of each stage of regeneration, the final percentage of activity recovered, and the physicochemical characterization sampling data of the catalyst after regeneration.

[0140] In this embodiment, in S8, the adaptive adjustment of the reaction system operation strategy specifically involves:

[0141] Based on the "residual efficiency index" calculated by S7, the optimal operating strategy is automatically generated and executed:

[0142] Strategy A: If the remaining efficiency is predicted to be high, the catalyst will continue to be used in the main bed of the original reactor. The process parameters will be fine-tuned, the space velocity will be slightly reduced or the feed ratio will be optimized to match the current optimal performance range of the catalyst, so as to extend its stable operation time while ensuring the conversion rate.

[0143] Strategy B: If the predicted remaining efficiency is moderate and the diagnostic model shows that its deactivation mode tends to be structural aging, the system automatically performs "internal rotation" of the catalyst bed, transferring some of the catalyst originally located in the high-load zone to the later stage of the reactor or the reactor with a lower load, while replenishing the core position with fresh or deeply regenerated catalyst with higher efficiency. This realizes the cascade utilization of catalyst assets and maximizes their economic value.

[0144] Strategy C: If the predicted remaining efficiency is close to the economic tipping point, and the "digital twin" model determines that the cost of the next regeneration will be higher than the value it generates, an early warning will be issued, a catalyst replacement plan will be generated, and the optimal replacement window will be accurately calculated to guide the ordering of new catalysts.

[0145] In this embodiment, the actual operating effect data after the execution of strategy S8 is compared with the prediction of S7. The deviation data is used to continuously calibrate and optimize the "digital twin" health model and strategy generation algorithm, forming a complete intelligent closed loop of "data acquisition - diagnosis and regeneration - evaluation and prediction - strategy execution - feedback learning", so that the decision-making of the entire catalyst management system becomes more and more accurate over time.

[0146] In this embodiment, in step S4, sensors are deployed inside the reactor to monitor regeneration-specific parameters in real time, including:

[0147] Microwave energy absorption spectrum: Analyze the reflection and absorption characteristics of microwaves by the catalyst bed to indirectly determine the real-time progress of carbon removal or fluorination reaction.

[0148] Online composition analysis of regenerated exhaust gas: Using rapid mass spectrometry or Fourier transform infrared spectroscopy, the concentrations of CO2, CO, unreacted O2 or HF, and trace by-products that may be generated in the exhaust gas are analyzed in real time.

[0149] Bed micro-area temperature and hot spot monitoring: Using a distributed fiber optic temperature measurement system, the bed temperature field is mapped in real time to accurately detect local overheating that may be caused by the exothermic reaction of oxidation.

[0150] Test case

[0151] 1. Experimental Objective

[0152] Verify the accuracy of the intelligent diagnostic model in determining the catalyst deactivation mode.

[0153] Evaluate the effectiveness of in-situ microwave-assisted staged regeneration process based on diagnostic results.

[0154] To examine the role of "digital twin" health records and adaptive adjustment strategies in catalyst life management and system operation optimization.

[0155] 2. Test Subjects and Apparatus

[0156] Catalyst: Industrial chromium-based catalyst for the gas-phase fluorination of trichloroethylene (TCE) to prepare tetrafluoroethane (R134a) (approximately 6000 hours of operation).

[0157] Reaction apparatus: A pilot-scale fixed-bed reactor equipped with a microwave radiation system, distributed fiber optic temperature measurement (DTS), online mass spectrometry / infrared gas analyzer, and advanced process control (APC) system.

[0158] Data System: An Industrial Internet of Things (IIoT) platform integrating inactivation diagnostic models, digital twin health records, and strategy optimization algorithms.

[0159] 3. Test conditions and procedures

[0160] The parameters were referenced in Example 2 (base temperature 230°C, oxygen concentration in stage A 3%).

[0161] Initial baseline data acquisition: During the catalyst performance stabilization period, the average value of key process parameters was continuously monitored and recorded for 72 hours as a baseline.

[0162] Simulated operation and deactivation induction: The catalyst was run at space velocities close to the design limit for 1500 hours to accelerate its deactivation process.

[0163] Inactivation diagnosis and regeneration trigger: When the data collected by S1 shows signs of inactivation, the system automatically performs S2 diagnosis and prompts for regeneration.

[0164] Perform the regeneration process: Strictly follow steps S3 to S6 to execute the regeneration procedure and record all key parameters.

[0165] Effect verification and strategy execution: After regeneration is completed, the S6 effect verification is performed, and the system generates and executes subsequent operation strategies based on S7 and S8.

[0166] Long-term monitoring: Continue operation for 500 hours to monitor the catalyst performance degradation trend and verify the effectiveness of the strategy.

[0167] 4. Experimental Data and Results

[0168] Table 1: Catalyst Deactivation Status Diagnosis and Benchmark Comparison

[0169] Monitoring parameters Performance stabilization period (baseline) Regeneration triggered (inactive state) Diagnostic model output TCE conversion rate 98.5% 86.2% Conversion rate and selectivity both decreased by more than 10%. R134a selectivity 99.1% 88.7% Bed pressure drop (kPa) 45 82 The pressure differential increased significantly (>80%). Bed hot spot temperature (°C) 320 (Position: Upper section) 305 (Location: Lower Middle Section) Hotspot shift Concentration of impurities in exhaust gas Dichlorofluoroethylene: 0.05% Dichlorofluoroethylene: 0.25% A sharp increase in the concentration of a specific intermediate

[0170] Table 2: Results of Regeneration Effect Verification and Strategy Implementation

[0171] project Post-regeneration trial operation data Compared to the baseline recovery rate System Decision-Making and Follow-up TCE conversion rate 97.8% 99.3% (vs. benchmark 98.5%) Strategy A: Predicting high remaining efficiency, the system fine-tunes airspeed by 5%. R134a selectivity 98.5% 99.4% (vs. benchmark 99.1%) Bed pressure drop 50 kPa Restored to near the baseline level After running for another 500 hours, the conversion rate remained above 96.5%, and the performance degradation rate decreased by about 40% compared to before regeneration. "Residual Efficiency Index" Assessment -- The system evaluation score is 85 (out of 100). Health records predict that the next regeneration cycle is expected to last up to 1800 hours.

[0172] 5. Experimental Conclusions

[0173] High diagnostic accuracy: The intelligent diagnostic model successfully identified a combined deactivation mode of "carbon buildup" and "local acidic site deactivation", which is highly consistent with the observed phenomena during the regeneration process (CO2 generation, pressure drop recovery, HF consumption).

[0174] Significant regeneration effect: The in-situ microwave-assisted staged regeneration method is targeted and highly efficient. Both conversion and selective recovery rates exceed 99%, and the bed pressure drop is significantly reduced, demonstrating its effective removal of carbon deposits and blockages. The total regeneration time is reduced by approximately 40% compared to traditional thermal regeneration methods.

[0175] The advantages of intelligent management are becoming increasingly apparent:

[0176] Digital twin health record: Based on the data from this regeneration, a longer next operating cycle was successfully predicted, providing a basis for predictive maintenance.

[0177] The adaptive strategy is effective: the "strategy A" (fine-tuning process parameters) executed by the system effectively extended the stable operating time of the regenerated catalyst, achieving "life extension" operation.

[0178] Closed-loop learning: All data from this study has been entered into the knowledge base for future optimization of diagnostic and regeneration parameter settings for similar inactivation modes.

[0179] In summary, this experimental example demonstrates, through specific data, the superior performance of the proposed tetrafluoroethane catalyst regeneration method and intelligent management system in terms of accurate diagnosis, efficient regeneration, and full life cycle optimization, and possesses significant industrial application value.

[0180] The above description is only a preferred embodiment of this practice, but the scope of protection of this embodiment is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the scope of the technology disclosed in this embodiment, based on the technical solution and inventive concept of this embodiment, should be covered within the scope of protection of this embodiment.

Claims

1. A method for regenerating a tetrafluoroethane catalyst, characterized in that... This includes the following steps: S1: Real-time monitoring and data acquisition: During the normal operation of the reaction system, key process parameters are continuously monitored and acquired in real time; S2: Intelligent diagnosis and regeneration decision of deactivation mode: The real-time data collected in S1 is input into the preset catalyst deactivation diagnosis model. The diagnosis model outputs the specific deactivation type, severity and bed location information. When the degree of deactivation reaches the preset threshold, the system automatically or by operator confirmation triggers the regeneration program. S3: Reaction system pretreatment: purge residual reactants to create a stable initial environment for regeneration; S4: In-situ microwave-assisted staged regeneration: Based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in-situ in the reactor, and different regeneration media are introduced to carry out staged targeted treatment. S5: Post-regeneration treatment and stabilization: Stop microwave radiation, switch back to nitrogen purging to remove residual hydrogen fluoride and regeneration byproducts in the reactor, and at the same time, slowly adjust the system temperature to close to the normal reaction start temperature. S6: Regeneration effect verification and process restart; S7: Catalyst health record update and residual performance assessment; S8: Adaptive adjustment of the system's operating strategy; S9: Knowledge base iteration and strategy optimization.

2. The method for regenerating a tetrafluoroethane catalyst according to claim 1, characterized in that, In S1, during normal operation of the reaction system, key process parameters are continuously monitored and collected in real time. These key process parameters include the temperature distribution curves of each bed in the reactor, the pressure difference between the inlet and outlet of the system, the conversion rate of the raw material trichloroethylene, the selectivity of the product tetrafluoroethane, and the concentration change rate of impurities in the tail gas, including dichloroethylene monofluoroethylene and dichloroethylene difluoroethylene.

3. The method for regenerating a tetrafluoroethane catalyst according to claim 2, characterized in that, In step S2, the real-time data collected in step S1 is input into a preset catalyst deactivation diagnostic model. The catalyst deactivation diagnostic model is established based on historical data, catalyst characteristics, and reaction kinetics, and can correlate parameter change patterns with the main causes of deactivation. If the bed hotspot shifts downward and is accompanied by a significant increase in pressure drop, it is diagnosed as "carbon deposition-dominated" deactivation; If the conversion rate and selectivity decrease slowly in tandem, while the pressure drop does not change significantly, the diagnosis is "structural sintering or over-fluorination-dominated" deactivation. If the concentration of a specific intermediate in the exhaust gas rises abnormally sharply, it indicates that the acidic sites on the catalyst surface are locally deactivated. The diagnostic model outputs specific inactivation type, severity, and bed location information. When the inactivation level reaches a preset threshold, the system automatically or with operator confirmation triggers the regeneration procedure.

4. The method for regenerating a tetrafluoroethane catalyst according to claim 3, characterized in that, In step S3, the feeding of trichloroethylene feedstock into the reactor is stopped, while nitrogen and / or diluted hydrogen fluoride gas are continued to be fed in, gradually reducing the system load and adjusting the reactor temperature to the pre-regeneration base temperature, which is 200-250°C.

5. The method for regenerating a tetrafluoroethane catalyst according to claim 4, characterized in that, In step S4, based on the diagnostic results of S2, microwave radiation is applied to the catalyst bed in situ within the reactor, and different regeneration media are introduced to perform phased targeted treatment: Phase A: If carbon deposits are diagnosed, a nitrogen mixture containing a low concentration of oxygen is introduced into the reactor; under microwave action, the carbon deposits in the catalyst are selectively heated and undergo a mild oxidation reaction with oxygen to generate carbon dioxide, which is then discharged; the microwave power and the duration of this phase are adjusted according to the severity of carbon deposits. Phase B: Subsequently, the process switches to introducing pure hydrogen fluoride gas or a high-concentration hydrogen fluoride / nitrogen mixture. With microwave assistance, hydrogen fluoride molecules are efficiently activated, enabling them to penetrate into the interior of the catalyst's microcrystalline structure, repairing active sites damaged by sintering or over-reaction, and replenishing surface fluorine species to restore its fluorination capacity. The temperature in this phase is precisely controlled by microwave energy.

6. The method for regenerating a tetrafluoroethane catalyst according to claim 5, characterized in that, In step S6, a low-flow-rate trichloroethylene feedstock is reintroduced for a trial reaction. Preliminary conversion rate and selectivity data are monitored online. The data are compared with the baseline data in step S1. If the predetermined recovery index is reached, the regeneration is considered successful. Subsequently, the system gradually increases the load according to the optimization procedure until it returns to full-load normal production. The key parameters of the entire process are recorded and fed back to the inactivation diagnosis model in step S2 for model self-learning and optimization.

7. The method for regenerating a tetrafluoroethane catalyst according to claim 6, characterized in that, In S7, after each S6 regeneration effect verification is completed, the health record of this regeneration is entered into the exclusive "digital twin" of the catalyst batch. Using machine learning algorithms, the historical regeneration cycle, activity recovery decline trend, and deactivation mode evolution information are analyzed to predict the possible lifetime of the catalyst batch in the next operating cycle and the upper limit of activity that can be reached after the next regeneration, and to evaluate its "residual efficiency index". The complete record includes the deactivation type at the time of triggering, the specific parameters of each stage of regeneration, the final percentage of activity recovered, and the physicochemical characterization sampling data of the catalyst after regeneration.

8. The method for regenerating a tetrafluoroethane catalyst according to claim 7, characterized in that, In S8, the adaptive adjustment of the reaction system operation strategy specifically involves: Based on the "residual efficiency index" calculated by S7, the optimal operating strategy is automatically generated and executed: Strategy A: If the remaining efficiency is predicted to be high, the catalyst will continue to be used in the main bed of the original reactor. The process parameters will be fine-tuned, the space velocity will be slightly reduced or the feed ratio will be optimized to match the current optimal performance range of the catalyst, so as to extend its stable operation time while ensuring the conversion rate. Strategy B: If the predicted remaining efficiency is moderate and the diagnostic model shows that its deactivation mode tends to be structural aging, the system automatically performs "internal rotation" of the catalyst bed, transferring some of the catalyst originally located in the high-load zone to the later stage of the reactor or the reactor with a lower load, while replenishing the core position with fresh or deeply regenerated catalyst with higher efficiency. This realizes the cascade utilization of catalyst assets and maximizes their economic value. Strategy C: If the predicted remaining efficiency is close to the economic tipping point, and the "digital twin" model determines that the cost of the next regeneration will be higher than the value it generates, an early warning will be issued, a catalyst replacement plan will be generated, and the optimal replacement window will be accurately calculated to guide the ordering of new catalysts.

9. The method for regenerating a tetrafluoroethane catalyst according to claim 8, characterized in that, In S9, the actual operating effect data after the execution of strategy S8 is compared with the prediction of S7. The deviation data is used to continuously calibrate and optimize the "digital twin" health model and strategy generation algorithm, forming a complete intelligent closed loop of "data acquisition - diagnosis and regeneration - evaluation and prediction - strategy execution - feedback learning", so that the decision-making of the entire catalyst management system becomes more and more accurate over time.

10. The method for regenerating a tetrafluoroethane catalyst according to claim 9, characterized in that, In step S4, sensors are deployed inside the reactor to monitor regeneration-specific parameters in real time, including: Microwave energy absorption spectrum: Analyze the reflection and absorption characteristics of microwaves by the catalyst bed to indirectly determine the real-time progress of carbon removal or fluorination reaction. Online composition analysis of regenerated exhaust gas: Using rapid mass spectrometry or Fourier transform infrared spectroscopy, the concentrations of CO2, CO, unreacted O2 or HF, and trace by-products that may be generated in the exhaust gas are analyzed in real time. Bed micro-area temperature and hot spot monitoring: Using a distributed fiber optic temperature measurement system, the bed temperature field is mapped in real time to accurately detect local overheating that may be caused by the exothermic reaction of oxidation.

Images