On-line monitoring method for catalyst structure by using X-ray diffraction technology

Abstract

The invention discloses a catalyst structure on-line monitoring method by using an X-ray diffraction technology, which comprises the following steps: S1, placing an X-ray diffractometer near a chemical reactor to enable X-rays to cover the surface of a catalyst; s2, adjusting working parameters of the X-ray diffractometer in real time to adapt to the change of a reaction environment and optimize a data acquisition effect; s3, a continuous sampling mode is adopted, and X-ray diffraction signals reflected by the surface of the catalyst are automatically collected; s4, transmitting the acquired X-ray diffraction data to a special data processing system in real time; s5, analyzing the obtained catalyst structure information based on a data processing system; and S6, according to the real-time monitoring result of the catalyst structure, adjusting the reaction conditions of the reactor or suggesting to replace the catalyst through the control system. The method has the advantages of being high in real-time monitoring capacity, high in analysis precision, capable of improving the production efficiency, capable of optimizing the reaction process and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of catalyst structure online monitoring, and in particular to a catalyst structure online monitoring method using X-ray diffraction technology. Background Art

[0002] With the rapid development of the chemical industry, catalysts are playing an increasingly important role in chemical reactions. Catalysts can significantly improve the efficiency and selectivity of chemical reactions, reduce energy consumption and byproduct formation, and are therefore of great significance for environmental protection and resource conservation. However, during use, catalyst structure and performance can be affected by factors such as reaction conditions, reactants, and time, which in turn can affect the efficiency of the entire chemical reaction and product quality. Therefore, real-time monitoring and analysis of catalyst structural changes is crucial for timely adjusting reaction conditions, optimizing catalyst usage, and improving production efficiency.

[0003] In existing technologies, catalyst structural analysis and performance evaluation are mostly performed after the reaction, usually relying on offline physical and chemical analysis methods such as transmission electron microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy. Although these methods can provide detailed structural and chemical information about the catalyst, they have the following drawbacks:

[0004] 1. Insufficient real-time performance: Existing technologies cannot achieve real-time or quasi-real-time monitoring of catalyst structural changes, resulting in the inability to adjust chemical reaction conditions in a timely manner, affecting production efficiency and product quality.

[0005] 2. The analysis process is cumbersome: Offline analysis usually requires a complex sample preparation process, which is not only time-consuming and labor-intensive, but may also change the original state of the catalyst due to sample processing.

[0006] 3. Difficulty in non-invasive monitoring: Most traditional analytical methods require sampling and cannot monitor and analyze catalysts without interfering with chemical reactions.

[0007] 4. High analysis cost: Traditional catalyst analysis methods usually rely on expensive analytical instruments and complex operating procedures, which increases production costs.

[0008] Therefore, how to provide a method for online monitoring of catalyst structure using X-ray diffraction technology is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0009] One objective of the present invention is to provide a method for online catalyst structure monitoring using X-ray diffraction technology. This method fully utilizes X-ray diffraction technology, a real-time data processing system, and a machine learning algorithm, and describes in detail the steps required to achieve real-time catalyst structure monitoring and analysis. By automatically adjusting X-ray diffraction parameters, analyzing catalyst structural changes in real time, and automatically adjusting chemical reaction conditions or proposing catalyst replacement recommendations based on the analysis results, the present invention offers advantages such as strong real-time monitoring capabilities, high analytical accuracy, improved production efficiency, and the ability to optimize the reaction process.

[0010] According to an embodiment of the present invention, a method for online monitoring of catalyst structure using X-ray diffraction technology comprises the following steps:

[0011] S1. Place an X-ray diffractometer near a chemical reactor so that X-rays can cover the surface of the catalyst and capture the structural information of the catalyst during the chemical reaction.

[0012] S2. Real-time adjustment of X-ray diffractometer operating parameters to adapt to changes in the reaction environment and optimize data acquisition effects;

[0013] S3, using a continuous sampling mode to automatically collect X-ray diffraction signals reflected from the catalyst surface to obtain continuous data on catalyst structural changes;

[0014] S4, transmitting the collected X-ray diffraction data in real time to a dedicated data processing system, which can quickly process and analyze the X-ray diffraction data;

[0015] S5. Based on the catalyst structure information obtained by the data processing system analysis, the structural characteristics of the catalyst are monitored in real time;

[0016] S6. Based on the real-time monitoring results of the catalyst structure, the control system adjusts the reaction conditions to the reactor or recommends replacing the catalyst to optimize the progress of the chemical reaction.

[0017] Optionally, the S1 specifically includes:

[0018] S11. Introducing an adaptive adjustment mechanism into the configuration of the X-ray diffractometer. By adjusting the incident angle θ and energy E of the X-rays through the adaptive adjustment mechanism, the efficiency of capturing catalyst structural information is optimized:

[0019] θ new =θ0+Δθ;

[0020] E new =E0+ΔE;

[0021] Where θ0 and E0 are the initial incident angle and energy, respectively, and Δθ and ΔE are the angle and energy changes that are dynamically adjusted based on the reaction conditions.

[0022] S12. Introduce a real-time feedback system to dynamically adjust the incident angle and energy of the X-rays based on the intensity change ΔI of the X-ray signal reflected from the catalyst surface:

[0023] Δθ=k θ ·log(1+ΔI);

[0024]

[0025] Where Δθ is the adjustment amount of the incident angle, k θ is a constant coefficient used to adjust the sensitivity of signal strength changes to angle adjustment. ΔI is the change in signal strength. log(1+ΔI) ensures that appropriate adjustment can be made even when the signal strength changes slightly. ΔE is the energy adjustment. k E is another constant coefficient used to adjust the sensitivity of signal strength changes to energy adjustments. Used to provide a smooth adjustment process, suitable for handling situations where the signal strength changes greatly;

[0026] S13. Use high-precision positioning technology to ensure that the relative position between the X-ray diffractometer and the chemical reactor remains unchanged:

[0027]

[0028] Where (x1, y1, z1) and (x2, y2, z2) represent the spatial coordinates of the X-ray source and a point on the catalyst surface, respectively;

[0029] S14. Use a pattern recognition-based algorithm to predict possible structural changes in the catalyst during the chemical reaction process. Use historical monitoring data to predict catalyst structural changes and automatically adjust the scanning plan and parameter settings of the X-ray diffractometer:

[0030]

[0031] Where P(t) represents the catalyst structure parameters at time point t, α is the learning rate, which represents the sensitivity of the adjustment. It is the gradient of the change of the structural parameters at time point t, and predicts the catalyst structural parameters at the next time point t+1.

[0032] Optionally, the S2 specifically includes:

[0033] S21. Through an adaptive adjustment mechanism, the operating parameters of the X-ray diffractometer are adjusted in real time to adapt to changes in the reaction environment and optimize data acquisition. Based on the intensity of the X-ray signal reflected from the catalyst surface and changes in environmental parameters, the X-ray incident angle θ and energy E are automatically adjusted to maximize the capture efficiency of catalyst structural information:

[0034] θ new =θ0+Δθ signal +Δθ env ;

[0035] E new =E0+ΔE signal +ΔE env ;

[0036] Where θ0 and E0 represent the initial incident angle and energy, Δθ signal and ΔE signal The angle and energy changes are automatically adjusted based on the X-ray signal intensity changes, Δθ env and ΔE env The angle and energy changes are adjusted according to the changes in environmental parameters;

[0037] S22. Deploy a real-time monitoring and feedback system that collects the X-ray signal intensity I reflected from the catalyst surface and environmental parameters and adjusts the incident angle and energy of the X-rays:

[0038]

[0039] f(I)=a I (I-I0);

[0040]

[0041] Where f(I) and g(I) are functions of the signal intensity I and are used to calculate the contribution of the signal intensity change to the angle and energy adjustment. and is the adjustment factor;

[0042] S23. Implement environmental perception and adjustment strategies to automatically adjust X-ray parameters based on real-time monitored environmental parameter changes:

[0043]

[0044] h(T,P)=c T (T-T0)+c P (P-P0);

[0045] j(T,P)=d T log(1+|T-T0|)+d Plog(1+|P-P0|);

[0046] Among them, h(T,P) and j(T,P) are functions of temperature T and pressure P, and To adjust the coefficient, the X-ray diffraction parameters are automatically adjusted according to environmental changes;

[0047] S24. A data-driven model is used to predict the structural changes of catalysts during chemical reactions and automatically adjust the scanning plan and parameter settings of the X-ray diffractometer accordingly. The prediction model uses machine learning algorithms to analyze historical monitoring data and real-time feedback information:

[0048]

[0049] Among them, P next represents the predicted catalyst structural parameters at the next time point, P current is the structural parameter at the current time point, β is the adjustment parameter, representing the prediction step of the model, is the gradient of the structural parameter change, which can be calculated by the machine learning model based on historical data, X1, X2, ..., X n Represents the factors that affect the catalyst structure change, w1, w2, ..., w n is the weight of these factors.

[0050] Optionally, the S3 specifically includes:

[0051] S31. Set the X-ray diffractometer to perform continuous sampling, automatically collecting X-ray signals reflected from the catalyst surface at a fixed frequency f, to ensure continuous monitoring of the structural changes of the catalyst:

[0052]

[0053] Where Δt represents the sampling interval, which is determined according to the dynamic properties of the catalyst reaction and the rate of structural change;

[0054] S32. Implement data preprocessing to improve signal quality:

[0055] S processed =f denoise (S raw )+f baseline (S raw );

[0056] Among them, S raw is the original signal, S processed is the processed signal, f denoise is the denoising function, f baseline is the baseline correction function;

[0057] S33. Conduct in-depth analysis of X-ray diffraction data, using specific algorithms to identify peaks in the diffraction pattern and analyze the lattice structure characteristics of the catalyst:

[0058] P crystal =f analysis (S processed );

[0059] Among them, P crystal represents the lattice structure parameter of the catalyst, f analysis is the diffraction data analysis function;

[0060] S34. Ensure timely transmission and effective storage of collected data, establish stable data transmission channels and efficient storage mechanisms, and support long-term storage and rapid access to large-scale data:

[0061] D transmitted =f transmit (S processed );

[0062] D stored =f store (D transmitted );

[0063] Among them, D transmitted is the transmitted data, f transmit is the data transmission function, D stored is the stored data, f store It is a data storage function;

[0064] S35. Establish a real-time monitoring and alarm mechanism to automatically identify abnormal changes in the catalyst structure based on the analysis results, use the set threshold Θ to conduct real-time evaluation of the analysis data and promptly issue an alarm for structural abnormalities:

[0065] Alert=f alert (P crystal ,Θ);

[0066] Among them, Alert represents the alarm signal, f alert Based on the lattice structure parameter P crystal and the alarm trigger function with a threshold Θ.

[0067] Optionally, the S4 specifically includes:

[0068] S41. Transmit the pre-processed X-ray diffraction data in real time to a dedicated data processing system. This system uses a high-speed data communication protocol to enable rapid identification and analysis of catalyst structural changes.

[0069] D real-time =f transfer (Sprocessed ,T current );

[0070] Among them, D real-time Represents real-time transmitted data, S processed is the preprocessed data, T current is the current time, f transfer is the data transmission function;

[0071] S42. The data processing system processes the received data in real time and applies a data analysis algorithm to analyze the structural changes of the catalyst:

[0072] P analyzed =f AI (D real-time );

[0073] Among them, P analyzed represents the catalyst structural parameters obtained after analysis, f AI It is an artificial intelligence-based data analysis function;

[0074] S43. The data processing system automatically generates a catalyst structure change report based on the analysis results. The report describes the changes in the catalyst structure in detail:

[0075] Report=f report (P analyzed ,C reaction );

[0076] Among them, Report is the generated report, P analyzed is the catalyst structure analysis parameter, C reaction is the chemical reaction condition, f report Generate functions for reports;

[0077] S44. Implement an interface with the production control system, allowing automatic adjustment of chemical reaction parameters based on catalyst structure change reports. This interface supports real-time data exchange and command execution:

[0078] C adjust =f control (Report,C optimal );

[0079] Among them, C adjust represents the adjusted chemical reaction conditions, C optimal For ideal chemical reaction conditions, f control A function for production control based on reports;

[0080] S45. Establish a data feedback loop to input actual data from the production process back into the data processing system to achieve continuous monitoring and optimization:

[0081] Feedback=f feedback (C adjust ,D new );

[0082] Among them, Feedback represents the feedback mechanism, C adjust is the reaction condition after adjustment, D new is the new monitoring data, f feedback is the data feedback function.

[0083] Optionally, the S5 specifically includes:

[0084] S51. Based on the catalyst structure information obtained through analysis by the data processing system, the structural characteristics of the catalyst are monitored in real time:

[0085] F features =f structure (P analyzed );

[0086] Among them, F features represents the catalyst structural feature set, P analyzed is the catalyst structural parameter obtained from the data processing system, f structure is the structural feature analysis function;

[0087] S52. Compare the structural characteristics obtained through real-time monitoring with the preset structural stability standards to automatically identify structural change trends and potential structural anomalies:

[0088] Status=f compare (F features ,S standard );

[0089] Among them, Status represents the structural stability of the catalyst, S standard is the structural stability standard, f compare is the feature contrast function;

[0090] S53. For any potential structural anomalies identified, the system will automatically generate a detailed anomaly report and provide specific adjustment suggestions:

[0091] Report exception =f exception (Status,F features );

[0092] Among them, Report exception For exception report, f exception Generate functions for anomaly reports based on the structural stability state and structural characteristics of the catalyst;

[0093] S54. Display real-time monitoring results, abnormality reports, and adjustment suggestions to the operator through the user interface:

[0094] UI=f display (Report exception ,F features ,C adjust );

[0095] Among them, UI stands for user interface, f display The data display function is responsible for presenting abnormality reports, structural characteristics, and adjustment suggestions to the operator in graphical and textual forms;

[0096] S55. Implement necessary production process adjustments based on operator feedback and automation strategies to minimize the impact of catalyst structural anomalies on production efficiency and product quality:

[0097] Adjust=f adjust (Report exception ,C current );

[0098] Among them, Adjust represents the adjustment measures implemented, C current is the current production process condition, f adjust A function that is adjusted based on anomaly reports.

[0099] Optionally, the S6 specifically includes:

[0100] S61. Based on the catalyst structure information obtained from the data processing system analysis, the control system automatically adjusts the reaction conditions in the chemical reactor to maintain the catalyst at an optimal performance state:

[0101] C temp =C temp0 +ΔC temp (P deviation );

[0102] C pressure =C pressure0 +ΔC pressure (P deviation );

[0103] C feedrate =C feedrate0 +ΔC feedrate (P deviation );

[0104] Among them, C temp0 、C pressure0 and C feedrate0 Represent the baseline setting values ​​of temperature, pressure and reactant feeding rate, ΔC temp , ΔCpressure and ΔC feedrate Based on the catalyst structure deviation P deviation The adjustment amount, P deviation is the deviation between the current structural parameters of the catalyst and the target structural parameters;

[0105] S62. Monitor the relationship between catalyst structural changes and chemical reaction conditions, and use machine learning models to predict catalyst performance trends to achieve more precise reaction condition adjustments:

[0106] P trend =β0+β1·C current +β2·H data ;

[0107] Among them, P trend is the catalyst performance change trend, C current is the current reaction condition, H data is the historical monitoring data, f predict is the prediction function;

[0108] S63: When catalyst performance degradation or structural abnormality is detected, the system automatically adjusts the reaction conditions and recommends catalyst replacement to ensure chemical reaction efficiency and product quality.

[0109] Adjust plan =f plan (P trend ,S threshold );

[0110] Execute command =f execute (Adjust plan );

[0111] Among them, Adjust plan To adjust the plan, P trend is the performance change trend, S threshold is the threshold for performance degradation, f plan Execute is a function that formulates an adjustment plan. command To execute the adjustment instruction, f execute A function that executes instructions;

[0112] S64. Provide the operator with real-time catalyst performance status, predicted trends, and adjustment plans through the user interface, ensuring that the operator can understand the reaction status and make decisions in a timely manner:

[0113] UI display =f UI (P trend ,Adjust plan ,C current);

[0114] Among them, UI display Display content for the user interface, f UI Build functions for the interface to present catalyst performance trends, adjustment plans, and current reaction conditions to the operator in an intuitive form;

[0115] S65. Implement feedback loops to adjust machine learning models based on actual production process data to continuously optimize prediction accuracy and adjustment strategies:

[0116] Model update =f feedback (P actual ,C adjusted ,H data );

[0117] Among them, Model updatd represents the updated prediction model, P actual is the catalyst performance parameter actually monitored, C adjusted is the parameter of the implemented adjustment measures, H data is the accumulated historical monitoring data, f feedback A function that updates the prediction model based on actual monitoring data and adjustment results.

[0118] The beneficial effects of the present invention are:

[0119] (1) This invention achieves real-time monitoring and precise analysis of catalyst structural changes by adjusting X-ray diffractometer operating parameters in real time and continuously sampling X-ray signals reflected from the catalyst surface, combined with real-time data processing and machine learning algorithms. This real-time monitoring capability enables the system to promptly adjust chemical reaction conditions, optimize catalyst usage, improve production efficiency and product quality, and effectively address the challenges posed by catalyst performance changes.

[0120] (2) The online non-invasive analysis capability of the present invention allows catalysts to be monitored directly on the production line without interfering with the chemical reaction, thus reducing sample handling steps, lowering the risk of potential sample contamination or structural changes, and ensuring the authenticity and reliability of the monitoring data.

[0121] (3) This invention utilizes X-ray diffraction technology to provide highly accurate information about the catalyst's crystal structure, including lattice parameters and crystal defects, enabling precise monitoring of minute changes in the catalyst structure. This provides an important scientific basis for understanding catalyst behavior during reactions and optimizing catalyst design.

[0122] (4) This invention optimizes production and R&D efficiency through automated data analysis and predictive models, enabling immediate adjustments to the production process and continuous optimization of catalyst performance. This optimization not only reduces R&D costs but also accelerates the commercialization of new catalysts.

[0123] (5) The broad application potential of the present invention means that it is not only suitable for specific types of catalysts or specific chemical reactions, but can also be widely applied to a variety of chemical production processes. This makes the present invention suitable for promotion and application in a variety of fields such as petrochemicals, pharmaceuticals, and environmental protection, and has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0124] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0125] Figure 1 This is an overall flow chart of a method for online monitoring of catalyst structure using X-ray diffraction technology proposed by the present invention;

[0126] Figure 2 This is a schematic diagram of an X-ray diffractometer and its configuration with a chemical reactor for an online monitoring method of catalyst structure using X-ray diffraction technology proposed in the present invention. DETAILED DESCRIPTION

[0127] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0128] refer to Figure 1-Figure 2 A method for online monitoring of catalyst structure using X-ray diffraction technology, characterized by comprising the following steps:

[0129] S1. Place an X-ray diffractometer near a chemical reactor so that X-rays can cover the surface of the catalyst and capture the structural information of the catalyst during the chemical reaction.

[0130] In this embodiment, S1 specifically includes:

[0131] S11. Introducing an adaptive adjustment mechanism into the configuration of the X-ray diffractometer. By adjusting the incident angle θ and energy E of the X-rays through the adaptive adjustment mechanism, the efficiency of capturing catalyst structural information is optimized:

[0132] θ new =θ0+Δθ;

[0133] E new =E0+ΔE;

[0134] Where θ0 and E0 are the initial incident angle and energy, respectively, and Δθ and ΔE are the angle and energy changes that are dynamically adjusted based on the reaction conditions.

[0135] S12. Introduce a real-time feedback system to dynamically adjust the incident angle and energy of the X-rays based on the intensity change ΔI of the X-ray signal reflected from the catalyst surface:

[0136] Δθ=k θ ·log(1+ΔI);

[0137]

[0138] Where Δθ is the adjustment amount of the incident angle, k θ is a constant coefficient used to adjust the sensitivity of signal strength changes to angle adjustment. ΔI is the change in signal strength. log(1+ΔI) ensures that appropriate adjustment can be made even when the signal strength changes slightly. ΔE is the energy adjustment. k E is another constant coefficient used to adjust the sensitivity of signal strength changes to energy adjustments. Used to provide a smooth adjustment process, suitable for handling situations where the signal strength changes greatly;

[0139] S13. Use high-precision positioning technology to ensure that the relative position between the X-ray diffractometer and the chemical reactor remains unchanged:

[0140]

[0141] Where (x1, y1, z1) and (x2, y2, z2) represent the spatial coordinates of the X-ray source and a point on the catalyst surface, respectively;

[0142] S14. Use a pattern recognition-based algorithm to predict possible structural changes in the catalyst during the chemical reaction process. Use historical monitoring data to predict catalyst structural changes and automatically adjust the scanning plan and parameter settings of the X-ray diffractometer:

[0143]

[0144] Where P(t) represents the catalyst structure parameters at time point t, α is the learning rate, which represents the sensitivity of the adjustment. It is the gradient of the change of the structural parameters at time point t, and predicts the catalyst structural parameters at the next time point t+1.

[0145] S2. Real-time adjustment of X-ray diffractometer operating parameters to adapt to changes in the reaction environment and optimize data acquisition effects;

[0146] In this embodiment, S2 specifically includes:

[0147] S21. Through an adaptive adjustment mechanism, the operating parameters of the X-ray diffractometer are adjusted in real time to adapt to changes in the reaction environment and optimize data acquisition. Based on the intensity of the X-ray signal reflected from the catalyst surface and changes in environmental parameters, the X-ray incident angle θ and energy E are automatically adjusted to maximize the capture efficiency of catalyst structural information:

[0148] θ new =θ0+Δθ signal +Δθ env ;

[0149] E new =E0+ΔE signal +ΔE env ;

[0150] Where θ0 and E0 represent the initial incident angle and energy, Δθ signal and ΔE signal The angle and energy changes are automatically adjusted based on the X-ray signal intensity changes, Δθ env and ΔE env The angle and energy changes are adjusted according to the changes in environmental parameters;

[0151] S22. Deploy a real-time monitoring and feedback system that collects the X-ray signal intensity I reflected from the catalyst surface and environmental parameters and adjusts the incident angle and energy of the X-rays:

[0152]

[0153]

[0154] f(I)=a I (I-I0);

[0155]

[0156] Where f(I) and g(I) are functions of the signal intensity I and are used to calculate the contribution of the signal intensity change to the angle and energy adjustment. and is the adjustment factor;

[0157] S23. Implement environmental perception and adjustment strategies to automatically adjust X-ray parameters based on real-time monitored environmental parameter changes:

[0158]

[0159] h(T,P)=c T (T-T0)+c P (P-P0);

[0160] j(T,P)=d T log(1+|T-T0|)+d P log(1+|P-P0|);

[0161] Among them, h(T,P) and j(T,P) are functions of temperature T and pressure P, and To adjust the coefficient, the X-ray diffraction parameters are automatically adjusted according to environmental changes;

[0162] S24. A data-driven model is used to predict the structural changes of catalysts during chemical reactions and automatically adjust the scanning plan and parameter settings of the X-ray diffractometer accordingly. The prediction model uses machine learning algorithms to analyze historical monitoring data and real-time feedback information:

[0163]

[0164] Among them, P next represents the predicted catalyst structural parameters at the next time point, P current is the structural parameter at the current time point, β is the adjustment parameter, representing the prediction step of the model, is the gradient of the structural parameter change, which can be calculated by the machine learning model based on historical data, X1, X2, ..., X n Represents the factors that affect the catalyst structure change, w1, w2, ..., w n is the weight of these factors.

[0165] S3, using a continuous sampling mode to automatically collect X-ray diffraction signals reflected from the catalyst surface to obtain continuous data on catalyst structural changes;

[0166] In this embodiment, S3 specifically includes:

[0167] S31. Set the X-ray diffractometer to perform continuous sampling, automatically collecting X-ray signals reflected from the catalyst surface at a fixed frequency f, to ensure continuous monitoring of the structural changes of the catalyst:

[0168]

[0169] Where Δt represents the sampling interval, which is determined according to the dynamic properties of the catalyst reaction and the rate of structural change;

[0170] S32. Implement data preprocessing to improve signal quality:

[0171] S processed =f denoise (S raw )+f baseline (S raw);

[0172] Among them, S raw is the original signal, S processed is the processed signal, f denoise is the denoising function, f baseline is the baseline correction function;

[0173] S33. Conduct in-depth analysis of X-ray diffraction data, using specific algorithms to identify peaks in the diffraction pattern and analyze the lattice structure characteristics of the catalyst:

[0174] P crystal =f analysis (S processed );

[0175] Among them, P crystal represents the lattice structure parameter of the catalyst, f analysis is the diffraction data analysis function;

[0176] S34. Ensure timely transmission and effective storage of collected data, establish stable data transmission channels and efficient storage mechanisms, and support long-term storage and rapid access to large-scale data:

[0177] D transmitted =f transmit (S processed );

[0178] D stored =f store (D transmitted );

[0179] Among them, D transmitted is the transmitted data, f transmit is the data transmission function, D stored is the stored data, f store It is a data storage function;

[0180] S35. Establish a real-time monitoring and alarm mechanism to automatically identify abnormal changes in the catalyst structure based on the analysis results, use the set threshold Θ to conduct real-time evaluation of the analysis data and promptly issue an alarm for structural abnormalities:

[0181] Alert=f alert (P crystal ,Θ);

[0182] Among them, Alert represents the alarm signal, f alert Based on the lattice structure parameter P crystal and the alarm trigger function with a threshold Θ.

[0183] S4, transmitting the collected X-ray diffraction data in real time to a dedicated data processing system, which can quickly process and analyze the X-ray diffraction data;

[0184] In this embodiment, S4 specifically includes:

[0185] S41. Transmit the pre-processed X-ray diffraction data in real time to a dedicated data processing system. This system uses a high-speed data communication protocol to enable rapid identification and analysis of catalyst structural changes.

[0186] D real-time =f transfer (S processed ,T current );

[0187] Among them, D real-time Represents real-time transmitted data, S processed is the preprocessed data, T current is the current time, f transfer is the data transmission function;

[0188] S42. The data processing system processes the received data in real time and applies a data analysis algorithm to analyze the structural changes of the catalyst:

[0189] P analyzed =f AI (D real-time );

[0190] Among them, P analyzed represents the catalyst structural parameters obtained after analysis, f AI It is an artificial intelligence-based data analysis function;

[0191] S43. The data processing system automatically generates a catalyst structure change report based on the analysis results. The report describes the changes in the catalyst structure in detail:

[0192] Report=f report (P analyzed ,C reaction );

[0193] Among them, Report is the generated report, P analyzed is the catalyst structure analysis parameter, C reaction is the chemical reaction condition, f report Generate functions for reports;

[0194] S44. Implement an interface with the production control system, allowing automatic adjustment of chemical reaction parameters based on catalyst structure change reports. This interface supports real-time data exchange and command execution:

[0195] Cadjust =f control (Report,C optimal );

[0196] Among them, C adjust represents the adjusted chemical reaction conditions, C optimal For ideal chemical reaction conditions, f control A function for production control based on reports;

[0197] S45. Establish a data feedback loop to input actual data from the production process back into the data processing system to achieve continuous monitoring and optimization:

[0198] Feedback=f feedback (C adjust ,D new );

[0199] Among them, Feedback represents the feedback mechanism, C adjust is the reaction condition after adjustment, D new is the new monitoring data, f feedback is the data feedback function.

[0200] S5. Based on the catalyst structure information obtained by the data processing system analysis, the structural characteristics of the catalyst are monitored in real time;

[0201] In this embodiment, S5 specifically includes:

[0202] S51. Based on the catalyst structure information obtained through analysis by the data processing system, the structural characteristics of the catalyst are monitored in real time:

[0203] F features =f structure (P analyzed );

[0204] Among them, F features represents the catalyst structural feature set, P analyzed is the catalyst structural parameter obtained from the data processing system, f structure is the structural feature analysis function;

[0205] S52. Compare the structural characteristics obtained through real-time monitoring with the preset structural stability standards to automatically identify structural change trends and potential structural anomalies:

[0206] Status=f compare (F features ,S standard );

[0207] Among them, Status represents the structural stability of the catalyst, S standardis the structural stability standard, f compare is the feature contrast function;

[0208] S53. For any potential structural anomalies identified, the system will automatically generate a detailed anomaly report and provide specific adjustment suggestions:

[0209] Report exception =f exception (Status,F features );

[0210] Among them, Report exception For exception report, f exception Generate functions for anomaly reports based on the structural stability state and structural characteristics of the catalyst;

[0211] S54. Display real-time monitoring results, abnormality reports, and adjustment suggestions to the operator through the user interface:

[0212] UI=f display (Report exception ,F features ,C adjust );

[0213] Among them, UI stands for user interface, f display The data display function is responsible for presenting abnormality reports, structural characteristics, and adjustment suggestions to the operator in graphical and textual forms;

[0214] S55. Implement necessary production process adjustments based on operator feedback and automation strategies to minimize the impact of catalyst structural anomalies on production efficiency and product quality:

[0215] Adjust=f adjust (Report exception ,C current );

[0216] Among them, Adjust represents the adjustment measures implemented, C current is the current production process condition, f adjust A function that is adjusted based on anomaly reports.

[0217] S6. Based on the real-time monitoring results of the catalyst structure, the control system adjusts the reaction conditions to the reactor or recommends replacing the catalyst to optimize the progress of the chemical reaction.

[0218] In this embodiment, S6 specifically includes:

[0219] S61. Based on the catalyst structure information obtained from the data processing system analysis, the control system automatically adjusts the reaction conditions in the chemical reactor to maintain the catalyst at an optimal performance state:

[0220] C temp =C temp0 +ΔC temp (P deviation );

[0221] C pressure =C pressure0 +C pressure (P deviation );

[0222] C feedrate =C feedrate0 +ΔC feedrate (P deviation );

[0223] Among them, C temp0 , G pressure0 and C feedrate0 Represent the baseline setting values ​​of temperature, pressure and reactant feeding rate, ΔC temp , ΔC pressure and ΔC feedrate Based on the catalyst structure deviation P deviation The adjustment amount, P deviation is the deviation between the current structural parameters of the catalyst and the target structural parameters;

[0224] S62. Monitor the relationship between catalyst structural changes and chemical reaction conditions, and use machine learning models to predict catalyst performance trends to achieve more precise reaction condition adjustments:

[0225] P trend =β0+β1·C current +β2·H data ;

[0226] Among them, P trend is the catalyst performance change trend, C current is the current reaction condition, H data is the historical monitoring data, f predict is the prediction function;

[0227] S63: When catalyst performance degradation or structural abnormality is detected, the system automatically adjusts the reaction conditions and recommends catalyst replacement to ensure chemical reaction efficiency and product quality.

[0228] Adjust plan =f plan (P trend ,(S threshold );

[0229] Execute command =f execute (Adjust plan );

[0230] Among them, Adjust plan To adjust the plan, P trend is the performance change trend, S threshold is the threshold for performance degradation, f plan Execute is a function that formulates an adjustment plan. command To execute the adjustment instruction, f execute A function that executes instructions;

[0231] S64. Provide the operator with real-time catalyst performance status, predicted trends, and adjustment plans through the user interface, ensuring that the operator can understand the reaction status and make decisions in a timely manner:

[0232] UI display =f UI (P trend ,Adjust plan ,C current );

[0233] Among them, UI display Display content for the user interface, f UI Build functions for the interface to present catalyst performance trends, adjustment plans, and current reaction conditions to the operator in an intuitive form;

[0234] S65. Implement feedback loops to adjust machine learning models based on actual production process data to continuously optimize prediction accuracy and adjustment strategies:

[0235] Model update =f feedback (P actual ,C adjusted ,H data );

[0236] Among them, Model update represents the updated prediction model, P actual is the catalyst performance parameter actually monitored, C adjusted is the parameter of the implemented adjustment measures, H data is the accumulated historical monitoring data, f feedback A function that updates the prediction model based on actual monitoring data and adjustment results.

[0237] Example 1:

[0238] To demonstrate the practical application of this invention, we selected a large chemical company for implementation. A major challenge facing this company during production is the impact of catalyst performance fluctuations on production efficiency and product quality. Because traditional monitoring methods suffer from issues such as limited real-time performance and cumbersome analysis, the company urgently needs a method that can monitor and analyze catalyst structural changes in real time to optimize production processes and improve product quality.

[0239] In this embodiment, the present invention achieves online, real-time monitoring and analysis of catalyst structure by installing an X-ray diffractometer next to a chemical reactor and combining it with a real-time data processing system and machine learning algorithms. The system can automatically adjust key chemical reaction parameters, such as temperature and pressure, based on real-time changes in the catalyst structure to ensure that the catalyst operates at optimal conditions, thereby improving production efficiency and product quality. Specific data before and after the implementation of the present invention are shown in the following table:

[0240] Table 1 Catalyst performance monitoring and production efficiency improvement report of chemical enterprises

[0241] Data Category Before deploying the system After deploying the system Catalyst replacement frequency 4 times 1 time Production line downtime 20 hours 5 hours Product qualification rate 85.0％ 95.0％ production costs 1.2 million yuan 1 million yuan Catalyst utilization efficiency 100 tons / ton catalyst 150 tons / ton catalyst

[0242] Comparing data before and after implementing this invention reveals that the frequency of catalyst replacement has significantly decreased since the system was deployed, requiring only one replacement per quarter, reducing catalyst consumption and replacement costs. Unplanned downtime on the production line has been reduced from 20 hours per month to 5 hours, significantly improving production efficiency. Furthermore, the product qualification rate has increased from 85.0% to 95.0%, and production costs have been reduced from 1.2 million yuan per month to 1 million yuan, significantly improving catalyst utilization efficiency.

[0243] By monitoring minute changes in catalyst structure in real time and automatically adjusting reaction conditions, a potential production accident was successfully avoided, which could have resulted in hours of downtime and hundreds of thousands of dollars in losses. Through the application of this invention, the company not only optimized catalyst use but also achieved continuous improvement and optimization of the production process, significantly improving production efficiency and product quality while reducing production costs.

[0244] This example demonstrates the practical application value of the present invention in chemical production, demonstrating the effectiveness of optimizing catalyst performance, improving production efficiency and product quality by real-time monitoring of catalyst structural changes and automatically adjusting reaction conditions. The application of the present invention not only improves the stability and controllability of chemical reactions, but also significantly reduces the risks and costs in the production process. In addition, by reducing the frequency of catalyst replacement and unplanned downtime, enterprises can use resources more efficiently, reduce energy consumption and raw material consumption, and further enhance the environmental friendliness of production.

[0245] The implementation of this invention not only improves the operational efficiency of the production line but also has a positive impact on the company's long-term development strategy. Through real-time monitoring and data analysis, the company can gain a deeper understanding of the catalyst's performance under different production conditions, providing valuable data support for catalyst research and development and optimization. This data-driven R&D model can accelerate the development cycle of new catalysts, improve the success rate of R&D, and lay a solid foundation for the company's continued innovation and technological progress.

[0246] The application of this invention also promotes the intelligent and digital transformation of production processes. By integrating advanced data processing technologies and intelligent control systems, enterprises can achieve more precise and flexible production management, improving their responsiveness and flexibility to market changes. This not only enhances the market competitiveness of enterprises but also provides strong support for their continued growth in a complex and volatile market environment.

[0247] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for online monitoring of catalyst structure using X-ray diffraction technology, characterized in that: The steps include: S1. Place an X-ray diffractometer near a chemical reactor so that X-rays can cover the surface of the catalyst and capture the structural information of the catalyst during the chemical reaction. S2. Real-time adjustment of X-ray diffractometer operating parameters to adapt to changes in the reaction environment and optimize data acquisition effects; S3, using a continuous sampling mode to automatically collect X-ray diffraction signals reflected from the catalyst surface to obtain continuous data on catalyst structural changes; S4, transmitting the collected X-ray diffraction data in real time to a dedicated data processing system, which can quickly process and analyze the X-ray diffraction data; S5. Based on the catalyst structure information obtained by the data processing system analysis, the structural characteristics of the catalyst are monitored in real time; S6. Based on the real-time monitoring results of the catalyst structure, the control system adjusts the reaction conditions to the reactor or recommends replacing the catalyst to optimize the progress of the chemical reaction.

2. The method for online monitoring of catalyst structure using X-ray diffraction technology according to claim 1, characterized in that: Said S1 specifically includes: S11. Introducing an adaptive adjustment mechanism into the configuration of the X-ray diffractometer. By adjusting the incident angle θ and energy E of the X-rays through the adaptive adjustment mechanism, the efficiency of capturing catalyst structural information is optimized: i new =θ0+Δθ; E new =E0+ΔE; Where θ0 and E0 are the initial incident angle and energy, respectively, and Δθ and ΔE are the angle and energy changes that are dynamically adjusted based on the reaction conditions. S12. Introduce a real-time feedback system to dynamically adjust the incident angle and energy of the X-rays based on the intensity change ΔI of the X-ray signal reflected from the catalyst surface: Δθ=k θ ·log1+ΔI); Where Δθ is the adjustment amount of the incident angle, k θ is a constant coefficient used to adjust the sensitivity of signal strength changes to angle adjustment. ΔI is the change in signal strength. log(1+ΔI) ensures that appropriate adjustment can be made even when the signal strength changes slightly. ΔE is the energy adjustment. k E is another constant coefficient used to adjust the sensitivity of signal strength changes to energy adjustments. Used to provide a smooth adjustment process, suitable for handling situations where the signal strength changes greatly; S13. Use high-precision positioning technology to ensure that the relative position between the X-ray diffractometer and the chemical reactor remains unchanged: Where (x1, y1, z1) and (x2, y2, z2) represent the spatial coordinates of the X-ray source and a point on the catalyst surface, respectively; S14. Use a pattern recognition-based algorithm to predict possible structural changes in the catalyst during the chemical reaction process. Use historical monitoring data to predict catalyst structural changes and automatically adjust the scanning plan and parameter settings of the X-ray diffractometer: Where P(t) represents the catalyst structure parameters at time point t, α is the learning rate, which represents the sensitivity of the adjustment. It is the gradient of the change of the structural parameters at time point t, and predicts the catalyst structural parameters at the next time point t+1.

3. The method for online monitoring of catalyst structure using X-ray diffraction technology according to claim 1, characterized in that: The S2 specifically includes: S21. Through an adaptive adjustment mechanism, the operating parameters of the X-ray diffractometer are adjusted in real time to adapt to changes in the reaction environment and optimize data acquisition. Based on the intensity of the X-ray signal reflected from the catalyst surface and changes in environmental parameters, the X-ray incident angle θ and energy E are automatically adjusted to maximize the capture efficiency of catalyst structural information: i new =θ0+Δθ signal +Δθ env ; E new =E0+ΔE signal +ΔE env ; Where θ0 and E0 represent the initial incident angle and energy, Δθ signal and ΔE signal The angle and energy changes are automatically adjusted based on the X-ray signal intensity changes, Δθ env and ΔE env The angle and energy changes are adjusted according to the changes in environmental parameters; S22. Deploy a real-time monitoring and feedback system that collects the X-ray signal intensity I reflected from the catalyst surface and environmental parameters and adjusts the incident angle and energy of the X-rays: f(I)=a I ·(I-I0); Where f(I) and g(I) are functions of the signal intensity I and are used to calculate the contribution of the signal intensity change to the angle and energy adjustment. and is the adjustment factor; S23. Implement environmental perception and adjustment strategies to automatically adjust X-ray parameters based on real-time monitored environmental parameter changes: h(T,P)=c T ·(T-T0)+c P ·(P-P0); j(T,P)=d T ·log(1+|T-T0|)+d P ·log(1+|P-P0|); Among them, h(T,P) and j(T,P) are functions of temperature T and pressure P, and To adjust the coefficient, the X-ray diffraction parameters are automatically adjusted according to environmental changes; S24. A data-driven model is used to predict the structural changes of catalysts during chemical reactions and automatically adjust the scanning plan and parameter settings of the X-ray diffractometer accordingly. The prediction model uses machine learning algorithms to analyze historical monitoring data and real-time feedback information: Among them, P next represents the predicted catalyst structural parameters at the next time point, P current is the structural parameter at the current time point, β is the adjustment parameter, representing the prediction step of the model, is the gradient of the structural parameter change, which can be calculated by the machine learning model based on historical data, X1, X2, ..., X n Represents the factors that affect the catalyst structure change, w1, w2, ..., w n is the weight of these factors.

4. The method for online monitoring of catalyst structure using X-ray diffraction technology according to claim 1, characterized in that: The S3 specifically includes: S31. Set the X-ray diffractometer to perform continuous sampling, automatically collecting X-ray signals reflected from the catalyst surface at a fixed frequency f, to ensure continuous monitoring of the structural changes of the catalyst: Where Δt represents the sampling interval, which is determined according to the dynamic properties of the catalyst reaction and the rate of structural change; S32. Implement data preprocessing to improve signal quality: S processed =f denoise (S raw )+f baseline (S raw ); Among them, S raw is the original signal, S processed is the processed signal, f denoise is the denoising function, f baseline is the baseline correction function; S33. Conduct in-depth analysis of X-ray diffraction data, using specific algorithms to identify peaks in the diffraction pattern and analyze the lattice structure characteristics of the catalyst: P crystal =f analysis (S processed ); Among them, P crystal represents the lattice structure parameter of the catalyst, f analysis is the diffraction data analysis function; S34. Ensure timely transmission and effective storage of collected data, establish stable data transmission channels and efficient storage mechanisms, and support long-term storage and rapid access to large-scale data: D transmitted =f transmit (S processed ); D stored =f store (D transmitted ); Among them, D transmitted is the transmitted data, f transmit is the data transmission function, D stored is the stored data, f store It is a data storage function; S35. Establish a real-time monitoring and alarm mechanism to automatically identify abnormal changes in the catalyst structure based on the analysis results, use the set threshold Θ to conduct real-time evaluation of the analysis data and promptly issue an alarm for structural anomalies: Alert=f alert (P crystal ,I); Among them, Alert represents the alarm signal, f alert Based on the lattice structure parameter P crystal and the alarm trigger function with a threshold Θ.

5. The method for online monitoring of catalyst structure using X-ray diffraction technology according to claim 1, characterized in that: The S4 specifically includes: S41. Transmit the pre-processed X-ray diffraction data in real time to a dedicated data processing system. This system uses a high-speed data communication protocol to enable rapid identification and analysis of catalyst structural changes. D real-time =f transfer (S processed ,T current ); Among them, D real-time Represents real-time transmitted data, S processed is the preprocessed data, T current is the current time, f transfer is the data transmission function; S42. The data processing system processes the received data in real time and applies a data analysis algorithm to analyze the structural changes of the catalyst: P analyzed =f AI (D real-time ); Among them, P analyzed represents the catalyst structural parameters obtained after analysis, f AI It is an artificial intelligence-based data analysis function; S43. The data processing system automatically generates a catalyst structure change report based on the analysis results. The report describes the changes in the catalyst structure in detail: Report=f report (P analyzed ,C reaction ); Among them, Report is the generated report, P analyzed is the catalyst structure analysis parameter, C reaction is the chemical reaction condition, f report Generate functions for reports; S44. Implement an interface with the production control system, allowing automatic adjustment of chemical reaction parameters based on catalyst structure change reports. This interface supports real-time data exchange and command execution: C adjust =f control (Report,C optimal ); Among them, C adjust represents the adjusted chemical reaction conditions, C optimal For ideal chemical reaction conditions, f control A function for production control based on reports; S45. Establish a data feedback loop to input actual data from the production process back into the data processing system to achieve continuous monitoring and optimization: Feedback=f feedback (C adjust ,D new ); Among them, Feedback represents the feedback mechanism, C adjust is the reaction condition after adjustment, D new is the new monitoring data, f feedback is the data feedback function.

6. The method for online monitoring of catalyst structure using X-ray diffraction technology according to claim 1, characterized in that: The S5 specifically includes: S51. Based on the catalyst structure information obtained through analysis by the data processing system, the structural characteristics of the catalyst are monitored in real time: F features =f structure (P analyzed ); Among them, F features represents the catalyst structural feature set, P analyzed is the catalyst structural parameter obtained from the data processing system, f structure is the structural feature analysis function; S52. Compare the structural characteristics obtained through real-time monitoring with the preset structural stability standards to automatically identify structural change trends and potential structural anomalies: Status=f compare (F features ,S standard ); Among them, Status represents the structural stability of the catalyst, S standard is the structural stability standard, f compare is the feature contrast function; S53. For any potential structural anomalies identified, the system will automatically generate a detailed anomaly report and provide specific adjustment suggestions: Report exception =f exception (Status,F features ); Among them, Report exception For exception report, f exception Generate functions for anomaly reports based on the structural stability state and structural characteristics of the catalyst; S54. Display real-time monitoring results, abnormality reports, and adjustment suggestions to the operator through the user interface: UI=f display (Report exception ,F features ,C adjust ; Among them, UI stands for user interface, f display The data display function is responsible for presenting abnormality reports, structural characteristics, and adjustment suggestions to the operator in graphical and textual forms; S55. Implement necessary production process adjustments based on operator feedback and automation strategies to minimize the impact of catalyst structural anomalies on production efficiency and product quality: Adjust=f adjust (Report exception ,C current ); Among them, Adjust represents the adjustment measures implemented, C current is the current production process condition, f adjust A function that is adjusted based on anomaly reports.

7. The method for online monitoring of catalyst structure using X-ray diffraction technology according to claim 1, characterized in that: The S6 specifically includes: S61. Based on the catalyst structure information obtained from the data processing system analysis, the control system automatically adjusts the reaction conditions in the chemical reactor to maintain the catalyst at an optimal performance state: C temp =C temp0 +ΔC temp (P deviation ); C pressure =C pressure0 +ΔC prssure (P deviation ); C feedrate =C feedrate0 +ΔC feedrate (P deviation ); Among them, C temp0 、C pressure0 and C feedrate0 Represent the baseline setting values ​​of temperature, pressure and reactant feeding rate, ΔC temp , ΔC pressure and ΔC feedrate Based on the catalyst structure deviation P deviation The adjustment amount, P deviation is the deviation between the current structural parameters of the catalyst and the target structural parameters; S62. Monitor the relationship between catalyst structural changes and chemical reaction conditions, and use machine learning models to predict catalyst performance trends to achieve more precise reaction condition adjustments: P trend =β0+β1·C current +β2·H data ; Among them, P trend is the catalyst performance change trend, C current is the current reaction condition, H data is the historical monitoring data, f predict is the prediction function; S63: When catalyst performance degradation or structural abnormality is detected, the system automatically adjusts the reaction conditions and recommends catalyst replacement to ensure chemical reaction efficiency and product quality. djust plan =f plan (P trend ,S th resh old ); Execute command =f execute (Adjust plan ); Among them, Adjust plan To adjust the plan, P trend is the performance change trend, S th resh old is the threshold for performance degradation, f plan Execute is a function that formulates an adjustment plan. command To execute the adjustment instruction, f execute A function that executes instructions; S64. Provide the operator with real-time catalyst performance status, predicted trends, and adjustment plans through the user interface, ensuring that the operator can understand the reaction status and make decisions in a timely manner: UI display =f UI (P trend ,Adjust plan ,C current ); Among them, UI display Display content for the user interface, f UI Build functions for the interface to present catalyst performance trends, adjustment plans, and current reaction conditions to the operator in an intuitive form; S65. Implement feedback loops to adjust machine learning models based on actual production process data to continuously optimize prediction accuracy and adjustment strategies: Model updatr =f feedback (P actual ,C adjusted ,H data ); Among them, Model updatd represents the updated prediction model, P actual is the catalyst performance parameter actually monitored, C adjusted is the parameter of the implemented adjustment measures, H data is the accumulated historical monitoring data, f feedback A function that updates the prediction model based on actual monitoring data and adjustment results.

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