Oil shale pyrolysis process online monitoring system based on laser confocal fast fluorescence lifetime

Abstract

The invention discloses an oil shale pyrolysis process online monitoring system based on laser confocal fast fluorescence lifetime, and belongs to the technical field of oil shale pyrolysis. In order to solve the problems that the traditional monitoring technology cannot realize real-time and dynamic monitoring of a pyrolysis process and limits deep understanding of a pyrolysis mechanism and optimization of pyrolysis efficiency, a high-temperature environment for organic matter conversion reaction is provided for an oil shale sample. The method comprises the following steps: performing pyrolytic reaction on an oil shale sample, capturing a fluorescence lifetime image in real time through a laser confocal microscope, providing dynamic regulation and control pyrolysis temperature when the oil shale sample is subjected to organic matter conversion reaction, and reconstructing a visual three-dimensional model based on the fluorescence lifetime image; real-time and dynamic monitoring of organic matter conversion in the oil shale pyrolysis process is provided, a unique real-time monitoring means is provided for the oil shale pyrolysis process, fluorescence lifetime images are captured in real time through a laser confocal microscope, and therefore chemical conversion and morphological change of organic matter in the pyrolysis process can be deeply known.

Description

Technical Field

[0001] The invention relates to the technical field of oil shale pyrolysis, and in particular to an oil shale pyrolysis process online monitoring system based on laser confocal fast fluorescence lifetime. Background Art

[0002] Oil shale (also known as kerogen) is a high-ash sedimentary rock containing combustible organic matter. Oil shale is an unconventional oil and gas resource, and is listed as a very important replacement energy source in the 21st century due to its rich resources and feasibility of development and utilization. The main use of oil shale is to refine shale oil, and many by-products can also be obtained, such as ammonium sulfate (which can be used as fertilizer), phenols and pyridine (which can be used as chemical raw materials for the production of synthetic fibers, plastics, dyes, and drugs). The discharged gas can be used as gas fuel, and the remaining shale ash can be used to make cement clinker, ceramic fiber, ceramsite and other construction materials.

[0003] The pyrolysis of oil shale is the key process to convert organic matter into oil and gas. Traditional monitoring technology cannot achieve real-time and dynamic monitoring of the pyrolysis process, which limits the in-depth understanding of the pyrolysis mechanism and the optimization of pyrolysis efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: an online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime, comprising:

[0006] Peripheral components for:

[0007] Providing a high-temperature environment for the oil shale sample to undergo an organic matter conversion reaction, performing a pyrolysis reaction on the oil shale sample and capturing a fluorescence lifetime image in real time through a laser confocal microscope, and providing dynamic control of the pyrolysis temperature when the oil shale sample undergoes an organic matter conversion reaction, wherein the peripheral component is connected to a real-time data processing module, a dynamic temperature control module, and a three-dimensional reconstruction visualization platform signal, and real-time acquisition of reaction data and fluorescence lifetime images are sent to the real-time data processing module;

[0008] Real-time data processing module for:

[0009] Based on the reaction data and fluorescence lifetime images generated by the organic matter conversion reaction provided by the peripheral components to the oil shale samples, data processing is performed and the fluorescence lifetime changes are analyzed to monitor the chemical conversion of organic matter in the oil shale pyrolysis process in real time;

[0010] Dynamic temperature control module for:

[0011] Generate a pyrolysis temperature adjustment instruction based on the reaction data and fluorescence lifetime image data acquired by the real-time data processing module, and send the pyrolysis temperature adjustment instruction to the peripheral component;

[0012] 3D reconstruction visualization platform for:

[0013] Imaging processing is performed based on the reaction data and fluorescence lifetime images obtained in real time. According to the fluorescence lifetime image data and imaging results, a three-dimensional model of organic matter transformation during oil shale pyrolysis is reconstructed and visualized.

[0014] Furthermore, the peripheral component includes:

[0015] A laser confocal microscope is used to provide high-resolution and high-contrast microscopic images and capture fluorescence lifetime images in real time. The laser confocal microscope includes a microscope, a laser light source, a scanning device, a detector, an image output device, an optical device and a confocal system. The laser confocal microscope is connected to the three-dimensional reconstruction visualization platform by signal;

[0016] A high temperature adaptable sample cabin is used to provide a high temperature environment for the oil shale sample for the organic matter conversion reaction to occur. The high temperature adaptable sample cabin is made of high temperature resistant material and allows laser penetration;

[0017] The pyrolysis reactor is used to provide the reaction conditions required for the pyrolysis of oil shale and adjust the pyrolysis temperature in real time through dynamic temperature control. The pyrolysis reactor includes a reactor body, a heating element, a temperature sensor and a control system, wherein the temperature sensor and the control system are both connected to the real-time data processing module and the dynamic temperature control module signal.

[0018] Furthermore, the real-time data processing module includes:

[0019] Data pre-processing unit, used for:

[0020] Receive the raw reaction data and fluorescence lifetime images sent by peripheral components in real time, perform preliminary data cleaning, format conversion and standardization on the raw reaction data and fluorescence lifetime images, identify and eliminate outliers, duplicate values ​​or invalid data in the data, and convert the received data into a unified format.

[0021] Furthermore, the real-time data processing module further includes:

[0022] Fluorescence Lifetime Analysis Unit for:

[0023] The preprocessed fluorescence lifetime image data is loaded, based on the preprocessed fluorescence lifetime image data, TCSPC is used to accurately measure the time when the fluorescence photons reach the detector, a fluorescence decay curve is constructed, the fluorescence lifetime is calculated, the fluorescence decay curve is fitted, and the fluorescence lifetime value is obtained, the chemical transformation of organic matter during the pyrolysis of oil shale is monitored in real time, the fluorescence lifetime value is correlated with the chemical transformation of organic matter during the pyrolysis of oil shale, and a monitoring report is generated, the content of the monitoring report includes the changing trend of the fluorescence lifetime, the degree of chemical transformation of organic matter and the rate of the pyrolysis process.

[0024] Furthermore, the dynamic temperature control module includes:

[0025] The adjustment instruction generation unit is used to:

[0026] The reaction data and fluorescence lifetime image data provided by the real-time data processing module are used as input parameters of the algorithm. A preset temperature adjustment algorithm is used to generate a pyrolysis temperature adjustment instruction, which includes a temperature setting value, an adjustment rate, and an adjustment direction. The generated pyrolysis temperature adjustment instruction is sent to the peripheral component to guide the peripheral component to dynamically control the pyrolysis temperature.

[0027] Furthermore, the dynamic temperature control module further includes:

[0028] Regulation effect evaluation unit, used for:

[0029] Collect the actual temperature data of the peripheral components after receiving the temperature adjustment command, compare the actual temperature data with the preset temperature range, evaluate the effect of dynamic temperature adjustment, and make feedback adjustments to the temperature adjustment command based on the evaluation results.

[0030] Furthermore, the three-dimensional reconstruction visualization platform includes:

[0031] 3D imaging processing unit, used for:

[0032] Based on the reaction data and fluorescence lifetime images obtained in real time, the received data are voxelized and surface reconstructed using a three-dimensional reconstruction algorithm, and the reaction data and fluorescence lifetime images are converted into voxel data in three-dimensional space. On the basis of voxelization, isosurface extraction is used to construct a three-dimensional surface model of organic matter transformation during pyrolysis, and a three-dimensional model of organic matter transformation during oil shale pyrolysis is generated. The three-dimensional model includes the spatial distribution of organic matter, morphological changes, and possible chemical reaction pathways.

[0033] Furthermore, the three-dimensional reconstruction visualization platform further includes:

[0034] Visual display unit for:

[0035] Load the optimized 3D model data, parse the 3D model data, initialize the AR environment, build a virtual scene to display the 3D model, render the parsed 3D model data into the virtual scene, visualize the reconstructed 3D model based on AR, and provide interactive functions for the visualization by implementing user input processing logic. The interactive functions include rotation, scaling, and translation. When the user performs interactive operations, the visualization display unit updates the 3D model status in the virtual scene in real time and displays the updated visual feedback.

[0036] Furthermore, the online monitoring system also includes a pyrolysis environmental impact analysis module, and the pyrolysis environmental impact analysis module includes:

[0037] Fluorescence decay reference curve unit, used for:

[0038] The initial fluorescence intensity during the pyrolysis process was obtained, and the fluorescence decay reference curve was obtained by plotting the initial fluorescence intensity, the set standard fluorescence lifetime, and the following fluorescence decay function:

[0039]

[0040] Among them, I 0 is the fluorescence intensity decay function; I0 is the initial fluorescence intensity used for pyrolysis; k i is the relative weight of the ith fluorescence component; t is the time of fluorescence; e is a natural constant; τ0 is the set standard fluorescence lifetime;

[0041] Attenuation comparison unit, used for:

[0042] The fluorescence decay curve constructed using the preprocessed fluorescence lifetime image data is compared with the fluorescence decay reference curve, and the similarity between the two is calculated using the formula:

[0043]

[0044] Where S is the similarity between the fluorescence decay curve and the fluorescence decay reference curve; n is the total number of corresponding comparison data points between the fluorescence decay curve and the fluorescence decay reference curve; I j is the fluorescence intensity of the jth corresponding comparison data point on the fluorescence decay curve; I 0 j is the fluorescence intensity of the jth corresponding comparison data point on the fluorescence decay reference curve;

[0045] Pyrolysis Evaluation and Anomaly Analysis Unit for:

[0046] The similarity between the calculated fluorescence decay curve and the fluorescence decay reference curve is compared with the similarity threshold. If the similarity is less than the similarity threshold, it means that there is an abnormality in the pyrolysis process. Based on the environmental data obtained by synchronous measurement during the pyrolysis process, the pyrolysis impact analysis of the environmental data is performed, and the pyrolysis environmental data control plan is output according to the analysis results.

[0047] Furthermore, the temperature adjustment algorithm preset by the dynamic temperature control module includes a demand process temperature prediction model, and the demand process temperature prediction model is constructed in the following manner:

[0048] First, a convolutional neural network including input layer, hidden layer and output layer is constructed. The hidden layer includes convolution layer, pooling layer and fully connected layer. The convolution kernel, convolution layer parameters and activation function in the convolution layer are determined.

[0049] Secondly, the pyrolysis process data of oil shale in historical records are obtained. The pyrolysis process data includes historical reaction data, historical fluorescence lifetime image data and historical process temperature data. The data is cleaned to remove abnormal data.

[0050] Thirdly, the pyrolysis process data are selected and grouped, and the training data set, the validation data set and the test data set are respectively established with the pyrolysis process data of different groups;

[0051] Finally, the training data set, validation data set and test data set are used in turn to train the convolutional neural network, verify the parameters and test them until the test results meet the predetermined accuracy requirements, and the required process temperature prediction model is obtained.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention can provide real-time and dynamic monitoring of organic matter transformation during oil shale pyrolysis by integrating peripheral components, real-time data processing modules, dynamic temperature control modules and three-dimensional reconstruction visualization platforms, providing a unique real-time monitoring method for oil shale pyrolysis. The fluorescence lifetime images are captured in real time by laser confocal microscopy, so as to gain an in-depth understanding of the chemical transformation and morphological changes of organic matter during pyrolysis.

[0054] 2. The present invention can accurately construct a fluorescence decay curve and calculate the fluorescence lifetime by loading the pre-processed fluorescence lifetime image data, which provides key parameters for analyzing the chemical transformation of organic matter during the pyrolysis of oil shale, monitors the chemical transformation of organic matter during the pyrolysis of oil shale in real time, helps to analyze the performance of the pyrolysis process, and can also guide actual production operations and improve the efficiency and economic benefits of the pyrolysis process of oil shale.

[0055] 3. By receiving the reaction data and fluorescence lifetime image data provided by the real-time data processing module, the adjustment instruction generation unit of the present invention can use a preset temperature adjustment algorithm to generate accurate pyrolysis temperature adjustment instructions, thereby ensuring that the pyrolysis process is carried out within the optimal temperature range, thereby optimizing the pyrolysis efficiency and reducing unnecessary energy consumption. By collecting the actual temperature data of the peripheral components after receiving the temperature adjustment instructions and comparing it with the preset temperature range, the adjustment effect evaluation unit can evaluate the effect of dynamic temperature adjustment.

[0056] 4. The present invention constructs a three-dimensional model of organic matter transformation during pyrolysis by combining reaction data and fluorescence lifetime images, so that the spatial distribution, morphological changes and possible chemical reaction paths of organic matter can be intuitively displayed, providing researchers with a powerful visualization tool. This unit can display the reconstructed three-dimensional model to users in a more realistic and intuitive way, making the monitoring results more real-time and dynamic. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the module principle of the online monitoring system for oil shale pyrolysis process of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] In order to solve the technical problem that traditional monitoring technology cannot realize real-time and dynamic monitoring of the pyrolysis process, which limits the in-depth understanding of the pyrolysis mechanism and the optimization of pyrolysis efficiency, please refer to Figure 1 , the present invention provides the following technical solutions:

[0060] The online monitoring system of oil shale pyrolysis process based on laser confocal fast fluorescence lifetime includes:

[0061] Peripheral components for:

[0062] Providing a high-temperature environment for the oil shale sample to undergo an organic matter conversion reaction, performing a pyrolysis reaction on the oil shale sample and capturing a fluorescence lifetime image in real time through a laser confocal microscope, and providing dynamic control of the pyrolysis temperature when the oil shale sample undergoes an organic matter conversion reaction, wherein the peripheral component is connected to a real-time data processing module, a dynamic temperature control module, and a three-dimensional reconstruction visualization platform signal, and real-time acquisition of reaction data and fluorescence lifetime images are sent to the real-time data processing module;

[0063] Real-time data processing module for:

[0064] Based on the reaction data and fluorescence lifetime images generated by the organic matter conversion reaction provided by the peripheral components to the oil shale samples, data processing is performed and the fluorescence lifetime changes are analyzed to monitor the chemical conversion of organic matter in the oil shale pyrolysis process in real time;

[0065] Dynamic temperature control module for:

[0066] Generate a pyrolysis temperature adjustment instruction based on the reaction data and fluorescence lifetime image data acquired by the real-time data processing module, and send the pyrolysis temperature adjustment instruction to the peripheral component;

[0067] 3D reconstruction visualization platform for:

[0068] Imaging processing is performed based on the reaction data and fluorescence lifetime images obtained in real time. According to the fluorescence lifetime image data and imaging results, a three-dimensional model of organic matter transformation during oil shale pyrolysis is reconstructed and visualized.

[0069] Specifically, by integrating peripheral components, real-time data processing modules, dynamic temperature control modules and three-dimensional reconstruction visualization platforms, a unique real-time monitoring method is provided for the oil shale pyrolysis process. Fluorescence lifetime images are captured in real time by laser confocal microscopy, which enables in-depth understanding of the chemical transformation and morphological changes of organic matter during the pyrolysis process.

[0070] In the above embodiment, the peripheral component provides a high temperature environment for the oil shale sample to cause an organic matter conversion reaction. At the same time, this component can also capture fluorescence lifetime images in real time, and send reaction data and images to the corresponding processing modules through signal connections with the real-time data processing module, the dynamic temperature control module and the 3D reconstruction visualization platform.

[0071] In the above embodiment, the real-time data processing module is responsible for processing the received reaction data and fluorescence lifetime images, analyzing the changes in fluorescence lifetime, and thus real-time monitoring of the chemical transformation of organic matter during the pyrolysis of oil shale, thereby improving the accuracy and real-time performance of monitoring.

[0072] In the above embodiment, the dynamic temperature control module generates pyrolysis temperature adjustment instructions based on the reaction data and fluorescence lifetime image data obtained by the real-time data processing module to optimize the pyrolysis process. This dynamic temperature control capability enables the system to adapt to different oil shale samples and reaction conditions, thereby improving the pyrolysis efficiency and the quality of organic matter conversion.

[0073] In the above embodiment, the three-dimensional reconstruction visualization platform uses the reaction data and fluorescence lifetime images obtained in real time for imaging processing to reconstruct a three-dimensional model of organic matter transformation during the pyrolysis of oil shale and visualize the model, which not only provides users with an intuitive and vivid monitoring interface, but also helps to further analyze the pyrolysis mechanism and optimize the pyrolysis process.

[0074] Peripheral components, including:

[0075] A laser confocal microscope is used to provide high-resolution and high-contrast microscopic images and capture fluorescence lifetime images in real time. The laser confocal microscope includes a microscope, a laser light source, a scanning device, a detector, an image output device, an optical device and a confocal system. The laser confocal microscope is connected to the three-dimensional reconstruction visualization platform by signal;

[0076] A high temperature adaptable sample cabin is used to provide a high temperature environment for the oil shale sample for the organic matter conversion reaction to occur. The high temperature adaptable sample cabin is made of high temperature resistant material and allows laser penetration;

[0077] The pyrolysis reactor is used to provide the reaction conditions required for the pyrolysis of oil shale and adjust the pyrolysis temperature in real time through dynamic temperature control. The pyrolysis reactor includes a reactor body, a heating element, a temperature sensor and a control system, wherein the temperature sensor and the control system are both connected to the real-time data processing module and the dynamic temperature control module signal.

[0078] In the above embodiment, the laser confocal microscope can provide high-resolution and high-contrast microscopic images, which enables the system to more accurately capture subtle changes in the oil shale sample during the pyrolysis process. By capturing fluorescence lifetime images in real time, the laser confocal microscope can reveal the chemical transformation dynamics of organic matter during the pyrolysis process and provide rich information for analysis. The laser confocal microscope is connected to the three-dimensional reconstruction and visualization platform signal, so that the captured fluorescence lifetime images can be directly used for the reconstruction and visualization of the three-dimensional model, enhancing the intuitiveness and real-time nature of the monitoring.

[0079] In the above embodiment, the high-temperature adaptive sample chamber is made of high-temperature resistant materials and can withstand the high-temperature environment required for oil shale pyrolysis to ensure the continuity and stability of the experiment. The sample chamber allows laser penetration, which ensures that the laser confocal microscope can observe the sample in real time in a high-temperature environment without affecting the monitoring of the pyrolysis process.

[0080] In the above embodiment, the pyrolysis reactor adjusts the pyrolysis temperature in real time through the dynamic temperature control module, providing flexible reaction conditions for the pyrolysis of oil shale, so that the system can adapt to the pyrolysis requirements of different samples. The built-in temperature sensor can monitor the temperature inside the reactor in real time, and connect the signals of the control system with the real-time data processing module and the dynamic temperature control module to ensure that the pyrolysis process is carried out under precise temperature conditions. Through real-time data feedback and dynamic regulation, the pyrolysis reactor can optimize the pyrolysis efficiency and improve the conversion rate and product quality of oil shale.

[0081] Real-time data processing module, including:

[0082] Data pre-processing unit, used for:

[0083] Receive the raw reaction data and fluorescence lifetime images sent by peripheral components in real time, perform preliminary data cleaning, format conversion and standardization on the raw reaction data and fluorescence lifetime images, identify and eliminate outliers, duplicate values ​​or invalid data in the data, and convert the received data into a unified format.

[0084] Fluorescence Lifetime Analysis Unit for:

[0085] The preprocessed fluorescence lifetime image data is loaded, based on the preprocessed fluorescence lifetime image data, TCSPC is used to accurately measure the time when the fluorescence photons reach the detector, a fluorescence decay curve is constructed, the fluorescence lifetime is calculated, the fluorescence decay curve is fitted, and the fluorescence lifetime value is obtained, the chemical transformation of organic matter during the pyrolysis of oil shale is monitored in real time, the fluorescence lifetime value is correlated with the chemical transformation of organic matter during the pyrolysis of oil shale, and a monitoring report is generated, the content of the monitoring report includes the changing trend of the fluorescence lifetime, the degree of chemical transformation of organic matter and the rate of the pyrolysis process.

[0086] In the above embodiment, by performing preliminary data cleaning, format conversion and standardization on the raw reaction data and fluorescence lifetime images, the data preprocessing unit can ensure that the data input into the subsequent analysis module is clean, accurate and in a uniform format, avoiding analysis errors caused by data quality problems and improving the reliability of the entire system. Identifying and eliminating outliers, duplicate values ​​or invalid data in the data further improves the purity and accuracy of the data and provides a more reliable data basis for subsequent analysis. Converting the data into a uniform format enables the system to process data from different sources and formats, enhancing the flexibility and versatility of the system.

[0087] In the above embodiment, by loading the pre-processed fluorescence lifetime image data and using TCSPC technology to accurately measure the time when the fluorescent photons reach the detector, the unit can accurately construct the fluorescence decay curve and calculate the fluorescence lifetime, which provides key parameters for analyzing the chemical transformation of organic matter during the pyrolysis of oil shale. The fluorescence lifetime analysis unit can monitor the chemical transformation of organic matter during the pyrolysis of oil shale in real time, and associate the fluorescence lifetime value with the chemical transformation of organic matter. The correlation analysis helps to deeply understand the change mechanism of organic matter during the pyrolysis process, and provides strong support for optimizing pyrolysis conditions and controlling product quality. The generation of a monitoring report containing the fluorescence lifetime change trend, the degree of chemical transformation of organic matter, and the rate of the pyrolysis process provides intuitive and comprehensive information for scientific researchers and managers, helps to analyze the performance of the pyrolysis process, and can also guide actual production operations to improve the efficiency and economic benefits of the pyrolysis process of oil shale.

[0088] Dynamic temperature control module, including:

[0089] The adjustment instruction generation unit is used to:

[0090] The reaction data and fluorescence lifetime image data provided by the real-time data processing module are used as input parameters of the algorithm. A preset temperature adjustment algorithm is used to generate a pyrolysis temperature adjustment instruction, which includes a temperature setting value, an adjustment rate, and an adjustment direction. The generated pyrolysis temperature adjustment instruction is sent to the peripheral component to guide the peripheral component to dynamically control the pyrolysis temperature.

[0091] Regulation effect evaluation unit, used for:

[0092] Collect the actual temperature data of the peripheral components after receiving the temperature adjustment command, compare the actual temperature data with the preset temperature range, evaluate the effect of dynamic temperature adjustment, and make feedback adjustments to the temperature adjustment command based on the evaluation results.

[0093] In the above embodiment, by receiving the reaction data and fluorescence lifetime image data provided by the real-time data processing module, the adjustment instruction generation unit can use the preset temperature adjustment algorithm to generate accurate pyrolysis temperature adjustment instructions, ensuring that the pyrolysis process is carried out within the optimal temperature range, thereby optimizing the pyrolysis efficiency and reducing unnecessary energy consumption. The generated adjustment instructions include temperature setting values, adjustment rates, and adjustment directions, so that the system can flexibly adjust temperature parameters according to real-time changes in the pyrolysis process. The dynamic temperature control strategy helps to improve the stability and controllability of the pyrolysis process. The adjustment instruction generation unit can generate temperature adjustment instructions in real time and send the instructions to the peripheral components to guide the peripheral components to adjust the pyrolysis temperature in real time, ensuring that the pyrolysis process can quickly respond to changes in external conditions, thereby improving the response speed and adaptability of the system.

[0094] In the above embodiment, by collecting the actual temperature data of the peripheral components after receiving the temperature adjustment instruction and comparing it with the preset temperature range, the adjustment effect evaluation unit can evaluate the effect of dynamic temperature adjustment. If the actual temperature data does not match the preset range, the evaluation unit will make feedback adjustments to optimize the temperature adjustment instructions, thereby improving the accuracy and stability of temperature control. The feedback adjustment mechanism based on the evaluation results enables the system to continuously learn and improve, optimize the temperature control strategy, and the continuous improvement process helps to improve the efficiency, stability and reliability of the pyrolysis process, and ultimately improve the performance of the entire online monitoring system.

[0095] 3D reconstruction visualization platform, including:

[0096] 3D imaging processing unit, used for:

[0097] Based on the reaction data and fluorescence lifetime images obtained in real time, the received data are voxelized and surface reconstructed using a three-dimensional reconstruction algorithm, and the reaction data and fluorescence lifetime images are converted into voxel data in three-dimensional space. On the basis of voxelization, isosurface extraction is used to construct a three-dimensional surface model of organic matter transformation during pyrolysis, and a three-dimensional model of organic matter transformation during oil shale pyrolysis is generated. The three-dimensional model includes the spatial distribution of organic matter, morphological changes, and possible chemical reaction pathways.

[0098] Visual display unit for:

[0099] Load the optimized 3D model data, parse the 3D model data, initialize the AR environment, build a virtual scene to display the 3D model, render the parsed 3D model data into the virtual scene, visualize the reconstructed 3D model based on AR, and provide interactive functions for the visualization by implementing user input processing logic. The interactive functions include rotation, scaling, and translation. When the user performs interactive operations, the visualization display unit updates the 3D model status in the virtual scene in real time and displays the updated visual feedback.

[0100] In the above embodiment, by using a three-dimensional reconstruction algorithm, the unit can accurately convert reaction data and fluorescence lifetime images into voxel data in three-dimensional space. Through isosurface extraction and surface reconstruction, the unit can construct a three-dimensional surface model of organic matter transformation during pyrolysis, so that the spatial distribution, morphological changes and possible chemical reaction paths of organic matter can be intuitively displayed, providing researchers with a powerful visualization tool. By optimizing the three-dimensional model, the unit can further improve the accuracy and stability of the model. The optimized model more accurately reflects the actual situation of the oil shale pyrolysis process, providing a more reliable data basis for subsequent monitoring and analysis.

[0101] In the above embodiment, through AR technology, the unit can display the reconstructed three-dimensional model to the user in a more realistic and intuitive way, which not only improves the user's sense of immersion, but also makes the monitoring results easier to understand and accept. By realizing interactive functions such as rotation, scaling, and translation, the unit provides users with a more flexible and convenient operation experience. Users can freely adjust the viewing angle, zoom in and out of the model, and translate the observation position according to their needs, so as to have a more comprehensive understanding of the details and characteristics of the oil shale pyrolysis process. When the user performs interactive operations, the unit can update the status of the three-dimensional model in the virtual scene in real time and display the updated visual feedback to the user. This real-time visual feedback not only improves the user's operating experience, but also makes the monitoring results more real-time and dynamic.

[0102] On the basis of the above-mentioned embodiment, the online monitoring system further includes a pyrolysis environment impact analysis module, and the pyrolysis environment impact analysis module includes:

[0103] Fluorescence decay reference curve unit, used for:

[0104] The initial fluorescence intensity during the pyrolysis process was obtained, and the fluorescence decay reference curve was obtained by plotting the initial fluorescence intensity, the set standard fluorescence lifetime, and the following fluorescence decay function:

[0105]

[0106] Among them, I 0 is the fluorescence intensity decay function; I0 is the initial fluorescence intensity used for pyrolysis; k i is the relative weight of the ith fluorescence component; t is the time of fluorescence; e is a natural constant; τ0 is the set standard fluorescence lifetime;

[0107] Attenuation comparison unit, used for:

[0108] The fluorescence decay curve constructed using the preprocessed fluorescence lifetime image data is compared with the fluorescence decay reference curve, and the similarity between the two is calculated using the formula:

[0109]

[0110] Where S is the similarity between the fluorescence decay curve and the fluorescence decay reference curve; n is the total number of corresponding comparison data points between the fluorescence decay curve and the fluorescence decay reference curve; I j is the fluorescence intensity of the jth corresponding comparison data point on the fluorescence decay curve; I 0 j is the fluorescence intensity of the jth corresponding comparison data point on the fluorescence decay reference curve;

[0111] Pyrolysis Evaluation and Anomaly Analysis Unit for:

[0112] The similarity between the calculated fluorescence decay curve and the fluorescence decay reference curve is compared with the similarity threshold. If the similarity is less than the similarity threshold, it means that there is an abnormality in the pyrolysis process. Based on the environmental data obtained by synchronous measurement during the pyrolysis process, the pyrolysis impact analysis of the environmental data is performed, and the pyrolysis environmental data control plan is output according to the analysis results.

[0113] In the above embodiment, by introducing the fluorescence decay function, according to the standard fluorescence lifetime set for the pyrolysis process with better effect, using the determined initial fluorescence intensity condition, substituting multi-point time data into the calculation, and drawing a curve according to the multi-point calculation results, a fluorescence decay reference curve is obtained as a reference; the fluorescence decay curve constructed according to the real-time data is compared with the fluorescence decay reference curve; the similarity calculation formula used for the comparison is used, and when the two curves are exactly the same, the similarity is 1; the smaller the similarity, the more the fluorescence decay curve deviates from the fluorescence decay reference curve, combined with the similarity threshold, it is judged whether the pyrolysis process reflected by the fluorescence decay curve is abnormal, and the use of an algorithm scheme can enhance objectivity and improve reliability; if an abnormality occurs, a pyrolysis impact analysis is performed on the environmental data obtained by synchronous measurement during the pyrolysis process. The environmental data refers to the peripheral condition data that affects the process of the pyrolysis process, such as climate, process temperature, etc. The pyrolysis environment data control scheme is output according to the analysis results to adjust the pyrolysis process control, correct the deviation in time, and ensure process efficiency, yield and product quality.

[0114] On the basis of the above-mentioned embodiment, the temperature adjustment algorithm preset by the dynamic temperature control module includes a demand process temperature prediction model, and the demand process temperature prediction model is constructed in the following manner:

[0115] First, a convolutional neural network including input layer, hidden layer and output layer is constructed. The hidden layer includes convolution layer, pooling layer and fully connected layer. The convolution kernel, convolution layer parameters and activation function in the convolution layer are determined.

[0116] Secondly, the pyrolysis process data of oil shale in historical records are obtained. The pyrolysis process data includes historical reaction data, historical fluorescence lifetime image data and historical process temperature data. The data is cleaned to remove abnormal data.

[0117] Thirdly, the pyrolysis process data are selected and grouped, and the training data set, the validation data set and the test data set are respectively established with the pyrolysis process data of different groups;

[0118] Finally, the training data set, validation data set and test data set are used in turn to train the convolutional neural network, verify the parameters and test them until the test results meet the predetermined accuracy requirements, and the required process temperature prediction model is obtained.

[0119] In the above embodiment, a demand process temperature prediction model is constructed and placed in the temperature adjustment algorithm preset in the dynamic temperature control module. The demand process temperature prediction model is used to predict the process temperature required at the next moment during the pyrolysis process, so as to be used for process temperature control of the pyrolysis process, thereby realizing the dynamics and adaptability of process temperature control, improving the accuracy of process temperature control, and ensuring the pyrolysis effect and efficiency. This solution provides a method for constructing and training a demand process temperature prediction model, and uses a convolutional neural network as the basis of the model. Through the above training, the prediction results of the demand process temperature prediction model have higher accuracy, thereby achieving the prediction purpose.

[0120] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime, characterized in that: include: Peripheral components for: Providing a high-temperature environment for the oil shale sample to undergo an organic matter conversion reaction, performing a pyrolysis reaction on the oil shale sample and capturing a fluorescence lifetime image in real time through a laser confocal microscope, and providing dynamic control of the pyrolysis temperature when the oil shale sample undergoes an organic matter conversion reaction, wherein the peripheral component is connected to a real-time data processing module, a dynamic temperature control module, and a three-dimensional reconstruction visualization platform signal, and real-time acquisition of reaction data and fluorescence lifetime images are sent to the real-time data processing module; Real-time data processing module for: Based on the reaction data and fluorescence lifetime images generated by the organic matter conversion reaction provided by the peripheral components to the oil shale samples, data processing is performed and the fluorescence lifetime changes are analyzed to monitor the chemical conversion of organic matter in the oil shale pyrolysis process in real time; Dynamic temperature control module for: Generate a pyrolysis temperature adjustment instruction based on the reaction data and fluorescence lifetime image data acquired by the real-time data processing module, and send the pyrolysis temperature adjustment instruction to the peripheral component; 3D reconstruction visualization platform for: Imaging processing is performed based on the reaction data and fluorescence lifetime images obtained in real time. According to the fluorescence lifetime image data and imaging results, a three-dimensional model of organic matter transformation during oil shale pyrolysis is reconstructed and visualized.

2. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The peripheral component comprises: A laser confocal microscope is used to provide high-resolution and high-contrast microscopic images and capture fluorescence lifetime images in real time. The laser confocal microscope includes a microscope, a laser light source, a scanning device, a detector, an image output device, an optical device and a confocal system. The laser confocal microscope is connected to the three-dimensional reconstruction visualization platform by signal; High temperature adaptable sample chamber, used to provide a high temperature environment for the organic matter conversion reaction to occur in oil shale samples; The pyrolysis reactor is used to provide the reaction conditions required for the pyrolysis of oil shale and adjust the pyrolysis temperature in real time through dynamic temperature control. The pyrolysis reactor includes a reactor body, a heating element, a temperature sensor and a control system, wherein the temperature sensor and the control system are both connected to the real-time data processing module and the dynamic temperature control module signal.

3. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The real-time data processing module comprises: Data pre-processing unit, used for: Receive the raw reaction data and fluorescence lifetime images sent by peripheral components in real time, perform preliminary data cleaning, format conversion and standardization on the raw reaction data and fluorescence lifetime images, identify and eliminate outliers, duplicate values ​​and invalid data in the data, and convert the received data into a unified format.

4. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 3 is characterized in that: The real-time data processing module further includes: Fluorescence Lifetime Analysis Unit for: The preprocessed fluorescence lifetime image data is loaded, based on the preprocessed fluorescence lifetime image data, the time when the fluorescence photons reach the detector is measured by TCSPC and a fluorescence decay curve is constructed, the fluorescence lifetime is calculated, the fluorescence decay curve is fitted to obtain a fluorescence lifetime value, the chemical transformation of organic matter during the pyrolysis of oil shale is monitored in real time, the fluorescence lifetime value is correlated with the chemical transformation of organic matter during the pyrolysis of oil shale, and a monitoring report is generated, wherein the content of the monitoring report includes the changing trend of the fluorescence lifetime, the degree of chemical transformation of organic matter and the rate of the pyrolysis process.

5. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The dynamic temperature control module comprises: The adjustment instruction generation unit is used to: The reaction data and fluorescence lifetime image data provided by the real-time data processing module are used as input parameters of the algorithm. A preset temperature adjustment algorithm is used to generate a pyrolysis temperature adjustment instruction, which includes a temperature setting value, an adjustment rate, and an adjustment direction. The generated pyrolysis temperature adjustment instruction is sent to the peripheral component to guide the peripheral component to dynamically control the pyrolysis temperature.

6. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 5, characterized in that: The dynamic temperature control module further includes: Regulation effect evaluation unit, used for: Collect the actual temperature data of the peripheral components after receiving the temperature adjustment command, compare the actual temperature data with the preset temperature range, evaluate the effect of dynamic temperature adjustment, and make feedback adjustments to the temperature adjustment command based on the evaluation results.

7. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 1, characterized in that: The three-dimensional reconstruction visualization platform comprises: 3D imaging processing unit, used for: Based on the reaction data and fluorescence lifetime images obtained in real time, the received data are voxelized and surface reconstructed using a three-dimensional reconstruction algorithm, and the reaction data and fluorescence lifetime images are converted into voxel data in three-dimensional space. On the basis of voxelization, isosurface extraction is used to construct a three-dimensional surface model of organic matter transformation during pyrolysis, and a three-dimensional model of organic matter transformation during oil shale pyrolysis is generated. The three-dimensional model includes the spatial distribution of organic matter, morphological changes, and possible chemical reaction pathways.

8. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 7, characterized in that: The three-dimensional reconstruction visualization platform also includes: Visual display unit for: Load the optimized 3D model data, parse the 3D model data, initialize the AR environment, build a virtual scene to display the 3D model, render the parsed 3D model data into the virtual scene, visualize the reconstructed 3D model based on AR, and provide interactive functions for the visualization by implementing user input processing logic. The interactive functions include rotation, scaling, and translation. When the user performs interactive operations, the visualization display unit updates the 3D model status in the virtual scene in real time and displays the updated visual feedback.

9. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 4, characterized in that: It also includes a pyrolysis environmental impact analysis module, which includes: Fluorescence decay reference curve unit, used for: The initial fluorescence intensity during the pyrolysis process was obtained, and the fluorescence decay reference curve was obtained by plotting the initial fluorescence intensity, the set standard fluorescence lifetime, and the following fluorescence decay function: Among them, I 0 is the fluorescence intensity decay function; I0 is the initial fluorescence intensity used for pyrolysis; k i is the relative weight of the th fluorescence component; t is the time of fluorescence; e is a natural constant; τ0 is the set standard fluorescence lifetime; Attenuation comparison unit, used for: The fluorescence decay curve constructed using the preprocessed fluorescence lifetime image data is compared with the fluorescence decay reference curve, and the similarity between the two is calculated using the formula: Where S is the similarity between the fluorescence decay curve and the fluorescence decay reference curve; n is the total number of corresponding comparison data points between the fluorescence decay curve and the fluorescence decay reference curve; I j is the fluorescence intensity of the corresponding comparison data point on the fluorescence decay curve; I 0 j is the fluorescence intensity of the corresponding comparison data point on the fluorescence decay reference curve; Pyrolysis Evaluation and Anomaly Analysis Unit for: The similarity between the calculated fluorescence decay curve and the fluorescence decay reference curve is compared with the similarity threshold. If the similarity is less than the similarity threshold, it means that there is an abnormality in the pyrolysis process. Based on the environmental data obtained by synchronous measurement during the pyrolysis process, the pyrolysis impact analysis of the environmental data is performed, and the pyrolysis environmental data control plan is output according to the analysis results.

10. The online monitoring system for oil shale pyrolysis process based on laser confocal fast fluorescence lifetime according to claim 5, characterized in that: The temperature adjustment algorithm preset by the dynamic temperature control module includes a demand process temperature prediction model, and the demand process temperature prediction model is constructed in the following manner: First, a convolutional neural network including input layer, hidden layer and output layer is constructed. The hidden layer includes convolution layer, pooling layer and fully connected layer. The convolution kernel, convolution layer parameters and activation function in the convolution layer are determined. Secondly, the pyrolysis process data of oil shale in historical records are obtained. The pyrolysis process data includes historical reaction data, historical fluorescence lifetime image data and historical process temperature data. The data is cleaned to remove abnormal data. Thirdly, the pyrolysis process data are selected and grouped, and the training data set, the validation data set and the test data set are respectively established with the pyrolysis process data of different groups; Finally, the training data set, validation data set and test data set are used in turn to train the convolutional neural network, verify the parameters and test them until the test results meet the predetermined accuracy requirements, and the required process temperature prediction model is obtained.

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