Industrial waste gas purification treatment control system based on multiple sensors

Abstract

The invention relates to the technical field of industrial waste gas purification, in particular to an industrial waste gas purification treatment control system based on multiple sensors, which comprises a multi-scale sensor data fusion and compensation module, a multi-dimensional degradation analysis module, a feature decoupling module, a control decoupling module and a feedforward-feedback composite control module, the multi-scale sensor data fusion and compensation module outputs a space-time aligned sensor data matrix; the multi-dimensional degradation analysis module generates a multi-dimensional calibration data packet including sensor aging compensation parameters; the feature decoupling module generates an independently decoupled process feature vector; the control decoupling module outputs a control parameter group with a decoupling relation; and the feedforward-feedback composite control module outputs a cooperative control instruction set. According to the invention, the flexibility and accuracy of the waste gas purification system are enhanced, and the response capability of the system to dynamic environment change is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial waste gas purification, and particularly to a control system for industrial waste gas purification and treatment based on multi-sensors. Background Art

[0002] With the continuous advancement of the industrialization process, the impact of industrial waste gas emissions on the environment has become increasingly serious. To solve this problem, the research and application of waste gas purification and treatment systems have become an important topic in the field of environmental protection. The waste gas purification and treatment system uses various technical means and various sensors to monitor multiple parameters such as waste gas composition, flow field turbulence intensity, and equipment vibration in real time, so as to achieve effective control of waste gas emissions. However, existing waste gas purification systems often face many challenges in practical applications, especially in aspects such as multi-sensor data fusion, sensor performance degradation compensation, and control strategy optimization.

[0003] Currently, most waste gas purification systems rely on traditional sensor data processing and control methods. These methods often cannot effectively cope with problems such as differences in sensor layout positions and different sampling frequencies, resulting in spatio-temporal inconsistencies in system data, affecting the accuracy and efficiency of waste gas purification. Especially in the case of sensor aging and performance degradation, existing systems often lack a real-time compensation mechanism, resulting in accumulated errors in the long-term operation data of sensors, and further affecting the accuracy of control strategies. In addition, existing control algorithms usually fail to effectively decouple the strong coupling relationship between multiple control parameters, making the control adjustment flexibility in the waste gas purification process poor, the response speed slow, and it is difficult to adapt to dynamic working conditions.

[0004] To solve the deficiencies in the prior art, the present invention proposes a control system for industrial waste gas purification and treatment based on multi-sensors, which improves the response ability and accuracy of the waste gas purification system, thus realizing real-time adjustment and optimal operation in the waste gas purification process, improving the waste gas treatment efficiency and reducing environmental pollution. Summary of the Invention

[0005] The present invention provides a control system for industrial waste gas purification and treatment based on multi-sensors.

[0006] A control system for industrial waste gas purification and treatment based on multi-sensors includes a multi-scale sensor data fusion and compensation module, a multi-dimensional degradation analysis module, a feature decoupling module, a control decoupling module, and a feedforward-feedback composite control module, wherein; The multi-scale sensor data fusion and compensation module receives sensor data arranged in different process sections of the waste gas treatment pipeline. By performing spatio-temporal compensation on the sensor data, it compensates for the acquisition time delay between sensors, uses a spatial flow field interpolation model to correct the monitoring value offset caused by differences in sensor installation positions, and adopts a multi-scale convolutional neural network (MS-CNN) model to fuse the spatio-temporally aligned sensor data, outputting a spatio-temporally aligned sensor data matrix; The multi-dimensional degradation analysis module receives the spatio-temporally aligned sensor data matrix. By constructing a sensor degradation state observer, it calculates the zero-point drift coefficient and sensitivity attenuation factor of each sensor in real time, generating a multi-dimensional calibration data packet including sensor aging compensation parameters; The feature decoupling module receives the multi-dimensional calibration data packet. Using tensor decomposition technology, it separates the waste gas components from the coupled data space into concentration, flow field turbulence intensity, and equipment vibration spectrum, generating an independently decoupled process feature vector; The control decoupling module receives the independently decoupled process feature vector. By using the non-linear differential geometry method to construct a control parameter decoupling surface, it converts the three control variables of catalyst injection amount, plasma frequency, and adsorption bed pressure drop from a strongly coupled state to a weakly coupled state, outputting a set of control parameters with a decoupling relationship; The feedforward-feedback composite control module receives the decoupled set of control parameters. In the feedforward channel, it generates a reference control variable using fuzzy PID control based on the prediction of waste gas concentration fluctuations, and in the feedback channel, it generates a correction control variable using dynamic inverse compensation based on the analysis of the equipment impedance spectrum, outputting a fused collaborative control instruction set.

[0007] Optionally, the multi-scale sensor data fusion and compensation module includes: Spatio-temporal compensation and delay correction: Performing spatio-temporal compensation on sensor data through a spatio-temporal compensation algorithm to make up for the time delay differences caused by different sensor installation positions and sampling frequencies; Spatial flow field interpolation and position correction: Correcting the monitoring value offset caused by differences in sensor installation positions through a spatial flow field interpolation model; Data fusion and multi-scale feature extraction: After spatio-temporal compensation and position correction, using a multi-scale convolutional neural network (MS-CNN) model to fuse the spatio-temporally aligned sensor data, learning multi-scale feature information, extracting features from various types of sensor data, and outputting a spatio-temporally aligned sensor data matrix.

[0008] Optionally, the spatio-temporal compensation and delay correction include: Sensor data acquisition: Arranging quantum dot gas sensors, resonant micro-particle sensors, and multi-band infrared sensors in different process sections of the waste gas treatment pipeline and collecting raw data; Compensation and Calibration: Use the Dynamic Time Warping (DTW) algorithm to perform spatio-temporal compensation on data from different sensors.

[0009] Optionally, the spatial flow field interpolation and position correction include: Interpolation weight definition: Let each sensor at position collect the data value , then the interpolation weight is calculated by the distance between the sensor and the target position ; Interpolation calculation: According to the positions and collected data values of each sensor, calculate the interpolation data value of the target position , expressed as: ; Among them, is the monitoring value collected by sensor at position , is the weighting coefficient of sensor for the target position , is the number of sensors participating in interpolation or calculation; Output monitoring value: Output the corrected target position data , that is, the monitoring value corrected by spatial interpolation .

[0010] Optionally, the multi-scale convolutional neural network (MS-CNN) model includes: Multi-scale feature extraction layer: Extract features through convolutional kernels of different scales, capture sensor data features at different scales, and use the weighted summation method to fuse the sensor data features extracted at different scales to generate a comprehensive feature matrix ; Feature processing and non-linear transformation: Input the fused comprehensive feature matrix into the ReLU activation function for non-linear transformation; Feature pooling and dimensionality reduction: Reduce the dimension by selecting the maximum value in the local area; Output layer and prediction: Integrate the extracted features through the fully connected layer, and generate the final prediction value ; Restore the spatio-temporal alignment data matrix from the network output: Based on the final prediction value , convert it into a spatio-temporally aligned sensor data matrix .

[0011] ​​Optionally, the multi-dimensional degradation analysis module includes: Construct a sensor degradation state observer: By constructing a sensor degradation state observer, the performance degradation of each sensor is monitored and evaluated in real time, and the zero-point drift coefficient and sensitivity attenuation factor are calculated; Generate a multi-dimensional calibration data packet: Based on the calculated zero-point drift coefficient and sensitivity attenuation factor, a multi-dimensional calibration data packet is generated .

[0012] Optionally, the construction of the sensor degradation state observer includes: Zero-point drift coefficient calculation: The zero-point drift is obtained by calculating the difference between the actual measurement value and the ideal measurement value of the sensor ; ; Sensitivity attenuation factor calculation: The sensitivity attenuation factor is calculated based on the ratio of the actual output value to the ideal output value of the sensor .

[0013] Optionally, the feature decoupling module includes: Correct the spatio-temporal alignment data: Apply the multi-dimensional calibration data packet to correct the spatio-temporal aligned sensor data matrix , and generate a corrected sensor data matrix ; Obtain the coupled data: Based on the corrected sensor data matrix , obtain the coupled data matrix ; Feature decoupling: Apply tensor decomposition technology to separate the exhaust gas components, flow field turbulence intensity, and equipment vibration spectrum in the coupled data matrix from the high-dimensional data space into independent matrices; Output the independent decoupled process feature vectors: Based on the separated independent matrices, output the independent decoupled process feature vectors, including the independent decoupled feature vectors of the exhaust gas concentration, the independent decoupled feature vectors of the flow field turbulence intensity, and the independent decoupled feature vectors of the equipment vibration spectrum.

[0014] Optionally, the control decoupling module includes: Convert the control quantity from a strongly coupled state to a weakly coupled state: Through the decoupling surface, convert the strong coupling relationship between the control parameters (catalyst injection amount, plasma frequency, adsorption bed pressure drop) into a weak coupling relationship; Output the control parameter group: Output the control parameter group with a decoupled relationship, including the decoupled catalyst injection amount , the decoupled plasma frequency , and the decoupled adsorption bed pressure drop .

[0015] Optionally, the feedforward-feedback composite control module includes: Generating a reference control quantity: Based on the predicted fluctuation of the exhaust gas concentration, fuzzy PID control generates a reference control quantity ; Generating a correction control quantity: In the feedback channel, a dynamic inverse compensation control quantity is generated based on the device impedance spectrum analysis ; Outputting a collaborative control instruction set: Combining the reference control quantity and the correction control quantity, and outputting a fused collaborative control instruction set .

[0016] Advantages of the present invention: In the present invention, by combining multi-scale sensor data fusion and compensation technology, the exhaust gas purification system can effectively solve the errors caused by differences in sensor layout positions and sampling time delays. The spatio-temporal compensation and position correction technology ensure the precise alignment of data from different sensors in time and space, while the multi-scale convolutional neural network model further enhances the accuracy and reliability of the data. This technology optimization enables the system to provide high-quality, real-time, and consistent data input, providing a precise control basis for the subsequent exhaust gas purification process, thereby improving the overall accuracy and stability of the system.

[0017] In the present invention, by introducing sensor performance degradation analysis and feature decoupling, the exhaust gas purification system can accurately identify the aging effect of sensors and ensure the accuracy of data through real-time compensation. This not only improves the adaptability of the system under complex working conditions but also realizes the independent adjustment of control quantities such as catalyst injection amount, plasma frequency, and adsorption bed pressure drop through decoupling control parameters, optimizing the control strategy, effectively enhancing the flexibility and precision of the exhaust gas purification system, improving the system's response ability to dynamic environmental changes, and enabling it to always operate efficiently under different working conditions.

[0018] In the present invention, by combining fuzzy PID control and dynamic inverse compensation mechanism, the response ability of the system to exhaust gas concentration fluctuations and device dynamic behaviors is enhanced. The feedforward channel can adjust the control quantity in advance based on the predicted exhaust gas concentration fluctuations, while the feedback channel corrects the control quantity in real-time through device impedance spectrum analysis to ensure that the system can adapt to complex environments and dynamic changes, thereby optimizing the operation efficiency of the exhaust gas purification process. This control mechanism not only improves the real-time adjustment and optimization ability during the exhaust gas purification process but also can significantly improve the treatment efficiency, reduce environmental pollution, lower energy consumption and operation risks, and enhance the intelligent level and economic benefits of the exhaust gas purification system. Description of the Drawings

[0019] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0020] Figure 1 Schematic diagram of the system function modules of the embodiment of the present invention; Figure 2 Schematic diagram of the multi-scale sensor data fusion and compensation module of the embodiment of the present invention. Detailed implementation manners

[0021] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawing part is only for more specifically describing the embodiments and is not intended to specifically limit the present invention.

[0022] It should be pointed out that in the specification, when referring to "an embodiment", "embodiments", "exemplary embodiments", "some embodiments", etc., it indicates that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. Additionally, when combining embodiments to describe specific features, structures or characteristics, implementing such features, structures or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0023] Generally, terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather, at least in part depending on the context, can allow for the existence of other factors that may not be explicitly described.

[0024] As Figure 1 - Figure 2 shown, an industrial waste gas purification and treatment control system based on multi-sensors includes a multi-scale sensor data fusion and compensation module, a multi-dimensional degradation analysis module, a feature decoupling module, a control decoupling module, and a feedforward-feedback composite control module, wherein; The multi-scale sensor data fusion and compensation module receives sensor data arranged in different process sections of the waste gas treatment pipeline. By performing spatio-temporal compensation on the sensor data, it compensates for the acquisition time delay between sensors, uses a spatial flow field interpolation model to correct the monitoring value deviation caused by the difference in sensor layout positions, and adopts a multi-scale convolutional neural network (MS-CNN) model to fuse the spatio-temporally aligned sensor data, outputting a spatio-temporally aligned sensor data matrix; The multi-dimensional degradation analysis module receives the spatio-temporally aligned sensor data matrix. By constructing a sensor degradation state observer, it calculates the zero-point drift coefficient and sensitivity attenuation factor of each sensor in real time, generating a multi-dimensional calibration data packet including sensor aging compensation parameters; The feature decoupling module receives the multi-dimensional calibration data packet. Using tensor decomposition technology, it separates the waste gas components from the coupled data space into concentration, flow field turbulence intensity, and equipment vibration spectrum, and generates an independently decoupled process feature vector; The control decoupling module receives the independently decoupled process feature vector. By constructing a control parameter decoupling surface using the non-linear differential geometry method, it converts the three control quantities of catalyst injection amount, plasma frequency, and adsorption bed pressure drop from a strongly coupled state to a weakly coupled state, outputting a control parameter group with a decoupled relationship; The feedforward-feedback composite control module receives the decoupled control parameter group. In the feedforward channel, it generates a reference control quantity using fuzzy PID control based on the prediction of waste gas concentration fluctuations. In the feedback channel, it generates a correction control quantity using dynamic inverse compensation based on the analysis of the equipment impedance spectrum, outputting a fused collaborative control instruction set to achieve the dynamic balance and optimal operation of the waste gas purification system; Through the above content, the spatio-temporal difference problem between sensors is effectively solved, ensuring high-precision data input. By introducing sensor performance degradation analysis and feature decoupling, the reliability and stability of the system are further improved. The combination of control decoupling and feedforward-feedback composite control mechanisms optimizes the control parameters, improves the flexibility and accuracy of system response, realizes real-time adjustment and optimal operation during the waste gas purification process, can greatly improve the waste gas treatment efficiency, and reduce environmental pollution.

[0025] The multi-scale sensor data fusion and compensation module includes: Spatio-temporal compensation and delay correction: Spatio-temporal compensation is performed on the sensor data through a spatio-temporal compensation algorithm to make up for the time delay difference caused by different sensor layout positions and sampling frequencies; Spatial flow field interpolation and position correction: The monitoring value deviation caused by the difference in sensor layout positions is corrected through a spatial flow field interpolation model; Data Fusion and Multi-Scale Feature Extraction: After spatio-temporal compensation and position correction, a multi-scale convolutional neural network (MS-CNN) model is used to fuse the spatio-temporally aligned sensor data, learn multi-scale feature information, extract features from various types of sensor data, and output a spatio-temporally aligned sensor data matrix; Through the above content, the quality of sensor data in the exhaust gas purification system is improved. Spatio-temporal compensation and delay correction ensure the precise alignment of data from different sensors in time and space, eliminating errors caused by differences in installation positions and sampling delays. Spatial flow field interpolation corrects data biases caused by differences in sensor positions, further improving the spatial consistency of the data. Using a multi-scale convolutional neural network model to fuse and extract features from the data can automatically capture key feature information at multiple levels, enhancing the accuracy and reliability of the data, providing high-quality, real-time, and consistent data support for the exhaust gas purification system, and optimizing the control and decision-making effects of the entire process.

[0026] Spatio-temporal Compensation and Delay Correction include: Sensor Data Acquisition: Quantum dot gas sensors, resonant microparticle sensors, and multi-band infrared sensors are arranged at different process sections of the exhaust gas treatment pipeline, and the raw data is collected; Compensation and Correction: In order to compensate for the time delay differences between different sensors, the dynamic time warping (DTW) algorithm is used to perform spatio-temporal compensation on the data of different sensors, mapping the data of different sensors to a unified time coordinate system to eliminate the influence of sampling time delay; The dynamic time warping (DTW) algorithm includes: (1) Calculate the distance between two sensor data points: Suppose there are two sensors and , and the time series and are respectively collected, where , are the sampling timestamps of the sensors, and there is a time delay difference between the two sets of data. The distance is expressed as: ; Among them, is the distance between two sensor data points, is the Euclidean distance; (2) Construct the cumulative distance matrix: Construct the cumulative distance matrix , which represents the minimum cumulative distance from to , and is expressed as: ; Among them, is the minimum cumulative distance to reach the point ; (3) Solve the alignment path: By backtracking the cumulative distance matrix, obtain the optimal alignment path to determine the time alignment relationship, expressed as: ; (4) Output parameters: The aligned time series and , that is, map the data of the two sensors to the same time reference; Through the above content, the time delay problem caused by the differences in the sensor layout positions and sampling frequencies is effectively solved. It can accurately perform spatio-temporal alignment on the data of different sensors, thereby eliminating the errors caused by time asynchrony and position deviation. Through this compensation and correction, the system can fuse multi-source sensor data in a more accurate time and space coordinate system, providing consistent and high-quality data input for subsequent data analysis, feature extraction, and control optimization. This not only improves the operation accuracy and reliability of the exhaust gas purification control system but also enhances the system's ability to handle complex environments and dynamic changes.

[0027] Spatial flow field interpolation and position correction include: Interpolation weight definition: Let each sensor at position collect the data value , then the interpolation weight is calculated by the distance between the sensor and the target position , expressed as: ; Among them, is the Euclidean distance between the sensor position and the target position, is a constant (take ); Interpolation calculation: According to the positions of each sensor and the collected data values, calculate the interpolation data value of the target position through the spatial flow field interpolation model, expressed as: ; Among them, is the monitoring value collected by the sensor at the position , is the weighting coefficient of the sensor for the target position , is the number of sensors participating in the interpolation or calculation; Output the monitoring value: Output the corrected target position data , that is, the monitoring value corrected by spatial interpolation; Through the above, the monitoring value offset caused by the difference in the sensor layout position can be effectively corrected. According to the position of the sensor and the characteristics of the waste gas flow field, the sensor data is adjusted to a unified spatial coordinate system through the spatial flow field interpolation model, ensuring the spatial consistency of the data, improving the accuracy of the data, making the monitoring results of the sensor consistent in space, reducing the error caused by the layout difference, and thus providing a more reliable and accurate input for the subsequent waste gas purification process. This not only optimizes the performance and control accuracy of the system, but also enhances the data comparability and stability in the waste gas treatment process, improving the efficiency and intelligent level of the overall system.

[0028] The multi-scale convolutional neural network (MS-CNN) model includes: Multi-scale feature extraction layer: Feature extraction is performed through convolutional kernels of different scales to capture the characteristics of sensor data at different scales, so as to improve the sensitivity of the model to different data changes in the waste gas treatment process. The multi-scale convolution operation can process both coarse-grained and fine-grained features simultaneously, adapt to data with different precisions and time scales in the waste gas treatment process, and use the weighted summation method to fuse the sensor data features extracted at different scales to generate a comprehensive feature matrix , specifically including: (1) Let the input data be a matrix of size , be the height, be the width, be the number of channels (i.e., the feature dimension of the sensor data). The multi-scale convolutional kernels have different sizes respectively ( ). For each scale of convolutional kernel , the convolution operation is expressed as: ; Among them, represents the convolution operation of the input data and the convolutional kernel , and the convolution feature of the -th layer is obtained. is the number of multi-scale convolutional kernels; (2) Multi-scale feature fusion: In order to fuse the features extracted at different scales, the weighted summation method is used to fuse the features of each layer into a comprehensive feature matrix , which is expressed as: ; Among them, is the weight of each scale feature; Feature processing and non-linear transformation: The fused comprehensive feature matrix It is input into the ReLU activation function for non - linear transformation, thereby enhancing the network's learning ability for complex data patterns, expressed as: ; wherein, ; Feature pooling and dimensionality reduction: Dimensionality reduction is achieved by selecting the maximum value in a local area. Let the pooling window be , then the pooling operation is expressed as: ; wherein, is the feature matrix after pooling; Output layer and prediction: The features extracted are integrated through a fully - connected layer, and the final prediction value is generated through the prediction layer for subsequent waste gas treatment control decisions, specifically including: ; wherein, is the output of the fully - connected layer, is the weight matrix of the fully - connected layer, is the feature matrix after pooling, is the bias term of the fully - connected layer; Restoring the spatio - temporal alignment data matrix from the network output: Based on the final prediction value , it is transformed into a spatio - temporal aligned sensor data matrix , ensuring that the features output by the network and the final data can be directly applied to the control and optimization in the waste gas treatment system, expressed as: ; wherein, is the process of converting the network output back to the spatio - temporal aligned sensor data matrix ; Through the above content, data features at different scales can be processed simultaneously, effectively capturing multi - level information in the waste gas purification process. Secondly, the model automatically extracts important spatio - temporal features by fusing data from different sensors and can perform deep learning tasks on data after spatio - temporal compensation and position correction, enhancing the accuracy and consistency of the data. Further, after feature pooling and dimensionality reduction, the model not only reduces the computational complexity but also retains the key information in the data, ensuring efficient data processing and decision - making capabilities. Finally, the spatio - temporal aligned sensor data matrix output by the model can directly provide accurate data input for the waste gas treatment system, optimizing the control accuracy, stability, and response speed of the waste gas purification system, improving the adaptability and precision in processing complex environmental data, and enhancing the overall efficiency and intelligent level of the waste gas purification system.

[0029] The multi-dimensional degradation analysis module includes: Construct a sensor degradation state observer: By constructing a sensor degradation state observer, the performance degradation of each sensor is monitored and evaluated in real time, and the zero-point drift coefficient and the sensitivity attenuation factor are calculated; Generate a multi-dimensional calibration data packet: Based on the calculated zero-point drift coefficient and sensitivity attenuation factor, a multi-dimensional calibration data packet is generated , expressed as: ; Through the above, the performance degradation of the sensor during long-term use can be effectively compensated, the zero-point drift and sensitivity attenuation caused by sensor aging can be accurately identified and corrected, ensuring the accuracy and consistency of the sensor output data. This not only improves the stability and reliability of the exhaust gas purification system, but also extends the service life of the sensor, reduces maintenance costs. Through the real-time compensation of the aging effect, high-precision exhaust gas monitoring and control can be always maintained, effectively improving the system's response ability under dynamic and complex working conditions, thereby optimizing the overall efficiency and intelligent level of the exhaust gas purification process.

[0030] Constructing the sensor degradation state observer includes: Zero-point drift coefficient calculation: The zero-point drift is obtained by calculating the difference between the actual measurement value and the ideal measurement value of the sensor , expressed as: ; Among them, is the actual measurement value of the sensor at time , is the ideal measurement value of the sensor (based on the factory calibration of the sensor); Sensitivity attenuation factor calculation: The sensitivity attenuation factor is calculated according to the ratio of the actual output value to the ideal output value of the sensor, expressed as: ; Among them, is the actual output value of the sensor at time , is the ideal output value of the sensor; ​Through the above, the performance degradation of the sensor can be accurately monitored and compensated in a timely manner. By calculating the difference (zero drift) between the actual measurement value and the ideal measurement value and the sensitivity attenuation coefficient, the aging effect of the sensor during long-term use can be effectively identified, and the degraded effects can be corrected by adjusting the output data of the sensor. This process can maintain the data accuracy during the long-term operation of the exhaust gas purification system, avoid the accumulation of errors caused by the performance decline of the sensor, and the application of the sensor degradation state observer greatly enhances the precise control ability during the exhaust gas treatment process, contributing to improving the treatment efficiency and reducing the equipment maintenance cost.

[0031] The feature decoupling module includes: Correct the spatio-temporal alignment data: Apply the multi-dimensional calibration data packet to correct the sensor data matrix of spatio-temporal alignment , and generate the corrected sensor data matrix , which is expressed as: ; Among them, is the corrected sensor data matrix, is the sensor data matrix of spatio-temporal alignment; Obtain the coupled data: Based on the corrected sensor data matrix , obtain the coupled data matrix , which is expressed as: ; Among them, are respectively the corrected sensor data matrices of the rd sensor at time ; Feature decoupling: Apply the tensor decomposition technology to separate the exhaust gas components, flow field turbulence intensity, and equipment vibration spectrum in the coupled data matrix from the high-dimensional data space into independent matrices, which is expressed as: ; Among them, is the input coupled data matrix, is the low-dimensional matrix obtained through tensor decomposition, representing the independent features of exhaust gas concentration, flow field turbulence intensity, and equipment vibration spectrum respectively; Output the independent decoupled process feature vectors: Based on the separated independent matrices, output the independent decoupled process feature vectors, including the independent decoupled feature vectors of exhaust gas concentration, the independent decoupled feature vectors of flow field turbulence intensity, and the independent decoupled feature vectors of equipment vibration spectrum, which are expressed as: ; ; ; Among them, , , are respectively the independent decoupled eigenvectors of waste gas concentration, the independent decoupled eigenvector of flow field turbulence intensity, and the independent decoupled eigenvector of equipment vibration spectrum, , , are respectively the eigenvectors of the extracted waste gas concentration characteristics, the eigenvectors of the flow field turbulence intensity characteristics, and the eigenvectors of the equipment vibration spectrum characteristics; Through the above content, these key features can be effectively separated and the mutual interference between them can be eliminated. This not only improves the interpretability of the data but also enhances the independent analysis ability of each process feature. By extracting independent features from multi-source sensor data, each process link can be controlled and optimized more precisely. In addition, the decoupled eigenvectors can more clearly reflect various process parameters, greatly improving the accuracy and efficiency in the waste gas purification process and enhancing the adaptability of the system in complex environments. Finally, a more efficient and stable waste gas treatment system is achieved.

[0032] The control decoupling module includes: Converting the control quantity from a strongly coupled state to a weakly coupled state: Through the decoupling surface, the strong coupling relationship between control parameters (catalyst injection amount, plasma frequency, adsorption bed pressure drop) is converted into a weak coupling relationship, enabling each control parameter to function independently and avoiding the mutual interference between different control quantities. The three control quantities of catalyst injection amount, plasma frequency, and adsorption bed pressure drop can be adjusted independently, expressed as: ; ; ; Among them, is the decoupled catalyst injection amount, is the decoupled plasma frequency, is the decoupled adsorption bed pressure drop, , , , , , , , , are adjustment coefficients; Outputting a control parameter group: Outputting a control parameter group with a decoupled relationship, including the decoupled catalyst injection amount , the decoupled plasma frequency , and the decoupled adsorption bed pressure drop ; Adjustment coefficient and and Calculated by the nonlinear differential geometry method, specifically including:[[]] Nonlinear mapping model: Let the control parameter have a nonlinear relationship with the process characteristics (waste gas concentration, flow field turbulence intensity, equipment vibration). For each control parameter , its change is expressed as:[[]] ;[[]] where is the adjustment coefficient, representing the coupling relationship between the control parameter and the process characteristics;[[]] Gradient calculation: The nonlinear differential geometry method describes the rate of change between the control quantity and the process characteristics by calculating the gradient, defines the relationship between the control parameter and the process characteristics, and calculates the gradient , expressed as:[[]] ;[[]] Coefficient calculation: In order to obtain the coefficient , these coefficients are solved by the fitting method, expressed as:[[]] ;[[]] where is the observed value of the control parameter at time , , , are the process characteristic vectors corresponding to time respectively,[[]] is the number of time steps;[[]] Through the above content, the flexibility and precision of the system are improved. Through the decoupled process characteristic vectors, each control parameter can be independently adjusted, such as the catalyst injection amount, plasma frequency, and adsorption bed pressure drop. This not only improves the response speed of the waste gas purification system but also enhances the adaptability and stability of the system under complex working conditions. Through precise control, the system can achieve a more efficient and stable waste gas treatment process, ultimately reducing energy consumption, reducing operation risks, and improving the overall operation efficiency and automation level.[[]]

[0033] The feedforward-feedback composite control module includes:[[]] Generate the reference control quantity: Based on the predicted fluctuation of the waste gas concentration, the fuzzy PID control generates the reference control quantity , expressed as:[[]] ;[[]] where is the error of the waste gas concentration fluctuation prediction,[[]] is the proportional coefficient,[[]] is the integral coefficient,[[]] is the differential coefficient[[]] is the reference control quantity calculated based on the prediction of the waste gas concentration fluctuation; Generate a correction control quantity: In the feedback channel, generate a dynamic inverse compensation control quantity based on the equipment impedance spectrum analysis , expressed as: ; Wherein, is the result of the spectrum impedance analysis of the equipment, is the correction control quantity based on the impedance spectrum, is the inverse transformation of the equipment impedance spectrum; ; Wherein, is the equipment response voltage, is the input current; Output a collaborative control instruction set: Combine the reference control quantity and the correction control quantity, and output the fused collaborative control instruction set , expressed as: ; Wherein, is the fusion coefficient; The error of the waste gas concentration fluctuation prediction is obtained based on the gap between the predicted value and the actual value, expressed as: ; Wherein, is the predicted value of the waste gas concentration, is the actually measured waste gas concentration; The predicted value of the waste gas concentration is calculated based on the time series prediction method, expressed as: ; Wherein, is the predicted waste gas concentration at time , is the actual waste gas concentration at the historical time , is the constant term (mean value), is the autoregressive coefficient, indicating the influence of past data on the current value, is the moving average coefficient, indicating the influence of the error term on the predicted value, is the white noise error term, indicating the prediction error; Through the above, precise and efficient control can be achieved in the exhaust gas purification system. The feedforward channel predicts based on the fluctuation of the exhaust gas concentration, responds in advance and generates a reference control quantity, thereby effectively preventing potential system fluctuations. The feedback channel uses the impedance spectrum analysis of the equipment for dynamic inverse compensation and corrects the control quantity in real time to ensure that the system can cope with the changes and disturbances in the actual working conditions. Through this composite control method, the system can not only quickly respond and adjust the control quantity, but also maintain stability in the face of the dynamic changes of the equipment and the environment.

[0034] This invention covers any alternatives, modifications, equivalent methods and solutions made within the spirit and scope of this invention. To enable the public to have a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments of this invention. However, those skilled in the art can fully understand this invention even without the description of these details. In addition, well-known methods, processes, procedures, components and circuits are not described in detail to avoid unnecessary confusion to the essence of this invention.

[0035] The above are only the preferred embodiments of this invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of this invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this invention.

Claims

1. A multi-sensor based industrial waste gas purification and control system, characterized in that: It includes a multi-scale sensor data fusion and compensation module, a multi-dimensional degradation analysis module, a feature decoupling module, a control decoupling module, and a feedforward-feedback composite control module, where; The multi-scale sensor data fusion and compensation module receives sensor data arranged in different process sections of the waste gas treatment pipeline. By performing spatio-temporal compensation on the sensor data, it compensates for the acquisition time lag between sensors, uses a spatial flow field interpolation model to correct the monitoring value offset caused by the difference in sensor layout positions, and uses a multi-scale convolutional neural network model to fuse the spatio-temporally aligned sensor data, outputting a spatio-temporally aligned sensor data matrix; The multi-dimensional degradation analysis module receives the spatio-temporally aligned sensor data matrix. By constructing a sensor degradation state observer, it calculates the zero drift coefficient and sensitivity attenuation factor of each sensor in real time, generating a multi-dimensional calibration data packet including sensor aging compensation parameters; The feature decoupling module receives the multi-dimensional calibration data packet and uses tensor decomposition technology to separate the waste gas components from the coupled data space into concentration, flow field turbulence intensity, and equipment vibration spectrum, generating an independently decoupled process feature vector; The control decoupling module receives the independently decoupled process feature vector. By using the non-linear differential geometry method to construct a control parameter decoupling surface, it converts the three control quantities of catalyst injection amount, plasma frequency, and adsorption bed pressure drop from a strongly coupled state to a weakly coupled state, outputting a set of control parameters with a decoupled relationship; The feedforward-feedback composite control module receives the decoupled control parameter set. In the feedforward channel, it uses fuzzy PID control based on waste gas concentration fluctuation prediction to generate a reference control quantity, and in the feedback channel, it uses dynamic inverse compensation based on equipment impedance spectrum analysis to generate a correction control quantity, outputting a fused collaborative control instruction set.

2. The multi-sensor based industrial waste gas purification and control system according to claim 1 is characterized in that: The multi-scale sensor data fusion and compensation module includes: Spatio-temporal compensation and delay correction: Spatio-temporal compensation is performed on the sensor data through a spatio-temporal compensation algorithm to make up for the time lag difference caused by different sensor layout positions and sampling frequencies; Spatial flow field interpolation and position correction: The monitoring value offset caused by the difference in sensor layout positions is corrected through a spatial flow field interpolation model; Data fusion and multi-scale feature extraction: After spatio-temporal compensation and position correction, a multi-scale convolutional neural network model is used to fuse the spatio-temporally aligned sensor data, learn multi-scale feature information, extract the features in various types of sensor data, and output a spatio-temporally aligned sensor data matrix.

3. The industrial waste gas purification control system based on multi-sensors according to claim 2, characterized in that The spatio-temporal compensation and delay correction include: Sensor data acquisition: Quantum dot gas sensors, resonant micro-particle sensors, and multi-band infrared sensors are arranged in different process sections of the waste gas treatment pipeline, and raw data is collected; Compensation and correction: The dynamic time warping algorithm is used to perform spatio-temporal compensation on the data of different sensors.

4. The industrial waste gas purification and treatment control system based on multi-sensors according to claim 3, wherein, The spatial flow field interpolation and position correction include: Interpolation weight definition: Let each sensor at position collect the data value , then the interpolation weight is calculated by the distance between the sensor and the target position ; Interpolation calculation: Based on the positions and the collected data values of each sensor, calculate the interpolation data value of the target position through the spatial flow field interpolation model of the interpolation data value , expressed as: ； Among them, is the monitoring value collected by the sensor at the position ; is the weighting coefficient of the sensor for the target position ; is the number of sensors participating in interpolation or calculation. Output monitoring value: Output the corrected target position data , that is, the monitoring value corrected by spatial interpolation .

5. The industrial waste gas purification control system based on multi-sensors according to claim 4, characterized in that, The multi-scale convolutional neural network model includes: Multi-scale feature extraction layer: Feature extraction is performed using convolutional kernels of different scales to capture sensor data features at different scales, and the weighted summation method is used to fuse the sensor data features extracted at different scales to generate a comprehensive feature matrix ; Feature processing and non-linear transformation: Input the fused comprehensive feature matrix into the ReLU activation function for non-linear transformation; Feature pooling and dimensionality reduction: Dimensionality reduction is achieved by selecting the maximum value in a local area; Output layer and prediction: Integrate the extracted features through a fully connected layer, and generate the final predicted value through the prediction layer ; Recovering the spatio-temporal aligned data matrix from the network output: Based on the final predicted values , convert it into a spatio-temporal aligned sensor data matrix .

6. The industrial waste gas purification and treatment control system based on multi-sensors according to claim 5, characterized in that, The multi-dimensional degradation analysis module includes: Construct a sensor degradation state observer: By constructing a sensor degradation state observer, the performance degradation of each sensor is monitored and evaluated in real time, and the zero drift coefficient and the sensitivity attenuation factor are calculated; Generate a multi-dimensional calibration data packet: Generate a multi-dimensional calibration data packet based on the calculated zero drift coefficient and sensitivity attenuation factor .

7. The industrial waste gas purification control system based on multi-sensors according to claim 6, characterized in that, The construction of the sensor degradation state observer includes: Zero drift coefficient calculation: By calculating the difference between the actual measured value and the ideal measured value of the sensor , the zero drift is obtained as ; Calculation of sensitivity attenuation factor: Calculate the sensitivity attenuation factor based on the ratio of the actual output value to the ideal output value of the sensor .

8. The multi-sensor based industrial waste gas purification and control system according to claim 7, characterized in that: The feature decoupling module includes: Correct spatio-temporal alignment data: Apply a multi-dimensional calibration data packet to correct the spatio-temporal aligned sensor data matrix , and generate a corrected sensor data matrix ; Obtain coupled data: Based on the corrected sensor data matrix , obtain the coupled data matrix ; Feature decoupling: Applying tensor decomposition technology to separate the exhaust gas components, flow field turbulence intensity, and equipment vibration spectrum in the coupled data matrix from the high-dimensional data space into independent matrices; Output independent decoupled process feature vectors: Based on the separated independent matrix, output independent decoupled process feature vectors, including independent decoupled feature vectors of waste gas concentration, independent decoupled feature vectors of flow field turbulence intensity, and independent decoupled feature vectors of equipment vibration spectrum.

9. The industrial waste gas purification control system based on multi-sensors according to claim 8, characterized in that, The control decoupling module includes: Convert the control quantity from a strongly coupled state to a weakly coupled state: Through the decoupling surface, convert the strong coupling relationship between control parameters into a weak coupling relationship; Output control parameter group: Output control parameter groups with decoupling relationships, including the decoupled catalyst injection amount , the decoupled plasma frequency , the decoupled adsorption bed pressure drop .

10. The multi-sensor based industrial waste gas purification and control system according to claim 9, characterized in that: The feedforward-feedback composite control module includes: Generate a reference control quantity: Based on the predicted fluctuation of the exhaust gas concentration, the fuzzy PID control generates a reference control quantity ; Generate a correction control quantity: In the feedback channel, generate a dynamic inverse compensation control quantity based on the device impedance spectrum analysis ; Output collaborative control instruction set: Combine the reference control quantity and the correction control quantity to output the fused collaborative control instruction set .

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