DCS automatic control system for natural gas purification station technological process

Abstract

The invention relates to the technical field of natural gas purification and industrial automation control, in particular to a DCS (Distributed Control System) automatic control system for a natural gas purification station technological process, which comprises a data acquisition step: calling process monitoring data; a manifold mapping step: mapping the data into a real-time state tensor, and judging a steady state or abrupt change evolution signal by using a manifold curvature; a trend prediction step of analyzing Jacobian field distribution information to feed back future trajectory deviation when the abrupt change signal is generated; a steady-state navigation step: when a steady-state signal is generated, acquiring an optimal path coefficient based on a geodesic distance, and generating a navigation correction coefficient; a path correction and execution step: performing quantitative matching on the correction coefficient by using a covariant derivative, generating a dynamic set value and issuing a maintaining or adjusting instruction; geometric navigation type precise control of the natural gas purification process is achieved, energy consumption is reduced, and the product gas quality is improved.

Description

Technical Field

[0001] This invention relates to the field of natural gas purification and industrial automation control technology, specifically to a DCS automatic control system for the process flow of a natural gas purification station. Background Technology

[0002] In the field of automatic control of natural gas purification station processes, distributed control systems have gradually become the core hub for ensuring production safety and product quality due to their high reliability and flexibility. Currently, distributed control systems (DCS) generally support feedback regulation strategies based on classical control theory. If the system needs to cope with the complex chemical reactions in the natural gas purification process, proportional-integral-derivative (PID) control loops or predictive control methods based on linear models are usually adopted. By collecting physical quantities such as temperature, pressure, and flow rate, steady-state regulation of the purification process can be achieved. However, when controlling the process flow in the above way, the chemical process variables with strong multidimensional coupling characteristics are often treated as independent data points in Euclidean space. This leads to the neglect of the inherent manifold topology of chemical reaction dynamics, the inability to detect sudden changes in operating conditions in advance through curvature changes, and the inability to plan the optimal path that balances energy consumption and quality based on geodesic distance when large lag occurs in nonlinear systems. This results in problems such as control response lag and high energy consumption. At the same time, this approach lacks a geometric constraint mechanism based on covariant derivatives, making it difficult to ensure that the control commands move parallel to the tangent space of the reaction manifold during the adjustment process. This can lead to the control strategy violating chemical thermodynamic constraints and affecting system stability. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the present invention provides a DCS automatic control system for the process flow of a natural gas purification station. Specifically, the technical solution of the present invention includes: The system includes a data acquisition center, a manifold mapping unit, a trend prediction unit, a steady-state navigation unit, a path correction unit, and a closed-loop execution unit. The data acquisition center is used to retrieve process monitoring data from natural gas purification stations and send the process monitoring data to the manifold mapping unit for Riemannian manifold spatial mapping analysis to obtain real-time state tensors. The manifold curvature values ​​of the real-time state tensors are then processed to obtain steady-state evolution signals or abrupt evolution signals. When a mutation evolution signal is generated, the trend prediction unit is used to perform future trajectory deviation evaluation and feedback analysis on the Jacobian field distribution information of the collected real-time state tensor to obtain a safe trajectory signal or a divergent trajectory signal. When a steady-state evolution signal is generated, the steady-state navigation unit is used to perform optimal path coefficient acquisition analysis on the geodesic distance information of the acquired real-time state tensor, process the obtained energy consumption characteristic value and quality characteristic value to obtain navigation correction coefficient, and the path correction unit is used to perform covariant derivative control quantization matching analysis on the acquired navigation correction coefficient, compare and analyze the obtained dynamic setpoint to obtain hold command or adjustment command.

[0004] Preferably, the Riemannian manifold space mapping analysis process is as follows: The current sampling time of the natural gas purification station is collected and set as the time anchor point. Each sensor in the natural gas purification station is set as a data node. The physical monitoring values ​​of each data node within the time anchor point are obtained. The physical monitoring values ​​represent temperature, pressure, flow rate, and component concentration. The physical monitoring values ​​of the data nodes are tensorized. If a high-dimensional tensor of the physical monitoring values ​​of the data nodes is generated, a manifold projection command is generated. At the same time, the high-dimensional tensor corresponding to the manifold projection command is set as the real-time state tensor. If no physical monitoring values ​​of the data nodes are generated, an abnormal alarm command is generated.

[0005] Preferably, a preset curvature threshold of the real-time state tensor within the time anchor point is obtained, and the Ricci curvature tensor magnitude of the manifold region where the real-time state tensor is located within the time anchor point is obtained. The Ricci curvature tensor magnitude is set as the current curvature value, and the value obtained by subtracting the preset curvature threshold from the current curvature value is set as the curvature deviation value. The curvature deviation value is then processed. If the curvature deviation value is greater than zero, a sudden evolution signal is generated. If the curvature deviation value is less than or equal to zero, a steady-state evolution signal is generated.

[0006] Preferably, the future trajectory deviation assessment and feedback analysis process is as follows: Tangent vector information of the real-time state tensor within the time anchor point is obtained. The tangent vector information includes the reaction rate vector and the mass transfer driving force vector. The direction of the reaction rate vector is extracted, and the vector formed by the extracted direction of the reaction rate vector is set as the evolution direction vector. Simultaneously, the geodesic equation of the ideal manifold is obtained, and the vector formed by the extracted tangent vector of the geodesic equation is set as the ideal direction vector. The Jacobian field deviation value between the evolution direction vector and the ideal direction vector is calculated. If the Jacobian field deviation value is less than a preset safety deviation threshold, a safe trajectory signal is generated; if the Jacobian field deviation value is greater than or equal to the preset safety deviation threshold, a divergent trajectory signal is generated.

[0007] Preferably, when generating the divergent trajectory signal, the Riemann distance between the predicted state point at the future time and the ideal manifold is compared and analyzed with a preset critical distance threshold: if the Riemann distance is less than the preset critical distance threshold, an adjustment command is generated; if the Riemann distance is greater than or equal to the preset critical distance threshold, an emergency cut-off command is generated; the Riemann distance represents the product obtained by multiplying the path integral length of the real-time state tensor projected along the geodesic onto the ideal manifold with the dimensionless normalized value of the Jacobian field divergence coefficient, and the Jacobian field divergence coefficient represents the separation velocity of adjacent geodesics during the evolution process.

[0008] Preferably, the optimal path coefficient acquisition and analysis process is as follows: The geodesic distance information of the real-time state tensor within the time anchor point is obtained. The geodesic distance information includes energy consumption characteristic values ​​and quality characteristic values. The energy consumption characteristic values ​​and quality characteristic values ​​are compared and analyzed with preset energy consumption thresholds and preset quality thresholds. The weighted sum of the energy consumption characteristic values ​​and quality characteristic values ​​that are greater than or equal to the corresponding preset energy consumption thresholds and preset quality thresholds is obtained. Simultaneously, the composition ratio of lean liquid circulation flow rate to reboiler steam consumption is calculated. If the composition ratio deviates from the preset equilibrium range, an inertial compensation factor is generated to dynamically correct the weight of the weighted sum. The corrected weighted sum is set as the navigation correction coefficient.

[0009] Preferably, the energy consumption characteristic value represents the product obtained by multiplying the lean liquid circulation flow rate of the natural gas purification station by the reboiler steam consumption after data normalization. The lean liquid circulation flow rate represents the total amount of amine liquid entering the absorption tower per unit time, and the reboiler steam consumption represents the steam enthalpy value required to maintain the temperature of the regeneration tower. The mass characteristic value represents the product obtained by multiplying the predicted sulfur content of the product gas from the real-time state tensor by the preset sulfur content standard value after data normalization. The predicted sulfur content of the product gas represents the hydrogen sulfide concentration at the time of outlet along the current geodesic.

[0010] Preferably, the covariant derivative control quantization matching analysis process is as follows: The control constraint information of the real-time state tensor within the time anchor point is obtained. This control constraint information represents the equipment's safe operating boundary. The value obtained by multiplying the navigation correction coefficient by the value corresponding to the equipment's safe operating boundary is set as the dynamic setpoint. The absolute value of the difference between the dynamic setpoint and the current setpoint of the DCS system is calculated. If the absolute value of the difference is less than or equal to the preset adjustment dead zone value, a hold command is generated; if the absolute value of the difference is greater than the preset adjustment dead zone value, an adjustment command is generated. The analysis process of the equipment's safe operating boundary is as follows: The total number of flooding factors in the absorber tower within the time anchor point is obtained, along with the occurrence frequency of mist entrainment in the absorber tower and the corrosion rate monitoring value of the regeneration tower. The corresponding values ​​of the total number of flooding factors in the absorber tower, the occurrence frequency of mist entrainment, and the corrosion rate monitoring value are multiplied together. The product obtained by multiplying the corresponding values ​​of the total number of flooding factors in the absorber tower, the occurrence frequency of mist entrainment, and the corrosion rate monitoring value is set as the equipment's safe operating boundary.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses the Isomap nonlinear dimensionality reduction algorithm called by the manifold mapping unit to map multi-source heterogeneous sensor data into real-time state tensors, and uses the Ollivier-Ricci discrete curvature algorithm to calculate local manifold curvature values. This realizes the transformation of multi-dimensional strongly coupled variables in the natural gas purification process into topological structural features in geometric space. It can detect sudden changes in operating conditions in advance through drastic changes in manifold curvature, and solves the problem that traditional control methods treat chemical process variables as independent data points in Euclidean space and ignore the inherent manifold structure of chemical reaction dynamics. 2. This invention analyzes the Jacobian field distribution information of the real-time state tensor through a trend prediction unit, describes the diffusion or convergence trend of the geodesic bundle based on the geodesic deviation equation, and compares the Riemann distance between the predicted state point and the ideal manifold surface. This enables accurate evaluation and feedback of the future trajectory deviation of the system when abrupt signals are generated, and timely generation of safe or divergent trajectory signals. This solves the problem that it is difficult to accurately predict the safe state of nonlinear systems during dynamic evolution. 3. This invention uses a steady-state navigation unit to calculate the geodesic distance from the current state point to the ideal operating point using the variational method, and performs weighted analysis and structured identification on the energy consumption characteristic value and the quality characteristic value. It establishes the optimal path to approach the quality target along the surface of the reaction manifold, effectively reducing system energy consumption and improving the stability of product gas quality. It solves the problem that nonlinear systems cannot plan the optimal control path that takes into account both energy consumption and quality under large time delay conditions. 4. This invention uses a path correction unit to calculate covariant derivatives using Levi-Civita communication to quantify and match navigation correction coefficients, ensuring that the adjustment direction of the control quantity always moves parallel to the manifold tangent space. It also generates dynamic setpoints by combining the equipment safety operating boundary determined by flooding factor, mist entrainment, and corrosion rate, ensuring that the control commands strictly comply with chemical thermodynamic constraints and solving the problem that the control strategy is prone to violate physical constraints during adjustment, thus affecting system stability. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 A DCS automatic control system for the process flow of a natural gas purification station includes a data acquisition center, a manifold mapping unit, a trend prediction unit, a steady-state navigation unit, a path correction unit, and a closed-loop execution unit. The data acquisition center is used to retrieve process monitoring data from natural gas purification stations and send the process monitoring data to the manifold mapping unit for Riemannian manifold spatial mapping analysis to obtain real-time state tensors. The manifold curvature values ​​of the real-time state tensors are then processed to obtain steady-state evolution signals or abrupt evolution signals. When a mutation evolution signal is generated, the trend prediction unit is used to perform future trajectory deviation evaluation and feedback analysis on the Jacobian field distribution information of the collected real-time state tensor to obtain a safe trajectory signal or a divergent trajectory signal. When a steady-state evolution signal is generated, the steady-state navigation unit is used to perform optimal path coefficient acquisition analysis on the geodesic distance information of the acquired real-time state tensor, process the obtained energy consumption characteristic value and quality characteristic value to obtain navigation correction coefficient, and the path correction unit is used to perform covariant derivative control quantization matching analysis on the acquired navigation correction coefficient, compare and analyze the obtained dynamic setpoint to obtain hold command or adjustment command.

[0015] This embodiment provides an automatic control system for a natural gas purification station DCS based on Riemannian manifold geometry theory. The system is deployed in an industrial edge computing workstation of a DCS, such as Honeywell Experion PKS or Yokogawa CENTUMVP, communicating via the OPCUA protocol. The core logic of this embodiment is to regard the multidimensional coupled variables in the natural gas purification process as a point in a high-dimensional Riemannian manifold space, and to regard the dynamic evolution of the chemical reaction as a curve on the manifold surface, thereby solving the problem of large time delay control in nonlinear systems. The data acquisition center retrieves real-time process monitoring data from the natural gas purification station, including feed gas flow rate, lean liquid inlet flow rate, reboiler steam flow rate, and temperature and pressure of each tray, providing raw physical quantities for subsequent calculations. The manifold mapping unit performs Riemannian manifold space mapping analysis. This unit calls the Isomap nonlinear dimensionality reduction algorithm to map discrete sensor data into real-time state tensors in manifold space, and uses the Ollivier-Ricci discrete curvature algorithm to calculate the manifold curvature value of the local region where the state tensor is located. A drastic change in curvature indicates a change in the reaction dynamics mechanism, such as a disruption of gas-liquid equilibrium, which the system identifies as a sudden evolution signal. A stable curvature indicates that the system is under stable reaction dynamic constraints, which the system identifies as a steady-state evolution signal. The trend prediction unit responds to sudden signals by calculating the Jacobian field of the real-time state tensor when a sudden change is detected. This field describes the diffusion or convergence trend of geodesic bundles based on the geodesic deviation equation. By analyzing its distribution information, it determines whether the future state of the system will deviate from the safe zone, thereby generating a safe trajectory signal or a divergent trajectory signal. Based on this, the steady-state navigation unit responds to the steady-state signal. When the system is stable, it calculates the geodesic distance from the current state point to the ideal operating point using the variational method. This distance is not a Euclidean straight-line distance, but rather the shortest path along the surface of the reaction manifold. The system performs a weighted analysis of energy consumption characteristic values ​​and quality characteristic values ​​to generate navigation correction coefficients, aiming to approach the quality target with minimal energy consumption. The path correction unit uses the Levi-Civita connection to calculate covariant derivatives to quantize and match the navigation correction coefficients. The covariant derivatives ensure that the adjustment direction of the control quantity always moves parallel to the tangent space of the manifold, avoiding the control command from violating the chemical thermodynamic constraints. The closed-loop execution unit compares the calculated dynamic setpoint with the current setpoint of the DCS and generates hold or adjust commands, which are directly sent to the DCS's underlying PID loop. This embodiment introduces Riemannian manifold mapping, which enables the system to transform complex nonlinear coupling relationships in chemical engineering into topological features in geometric space. It uses manifold curvature to identify sudden changes in operating conditions, geodesic distance to find the optimal energy consumption path, and covariant derivatives to ensure that control commands comply with the laws of thermodynamics. This achieves geometric navigation-style precise control of the natural gas purification process, significantly reducing energy consumption and improving the stability of product gas quality. Regarding the real-time implementation methods not covered in the specification, this embodiment provides supplementary explanations: To implement the aforementioned millisecond-level algorithm on an industrial edge computing workstation, the system adopts a heterogeneous computing architecture; data acquisition and preprocessing are performed on the CPU, while computationally intensive tensor operations, manifold mapping, and curvature calculations are offloaded to the onboard industrial-grade GPU, such as an NVIDIA T4 or Jetson AGXXavier, and accelerated in parallel via CUDA cores; in addition, the system adopts a two-layer architecture of offline learning and online matching: the construction of the manifold topology, i.e., Isomap training, is updated in the background at minute-level cycles to generate the mapping matrix; the real-time state tensor is projected only through matrix multiplication, thereby avoiding the high computational complexity of performing full feature decomposition online and ensuring that the control loop response time is less than 50ms.

[0016] Example 2: The Riemannian manifold space mapping analysis process is as follows: The system acquires the current sampling time of the natural gas purification station and sets it as the time anchor point. Each sensor in the natural gas purification station is designated as a data node. The system obtains the physical monitoring values ​​of each data node within the time anchor point, representing temperature, pressure, flow rate, and component concentration. The physical monitoring values ​​of the data nodes are then tensorized. If a high-dimensional tensor of the physical monitoring values ​​of the data node is generated, a manifold projection command is generated, and the high-dimensional tensor corresponding to the manifold projection command is set as the real-time state tensor. If no physical monitoring values ​​of the data node are generated, an abnormal alarm command is generated.

[0017] This embodiment details the specific process of Riemannian manifold space mapping analysis, a crucial step in transforming physical world data into geometric space tensors. This aims to eliminate data redundancy and extract the essential characteristics of the chemical process. The system acquires the current sampling time of the natural gas purification station with millisecond-level precision, defining it as a time anchor point. This anchor point serves as the time reference for all subsequent tensor constructions. Sensors within the station are defined as data nodes, acquiring the physical monitoring values ​​of each data node at the time anchor point, including temperature, pressure, flow rate, and component concentration. The system then constructs a high-dimensional tensor of the physical monitoring values. In this step, to eliminate scale differences caused by different physical dimensions, such as temperature in degrees Celsius and pressure in Pascals, the system first performs Z-score standardization on all monitored values. The specific calculation formula is as follows: , in, and These are the mean and standard deviation of the sensor over a sliding window of the past 24 hours, respectively, with a mean of 0 and a variance of 1. In this embodiment... It is a third-order tensor, with its dimension defined as sensor type, corresponding to the number of data nodes, spatial location, physical distribution of sensors, and time window, set to 50 sampling periods tracing back from the time anchor point; in response to successful system generation This triggers a manifold projection instruction, which invokes the improved Isomap manifold learning algorithm; The specific steps are as follows: Constructing the neighborhood graph and database: Following the historical database construction guidelines mentioned in the manual, the system pre-builds a historical tensor database containing a large amount of historical normal operating condition data. The specific construction method is as follows: Collect the station's operating data from the past 3-6 months, and use the K-Means clustering algorithm to select the most representative L landmarks (e.g., L=1000) and store them in the database, rather than storing all massive historical data, thus controlling the computational scale; Calculate the current real-time state tensor. The Frobenius norm of these landmarks As a distance metric, the k nearest neighbors are selected. In this embodiment, k=12, and the value range is 8-20 to balance local linearity and global connectivity, and an adjacency graph is constructed. Approximating the geodesic distance: Using Dijkstra's algorithm to calculate the shortest path between any two points on the adjacency graph, thereby approximating the geodesic distance on the manifold; Multidimensional scaling and approximation acceleration: To address the high computational complexity of the Isomap algorithm, this embodiment employs the Landmark-Isomap algorithm for simplification; the system utilizes The approximation method only requires eigenvalue decomposition of the L×L landmark distance matrix. It can be pre-computed offline, and during online computation, it only needs to multiply the distance vector between the real-time point and the landmark point with the pre-computed eigenvector matrix to quickly solve for the embedded coordinates. This method avoids performing eigenvalue decomposition on the entire N×N data matrix online, reducing the computational complexity from O(N) to O(N). 3 The value is reduced to O(L·N), allowing manifold projection to be completed within milliseconds; the calculation formula is as follows: , in, The source is the result of mapping calculation, and its physical meaning is the real-time state tensor, which represents the coordinate points of the system on the reaction dynamics manifold; The source is the Isomap operator, which physically means manifold mapping operator; The source is aggregated sensor data, and its physical meaning is a high-dimensional tensor of the original physical monitoring values; it responds to situations where sensor malfunction prevents generation. The system immediately generates an alarm command indicating missing data; This embodiment constructs a high-dimensional tensor based on time anchor points and performs manifold projection, which can fuse multi-source heterogeneous sensor data into a unified geometric object. This mapping eliminates redundancy and noise in the data and extracts the essential characteristics of the chemical process, namely the coordinates on the manifold, providing a high-confidence mathematical basis for subsequent curvature analysis and geodesic tracing.

[0018] Example 3: The preset curvature threshold of the real-time state tensor within the time anchor point is obtained, and the Ricci curvature tensor magnitude of the manifold region where the real-time state tensor is located within the time anchor point is also obtained. The Ricci curvature tensor magnitude is set as the current curvature value, and the value obtained by subtracting the preset curvature threshold from the current curvature value is set as the curvature deviation value. The curvature deviation value is then processed. If the curvature deviation value is greater than zero, a sudden evolution signal is generated. If the curvature deviation value is less than or equal to zero, a steady-state evolution signal is generated.

[0019] This embodiment details the process of generating operating condition evolution signals based on curvature discrimination, aiming to utilize geometric topological characteristics to perceive essential changes in operating conditions in advance. The system obtains the Ricci curvature tensor magnitude of the manifold region where the real-time state tensor is located within the time anchor point and sets it as the current curvature value. Since the data collected by the DCS system is a discrete point cloud, continuous differentiation operations cannot be performed directly. Therefore, this embodiment uses the Ollivier-Ricci discrete curvature algorithm to solve the problem of the Ricci curvature tensor being non-differentiable on discrete data. The corrected calculation formula is as follows: , in, The source is the algorithm output, and its physical meaning is the discretized current curvature value, representing the curvature value at which the curvature is reduced. The degree of local contraction or divergence of the manifold centered on the target; The source is the Wasserstein distance algorithm; to address the problem that the traditional linear programming solution for Wasserstein distance has high complexity and cannot meet real-time requirements, this embodiment adopts the Sinkhorn-Knopp algorithm; by introducing an entropy regularization term, the original optimization problem is transformed into a matrix scaling problem, which can be solved by parallel iteration using GPU, and converges in near linear time, ensuring real-time calculation of discrete curvature within the DCS control cycle; The specific iterative algorithm process is as follows: Set the entropy regularization coefficient. =0.5, suggested value range 0.01-0.1; Construct the cost matrix: , in, They are respectively and Supporting points in the set; calculating the Gibbs kernel matrix. Initialize auxiliary vectors Given a vector of all 1s; perform alternating iterative updates: , , in, This represents element-wise division, up to... Convergence; Calculate the final distance ; The source is an adjacency graph. Define the current state point In its Nearest neighbor region, by Construct a uniform probability measure on the network, that is, for neighboring nodes probability ; To go back along the timeline Historical state point of time The neighborhood probability measure; The source is the manifold geodesic distance, which is the shortest path length calculated in Example 2. The source is the numerical stability constant, with a value of 1×10⁻⁶. -6 This is used to prevent the system from becoming absolutely steady-state. and When the circuit breaker is triggered by overlapping (d=0), the denominator is zero; if... The distance is significantly smaller ,but A value greater than 0 indicates that the geodesics converge and the system is stable; conversely, a value greater than 0 indicates that the system diverges. Obtain the preset curvature threshold Regarding the lack of detail in the threshold determination method in the specification, this embodiment supplements the specific statistical calculation process: During the initialization phase, the system performs baseline manifold learning: it retrieves historical data from the past 30 days that have been manually confirmed as stable and of high quality; and calculates the curvature values ​​of all time steps within that period using the Ollivier-Ricci algorithm described above, forming a curvature distribution set. ;right By fitting a normal distribution, the mean can be obtained. and standard deviation Based on statistical principles, a preset curvature threshold is set. That is, the upper bound of the 99.7% confidence interval; this method ensures the objectivity and adaptability of the threshold setting and avoids the arbitrariness of human experience in assigning values. Calculate curvature deviation value ; Discrimination processing of curvature deviation values: Response to A value greater than zero indicates a severe local bending of the manifold, i.e., an abnormal contraction or expansion of the Wasserstein distance. This typically corresponds to a disruption of the gas-liquid phase equilibrium, the entry of slug flow, or a sudden change in the reaction front, generating a signal indicating a sudden evolution of the system's dynamics. A value less than or equal to zero indicates that the manifold is locally flat, the system is in a stable reaction region, and a steady-state evolution signal is generated. In this embodiment, Ollivier-Ricci discrete curvature is used as a criterion to transform the complex differential geometry concept into an encodeable graph theory algorithm, thereby realizing the quantitative perception of topological changes in chemical processes.

[0020] Example 4: The future trajectory deviation assessment and feedback analysis process is as follows: The tangent vector information of the real-time state tensor within the time anchor point is obtained. The tangent vector information includes the reaction rate vector and the mass transfer driving force vector. The direction of the reaction rate vector is extracted, and the vector formed by the extracted direction of the reaction rate vector is set as the evolution direction vector. At the same time, the geodesic equation of the ideal manifold is obtained, and the vector formed by the extracted tangent vector of the geodesic equation is set as the ideal direction vector. The Jacobian field deviation value between the evolution direction vector and the ideal direction vector is calculated. If the Jacobian field deviation value is less than the preset safety deviation threshold, a safe trajectory signal is generated. If the Jacobian field deviation value is greater than or equal to the preset safety deviation threshold, a divergent trajectory signal is generated. When generating the divergent trajectory signal, the Riemann distance between the predicted state point at future time and the ideal manifold is compared and analyzed with a preset critical distance threshold: If the Riemann distance is less than the preset critical distance threshold, an adjustment command is generated; if the Riemann distance is greater than or equal to the preset critical distance threshold, an emergency cut-off command is generated. The Riemann distance represents the product of the path integral length of the real-time state tensor projected along the geodesic onto the ideal manifold and the dimensionless normalized value of the Jacobian field divergence coefficient. The Jacobian field divergence coefficient represents the separation rate of adjacent geodesics during the evolution process.

[0021] This embodiment details the evaluation and feedback analysis of future trajectory deviation and the processing of divergent trajectory signals triggered by a mutation evolution signal. Addressing the lack of clarity in the specification regarding the construction methods of ideal manifolds and ideal geodesics, as well as the source of ideal direction vectors, this embodiment supplements these fundamental definitions and generation steps, followed by a clear mathematical definition of the Jacobian field operator. Before system commissioning, the construction of the ideal manifold and geodesic involves an offline process: Data filtering: Selecting the top 5% of operating batches with the lowest energy consumption and qualified product quality from the historical database, marking them as the "golden batches"; Manifold fitting: Mapping the data points of these golden batches to a low-dimensional space using Isomap to form a point cloud band; Using the master curve algorithm or Gaussian process regression, fitting a smooth curve passing through the center of this point cloud band, which is defined as the ideal geodesic. ,in, The arc length parameter; manifold definition: Calculate the average orthogonal distance from the golden batch point cloud to the curve, centered on the ideal geodesic. , defined by With axis and radius The tubular neighborhood is an ideal manifold; the ideal direction vector is obtained by considering the current real-time state tensor. ,exist Find the nearest point Extract the tangent vector at that point. Set it as the ideal direction vector This solved The source of the problem; To construct the local manifold geometry for computing the Jacobian field, the system needs to establish the Riemannian metric of the local space; with the current state... Corresponding original high-dimensional point Centered on a given point, search for k=15 nearest neighbors in the historical database to form a neighborhood set; perform PCA on this set to extract the first d=3 principal components to obtain the local coordinates. Constructing a mapping function using an RBF network And calculate the Jacobian matrix. The Riemannian metric tensor is obtained. And the second type of Christo Offel symbol ; Tangent vector extraction and Jacobian field definition are used to obtain tangent vector information of the real-time state tensor; the reaction rate vector, i.e., the evolution direction vector, is used. It is obtained through finite difference calculation within a sliding time window; here, the Jacobian field operator is defined. In this system, Defined as a vector field The covariant differential tensor along the manifold, which characterizes the rate of structural change of the vector field in curved space, is a generalization of the Jacobian matrix to the Riemannian manifold, and is calculated as follows: , in, It is obtained by using the central difference approximation of the vector field in the neighborhood; this operator maps a tangent vector to a second-order tensor, describing the divergence or curl characteristics of the vector field in the local neighborhood; Jacobian field deviation calculation and determination are based on the above definition, and the Jacobian field deviation value is calculated. : , This formula calculates the Frobenius norm difference between the actual evolutionary dynamic field and the ideal dynamic field in the covariant differential sense; regarding the preset safety deviation threshold, the system statistically analyzes the maximum value under historical conditions where minor disturbances occurred but automatic recovery occurred. Set the threshold to ;like A threshold is set to trigger a divergent trajectory signal; otherwise, a safe trajectory signal is generated. Based on the Riemann distance, the subsequent control response to the divergent trajectory signal is used to further calculate the Riemann distance from the predicted state point to the ideal manifold. At this point, the calculation from step one is used. Perform path integration: , Among them, the Jacobian field divergence coefficient Characterizing the expansion rate of a volume element; With respect to the preset critical distance threshold Comparison: If Generate adjustment instructions; if Generate an emergency shutdown command; This embodiment solves the programmability problem of Jacobi field computation by using a strictly defined covariant differential operator.

[0022] Example 5: The process of obtaining and analyzing the optimal path coefficients is as follows: The geodesic distance information of the real-time state tensor within the time anchor point is obtained. The geodesic distance information includes energy consumption characteristic values ​​and mass characteristic values. The energy consumption characteristic values ​​and mass characteristic values ​​are compared and analyzed with preset energy consumption thresholds and preset mass thresholds. The weighted sum of the energy consumption characteristic values ​​and mass characteristic values ​​that are greater than or equal to the corresponding preset energy consumption thresholds and preset mass thresholds is obtained. At the same time, the composition ratio of lean liquid circulation flow rate and reboiler steam consumption is calculated. If the composition ratio deviates from the preset equilibrium range, an inertial compensation factor is generated to dynamically correct the weight of the weighted sum. The corrected weighted sum is set as the navigation correction coefficient. The energy consumption characteristic value represents the product of the lean liquid circulation flow rate and the reboiler steam consumption of the natural gas purification station after data normalization. The lean liquid circulation flow rate represents the total amount of amine liquid entering the absorption tower per unit time, and the reboiler steam consumption represents the steam enthalpy value required to maintain the temperature of the regeneration tower. The quality characteristic value represents the product obtained by multiplying the predicted sulfur content of the product gas from the real-time state tensor and the preset sulfur content standard value after data normalization. The predicted sulfur content of the product gas represents the hydrogen sulfide concentration that evolves along the current geodesic to the time of exit.

[0023] This embodiment details the analysis of obtaining the optimal path coefficients under the triggering of steady-state evolution signals; in order to address the potential product trap of product-type eigenvalues, i.e., different combinations of operating conditions produce the same eigenvalues, this embodiment introduces structural identification logic in the weighted analysis stage; Calculate energy consumption characteristic value and quality characteristic value Normalization function Using a non-zero lower bound - Mapping; performing threshold comparison and weighted sum calculation to obtain navigation correction coefficients. The calculation formula is: , To address the discrimination deficiency caused by the product trap, this embodiment adjusts the weighting factor. The calculation has been structurally improved: the system not only calculates the basic weights based on the entropy weight method, i.e., constructs the feature matrix, but also calculates the information entropy. Determine the difference coefficients of each indicator, and then obtain the normalized basic weights. It also calculates the energy consumption composition ratio in real time. ;like This indicates that the flow rate matches the steam consumption, maintaining the basic weight; if This refers to the high-flow-rate, low-energy-consumption mode, indicating that the system is in a high-flow-rate flushing state. Even under these conditions... Similar to the low-flow, high-energy-consumption state, its system inertia is greater; The system automatically introduces an inertia compensation factor and dynamically adjusts it. The value, for example, increasing by 20%, forces the navigation correction factor to... Increase, thus generating stronger regulatory force in subsequent control; if This indicates a low-flow, high-energy-consumption mode, signifying low thermal efficiency and requiring dynamic adjustment of the system. Or keep Keep the PID parameter set unchanged; Through this dynamic adjustment of weights based on composition ratios, the system can effectively distinguish... The same but physically different operating conditions ensure that the navigation correction coefficient can guide the system to optimize in a targeted manner, that is, reduce the pump speed for excessive flow or reduce the heat source for excessive steam, thereby achieving true Pareto optimal boundary guidance.

[0024] Example 6: The covariant derivative control quantization matching analysis process is as follows: The control constraint information of the real-time state tensor within the time anchor point is obtained. The control constraint information represents the safe operation boundary of the equipment. The value obtained by multiplying the navigation correction coefficient by the value corresponding to the safe operation boundary of the equipment is set as the dynamic setpoint. The absolute value of the dynamic setpoint is calculated. If the absolute value is less than or equal to the preset adjustment dead zone value, a hold command is generated; if the absolute value is greater than the preset adjustment dead zone value, an adjustment command is generated. The analysis process for the equipment's safe operating boundaries is as follows: The total number of flooding factors in the absorption tower within the time anchor point is obtained, along with the occurrence frequency of mist entrainment in the absorption tower and the corrosion rate monitoring value of the regeneration tower. The corresponding values ​​of the total number of flooding factors in the absorption tower, the occurrence frequency of mist entrainment, and the corrosion rate monitoring value are multiplied together, and the product value is set as the safe operating boundary of the equipment. This embodiment details the calculation of covariant derivative control quantization matching and equipment safe operating boundaries; it specifically addresses the calculation of covariant derivative calibration coefficients. The required metric tensor partial derivative This embodiment supplements a reproducible local mesh difference algorithm; Calculate physical safety boundary values Regarding the formula Regarding the issue of directly multiplying parameters with different dimensions, this embodiment clarifies that the corresponding values ​​in the embodiment refer to the risk factors after dimensionless normalization, rather than the original physical quantities. The specific calculation steps are as follows: Obtain the total number of times the liquid flooding factor of the absorber tower is obtained. Divide it by the maximum number of oscillations allowed by the equipment. For example, the limit values ​​in the design specifications yield dimensionless terms. ; Get the frequency of occurrence The unit is Multiply it by the characteristic time constant or divided by the maximum permissible frequency , thus obtaining the dimensionless term ; Obtain corrosion rate monitoring values The unit is Divide it by the maximum allowable rate corresponding to the equipment corrosion allowance. , thus obtaining the dimensionless term ; Perform security confidence calculation ;at this time This is a dimensionless equipment safety capacity index, ranging from 0 to 1. A value closer to 1 indicates a larger safety margin and higher permissible control setpoints; a value closer to 0 indicates the equipment is approaching its limits, requiring control output restrictions. This has a clear physical meaning and avoids dimensional conflicts. The covariant derivative calibration coefficient is calculated using the following formula: , in, The Riemann curvature sensitivity coefficient is set to a value range of 0.5-2.0, and is preferably used in this embodiment. ; The evolution direction vector obtained in Example 4 The inverted component; The L2 norm of a vector; The computation depends on the metric tensor and its partial derivatives; to ensure this step is executable, the system performs the following real-time mesh construction and difference calculation steps: Inherit the RBF network model trained in Example 4 and the local coordinates of the current point Set a small step size =10 -3 Generate in the local tangent space with A five-point cross-shaped grid centered on the center: , in, For the first A unit vector of dimension; Grid point metric reconstruction: Calculate the metric for each point in the grid using the EBF analytical expression. Jacobian matrix at the location Obtain the metric tensor at that point. ; Using the metric tensor values ​​of the grid points, the partial derivatives are calculated using the fourth-order precision central difference formula: , Substituting the calculated partial derivatives into the Levi-Civita connection formula yields the exact result. The specific calculation process for the communication symbol is as follows: For the metric tensor of the current point... Perform matrix inversion to obtain the inverse metric tensor. Tensor shrinking operations are performed using the fundamental theorem of Riemannian geometry. , From this obtained It accurately describes the nonlinear correction caused by the curvature of the coordinate system when the control quantity moves in the curved manifold space; Calculate the final safe operating boundary of the equipment And calculate the dynamic setpoint, using the following formula: , in, This refers to the engineering range span value of the DCS control loop, used to restore the dimensionless normalization coefficients to specific physical adjustment ranges; and to convert the dynamic setpoints... The absolute value is compared with the preset dead zone value to generate a hold or adjust command; this process realizes the concrete implementation of Riemann geometric constraints in industrial control systems through explicit mesh construction and numerical difference.

[0025] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A DCS automatic control system for the process flow of a natural gas purification station, characterized in that, It includes a data acquisition center, a manifold mapping unit, a trend prediction unit, a steady-state navigation unit, a path correction unit, and a closed-loop execution unit; The data acquisition center is used to retrieve process monitoring data from natural gas purification stations and send the process monitoring data to the manifold mapping unit for Riemannian manifold space mapping analysis to obtain real-time state tensors. The manifold curvature values ​​of the real-time state tensors are then processed to obtain steady-state evolution signals or abrupt evolution signals. When a mutation evolution signal is generated, the trend prediction unit is used to perform future trajectory deviation evaluation and feedback analysis on the Jacobian field distribution information of the collected real-time state tensor to obtain a safe trajectory signal or a divergent trajectory signal. When a steady-state evolution signal is generated, the steady-state navigation unit is used to perform optimal path coefficient acquisition analysis on the geodesic distance information of the acquired real-time state tensor, process the obtained energy consumption characteristic value and quality characteristic value to obtain navigation correction coefficient, and the path correction unit is used to perform covariant derivative control quantization matching analysis on the acquired navigation correction coefficient, compare and analyze the obtained dynamic setpoint to obtain hold command or adjustment command.

2. The DCS automatic control system for a natural gas purification station process according to claim 1, characterized in that, The Riemannian manifold space mapping analysis process is as follows: The current sampling time of the natural gas purification station is collected and set as the time anchor point. Each sensor in the natural gas purification station is set as a data node. The physical monitoring values ​​of each data node within the time anchor point are obtained. The physical monitoring values ​​represent temperature, pressure, flow rate and component concentration. The physical monitoring values ​​of the data nodes are then subjected to tensor quantization processing. If a high-dimensional tensor of the physical monitoring values ​​of the data node is generated, a manifold projection instruction is generated, and the high-dimensional tensor corresponding to the manifold projection instruction is set as the real-time state tensor. If no physical monitoring values ​​of the data node are generated, an abnormal alarm instruction is generated.

3. The DCS automatic control system for a natural gas purification station process according to claim 2, characterized in that, Obtain the preset curvature threshold of the real-time state tensor within the time anchor point, and at the same time obtain the Ricci curvature tensor magnitude of the manifold region where the real-time state tensor is located within the time anchor point, and set the Ricci curvature tensor magnitude as the current curvature value; The current curvature value is subtracted from the preset curvature threshold and the resulting value is set as the curvature deviation value. The curvature deviation value is then processed. If the curvature deviation value is greater than zero, a sudden evolution signal is generated. If the curvature deviation value is less than or equal to zero, a steady-state evolution signal is generated.

4. The DCS automatic control system for a natural gas purification station process flow according to claim 1, characterized in that, The future trajectory deviation assessment and feedback analysis process is as follows: The tangent vector information of the real-time state tensor within the time anchor point is obtained. The tangent vector information includes the reaction rate vector and the mass transfer driving force vector. The direction of the reaction rate vector is extracted, and the vector formed by the extracted direction of the reaction rate vector is set as the evolution direction vector. Simultaneously, the geodesic equation of the ideal manifold is obtained, and the vector formed by extracting the tangent vector of the geodesic equation is set as the ideal direction vector. The Jacobian field deviation value between the evolution direction vector and the ideal direction vector is calculated. If the Jacobian field deviation value is less than the preset safety deviation threshold, a safe trajectory signal is generated. If the Jacobian field deviation value is greater than or equal to the preset safety deviation threshold, a divergent trajectory signal is generated.

5. The DCS automatic control system for a natural gas purification station process according to claim 4, characterized in that, When generating the divergent trajectory signal, the Riemann distance between the predicted state point at future time and the ideal manifold is compared and analyzed with a preset critical distance threshold: If the Riemann distance is less than the preset critical distance threshold, an adjustment command is generated; if the Riemann distance is greater than or equal to the preset critical distance threshold, an emergency cut-off command is generated. The Riemann distance represents the product of the path integral length of the real-time state tensor projected along the geodesic onto the ideal manifold and the dimensionless normalized value of the Jacobian field divergence coefficient. The Jacobian field divergence coefficient represents the separation velocity of adjacent geodesics during the evolution process.

6. The DCS automatic control system for a natural gas purification station process according to claim 1, characterized in that, The process for obtaining and analyzing the optimal path coefficients is as follows: The geodesic distance information of the real-time state tensor within the time anchor point is obtained. The geodesic distance information includes energy consumption characteristic value and quality characteristic value. The energy consumption characteristic value and quality characteristic value are compared and analyzed with preset energy consumption threshold and preset quality threshold. Obtain the weighted sum of energy consumption characteristic values ​​and quality characteristic values ​​that are greater than or equal to the corresponding preset energy consumption threshold and preset quality threshold; at the same time, calculate the composition ratio of lean liquid circulation flow rate value and reboiler steam consumption. If the composition ratio deviates from the preset equilibrium range, generate an inertia compensation factor to dynamically correct the weight of the weighted sum, and set the corrected weighted sum as the navigation correction coefficient.

7. The DCS automatic control system for a natural gas purification station process flow according to claim 6, characterized in that: The energy consumption characteristic value represents the product of the lean liquid circulation flow rate and the reboiler steam consumption of the natural gas purification station after data normalization. The lean liquid circulation flow rate represents the total amount of amine liquid entering the absorption tower per unit time, and the reboiler steam consumption represents the steam enthalpy value required to maintain the temperature of the regeneration tower. The quality characteristic value represents the product obtained by multiplying the predicted sulfur content of the product gas from the real-time state tensor and the preset sulfur content standard value after data normalization. The predicted sulfur content of the product gas represents the hydrogen sulfide concentration that evolves along the current geodesic to the time of exit.

8. The DCS automatic control system for a natural gas purification station process flow according to claim 6, characterized in that, The covariant derivative control quantization matching analysis process is as follows: The control constraint information of the real-time state tensor within the time anchor point is obtained. The control constraint information represents the safe operation boundary of the equipment. The value obtained by multiplying the navigation correction coefficient by the value corresponding to the safe operation boundary of the equipment is set as the dynamic setting value. Calculate the absolute value of the difference between the dynamic setpoint and the current setpoint of the DCS system. If the absolute value of the difference is less than or equal to the preset adjustment dead zone value, generate a hold command. If the absolute value of the difference is greater than the preset adjustment dead zone value, generate an adjustment command. The analysis process for the safe operating boundary of the equipment is as follows: The total number of flooding factors in the absorption tower within the time anchor point is obtained, along with the occurrence frequency of mist entrainment in the absorption tower and the corrosion rate monitoring value of the regeneration tower. The corresponding values ​​of the total number of flooding factors in the absorption tower, the occurrence frequency of mist entrainment, and the corrosion rate monitoring value are multiplied together, and the product value is set as the safe operating boundary of the equipment.

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