APC control method and system based on multivariable coupling LNG device

Abstract

The invention belongs to the technical field of industrial automation control, and relates to an APC control method and system based on a multivariable coupling LNG device, the system realizes real-time process parameter acquisition of three processes through a data acquisition and secure transmission module, and the process parameters are securely transmitted to an APC server through an OPC Server; the PID loop management and optimization module is used for accurately analyzing loop performance, dynamically modeling and optimizing PID parameters, so that the control precision and the loop stability are improved; the multivariable prediction and cross-process coordination control module implements multivariable prediction control, optimizes operation variables of each process and enhances the response speed of the whole system; the soft instrument calculation module improves the control precision through dynamic correction; the unified operation interface optimization module enhances the working efficiency and the management level of operators by improving the visualization effect. The control precision, the processing capacity and the operation efficiency of the LNG device can be effectively improved, and the stability and the production benefits of the device are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and specifically to an APC control method and system based on a multivariable coupled LNG device. Background Technology

[0002] In existing technologies, with the continuous development of intelligent control technology and information systems, liquefied natural gas (LNG) production units face complex and ever-changing process characteristics and control requirements in operation and management. Especially in key processes such as desulfurization, methanation, and ammonia synthesis, LNG units often involve a large number of highly coupled and dynamically variable process variables, posing a significant challenge to traditional control modes based on a single PID strategy. The complex coupling relationships between units in the LNG production process mean that conventional distributed control systems (DCS) often struggle to meet the demands for overall system performance optimization and dynamic predictive control, resulting in large control fluctuations, high energy consumption, and heavy operational burdens for personnel.

[0003] In the face of fierce competition in the steel manufacturing industry, the applicant is at a critical juncture of improving quality and efficiency and transforming towards intelligent manufacturing. The company urgently needs to enhance the automation, information technology, and intelligent control levels in the LNG production process. Although existing LNG plants possess a certain degree of automation, control strategies and equipment issues still exist.

[0004] To this end, invention patent CN211577728U discloses an advanced control system for liquefied natural gas (LNG) preparation and an LNG production system for use in LNG preparation devices. The LNG preparation device comprises a purification system, a synthesis system, a liquefaction system, and a regulating component. The purification system, synthesis system, and liquefaction system are connected sequentially, and the regulating component is located within the purification system, synthesis system, and liquefaction system. The provided advanced control system for LNG preparation, through automatic and precise regulation, can promptly issue control commands according to predetermined operating standards, ensuring that the operating data of each part during the LNG preparation process remains within the optimized range, ensuring that process parameters fluctuate within the allowable correction error range, reducing energy consumption in the natural gas preparation process, and achieving a high degree of automation.

[0005] The above-mentioned technical solutions have made progress in liquefied natural gas production, but the following technical problems still exist: (1) The key processes of fine desulfurization, methanation and ammonia synthesis have not been covered by independent and coupled process models. There is a lack of synergistic optimization strategies for the material and heat coupling relationship between processes (such as the H / N ratio of syngas, the heat balance of methanation reaction and the conversion rate of ammonia synthesis), and it is impossible to achieve unified prediction and coordinated control of the whole process. (2) Lack of PID loop performance optimization mechanism; the existing scheme does not monitor the performance and optimize the parameters of the key PID loops for temperature control of the desulfurization tower, segmented temperature control of the methanation reactor, and temperature and pressure control of the ammonia synthesis tower. It lacks online tuning and adaptive adjustment methods based on dynamic modeling, which can easily lead to large control fluctuations, slow response and performance degradation under long-term operation. (3) Lack of dynamic adaptive and online correction mechanism; the existing system does not provide soft measurement and dynamic correction functions for key indicators that cannot be directly measured, such as sulfur content at the desulfurization outlet, methanation temperature distribution, and ammonia conversion rate. It also does not have the ability to automatically adjust PID parameters and optimize multivariable control strategies based on real-time operating condition changes, resulting in a decrease in control accuracy and stability under conditions such as changes in production load and fluctuations in raw gas composition.

[0006] In view of this, it is very necessary to provide an APC control method and system based on a multivariable coupled LNG unit to solve the above-mentioned defects in the prior art. Summary of the Invention

[0007] The purpose of this invention is to address the problems existing in current LNG production plants, such as the lack of independent and coupled process models, insufficient multivariate collaborative optimization, lack of PID loop performance optimization, and lack of dynamic adaptive and online correction mechanisms in key process stages of desulfurization, methanation, and ammonia synthesis. In response to the technical deficiencies of the prior art, this invention provides an APC control method and system based on a multivariate coupled LNG plant to solve the aforementioned technical problems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: An APC control method for a multivariable coupled LNG unit includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0009] Furthermore, the present invention also provides an APC control system based on a multivariable coupled LNG unit, comprising: The data acquisition and secure transmission module is used to collect real-time process parameters for the desulfurization, methanation, and ammonia synthesis processes. After preprocessing by the DCS, the parameters are transmitted to the APC server via the OPC Server. It supports historical data storage and provides data support.

[0010] The PID loop management and optimization module is used for the analysis, modeling, and optimization of critical control loops to ensure loop stability. It includes three sub-modules: loop performance evaluation, dynamic modeling, and PID parameter optimization and online application. The loop performance evaluation sub-module analyzes the performance indicators of each loop, the dynamic modeling sub-module establishes a multivariate dynamic model based on process characteristics, and the PID parameter optimization and online application sub-module combines optimization methods to tune PID parameters and implement online performance monitoring to improve loop control stability.

[0011] Critical control loops (CLOs) are further selected from the critical loops to identify key loops that require inclusion in Advanced Process Control (APC) modeling, optimization, and prediction. These loops are not only involved in safe and stable operation but are also closely related to multivariate coupling relationships, constraints, and energy efficiency optimization. In this invention, CLOs include the temperature control loop of the desulfurization tower, the segmented temperature control loop of the methanation reactor, and the temperature-pressure coupling loop of the ammonia synthesis tower. Critical loops refer to the most important process loops in the operation of an LNG plant, directly affecting the system's safety, stability, and product quality. In this invention, CLOs include the core temperature, pressure, and flow control links of the desulfurization, methanation, and ammonia synthesis processes.

[0012] The multivariate prediction and cross-process coordination control module is based on a dynamic process model to perform multivariate predictive control, coordinate the operation variables of the three major processes, and achieve precise control and improve processing capacity.

[0013] The soft instrument calculation and dynamic correction module is used to calculate key indicators that are not measurable and to correct them in combination with actual detection values ​​to ensure that the control accuracy is consistent with the actual value.

[0014] The Unified User Interface Optimization and Visual Management Module integrates the user interface, optimizes parameter display, sets alarm prompts, supports one-click operation, and improves operational efficiency.

[0015] The modules work together to achieve a complete APC control process, from data acquisition, loop performance analysis, parameter optimization, advanced control to user interface visualization.

[0016] The beneficial effects of this invention are as follows: By acquiring and securely transmitting multi-source process data, combined with historical data storage mechanisms and real-time data processing, this invention improves the measurement accuracy of on-site testing equipment and the control stability of actuators such as pumps, valves, and compressors through real-time monitoring of their status. This can meet the production control requirements of key processes such as desulfurization, methanation, and ammonia synthesis, thereby enhancing the monitoring and operational efficiency of production operators. By evaluating the performance of key PID loops and dynamic modeling, combined with PID parameter optimization and online application, and the application of advanced APC control technology, this invention can achieve stable operation of key control indicators such as temperature control of the desulfurization tower, temperature difference regulation of the methanation reactor, top pressure of the ammonia synthesis tower, and H / N ratio. At the same time, it reduces the need for manual intervention by operators, reduces labor intensity, and improves control stability and operational reliability. By utilizing multivariate prediction and cross-process coordinated control, as well as soft instrument calculation and dynamic correction mechanisms, this invention can achieve edge-controlled operation, maximizing the processing capacity of the desulfurization, methanation, and ammonia synthesis units, reducing energy consumption, and increasing product yield. While ensuring accurate H / N ratio in syngas, it can increase ammonia production by more than 0.5% and simultaneously increase liquid ammonia production in LNG units, thereby bringing significant economic benefits and improving the overall production efficiency of enterprises. Dynamic model prediction, constraint management, and real-time monitoring have enhanced the inherent safety of the desulfurization, methanation, and ammonia synthesis units, preventing unplanned shutdowns and ensuring the continuity and stability of the production process. Meanwhile, the optimized unified operating interface enables operators to respond quickly to abnormal situations, further improving the safety of unit operation. This invention integrates the scattered operation screens, realizing a unified integrated operation interface for the three major processes of fine desulfurization, methanation, and ammonia synthesis. It optimizes the parameter display layout, hierarchical alarm prompts, and quick switching and one-click operation functions, which not only improves the operation efficiency and visualization management level, but also facilitates cross-process monitoring and operation by engineers, forming a complete and intelligent production management system.

[0017] Therefore, it is evident that the present invention has outstanding substantive features and significant progress compared with the prior art, and the beneficial effects of its implementation are also obvious. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart of an APC control method for a multivariable coupled LNG unit; Figure 2 This is a block diagram of an APC control system based on a multivariable coupled LNG unit. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following implementation methods.

[0021] Example 1: like Figure 1 As shown in the figure, this embodiment provides an APC control method based on a multivariable coupled LNG unit, which includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0022] Example 2: like Figure 1 As shown in the figure, this embodiment provides an APC control method based on a multivariable coupled LNG unit, which includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0023] In step S1, for key processes such as desulfurization, methanation, and ammonia synthesis, multi-source real-time process data, including temperature, pressure, flow rate, component analysis, H / N ratio, and operating status of major equipment, are collected through field instruments. The data undergoes preprocessing by the DCS system, including signal filtering, outlier removal, and data compression, before being securely transmitted to the APC server via an encrypted transmission protocol through the OPC Server. Simultaneously, a historical data storage mechanism is established, classifying and archiving data according to process segment, equipment type, and time dimension, providing a high-quality data foundation for subsequent model building, performance analysis, and control optimization.

[0024] This step, by establishing a safe, stable, and complete multi-source process data acquisition and transmission channel, can ensure the reliability of data for subsequent control optimization and reduce the risk of control instability caused by data delays or missing data.

[0025] Example 3: like Figure 1 As shown in the figure, this embodiment provides an APC control method based on a multivariable coupled LNG unit, which includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0026] In step S1, for key processes such as desulfurization, methanation, and ammonia synthesis, multi-source real-time process data, including temperature, pressure, flow rate, component analysis, H / N ratio, and operating status of major equipment, are collected through field instruments. The data undergoes preprocessing by the DCS system, including signal filtering, outlier removal, and data compression, before being securely transmitted to the APC server via an encrypted transmission protocol through the OPC Server. Simultaneously, a historical data storage mechanism is established, classifying and archiving data according to process segment, equipment type, and time dimension, providing a high-quality data foundation for subsequent model building, performance analysis, and control optimization.

[0027] This step, by establishing a safe, stable, and complete multi-source process data acquisition and transmission channel, can ensure the reliability of data for subsequent control optimization and reduce the risk of control instability caused by data delays or missing data.

[0028] In step S2: key PID loops are selected for the temperature control loops of the desulfurization tower, the methanation reactor bed, and the ammonia synthesis tower pressure and temperature control loop. All PID loops are tuned, their step response data are collected, and key performance indicators such as standard deviation, overshoot rate, and response time of all PID loops are analyzed to determine their dynamic characteristics and control quality. Combining process coupling characteristics and material balance relationships, a multivariate dynamic process model across processes is established using system identification methods, providing a mathematical description and prediction basis for the APC controller.

[0029] A critical PID loop refers to the underlying execution loop implemented by the PID controller in the field DCS system within the critical control loop. These loops are the actual execution units of the APC system optimization results. In this embodiment of the invention, the critical PID loops include the desulfurization tower temperature control loop, the methanation reactor bed temperature control loop, and the ammonia synthesis tower pressure and temperature control loop.

[0030] Specifically, tuning all PID loops involves: establishing a monitoring mechanism for all PID loops in the unit; diagnosing faulty loops; analyzing instrumentation, process, and PID parameter issues; and taking measures such as repairing or replacing instruments, modifying control schemes, or retuning PID parameters for different problems. Furthermore, as the unit's production conditions change, continuous full-lifecycle loop performance monitoring and parameter tuning are conducted. This dynamic adjustment mechanism ensures that the control system can maintain an optimized state at different production stages and can cope with fluctuations and changes in the actual process.

[0031] This step, through performance evaluation and dynamic modeling, can obtain real and accurate process dynamic characteristics, providing targeted input for control strategy optimization and reducing the deviation between the model and actual operation.

[0032] Example 4: like Figure 1 As shown in the figure, this embodiment provides an APC control method based on a multivariable coupled LNG unit, which includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0033] In step S1, for key processes such as desulfurization, methanation, and ammonia synthesis, multi-source real-time process data, including temperature, pressure, flow rate, component analysis, H / N ratio, and operating status of major equipment, are collected through field instruments. The data undergoes preprocessing by the DCS system, including signal filtering, outlier removal, and data compression, before being securely transmitted to the APC server via an encrypted transmission protocol through the OPC Server. Simultaneously, a historical data storage mechanism is established, classifying and archiving data according to process segment, equipment type, and time dimension, providing a high-quality data foundation for subsequent model building, performance analysis, and control optimization.

[0034] This step, by establishing a safe, stable, and complete multi-source process data acquisition and transmission channel, can ensure the reliability of data for subsequent control optimization and reduce the risk of control instability caused by data delays or missing data.

[0035] In step S2: key PID loops are selected for the temperature control loops of the desulfurization tower, the methanation reactor bed, and the ammonia synthesis tower pressure and temperature control loop. All PID loops are tuned, their step response data are collected, and key performance indicators such as standard deviation, overshoot rate, and response time of all PID loops are analyzed to determine their dynamic characteristics and control quality. Combining process coupling characteristics and material balance relationships, a multivariate dynamic process model across processes is established using system identification methods, providing a mathematical description and prediction basis for the APC controller.

[0036] A critical PID loop refers to the underlying execution loop implemented by the PID controller in the field DCS system within the critical control loop. These loops are the actual execution units of the APC system optimization results. In this embodiment of the invention, the critical PID loops include the desulfurization tower temperature control loop, the methanation reactor bed temperature control loop, and the ammonia synthesis tower pressure and temperature control loop.

[0037] Specifically, tuning all PID loops involves: establishing a monitoring mechanism for all PID loops in the unit; diagnosing faulty loops; analyzing instrumentation, process, and PID parameter issues; and taking measures such as repairing or replacing instruments, modifying control schemes, or retuning PID parameters for different problems. Furthermore, as the unit's production conditions change, continuous full-lifecycle loop performance monitoring and parameter tuning are conducted. This dynamic adjustment mechanism ensures that the control system can maintain an optimized state at different production stages and can cope with fluctuations and changes in the actual process.

[0038] This step, through performance evaluation and dynamic modeling, can obtain real and accurate process dynamic characteristics, providing targeted input for control strategy optimization and reducing the deviation between the model and actual operation.

[0039] In step S3: using the dynamic model established in S2, the proportional, integral, and derivative parameters of each PID loop are tuned using computational optimization algorithms (such as least squares method, gradient descent method) or engineering experience methods. The tuning effect and steady-state performance are verified through an offline simulation platform. After successful verification, the optimized PID parameters are downloaded to the DCS system in batches for operation, and the loop response is monitored in real time during operation, with adaptive corrections made according to changes in operating conditions.

[0040] This step, through model-based PID parameter tuning and online adjustment, can significantly improve the accuracy and stability of loop control, reduce frequent manual adjustments, and ensure the long-term stable operation of the production unit.

[0041] Example 5: like Figure 1 As shown in the figure, this embodiment provides an APC control method based on a multivariable coupled LNG unit, which includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0042] In step S1, for key processes such as desulfurization, methanation, and ammonia synthesis, multi-source real-time process data, including temperature, pressure, flow rate, component analysis, H / N ratio, and operating status of major equipment, are collected through field instruments. The data undergoes preprocessing by the DCS system, including signal filtering, outlier removal, and data compression, before being securely transmitted to the APC server via an encrypted transmission protocol through the OPC Server. Simultaneously, a historical data storage mechanism is established, classifying and archiving data according to process segment, equipment type, and time dimension, providing a high-quality data foundation for subsequent model building, performance analysis, and control optimization.

[0043] This step, by establishing a safe, stable, and complete multi-source process data acquisition and transmission channel, can ensure the reliability of data for subsequent control optimization and reduce the risk of control instability caused by data delays or missing data.

[0044] In step S2: key PID loops are selected for the temperature control loops of the desulfurization tower, the methanation reactor bed, and the ammonia synthesis tower pressure and temperature control loop. All PID loops are tuned, their step response data are collected, and key performance indicators such as standard deviation, overshoot rate, and response time of all PID loops are analyzed to determine their dynamic characteristics and control quality. Combining process coupling characteristics and material balance relationships, a multivariate dynamic process model across processes is established using system identification methods, providing a mathematical description and prediction basis for the APC controller.

[0045] A critical PID loop refers to the underlying execution loop implemented by the PID controller in the field DCS system within the critical control loop. These loops are the actual execution units of the APC system optimization results. In this embodiment of the invention, the critical PID loops include the desulfurization tower temperature control loop, the methanation reactor bed temperature control loop, and the ammonia synthesis tower pressure and temperature control loop.

[0046] Specifically, tuning all PID loops involves: establishing a monitoring mechanism for all PID loops in the unit; diagnosing faulty loops; analyzing instrumentation, process, and PID parameter issues; and taking measures such as repairing or replacing instruments, modifying control schemes, or retuning PID parameters for different problems. Furthermore, as the unit's production conditions change, continuous full-lifecycle loop performance monitoring and parameter tuning are conducted. This dynamic adjustment mechanism ensures that the control system can maintain an optimized state at different production stages and can cope with fluctuations and changes in the actual process.

[0047] This step, through performance evaluation and dynamic modeling, can obtain real and accurate process dynamic characteristics, providing targeted input for control strategy optimization and reducing the deviation between the model and actual operation.

[0048] In step S3: using the dynamic model established in S2, the proportional, integral, and derivative parameters of each PID loop are tuned using computational optimization algorithms (such as least squares method, gradient descent method) or engineering experience methods. The tuning effect and steady-state performance are verified through an offline simulation platform. After successful verification, the optimized PID parameters are downloaded in batches to the DCS system for operation, and the loop response is monitored in real time during operation, with adaptive corrections made according to changes in operating conditions.

[0049] This step, through model-based PID parameter tuning and online adjustment, can significantly improve the accuracy and stability of loop control, reduce frequent manual adjustments, and ensure the long-term stable operation of the production unit.

[0050] In step S4: Based on the multivariate predictive control (MPC) method, the dynamic process model from S2 is used to predict future trends in process variables. Taking into account equipment operating constraints, safety limitations, and energy balance, key operating variables (such as feed flow rate, reaction temperature, tower top pressure, and H / N ratio) of the three major processes—desulfurization, methanation, and ammonia synthesis—are coordinated and controlled to achieve optimal operating strategies across processes. This control strategy is uniformly distributed to each DCS control unit through the APC system, thereby achieving coordinated optimization of the entire process.

[0051] This step, through cross-process prediction and coordinated control, enables edge operation while ensuring safe operation, thereby increasing production capacity and reducing energy consumption.

[0052] Example 6: like Figure 1 As shown in the figure, this embodiment provides an APC control method based on a multivariable coupled LNG unit, which includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0053] In step S1, for key processes such as desulfurization, methanation, and ammonia synthesis, multi-source real-time process data, including temperature, pressure, flow rate, component analysis, H / N ratio, and operating status of major equipment, are collected through field instruments. The data undergoes preprocessing by the DCS system, including signal filtering, outlier removal, and data compression, before being securely transmitted to the APC server via an encrypted transmission protocol through the OPC Server. Simultaneously, a historical data storage mechanism is established, classifying and archiving data according to process segment, equipment type, and time dimension, providing a high-quality data foundation for subsequent model building, performance analysis, and control optimization.

[0054] This step, by establishing a safe, stable, and complete multi-source process data acquisition and transmission channel, can ensure the reliability of data for subsequent control optimization and reduce the risk of control instability caused by data delays or missing data.

[0055] In step S2: key PID loops are selected for the temperature control loops of the desulfurization tower, the methanation reactor bed, and the ammonia synthesis tower pressure and temperature control loop. All PID loops are tuned, their step response data are collected, and key performance indicators such as standard deviation, overshoot rate, and response time of all PID loops are analyzed to determine their dynamic characteristics and control quality. Combining process coupling characteristics and material balance relationships, a multivariate dynamic process model across processes is established using system identification methods, providing a mathematical description and prediction basis for the APC controller.

[0056] A critical PID loop refers to the underlying execution loop implemented by the PID controller in the field DCS system within the critical control loop. These loops are the actual execution units of the APC system optimization results. In this embodiment of the invention, the critical PID loops include the desulfurization tower temperature control loop, the methanation reactor bed temperature control loop, and the ammonia synthesis tower pressure and temperature control loop.

[0057] Specifically, tuning all PID loops involves: establishing a monitoring mechanism for all PID loops in the unit; diagnosing faulty loops; analyzing instrumentation, process, and PID parameter issues; and taking measures such as repairing or replacing instruments, modifying control schemes, or retuning PID parameters for different problems. Furthermore, as the unit's production conditions change, continuous full-lifecycle loop performance monitoring and parameter tuning are conducted. This dynamic adjustment mechanism ensures that the control system can maintain an optimized state at different production stages and can cope with fluctuations and changes in the actual process.

[0058] This step, through performance evaluation and dynamic modeling, can obtain real and accurate process dynamic characteristics, providing targeted input for control strategy optimization and reducing the deviation between the model and actual operation.

[0059] In step S3: using the dynamic model established in S2, the proportional, integral, and derivative parameters of each PID loop are tuned using computational optimization algorithms (such as least squares method, gradient descent method) or engineering experience methods. The tuning effect and steady-state performance are verified through an offline simulation platform. After successful verification, the optimized PID parameters are downloaded to the DCS system in batches for operation, and the loop response is monitored in real time during operation, with adaptive corrections made according to changes in operating conditions.

[0060] This step, through model-based PID parameter tuning and online adjustment, can significantly improve the accuracy and stability of loop control, reduce frequent manual adjustments, and ensure the long-term stable operation of the production unit.

[0061] In step S4: Based on the multivariate predictive control (MPC) method, the dynamic process model from S2 is used to predict future trends in process variables. Taking into account equipment operating constraints, safety limitations, and energy balance, key operating variables (such as feed flow rate, reaction temperature, tower top pressure, and H / N ratio) of the three major processes—desulfurization, methanation, and ammonia synthesis—are coordinated and controlled to achieve optimal operating strategies across processes. This control strategy is uniformly distributed to each DCS control unit through the APC system, thereby achieving coordinated optimization of the entire process.

[0062] This step, through cross-process prediction and coordinated control, enables edge operation while ensuring safe operation, thereby increasing production capacity and reducing energy consumption.

[0063] In step S5, a soft-sensor model is established to calculate key process indicators that cannot be directly detected online, such as trace sulfur content at the desulfurization outlet, residual CO content in methanation, and ammonia conversion rate. This model uses real-time data of measurable variables for calculation and dynamically compares and corrects the calculated values ​​with laboratory test data or online analytical instrument data to maintain consistency between the soft-sensor output and the actual values.

[0064] This step, through soft measurement and dynamic correction, can improve the comprehensiveness and accuracy of process monitoring without increasing hardware testing costs, thereby optimizing the execution effect of control decisions.

[0065] Example 7: like Figure 1 As shown in the figure, this embodiment provides an APC control method based on a multivariable coupled LNG unit, which includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariable prediction and cross-process coordinated control are to implement multivariable predictive control (MPC) based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control to improve the unit's output and intrinsic safety. S5: The steps of soft instrument calculation and dynamic correction are to build a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values ​​to improve the accuracy of APC and PID control. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, optimizing parameter display and alarm prompts, supporting one-click operation and quick switching, improving operation efficiency and visualization management level.

[0066] In step S1, for key processes such as desulfurization, methanation, and ammonia synthesis, multi-source real-time process data, including temperature, pressure, flow rate, component analysis, H / N ratio, and operating status of major equipment, are collected through field instruments. The data undergoes preprocessing by the DCS system, including signal filtering, outlier removal, and data compression, before being securely transmitted to the APC server via an encrypted transmission protocol through the OPC Server. Simultaneously, a historical data storage mechanism is established, classifying and archiving data according to process segment, equipment type, and time dimension, providing a high-quality data foundation for subsequent model building, performance analysis, and control optimization.

[0067] This step, by establishing a safe, stable, and complete multi-source process data acquisition and transmission channel, can ensure the reliability of data for subsequent control optimization and reduce the risk of control instability caused by data delays or missing data.

[0068] In step S2: key PID loops are selected for the temperature control loops of the desulfurization tower, the methanation reactor bed, and the ammonia synthesis tower pressure and temperature control loop. All PID loops are tuned, their step response data are collected, and key performance indicators such as standard deviation, overshoot rate, and response time of all PID loops are analyzed to determine their dynamic characteristics and control quality. Combining process coupling characteristics and material balance relationships, a multivariate dynamic process model across processes is established using system identification methods, providing a mathematical description and prediction basis for the APC controller.

[0069] A critical PID loop refers to the underlying execution loop implemented by the PID controller in the field DCS system within the critical control loop. These loops are the actual execution units of the APC system optimization results. In this embodiment of the invention, the critical PID loops include the desulfurization tower temperature control loop, the methanation reactor bed temperature control loop, and the ammonia synthesis tower pressure and temperature control loop.

[0070] Specifically, tuning all PID loops involves: establishing a monitoring mechanism for all PID loops in the unit; diagnosing faulty loops; analyzing instrumentation, process, and PID parameter issues; and taking measures such as repairing or replacing instruments, modifying control schemes, or retuning PID parameters for different problems. Furthermore, as the unit's production conditions change, continuous full-lifecycle loop performance monitoring and parameter tuning are conducted. This dynamic adjustment mechanism ensures that the control system can maintain an optimized state at different production stages and can cope with fluctuations and changes in the actual process.

[0071] This step, through performance evaluation and dynamic modeling, can obtain real and accurate process dynamic characteristics, providing targeted input for control strategy optimization and reducing the deviation between the model and actual operation.

[0072] In step S3: using the dynamic model established in S2, the proportional, integral, and derivative parameters of each PID loop are tuned using computational optimization algorithms (such as least squares method, gradient descent method) or engineering experience methods. The tuning effect and steady-state performance are verified through an offline simulation platform. After successful verification, the optimized PID parameters are downloaded to the DCS system in batches for operation, and the loop response is monitored in real time during operation, with adaptive corrections made according to changes in operating conditions.

[0073] This step, through model-based PID parameter tuning and online adjustment, can significantly improve the accuracy and stability of loop control, reduce frequent manual adjustments, and ensure the long-term stable operation of the production unit.

[0074] In step S4: Based on the multivariate predictive control (MPC) method, the dynamic process model from S2 is used to predict future trends in process variables. Taking into account equipment operating constraints, safety limitations, and energy balance, key operating variables (such as feed flow rate, reaction temperature, tower top pressure, and H / N ratio) of the three major processes—desulfurization, methanation, and ammonia synthesis—are coordinated and controlled to achieve optimal operating strategies across processes. This control strategy is uniformly distributed to each DCS control unit through the APC system, thereby achieving coordinated optimization of the entire process.

[0075] This step, through cross-process prediction and coordinated control, enables edge operation while ensuring safe operation, thereby increasing production capacity and reducing energy consumption.

[0076] In step S5, a soft-sensor model is established to calculate key process indicators that cannot be directly detected online, such as trace sulfur content at the desulfurization outlet, residual CO content in methanation, and ammonia conversion rate. This model uses real-time data of measurable variables for calculation and dynamically compares and corrects the calculated values ​​with laboratory test data or online analytical instrument data to maintain consistency between the soft-sensor output and the actual values.

[0077] This step, through soft measurement and dynamic correction, can improve the comprehensiveness and accuracy of process monitoring without increasing hardware testing costs, thereby optimizing the execution effect of control decisions.

[0078] In step S6, the independent operation interfaces of the three major processes—desulfurization, methanation, and ammonia synthesis—are integrated to construct a unified operation and monitoring platform. This platform optimizes the parameter display layout, centrally presenting key variables, alarm information, and soft measurement results. It adds one-click switching and quick navigation functions, supports multi-level alarm prompts, and allows for different levels of operation and adjustment based on access permissions.

[0079] This step, through integrated interface and visualization optimization, can improve the monitoring efficiency and response speed of operators, reduce the error rate, and enhance the security and maintainability of system operation.

[0080] Example 8: like Figure 2 As shown in the figure, this embodiment provides an APC control system based on a multivariable coupled LNG unit, including: The data acquisition and secure transmission module (1) is used to collect real-time process parameters and equipment status information for the three major processes of desulfurization, methanation, and ammonia synthesis, including temperature, pressure, flow rate, gas composition, H / N ratio, and load status of major equipment. After the collected data is preprocessed by the DCS system, it is securely transmitted to the APC server through the OPC Server. At the same time, a historical data storage mechanism is established to provide data support for subsequent PID loop performance analysis, dynamic model construction, and APC control.

[0081] This module ensures the timeliness and accuracy of process data by acquiring and transmitting it in real time, providing reliable data support for subsequent control and optimization, and improving the system's stability and responsiveness.

[0082] The PID loop management and optimization module (2) is responsible for the analysis, modeling, and optimization of key control loops in the three major processes of desulfurization, methanation, and ammonia synthesis, ensuring loop stability and overall control accuracy of the unit. This module is divided into three sub-modules: Loop Performance Evaluation Submodule (201): For the temperature control loop of the desulfurization tower, the segmented temperature control loop of the methanation reactor, and the temperature and pressure control loop of the ammonia synthesis tower, analyze the performance indicators such as standard deviation, overshoot rate, and response time of each loop, identify the fluctuation or lag problems in the loop, and provide a basis for subsequent modeling and parameter optimization.

[0083] Dynamic Modeling Submodule (202): Based on the experimental data of step response and the process coupling characteristics of the three major processes, a multivariate dynamic process model is established. Key parameters such as H / N ratio, reaction exothermics and heat recovery are introduced into the model to accurately reflect the material and energy coupling relationship across processes, providing a reliable foundation for APC control and PID parameter optimization.

[0084] PID Parameter Optimization and Online Application Submodule (203): This submodule tunes the PID parameters using engineering experience or computational optimization methods, and then downloads them to the DCS system after offline simulation verification. Simultaneously, this submodule establishes an online performance monitoring mechanism to achieve adaptive adjustment of the PID loop, improving loop control stability and overall device operating efficiency.

[0085] This module improves loop control accuracy, reduces fluctuations and hysteresis, and enhances the overall control stability and response efficiency of the device by accurately analyzing and optimizing loop performance.

[0086] The multivariate prediction and cross-process coordination control module (3) implements multivariate predictive control (MPC) based on the established dynamic process model. Under the premise of considering the equipment operation constraints, it coordinates the operating variables of the three major processes of fine desulfurization, methanation, and ammonia synthesis to achieve stable sulfur content at the outlet of fine desulfurization, equalization of temperature difference in the methanation reactor, and precise control of pressure and H / N ratio in the ammonia synthesis tower. Through constraint management and cross-process optimization strategies, it achieves edge operation of the unit, improves processing capacity and material recovery rate.

[0087] This module effectively improves the stability and processing capacity of the production process by optimizing the coordinated control of operational variables, while also enhancing energy utilization and material recovery efficiency.

[0088] The soft instrument calculation and dynamic correction module (4) is used to calculate key indicators that cannot be directly measured, such as the trace sulfur content at the desulfurization outlet, the residual CO content of methanation and the conversion rate of synthetic ammonia, and to perform dynamic correction in combination with the actual detection values ​​to improve the accuracy of MPC and PID control and ensure that the predicted results are consistent with the actual operation.

[0089] This module improves control accuracy by dynamically correcting the calculation results of the software instruments, ensuring the accuracy and reliability of system operation, and reducing control deviations caused by measurement errors.

[0090] The unified operation interface optimization and visualization management module (5) integrates the independent operation screens of the three major processes into an integrated interface, optimizes the parameter display layout, centrally displays the H / N ratio, daily output of synthetic ammonia, key temperature and pressure, equipment status and soft measurement indicators, sets hierarchical alarm prompts, and designs support one-click operation and quick switching functions to improve operation efficiency and visualization management level.

[0091] This module enhances operator efficiency, reduces operational errors and response time, and improves system operability and management efficiency by optimizing the interface layout and improving visualization.

[0092] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0093] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0094] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0095] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0097] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0098] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0099] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0100] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. An APC control method for a multivariable coupled LNG unit, characterized in that, Includes the following steps: S1: Steps for multi-source process data acquisition and secure transmission: real-time process parameters and equipment operating status of key processes such as fine desulfurization, methanation, and ammonia synthesis are collected, pre-processed by the DCS system, and securely transmitted to the APC server via the OPC Server. At the same time, a historical data storage mechanism is established. S2: Steps for performance evaluation and dynamic modeling of key PID loops, performance analysis of key PID loops in the desulfurization tower, methanation reactor and ammonia synthesis tower, system identification based on step response data, establishment of multivariable coupled dynamic process model, and provision of a foundation for APC control. S3: Steps for PID parameter optimization and online application: Combine dynamic models, use calculation optimization or engineering experience methods to tune PID parameters, verify them through offline simulation, download them to the DCS system, and monitor them in real time to achieve adaptive adjustment; S4: The steps of multivariate prediction and cross-process coordinated control are to implement multivariate predictive control based on dynamic models, coordinate the operating variables of the three major processes of fine desulfurization, methanation and ammonia synthesis, and achieve cross-process optimization through APC control. S5: The steps of soft instrument calculation and dynamic correction are to construct a soft measurement model to calculate key indicators that cannot be directly measured, and to perform dynamic correction based on actual detection values. S6: Unified operation interface optimization and visualization steps, integrating the operation screens of the three major processes of fine desulfurization, methanation and ammonia synthesis into a unified interface, and optimizing parameter display and alarm prompts.

2. The APC control method for a multivariable coupled LNG unit according to claim 1, characterized in that, In step S1: For key processes such as fine desulfurization, methanation, and ammonia synthesis, multi-source real-time process data is collected through field instruments. The data is preprocessed by the DCS system, including signal filtering, outlier removal, and data compression. Then, it is securely transmitted to the APC server via the OPCServer using an encrypted transmission protocol. At the same time, a historical data storage mechanism is established to classify and archive data according to process segment, equipment type, and time dimension.

3. The APC control method for a multivariable coupled LNG unit according to claim 1 or 2, characterized in that, In step S2: key PID loops are selected and all PID loops are tuned, their step response data are collected, and key performance indicators are analyzed to determine their dynamic characteristics and control quality; combined with process coupling characteristics and material balance relationships, a multivariate dynamic process model across processes is established using system identification methods to provide a mathematical description and prediction basis for the APC controller.

4. The APC control method for a multivariable coupled LNG unit according to claim 3, characterized in that, In step S3: using the dynamic model established in S2, the proportional, integral, and derivative parameters of each PID loop are tuned by combining computational optimization algorithms or engineering experience methods, and the adjustment effect and steady-state performance are verified through an offline simulation platform; after verification, the optimized PID parameters are downloaded to the DCS system in batches for operation, and the loop response is monitored in real time during operation, and adaptive correction is made according to changes in operating conditions.

5. The APC control method for a multivariable coupled LNG unit according to claim 4, characterized in that, In step S4: Based on the multivariate predictive control method, the dynamic process model in S2 is used to predict the future trend of process variable changes. Taking into account equipment operation constraints, safety limitations and energy balance, the key operation variables of the three major processes of fine desulfurization, methanation and ammonia synthesis are coordinated and controlled to achieve the optimal operation strategy across processes.

6. The APC control method for a multivariable coupled LNG unit according to claim 5, characterized in that, In step S5: a soft measurement model is established to calculate key process indicators that cannot be directly detected online. The calculation is performed using real-time data of measurable variables, and the calculated values ​​are dynamically compared and error corrected with laboratory test data or online analytical instrument data to maintain the consistency between the soft measurement output and the true value.

7. The APC control method for a multivariable coupled LNG unit according to claim 6, characterized in that, In step S6, the independent operation interfaces of the three major processes of fine desulfurization, methanation, and ammonia synthesis are integrated to build a unified operation and monitoring platform. This platform optimizes the parameter display layout, centrally presents key variables, alarm information, and soft measurement results, and enables different levels of operation and adjustment based on access control.

8. The APC control method for a multivariable coupled LNG unit according to claim 7, characterized in that, Specifically, the tuning of all PID loops involves establishing a monitoring mechanism for all PID loops in the unit, diagnosing faulty loops, analyzing instrument, process, and PID parameter issues, and taking measures such as repairing or replacing instruments, modifying control schemes, or retuning PID parameters for different problems. Furthermore, as the unit's production conditions change, the loop performance is continuously monitored and parameters are tuned throughout its entire lifecycle.

9. An APC control system based on a multivariable coupled LNG unit, characterized in that, include: The module includes a data acquisition and secure transmission module (1), a PID loop management and optimization module (2), a multivariable prediction and cross-process coordination control module (3), a soft instrument calculation and dynamic correction module (4), and a unified operation interface optimization and visualization management module (5). The data acquisition and secure transmission module (1) acquires real-time process parameters for the desulfurization, methanation and ammonia synthesis processes. After DCS preprocessing, the parameters are transmitted to the APC server via the OPC Server. It supports historical data storage and provides data support. The PID loop management and optimization module (2) is responsible for the analysis, modeling and optimization of key control loops to ensure loop stability; The multivariate prediction and cross-process coordination control module (3) performs multivariate prediction control based on the dynamic process model, coordinates the operation variables of the three major processes, and achieves precise control and improves processing capacity. The soft instrument calculation and dynamic correction module (4) calculates the key indicators that cannot be measured and corrects them in combination with the actual detection values ​​to ensure that the control accuracy is consistent with the actual value. The unified operation interface optimization and visualization management module (5) integrates the operation interface, optimizes parameter display, and sets alarm prompts.

10. The APC control system for a multivariable coupled LNG unit according to claim 9, characterized in that, The PID loop management and optimization module (2) includes three sub-modules: loop performance evaluation sub-module (201), dynamic modeling sub-module (202), and PID parameter optimization and online application sub-module (203). The loop performance evaluation submodule (201) is used to analyze the performance indicators of each loop. The dynamic modeling submodule (202) establishes a multivariate dynamic model based on process characteristics. The PID parameter optimization and online application submodule (203) combines optimization methods to tune PID parameters and implement online performance monitoring to improve the stability of loop control.

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