Energy consumption control method and system for preparing HSR dry powder denitration agent

Abstract

The invention relates to the technical field of energy conservation and emission reduction, and discloses an energy consumption control method and system for preparation of an HSR dry powder denitration agent, and the system comprises an energy consumption data collection module, an energy consumption optimization analysis module, an energy consumption control execution module, a dynamic environment simulation module, a real-time energy efficiency evaluation module and a self-adaptive optimization module. Static energy consumption data and dynamic energy consumption data are synchronously collected, sampling frequency consistency judgment and adjustment are carried out on the static energy consumption data and the dynamic energy consumption data, time sequence alignment and fusion precision of energy consumption data of different sources are guaranteed, the problem of energy consumption analysis errors caused by mismatching of data collection frequencies is solved, a data basis is provided for subsequent optimization control, and the energy consumption analysis efficiency is improved. The integrity and consistency of energy consumption parameter analysis are improved; the real-time energy consumption parameter combination is intelligently matched with multiple standard optimization algorithms, the energy consumption optimization algorithm most suitable for the current production working condition is automatically identified and called, and the limitation that a traditional single control strategy is poor in adaptability and cannot cope with complex working condition changes is overcome.

Description

Technical Field

[0001] This invention relates to the field of energy conservation and emission reduction technology, specifically to an energy consumption control method and system for the preparation of HSR dry powder denitrification agent. Background Technology

[0002] The rapid pace of industrialization has led to massive energy consumption and exacerbated air pollution. Nitrogen oxides, as a major pollutant in flue gas, pose a threat to the environment and human health. Faced with increasingly stringent emission standards and the dual pressures of energy conservation and emission reduction, traditional flue gas denitrification technologies have revealed significant shortcomings in terms of energy consumption, equipment complexity, and adaptability. There is an urgent need to develop new denitrification materials and processes that are highly efficient, easy to operate, and have high energy utilization. HSR dry powder denitrification agent, with its unique chemical reaction mechanism and process adaptability, achieves high-temperature and high-efficiency denitrification without altering the existing equipment structure, making it a worthy area of ​​in-depth research in the field of industrial flue gas treatment.

[0003] Currently, in the preparation process of HSR dry powder denitrification agent, due to the involvement of multiple high-energy-consuming processes, energy consumption data collection includes both static non-operational state and dynamic operational state. It is impossible to guarantee the consistency of sampling frequency between static and dynamic energy consumption data in real time. Excessive frequency deviation may cause large errors in the fusion analysis of energy consumption parameters, making it impossible to guarantee the accuracy of subsequent optimization control.

[0004] Therefore, a method and system for controlling energy consumption in the preparation of HSR dry powder denitrification agent is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an energy consumption control method and system for the preparation of HSR dry powder denitrification agent, which solves the problem mentioned in the background technology that the error in the fusion analysis of energy consumption parameters is large and cannot guarantee the accuracy of subsequent optimization control.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an energy consumption control method for the preparation of HSR dry powder denitrification agent, the method comprising the following steps: S1. Collect static and dynamic energy consumption data during the preparation process of HSR dry powder denitrification agent; S2. Based on the static energy consumption data and dynamic energy consumption data, perform energy consumption parameter sampling frequency measurement and processing to generate static energy consumption frequency data and dynamic energy consumption frequency data. S3. Based on the static energy consumption frequency data and dynamic energy consumption frequency data, perform energy consumption parameter sampling frequency consistency judgment processing to generate energy consumption sampling frequency consistency judgment data. When the frequencies are consistent, directly execute step S5. S4. When the frequencies are inconsistent, static energy consumption parameter sampling frequency adjustment processing is performed based on the static energy consumption data, static energy consumption frequency data, and dynamic energy consumption frequency data to generate static energy consumption adjustment data. S5. Based on the static energy consumption data, static energy consumption adjustment data, and dynamic energy consumption data, perform energy consumption parameter combination processing to construct energy consumption parameter combination data; S6. Based on the energy consumption parameter combination data and the standard energy consumption parameter combination data corresponding to different energy consumption optimization algorithms, perform energy consumption optimization algorithm type matching processing to generate target energy consumption optimization algorithm type feature data. S7. Construct energy consumption control summary data to perform energy consumption control execution processing for the preparation process of HSR dry powder denitrification agent, and generate energy consumption optimization control data.

[0007] Preferably, the step S1, which involves collecting static and dynamic energy consumption data during the preparation of HSR dry powder denitrification agent, includes the following steps: S11. Collect energy consumption parameters of different steps in the preparation process of HSR dry powder denitrification agent under non-operation state by equipping the preparation equipment with a static energy consumption sensor, and generate static energy consumption data; S12. Static energy consumption data represents the combination of time parameters and energy consumption parameters established with the process time series as a one-dimensional coordinate axis. Static energy consumption sensors include temperature sensors, pressure sensors, and power meters. Energy consumption parameters include raw material mixing energy consumption, high-temperature reaction energy consumption, and drying energy consumption. S13, where the temperature sensor has an accuracy of ±0.5℃, the pressure sensor has a range of 0-10MPa, and the power meter uses a three-phase energy meter to record power consumption data at 0.5s intervals in real time. S14. The energy consumption parameters of different steps in the preparation process of HSR dry powder denitrification agent under the operation state are collected by the dynamic energy consumption sensor equipped on the preparation equipment, and dynamic energy consumption data is generated. S15. Dynamic energy consumption data represents the combined data of time parameters and energy consumption parameters established with the process time series as a one-dimensional coordinate axis under the operating state. Dynamic energy consumption sensors include ultrasonic flow meters, infrared thermal imagers and vibration sensors, which capture the instantaneous energy consumption peak caused by material conveying heat loss, mechanical vibration energy loss of the reactor and airflow fluctuations.

[0008] Preferably, the energy consumption parameter sampling frequency measurement processing in step S2 includes the following steps: S21. Import the generated static energy consumption data and dynamic energy consumption data into the energy consumption monitoring platform. Use a bidirectional search algorithm to search for the sampling frequency information corresponding to the target static energy consumption data and target dynamic energy consumption data in the energy consumption monitoring platform according to frequency keywords, and generate static energy consumption frequency data and dynamic energy consumption frequency data, both in Hertz. S22. The static energy consumption frequency data range is fixed at 1Hz, and the dynamic energy consumption frequency data range is adjustable from 5 to 50Hz. The bidirectional search algorithm first traverses the timestamp index of the dynamic energy consumption data, and then matches the device tag of the static energy consumption data in reverse.

[0009] Preferably, the energy consumption parameter sampling frequency consistency judgment process in S3 includes the following steps: S31. Obtain the static energy consumption frequency data and the dynamic energy consumption frequency data; S32. Compare the frequency values ​​of the static energy consumption frequency data and the dynamic energy consumption frequency data, and generate energy consumption sampling frequency consistency judgment data based on the comparison results. S33. When the frequency values ​​are consistent, output the frequency consistency judgment data and execute S5.

[0010] Preferably, the static energy consumption parameter sampling frequency adjustment process in step S4 includes the following steps: S41. When the frequency values ​​are inconsistent, a linear interpolation algorithm is used to adjust the sampling frequency of the static energy consumption data according to the dynamic energy consumption frequency data to generate static energy consumption adjustment data. Specifically, this includes: using the dynamic energy consumption frequency as a benchmark, inserting virtual data points between the original sampling points of the static energy consumption data, and generating the values ​​of the virtual data points based on the slope of two adjacent points, ultimately reconstructing a static energy consumption time series consistent with the dynamic energy consumption frequency.

[0011] Preferably, in step S5, energy consumption parameter combination processing is performed to construct energy consumption parameter combination data, including the following steps: S51. The static energy consumption data, static energy consumption adjustment data and dynamic energy consumption data are aligned and arranged according to the process time parameters to construct energy consumption parameter combination data, and the combination form is a two-dimensional matrix structure. S52. The row vectors of the matrix are process timestamps, and the column vectors are static energy consumption parameters and dynamic energy consumption parameters. Missing values ​​are filled by nearest neighbor interpolation.

[0012] Preferably, the energy consumption optimization algorithm type matching process in step S6 includes the following steps: S61. Establish a standard energy consumption parameter combination data matrix corresponding to different energy consumption optimization algorithms, including temperature optimization algorithm, pressure optimization algorithm and hybrid optimization algorithm. The standard matrix corresponding to each algorithm contains 100 sets of historical best energy consumption parameter combinations. S62. Match the energy consumption parameter combination data with the standard energy consumption parameter combination data matrix to generate target energy consumption optimization algorithm type feature data; S63. The matching process initializes and optimizes the algorithm to search for the osprey population. In the exploration phase, the current position is randomly perturbed to find a better solution. In the development phase, the population position is updated based on the fitness value. After 50 iterations, the algorithm type label and confidence score with the highest matching degree are output.

[0013] Preferably, generating energy consumption optimization control data in step S7 includes the following steps: S71. Combine the energy consumption parameter combination data and the target energy consumption optimization algorithm type feature data to construct energy consumption control summary data. The data packet format is JSON structure, which includes timestamp, device number, algorithm type code and energy consumption parameter matrix hash value. S72. The energy consumption monitoring platform calls the optimization program to extract parameter information from the aggregated data according to the target algorithm type, performs control processing on the dynamic energy consumption data, and generates energy consumption optimization control data. S73. Control processing includes: temperature optimization algorithm to adjust the heating power of the reactor, pressure optimization algorithm to adjust the start-stop cycle of the compressor, and finally output the real-time energy consumption threshold and fluctuation tolerance range of each process.

[0014] Preferably, it also includes an energy consumption feedback calibration step: S81. Based on the energy consumption optimization control data, compare it with the preset energy consumption threshold in real time. When the threshold is exceeded for 3 consecutive sampling cycles, the parameter is re-adjusted. S82. The readjustment process includes: backtracking the energy consumption data of the previous 10 minutes, re-executing the S4 frequency adjustment and S6 algorithm matching until the energy consumption returns to the threshold range, forming a closed-loop control chain.

[0015] An energy consumption control system for the preparation of HSR dry powder denitrification agent, the system comprising: The energy consumption data acquisition module uses a static energy consumption acquisition unit to acquire basic energy consumption parameters of the equipment, captures real-time energy consumption data during operation through a dynamic energy consumption acquisition unit, and outputs standardized frequency data through an energy consumption frequency measurement unit. The energy consumption optimization analysis module receives the standardized frequency data, constructs a multi-dimensional energy consumption matrix through the energy consumption parameter combination unit, calls the pre-stored optimization algorithm library through the standard algorithm data storage unit, and outputs the target optimization algorithm identifier through the algorithm type matching unit. The energy consumption control execution module receives the target optimization algorithm identifier, integrates real-time operating condition data through the control parameter collection unit, drives the actuator to adjust the operating parameters through the control execution unit, and outputs a closed-loop control signal through the energy consumption feedback calibration unit. The dynamic environment simulation module receives the closed-loop control signal, simulates the energy consumption environment under different production loads through the working condition scenario reconstruction unit, sets the energy consumption boundary conditions through the safety threshold configuration unit, and outputs test scenario data through the disturbance event generation unit. The real-time energy efficiency assessment module receives the test scenario data, records the system response parameters through the energy efficiency data acquisition unit, calculates the energy efficiency index using the multi-objective optimization unit, and outputs the assessment results through the regulation effect assessment unit. The adaptive optimization module receives the evaluation results, analyzes the system's energy efficiency characteristics through the energy efficiency profile construction unit, locates high-energy-consuming nodes through the weak link identification unit, and generates optimization strategy instructions through the operation parameter control unit.

[0016] Beneficial effects Compared with the prior art, the present invention provides an energy consumption control method and system for the preparation of HSR dry powder denitrification agent, which has the following beneficial effects: 1. In this invention, when energy consumption is controlled during the preparation of HSR dry powder denitrification agent, both static and dynamic energy consumption data are collected simultaneously, and the consistency of their sampling frequencies is judged and adjusted to ensure the time series alignment and fusion accuracy of energy consumption data from different sources. This solves the problem of energy consumption analysis error caused by mismatch in data acquisition frequencies, provides a data basis for subsequent optimization control, and improves the completeness and consistency of energy consumption parameter analysis.

[0017] 2. In this invention, during the process of achieving energy consumption optimization control, the real-time energy consumption parameters are intelligently matched with multiple standard optimization algorithms. The system automatically identifies and calls the energy consumption optimization algorithm most suitable for the current production conditions, overcoming the limitations of traditional single control strategies in terms of poor adaptability and inability to cope with complex changes in operating conditions. This enables the system to maintain energy consumption control effectiveness under different production loads, equipment states, and process stages, thereby enhancing the system's intelligence and algorithm generalization ability.

[0018] 3. In this invention, a closed-loop feedback calibration mechanism is introduced while energy consumption control is being executed. By comparing the optimized control results with the preset energy consumption threshold in real time, the parameters are dynamically re-adjusted and the algorithm is re-matched, so as to realize the rapid identification and adaptive correction of energy consumption anomalies, avoid energy consumption runaway and parameter drift problems, ensure the continuous stability and reliability of energy consumption control in the preparation process of HSR dry powder denitrification agent, and improve the robustness and overall energy efficiency of the system. Attached Figure Description

[0019] Figure 1 A flowchart illustrating the energy consumption control method for preparing the HSR dry powder denitrification agent of this invention; Figure 2 This is a schematic diagram of the energy consumption control system for the preparation of the HSR dry powder denitrification agent of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Specific embodiment: Energy consumption control method for preparing HSR dry powder denitrification agent, the method includes the following steps: S1. Collect static and dynamic energy consumption data during the preparation process of HSR dry powder denitrification agent; S2. Based on static energy consumption data and dynamic energy consumption data, perform energy consumption parameter sampling frequency measurement and processing to generate static energy consumption frequency data and dynamic energy consumption frequency data. S3. Based on the static energy consumption frequency data and the dynamic energy consumption frequency data, perform energy consumption parameter sampling frequency consistency judgment processing to generate energy consumption sampling frequency consistency judgment data. When the frequencies are consistent, directly execute step S5. S4. When the frequencies are inconsistent, static energy consumption parameter sampling frequency adjustment processing is performed based on static energy consumption data, static energy consumption frequency data, and dynamic energy consumption frequency data to generate static energy consumption adjustment data. S5. Based on static energy consumption data, static energy consumption adjustment data, and dynamic energy consumption data, perform energy consumption parameter combination processing to construct energy consumption parameter combination data; S6. Based on the energy consumption parameter combination data and the standard energy consumption parameter combination data corresponding to different energy consumption optimization algorithms, perform energy consumption optimization algorithm type matching processing to generate target energy consumption optimization algorithm type feature data; S7. Construct energy consumption control summary data to perform energy consumption control execution processing for the preparation process of HSR dry powder denitrification agent, and generate energy consumption optimization control data.

[0022] S1 collects static and dynamic energy consumption data for the preparation process of HSR dry powder denitrification agent, including the following steps: S11. Collect energy consumption parameters of different steps in the preparation process of HSR dry powder denitrification agent under non-operation state by equipping the preparation equipment with a static energy consumption sensor, and generate static energy consumption data; S12. Static energy consumption data represents the combination of time parameters and energy consumption parameters established with the process time series as a one-dimensional coordinate axis. Static energy consumption sensors include temperature sensors, pressure sensors, and power meters. Energy consumption parameters include raw material mixing energy consumption, high-temperature reaction energy consumption, and drying energy consumption. S13, where the temperature sensor has an accuracy of ±0.5℃, the pressure sensor has a range of 0-10MPa, and the power meter uses a three-phase energy meter to record power consumption data at 0.5s intervals in real time. S14. The energy consumption parameters of different steps in the preparation process of HSR dry powder denitrification agent under the operation state are collected by the dynamic energy consumption sensor equipped on the preparation equipment, and dynamic energy consumption data is generated. S15. Dynamic energy consumption data represents the combined data of time parameters and energy consumption parameters established with the process time series as a one-dimensional coordinate axis under the operating state. Dynamic energy consumption sensors include ultrasonic flow meters, infrared thermal imagers and vibration sensors, which capture the instantaneous energy consumption peak caused by material conveying heat loss, mechanical vibration energy loss of the reactor and airflow fluctuations.

[0023] The energy consumption parameter sampling frequency measurement and processing in S2 includes the following steps: S21. Import the generated static energy consumption data and dynamic energy consumption data into the energy consumption monitoring platform. Use a bidirectional search algorithm to search for the sampling frequency information corresponding to the target static energy consumption data and target dynamic energy consumption data in the energy consumption monitoring platform according to frequency keywords, and generate static energy consumption frequency data and dynamic energy consumption frequency data, both in Hertz. The specific implementation steps of the bidirectional search algorithm are as follows: First, the algorithm initializes two search pointers: one that traverses forward from the beginning of the timestamp index of the dynamic energy consumption data, and the other that traverses backward from the end; the search keyword is "sampling frequency"; the forward traversal prioritizes scanning each timestamp of the dynamic energy consumption data and extracts its corresponding frequency value; at the same time, the backward traversal matches the device tags of the static energy consumption data to verify the data source.

[0024] When the two pointers meet and cross, the algorithm compares the forward and reverse search results: if the timestamp and device tag match, the corresponding static energy consumption frequency data and dynamic energy consumption frequency data are output; otherwise, an error is returned and the search is reinitialized.

[0025] S22. The static energy consumption frequency data range is fixed at 1Hz, and the dynamic energy consumption frequency data range is adjustable from 5 to 50Hz. The bidirectional search algorithm first traverses the timestamp index of the dynamic energy consumption data, and then matches the device tag of the static energy consumption data in reverse.

[0026] The consistency judgment and processing of energy consumption parameter sampling frequency in S3 includes the following steps: S31. Obtain static energy consumption frequency data and dynamic energy consumption frequency data; S32. Compare the frequency values ​​of static energy consumption frequency data with dynamic energy consumption frequency data, and generate energy consumption sampling frequency consistency judgment data based on the comparison results. S33. When the frequency values ​​are consistent, output the frequency consistency judgment data and execute S5.

[0027] The static energy consumption parameter sampling frequency adjustment process in S4 includes the following steps: S41. When the frequency values ​​are inconsistent, a linear interpolation algorithm is used to adjust the sampling frequency of the static energy consumption data according to the dynamic energy consumption frequency data to generate static energy consumption adjustment data. Specifically, this includes: using the dynamic energy consumption frequency as a benchmark, inserting virtual data points between the original sampling points of static energy consumption data, and generating the values ​​of the virtual data points based on the slope of two adjacent points, ultimately reconstructing a static energy consumption time series consistent with the dynamic energy consumption frequency; ; in This is the interpolated static energy consumption value. For the new sampling time point, and are adjacent original time points and satisfy , and This represents the original static energy consumption value. It is a linear weighting factor.

[0028] In S5, energy consumption parameter combination processing is performed to construct energy consumption parameter combination data, including the following steps: S51. Arrange the static energy consumption data, static energy consumption adjustment data and dynamic energy consumption data in alignment according to the process time parameters to construct energy consumption parameter combination data. The combination form is a two-dimensional matrix structure. S52. The row vectors of the matrix are process timestamps, and the column vectors are static energy consumption parameters and dynamic energy consumption parameters. Missing values ​​are filled by nearest neighbor interpolation. The specific steps for implementing the nearest neighbor interpolation method are as follows: First, the energy consumption parameter combination data is organized into a two-dimensional matrix, with the row vector representing process timestamps and the column vector representing static and dynamic energy consumption parameters.

[0029] When a missing value is detected, the algorithm searches for the nearest neighbor in that row and column: calculating the Euclidean distance, prioritizing adjacent parameter columns with the same timestamp, otherwise selecting the row with the nearest timestamp in the time dimension. Missing values ​​are directly copied from their nearest neighbors. Finally, the integrity of the padded matrix is ​​verified, and the energy consumption parameter combination data without missing values ​​is output.

[0030] The energy consumption optimization algorithm type matching process in S6 includes the following steps: S61. Establish a standard energy consumption parameter combination data matrix corresponding to different energy consumption optimization algorithms, including temperature optimization algorithm, pressure optimization algorithm and hybrid optimization algorithm. The standard matrix corresponding to each algorithm contains 100 sets of historical best energy consumption parameter combinations. Temperature optimization algorithm formula: ; in To adjust the output heating power. The temperature error is defined as the set temperature minus the real-time temperature. , , This refers to the PID gain coefficient; Stress optimization algorithm formula: ; in This is the compressor start-stop cycle adjustment value. and To set pressure and real-time pressure, , These are the weighting coefficients; Hybrid optimization algorithm formula: ; in For comprehensive control output, and The output function is for temperature optimization and pressure optimization. As a weighting factor; S62. Match the energy consumption parameter combination data with the standard energy consumption parameter combination data matrix to generate target energy consumption optimization algorithm type feature data; The specific implementation steps are as follows: First, the real-time energy consumption parameter combination data is compared with each standard algorithm matrix for similarity calculation: Euclidean distance is used to measure similarity, with smaller distances indicating higher matching degrees; formula: ; in For real-time data matrix, This is a standard matrix.

[0031] Then, the Osprey optimization algorithm is initialized to search for the optimal match: a candidate algorithm type population is generated, the position is iteratively optimized, and finally the algorithm type with the highest similarity is selected as the target output; S63. The matching process initializes and optimizes the algorithm to search for the osprey population. In the exploration phase, the current position is randomly perturbed to find a better solution. In the development phase, the population position is updated based on the fitness value. After 50 iterations, the algorithm type label with the highest matching degree and the confidence score are output. The Osprey optimization algorithm is applicable to the matching process, including the exploration and development phases, and the formula is: Exploration phase: ; Development phase: ; For position vectors, The solution is randomly generated. This is the current optimal solution. It is a random perturbation factor and its range is [0,1].

[0032] Generating energy consumption optimization control data in S7 includes the following steps: S71. Combine the energy consumption parameter combination data and the target energy consumption optimization algorithm type feature data to construct the energy consumption control summary data. The data packet format is JSON structure, which includes timestamp, device number, algorithm type code and energy consumption parameter matrix hash value. S72. The energy consumption monitoring platform calls the optimization program to extract parameter information from the aggregated data according to the target algorithm type, performs control processing on the dynamic energy consumption data, and generates energy consumption optimization control data. S73. Control processing includes: temperature optimization algorithm to adjust the heating power of the reactor, pressure optimization algorithm to adjust the start-stop cycle of the compressor, and finally output the real-time energy consumption threshold and fluctuation tolerance range of each process.

[0033] It also includes an energy consumption feedback calibration step: S81. Based on the energy consumption optimization control data, compare it with the preset energy consumption threshold in real time. When the threshold is exceeded for 3 consecutive sampling cycles, the parameter is re-adjusted. S82. The readjustment process includes: backtracking the energy consumption data of the previous 10 minutes, re-executing the S4 frequency adjustment and S6 algorithm matching until the energy consumption returns to the threshold range, forming a closed-loop control chain.

[0034] An energy consumption control system for the preparation of HSR dry powder denitrification agent, the system including: The energy consumption data acquisition module uses a static energy consumption acquisition unit to acquire basic energy consumption parameters of the equipment, captures real-time energy consumption data during operation through a dynamic energy consumption acquisition unit, and outputs standardized frequency data through an energy consumption frequency measurement unit. The energy consumption optimization and analysis module receives standardized frequency data, constructs a multi-dimensional energy consumption matrix through the energy consumption parameter combination unit, calls the pre-stored optimization algorithm library using the standard algorithm data storage unit, and outputs the target optimization algorithm identifier through the algorithm type matching unit. The energy consumption control execution module receives the target optimization algorithm identifier, integrates real-time operating condition data through the control parameter collection unit, drives the actuator to adjust the operating parameters through the control execution unit, and outputs closed-loop control signals through the energy consumption feedback calibration unit. The dynamic environment simulation module receives closed-loop control signals, simulates energy consumption environments under different production loads through the working condition scenario reconstruction unit, sets energy consumption boundary conditions through the safety threshold configuration unit, and outputs test scenario data through the disturbance event generation unit. The real-time energy efficiency assessment module receives test scenario data, records system response parameters through the energy efficiency data acquisition unit, calculates energy efficiency indicators using the multi-objective optimization unit, and outputs assessment results through the regulation effect assessment unit. The adaptive optimization module receives the evaluation results, analyzes the system's energy efficiency characteristics through the energy efficiency profile building unit, locates high-energy-consuming nodes through the weak link identification unit, and generates optimization strategy instructions through the operation parameter control unit.

[0035] The method and system operation steps are as follows: Step 1: Energy Consumption Data Acquisition and Alignment Processing Dual-modal data acquisition: Static energy consumption data: When the equipment is not running, basic energy consumption parameters of the raw material mixing, high-temperature reaction and drying processes are collected by high-precision temperature sensors, pressure sensors and three-phase energy meters to form a data sequence with process time as the axis.

[0036] Dynamic energy consumption data: During equipment operation, the heat loss of material conveying, energy loss of mechanical vibration of the reactor, and instantaneous energy consumption peak are captured in real time by ultrasonic flow meters, infrared thermal imagers and vibration sensors to generate operating time series data.

[0037] Frequency consistency guarantee: A bidirectional search algorithm is used to retrieve the sampling frequency of two types of data in the energy consumption monitoring platform, prioritizing the matching of dynamic data timestamp indexes.

[0038] If the frequencies are inconsistent, the static data is reconstructed using a linear interpolation algorithm: based on the dynamic frequency, virtual data points are inserted between the original static sampling points to generate static energy consumption adjustment data consistent with the dynamic frequency.

[0039] Multi-source data fusion: The adjusted static data, original static data, and dynamic data are aligned according to the process timestamp to construct a two-dimensional matrix, and missing values ​​are filled by nearest neighbor interpolation.

[0040] Step 2: Intelligent Optimization Algorithm Matching and Control Execution Algorithm library matching mechanism: Standard energy consumption parameter matrices for pre-stored temperature optimization algorithm, pressure optimization algorithm and hybrid optimization algorithm.

[0041] Employs an intelligent search mechanism: Exploration phase: Randomly perturb the current parameter position to find a better solution; Development phase: Update the population position based on fitness value, and after 50 iterations, output the algorithm type label and confidence score with the highest matching degree.

[0042] Dynamic energy consumption control: The energy consumption parameter combination data and the target algorithm type are encapsulated into JSON format control instructions.

[0043] The energy consumption monitoring platform uses matching algorithms to adjust equipment in real time. Temperature optimization: Adjust the heating power of the reactor; Pressure optimization: Adjust compressor start-stop cycle; Output the real-time energy consumption threshold and fluctuation tolerance range for each process.

[0044] Step 3: Closed-loop feedback and system adaptation Real-time calibration mechanism: If the optimized control data is compared with the preset energy consumption threshold, and the threshold is exceeded for three consecutive sampling periods, a readjustment is triggered: the data from the previous 10 minutes is reviewed, and the frequency adjustment and algorithm matching are re-executed until the threshold range is returned.

[0045] System-level optimized architecture: Energy consumption data acquisition module: integrates static and dynamic sensor data and outputs standardized frequencies; Energy consumption optimization analysis module: Constructs a multi-dimensional energy consumption matrix and matches the optimal algorithm type; Energy consumption control execution module: drives the actuator to adjust parameters and provides feedback closed-loop signals; Dynamic environment simulation module: Simulates disturbance scenarios under different production loads; Real-time energy efficiency assessment module: calculates energy efficiency indicators and evaluates the effect of regulation; Adaptive optimization module: Locates high-energy-consuming nodes and generates policy instructions.

[0046] 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.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for controlling energy consumption in the preparation of HSR dry powder denitrification agent, characterized in that: The method includes the following steps: S1. Collect static and dynamic energy consumption data during the preparation process of HSR dry powder denitrification agent; S2. Based on the static energy consumption data and dynamic energy consumption data, perform energy consumption parameter sampling frequency measurement and processing to generate static energy consumption frequency data and dynamic energy consumption frequency data. S3. Based on the static energy consumption frequency data and dynamic energy consumption frequency data, perform energy consumption parameter sampling frequency consistency judgment processing to generate energy consumption sampling frequency consistency judgment data. When the frequencies are consistent, directly execute step S5. S4. When the frequencies are inconsistent, static energy consumption parameter sampling frequency adjustment processing is performed based on the static energy consumption data, static energy consumption frequency data, and dynamic energy consumption frequency data to generate static energy consumption adjustment data. S5. Based on the static energy consumption data, static energy consumption adjustment data, and dynamic energy consumption data, perform energy consumption parameter combination processing to construct energy consumption parameter combination data; S6. Based on the energy consumption parameter combination data and the standard energy consumption parameter combination data corresponding to different energy consumption optimization algorithms, perform energy consumption optimization algorithm type matching processing to generate target energy consumption optimization algorithm type feature data. S7. Construct energy consumption control summary data to perform energy consumption control execution processing for the preparation process of HSR dry powder denitrification agent, and generate energy consumption optimization control data.

2. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: The static and dynamic energy consumption data of the HSR dry powder denitrification agent preparation process collected in S1 include the following steps: S11. Collect energy consumption parameters of different steps in the preparation process of HSR dry powder denitrification agent under non-operation state by equipping the preparation equipment with a static energy consumption sensor, and generate static energy consumption data; S12. Static energy consumption data represents the combination of time parameters and energy consumption parameters established with the process time series as a one-dimensional coordinate axis. Static energy consumption sensors include temperature sensors, pressure sensors, and power meters. Energy consumption parameters include raw material mixing energy consumption, high-temperature reaction energy consumption, and drying energy consumption. S13, where the temperature sensor has an accuracy of ±0.5℃, the pressure sensor has a range of 0-10MPa, and the power meter uses a three-phase energy meter to record power consumption data at 0.5s intervals in real time. S14. The energy consumption parameters of different steps in the preparation process of HSR dry powder denitrification agent under the operation state are collected by the dynamic energy consumption sensor equipped on the preparation equipment, and dynamic energy consumption data is generated. S15. Dynamic energy consumption data represents the combined data of time parameters and energy consumption parameters established with the process time series as a one-dimensional coordinate axis under the operating state. Dynamic energy consumption sensors include ultrasonic flow meters, infrared thermal imagers and vibration sensors, which capture the instantaneous energy consumption peak caused by material conveying heat loss, mechanical vibration energy loss of the reactor and airflow fluctuations.

3. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: The energy consumption parameter sampling frequency measurement and processing in S2 includes the following steps: S21. Import the generated static energy consumption data and dynamic energy consumption data into the energy consumption monitoring platform. Use a bidirectional search algorithm to search for the sampling frequency information corresponding to the target static energy consumption data and target dynamic energy consumption data in the energy consumption monitoring platform according to frequency keywords, and generate static energy consumption frequency data and dynamic energy consumption frequency data, both in Hertz. S22. The static energy consumption frequency data range is fixed at 1Hz, and the dynamic energy consumption frequency data range is adjustable from 5 to 50Hz. The bidirectional search algorithm first traverses the timestamp index of the dynamic energy consumption data, and then matches the device tag of the static energy consumption data in reverse.

4. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: The energy consumption parameter sampling frequency consistency judgment process in S3 includes the following steps: S31. Obtain the static energy consumption frequency data and the dynamic energy consumption frequency data; S32. Compare the frequency values ​​of the static energy consumption frequency data and the dynamic energy consumption frequency data, and generate energy consumption sampling frequency consistency judgment data based on the comparison results. S33. When the frequency values ​​are consistent, output the frequency consistency judgment data and execute S5.

5. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: The static energy consumption parameter sampling frequency adjustment process in step S4 includes the following steps: S41. When the frequency values ​​are inconsistent, a linear interpolation algorithm is used to adjust the sampling frequency of the static energy consumption data according to the dynamic energy consumption frequency data to generate static energy consumption adjustment data. Specifically, this includes: using the dynamic energy consumption frequency as a benchmark, inserting virtual data points between the original sampling points of the static energy consumption data, and generating the values ​​of the virtual data points based on the slope of two adjacent points, ultimately reconstructing a static energy consumption time series consistent with the dynamic energy consumption frequency.

6. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: In step S5, energy consumption parameter combination processing is performed to construct energy consumption parameter combination data, including the following steps: S51. The static energy consumption data, static energy consumption adjustment data and dynamic energy consumption data are aligned and arranged according to the process time parameters to construct energy consumption parameter combination data, and the combination form is a two-dimensional matrix structure. S52. The row vectors of the matrix are process timestamps, and the column vectors are static energy consumption parameters and dynamic energy consumption parameters. Missing values ​​are filled by nearest neighbor interpolation.

7. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: The energy consumption optimization algorithm type matching process in step S6 includes the following steps: S61. Establish a standard energy consumption parameter combination data matrix corresponding to different energy consumption optimization algorithms, including temperature optimization algorithm, pressure optimization algorithm and hybrid optimization algorithm. The standard matrix corresponding to each algorithm contains 100 sets of historical best energy consumption parameter combinations. S62. Match the energy consumption parameter combination data with the standard energy consumption parameter combination data matrix to generate target energy consumption optimization algorithm type feature data; S63. The matching process initializes and optimizes the algorithm to search for the osprey population. In the exploration phase, the current position is randomly perturbed to find a better solution. In the development phase, the population position is updated based on the fitness value. After 50 iterations, the algorithm type label and confidence score with the highest matching degree are output.

8. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: The generation of energy consumption optimization control data in S7 includes the following steps: S71. Combine the energy consumption parameter combination data and the target energy consumption optimization algorithm type feature data to construct energy consumption control summary data. The data packet format is JSON structure, which includes timestamp, device number, algorithm type code and energy consumption parameter matrix hash value. S72. The energy consumption monitoring platform calls the optimization program to extract parameter information from the aggregated data according to the target algorithm type, performs control processing on the dynamic energy consumption data, and generates energy consumption optimization control data. S73. Control processing includes: temperature optimization algorithm to adjust the heating power of the reactor, pressure optimization algorithm to adjust the start-stop cycle of the compressor, and finally output the real-time energy consumption threshold and fluctuation tolerance range of each process.

9. The energy consumption control method for preparing HSR dry powder denitrification agent according to claim 1, characterized in that: It also includes an energy consumption feedback calibration step: S81. Based on the energy consumption optimization control data, compare it with the preset energy consumption threshold in real time. When the threshold is exceeded for 3 consecutive sampling cycles, the parameter is re-adjusted. S82. The readjustment process includes: backtracking the energy consumption data of the previous 10 minutes, re-executing the S4 frequency adjustment and S6 algorithm matching until the energy consumption returns to the threshold range, forming a closed-loop control chain.

10. An energy consumption control system for the preparation of HSR dry powder denitrification agent, used to implement the energy consumption control method for the preparation of HSR dry powder denitrification agent according to any one of claims 1-9, characterized in that: The system includes: The energy consumption data acquisition module uses a static energy consumption acquisition unit to acquire basic energy consumption parameters of the equipment, captures real-time energy consumption data during operation through a dynamic energy consumption acquisition unit, and outputs standardized frequency data through an energy consumption frequency measurement unit. The energy consumption optimization analysis module receives the standardized frequency data, constructs a multi-dimensional energy consumption matrix through the energy consumption parameter combination unit, calls the pre-stored optimization algorithm library through the standard algorithm data storage unit, and outputs the target optimization algorithm identifier through the algorithm type matching unit. The energy consumption control execution module receives the target optimization algorithm identifier, integrates real-time operating condition data through the control parameter collection unit, drives the actuator to adjust the operating parameters through the control execution unit, and outputs a closed-loop control signal through the energy consumption feedback calibration unit. The dynamic environment simulation module receives the closed-loop control signal, simulates the energy consumption environment under different production loads through the working condition scenario reconstruction unit, sets the energy consumption boundary conditions through the safety threshold configuration unit, and outputs test scenario data through the disturbance event generation unit. The real-time energy efficiency assessment module receives the test scenario data, records the system response parameters through the energy efficiency data acquisition unit, calculates the energy efficiency index using the multi-objective optimization unit, and outputs the assessment results through the regulation effect assessment unit. The adaptive optimization module receives the evaluation results, analyzes the system's energy efficiency characteristics through the energy efficiency profile construction unit, locates high-energy-consuming nodes through the weak link identification unit, and generates optimization strategy instructions through the operation parameter control unit.

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