Method for improving gradient utilization rate of steam and comprehensive energy-saving control system

Abstract

The invention discloses a method for improving the steam step utilization rate, which comprises the following steps of: dividing a steam step into at least two areas, and setting a sensor network in each area for acquiring temperature, pressure and flow parameters in real time; the method comprises the following steps: preprocessing sensor data through a data acquisition and processing module, including data cleaning and abnormal value elimination, and calculating an energy efficiency index; and training the preprocessed historical energy efficiency data by using a machine learning algorithm. The working parameters of the steam step are monitored in real time, an energy efficiency prediction model is established through a machine learning algorithm, accurate prediction and optimal regulation and control of the energy efficiency of the steam step are achieved, and therefore the energy efficiency utilization rate of the steam step is remarkably improved. According to the comprehensive energy-saving control system, an intelligent control strategy is adopted, the working parameters of the steam steps can be automatically adjusted according to an energy efficiency prediction model and an optimization target, and intelligent management and automatic control are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and utilization, and in particular to a method for improving steam cascade utilization and a comprehensive energy-saving control system. Background Art

[0002] In the traditional steam cascade utilization process, the low efficiency of steam transmission and conversion between different cascades often leads to energy waste and low energy efficiency. At present, although there are some methods trying to optimize the utilization rate of steam cascades, most of them rely on manual experience and simple control strategies, and it is difficult to achieve intelligent energy efficiency management and optimization. Therefore, how to effectively improve the utilization rate of steam cascades and realize refined energy management and intelligent control has become an urgent problem to be solved in the current field of industrial energy management technology. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned background technical problems and to propose a method for improving the utilization rate of steam cascade and a comprehensive energy-saving control system.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A method for improving steam step utilization comprises the following steps:

[0006] Divide the steam stage into at least two areas, and set a sensor network in each area for real-time acquisition of temperature, pressure and flow parameters;

[0007] The sensor data is preprocessed through the data acquisition and processing module, including data cleaning and outlier removal, and the energy efficiency index is calculated;

[0008] Use machine learning algorithms to train preprocessed historical energy efficiency data and establish an energy efficiency prediction model;

[0009] According to the energy efficiency prediction model and optimization objectives, the working parameters of the steam stage are adjusted through the controller module to achieve intelligent control;

[0010] The actuator module adjusts the working state of the steam stage according to the instructions of the controller module to achieve energy efficiency optimization.

[0011] A comprehensive energy-saving control system for improving steam cascade utilization, characterized in that it includes a sensor network module deployed in each area of ​​the steam cascade for real-time monitoring of the working parameters of the steam cascade;

[0012] The data acquisition and processing module receives the data from the sensor network module, performs preprocessing, and calculates the energy efficiency index;

[0013] The machine learning module builds an energy efficiency prediction model based on preprocessed historical data and updates the model in real time;

[0014] The controller module outputs control instructions and adjusts the working parameters of the steam cascade according to the energy efficiency prediction model and optimization objectives;

[0015] The actuator module adjusts the working state of the steam stage according to the control instructions of the controller module;

[0016] Redundant design modules provide backup for key components to improve system reliability and stability;

[0017] The user interface module provides real-time data monitoring, energy efficiency analysis reports, remote control functions and energy efficiency optimization suggestions.

[0018] As a further solution of the present invention: the machine learning module also includes a model verification function, which continuously iterates and optimizes the prediction model by comparing the actual energy efficiency with the predicted energy efficiency. The model verification function further includes using cross-validation, holdout method or bootstrap method and other techniques to verify the model.

[0019] As a further solution of the present invention: the controller module also has a fault warning function. When the energy efficiency index is monitored to be lower than a preset threshold or it is predicted that the energy efficiency will drop significantly, a warning signal is automatically triggered. The warning signal includes but is not limited to a sound alarm, a light signal alarm or sending an alarm message to a remote terminal.

[0020] As a further solution of the present invention: the energy efficiency optimization provided by the user interface module is based on historical data and a prediction model, and the energy efficiency optimization includes adjusting the working parameters of the steam stage, replacing equipment or optimizing the operating process.

[0021] As a further solution of the present invention: the system also includes a safety monitoring module for real-time monitoring of the safety status of the steam stage, and automatically triggering a safety protection mechanism when an abnormality or potential danger is detected.

[0022] As a further solution of the present invention: the system also has remote monitoring and remote control functions, allowing the user to view the working status, energy efficiency data and alarm information of the steam stage in real time through a remote terminal, and remotely control the working parameters of the steam stage.

[0023] Beneficial effects of the present invention:

[0024] Improve energy efficiency utilization: By real-time monitoring of the working parameters of the steam cascade and using machine learning algorithms to establish an energy efficiency prediction model, accurate prediction and optimal regulation of the steam cascade energy efficiency are achieved, thereby significantly improving the energy efficiency utilization of the steam cascade.

[0025] Intelligent management: The comprehensive energy-saving control system of the present invention adopts an intelligent control strategy, which can automatically adjust the working parameters of the steam cascade according to the energy efficiency prediction model and optimization objectives, thereby realizing intelligent management and automatic control. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described below in conjunction with the accompanying drawings.

[0027] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] For example, see Figure 1 As shown, the present invention is a method for improving steam step utilization, characterized in that it comprises the following steps:

[0030] Divide the steam stage into at least two areas, and set a sensor network in each area for real-time acquisition of temperature, pressure and flow parameters;

[0031] The sensor data is preprocessed through the data acquisition and processing module, including data cleaning and outlier removal, and the energy efficiency index is calculated;

[0032] Use machine learning algorithms to train preprocessed historical energy efficiency data and establish an energy efficiency prediction model;

[0033] According to the energy efficiency prediction model and optimization objectives, the working parameters of the steam stage are adjusted through the controller module to achieve intelligent control;

[0034] The actuator module adjusts the working state of the steam stage according to the instructions of the controller module to achieve energy efficiency optimization.

[0035] Embodiment 2, a comprehensive energy-saving control system for improving steam cascade utilization, characterized in that it includes a sensor network module deployed in each area of ​​the steam cascade for real-time monitoring of the working parameters of the steam cascade;

[0036] The data acquisition and processing module receives the data from the sensor network module, performs preprocessing, and calculates the energy efficiency index;

[0037] The machine learning module builds an energy efficiency prediction model based on preprocessed historical data and updates the model in real time;

[0038] The controller module outputs control instructions and adjusts the working parameters of the steam cascade according to the energy efficiency prediction model and optimization objectives;

[0039] The actuator module adjusts the working state of the steam stage according to the control instructions of the controller module;

[0040] Redundant design modules provide backup for key components to improve system reliability and stability;

[0041] The user interface module provides real-time data monitoring, energy efficiency analysis reports, remote control functions and energy efficiency optimization suggestions.

[0042] In another embodiment, the machine learning module also includes a model validation function, which continuously iterates and optimizes the prediction model by comparing the actual energy efficiency with the predicted energy efficiency. The model validation function further includes using cross-validation, holdout method or bootstrap method and other techniques to validate the model.

[0043] The controller module also has a fault warning function. When the energy efficiency index is monitored to be lower than a preset threshold or it is predicted that the energy efficiency will drop significantly, a warning signal is automatically triggered. The warning signal includes but is not limited to a sound alarm, a light signal alarm or sending an alarm message to a remote terminal.

[0044] The energy efficiency optimization provided by the user interface module is based on historical data and a prediction model, and the energy efficiency optimization includes adjusting the working parameters of the steam stage, replacing equipment, or optimizing the operation process.

[0045] The system also includes a safety monitoring module for real-time monitoring of the safety status of the steam cascade, which automatically triggers the safety protection mechanism when an abnormality or potential danger is detected.

[0046] The system also has remote monitoring and remote control functions, allowing users to view the working status, energy efficiency data and alarm information of the steam cascade in real time through the remote terminal, and remotely control the working parameters of the steam cascade.

[0047] Worth mentioning:

[0048] A method for improving the utilization rate of steam cascade: the method includes dividing the steam cascade into at least two areas, and setting a sensor network in each area to collect parameters such as temperature, pressure, flow and the like in real time. Then, the sensor data is preprocessed by the data acquisition and processing module, and the energy efficiency index E is calculated. The calculation formula of the energy efficiency index E is: E = α × (T / T_opt)2 / P_opt0.5, where α, β, and γ are weight coefficients determined according to the characteristics of the steam cascade and historical data, T, P, and Q are respectively the temperature, pressure, and flow collected in real time, and T_opt, P_opt, and Q_opt are respectively the optimal values ​​of temperature, pressure, and flow. Then, the preprocessed historical energy efficiency data is trained using a machine learning algorithm to establish an energy efficiency prediction model. Finally, according to the energy efficiency prediction model and the optimization target, the working parameters of the steam cascade are adjusted through the controller module to realize intelligent control.

[0049] A comprehensive energy-saving control system: The system includes a sensor network module, a data acquisition and processing module, a machine learning module, a controller module, an actuator module, a redundant design module and a user interface module. Among them, the sensor network module is used to monitor the working parameters of the steam cascade in real time; the data acquisition and processing module is used to receive sensor data and perform preprocessing; the machine learning module is used to establish an energy efficiency prediction model and update it in real time; the controller module is used to adjust the working parameters of the steam cascade according to the prediction results; the actuator module is used to execute the instructions of the controller module; the redundant design module is used to improve the reliability and stability of the system; the user interface module is used to provide real-time data monitoring, energy efficiency analysis reports, remote control functions and energy efficiency optimization suggestions.

[0050] Sensor network module: Multiple sensors, including temperature sensors, pressure sensors, and flow sensors, are set in each steam step area to collect the working parameters of the steam step in real time. These sensors transmit data to the data acquisition and processing module via wired or wireless means.

[0051] Data acquisition and processing module: Receives data from the sensor network module and performs preprocessing operations such as data cleaning and outlier removal. Then, the real-time energy efficiency index E is calculated according to the calculation formula of the energy efficiency index E.

[0052] Machine learning module: Use pre-processed historical energy efficiency data to establish an energy efficiency prediction model through machine learning algorithms (such as support vector machines, neural networks, etc.). This model can predict the future energy efficiency index E based on current working parameters. At the same time, this module also includes a model verification function, which continuously iterates and optimizes the prediction model by comparing actual energy efficiency with predicted energy efficiency.

[0053] Controller module: According to the energy efficiency prediction model and optimization target, the intelligent control algorithm (such as PID control, fuzzy control, etc.) is used to adjust the working parameters of the steam cascade (such as temperature, pressure, flow, etc.) to achieve energy efficiency optimization. At the same time, the module also has a fault warning function. When the energy efficiency index E is monitored to be lower than the preset threshold or the energy efficiency is predicted to drop significantly, the warning signal is automatically triggered.

[0054] Actuator module: According to the instructions of the controller module, it adjusts the working status of the steam cascade (such as opening or closing the valve, adjusting the motor speed, etc.) to achieve energy efficiency optimization and fault warning.

[0055] Redundant design module: Provide backup for key components to improve system reliability and stability. For example, redundant sensors, controllers, and actuators can be set up to ensure that the system can still operate normally when a single component fails.

[0056] User interface module: provides real-time data monitoring, energy efficiency analysis reports, remote control functions and energy efficiency optimization suggestions. Users can use this module to view the working status, energy efficiency data and alarm information of the steam cascade in real time, and remotely control the working parameters of the steam cascade. At the same time, this module can also provide users with energy efficiency optimization suggestions based on historical data and prediction models to help users better manage steam cascades.

[0057] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of application of the present invention should still fall within the scope of the claims of the present invention.

Claims

1. A method for improving steam cascade utilization, characterized in that: The following steps are involved: Divide the steam stage into at least two areas, and set a sensor network in each area for real-time acquisition of temperature, pressure and flow parameters; The sensor data is preprocessed through the data acquisition and processing module, including data cleaning and outlier removal, and the energy efficiency index is calculated; Use machine learning algorithms to train preprocessed historical energy efficiency data and establish an energy efficiency prediction model; According to the energy efficiency prediction model and optimization objectives, the working parameters of the steam stage are adjusted through the controller module to achieve intelligent control; The actuator module adjusts the working state of the steam stage according to the instructions of the controller module to achieve energy efficiency optimization.

2. A comprehensive energy-saving control system for improving steam cascade utilization, characterized in that: It includes a sensor network module, which is deployed in various areas of the steam cascade to monitor the working parameters of the steam cascade in real time; The data acquisition and processing module receives the data from the sensor network module, performs preprocessing, and calculates the energy efficiency index; The machine learning module builds an energy efficiency prediction model based on preprocessed historical data and updates the model in real time; The controller module outputs control instructions and adjusts the working parameters of the steam cascade according to the energy efficiency prediction model and optimization objectives; The actuator module adjusts the working state of the steam stage according to the control instructions of the controller module; Redundant design modules provide backup for key components to improve system reliability and stability; The user interface module provides real-time data monitoring, energy efficiency analysis reports, remote control functions and energy efficiency optimization suggestions.

3. A method for improving steam cascade utilization and a comprehensive energy-saving control system according to claim 2, characterized in that: The machine learning module also includes a model validation function, which continuously iterates and optimizes the prediction model by comparing the actual energy efficiency with the predicted energy efficiency. The model validation function further includes using cross-validation, holdout method or bootstrap method to validate the model.

4. A method for improving steam cascade utilization and a comprehensive energy-saving control system according to claim 2, characterized in that: The controller module also has a fault warning function. When the energy efficiency index is monitored to be lower than a preset threshold or it is predicted that the energy efficiency will drop significantly, a warning signal is automatically triggered. The warning signal includes but is not limited to a sound alarm, a light signal alarm or sending an alarm message to a remote terminal.

5. A method for improving steam cascade utilization and a comprehensive energy-saving control system according to claim 2, characterized in that: The energy efficiency optimization provided by the user interface module is based on historical data and a prediction model, and the energy efficiency optimization includes adjusting the working parameters of the steam stage, replacing equipment, or optimizing the operation process.

6. A method for improving steam cascade utilization and a comprehensive energy-saving control system according to claim 2, characterized in that: The system also includes a safety monitoring module for real-time monitoring of the safety status of the steam cascade, automatically triggering the safety protection mechanism when an abnormality or potential danger is detected.

7. A method for improving steam cascade utilization and a comprehensive energy-saving control system according to claim 2, characterized in that: The system also has remote monitoring and remote control functions, allowing users to view the working status, energy efficiency data and alarm information of the steam cascade in real time through the remote terminal, and remotely control the working parameters of the steam cascade.

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