Automatic pressure control device of vacuum hot water boiler and control method of automatic pressure control device

Abstract

The invention relates to the technical field of vacuum hot water boilers, in particular to a vacuum hot water boiler automatic pressure control device and a control method thereof.The vacuum hot water boiler automatic pressure control device comprises a boiler body module and a heater module, the heater module is installed on one side of the boiler body module, and a heat exchange module is installed on one side of the boiler body module; the water inlet end and the water outlet end of the heat exchange module are each provided with a temperature sensor module, a pressure adjusting module is installed on one side of the boiler body module, and controller modules are installed on one sides of the temperature sensor modules and one side of the pressure adjusting module. According to the system, the boiler outlet water temperature and the pressure in the boiler are monitored in real time through the temperature sensor and the pressure sensor, and accurate control over the outlet water temperature is ensured in combination with the calculation module and the neural network optimization module in the controller; through analysis and optimization of real-time data and historical data, temperature fluctuation can be effectively reduced, and the precision of temperature control is improved.

Description

Technical Field

[0001] The invention relates to the technical field of vacuum hot water boilers, in particular to an automatic pressure control device for a vacuum hot water boiler and a control method thereof. Background Art

[0002] Vacuum hot water boilers are widely used in industrial and civil heating systems; traditional vacuum hot water boilers usually control the outlet water temperature by manually adjusting the pressure and temperature; however, this manual adjustment method has many disadvantages: slow response speed, low adjustment accuracy, and inability to respond to load changes in real time, resulting in large fluctuations in the outlet water temperature, affecting the stability and efficiency of the heating system; in addition, the traditional system lacks intelligent control means, making it difficult to achieve accurate and stable temperature control.

[0003] In the current vacuum hot water boiler system, there is a lack of effective automatic control devices to realize real-time monitoring and adjustment of boiler pressure and temperature; especially in the case of large load changes, the lag and low precision of manual adjustment seriously affect the operating efficiency and energy utilization of the system; therefore, in order to solve the above problems, an automatic pressure control device for vacuum hot water boiler and a control method thereof are proposed. Summary of the invention

[0004] The purpose of the present invention is to provide an automatic pressure control device for a vacuum hot water boiler and a control method thereof, which can monitor and adjust the pressure and outlet water temperature in the boiler in real time by adding a sensor module and optimizing the device using a neural network model algorithm, thereby achieving accurate and stable temperature control.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An automatic pressure control device for a vacuum hot water boiler comprises a boiler body module and a heater module, wherein the heater module is installed on one side of the boiler body module, a heat exchange module is installed on one side of the boiler body module, a temperature sensor module is installed on both the water inlet and the water outlet of the heat exchange module, a pressure regulating module is installed on one side of the boiler body module, and a controller module is installed on one side of the temperature sensor module and one side of the pressure regulating module;

[0007] The controller module comprises:

[0008] A data processing module, used for processing the measurement data of the temperature sensor and the pressure sensor;

[0009] A calculation module for calculating the required boiler pressure based on the Antoine equation and the target outlet water temperature;

[0010] A control module, used for controlling the opening of the pressure regulating valve according to the pressure value calculated by the calculation module;

[0011] Neural network optimization module, used to optimize control strategies based on real-time data and historical operation data.

[0012] Preferably, the calculation module calculates the required boiler pressure using the following formula:

[0013] T out =T in +η·(T source -T in )

[0014]

[0015] Among them, T source is the heat source temperature, T out is the outlet water temperature, T in is the inlet water temperature, η is the boiler thermal efficiency, and P is the pressure.

[0016] Preferably, the pressure regulating module is a proportional valve, which accurately adjusts the pressure in the boiler by changing the valve opening; the heater module is an electric heater, a gas heater or a steam heater, and is adjusted by a controller according to real-time temperature data.

[0017] Preferably, the device further comprises an alarm module for issuing an alarm signal when the outlet water temperature or the boiler pressure exceeds a safe range; the alarm module comprises an audible alarm and a visual alarm to ensure that the operator takes timely measures.

[0018] Preferably, the neural network model adopts a multi-layer perceptron (MLP) architecture, including an input layer, a hidden layer and an output layer, and the model parameters are optimized by training the data set. The formula is as follows:

[0019]

[0020] Among them, y is the output, x i is the input, w i is the weight, b is the bias, and f is the activation function.

[0021] Preferably, the optimization algorithm of the neural network model adopts an adaptive moment estimation (Adam) optimizer, and the algorithm is as follows:

[0022] m t =β 1 m t-1 +(1-β 1 ) t

[0023]

[0024] Where m t and v tare the first-order moment estimate and the second-order moment estimate of the gradient, β 1 and β 2 is the decay rate, α is the learning rate, ∈ is the smoothing term, θ t as a parameter.

[0025] A control method for an automatic pressure control device for a vacuum hot water boiler comprises the following steps:

[0026] Step 1: Real-time measurement of boiler outlet water temperature and pressure inside the boiler;

[0027] Step 2: input the measured data into the data processing module of the controller module;

[0028] Step 3: The controller module calculates the required boiler pressure through the calculation module according to the target outlet water temperature and the current pressure;

[0029] Step 4: The controller adjusts the opening of the pressure regulating valve through the pressure regulating module according to the calculation results to achieve the required pressure;

[0030] Step 5: Adjust the power of the heater module in the boiler to stabilize the outlet water temperature at the target value;

[0031] Step 6: Analyze historical data and real-time data through the neural network model and optimize the control strategy to further improve the accuracy and response speed of temperature control.

[0032] Preferably, the target outlet water temperature is pre-set according to user needs and can be adjusted through a human-machine interface, and the controller can dynamically adapt to different set temperatures.

[0033] Preferably, the controller module uses a PID control algorithm to achieve accurate regulation of the boiler pressure, and the parameters of the PID algorithm are adjusted in real time through an adaptive algorithm to optimize the control effect; the PID control algorithm includes the following detailed steps:

[0034] Step 1: Proportional control: Calculate the proportional control amount K based on the current error e(t) p e(t), where K p is the proportionality coefficient;

[0035] Step 2: Integral control: Accumulate historical errors and calculate the integral control amount Where K i is the integration coefficient;

[0036] Step 3: Differential control: Calculate the error change rate and the differential control amount Where K d is the differential coefficient;

[0037] Step 4: Control output: Combine proportional, integral and differential control quantities to calculate the final control output

[0038] Preferably, the parameters of the PID control algorithm are adjusted in real time by an adaptive algorithm to optimize the control effect; the specific steps of the adaptive algorithm are as follows:

[0039] Step 1: Initial parameter setting: Set the initial PID parameter K according to system characteristics p , K i and K d ;

[0040] Step 2: Real-time error analysis: monitor the real-time error and response curve during the operation of the system;

[0041] Step 3: Parameter optimization: According to the real-time error analysis results, dynamically adjust the PID parameters through the adaptive algorithm to achieve the best control effect of the system;

[0042] Step 4: Parameter update: Apply the optimized parameters to the PID controller and adjust the control output in real time.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. In the present invention, the outlet water temperature of the boiler and the pressure in the boiler are monitored in real time by temperature sensors and pressure sensors, and the calculation module and neural network optimization module in the controller are combined to ensure accurate control of the outlet water temperature; through the analysis and optimization of real-time data and historical data, the temperature fluctuation can be effectively reduced, the accuracy of temperature control can be improved, and the stability and efficient operation of the heating system can be ensured.

[0045] 2. In the present invention, a controller is used to automatically adjust the pressure in the boiler and the power of the heater without manual intervention; by using PID control algorithm and adaptive algorithm, the system can quickly respond to changes in temperature and pressure, achieve efficient automatic adjustment, and greatly improve the response speed and operating efficiency of the system.

[0046] 3. In the present invention, by optimizing the pressure regulation strategy in the boiler and the power output of the heater, the present invention can reduce energy consumption while meeting the outlet water temperature requirements; the neural network model further optimizes the control strategy by learning historical operation data, so that the system can achieve the best energy efficiency under different working conditions and save energy costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is the overall module diagram of the present invention. DETAILED DESCRIPTION

[0048] See also Figure 1, the present invention provides a technical solution:

[0049] An automatic pressure control device for a vacuum hot water boiler comprises a boiler body module and a heater module, wherein the heater module is installed on one side of the boiler body module, a heat exchange module is installed on one side of the boiler body module, a temperature sensor module is installed on both the water inlet and the water outlet of the heat exchange module, a pressure regulating module is installed on one side of the boiler body module, and a controller module is installed on one side of the temperature sensor module and one side of the pressure regulating module;

[0050] The controller module comprises:

[0051] A data processing module, used for processing the measurement data of the temperature sensor and the pressure sensor;

[0052] A calculation module for calculating the required boiler pressure based on the Antoine equation and the target outlet water temperature;

[0053] A control module, used for controlling the opening of the pressure regulating valve according to the pressure value calculated by the calculation module;

[0054] Neural network optimization module, used to optimize control strategies based on real-time data and historical operation data.

[0055] By measuring and controlling the boiler pressure and temperature in real time, the stability and accuracy of the outlet water temperature are ensured, thus improving the overall efficiency and reliability of the system. Through modular design, efficient data processing and precise calculation are achieved, ensuring the timeliness and accuracy of pressure and temperature regulation, thus improving the response speed of the system.

[0056] The calculation module calculates the required boiler pressure using the following formula:

[0057] T out =T i +η·(T source -T in )

[0058]

[0059] Among them, T source is the heat source temperature, T out is the outlet water temperature, T in is the inlet water temperature, η is the boiler thermal efficiency, and P is the pressure.

[0060] By using accurate mathematical models and formulas, the outlet water temperature can be accurately predicted and controlled, thus improving the temperature regulation accuracy of the system.

[0061] The pressure regulating module is a proportional valve, which accurately adjusts the pressure in the boiler by changing the valve opening; the heater module is an electric heater, a gas heater or a steam heater, and is adjusted by a controller according to real-time temperature data.

[0062] The use of proportional valves makes pressure regulation more precise and flexible, which helps to achieve higher-precision temperature control; the flexible selection of multiple heating methods improves the adaptability and energy efficiency of the system, ensuring stable heating under different working conditions.

[0063] The device also includes an alarm module for sending out an alarm signal when the outlet water temperature or the boiler pressure exceeds a safe range; the alarm module includes an audible alarm and a visual alarm to ensure that the operator can take timely measures.

[0064] The setting of the alarm module can promptly remind operators when abnormalities occur in the system, prevent potential risks, and ensure the safe operation of the system.

[0065] The neural network model adopts a multi-layer perceptron (MLP) architecture, including an input layer, a hidden layer, and an output layer. The model parameters are optimized through a training data set. The formula is as follows:

[0066]

[0067] Among them, y is the output, x i is the input, w i is the weight, b is the bias, and f is the activation function.

[0068] The multilayer perceptron model can be trained with a large amount of historical data to optimize the system's control strategy and improve the accuracy and stability of temperature and pressure control.

[0069] The optimization algorithm of the neural network model adopts the adaptive moment estimation (Adam) optimizer, and the algorithm is as follows:

[0070] m t =β 1 m t-1 +(1-β 1 ) t

[0071]

[0072] Where m t and v t are the first-order moment estimate and the second-order moment estimate of the gradient, β 1 and β 2 is the decay rate, α is the learning rate, ∈ is the smoothing term, θ t as a parameter.

[0073] The Adam optimization algorithm can effectively improve the training speed and convergence of the neural network model and improve the optimization efficiency and accuracy of the control strategy by adaptively adjusting the learning rate.

[0074] A control method for an automatic pressure control device for a vacuum hot water boiler comprises the following steps:

[0075] Step 1: Real-time measurement of boiler outlet water temperature and pressure inside the boiler;

[0076] Step 2: input the measured data into the data processing module of the controller module;

[0077] Step 3: The controller module calculates the required boiler pressure through the calculation module according to the target outlet water temperature and the current pressure;

[0078] Step 4: The controller adjusts the opening of the pressure regulating valve through the pressure regulating module according to the calculation results to achieve the required pressure;

[0079] Step 5: Adjust the power of the heater module in the boiler to stabilize the outlet water temperature at the target value;

[0080] Step 6: Analyze historical data and real-time data through the neural network model and optimize the control strategy to further improve the accuracy and response speed of temperature control.

[0081] Through automation and intelligent control, the system's operating efficiency and stability are significantly improved, the need for manual intervention is reduced, and the stability of the outlet water temperature is ensured.

[0082] The target outlet water temperature is preset according to user needs and can be adjusted through a human-machine interface. The controller can dynamically adapt to different set temperatures.

[0083] Users can flexibly set and adjust the target water outlet temperature according to actual needs. The system can dynamically respond and adapt to different temperature settings, improving the user-friendliness and flexibility of the system.

[0084] The controller module uses a PID control algorithm to achieve precise regulation of the boiler pressure. The parameters of the PID algorithm are adjusted in real time through an adaptive algorithm to optimize the control effect. The PID control algorithm includes the following detailed steps:

[0085] Step 1: Proportional control: Calculate the proportional control amount K based on the current error e(t) p e(t), where K p is the proportionality coefficient;

[0086] Step 2: Integral control: Accumulate historical errors and calculate the integral control amount Where K i is the integration coefficient;

[0087] Step 3: Differential control: Calculate the error change rate and the differential control amount Where K d is the differential coefficient;

[0088] Step 4: Control output: Combine proportional, integral and differential control quantities to calculate the final control output

[0089] The PID control algorithm can effectively reduce overshoot and oscillation during temperature and pressure regulation. The PID parameters are adjusted in real time through an adaptive algorithm, further improving the response speed and stability of the control system.

[0090] The parameters of the PID control algorithm are adjusted in real time through an adaptive algorithm to optimize the control effect; the specific steps of the adaptive algorithm are as follows:

[0091] Step 1: Initial parameter setting: Set the initial PID parameter K according to system characteristics p , K i and K d ;

[0092] Step 2: Real-time error analysis: monitor the real-time error and response curve during the operation of the system;

[0093] Step 3: Parameter optimization: According to the real-time error analysis results, dynamically adjust the PID parameters through the adaptive algorithm to achieve the best control effect of the system;

[0094] Step 4: Parameter update: Apply the optimized parameters to the PID controller and adjust the control output in real time.

[0095] The adaptive algorithm can optimize PID parameters in real time according to actual working conditions, ensuring that the system can achieve the best control effect under different working conditions, thereby improving the adaptability and control accuracy of the system.

[0096] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. The above is only a preferred implementation of the present invention. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the protection scope of the present invention.

Claims

1. An automatic pressure control device for a vacuum hot water boiler, characterized in that: It comprises a boiler body module and a heater module, wherein the heater module is installed on one side of the boiler body module, the heat exchange module is installed on one side of the boiler body module, the water inlet and water outlet of the heat exchange module are both installed with temperature sensor modules, the pressure regulating module is installed on one side of the boiler body module, and the controller module is installed on one side of the temperature sensor module and one side of the pressure regulating module; The controller module comprises: A data processing module, used for processing the measurement data of the temperature sensor and the pressure sensor; A calculation module for calculating the required boiler pressure based on the Antoine equation and the target outlet water temperature; A control module, used for controlling the opening of the pressure regulating valve according to the pressure value calculated by the calculation module; Neural network optimization module, used to optimize control strategies based on real-time data and historical operation data.

2. The automatic pressure control device for a vacuum hot water boiler according to claim 1, characterized in that: The calculation module calculates the required boiler pressure using the following formula: T out =T in +η·(T source -T in ) Among them, T source is the heat source temperature, T out is the outlet water temperature, T in is the inlet water temperature, η is the boiler thermal efficiency, and P is the pressure.

3. The automatic pressure control device for a vacuum hot water boiler according to claim 1, characterized in that: The pressure regulating module is a proportional valve, which accurately adjusts the pressure in the boiler by changing the valve opening; the heater module is an electric heater, a gas heater or a steam heater, and is adjusted by a controller according to real-time temperature data.

4. The automatic pressure control device for a vacuum hot water boiler according to claim 1, characterized in that: The device also includes an alarm module for sending out an alarm signal when the outlet water temperature or the boiler pressure exceeds a safe range; the alarm module includes an audible alarm and a visual alarm to ensure that the operator can take timely measures.

5. The automatic pressure control device for a vacuum hot water boiler according to claim 1, characterized in that: The neural network model adopts a multi-layer perceptron (MLP) architecture, including an input layer, a hidden layer, and an output layer. The model parameters are optimized through a training data set. The formula is as follows: Among them, y is the output, x i is the input, w i is the weight, b is the bias, and f is the activation function.

6. The automatic pressure control device for a vacuum hot water boiler according to claim 1, characterized in that: The optimization algorithm of the neural network model adopts the adaptive moment estimation (Adam) optimizer, and the algorithm is as follows: m t =β1m t-1 +(1-β1)g t Where m t and v t are the first-order moment estimate and the second-order moment estimate of the gradient, β1 and β2 are the decay rates, α is the learning rate, ∈ is the smoothing term, and θ t as a parameter.

7. A control method for an automatic pressure control device for a vacuum hot water boiler, characterized in that: The following steps are involved: Step 1: Real-time measurement of boiler outlet water temperature and pressure inside the boiler; Step 2: input the measured data into the data processing module of the controller module; Step 3: The controller module calculates the required boiler pressure through the calculation module according to the target outlet water temperature and the current pressure; Step 4: The controller adjusts the opening of the pressure regulating valve through the pressure regulating module according to the calculation results to achieve the required pressure; Step 5: Adjust the power of the heater module in the boiler to stabilize the outlet water temperature at the target value; Step 6: Analyze historical data and real-time data through the neural network model and optimize the control strategy to further improve the accuracy and response speed of temperature control.

8. The control method of the automatic pressure control device for a vacuum hot water boiler according to claim 7, characterized in that: The target outlet water temperature is preset according to user needs and can be adjusted through a human-machine interface. The controller can dynamically adapt to different set temperatures.

9. The control method of the automatic pressure control device for a vacuum hot water boiler according to claim 8, characterized in that: The controller module uses a PID control algorithm to achieve precise regulation of the boiler pressure. The parameters of the PID algorithm are adjusted in real time through an adaptive algorithm to optimize the control effect. The PID control algorithm includes the following detailed steps: Step 1: Proportional control: Calculate the proportional control amount K based on the current error e(t) p e(t), where K p is the proportionality coefficient; Step 2: Integral control: Accumulate historical errors and calculate the integral control amount Where K i is the integration coefficient; Step 3: Differential control: Calculate the error change rate and the differential control amount Where K d is the differential coefficient; Step 4: Control output: Combine proportional, integral and differential control quantities to calculate the final control output 10. The control method of the automatic pressure control device for a vacuum hot water boiler according to claim 9, characterized in that: The parameters of the PID control algorithm are adjusted in real time through an adaptive algorithm to optimize the control effect; The specific steps of the adaptive algorithm are as follows: Step 1: Initial parameter setting: Set the initial PID parameter K according to system characteristics p , K i and K d ; Step 2: Real-time error analysis: monitor the real-time error and response curve during the operation of the system; Step 3: Parameter optimization: According to the real-time error analysis results, dynamically adjust the PID parameters through the adaptive algorithm to achieve the best control effect of the system; Step 4: Parameter update: Apply the optimized parameters to the PID controller and adjust the control output in real time.

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