Model and data driven intelligent control method for radon scrubber

Abstract

The application discloses a radon purifier intelligent control method based on model and data driving, and specifically comprises the following steps: firstly, data acquisition; secondly, relevant parameter fitting according to the collected data; thirdly, obtaining purification efficiency λ v , radon source term concentration alpha through least square fitting of an indoor radon concentration change equation; and finally, intelligently controlling the radon purifier according to λ v and alpha obtained through model and data driving. The radon purifier intelligent control method based on model and data driving is adopted, the radon purifier purification time, purification efficiency and indoor radon source term concentration are predicted through the intelligent control method driven by data and model according to the real-time monitored radon concentration, temperature, humidity and other data in the room, and the radon purifier is used to purify indoor air with the lowest energy consumption and the highest material utilization.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, and in particular to a model- and data-driven intelligent control method for radon purifiers. Background Technology

[0002] Radon is a colorless, odorless, and radioactive inert gas. While inherently inactive, it decays into radon progeny. These progeny, inhaled, accumulate in the respiratory tract and lungs. The energy released during their continued decay damages normal cells, eventually leading to lung cancer. Studies show that inhalation of radon and its radioactive progeny is the leading cause of lung cancer in humans, second only to smoking. Long-term exposure to high radon concentrations can also trigger serious illnesses such as stomach cancer and leukemia, severely impacting human health.

[0003] In recent decades, with the dramatic changes in building materials and the vigorous promotion of energy-efficient housing in my country, indoor radon levels have been steadily increasing. Indoor radon mainly originates from building and decoration materials, with radon from building materials accounting for up to 19.5% of indoor radon concentration. Compared to the 1980s, indoor radon levels in my country have increased by 20% to 57%, and given my country's vast territory and complex geological structure, soil radon levels... 238 U and 232 The activity concentration of Th is higher than the world average, and there are many potentially high-radon areas. In order to solve the problem of excessive radon in residential buildings and reduce the health impact of radon on residents, it is urgent to carry out research on radon reduction devices.

[0004] Existing radon removal methods mainly include air purification technology and ventilation. Air purification for radon reduction primarily involves filtration and adsorption by purification devices to achieve radon reduction. Key methods include adsorption purification, electrostatic dust removal, and fiber filtration. Ventilation aims to reduce radon concentration by improving air circulation between indoors and outdoors, and is a crucial means of radon control. However, ventilation systems are influenced by many factors, and continuous, single-mode ventilation can lead to high building energy consumption. Considering my country's specific circumstances, using purification devices to remove indoor radon and its decay products is a relatively economical and effective technical approach.

[0005] Air purification for radon reduction is currently one of the main methods for indoor radon removal. This method is not limited by the source or pathway of radon and can be used in places where house renovations and ventilation are difficult. It is particularly economical and fast for places with short-term occupancy and indoor spaces where long-term stay is not required. However, traditional radon purification methods lack systematicity and intelligence, and often suffer from low consumable utilization and high energy consumption. Therefore, it is essential to develop an intelligent control method for radon purifiers to achieve intelligent control, reduce indoor radon and its decay product concentrations, lower energy consumption, improve air quality, and protect the health of residents. Summary of the Invention

[0006] The purpose of this invention is to provide a model- and data-driven intelligent control method for radon purifiers, applicable to various user scenarios. The radon reduction strategy is adjusted according to the radon reduction requirements of different scenarios, ensuring that the indoor radon concentration remains below a specified value when personnel are on duty. This achieves efficient and energy-saving radon reduction for various indoor scenarios, offering advantages such as high intelligence, real-time monitoring, dynamic adjustment, and energy conservation.

[0007] To achieve the above objectives, this invention provides a model- and data-driven intelligent control method for radon air purifiers, the specific steps of which are as follows:

[0008] S1, Data Acquisition;

[0009] S2. Fit relevant parameters based on the data collected in step S1;

[0010] S3. The purification efficiency λ is obtained by fitting the equation of indoor radon concentration change using the least squares method. v Radon source concentration α;

[0011] S4. λ is obtained based on the model and data obtained in step S3. v And α, to intelligently control the radon purifier.

[0012] Preferably, in step S1, data acquisition involves obtaining real-time radon concentration, temperature, and humidity values ​​through sensors on the purifier, collecting space volume and wall area, and calculating the porosity of indoor materials.

[0013] Preferably, in step S2, relevant parameters are fitted based on the collected data. The effective diffusion coefficient D of radon is obtained by fitting the data collected in step S1, and the equivalent decay coefficient λ of radon is further obtained by fitting. e .

[0014] Preferably, in step S4, λ is obtained based on the model and data-driven approach. vAnd α, to achieve intelligent control of the radon purifier, the control scheme is divided into two scenarios: unmanned and manned. In the unmanned scenario, the pre-purification time t is calculated through the drive model. s This ensures that the indoor radon concentration is below the threshold when the system starts operating. When someone is on duty, the radon sensor detects the indoor radon concentration in real time. Once the set threshold is exceeded, the purifier starts to purify and reduce the radon until the radon concentration is reduced to below the limit radon concentration value.

[0015] Preferably, the purification efficiency λ obtained by fitting the data in step S2 is... v This serves as a basis for determining whether the air purifier's consumables need to be replaced.

[0016] Therefore, the beneficial effects of the above-mentioned model- and data-driven intelligent control method for radon purifiers in this invention are as follows:

[0017] 1. This invention collects scene parameters from the user side, and the model and data-driven control method can meet the radon removal requirements in multiple indoor scenarios. It is applicable to most above-ground buildings that require radon removal and meets the radon reduction and progeny requirements of underground engineering for air isolation under specific circumstances.

[0018] 2. This invention utilizes a model- and data-driven control method to fit the purification efficiency of the radon purifier every 2 hours, using this efficiency as the basis for determining whether the purifier's consumables need to be replaced. This overcomes the drawback of traditional purification devices requiring timely replacement of consumables, making consumable replacement more intelligent and reliable.

[0019] 3. This invention is based on a self-developed radon and its decay product testing module. Using real-time indoor radon concentration and temperature data, an intelligent control method predicts the purification time, purification efficiency, and indoor radon source concentration of the radon purifier, enabling it to purify indoor air with minimal energy consumption and maximum material utilization. This overcomes the shortcomings of traditional radon purification methods, such as lack of systematicity and intelligence, low material utilization, and high energy consumption.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a technical roadmap for a model- and data-driven intelligent control method for radon purifiers according to the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] like Figure 1As shown, this invention discloses a model- and data-driven intelligent control method for radon purifiers, employing a PLC touchscreen all-in-one machine for intelligent control. The specific algorithm is as follows:

[0024] S1. Data Collection

[0025] The air radon concentration C (Bq / m³) is collected every 10 minutes using sensors. 3 ), air temperature T (°C), air humidity w (RH), and collection space volume V (m³) 3 ), wall area S (m 2 Based on the relevant parameters of the wall material, the porosity is calculated using the following formula:

[0026]

[0027] In the formula, n is the porosity of the wall material. V0 is the volume of the material in its natural state, or apparent volume, in cm³. 3 / m 3 V is the absolute dense volume of the material, in cm. 3 / m 3 P0 is the bulk density of the material, in g / cm³. 3 or kg / m 3 P is the material density, in g / cm³. 3 or kg / m 3 .

[0028] S2. Calculate relevant parameters

[0029] S21. After collecting and recording 12 sets of data in step S1 (2 hours), begin the fitting calculation, and correct for errors by incorporating air temperature T and humidity w, to obtain the effective radon diffusion coefficient D, calculated as follows:

[0030]

[0031] Using the above parameters, calculate the equivalent decay coefficient of radon:

[0032]

[0033] After defining the equivalent decay coefficient of radon, the amount of radon released into the ventilated space can be considered as a constant, thus simplifying the calculation process.

[0034] S22. During the radon purification process, under stable ventilation conditions, the change in indoor radon concentration is described by the following formula:

[0035]

[0036] In the formula, C represents the radon concentration in the indoor space, which is detected in real time by a radon sensor; k t Let k be the turbulent diffusion coefficient.t =1; λ e The equivalent decay coefficient of radon is calculated from equation (3); λ v α represents the purification efficiency of the radon purifier; α represents the radon source concentration; C0 represents the first of the 12 radon concentration data.

[0037] When ventilation begins at t=0, C=C0, and the analytical solution of equation (3) is:

[0038]

[0039] make D=λ e +k t λ v ,but

[0040] C = A + Be -Dt (6)

[0041] Equation (6) is the driving model of the intelligent control algorithm of this invention. The least squares method is used to fit Equation (6) with radon concentration data detected in real time by the radon sensor, thus obtaining A, B, and D. This control method sets the radon sensor to fit once every 12 radon concentration data points. Since radon concentration data is collected every 10 minutes, the time to collect 12 data points is 2 hours. During system operation, a set of A, B, and D values ​​is fitted every 2 hours. The purification efficiency λ of the radon purifier is calculated using the following formulas. v Radon source concentration α:

[0042]

[0043] α=AD (8)

[0044] λ is obtained based on the model and data-driven approach. v And α, to intelligently control the radon purifier.

[0045] S3, Intelligent Control Solution

[0046] S31. In the absence of personnel (taking the period from 18:00 the day before yesterday to 08:00 today as an example)

[0047] If the existing indoor radon concentration exceeds the standard, purification should be carried out for a necessary period of time before people enter to reduce the radon concentration to the target level. The following formula can be used to estimate the time the air purifier should be turned on before 08:00 each day to ensure that the indoor radon concentration is below the threshold during operating hours.

[0048]

[0049] In the formula: C L This represents the limiting radon concentration value. Bq / m3 ;λ v α represents the closest fitting result in time during step S2. P The radon concentration threshold is set according to relevant national standards. Taking the pollutant limits for Class II civil building projects in GB 50325-2010 as an example, C... P =400Bq / m 3 .

[0050] Table 1 Indoor Radon Concentration Threshold Standards

[0051]

[0052] Assuming a ventilation allowance of m = 0.1, the required time is:

[0053]

[0054] S32. In the presence of people (08:00-18:00 on the same day)

[0055] Under normal personnel on duty, indoor radon concentration levels will continue to rise over time. To protect the health of personnel on duty, when the indoor radon concentration exceeds a set threshold, the air purifier will activate to reduce radon levels until they are below the limit. The specific implementation plan is as follows:

[0056] When the radon concentration value measured by the sensor is greater than C P =400Bq / m 3 At that time, turn on the air purifier until the measured radon concentration value is less than C. L =200Bq / m 3 The air purifier should be turned on for at least 30 minutes each time (avoid frequent on / off cycles).

[0057] S33, Supplementary Explanation

[0058] The purification efficiency λ is calculated according to equation (7). v To determine whether your air purifier's consumables need replacing, follow these steps:

[0059] When the most recently fitted purification efficiency λ v Less than 0.3 times the initial λ v When the results of the first fitting are obtained, it is determined that the air purifier consumables have reached their maximum utilization rate and need to be replaced in time.

[0060] Therefore, the present invention employs the aforementioned model- and data-driven intelligent control method for radon purifiers, which can be applied to different user-side scenarios. This invention can adjust the radon reduction strategy according to the radon reduction requirements of different scenarios, ensuring that the indoor radon concentration remains below a specified value when personnel are on duty. This achieves efficient and energy-saving radon reduction for various indoor scenarios, and possesses advantages such as high intelligence, real-time monitoring, dynamic adjustment, energy saving, and environmental protection.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A model and data driven based intelligent control method for radon scrubber, characterized in that, The specific steps are as follows: S1, data acquisition; S2, fitting relevant parameters according to the data collected in step S1; According to the collected data, the relevant parameters are fitted, and the effective diffusion coefficient D of radon is obtained by fitting the data collected in step S1, and the equivalent decay coefficient λ of radon is further fitted e ; S3, the purification efficiency λ is obtained by least square fitting of the indoor radon concentration change equation v , the radon source term concentration α; S4. λ is obtained by driving the model and data according to step S3 v and α, the radon purifier is intelligently controlled, and the control scheme is divided into two cases of unmanned and manned: in the unmanned case, the pre-purification time t s is calculated by driving the model, so that the indoor radon concentration is below the threshold value when the work starts; in the manned case, the indoor radon concentration value is detected in real time by the radon sensor, and once the set threshold value is exceeded, the purifier starts to purify and reduce radon until the radon concentration is reduced to below the limit radon concentration value.

2. The model and data driven based intelligent control method of a radon scrubber according to claim 1, wherein: In step S1, the data acquisition, the radon concentration value, the temperature value and the humidity value detected in real time are obtained through the sensor on the purifier, the space volume and the wall area are collected, and the porosity of the indoor material is calculated.

3. The model and data driven based intelligent control method of a radon scrubber according to claim 1, wherein: The purification efficiency λ obtained by fitting the data in step S3 v as a basis for determining whether the purifier consumables need to be replaced.

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