CN114935892A - Air flow rate adaptive control modeling method of air water generator - Google Patents
Air flow rate adaptive control modeling method of air water generator Download PDFInfo
- Publication number
- CN114935892A CN114935892A CN202210651924.2A CN202210651924A CN114935892A CN 114935892 A CN114935892 A CN 114935892A CN 202210651924 A CN202210651924 A CN 202210651924A CN 114935892 A CN114935892 A CN 114935892A
- Authority
- CN
- China
- Prior art keywords
- water
- fan
- air
- water production
- rotating speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
Abstract
The invention provides a modeling analysis method for air flow rate parameters of a water making machine, aiming at the defects of the existing selection method for the air flow rate parameters of the water making machine. The method comprises the following steps: step one, setting parameter sampling time of an air water generator to sample the rotating speed of an air inlet fan to obtain a rotating speed vector; sampling the water production quantity of a water production machine at different rotating speeds, wherein the water production quantity data form a vector; step two, setting a regression function model; and step three, obtaining a theoretical water production vector according to the regression function model and obtaining a distance definition between the water production vectors through a test, thereby obtaining a parameter vector of the water production model. The modeling analysis method provided by the invention can realize the self-adaptive intelligent control of the air water generator, improve the water generation efficiency of the air water generator and reduce the energy consumption of the water generator.
Description
Technical Field
The invention relates to an adaptive control modeling method for air flow rate, in particular to an adaptive control signal modeling algorithm for air suction speed of an air water generator, which is used for effectively improving water generation efficiency of the air water generator.
Background
The air water generator is a device for taking water from air, and condenses water vapor in air into water by adopting a condensation method. An air inlet fan in the air water machine can control the flow rate of the inflowing air. Thus, increasing the speed of the inlet fan brings more water vapour, but also increases the rate of evaporation of the condensed water. Therefore, the rotation speed of the air inlet fan and the air amount flowing into the water making machine are nonlinear, and the optimal rotation speed parameter is difficult to obtain through experiments so as to achieve the optimal water making efficiency. Therefore, it is necessary to model and analyze the rotation speed of the air intake fan of the air water generator and the water generation efficiency of the air water generator.
Disclosure of Invention
The invention provides a modeling and analyzing method for the air flow rate parameter of a water generator, aiming at the defects of the existing selection method for the air flow rate parameter of the water generator.
The method provided by the invention comprises the following steps:
step one, setting a parameter sampling time interval of an air water making machine as delta T, sampling the rotating speed of an air inlet fan, and obtaining a rotating speed vector omega ═ omega 1 ,ω 2 ,ω 3 ,...,ω k ]Sampling the water production quantity of the water production machine at different rotating speeds, wherein a vector formed by water production quantity data is as follows: v ═ V 1 ,v 2 ,v 3 ,...,v k ]；
Step two, setting a regression function model as
Wherein w ═ w 1 ,w 2 w 3 ,...,w n ]Is a parameter vector, k is the number of signal samples in one round, and i is the power number of the function model;
step three, the distance between the theoretical water production vector obtained according to the regression function model and the water production vector obtained by the experiment is defined as:
get
When the measured value is greater than the predetermined value, find w ═ w 1 ,w 2 w 3 ,...,w n ]And obtaining the parameter vector of the water maker model.
Preferably, the water production machine water volume is sampled at different rotation speeds, and the following steps are used:
substep a, setting the initial of the fanRotating speed n, setting the timing time of the water making machine, starting a timer, and measuring the height h of the existing water in the water tank 1 ，
And a substep b, after the timer times t, measuring the water storage height h of the water tank 2 Setting the perimeter c of the bottom of the rectangular water tank, the water production quantity delta v of the water production machine at time t can be calculated 1 ＝c*(h 2 -h 1 )；
A substep c of converting the amount of water production Δ v during the time t 1 And fan speed n 1 Storing the data in a memory;
substep d, increasing fan speed increment to be Deltan and marking the speed to be n 2 After the timer times t, calculating to obtain the water production quantity delta v 2 ；
Sub-step e, determining Δ v 2 Whether greater than Δ v 1 If Δ v 1 <△v 2 Continuously increasing the fan speed increment to be delta n, namely, the fan speed is n 3 The amount of produced water is delta v 3 ,., until the k time adjusts the fan speed, the water production quantity delta v k-1 >△v k When the fan is started, stopping adjusting the rotating speed of the fan;
sub-step f, if Δ v 1 >△v 2 Reducing the increment of the rotating speed of the fan delta n until the rotating speed of the fan is adjusted for the kth time, and controlling the water quantity delta v k-1 >△v k And stopping adjusting the rotating speed of the fan.
The modeling analysis method provided by the invention can realize the self-adaptive intelligent control of the air water generator, improve the water generation efficiency of the air water generator and reduce the energy consumption of the water generator.
Drawings
FIG. 1 is a flow chart of water production per unit time by a fan according to the present invention;
fig. 2 is a flow chart of the adaptive control of the fan of the air water generator of the present invention.
Detailed Description
The technical solution of the present invention is further specifically described below by way of specific examples in conjunction with the accompanying drawings.
Example 1
A modeling and analyzing method for air flow rate parameters of a water generator comprises the following specific steps:
(1) assuming that the sampling time interval of the parameters of the air water making machine is 10 minutes, sampling the rotating speed of the air inlet fan, wherein the rotating speed vector is as follows: omega-omega ═ omega 1 ,ω 2 ,ω 3 ,...,ω k ]Sampling the water production quantity of the water production machine at different rotating speeds, wherein a vector formed by water production quantity data is as follows: v ═ V 1 ,v 2 ,v 3 ,...,v k ]。
(2) Setting the regression function model as
Wherein w ═ w 1 ,w 2 w 3 ,...,w n ]For the parameter vector, k is the number of signal samples in a round, and i is the power of the function model.
(3) The distance between the theoretical water production vector obtained according to the regression function model and the water production vector obtained by the experiment is defined as:
get
When it is determined that w is [ w ] 1 ,w 2 w 3 ,...,w n ]And obtaining the parameter vector of the water maker model.
(4) The method for collecting the data of the water generator comprises the following steps: the flow of a fan control module in an air water generator is shown in figure 1. The module can obtain better technical effect, sets the initial rotating speed n of the fan according to the existing test result, then sets the timing time of the water making machine, starts the timer and measures the existing water height h of the water tank 1 After the timer times t, the height h of the water stored in the water tank is measured 2 Setting the perimeter c of the bottom of the rectangular water tank, the water production quantity delta v of the water production machine at time t can be calculated 1 ＝c*(h 2 -h 1 ). Then the water production quantity delta v in the time t 1 And fan speed n 1 And storing the data in a memory. A single speed adjustment as shown in fig. 2 does not yield sufficient data and thus requires multiple measurements of the fan speed parameter. FIG. 2 is an air to water systemAnd (3) a self-adaptive intelligent control flow chart of the fan. The first water making amount of a fan control module in the air water making machine is used as a reference standard, then the fan rotating speed increment is increased to be delta n, and the marked rotating speed is n 2 The amount of produced water is delta v 2 If Δ v 1 <△v 2 Continuously increasing the fan speed increment to be delta n, namely, the fan speed is n 3 The amount of produced water is delta v 3 ,. until the k time, the rotation speed of the fan is adjusted, and the water production quantity delta v k-1 >△v k And stopping adjusting the rotating speed of the fan. If Δ v 1 >△v 2 Reducing the increment of the rotating speed of the fan delta n until the rotating speed of the fan is adjusted for the kth time, and controlling the water quantity delta v k-1 >△v k And stopping adjusting the rotating speed of the fan.
Claims (2)
1. An adaptive control modeling method for air flow rate of an air water generator is characterized by comprising the following steps:
step one, setting a parameter sampling time interval of an air water making machine as delta T, sampling the rotating speed of an air inlet fan, and obtaining a rotating speed vector omega ═ omega 1 ,ω 2 ,ω 3 ,...,ω k ]Sampling the water production quantity of the water production machine at different rotating speeds, wherein a vector formed by water production quantity data is as follows: v ═ V 1 ,v 2 ,v 3 ,...,v k ]；
Step two, setting a regression function model as
Wherein w ═ w 1 ,w 2 w 3 ,...,w n ]Is a parameter vector, k is the number of signal samples in one round, and i is the power number of the function model;
step three, the distance between the theoretical water production vector obtained according to the regression function model and the water production vector obtained by the experiment is defined as:
get
When it is determined that w is [ w ] 1 ,w 2 w 3 ,...,w n ]And obtaining a parameter vector of the water maker model.
2. An adaptive air flow rate control modeling method for an air water generator as claimed in claim 1 wherein sampling of the water flow rate of the water generator at different rotational speeds comprises the steps of:
a substep a, setting the initial rotating speed n of the fan, setting the timing time of the water generator, starting a timer, and measuring the height h of the existing water in the water tank 1 ，
And a substep b, after the timer times t, measuring the water storage height h of the water tank 2 Setting the perimeter c of the bottom of the rectangular water tank, and calculating the water production quantity delta v of the water production machine at time t 1 ＝c*(h 2 -h 1 )；
A substep c of converting the amount of water production Δ v during the time t 1 And fan speed n 1 Storing the data in a memory;
substep d, increasing fan speed increment to be Deltan and marking the speed to be n 2 After the timer times t, calculating to obtain the water production quantity delta v 2 ；
Sub-step e, determining Δ v 2 Whether greater than Δ v 1 If Δ v 1 <△v 2 Continuously increasing the fan speed increment to be delta n, namely the fan speed is n 3 The amount of produced water is delta v 3 ,. until the k time, the rotation speed of the fan is adjusted, and the water production quantity delta v k-1 >△v k When the fan is started, stopping adjusting the rotating speed of the fan;
sub-step f, if Δ v 1 >△v 2 Reducing the increment of the rotating speed of the fan delta n until the rotating speed of the fan is adjusted for the kth time, and controlling the water quantity delta v k-1 >△v k And when the fan is started, stopping adjusting the rotating speed of the fan.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210651924.2A CN114935892B (en) | 2022-06-10 | 2022-06-10 | Air flow rate self-adaptive control modeling method for air water generator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210651924.2A CN114935892B (en) | 2022-06-10 | 2022-06-10 | Air flow rate self-adaptive control modeling method for air water generator |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114935892A true CN114935892A (en) | 2022-08-23 |
| CN114935892B CN114935892B (en) | 2023-10-03 |
Family
ID=82866564
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210651924.2A Active CN114935892B (en) | 2022-06-10 | 2022-06-10 | Air flow rate self-adaptive control modeling method for air water generator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114935892B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080162388A1 (en) * | 2006-12-28 | 2008-07-03 | De La Guardia Rafael | Adaptive system for fan management |
| CN102902257A (en) * | 2012-10-30 | 2013-01-30 | 威水星空（北京）环境技术有限公司 | Sewage treatment process optimization and energy-saving control system and method |
| CN104166405A (en) * | 2014-08-06 | 2014-11-26 | 东北大学 | Liquid level system PI control method based on virtual unmodeled dynamics compensation |
| CN107429927A (en) * | 2015-03-06 | 2017-12-01 | 三菱电机株式会社 | Air-conditioning system and the system and method for the work for controlling air-conditioning system |
| CN108108889A (en) * | 2017-12-18 | 2018-06-01 | 杭州电子科技大学 | A kind of water monitoring data on-line processing method and device |
| CN112101796A (en) * | 2020-09-16 | 2020-12-18 | 清华大学合肥公共安全研究院 | Water environment pollution risk comprehensive perception and recognition system |
| CN112616292A (en) * | 2020-11-27 | 2021-04-06 | 湖南大学 | Data center energy efficiency optimization control method based on neural network model |
-
2022
- 2022-06-10 CN CN202210651924.2A patent/CN114935892B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080162388A1 (en) * | 2006-12-28 | 2008-07-03 | De La Guardia Rafael | Adaptive system for fan management |
| CN102902257A (en) * | 2012-10-30 | 2013-01-30 | 威水星空（北京）环境技术有限公司 | Sewage treatment process optimization and energy-saving control system and method |
| CN104166405A (en) * | 2014-08-06 | 2014-11-26 | 东北大学 | Liquid level system PI control method based on virtual unmodeled dynamics compensation |
| CN107429927A (en) * | 2015-03-06 | 2017-12-01 | 三菱电机株式会社 | Air-conditioning system and the system and method for the work for controlling air-conditioning system |
| CN108108889A (en) * | 2017-12-18 | 2018-06-01 | 杭州电子科技大学 | A kind of water monitoring data on-line processing method and device |
| CN112101796A (en) * | 2020-09-16 | 2020-12-18 | 清华大学合肥公共安全研究院 | Water environment pollution risk comprehensive perception and recognition system |
| CN112616292A (en) * | 2020-11-27 | 2021-04-06 | 湖南大学 | Data center energy efficiency optimization control method based on neural network model |
Non-Patent Citations (1)
| Title |
|---|
| 王亦斌: "基于EMD-LSTM模型的河流水量水位预测", 《》, vol. 40, no. 6, pages 40 - 48 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114935892B (en) | 2023-10-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2019196490A1 (en) | Air conditioner start control method, device, storage medium and air conditioner | |
| KR20210047880A (en) | Automatic yaw calibration for wind turbine generators | |
| CN100490658C (en) | Method and apparatus for ventilating and modifying of grain storage | |
| CN111734670B (en) | Test system and test method of magnetic suspension blower | |
| CN204872113U (en) | Rice device is stored up to intelligence | |
| CN108397853A (en) | Air-conditioner set control method and device | |
| JP2008223669A (en) | Engine load estimating apparatus and engine load estimating method | |
| CN101122546A (en) | Blower performance test system and test method | |
| CN108955002A (en) | A kind of control method for overheat and air source heat pump of iteration optimization | |
| CN105115212B (en) | A kind of control method of controlled evaporation temperature dehumidifier | |
| CN101349499B (en) | Method for controlling dehumidification rate and heat pump drier for implementing dehumidification rate control | |
| CN114935892A (en) | Air flow rate adaptive control modeling method of air water generator | |
| CN111472733A (en) | Intelligent intermittent drainage gas production system and control method thereof | |
| CN104990227B (en) | A kind of control method of energy-saving type refrigeration and dehumidification machine | |
| CN111692788A (en) | Efficient and accurate fuzzy defrosting control method | |
| EP3889518A1 (en) | Control method for heat-pump water heater, and heat-pump water heater | |
| CN113809370A (en) | Method, device and system for controlling operation temperature of fuel cell stack | |
| CN116954283B (en) | Temperature control system and method for edible fungi industrial production | |
| CN113154653A (en) | Air conditioner frequency and outer fan control method and device and air conditioner | |
| CN109091896A (en) | A kind of low temperature method for concentration of liquid material | |
| CN115573924A (en) | Centrifugal pump performance prediction method based on improved QP model | |
| CN203325853U (en) | Humidity feedback control flow correction system | |
| CN105892534B (en) | A kind of Large Box heater counter control and condensation alarm method | |
| WO2022170992A1 (en) | Method for determining blockage of filter mesh of clothes dryer, determining apparatus, and clothes dryer | |
| CN117366821A (en) | Dehumidification equipment based on fan speed regulation and control method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |