CN116378974B - Intelligent control system of water purifier - Google Patents
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- G—PHYSICS
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- 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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D61/00—Processes of separation using semi-permeable membranes, e.g. dialysis, osmosis or ultrafiltration; Apparatus, accessories or auxiliary operations specially adapted therefor
- B01D61/02—Reverse osmosis; Hyperfiltration ; Nanofiltration
- B01D61/025—Reverse osmosis; Hyperfiltration
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D61/00—Processes of separation using semi-permeable membranes, e.g. dialysis, osmosis or ultrafiltration; Apparatus, accessories or auxiliary operations specially adapted therefor
- B01D61/02—Reverse osmosis; Hyperfiltration ; Nanofiltration
- B01D61/12—Controlling or regulating
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/008—Control or steering systems not provided for elsewhere in subclass C02F
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/44—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis
- C02F1/441—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis by reverse osmosis
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D15/00—Control, e.g. regulation, of pumps, pumping installations or systems
- F04D15/0066—Control, e.g. regulation, of pumps, pumping installations or systems by changing the speed, e.g. of the driving engine
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/02—Temperature
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/11—Turbidity
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention discloses an intelligent control system of a water purifier, which relates to the field of self-adaptive control systems, is arranged in the water purifier and is used for controlling the water purifying process by self-adaptively adjusting a self-priming pump of the water purifier, and comprises the following steps: a target operation unit and an adaptive control unit. The target pump pressure can be set in real time according to the sensing information of the water purification process, and the target pump pressure is quickly realized by the aid of the main and auxiliary double loop control model with the auxiliary loop fine tuning and the main loop qualitative regulation and control, the self-priming pump is adaptively regulated according to the rotating speed state and the pump pressure error condition of the self-priming pump of the water purifier, the water purification efficiency is improved, the service life of the reverse osmosis membrane filter element is prolonged, and the reliability of the water purifier is enhanced.
Description
Technical Field
The invention relates to the field of self-adaptive control systems, in particular to an intelligent control system of a water purifier.
Background
The water purifier apparatus is generally composed of a pretreatment filter element (coarse filtration filter element), a self-priming pump, a reverse osmosis membrane filter element, and a post-filter element. In the water purification process, raw water is subjected to rough filtration through a pretreatment filter element, then a self-priming pump provides certain pressure, the raw water is pressed to a reverse osmosis membrane filter element to filter out pure water, and finally the water quality and taste are improved through a rear filter element. Therefore, the self-priming pump and the reverse osmosis membrane filter element are core components of the water purifier.
Because municipal tap water quality TDS, hardness, PH value etc. fluctuation range is big, and supply water pressure also has certain fluctuation, therefore the water purifier has suffered not negligible influence when using, and especially when the water pressure is low, the temperature is low, and water purification efficiency is extremely poor, and user experience is not good. Therefore, the self-priming pump of the water purifier needs to be adaptively controlled according to the actual working condition.
At present, a self-priming pump of a water purifier is usually accelerated and decelerated by simple logic judgment, and intelligent self-adaptive accurate control cannot be realized. In the existing field of adaptive control systems, no adaptive control model is fit for providing a solution for intelligent control of a water purifier.
Disclosure of Invention
Aiming at the defects in the prior art, the intelligent control system of the water purifier provided by the invention solves the problems that the speed of the self-priming pump is increased and decreased only by simple logic judgment in the existing control scheme of the water purifier, the accurate control cannot be realized, and the water purification efficiency is poor.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
an intelligent control system of a water purifier is arranged in the water purifier, and self-priming pumps of the water purifier are adjusted in a self-adaptive manner to control the water purification process, comprising: a target operation unit and an adaptive control unit;
the target operation unit is operated with a single-layer linear network model and is used for setting a target pump pressure according to the sensing information of the water purification process;
the self-adaptive control unit is operated with a main loop control model and a secondary loop control model, the main loop control model takes a rotating speed error feedback loop as an inner layer secondary loop, and takes a pumping pressure error feedback loop as an outer layer main loop, and the self-adaptive control unit is used for self-adaptively adjusting a self-priming pump so as to realize a target pumping pressure.
The beneficial effects of the invention are as follows: according to the invention, the target pump pressure can be set in real time according to the sensing information of the water purification process, and the self-priming pump is self-adaptively regulated according to the rotating speed state and the pump pressure error condition of the self-priming pump of the water purifier by a main and auxiliary double loop control model with fine tuning of the auxiliary loop and qualitative regulation and control of the main loop, so that the target pump pressure is quickly realized, the water purification efficiency is improved, the service life of a reverse osmosis membrane filter element is prolonged, and the reliability of the water purifier is enhanced.
Further, the water purification process sensing information includes:
raw water quality data measured by a turbidity sensor;
self-priming pump temperature data measured by a temperature sensor;
raw water pressure data measured by the first pressure transmitter.
Further, the single-layer linear network model has an operation expression as follows:
，
，
wherein ,pumping for the target>For the water purification process sensor information column vector, +.>Is raw water quality data>Is self priming pump temperature data, < >>Is raw water pressure data>For a single layer linear network parameter column vector, +.>Is a transposed transform.
Further, the single-layer linear network parameter column vector is obtained through iterative training:
，
，
wherein ,is->Single layer linear network parameter column vector at multiple iterations,/->Is->Single layer linear network parameter column vector at multiple iterations,/->To participate in->The past water purification process of the iteration senses information column vectors, < >>Is->Cost matrix at iteration time, +.>Is->Coefficient matrix at the time of iteration, +.>For the iteration rate +.>Is->Difference at the time of iteration.
The beneficial effects of the above-mentioned further scheme are: in order to train single-layer linear network parameter array vectors rapidly and accurately according to sensing information data in a past water purifying process, mean square error between a calculated value of a target pump pressure and an optimal pump pressure value is minimum, a cost matrix is set to approach an inverse matrix of a single-layer linear network model input data statistically autocorrelation matrix based on a mathematical statistics principle, the cost matrix is updated by the single-layer linear network model input data, the single-layer linear network parameter array vectors are updated by the cost matrix, and compared with an LMS minimum mean square algorithm established by using a gradient descent idea, convergence speed is better, and accuracy is higher.
Further, the firstThe calculated expression of the difference at the time of iteration is:
，
wherein ,for collecting the obtained and participating->Optimal pump pressure value corresponding to sensing information array vector of the past water purifying process of the next iteration;
the initial expression of the single-layer linear network parameter column vector is as follows:
，
wherein ,a single-layer linear network parameter column vector when iteration is not started;
the initial expression of the cost matrix is:
，
wherein ,is the cost matrix when iteration is not started.
The beneficial effects of the above-mentioned further scheme are: the cost matrix is used for approximating the inverse matrix of the data autocorrelation matrix input by the single-layer linear network model through iterative training, and the autocorrelation matrix has complex conjugate symmetry, so that the initial value of the cost matrix is set as a diagonal matrix, and the iterative training is facilitated.
Further, the expression of the primary and secondary dual loop control model includes:
，
，
wherein ,is->Pump error during wheel adaptive adjustment, +.>Is->Pump error during wheel adaptive adjustment, +.>Is->Pump error during wheel adaptive adjustment, +.>For the outer main loop proportionality coefficient, < >>Differential coefficient for outer main loop, < >>Integration coefficient for outer main loop, < >>Is->First buffered data at wheel adaptation time, < >>Is->Wheel-adaptive adjustment of the output control quantity, +.>Is the proportion coefficient of the inner secondary loop, +.>Is the differential coefficient of the inner secondary loop, +.>For the inner layer secondary loop integral coefficient, +.>Is->The rotational speed error during the self-adaptive adjustment of the wheel,is->Pump error rotational speed error during adaptive wheel adjustment, +.>Is->And the rotation speed error during the self-adaptive adjustment of the wheel.
Further, the method comprises the steps of,said firstThe calculation expression of the pump pressure error in the wheel self-adaptive adjustment is as follows:
，
wherein ,is->And the water pressure data of the reverse osmosis membrane filter core, which is measured by the second pressure transmitter, is measured during the self-adaptive adjustment of the wheel.
Further, the firstThe calculation expression of the rotation speed error during the wheel self-adaptive adjustment is as follows:
，
wherein ,is->Second buffered data at wheel adaptation time, < >>Is->And the rotation speed of the self-priming pump is measured by a rotation speed sensor during the self-adaptive adjustment of the wheel.
The beneficial effects of the above-mentioned further scheme are: the control quantity output by the main and auxiliary double loop control model directly controls the rotating speed of the self-priming pump, so as to control the pumping pressure of the self-priming pump, namely the reverse osmosis membrane filter core water pressure generated by pushing water in the water purifier, therefore, the feedback regulation of pumping pressure errors is used as an outer ring for qualitative regulation, the feedback regulation of rotating speed errors is used as an inner ring for fine adjustment, and the self-adaptive accurate regulation and control of the self-priming pump is realized. The regulation and control of the inner ring and the outer ring simultaneously use proportional regulation, integral regulation and differential regulation, can quickly converge to a target value, inhibit jitter, self-excitation and interference, and improve robustness, stability and reliability.
Further, the firstThe calculation expression of the second cache data in the round of adaptive adjustment is as follows:
，
wherein ,is->First buffered data at wheel adaptation time, < >>Is->Second buffered data at wheel adaptation time, < >>Is->Second buffered data at wheel adaptation time, < >>Differential equation for pumping speed>First coefficient of order, +>Differential equation for pumping speed>And (3) the second coefficient.
The beneficial effects of the above-mentioned further scheme are: the second-order differential equation is set, the second-order constant coefficient linear differential equation is simulated, and the internal and external relations of the double loop of the self-priming pump are modeled, so that the second cache data with the physical meaning equivalent to the rotating speed of the self-priming pump is calculated according to the first cache data obtained through the proportion, the integral and the differential adjustment of the outer loop, and the main and auxiliary double loop control model is enabled to be more fit with the natural rules of the control quantity of the self-priming pump, the rotating speed of the self-priming pump and the pumping pressure.
Drawings
Fig. 1 is a block diagram of an intelligent control system for a water purifier according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, in one embodiment of the present invention, an intelligent control system for a water purifier, which is disposed in the water purifier, controls a water purifying process by adaptively adjusting a self priming pump of the water purifier, includes: a target operation unit and an adaptive control unit;
the target operation unit is operated with a single-layer linear network model and is used for setting a target pump pressure according to the sensing information of the water purification process;
the self-adaptive control unit is operated with a main loop control model and a secondary loop control model, the main loop control model takes a rotating speed error feedback loop as an inner layer secondary loop, and takes a pumping pressure error feedback loop as an outer layer main loop, and the self-adaptive control unit is used for self-adaptively adjusting a self-priming pump so as to realize a target pumping pressure.
The water purification process sensing information includes:
raw water quality data measured by a TS300B type turbidity sensor;
self priming pump temperature data measured by a DS18B20 type temperature sensor;
raw water pressure data measured by the first pressure transmitter.
The operational expression of the single-layer linear network model is as follows:
，
，
wherein ,pumping for the target>For the water purification process sensor information column vector, +.>Is raw water quality data>Is self priming pump temperature data, < >>Is raw water pressure data>For a single layer linear network parameter column vector, +.>Is a transposed transform.
The single-layer linear network parameter column vector is obtained through the following iterative training:
，
，
wherein ,is->Single layer linear network parameter column vector at multiple iterations,/->Is->Single layer linear network parameter column vector at multiple iterations,/->To participate in->The past water purification process of the iteration senses information column vectors, < >>Is->Cost matrix at iteration time, +.>Is->Coefficient matrix at the time of iteration, +.>For the iteration rate +.>Is->Difference at the time of iteration.
In order to train single-layer linear network parameter array vectors rapidly and accurately according to sensing information data in a past water purifying process, mean square error between a calculated value of a target pump pressure and an optimal pump pressure value is minimum, a cost matrix is set to approach an inverse matrix of a single-layer linear network model input data statistically autocorrelation matrix based on a mathematical statistics principle, the cost matrix is updated by the single-layer linear network model input data, the single-layer linear network parameter array vectors are updated by the cost matrix, and compared with an LMS minimum mean square algorithm established by using a gradient descent idea, convergence speed is better, and accuracy is higher.
First, theThe calculated expression of the difference at the time of iteration is:
，
wherein ,for collecting the obtained and participating->Optimal pump pressure value corresponding to sensing information array vector of the past water purifying process of the next iteration;
the initial expression of the single-layer linear network parameter column vector is:
，
wherein ,a single-layer linear network parameter column vector when iteration is not started;
the initial expression of the cost matrix is:
，
wherein ,is the cost matrix when iteration is not started.
The cost matrix is used for approximating the inverse matrix of the data autocorrelation matrix input by the single-layer linear network model through iterative training, and the autocorrelation matrix has complex conjugate symmetry, so that the initial value of the cost matrix is set as a diagonal matrix, and the iterative training is facilitated.
The expression of the primary and secondary dual loop control model comprises:
，
，
wherein ,is->Pump error during wheel adaptive adjustment, +.>Is->Pump error during wheel adaptive adjustment, +.>Is->Pump error during wheel adaptive adjustment, +.>For the outer main loop proportionality coefficient, < >>Differential coefficient for outer main loop, < >>Integration coefficient for outer main loop, < >>Is->First buffered data at wheel adaptation time, < >>Is->Wheel-adaptive adjustment of the output control quantity, +.>Is the proportion coefficient of the inner secondary loop, +.>Is the differential coefficient of the inner secondary loop, +.>For the inner layer secondary loop integral coefficient, +.>Is->The rotational speed error during the self-adaptive adjustment of the wheel,is->Pump error rotational speed error during adaptive wheel adjustment, +.>Is->And the rotation speed error during the self-adaptive adjustment of the wheel.
First, theThe calculation expression of the pump pressure error in the wheel self-adaptive adjustment is as follows:
，
wherein ,is->And the water pressure data of the reverse osmosis membrane filter core, which is measured by the second pressure transmitter, is measured during the self-adaptive adjustment of the wheel.
First, theThe calculation expression of the rotation speed error during the wheel self-adaptive adjustment is as follows:
，
wherein ,is->Second buffered data at wheel adaptation time, < >>Is->And the rotation speed of the self-priming pump is measured by a rotation speed sensor during the self-adaptive adjustment of the wheel.
The control quantity output by the main and auxiliary double loop control model directly controls the rotating speed of the self-priming pump, so as to control the pumping pressure of the self-priming pump, namely the reverse osmosis membrane filter core water pressure generated by pushing water in the water purifier, therefore, the feedback regulation of pumping pressure errors is used as an outer ring for qualitative regulation, the feedback regulation of rotating speed errors is used as an inner ring for fine adjustment, and the self-adaptive accurate regulation and control of the self-priming pump is realized. The regulation and control of the inner ring and the outer ring simultaneously use proportional regulation, integral regulation and differential regulation, can quickly converge to a target value, inhibit jitter, self-excitation and interference, and improve robustness, stability and reliability.
First, theThe calculation expression of the second cache data in the round of adaptive adjustment is as follows:
，
wherein ,is->First buffered data at wheel adaptation time, < >>Is->Second buffered data at wheel adaptation time, < >>Is->Second buffered data at wheel adaptation time, < >>Differential equation for pumping speed>First coefficient of order, +>Differential equation for pumping speed>And (3) the second coefficient.
The expression is provided with a second-order differential equation, a second-order constant coefficient linear differential equation is simulated, and the internal and external relations of the double loop of the self-priming pump are modeled, so that second cache data with physical meaning equivalent to the rotating speed of the self-priming pump is calculated according to first cache data obtained through external loop proportion, integral and differential adjustment, and the main and auxiliary double loop control model is enabled to be more matched with the natural rules of the control quantity of the self-priming pump, the rotating speed of the self-priming pump and the pumping pressure.
In summary, the invention can set the target pump pressure in real time according to the sensing information of the water purifying process, and the self-priming pump is self-adaptively regulated according to the rotating speed state and the pump pressure error condition of the self-priming pump of the water purifier by the main and auxiliary double loop control model with the auxiliary loop fine tuning and the main loop qualitative regulation, thereby rapidly realizing the target pump pressure, improving the water purifying efficiency, prolonging the service life of the reverse osmosis membrane filter element and enhancing the reliability of the water purifier.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (1)
1. The utility model provides a water purifier intelligent control system which characterized in that sets up in the water purifier, through self-priming pump of self-adaptation regulation water purifier to control water purification process, include: a target operation unit and an adaptive control unit;
the target operation unit is operated with a single-layer linear network model and is used for setting a target pump pressure according to the sensing information of the water purification process;
the self-adaptive control unit is provided with a main loop control model and a secondary loop control model, a rotating speed error feedback loop is used as an inner layer secondary loop, and a pumping pressure error feedback loop is used as an outer layer main loop, so as to self-adaptively adjust the self-priming pump and realize a target pumping pressure;
the water purification process sensing information includes:
raw water quality data measured by a turbidity sensor;
self-priming pump temperature data measured by a temperature sensor;
raw water pressure data measured by a first pressure transmitter;
the operational expression of the single-layer linear network model is as follows:
，/>, wherein ,/>Pumping for the target>For the water purification process sensor information column vector, +.>Is raw water quality data>Is self priming pump temperature data, < >>Is raw water pressure data>For a single layer linear network parameter column vector, +.>Is transposed transformation;
the single-layer linear network parameter column vector is obtained through the following iterative training:
，
，
wherein ,is->Single layer linear network parameter column vector at multiple iterations,/->Is->Single layer linear network parameter column vector at multiple iterations,/->To participate in->The past water purification process of the next iteration senses information column vectors,is->Cost matrix at iteration time, +.>Is->Coefficient matrix at the time of iteration, +.>For the iteration rate +.>Is->Difference value at the time of iteration;
said firstThe calculated expression of the difference at the time of iteration is:
，
wherein ,for collecting the obtained and participating->Optimal pump pressure value corresponding to sensing information array vector of the past water purifying process of the next iteration;
the initial expression of the single-layer linear network parameter column vector is as follows:
, wherein ,/>A single-layer linear network parameter column vector when iteration is not started;
the initial expression of the cost matrix is:
, wherein ,/>The cost matrix is the cost matrix when iteration is not started;
the expression of the primary and secondary dual loop control model comprises:
，
，
wherein ,is->Pump error during wheel adaptive adjustment, +.>Is->Pump error during wheel adaptive adjustment, +.>Is->Pump error during wheel adaptive adjustment, +.>For the outer main loop proportionality coefficient, < >>Differential coefficient for outer main loop, < >>Integration coefficient for outer main loop, < >>Is->First buffered data at wheel adaptation time, < >>Is->Wheel-adaptive adjustment of the output control quantity, +.>Is the proportion coefficient of the inner secondary loop, +.>Is the differential coefficient of the inner secondary loop, +.>For the inner layer secondary loop integral coefficient, +.>Is->The rotational speed error during the self-adaptive adjustment of the wheel,is->Pump error rotational speed error during adaptive wheel adjustment, +.>Is->The rotating speed error during the self-adaptive adjustment of the wheel;
said firstThe calculation expression of the pump pressure error in the wheel self-adaptive adjustment is as follows:
，
wherein ,is->The reverse osmosis membrane filter core water pressure data measured by a second pressure transmitter during the wheel self-adaptive adjustment;
said firstThe calculation expression of the rotation speed error during the wheel self-adaptive adjustment is as follows:
，
wherein ,is->Second buffered data at wheel adaptation time, < >>Is->The rotation speed of the self-priming pump is measured by a rotation speed sensor during the self-adaptive adjustment of the wheel;
said firstThe calculation expression of the second cache data in the round of adaptive adjustment is as follows:
，
wherein ,is->First buffered data at wheel adaptation time, < >>Is->Second buffered data at wheel adaptation time, < >>Is->Second buffered data at wheel adaptation time, < >>Differential equation for pumping speed>First coefficient of order, +>Differential equation for pumping speed>And (3) the second coefficient.
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