CN114798005A - Intelligent regeneration control method and system for ion exchange system based on image recognition - Google Patents
Intelligent regeneration control method and system for ion exchange system based on image recognition Download PDFInfo
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- CN114798005A CN114798005A CN202210380856.0A CN202210380856A CN114798005A CN 114798005 A CN114798005 A CN 114798005A CN 202210380856 A CN202210380856 A CN 202210380856A CN 114798005 A CN114798005 A CN 114798005A
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- 238000011069 regeneration method Methods 0.000 title claims abstract description 95
- 230000008929 regeneration Effects 0.000 title claims abstract description 94
- 238000005342 ion exchange Methods 0.000 title claims abstract description 75
- 238000000034 method Methods 0.000 title claims abstract description 49
- 239000011347 resin Substances 0.000 claims abstract description 346
- 229920005989 resin Polymers 0.000 claims abstract description 346
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 129
- 238000011001 backwashing Methods 0.000 claims abstract description 74
- 238000005406 washing Methods 0.000 claims abstract description 54
- 239000007788 liquid Substances 0.000 claims abstract description 48
- 238000013136 deep learning model Methods 0.000 claims abstract description 33
- 238000007599 discharging Methods 0.000 claims abstract description 32
- 239000002253 acid Substances 0.000 claims abstract description 31
- 239000003513 alkali Substances 0.000 claims abstract description 29
- 238000002156 mixing Methods 0.000 claims abstract description 29
- 238000002347 injection Methods 0.000 claims abstract description 6
- 239000007924 injection Substances 0.000 claims abstract description 6
- 238000004458 analytical method Methods 0.000 claims description 40
- 150000001450 anions Chemical group 0.000 claims description 28
- 230000008569 process Effects 0.000 claims description 23
- 238000005349 anion exchange Methods 0.000 claims description 21
- 238000005341 cation exchange Methods 0.000 claims description 21
- 150000002500 ions Chemical class 0.000 claims description 21
- 238000004062 sedimentation Methods 0.000 claims description 17
- 238000012545 processing Methods 0.000 claims description 14
- 230000001172 regenerating effect Effects 0.000 claims description 11
- 150000001768 cations Chemical class 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 9
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 8
- 230000009471 action Effects 0.000 claims description 7
- 238000005507 spraying Methods 0.000 claims description 6
- 229910052681 coesite Inorganic materials 0.000 claims description 4
- 229910052906 cristobalite Inorganic materials 0.000 claims description 4
- 239000000377 silicon dioxide Substances 0.000 claims description 4
- 235000012239 silicon dioxide Nutrition 0.000 claims description 4
- 229910052682 stishovite Inorganic materials 0.000 claims description 4
- 229910052905 tridymite Inorganic materials 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 2
- NWUYHJFMYQTDRP-UHFFFAOYSA-N 1,2-bis(ethenyl)benzene;1-ethenyl-2-ethylbenzene;styrene Chemical compound C=CC1=CC=CC=C1.CCC1=CC=CC=C1C=C.C=CC1=CC=CC=C1C=C NWUYHJFMYQTDRP-UHFFFAOYSA-N 0.000 description 12
- 239000003456 ion exchange resin Substances 0.000 description 12
- 229920003303 ion-exchange polymer Polymers 0.000 description 12
- 239000000243 solution Substances 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 6
- 238000007689 inspection Methods 0.000 description 4
- 150000003839 salts Chemical class 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 239000002585 base Substances 0.000 description 2
- 238000010612 desalination reaction Methods 0.000 description 2
- 238000011033 desalting Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000007921 spray Substances 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- GPRLSGONYQIRFK-UHFFFAOYSA-N hydron Chemical compound [H+] GPRLSGONYQIRFK-UHFFFAOYSA-N 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-M hydroxide Chemical compound [OH-] XLYOFNOQVPJJNP-UHFFFAOYSA-M 0.000 description 1
- 238000001727 in vivo Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000001179 sorption measurement Methods 0.000 description 1
- 238000013517 stratification Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01J—CHEMICAL OR PHYSICAL PROCESSES, e.g. CATALYSIS OR COLLOID CHEMISTRY; THEIR RELEVANT APPARATUS
- B01J49/00—Regeneration or reactivation of ion-exchangers; Apparatus therefor
- B01J49/80—Automatic regeneration
- B01J49/85—Controlling or regulating devices therefor
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41845—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by system universality, reconfigurability, modularity
-
- 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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/33—Director till display
- G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
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- Chemical & Material Sciences (AREA)
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- Chemical Kinetics & Catalysis (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Treatment Of Water By Ion Exchange (AREA)
Abstract
The invention provides an intelligent regeneration control method and system of an ion exchange system based on image recognition, comprising the following steps: sending a regeneration instruction, backwashing the mixed ion exchange equipment, and collecting a difference value between the resin backwashing expansion height and a preset reference line A by an upper view mirror camera to judge whether the resin expansion height is proper or not; the method comprises the following steps that a middle view mirror camera collects resin pictures, and whether a resin layering interface is clear or not is judged according to a deep learning model so as to control backwashing layering; collecting a resin picture by a middle view mirror camera, and judging the difference value between the height of the upper liquid level of the resin and the height of the resin to finish water drainage; collecting resin layered photos by a downward-looking lens camera, judging whether resin is disturbed according to a deep learning model, and carrying out pre-injection, acid and alkali feeding and replacement according to the disturbed resin; full water forward washing, namely judging that the equipment is full of water through a liquid level switch of an exhaust pipe, and then performing small forward washing and forward washing; and discharging water, and mixing the resin and the image collected by the middle view mirror camera so as to control the mixing of the resin and carry out forward washing. The technical problems of time and labor consumption and low operation safety are solved.
Description
Technical Field
The invention relates to the technical field of power plant water treatment, in particular to an intelligent regeneration control method and system of an ion exchange system based on image recognition.
Background
The ion exchange method used as one of the traditional desalting technologies which are widely applied at present has the advantages of good effluent quality, lower production cost, mature technology and the like since the 20 th century and the 40 th generation. Ion exchange water treatment is still mainly used in conventional softening and desalination systems, especially in desalination systems for producing pure water and highly pure water. The ion exchange desalting is to utilize exchangeable hydrogen ion and hydroxyl ion on ion exchange resin to exchange with dissolved salt in water to eliminate salt in water. Due to the limited exchange capacity of the ion exchange resin and the reversibility of the ion exchange reaction, the ion exchange resin can be repeatedly utilized through exchange adsorption and regeneration.
After the ion resin is failed, the ion resin must be regenerated to recover the exchange capacity for standby. The regeneration of the resin is the most important link in the process of ion exchange water treatment, and the quality of the regeneration effect not only has direct influence on the working exchange capacity and the effluent quality, but also determines the running economy to a great extent. The chemical ion exchange water producing system adopts in vivo regeneration generally, and is carried out by program control, and the resin regeneration process of anion/cation exchange equipment comprises the following steps: backwash → drain → pre-spray → regeneration → displacement → small forward wash → reserve. The regeneration process of the resin of the mixed ion exchange equipment comprises the following steps: backwash stratification → standing → water discharge → pre-spray → regeneration → replacement → small forward wash → backup. The regeneration key steps comprise key links such as layering, mixing, backwashing, water drainage and the like. The patent application No. CN201610688398.1 discloses a regeneration control method for softening resin of a water softener, which divides the operation mode of the water softener into a normal operation mode and a holiday operation mode; the method comprises the steps that a water softener is controlled to be switched from a normal operation mode to a holiday operation mode through manual intervention, when the water softener is in the holiday operation mode, the actual water consumption of the water softener is monitored in real time, the numerical value of the actual water consumption and the numerical value of the set water consumption are judged, and if the actual water consumption is larger than or equal to the set water consumption, the water softener is immediately switched to the normal operation mode; when the water softener is in a normal operation mode, when the accumulated operation time of the water softener reaches integral multiple of the first set time, backwashing, salt absorption and forward washing operations are sequentially carried out on the softened resin, when the accumulated operation time of the water softener is in a holiday operation mode, when the accumulated operation time of the water softener reaches the second set time for the first time, backwashing, salt absorption and forward washing operations are sequentially carried out on the softened resin, and when the accumulated operation time of the water softener reaches integral multiple of the second set time, backwashing and forward washing operations are sequentially carried out on the softened resin. The resin regeneration control method in the prior patent only performs program control operation on the regeneration operation duration, so that the automation is low, and in the prior art, generally, the program control operation and the on-site inspection and matching of operators are required, so that the time and the labor are consumed. In addition, part of chemical professionals in the power plant are in short supply, and the confirmation of the key link of resin regeneration is inexperienced, so that the regeneration quality of the resin fluctuates, and the safe and economic operation of the ion exchange system is influenced. The prior art has the technical problems of time and labor consumption and low system operation safety.
Disclosure of Invention
The invention aims to solve the technical problems of time and labor consumption and low system operation safety in the prior art.
The invention adopts the following technical scheme to solve the technical problems: the mixed ion exchange equipment resin regeneration process in the intelligent regeneration control method of the ion exchange system based on image recognition comprises the following steps:
s1, enabling the mixed ion exchange equipment to enter a backwashing step according to a DCS program control regeneration instruction, collecting and analyzing a resin backwashing image so as to obtain resin expansion state data, judging to finish the backwashing step according to the resin expansion state data and preset expansion range data, and obtaining the resin to be layered and settled;
s2, performing layering and sedimentation steps on the resin to be layered and sedimentated, collecting and analyzing a resin layered interface image by using a deep learning model to obtain layered clear data, and triggering the mixed ion exchange equipment to perform standing sedimentation operation according to the layered clear data until the resin is completely sedimentated to obtain a first resin to be discharged;
s3, performing a water discharging step on the first resin to be discharged, collecting and analyzing a resin water discharging image to process to obtain a liquid level difference value on the first resin, and judging to finish the water discharging operation to obtain a first resin to be replaced;
s4, performing pre-injection, regeneration acid/alkali and replacement on the first resin to be replaced, collecting and processing a resin disturbance image by using the deep learning model to obtain resin disturbance state data, adjusting the frequency of a regeneration water pump until the first resin to be replaced is not disturbed, and controlling the mixed ion exchange equipment to perform the regeneration acid/alkali and replacement on the first resin to be replaced in sequence by using the resin disturbance state data to obtain the resin to be mixed;
s5, performing a mixing step on the resin to be mixed, collecting the action data of a liquid level switch of an exhaust valve of the mixed ion exchanger and the height difference of the upper liquid level of the second resin, controlling the ion exchange equipment to carry out operations of water refilling, small forward washing, forward washing and water drainage, collecting and processing a resin mixed image by using the deep learning model, and controlling the mixed ion exchange equipment to carry out mixing operation so as to obtain the first regenerated resin.
According to the invention, the monitoring images in the regeneration process of the ion exchange resin are obtained, and the monitoring images are identified according to the preset identification algorithm and the deep learning model, so that the liquid level of the resin is obtained, and the backwashing layering and mixing conditions of the resin are judged, thereby reducing the on-site inspection and matching of operators, saving the working hours and improving the resin regeneration efficiency. The method avoids the fluctuation of the regeneration quality of the resin caused by manual operation and experience in the traditional technology, and ensures the safe and economic operation of the ion exchange system.
In a more specific technical solution, the method for controlling intelligent regeneration of an ion exchange system based on image recognition further comprises an anion/cation exchange device resin regeneration process, wherein the process comprises the following steps:
s1', according to a DCS program control regeneration instruction, enabling an anion/cation exchange device to enter a backwashing step, acquiring by an intelligent camera of an upper view mirror and analyzing a resin backwashing image by utilizing a computer intelligent analysis unit so as to obtain resin expansion state data, and according to comparison between the resin expansion state data and preset expansion height range data, judging to finish the backwashing step and obtain a second resin to be settled, wherein the leakage state data of the resin is monitored by an outlet camera of a backwashing drainage pipe;
s2', performing a settling step on the second resin to be settled, and controlling the anion/cation exchange equipment to continuously perform standing settlement until the resin is completely settled to obtain a second resin to be discharged;
s3', a water discharging step is carried out on the second resin to be discharged, a water discharging image of the resin is collected and analyzed by an intelligent camera of a middle view mirror, the state data of a liquid level switch on the joint middle discharge is obtained by processing of the intelligent analysis unit of the computer, and the water discharging operation is judged to be finished so as to obtain the resin to be replaced;
s4', performing pre-injection, regeneration acid/alkali and replacement steps on the resin to be replaced, acquiring by the intelligent centre-view mirror camera and analyzing the resin disturbance image by the intelligent computer analysis unit through the deep learning model to obtain regenerated water pump frequency adjustment data, adjusting the regenerated water pump until the resin is not disturbed, controlling the anion/cation exchange device to perform regeneration acid/alkali operation, acquiring acid/alkali concentration change data by an acid/alkali concentration meter on a middle-row pipe, and triggering and controlling the anion/cation exchange device to perform replacement operation on the resin to be replaced so as to obtain the resin to be forward washed;
s5', carrying out small forward washing and forward washing operations on the resin to be forward washed by an anion exchanger and a cation exchanger, and judging that the forward washing step is finished when DD is not more than 5.0 mu S/cm, Si O2 is not more than 100 mu g/L and Na + is not more than 100 mu g/L of forward washing drainage of the cation exchanger so as to obtain a second regenerated resin.
In a more specific embodiment, step S1 includes:
s11, sending a regeneration command to the mixed ion exchange equipment by a computer DCS program control system;
s12, the mixed ion exchange equipment enters the backwashing step according to the regeneration instruction;
s13, collecting and transmitting the resin backwashing image to a computer intelligent analysis unit by using an upper view mirror camera, processing to obtain a difference value between the resin expansion height and a preset height so as to judge whether the resin expansion height is proper, and judging to obtain the resin to be layered and settled according to the state of the resin expansion height;
and S14, monitoring the resin leakage condition of the resin in the backwashing process by using the backwashing drain pipe outlet camera, and feeding back and adjusting the backwashing water pump frequency when the preset flow rate of the resin leakage is found.
In a more specific embodiment, step S2 includes:
s21, collecting the resin layered interface image through a down-view mirror intelligent camera;
s22, transmitting the resin layering interface image to a computer intelligent analysis unit;
and S23, comparing the resin layered interface image with a preset annotation image according to the deep learning model by the computer intelligent analysis unit to judge whether the resin layered interface is clear.
In a more specific technical solution, step S3 further includes:
s31, collecting the resin drainage image through an intelligent camera of a middle view mirror and transmitting the resin drainage image to an intelligent analysis unit of a computer;
and S32, processing the picture of the resin water discharge by the computer intelligent analysis unit to obtain a liquid level difference value on the first resin, and judging that the water discharge step of the resin is finished when the liquid level difference value on the first resin reaches 20 cm.
In a more specific embodiment, the difference in the liquid level above the first resin in step S32 is a difference between the liquid level above the resin and the resin height.
In a more specific technical solution, step S4 further includes:
s41, entering a pre-spraying step, collecting the resin disturbance image through a down-view mirror intelligent camera and transmitting the resin disturbance image to a computer intelligent analysis unit, judging whether the first resin to be replaced has disturbance or not through the computer intelligent analysis unit according to the deep learning model, and feeding back the resin disturbance state data to the regeneration water pump when the first resin to be replaced has severe disturbance so as to adjust the frequency of the regeneration water pump and enable the first resin to be replaced to enter a non-disturbance state.
S42, entering a resin regeneration acid/alkali entering step, collecting the resin disturbance image through a down-view mirror intelligent camera and transmitting the resin disturbance image to the computer intelligent analysis unit, and judging whether the resin is disturbed or not by the computer intelligent analysis unit according to the deep learning model to obtain numerical value disturbance state data;
and S43, entering a replacement step, and replacing the first resin to be replaced according to the resin disturbance state data to obtain the resin to be mixed.
According to the invention, the intelligent camera is introduced to directly observe and analyze the ion exchange resin in the equipment, so that the resin state of key links such as backwashing, layering, water drainage, regeneration, mixing and the like in the resin regeneration process is judged. The technology can realize the intelligent operation of unattended operation and a demineralized water making system. In addition, the regeneration quality of the ion exchange resin can be improved, the consumption of regenerated acid and alkali and the discharge of waste liquid are reduced, the resin loss rate is reduced, and the wage cost of personnel is reduced.
In a more specific technical solution, step S5 further includes:
s51, performing a full water step and a small forward washing step on the resin to be mixed;
s53, performing a forward washing step on the resin to be mixed, entering the water full step, collecting action data of a liquid level switch of an exhaust valve of the mixed ion exchanger, collecting a liquid level difference value on the second resin by using a middle view mirror intelligent camera, controlling the mixed ion exchange equipment to enter the water full step and the water discharging step, collecting the resin mixed image by using the middle view mirror intelligent camera, and controlling the mixed ion exchange equipment to mix to obtain the first regenerated resin.
In a more specific technical solution, step S53 further includes:
s531, entering the forward washing step, judging that the forward washing step is finished when forward washing drainage reaches 5.0 mu S/cm, and entering the full water step;
s532, when a liquid level switch of an exhaust valve of the mixed ion exchanger acts, the water filling step is completed, the mixed ion exchanger enters the water discharging step, the mixed ion exchanger is filled with water and discharged again through a resin liquid level height image acquired by an intelligent camera of a middle view mirror when the difference between the resin liquid level height and the resin height is smaller than 10-20 cm, otherwise, the mixed ion exchanger enters the mixing step;
s533, entering a mixing step, collecting the resin mixed image through an intelligent camera of a middle view mirror and transmitting the resin mixed image to the computer intelligent analysis unit, and judging whether the resin is fully mixed or not by the computer intelligent analysis unit according to the deep learning model;
and S534, entering the step of full water after the step of mixing is finished, judging that the full water is finished when a liquid level switch of an exhaust valve of the mixed ion exchanger is actuated, entering the step of forward washing, and judging that the step of forward washing is finished when forward washing drainage reaches DD not more than 0.20 MuS/cm and SiO2 not more than 20 Mug/L, thus obtaining the first regenerated resin.
In a more specific aspect, an ion exchange system intelligent regeneration control system based on image recognition comprises:
the backwashing unit is used for enabling the mixed ion exchange equipment to enter a backwashing step according to a DCS program control regeneration instruction, collecting and analyzing a resin backwashing image so as to obtain resin expansion state data, and judging to finish the backwashing step so as to obtain the resin to be subjected to layered sedimentation;
the layered sedimentation unit is used for performing layering and sedimentation steps on the resin to be layered and sedimentated, acquiring and analyzing a resin layered interface image by using a deep learning model to obtain layered clear data, triggering the mixed ion exchange equipment to perform standing sedimentation operation according to the layered clear data until the resin is completely sedimentated to obtain a first resin to be drained, and the layered sedimentation unit is connected with the backwashing unit;
the water discharging unit is used for performing a water discharging step on the first resin to be discharged, collecting and analyzing a resin water discharging image to process to obtain a liquid level height difference value on the first resin, and accordingly judging that water discharging operation is finished to obtain the first resin to be replaced, and the water discharging unit is connected with the layered sedimentation unit;
the replacement unit is used for carrying out the steps of pre-spraying, regenerating acid/alkali and replacing on the first resin to be replaced, acquiring and processing the resin disturbance image by using the deep learning model to obtain resin disturbance state data, adjusting the frequency of a regenerating water pump until the first resin to be replaced is not disturbed, controlling the mixed ion exchange equipment to carry out the operations of regenerating acid/alkali and replacing on the first resin to be replaced in sequence by using the resin disturbance state data to obtain the resin to be mixed, and the replacement unit is connected with the water discharge unit;
and the mixing unit is used for performing a mixing step on the resin to be mixed, collecting the action data of a liquid level switch of an exhaust valve of the mixed ion exchanger and the height difference value of the upper liquid level of the second resin, controlling the ion exchange equipment to perform operations of water refilling, small forward washing, forward washing and water drainage, collecting and processing a resin mixed image by using the deep learning model, controlling the mixed ion exchange equipment to perform a mixing operation so as to obtain a first regenerated resin, and is connected with the replacement unit.
Compared with the prior art, the invention has the following advantages: according to the invention, the monitoring images in the regeneration process of the ion exchange resin are obtained, and the monitoring images are identified according to the preset identification algorithm and the deep learning model, so that the liquid level of the resin is obtained, and the backwashing layering and mixing conditions of the resin are judged, thereby reducing the on-site inspection and matching of operators, saving the working hours and improving the resin regeneration efficiency. Avoids the fluctuation of the regeneration quality of the resin caused by manual operation and experience in the prior art, and ensures the safe and economic operation of the ion exchange system. According to the invention, the intelligent camera is introduced to directly observe and analyze the ion exchange resin in the equipment, so that the resin state of key links such as backwashing, layering, water drainage, regeneration, mixing and the like in the resin regeneration process is judged. The technology can realize unattended operation and intelligent operation of a desalted water production system. In addition, the regeneration quality of the ion exchange resin can be improved, the consumption of regenerated acid and alkali and the discharge of waste liquid are reduced, the resin loss rate is reduced, and the wage cost of personnel is reduced. The invention solves the technical problems of time and labor consumption and low system operation safety in the prior art.
Drawings
FIG. 1 is a schematic flow diagram of a process for regenerating an anion/cation exchange apparatus;
FIG. 2 is a schematic flow diagram of a regeneration process for a hybrid ion exchange device.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The outlet of the anion/cation exchange device upper view mirror, the outlet of the middle view mirror and the outlet of the backwashing drain pipe are respectively provided with a camera, the anion/cation exchange device upper view mirror camera is used for observing the expansion height of the resin during backwashing, and the anion/cation exchange device middle view mirror camera is used for observing the resin disturbance conditions of the regeneration pre-injection, acid/alkali feeding and replacement steps; and the outlet camera of the backwashing drain pipe of the anion/cation exchange equipment is used for observing whether resin leaks during resin backwashing. The camera monitoring image is transmitted to a computer intelligent analysis unit, and the intelligent analysis unit is connected with a computer DCS program control system.
As shown in fig. 1, the present embodiment adopts a method for intelligent regeneration optimization control of an ion exchange system based on image recognition, wherein the method for regenerating an anion/cation exchange device comprises the following steps:
step S01: and (4) sending a regeneration instruction by the computer DCS program control system, and enabling the anion/cation exchange equipment to enter a backwashing step. The intelligent camera through the upper view mirror collects the picture of the resin backwashing, transmits the picture to the computer intelligent analysis unit, and judges whether the resin expansion height is proper or not according to the resin expansion height and the preset height difference value. The outlet camera of the backwashing drain pipe of the anion/cation exchange equipment is used for observing whether a large amount of resin leaks during resin backwashing. The upper view mirror of the anion/cation exchange device is used for acquiring an expansion height image of resin in the device during backwashing, whether the expansion height of the resin is within a pre-estimated height range is analyzed through a preset algorithm, and the frequency of a backwashing water pump is fed back and adjusted if the expansion height exceeds the pre-estimated height.
Step S02: and after the backwashing of the resin is finished, a standing sedimentation stage is carried out, the step of discharging water is carried out after the resin is completely sedimentated, a picture of the resin discharged water is collected through an intelligent camera of a middle view mirror and is transmitted to a computer intelligent analysis unit, and the discharged water is not discharged when a liquid level switch on the middle view mirror is combined, so that the step of discharging water is judged to be finished.
Step S03: and after the water drainage of the resin is finished, the resin enters a pre-spraying step, resin pictures are collected through an intelligent camera of the middle view mirror and transmitted to the intelligent analysis unit of the computer, and whether the resin is disturbed or not is judged according to the deep learning model. And the resin is severely disturbed and fed back to a regenerated water pump, and the frequency is automatically adjusted until the resin is basically undisturbed. The resin is essentially undisturbed and enters the regeneration acid/base step.
Step S04: and in the step of regenerating the resin into acid/alkali, whether the regeneration is finished is judged through an acid/alkali concentration meter on the middle discharge pipe, the concentration of the acid/alkali concentration meter starts to rise and keeps unchanged, and the step of regenerating into acid and alkali is finished.
Step S05: and after the regeneration of the resin is finished, the resin enters a replacement step, whether replacement is finished or not is judged through an acid/alkali concentration meter on the middle discharge pipe, and the concentration of the acid/alkali concentration meter is kept unchanged after the concentration is reduced, so that the replacement is finished.
Step S06: and after the resin replacement is finished, performing a small forward washing step, after the small forward washing is finished, performing a forward washing step, when the forward washing drainage DD of the anion exchanger is less than or equal to 5.0 mu S/cm, the Si O2 is less than or equal to 100 mu g/L, performing the forward washing step of the anion exchanger, and the forward washing drainage Na + of the cation exchanger is less than or equal to 100 mu g/L, completing the forward washing step of the cation exchanger, and converting the anion/cation exchange equipment to be standby.
Example 2
The mixed ion exchange equipment upper view mirror, the mixed ion exchange equipment middle view mirror, the mixed ion exchange equipment lower view mirror and the outlet of the backwashing drain pipe are respectively provided with a camera, the mixed ion exchange equipment upper view mirror is used for observing the expansion height of the resin during backwashing, the mixed ion exchange equipment middle view mirror is used for observing the anion-cation resin interface during resin backwashing layering, and the mixed ion exchange equipment lower view mirror is used for observing the anion-cation resin interface during resin regeneration and the anion-cation resin disturbance state in the mixing step. The mixed ion exchange equipment backwashing drain pipe outlet camera is used for observing whether a large amount of resin leaks during resin backwashing.
As shown in fig. 2, the present embodiment adopts a method of intelligent regeneration optimization control of an ion exchange system based on image recognition, wherein the regeneration method of the hybrid ion exchange device comprises the following steps:
step S01': and sending a regeneration instruction by the computer DCS program control system, and enabling the mixed ion exchange equipment to enter a backwashing step. The intelligent camera through the upper view mirror collects the picture of the resin backwashing, transmits the picture to the computer intelligent analysis unit, and judges whether the resin expansion height is proper or not according to the resin expansion height and the preset height difference value. The mixed ion exchange equipment backwashing drain pipe outlet camera is used for observing whether a large amount of resin leaks during resin backwashing. The upper view mirror of the mixed ion exchange equipment is used for acquiring an expansion height image of resin in the equipment during backwashing, whether the expansion height of the resin is within a pre-estimated height range is analyzed through a preset algorithm, and the frequency of a backwashing water pump is fed back and adjusted if the expansion height exceeds the pre-estimated height.
Step S02': and after the resin is completely expanded, the resin enters a layering stage, a picture of a resin layering interface is collected through an intelligent camera of the middle view mirror and is transmitted to the computer intelligent analysis unit, and whether the resin layering interface is clear or not is judged according to the deep learning model. And if the layering interface is not clear, sending a command for continuing backwashing layering through the DCS program control system, and if the layering interface is clear, sending a next standing command through the DCS program control system. The endoscope camera of the mixed ion exchange equipment is used for acquiring a yin-yang resin layered interface in the equipment, and the image of the yin-yang resin layered interface is compared with a preset standard image to obtain an image difference value; and when the image difference value is greater than or equal to a preset difference value, generating a re-backwashing layering instruction, and re-backwashing layering the anion and cation resin according to the re-backwashing layering instruction. The mixed ion exchange equipment lower view mirror camera is used for collecting regeneration and replacement states of anion and cation resin in the equipment, and the anion and cation resin state image is compared with a preset standard image to obtain an image difference value; and when the image difference value is larger than a preset difference value, adjusting the flow of the backwashing water pump until the image difference value is smaller than or equal to the preset difference value.
Step S03': and after the resin layering is finished, a standing and settling stage is carried out, after the resin is completely settled, a water drainage step is carried out, a picture of resin water drainage is collected through an intelligent camera of a middle view mirror and is transmitted to an intelligent analysis unit of a computer, and the resin water drainage step is finished according to the difference value between the height of the upper liquid level of the resin and the height of the resin reaching 10-20 cm.
Step S04': and after the water drainage of the resin is finished, the resin enters a pre-spraying step, resin layered pictures are collected through an intelligent camera of a downward-looking mirror and transmitted to an intelligent analysis unit of a computer, and whether the resin is disturbed or not is judged according to a deep learning model. And the resin is severely disturbed and fed back to a regenerated water pump, and the frequency is automatically adjusted until the resin basically has no disturbance. The resin is essentially undisturbed and enters the regeneration acid/base step.
Step S05': and in the step of regenerating resin, collecting resin pictures through an intelligent camera of a lower sight lens, transmitting the resin pictures to an intelligent analysis unit of a computer, and judging whether the resin is disturbed or not according to a deep learning model.
Step S06': and after the regeneration of the resin is completed, the resin enters a replacement step, a picture of the resin is collected by an intelligent camera of a middle view mirror and is transmitted to an intelligent analysis unit of a computer, and whether the resin is disturbed or not is judged according to a deep learning model.
Step S07': and after the resin replacement is finished, the step of filling water is carried out, the exhaust valve of the mixed ion exchanger is provided with a liquid level switch, and when the liquid level switch acts, the step of filling water is finished. Then small forward washing is carried out, the forward washing step is carried out after the small forward washing is finished, and the forward washing step is finished and the water discharging step is carried out when the forward washing water discharge reaches 5.0 mu S/cm.
Step S08': the mixed ion exchanger enters a water discharging step, and water is filled and discharged again when the difference between the height of the liquid level of the resin and the height of the resin is less than 10-20 cm through resin photos collected by an intelligent camera of a middle view mirror; and when the height difference of the resin reaches 10-20 cm, entering a mixing step.
Step S09': and after the resin water discharging step is finished, the mixing step is carried out, the picture of the resin is collected through the intelligent camera of the middle view mirror and is transmitted to the intelligent analysis unit of the computer, and whether the resin is fully mixed or not is judged according to the deep learning model.
Step S10': and (3) after the resin mixing step is finished, filling water, performing opening and closing actions on the liquid level of an exhaust valve of the mixed ion exchange equipment, after the water filling is finished, performing a forward washing step, and when the forward washing drainage reaches DD (DD) less than or equal to 0.20 mu S/cm and the SiO2 (SiO) less than or equal to 20 mu g/L, completing the forward washing step, and converting the mixed ion exchanger into a standby state.
In conclusion, the invention obtains the monitoring images in the regeneration process of the ion exchange resin, identifies each monitoring image according to the preset identification algorithm and the deep learning model, obtains the liquid level height of the resin and judges the backwashing layering and mixing condition of the resin, reduces the on-site inspection and matching of operators, saves the working hours and improves the resin regeneration efficiency. Avoids the fluctuation of the regeneration quality of the resin caused by manual operation and experience in the prior art, and ensures the safe and economic operation of the ion exchange system. According to the invention, the intelligent camera is introduced to directly observe and analyze the ion exchange resin in the equipment, so that the resin state of key links such as backwashing, layering, water drainage, regeneration, mixing and the like in the resin regeneration process is judged. The technology can realize unattended operation and intelligent operation of a desalted water production system. In addition, the regeneration quality of the ion exchange resin can be improved, the consumption of regenerated acid and alkali and the discharge of waste liquid are reduced, the resin loss rate is reduced, and the wage cost of personnel is reduced. The invention solves the technical problems of time and labor consumption and low system operation safety in the prior art.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent regeneration control method of an ion exchange system based on image recognition is characterized in that a mixed ion exchange equipment resin regeneration process in the method comprises the following steps:
s1, enabling the mixed ion exchange equipment to enter a backwashing step according to a DCS program control regeneration instruction, collecting and analyzing a resin backwashing image so as to obtain resin expansion state data, judging to finish the backwashing step according to the resin expansion state data and preset expansion range data, and obtaining the resin to be layered and settled;
s2, performing layering and sedimentation steps on the resin to be layered and sedimentated, collecting and analyzing a resin layered interface image by using a deep learning model to obtain layered clear data, and triggering the mixed ion exchange equipment to perform standing sedimentation operation according to the layered clear data until the resin is completely sedimentated to obtain a first resin to be discharged;
s3, performing a water discharging step on the first resin to be discharged, collecting and analyzing a resin water discharging image to process to obtain a liquid level height difference value on the first resin, and judging to finish the water discharging operation to obtain a first resin to be replaced;
s4, performing pre-injection, regeneration acid/alkali and replacement on the first resin to be replaced, collecting and processing a resin disturbance image by using the deep learning model to obtain resin disturbance state data, adjusting the frequency of a regeneration water pump until the first resin to be replaced is not disturbed, and controlling the mixed ion exchange equipment to perform the regeneration acid/alkali and replacement on the first resin to be replaced in sequence by using the resin disturbance state data to obtain the resin to be mixed;
s5, performing a mixing step on the resin to be mixed, collecting the action data of a liquid level switch of an exhaust valve of the mixed ion exchanger and the height difference of the upper liquid level of the second resin, controlling the ion exchange equipment to carry out operations of water refilling, small forward washing, forward washing and water drainage, collecting and processing a resin mixed image by using the deep learning model, and controlling the mixed ion exchange equipment to carry out mixing operation so as to obtain the first regenerated resin.
2. The intelligent regeneration control method for ion exchange system based on image recognition as claimed in claim 1, wherein the method further comprises an anion/cation exchange device resin regeneration process, the process comprises the following steps:
s1', according to a DCS program control regeneration instruction, enabling the anion/cation exchange equipment to enter a backwashing step, collecting by an intelligent camera of an upper view mirror and analyzing a resin backwashing image by using an analysis unit so as to obtain resin expansion state data, and according to comparison between the resin expansion state data and preset expansion height range data, judging to finish the backwashing step and obtain a second resin to be settled, wherein the leakage state data of the resin is monitored by an outlet camera of a backwashing drainage pipe;
s2', performing a settling step on the second resin to be settled, and controlling the anion/cation exchange equipment to continuously perform standing settlement until the resin is completely settled to obtain a second resin to be discharged;
s3', a water discharging step is carried out on the second resin to be discharged, a water discharging image of the resin is collected and analyzed by an intelligent camera of a middle view mirror, the state data of a liquid level switch on the joint middle discharge is obtained by processing of the analysis unit, and accordingly, the water discharging operation is judged to be completed, so that the resin to be replaced is obtained;
s4', performing pre-injection, regeneration acid/alkali and replacement steps on the resin to be replaced, acquiring by the intelligent center view mirror camera and analyzing the resin disturbance image by the deep learning model through the analysis unit to obtain regeneration water pump frequency adjustment data, adjusting the regeneration water pump until the resin is not disturbed, controlling the anion/cation exchange device to perform regeneration acid/alkali operation, acquiring acid/alkali concentration change data by an acid/alkali concentration meter on a middle discharge pipe, and triggering and controlling the anion/cation exchange device to perform replacement operation on the resin to be replaced so as to obtain the resin to be washed;
s5', carrying out small forward washing and forward washing operations on the resin to be forward washed by an anion exchanger and a cation exchanger, and judging that the forward washing step is finished when DD is not more than 5.0 mu S/cm, Si O2 is not more than 100 mu g/L and Na + is not more than 100 mu g/L of forward washing drainage of the cation exchanger so as to obtain a second regenerated resin.
3. The method for controlling intelligent regeneration of an ion exchange system based on image recognition as claimed in claim 1, wherein said step S1 includes:
s11, sending a regeneration command to the mixed ion exchange equipment by a computer DCS program control system;
s12, the mixed ion exchange equipment enters the backwashing step according to the regeneration instruction;
s13, collecting and transmitting the resin backwashing image to a computer intelligent analysis unit by using an upper view mirror camera, processing to obtain a difference value between the resin expansion height and a preset height so as to judge whether the resin expansion height is proper, and judging to obtain the resin to be layered and settled according to the state of the resin expansion height;
and S14, monitoring the resin leakage condition of the resin in the backwashing process by using the backwashing drain pipe outlet camera, and feeding back and adjusting the backwashing water pump frequency when the preset flow rate of the resin leakage is found.
4. The method for controlling intelligent regeneration of an ion exchange system based on image recognition according to claim 1, wherein the step S2 includes:
s21, collecting the resin layered interface image through a down-view mirror intelligent camera;
s22, transmitting the resin layering interface image to a computer intelligent analysis unit;
and S23, comparing the resin layered interface image with a preset annotation image according to the deep learning model by the computer intelligent analysis unit to judge whether the resin layered interface is clear.
5. The method for controlling intelligent regeneration of an ion exchange system based on image recognition as claimed in claim 1, wherein said step S3 further comprises:
s31, collecting the resin drainage image through an intelligent camera of a middle view mirror and transmitting the resin drainage image to an intelligent analysis unit of a computer;
and S32, processing the picture of the resin water discharge by the computer intelligent analysis unit to obtain a liquid level difference value on the first resin, and judging that the water discharge step of the resin is finished when the liquid level difference value on the first resin reaches 20 cm.
6. The method as claimed in claim 5, wherein the difference between the liquid level on the first resin in step S32 is the difference between the liquid level on the resin and the resin height.
7. The method for controlling intelligent regeneration of an ion exchange system based on image recognition as claimed in claim 1, wherein said step S4 further comprises:
s41, entering a pre-spraying step, collecting the resin disturbance image through a down-view mirror intelligent camera and transmitting the resin disturbance image to a computer intelligent analysis unit, judging whether the first resin to be replaced has disturbance or not through the computer intelligent analysis unit according to the deep learning model, and feeding back the resin disturbance state data to the regeneration water pump when the first resin to be replaced has severe disturbance so as to adjust the frequency of the regeneration water pump and enable the first resin to be replaced to enter a non-disturbance state.
S42, entering a resin regeneration acid/alkali entering step, collecting the resin disturbance image through a down-view mirror intelligent camera and transmitting the resin disturbance image to the computer intelligent analysis unit, and judging whether the resin is disturbed or not by the computer intelligent analysis unit according to the deep learning model to obtain numerical value disturbance state data;
and S43, entering a replacement step, and replacing the first resin to be replaced according to the resin disturbance state data to obtain the resin to be mixed.
8. The method for controlling intelligent regeneration of an ion exchange system based on image recognition as claimed in claim 1, wherein said step S5 further comprises:
s51, performing a full water step and a small forward washing step on the resin to be mixed;
s53, performing a forward washing step on the resin to be mixed, entering the water full step, collecting action data of a liquid level switch of an exhaust valve of the mixed ion exchanger, collecting a liquid level difference value on the second resin by using a middle view mirror intelligent camera, controlling the mixed ion exchange equipment to enter the water full step and the water discharging step, collecting the resin mixed image by using the middle view mirror intelligent camera, and controlling the mixed ion exchange equipment to mix to obtain the first regenerated resin.
9. The method for controlling intelligent regeneration of an ion exchange system based on image recognition as claimed in claim 8, wherein said step S53 further comprises:
s531, entering the forward washing step, judging that the forward washing step is finished when forward washing drainage reaches 5.0 mu S/cm, and entering the full water step;
s532, when a liquid level switch of an exhaust valve of the mixed ion exchanger acts, the water filling step is completed, the mixed ion exchanger enters the water discharging step, the mixed ion exchanger is filled with water and discharged again through a resin liquid level height image acquired by an intelligent camera of a middle view mirror when the difference between the resin liquid level height and the resin height is smaller than 10-20 cm, otherwise, the mixed ion exchanger enters the mixing step;
s533, entering a mixing step, collecting the resin mixed image through an intelligent camera of a middle view mirror and transmitting the resin mixed image to the computer intelligent analysis unit, and judging whether the resin is fully mixed or not by the computer intelligent analysis unit according to the deep learning model;
and S534, entering the step of full water after the step of mixing is finished, judging that the full water is finished when a liquid level switch of an exhaust valve of the mixed ion exchanger is actuated, entering the step of forward washing, and judging that the step of forward washing is finished when forward washing drainage reaches DD not more than 0.20 MuS/cm and SiO2 not more than 20 Mug/L, thus obtaining the first regenerated resin.
10. An ion exchange system intelligent regeneration control system based on image recognition is characterized by comprising:
the backwashing unit is used for enabling the mixed ion exchange equipment to enter a backwashing step according to a DCS program control regeneration instruction, collecting and analyzing a resin backwashing image so as to obtain resin expansion state data, and judging to finish the backwashing step so as to obtain the resin to be subjected to layered sedimentation;
the layered sedimentation unit is used for performing layering and sedimentation steps on the resin to be layered and sedimentated, acquiring and analyzing a resin layered interface image by using a deep learning model to obtain layered clear data, triggering the mixed ion exchange equipment to perform standing sedimentation operation according to the layered clear data until the resin is completely sedimentated to obtain a first resin to be drained, and the layered sedimentation unit is connected with the backwashing unit;
the water discharging unit is used for performing a water discharging step on the first resin to be discharged, collecting and analyzing a resin water discharging image to process to obtain a liquid level height difference value on the first resin, and accordingly judging that water discharging operation is finished to obtain the first resin to be replaced, and the water discharging unit is connected with the layered sedimentation unit;
the replacement unit is used for carrying out the steps of pre-spraying, regenerating acid/alkali and replacing on the first resin to be replaced, acquiring and processing the resin disturbance image by using the deep learning model to obtain resin disturbance state data, adjusting the frequency of a regenerating water pump until the first resin to be replaced is not disturbed, controlling the mixed ion exchange equipment to carry out the operations of regenerating acid/alkali and replacing on the first resin to be replaced in sequence by using the resin disturbance state data to obtain the resin to be mixed, and the replacement unit is connected with the water discharge unit;
and the mixing unit is used for performing a mixing step on the resin to be mixed, collecting the action data of a liquid level switch of an exhaust valve of the mixed ion exchanger and the height difference value of the upper liquid level of the second resin, controlling the ion exchange equipment to perform operations of water refilling, small forward washing, forward washing and water drainage, collecting and processing a resin mixed image by using the deep learning model, controlling the mixed ion exchange equipment to perform a mixing operation so as to obtain a first regenerated resin, and is connected with the replacement unit.
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