CN112881637A - Visual osmotic membrane treatment wastewater detection device - Google Patents
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- 239000012528 membrane Substances 0.000 title claims abstract description 84
- 230000000007 visual effect Effects 0.000 title claims abstract description 21
- 238000001514 detection method Methods 0.000 title claims abstract description 15
- 230000003204 osmotic effect Effects 0.000 title claims abstract description 15
- 239000002351 wastewater Substances 0.000 title claims abstract description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 40
- 230000003287 optical effect Effects 0.000 claims abstract description 25
- 238000009292 forward osmosis Methods 0.000 claims abstract description 20
- 230000004907 flux Effects 0.000 claims abstract description 11
- 150000003839 salts Chemical class 0.000 claims abstract description 5
- 239000007788 liquid Substances 0.000 claims description 39
- 239000002994 raw material Substances 0.000 claims description 21
- 238000012545 processing Methods 0.000 claims description 9
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 2
- 238000003062 neural network model Methods 0.000 claims description 2
- 238000012800 visualization Methods 0.000 claims 2
- 238000012544 monitoring process Methods 0.000 abstract description 12
- 238000005516 engineering process Methods 0.000 abstract description 10
- 238000000034 method Methods 0.000 abstract description 10
- 230000008569 process Effects 0.000 abstract description 10
- 230000007613 environmental effect Effects 0.000 abstract description 5
- 238000013527 convolutional neural network Methods 0.000 abstract description 3
- 230000014759 maintenance of location Effects 0.000 abstract 1
- 239000000523 sample Substances 0.000 description 9
- 239000003344 environmental pollutant Substances 0.000 description 5
- 231100000719 pollutant Toxicity 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 239000012466 permeate Substances 0.000 description 4
- 238000004065 wastewater treatment Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000009285 membrane fouling Methods 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000000879 optical micrograph Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 239000002957 persistent organic pollutant Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000011780 sodium chloride Substances 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
- G01N2015/086—Investigating permeability, pore-volume, or surface area of porous materials of films, membranes or pellicules
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Abstract
The invention relates to a visual osmotic membrane treatment wastewater detection device, which comprises a membrane component, two water pumps, two three-position integrated measuring devices, a mechanical arm, an optical microscope, a central control device and a terminal control device, wherein the three-position integrated measuring devices are connected with the central control device; conductivity, temperature and weight data acquired by the three-dimensional integrated measuring device, weight data acquired by the balance and image data captured by the optical microscope are sent to the terminal control device in real time, and the terminal control device sends structural parameters, water flux, salt flux and retention rate data of the membrane module permeable membrane to the convolutional neural network model to establish the forward osmosis model. The device can be used for monitoring the whole process of the running state of the permeable membrane, providing all process data for establishing a membrane pollution model and a forward osmosis model, and the established model can be used for predicting the pollution condition and the running state of the membrane and obtaining the long-time running result in advance. The bottom layer of the equipment integrates automation and environmental engineering professional technologies, and meanwhile, the simple upper-layer operation of environmental engineering technicians can be met.
Description
Technical Field
The invention relates to the field of environmental engineering and automation, in particular to a visual osmotic membrane treatment wastewater detection device.
Background
With the rapid development of national industry, water resource shortage and water pollution become increasingly serious problems, especially water containing trace organic pollutants which are difficult to treat. The membrane separation technology has the advantages of low energy consumption and high efficiency, and is widely applied to water treatment, thereby arousing the research interest of a large number of scholars. Forward osmosis occurs widely in nature and achieves mass transfer of solvent from a high pressure to a low pressure region by the osmotic pressure differential of the solution. Research results show that the forward osmosis technology has a good effect on the pollutant interception process. In order to select better membrane materials and enable forward osmosis to operate under the optimal condition, the best mode is to carry out real-time visual automatic monitoring through a computer osmosis membrane process, so that the time of manual supervision is saved, and human errors are avoided. The research in this direction is relatively few, the related contents are not extensive enough, and the application in the actual industry is impossible, so further innovation is needed. The on-line monitoring technology is popular recently, and is based on a first principle, the operation state of the material and the equipment of the membrane is simulated in a physical mode, and the material performance and the forward osmosis operation effect of the membrane are predicted; or data is collected through a data monitoring technology, and the condition occurring at the next moment is predicted through data analysis performed by a data driving model.
Disclosure of Invention
The invention provides a visual osmotic membrane wastewater treatment detection device aiming at the problem of osmotic membrane wastewater treatment effect detection, which can be used for monitoring the whole process of the operation state of an osmotic membrane, providing all process data for establishing a membrane pollution model and a forward osmosis model, predicting the pollution condition and the operation state of the membrane by the established model, and obtaining the long-time operation result in advance.
The technical scheme of the invention is that the visual osmotic membrane treatment wastewater detection device comprises a membrane component, two water pumps, two three-position integrated measuring devices of conductivity, temperature and balance, a mechanical arm, an optical microscope, a central control device and a terminal control device;
placing the wide-mouth bottle filled with the raw material liquid on a first balance, and enabling the raw material liquid to enter the membrane module through a conduit and a first water pump; the wide-mouth bottle filled with the drawing liquid is placed on a second balance, and the drawing liquid is connected with the membrane assembly through a conduit and a second water pump; an optical microscope fixed on the mechanical arm is arranged right above the membrane component; the terminal control device outputs control signals to control the water flow rates of the two water pumps and the mechanical arm to work, and the mechanical arm drives the optical microscope to move; conductivity values, temperature and weight data acquired by the two conductivity, temperature and balance three-dimensional integrated measuring devices and image data captured by an optical microscope are sent to a terminal control device in real time, and the terminal control device sends structural parameters, water flux, salt flux and rejection rate data of a membrane module permeable membrane to an application convolution neural network model to establish a forward osmosis model; the terminal control device communicates with the central control device in real time.
Through the technical scheme, the whole process monitoring of the running state of the permeable membrane is realized.
Preferably: the terminal control device comprises a PLC and a data processor, the two three-position integrated measuring devices and the two water pumps are connected with the PLC, data are transmitted into the data processor in real time through a protocol of the PLC, and the data processor comprises an image processing module, a machine learning algorithm and a machine vision algorithm.
Through the technical scheme, the traditional PLC application, the image technology, the algorithm and the big data are combined, the workload and the cost of manual reading are saved, and intelligent management is realized.
Preferably: the optical microscope is fixed on the mechanical arm, and the terminal control device drives the mechanical arm to enable the optical microscope to move above the membrane module along the XY axes, so that the pollution condition of each position of the membrane module is detected.
By the technical scheme, the image amplification technology and the mechanical control technology are combined, and the automatic image capturing function is realized.
Preferably: the central control device is an operable platform with a visual system interface and is communicated with the terminal control device.
Through the technical scheme, the operable platform of the visual system interface is convenient to operate and information is visual and intuitive.
Preferably: the temperature condition of the raw material liquid is measured by a temperature detector in the three-position integrated measuring device, the terminal control device sends temperature data to the central control device, and a visual system interface is used for checking.
Through the technical scheme, the water temperature can be used for acquiring and visualizing the penetration associated data.
The invention has the beneficial effects that: the visual osmotic membrane treatment wastewater detection device can acquire data of each external device through the central control device, transmit the data to the terminal control device, and perform various algorithm operations and image processing, so that the monitoring of the forward osmosis running state and the detection of membrane pollution are achieved, and the purposes of integrating automation and environmental engineering professional technologies on the bottom layer of the device and meeting the requirement of simple upper-layer operation of environmental engineering technicians are achieved.
Drawings
FIG. 1 is a schematic structural diagram of a visual permeable membrane wastewater treatment detection device of the invention;
FIG. 2 is a diagram illustrating a device management platform transport according to the present invention;
FIG. 3 is a schematic view of a real-time monitoring interface of the apparatus of the present invention;
FIG. 4 is a flow chart of the operation of the visual permeable membrane wastewater treatment device of the invention.
Reference numerals: 1. a conductivity/temperature probe of the three-in-one measuring device; 2. a raw material liquid; 3. a weight measuring module of the three-in-one measuring device; 4. a terminal control device; 5. a water pump; 6. a central control device; 7. an optical microscope; 8. a mechanical arm; 9. a membrane module; 10. a water pump; 11. a conductivity/temperature probe of the three-in-one measuring device; 12. drawing the liquid; 13. three-in-one measuring device weight measuring module.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, the structure of the visual permeable membrane treatment wastewater detection device is schematically illustrated, and the device comprises a membrane module 9, water pumps 5 and 10, conductivity/temperature probes 1 and 11 of a three-position integrated measuring device, weight measuring modules 3 and 13 of the three-position integrated measuring device, a mechanical arm 8, an optical microscope 7, a central control device 6 and a terminal control device 4. The wide-mouth bottle filled with the raw material liquid 2 is arranged on the weight measuring module 3, and the raw material liquid 2 is connected with the membrane component 9 through a guide pipe by a water pump 5; the wide-mouth bottle filled with the draw solution 12 is arranged on a weight measuring module 13, and the draw solution 12 is connected with the membrane component 9 through a water pump 10 by a conduit; an optical microscope 7 fixed on the mechanical arm 8 is arranged right above the membrane assembly 9; the raw material liquid 2 and the drawing liquid 12 are respectively provided with a conductivity/temperature probe 1, 11, the conductivity/temperature probes 1, 11 are used for transmitting conductivity and temperature data, weight data of the weight measuring modules 3, 13 and optical microscope image data to the terminal control device 4 in real time, and the terminal control device 4 outputs control signals to control the two water pumps 5, 10 and the mechanical arm 8 to work; the terminal control device 4 communicates data with the central control device 6.
The central control device 6 is an operable platform with a visual system interface, such as a touch screen or a mobile phone, and communicates with the terminal control device 4.
The terminal control device 4 is used as a server and comprises a storage module for storing data and a calculation module for safely storing and processing the data through a MongoDB distributed database, wherein the calculation module comprises a module for fitting a water permeability coefficient (A), a solute permeability coefficient (B) and a membrane structure parameter (S) through experimental data; predicting water flux, salt flux and rejection rate through a forward osmosis model; membrane fouling was detected by three membrane fouling models (full plugging model, internal plugging model, full partial plugging model, layer plugging model). The transmission module is used for receiving and transmitting data, the display module is connected with a computer through various devices of the forward osmosis membrane for transmission and conversion of numerical value units, the display module is used for processing the graphical interface of the upper-layer platform, the terminal control device 4 serves as a server, an access interface is created and provided for other devices to use, and the wireless communication module is connected through a smart phone or other wireless central processing devices.
Two sets of gear water pumps 5 and 10 are connected with the membrane component through hoses, raw material liquid and drawing liquid independently circulate to enter the upper side and the lower side of the membrane component, part of liquid in the raw material liquid permeates into the drawing liquid through the permeable membrane, and the liquid returns to respective wide-mouth bottles after passing through the membrane component, so that the concentrations on the two sides change in a convergent manner. The two gear pumps respectively transmit the raw material liquid and the absorption liquid in the forward osmosis equipment, and the difference of the two gear pumps in other equipment is that the gear pumps are connected with a visual system software platform to digitally adjust the pump speed.
The conductivity/temperature probes 1 and 11 measure the conductivity values of the raw material solution and the draw solution in the forward osmosis device, respectively, and the terminal control device 4 converts the concentration of the solute and changes the unit of the concentration, thereby filtering out some data with wrong reception.
The weight measuring module measures the mass of the drawing liquid, the accuracy of the weight measuring module is 0.01, and the weight measuring module can detect, record and calibrate the weight measuring module through the terminal control device 4.
The temperature situation can be measured by the temperature probe in the conductivity/temperature probe 1 and viewed through the visual system interface on the central control device.
The optical microscope 7 is fixed on the mechanical arm 8, the position of the optical microscope 7 is moved through the mechanical arm 8, the image is transmitted to the terminal control device 4 in real time through the USB, and the terminal control device 4 detects membrane pollution by collecting image data through a convolutional neural network model.
The optical microscope 7 is fixed on the mechanical arm 8, and programs are written through the mechanical arm 8, so that the optical microscope 7 can move above the membrane assembly 9 along the XY axes, and the pollution condition of each position of the membrane is detected.
The two water pumps, the two three-position integrated measuring devices and the mechanical arm are connected with the terminal control device 4 in a serial port mode, the time for reading data each time is set to be 10ms, the data detected in real time are transmitted to the terminal control device 4 through an RS485 protocol, then the time for reading the database is set, and the data transmitted in real time are read into the mongoDB database. The optical microscope 7 is connected with the terminal control device 4 through a USB, sets the grabbing time, grabs the image in real time through the terminal control device 4 to measure the membrane pollution, and controls the orientation through the mechanical arm 8 to detect the pollution of different positions of the membrane. The device pumps raw material liquid 2 and drawing liquid 12 into a membrane module 9 at a certain speed through a water pump, and permeates water in the polluted raw material liquid through a membrane in the membrane module 9. Then, the pollutant is intercepted by using an interception mechanism of a semipermeable membrane, and water molecules of the pollutant penetrate through the permeable membrane to the absorption liquid, so that the membrane interception operation is finished. Then returns to the original solution through a hose. In the process, an optical microscope is arranged above the membrane component to measure membrane pollution; and a balance is arranged below the drawing liquid to record the solution mass of the drawing liquid in real time so as to calculate the water flux. The two conductivity/temperature probes are respectively placed in the raw material liquid and the drawing liquid to measure the conductivity of the solution, and then the concentration of the solution is calculated.
Example (b):
an asymmetric commercial membrane was selected as the membrane unit for this experiment in membrane module 9. The parameters are as follows: the rotating speed of the water pump is 240 mL/min; the transmission time interval of the external equipment is 5s, and each time data is transmitted to the terminal control device through an RS485 protocol and is imported into the MongoDB database. The optical microscope 7 also has a grabbing time interval of 5 s. 1.0mol/L sodium chloride solution is selected as the draw solution 12; the raw material liquid 2 was tested by selecting 10mg/L, 15mg/L and 20mg/L of organic wastewater as raw material liquid, and the operation was carried out for 8 hours.
First, all devices are turned on, the system interface is visualized in the central control means 6 as in fig. 2, and the apparatus is operated after turning green by clicking the device switch, the on/off button, and then the pop-up switch picture by touch. If the equipment is stopped, the pop-up switch picture is clicked, and the operation of the device can be stopped after the pop-up switch picture turns red. The real-time monitoring interface is accessed by sequentially clicking the meter monitoring and real-time monitoring buttons, and the schematic diagram is shown in fig. 3. The operation mechanism of one forward osmosis intelligent management device is shown in fig. 4, the device respectively pumps raw material liquid and drawing liquid into a membrane component through a water pump, water in the polluted raw material liquid permeates through a membrane in the membrane component and intercepts pollutants, water molecules permeate into the drawing liquid through a permeable membrane, and the interception operation of the membrane can be finished after waiting for time.
Next, the optical microscope is operated by sequentially clicking the machine vision and film contamination detection buttons on the central control device 6. The principle is that firstly, an image is grayed, then, a gray level histogram is passed through, a membrane area is preliminarily detected according to the color of the membrane, then, the membrane area is secondarily selected according to the characteristic histogram and the precondition of the degree of rectangle and the area. The image was cropped and all the images obtained were converted to a uniform size of 200 x 200. And (3) introducing the experimental water flux and pollutant concentration data of 10mg/L and 20mg/L at each moment as dependent variables and the image measured at each moment as independent variables into a convolutional neural network model for training. After obtaining the model, the accuracy of the model is checked by taking experimental data of 15mg/L as test data. If not, refit.
Finally, the data processing and data viewing button in the central control device 6 is clicked to derive the data of the experiment. The average water flux can be calculated to be 15.43LMH and the salt rejection rate can be calculated to be 92.65% through a data processing and operation processing button. Therefore, the forward osmosis intelligent management device can detect the membrane pollution and the forward osmosis running state in real time, process data and conveniently solve the problem.
A Programmable Logic Controller (PLC) is widely applied to data transmission between an upper computer and external equipment, and provides a basic premise for real-time monitoring. Meanwhile, machine learning, machine vision algorithm and database partition storage technology provide a powerful solution for accelerating calculation and reading real-time data of the forward osmosis device. Therefore, for the forward osmosis device, parameters such as quality, conductivity and flow can be detected through external equipment, and the forward osmosis device is connected with the terminal control device and is transmitted in real time through a protocol of the PLC, so that the workload and the cost of manual reading are saved.
Claims (5)
1. A visual osmotic membrane treatment wastewater detection device is characterized by comprising a membrane component, two water pumps, two three-position integrated measuring devices of conductivity, temperature and balance, a mechanical arm, an optical microscope, a central control device and a terminal control device;
placing the wide-mouth bottle filled with the raw material liquid on a first balance, and enabling the raw material liquid to enter the membrane module through a conduit and a first water pump; the wide-mouth bottle filled with the drawing liquid is placed on a second balance, and the drawing liquid is connected with the membrane assembly through a conduit and a second water pump; an optical microscope fixed on the mechanical arm is arranged right above the membrane component; the terminal control device outputs control signals to control the water flow rates of the two water pumps and the mechanical arm to work, and the mechanical arm drives the optical microscope to move; conductivity values, temperature and weight data acquired by the two conductivity, temperature and balance three-dimensional integrated measuring devices and image data captured by an optical microscope are sent to a terminal control device in real time, and the terminal control device sends structural parameters, water flux, salt flux and rejection rate data of a membrane module permeable membrane to an application convolution neural network model to establish a forward osmosis model; the terminal control device communicates with the central control device in real time.
2. The visual osmotic membrane treatment wastewater detection device according to claim 1, wherein the terminal control device comprises a PLC and a data processor, the two three-position integrated measuring devices and the two water pumps are connected with the PLC, data are transmitted into the data processor in real time through a protocol of the PLC, and the data processor comprises an image processing module, a machine learning algorithm and a machine vision algorithm.
3. The visual osmotic membrane treatment wastewater detection device according to claim 1, wherein the optical microscope is fixed on a mechanical arm, and the terminal control device drives the mechanical arm to move the optical microscope along the XY axes above the membrane module to detect the pollution condition of each position of the membrane module.
4. The apparatus for detecting wastewater treated by a visualized osmotic membrane according to claim 1, wherein said central control unit is an operable platform with a visualization system interface, and is in communication with said terminal control unit.
5. The apparatus for detecting wastewater treated by a visualized osmotic membrane according to claim 1, wherein the temperature condition of the raw material liquid is measured by a temperature detector in a three-in-one measuring device, the terminal control device sends the temperature data to the central control device, and the visualization system interface is used for viewing.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117684953A (en) * | 2023-12-11 | 2024-03-12 | 江苏省环境科学研究院 | Visual detection equipment and detection method for non-aqueous phase liquid pollutants of underground water |
| CN117684953B (en) * | 2023-12-11 | 2024-05-24 | 江苏省环境科学研究院 | Visual detection equipment and detection method for non-aqueous phase liquid pollutants of underground water |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101366046A (en) * | 2005-11-01 | 2009-02-11 | 卡姆赛特有限公司 | Method of dental microscopic procedure |
| CN101384342A (en) * | 2006-01-24 | 2009-03-11 | 加利福尼亚大学董事会 | Method and system for monitoring reverse osmosis membranes |
| CN205317631U (en) * | 2015-10-24 | 2016-06-15 | 宁波大学 | Positive osmotic membrane testing arrangement in laboratory |
| CN106082397A (en) * | 2016-06-12 | 2016-11-09 | 东华大学 | A kind of system and method synchronizing regeneration effluent and desalinization |
| CN106946320A (en) * | 2017-04-14 | 2017-07-14 | 东华大学 | One kind processing high salt dangerous waste percolate system and its application method |
| CN107287119A (en) * | 2017-08-23 | 2017-10-24 | 湖南开启时代生物科技有限责任公司 | A kind of cell culture counting device |
| US20180161730A1 (en) * | 2015-04-24 | 2018-06-14 | Nanyang Technological University | Method and apparatus for assessing a state of fouling of a reverse osmosis system |
| CN207866647U (en) * | 2018-02-05 | 2018-09-14 | 同沁（厦门）环保科技有限公司 | A kind of forward osmosis membrane test device |
| CN108921039A (en) * | 2018-06-07 | 2018-11-30 | 南京启德电子科技有限公司 | The forest fire detection method of depth convolution model based on more size convolution kernels |
| CN109001901A (en) * | 2018-07-13 | 2018-12-14 | 上海大学 | A kind of multi-angle microlens holder device based on industrial machinery arm |
| CN109191423A (en) * | 2018-07-18 | 2019-01-11 | 中国矿业大学 | A kind of porous media Permeability Prediction method based on machine image intelligence learning |
| CN110210682A (en) * | 2019-06-12 | 2019-09-06 | 云南电网有限责任公司大理供电局 | Distribution transforming heavy-overload method for early warning based on load data image conversion convolutional neural networks |
| CN209624306U (en) * | 2018-08-29 | 2019-11-12 | 无锡星月科技有限公司 | Film workshop semi-automatic water examines platform |
| CN110494395A (en) * | 2017-03-28 | 2019-11-22 | 东丽株式会社 | Wastewater treatment method, wastewater treatment equipment and the waste water treatment system management program of film separated activated sludge |
| US20190371147A1 (en) * | 2018-05-31 | 2019-12-05 | Boe Technology Group Co., Ltd. | Fire alarming method and device |
| US20200158618A1 (en) * | 2017-05-19 | 2020-05-21 | Hach Company | Membrane integrity monitoring in water treatment |
| WO2020254268A1 (en) * | 2019-06-18 | 2020-12-24 | Baxter Healthcare Sa | A reverse osmosis apparatus, and a method for controlling a reverse osmosis apparatus |
-
2021
- 2021-02-08 CN CN202110171530.2A patent/CN112881637A/en active Pending
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101366046A (en) * | 2005-11-01 | 2009-02-11 | 卡姆赛特有限公司 | Method of dental microscopic procedure |
| CN101384342A (en) * | 2006-01-24 | 2009-03-11 | 加利福尼亚大学董事会 | Method and system for monitoring reverse osmosis membranes |
| US20180161730A1 (en) * | 2015-04-24 | 2018-06-14 | Nanyang Technological University | Method and apparatus for assessing a state of fouling of a reverse osmosis system |
| CN205317631U (en) * | 2015-10-24 | 2016-06-15 | 宁波大学 | Positive osmotic membrane testing arrangement in laboratory |
| CN106082397A (en) * | 2016-06-12 | 2016-11-09 | 东华大学 | A kind of system and method synchronizing regeneration effluent and desalinization |
| CN110494395A (en) * | 2017-03-28 | 2019-11-22 | 东丽株式会社 | Wastewater treatment method, wastewater treatment equipment and the waste water treatment system management program of film separated activated sludge |
| CN106946320A (en) * | 2017-04-14 | 2017-07-14 | 东华大学 | One kind processing high salt dangerous waste percolate system and its application method |
| US20200158618A1 (en) * | 2017-05-19 | 2020-05-21 | Hach Company | Membrane integrity monitoring in water treatment |
| CN107287119A (en) * | 2017-08-23 | 2017-10-24 | 湖南开启时代生物科技有限责任公司 | A kind of cell culture counting device |
| CN207866647U (en) * | 2018-02-05 | 2018-09-14 | 同沁（厦门）环保科技有限公司 | A kind of forward osmosis membrane test device |
| US20190371147A1 (en) * | 2018-05-31 | 2019-12-05 | Boe Technology Group Co., Ltd. | Fire alarming method and device |
| CN108921039A (en) * | 2018-06-07 | 2018-11-30 | 南京启德电子科技有限公司 | The forest fire detection method of depth convolution model based on more size convolution kernels |
| CN109001901A (en) * | 2018-07-13 | 2018-12-14 | 上海大学 | A kind of multi-angle microlens holder device based on industrial machinery arm |
| CN109191423A (en) * | 2018-07-18 | 2019-01-11 | 中国矿业大学 | A kind of porous media Permeability Prediction method based on machine image intelligence learning |
| CN209624306U (en) * | 2018-08-29 | 2019-11-12 | 无锡星月科技有限公司 | Film workshop semi-automatic water examines platform |
| CN110210682A (en) * | 2019-06-12 | 2019-09-06 | 云南电网有限责任公司大理供电局 | Distribution transforming heavy-overload method for early warning based on load data image conversion convolutional neural networks |
| WO2020254268A1 (en) * | 2019-06-18 | 2020-12-24 | Baxter Healthcare Sa | A reverse osmosis apparatus, and a method for controlling a reverse osmosis apparatus |
Non-Patent Citations (2)
| Title |
|---|
| 王珏等: "3种正渗透膜对水中卡马西平的截留", 《环境工程学报》 * |
| 罗方 等: "正渗透膜特征参数测试方法研究进展", 《化工进展》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117684953A (en) * | 2023-12-11 | 2024-03-12 | 江苏省环境科学研究院 | Visual detection equipment and detection method for non-aqueous phase liquid pollutants of underground water |
| CN117684953B (en) * | 2023-12-11 | 2024-05-24 | 江苏省环境科学研究院 | Visual detection equipment and detection method for non-aqueous phase liquid pollutants of underground water |
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