CN117263464A - Energy-saving industrial sewage treatment system and sewage treatment method - Google Patents
Energy-saving industrial sewage treatment system and sewage treatment method Download PDFInfo
- Publication number
- CN117263464A CN117263464A CN202311492052.0A CN202311492052A CN117263464A CN 117263464 A CN117263464 A CN 117263464A CN 202311492052 A CN202311492052 A CN 202311492052A CN 117263464 A CN117263464 A CN 117263464A
- Authority
- CN
- China
- Prior art keywords
- stirring speed
- sewage
- value
- training
- time sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 239000010865 sewage Substances 0.000 title claims abstract description 147
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000003756 stirring Methods 0.000 claims abstract description 180
- 238000010219 correlation analysis Methods 0.000 claims abstract description 15
- 239000013598 vector Substances 0.000 claims description 111
- 230000003993 interaction Effects 0.000 claims description 65
- 238000012549 training Methods 0.000 claims description 64
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 57
- CDBYLPFSWZWCQE-UHFFFAOYSA-L Sodium Carbonate Chemical compound [Na+].[Na+].[O-]C([O-])=O CDBYLPFSWZWCQE-UHFFFAOYSA-L 0.000 claims description 34
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 32
- 238000006243 chemical reaction Methods 0.000 claims description 31
- 239000000126 substance Substances 0.000 claims description 29
- 238000004065 wastewater treatment Methods 0.000 claims description 28
- 238000005345 coagulation Methods 0.000 claims description 26
- 230000015271 coagulation Effects 0.000 claims description 26
- 239000010842 industrial wastewater Substances 0.000 claims description 26
- 239000002245 particle Substances 0.000 claims description 26
- 239000011159 matrix material Substances 0.000 claims description 25
- 150000002500 ions Chemical class 0.000 claims description 22
- 229910000029 sodium carbonate Inorganic materials 0.000 claims description 17
- 235000017550 sodium carbonate Nutrition 0.000 claims description 17
- 238000000926 separation method Methods 0.000 claims description 16
- 230000009471 action Effects 0.000 claims description 15
- 125000002091 cationic group Chemical group 0.000 claims description 15
- 238000000605 extraction Methods 0.000 claims description 13
- 238000001914 filtration Methods 0.000 claims description 13
- 238000013032 photocatalytic reaction Methods 0.000 claims description 13
- 238000001179 sorption measurement Methods 0.000 claims description 13
- 239000007788 liquid Substances 0.000 claims description 12
- 238000001556 precipitation Methods 0.000 claims description 12
- 238000003062 neural network model Methods 0.000 claims description 11
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 claims description 10
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 claims description 10
- 238000013019 agitation Methods 0.000 claims description 10
- 229910052791 calcium Inorganic materials 0.000 claims description 10
- 239000011575 calcium Substances 0.000 claims description 10
- 239000001913 cellulose Substances 0.000 claims description 10
- 229920002678 cellulose Polymers 0.000 claims description 10
- 229910001385 heavy metal Inorganic materials 0.000 claims description 10
- 239000012535 impurity Substances 0.000 claims description 10
- 229910052742 iron Inorganic materials 0.000 claims description 10
- 229910052749 magnesium Inorganic materials 0.000 claims description 10
- 239000011777 magnesium Substances 0.000 claims description 10
- 239000004964 aerogel Substances 0.000 claims description 9
- 230000002195 synergetic effect Effects 0.000 claims description 9
- 238000007667 floating Methods 0.000 claims description 8
- 230000001965 increasing effect Effects 0.000 claims description 8
- HRPVXLWXLXDGHG-UHFFFAOYSA-N Acrylamide Chemical compound NC(=O)C=C HRPVXLWXLXDGHG-UHFFFAOYSA-N 0.000 claims description 7
- GQOKIYDTHHZSCJ-UHFFFAOYSA-M dimethyl-bis(prop-2-enyl)azanium;chloride Chemical compound [Cl-].C=CC[N+](C)(C)CC=C GQOKIYDTHHZSCJ-UHFFFAOYSA-M 0.000 claims description 7
- 238000002156 mixing Methods 0.000 claims description 7
- 238000010526 radical polymerization reaction Methods 0.000 claims description 7
- 230000003247 decreasing effect Effects 0.000 claims description 6
- 239000005416 organic matter Substances 0.000 claims description 5
- 230000015556 catabolic process Effects 0.000 claims description 3
- 238000006731 degradation reaction Methods 0.000 claims description 3
- 238000004064 recycling Methods 0.000 claims description 3
- 230000002123 temporal effect Effects 0.000 claims 2
- 230000008859 change Effects 0.000 abstract description 15
- 230000008569 process Effects 0.000 abstract description 13
- 238000012545 processing Methods 0.000 abstract description 7
- 238000005265 energy consumption Methods 0.000 abstract description 6
- 238000003912 environmental pollution Methods 0.000 abstract description 4
- 230000001976 improved effect Effects 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 15
- 239000012530 fluid Substances 0.000 description 7
- 239000000047 product Substances 0.000 description 7
- 230000004913 activation Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000009826 distribution Methods 0.000 description 6
- 238000011176 pooling Methods 0.000 description 6
- 239000002244 precipitate Substances 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 239000003344 environmental pollutant Substances 0.000 description 5
- 231100000719 pollutant Toxicity 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000010170 biological method Methods 0.000 description 4
- ZFXVRMSLJDYJCH-UHFFFAOYSA-N calcium magnesium Chemical compound [Mg].[Ca] ZFXVRMSLJDYJCH-UHFFFAOYSA-N 0.000 description 4
- 239000003054 catalyst Substances 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 210000002569 neuron Anatomy 0.000 description 4
- 238000000053 physical method Methods 0.000 description 4
- HEMHJVSKTPXQMS-UHFFFAOYSA-M Sodium hydroxide Chemical compound [OH-].[Na+] HEMHJVSKTPXQMS-UHFFFAOYSA-M 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
- 239000002957 persistent organic pollutant Substances 0.000 description 3
- 150000003839 salts Chemical class 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000006555 catalytic reaction Methods 0.000 description 2
- 239000000701 coagulant Substances 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000001704 evaporation Methods 0.000 description 2
- 230000008020 evaporation Effects 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 239000000383 hazardous chemical Substances 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000001728 nano-filtration Methods 0.000 description 2
- 239000007800 oxidant agent Substances 0.000 description 2
- 230000001590 oxidative effect Effects 0.000 description 2
- 238000007146 photocatalysis Methods 0.000 description 2
- 230000000750 progressive effect Effects 0.000 description 2
- 239000000376 reactant Substances 0.000 description 2
- 238000001223 reverse osmosis Methods 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 238000000108 ultra-filtration Methods 0.000 description 2
- 238000012300 Sequence Analysis Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000002378 acidificating effect Effects 0.000 description 1
- 230000003213 activating effect Effects 0.000 description 1
- 239000003463 adsorbent Substances 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 238000005311 autocorrelation function Methods 0.000 description 1
- 231100000693 bioaccumulation Toxicity 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000007853 buffer solution Substances 0.000 description 1
- 239000013043 chemical agent Substances 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000009133 cooperative interaction Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000004090 dissolution Methods 0.000 description 1
- 238000011049 filling Methods 0.000 description 1
- 239000008394 flocculating agent Substances 0.000 description 1
- 238000005189 flocculation Methods 0.000 description 1
- 230000016615 flocculation Effects 0.000 description 1
- 238000005188 flotation Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 231100000086 high toxicity Toxicity 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- -1 hydrogen ions Chemical class 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012482 interaction analysis Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 230000007787 long-term memory Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000007783 nanoporous material Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 238000010587 phase diagram Methods 0.000 description 1
- 230000001699 photocatalysis Effects 0.000 description 1
- 229920000642 polymer Polymers 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 230000009257 reactivity Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 229910001415 sodium ion Inorganic materials 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000013517 stratification Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F9/00—Multistage treatment of water, waste water or sewage
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F5/00—Softening water; Preventing scale; Adding scale preventatives or scale removers to water, e.g. adding sequestering agents
- C02F5/02—Softening water by precipitation of the hardness
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/001—Processes for the treatment of water whereby the filtration technique is of importance
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/24—Treatment of water, waste water, or sewage by flotation
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/28—Treatment of water, waste water, or sewage by sorption
- C02F1/288—Treatment of water, waste water, or sewage by sorption using composite sorbents, e.g. coated, impregnated, multi-layered
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/30—Treatment of water, waste water, or sewage by irradiation
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/34—Treatment of water, waste water, or sewage with mechanical oscillations
- C02F1/36—Treatment of water, waste water, or sewage with mechanical oscillations ultrasonic vibrations
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/40—Devices for separating or removing fatty or oily substances or similar floating material
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/46—Treatment of water, waste water, or sewage by electrochemical methods
- C02F1/4608—Treatment of water, waste water, or sewage by electrochemical methods using electrical discharges
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/52—Treatment of water, waste water, or sewage by flocculation or precipitation of suspended impurities
- C02F1/54—Treatment of water, waste water, or sewage by flocculation or precipitation of suspended impurities using organic material
- C02F1/56—Macromolecular compounds
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/70—Treatment of water, waste water, or sewage by reduction
- C02F1/705—Reduction by metals
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/06—Controlling or monitoring parameters in water treatment pH
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Hydrology & Water Resources (AREA)
- Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Water Supply & Treatment (AREA)
- Chemical & Material Sciences (AREA)
- Organic Chemistry (AREA)
- Physical Water Treatments (AREA)
Abstract
The application discloses an energy-saving industrial sewage treatment system and a sewage treatment method, wherein the stirring speed value and the PH value in the softening process are collected, and a data processing and analyzing algorithm is introduced into the rear end to carry out time sequence collaborative correlation analysis of the stirring speed and the PH in the softening process, so that softening parameters are automatically controlled based on the collaboration of the stirring speed and the PH change, the treatment efficiency is improved, the energy consumption is reduced, and the performance of the industrial sewage treatment system is optimized. Therefore, industrial sewage can be purified more effectively, environmental pollution is reduced, and sustainable utilization of resources is realized.
Description
Technical Field
The present application relates to the field of wastewater treatment, and more particularly, to an energy-efficient industrial wastewater treatment system and a wastewater treatment method.
Background
The treatment of industrial sewage is very important because if its components are not treated, the direct discharge into the environment can cause serious pollution to the land and water sources. Particularly Persistent Organic Pollutants (POPs) which are capable of long-distance migration and long-term environmental presence, have long-term residual properties, bioaccumulation properties, semi-volatility and high toxicity. These substances pose serious health and environmental hazards to humans. Accordingly, research has been dedicated in recent years to thoroughly degrade these persistent organic pollutants during sewage treatment to prevent the generation of new pollutants.
The industrial sewage has complex water quality components and usually contains high suspended matters and turbidity, high salt content, high chemical oxygen demand, various heavy metals and the like. At present, the industrial sewage treatment mainly adopts comprehensive technical methods such as a physical method, a chemical method, a biological method and the like. Physical methods include multiple effect evaporation, ionization, reverse osmosis, ultrafiltration, nanofiltration, etc., but require a large amount of power resources and have high running costs. The chemical method is to treat sewage by adding a high molecular oxidant and a chemical flocculant, but has the defects of large dosage, long standing time and secondary pollution. The biological method has the advantages of stability and low cost in the aspect of treating domestic sewage, but cannot effectively treat industrial sewage with high heavy metal content, high organic matter concentration and high salt content.
Accordingly, an optimized energy efficient industrial wastewater treatment system is desired.
Disclosure of Invention
The present application has been made in order to solve the above technical problems. The embodiment of the application provides an energy-saving industrial sewage treatment system and a sewage treatment method, which are used for carrying out time sequence collaborative correlation analysis of stirring speed and PH in the softening process by collecting the stirring speed value and the PH value in the softening process and introducing a data processing and analyzing algorithm at the rear end so as to automatically control softening parameters based on the collaboration of the stirring speed and PH change, thereby improving the treatment efficiency, reducing the energy consumption and optimizing the performance of the industrial sewage treatment system. Therefore, industrial sewage can be purified more effectively, environmental pollution is reduced, and sustainable utilization of resources is realized.
According to one aspect of the present application, there is provided an energy-saving type industrial sewage treatment system, comprising:
the filtering subsystem is used for filtering the industrial sewage through a mechanical grid, and separating the filtered industrial sewage through an oil-water separator to remove impurities, floating oil and suspended matters so as to obtain pre-separated sewage;
the softening subsystem is used for enabling the pre-separated sewage to pass through the chemical reactor, adding sodium carbonate, stirring and mixing to remove calcium and magnesium hardness ions so as to soften the water body to generate first suspended floc particles and softened sewage;
the reinforced coagulation subsystem is used for introducing the softened sewage into a coagulation air floatation machine, and adding a cationic flocculant to generate second suspended floc particles and coagulation air floatation sewage, wherein the cationic flocculant is a product obtained by performing free radical polymerization reaction on acrylamide and dimethyl diallyl ammonium chloride through irradiation;
the organic matter degradation subsystem is used for introducing the coagulation air floatation sewage into a pulse discharge plasma-photocatalytic reactor for reaction so as to obtain pulse discharge plasma-photocatalytic reaction sewage;
the zero-valent iron treatment subsystem is used for carrying out ultrasonic synergistic reaction on the nanometer zero-valent iron and the pulse discharge plasma-photocatalytic reaction sewage under the ultrasonic action, and then carrying out solid-liquid separation treatment in a precipitation area to obtain separated sewage;
And the adsorption treatment subsystem is used for introducing the separated sewage into an adsorption tank, adding iron wire cage skeleton cellulose aerogel, separating the separated sewage from the water body under the action of a magnetic field, and desorbing and recycling heavy metal ions to obtain the treated sewage.
According to another aspect of the present application, there is provided an energy-saving industrial sewage treatment method, comprising:
filtering industrial sewage through a mechanical grid, and separating the filtered industrial sewage through an oil-water separator to remove impurities, floating oil and suspended matters to obtain pre-separated sewage;
the pre-separated sewage is passed through a chemical reactor, and is stirred and mixed after soda ash is added, so that calcium and magnesium hardness ions are removed to soften water bodies to generate first suspended floc particles and softened sewage;
introducing the softened sewage into a coagulation air floatation machine, and adding a cationic flocculant to generate second suspended floc particles and coagulation air floatation sewage, wherein the cationic flocculant is a product obtained by carrying out free radical polymerization reaction on acrylamide and dimethyl diallyl ammonium chloride through irradiation;
introducing the coagulation air floatation sewage into a pulse discharge plasma-photocatalytic reactor for reaction to obtain pulse discharge plasma-photocatalytic reaction sewage;
Under the ultrasonic action, carrying out ultrasonic synergistic reaction on the nano zero-valent iron and the pulse discharge plasma-photocatalytic reaction sewage, and then carrying out solid-liquid separation treatment in a precipitation area to obtain separated sewage;
and (3) introducing the separated sewage into an adsorption tank, adding iron wire cage skeleton cellulose aerogel, separating the separated sewage from the water body under the action of a magnetic field, and desorbing and recovering heavy metal ions to obtain the treated sewage.
Compared with the prior art, the energy-saving industrial sewage treatment system and the sewage treatment method provided by the application have the advantages that the stirring speed value and the PH value in the softening process are collected, the data processing and analysis algorithm is introduced into the rear end to carry out time sequence collaborative correlation analysis of the stirring speed and the PH in the softening process, so that the softening parameters are automatically controlled based on the collaboration of the stirring speed and the PH change, the treatment efficiency is improved, the energy consumption is reduced, and the performance of the industrial sewage treatment system is optimized. Therefore, industrial sewage can be purified more effectively, environmental pollution is reduced, and sustainable utilization of resources is realized.
Drawings
The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 is a block diagram of an energy efficient industrial wastewater treatment system according to an embodiment of the present application;
FIG. 2 is a block diagram of a softening subsystem in an energy efficient industrial wastewater treatment system according to an embodiment of the present application;
FIG. 3 is a system architecture diagram of an energy efficient industrial wastewater treatment system according to an embodiment of the present application;
FIG. 4 is a block diagram of a PH value-agitation speed timing cross-correlation analysis module in an energy-efficient industrial wastewater treatment system according to an embodiment of the present application;
fig. 5 is a flow chart of an energy-efficient industrial wastewater treatment method according to an embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
As used in this application and in the claims, the terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Although the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server. The modules are merely illustrative, and different aspects of the systems and methods may use different modules.
Flowcharts are used in this application to describe the operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
At present, the industrial sewage treatment mainly adopts comprehensive technical methods such as a physical method, a chemical method, a biological method and the like. Physical methods include multiple effect evaporation, ionization, reverse osmosis, ultrafiltration, nanofiltration, etc., but require a large amount of power resources and have high running costs. The chemical method is to treat sewage by adding a high molecular oxidant and a chemical flocculant, but has the defects of large dosage, long standing time and secondary pollution. The biological method has the advantages of stability and low cost in the aspect of treating domestic sewage, but cannot effectively treat industrial sewage with high heavy metal content, high organic matter concentration and high salt content. Accordingly, an optimized energy efficient industrial wastewater treatment system is desired.
In the technical scheme of the application, an energy-saving industrial sewage treatment system is provided. Fig. 1 is a block diagram of an energy efficient industrial wastewater treatment system according to an embodiment of the present application. As shown in fig. 1, an energy efficient industrial wastewater treatment system 300 according to an embodiment of the present application includes: the filtering subsystem 310 is used for filtering the industrial sewage through a mechanical grid, and separating the filtered industrial sewage through an oil-water separator to remove impurities, floating oil and suspended matters so as to obtain pre-separated sewage; the softening subsystem 320 is used for passing the pre-separated sewage through a chemical reactor, adding sodium carbonate, stirring and mixing to remove calcium magnesium hardness ions so as to soften the water body and generate first suspended floc particles and softened sewage; the reinforced coagulation subsystem 330 is used for introducing the softened sewage into a coagulation air floatation machine, and adding a cationic flocculant to generate second suspended floc particles and coagulated air floatation sewage, wherein the cationic flocculant is a product obtained by performing free radical polymerization reaction on acrylamide and dimethyl diallyl ammonium chloride through irradiation; the organic matter degradation subsystem 340 is used for introducing the coagulation air floatation sewage into a pulse discharge plasma-photocatalytic reactor for reaction so as to obtain pulse discharge plasma-photocatalytic reaction sewage; the zero-valent iron treatment subsystem 350 is used for carrying out ultrasonic synergistic reaction on the nanometer zero-valent iron and the pulse discharge plasma-photocatalytic reaction sewage under the ultrasonic action, and then carrying out solid-liquid separation treatment in a precipitation area to obtain separated sewage; and the adsorption treatment subsystem 360 is used for introducing the separated sewage into an adsorption tank, adding iron wire cage skeleton cellulose aerogel, separating from the water body under the action of a magnetic field, and desorbing and recovering heavy metal ions to obtain the treated sewage.
Specifically, the filtering subsystem 310 is configured to filter the industrial sewage through a mechanical grid, and separate the filtered industrial sewage through an oil-water separator to remove impurities, floating oil and suspended matters, thereby obtaining pre-separated sewage. It should be understood that by the combined treatment of the mechanical grating and the oil-water separator, impurities, floating oil and suspended matters in the industrial sewage can be effectively removed, so that the sewage is primarily separated and purified.
A mechanical grating is a device for filtering and separating substances. It is typically made up of a series of parallel or cross-aligned metal or plastic strips with some gaps between them. Mechanical grids may be used to treat fluids of liquids, gases, or solid particles to remove impurities, solid particles, or other unwanted substances therefrom. The mechanical grating works by directing a fluid at the inlet of the grating through the gaps of the grating. Larger particles or material will be trapped by the grating strips, while smaller particles or material will continue to flow through the interstices of the grating. In this way, filtration and separation of solid particles in the fluid can be achieved.
An oil-water separator is a device for separating a fluid in which oil and water are mixed into two phases of oil and water. It is one of the devices commonly used in liquid handling and environmental protection. The working principle of the oil-water separator is based on the density difference of oil and water. Because of its low density, the oil floats on the surface of the water forming an oil phase. The oil-water separator separates the oil and water in the mixed fluid by a series of physical processes such as gravity separation, precipitation, oil-water stratification, etc. The oil-water separator is usually formed by a container or tank body, in which a number of separating means and flow control means are arranged. After the mixed fluid enters the separator, the oil and water respectively flow along different paths by adjusting the flow rate and the flow direction of the fluid. Inside the separator, the separation process of oil and water can be realized by gravity separation, a sedimentation tank, an oil-water separator, an oil collector and the like. By these means, the oil and water can be separated and discharged separately.
Specifically, the softening subsystem 320 is configured to pass the pre-separated sewage through a chemical reactor, and add soda ash, and mix with stirring, so as to remove calcium and magnesium hardness ions, so that the water body is softened to generate first suspended floc particles and softened sewage. In particular, in one specific example of the present application, as shown in fig. 2 and 3, the softening subsystem 320 comprises: the data acquisition module 321 is configured to acquire stirring speed values and PH values at a plurality of predetermined time points within a predetermined period of time; a PH-stirring speed time sequence interaction correlation analysis module 322, configured to perform time sequence correlation analysis on the stirring speed values and PH values at the plurality of predetermined time points to obtain PH-stirring speed interaction characteristics; the stirring speed control module 323 is configured to determine, based on the PH-stirring speed interaction characteristic, whether the stirring speed value at the current time point should be increased or decreased.
Specifically, the data acquisition module 321 is configured to acquire stirring speed values and PH values at a plurality of predetermined time points within a predetermined period. Considering that in industrial wastewater treatment systems, the softening subsystem is an important component for removing calcium and magnesium hardness ions in water to soften water. The softening subsystem typically includes a chemical reactor and a stirred mixing apparatus. Soda ash is added into the chemical reactor to react with calcium and magnesium hardness ions to form first suspended floc particles, so that the water body is softened. The stirring and mixing device is used for promoting the chemical reaction and ensuring the uniformity of the reaction. During softening, the stirring speed and pH are important parameters for the softening subsystem to be cooperatively controlled. The reasonable control of the stirring speed can ensure the uniform mixing of the reaction substances and improve the reaction efficiency; control of the pH can affect the rate of reaction and product formation. Therefore, accurate control of the stirring speed and pH is critical to proper operation and performance optimization of the softening subsystem, which is also critical to ensuring the quality and efficiency of industrial wastewater treatment. Therefore, in the technical scheme of the application, firstly, stirring speed values and PH values at a plurality of preset time points in a preset time period are obtained. According to the embodiment of the application, the stirring speed values at a plurality of preset time points in a preset time period can be obtained through the stirring speed sensor, and the PH values at a plurality of preset time points in the preset time period can be obtained through the PH sensor.
The stirring speed sensor is a sensor for measuring the rotation speed of the stirring device or the speed of stirring the liquid. The device generally adopts a non-contact or contact measurement principle, can monitor the rotating speed of the stirring equipment in real time, and transmits a rotating speed signal to a control system or a display device.
The pH sensor is a sensor for measuring acid-base properties of a solution. It is capable of measuring the concentration of hydrogen ions in the solution to determine the acid-base level, i.e., the pH, of the solution. It should be noted that the pH sensor requires regular calibration and maintenance to ensure accuracy and stability of the measurement results. Calibration is typically accomplished by placing the sensor in a standard buffer solution of known pH for alignment.
Accordingly, in one possible implementation, the stirring speed value and the PH value at a plurality of predetermined time points within a predetermined period of time may be obtained by, for example: first determining a predetermined period of time; a stirring speed sensor and a pH sensor are installed in the stirring apparatus. Ensuring that the sensor is properly connected and connected with a monitoring system or a recording device; setting sampling frequency and data storage mode; for the pH sensor, preheating and calibration were performed according to manufacturer's recommendations. This may ensure accuracy and stability of the sensor; a plurality of time points are selected for data acquisition within a predetermined period of time. These points in time may be evenly distributed or selected according to a particular experimental or operational plan; at each predetermined time point, the stirring speed and pH were recorded. Ensuring that the recorded data corresponds to the time point, and storing the data according to the requirements of the equipment; the processed and analyzed data is recorded.
Specifically, the PH-stirring speed time sequence correlation analysis module 322 is configured to perform time sequence correlation analysis on the stirring speed values and PH values at the plurality of predetermined time points to obtain PH-stirring speed interaction characteristics. In particular, in one specific example of the present application, as shown in fig. 4, the PH-stirring speed timing cross-correlation analysis module 322 includes: a data parameter time sequence arrangement unit 3221, configured to arrange the stirring speed values and the PH values at the plurality of predetermined time points into a stirring speed time sequence input vector and a PH value time sequence input vector according to a time dimension, respectively; a data parameter time sequence feature extraction unit 3222, configured to perform feature extraction on the stirring speed time sequence input vector and the PH value time sequence input vector through a time sequence feature extractor based on a deep neural network model, so as to obtain a stirring speed time sequence feature vector and a PH value time sequence feature vector; and the PH-stirring speed sequential feature interaction unit 3223 is configured to perform feature interaction association encoding on the stirring speed sequential feature vector and the PH sequential feature vector to obtain a PH-stirring speed interaction feature vector as the PH-stirring speed interaction feature.
More specifically, the data parameter time sequence arrangement unit 3221 is configured to arrange the stirring speed values and the PH values at the plurality of predetermined time points into a stirring speed time sequence input vector and a PH value time sequence input vector according to a time dimension, respectively. In consideration of the fact that the stirring speed value and the PH value are continuously changed in the time dimension, the stirring speed value and the PH value have a time sequence dynamic change rule, that is, the stirring speed value and the PH value at a plurality of preset time points respectively have a time sequence association relation. In order to capture and characterize the dynamic change characteristics of the stirring speed value and the PH value in time sequence so as to perform time sequence change characteristic interaction analysis of the stirring speed value and the PH value, in the technical scheme of the application, the stirring speed value and the PH value at a plurality of preset time points are firstly required to be respectively arranged into a stirring speed time sequence input vector and a PH value time sequence input vector according to a time dimension, so that the distribution information of the stirring speed value and the PH value in time sequence is respectively integrated.
More specifically, the data parameter timing feature extraction unit 3222 is configured to perform feature extraction on the stirring speed timing input vector and the PH timing input vector by using a timing feature extractor based on a deep neural network model, so as to obtain a stirring speed timing feature vector and a PH timing feature vector. In the technical scheme of the application, the stirring speed time sequence input vector and the PH value time sequence input vector are subjected to feature mining in a time sequence feature extractor based on a one-dimensional convolution layer so as to extract time sequence dynamic associated feature information of the stirring speed value and the PH value in a time dimension respectively, so that the stirring speed time sequence feature vector and the PH value time sequence feature vector are obtained. Specifically, each layer using the one-dimensional convolution layer based timing feature extractor performs, in forward transfer of the layer, input data: carrying out convolution processing on input data to obtain a convolution characteristic diagram; pooling the convolution feature images based on a feature matrix to obtain pooled feature images; performing nonlinear activation on the pooled feature map to obtain an activated feature map; the output of the last layer of the one-dimensional convolution layer-based time sequence feature extractor is the stirring speed time sequence feature vector and the PH value time sequence feature vector, and the input of the first layer of the one-dimensional convolution layer-based time sequence feature extractor is the stirring speed time sequence input vector and the PH value time sequence input vector.
The one-dimensional convolution layer (1D Convolutional Layer) is a neural network layer in deep learning for processing data having a sequence structure, such as text, audio, or time-series data. It can capture local features in the input sequence and extract useful representations. The structure of the one-dimensional convolution layer is as follows: input: the input to a one-dimensional convolutional layer is a one-dimensional sequence of data, typically represented as a vector. For example, for text data, each word may be represented as a word vector, forming a sequence of words as input; convolution kernel: the convolution kernel is a learning parameter of a one-dimensional convolution layer used to extract local features in an input sequence. The convolution kernel is a small window that slides over the input sequence and convolves with a localized region of the input sequence; stride length: the stride specifies the step size by which the convolution kernel slides over the input sequence. A larger stride may reduce the length of the output sequence, while a smaller stride may retain more information; filling: padding is the addition of extra values (usually zeros) on both sides of the input sequence to control the length of the output sequence. The padding can keep the length of the input sequence unchanged, and can also increase the length of the output sequence; convolution operation: the convolution operation performs product accumulation of local areas on the input sequence through a sliding convolution kernel to generate an output sequence. The output of each convolution operation corresponds to a position of the input sequence; bias term: for each convolution operation output, a bias term may be added and an activation function applied. Offset can be introduced into the offset term, so that the model is more flexible; activation function: the activation function performs a nonlinear transformation on the output of the convolution operation, introducing a nonlinear characteristic representation. Common activation functions include ReLU, sigmoid, tanh, etc.; pooling operation: to reduce the length of the output sequence and extract more important features, the output of the convolutional layer may be downsampled using a pooling operation. Common pooling operations include maximum pooling and average pooling. A one-dimensional convolution layer may capture different features by stacking multiple convolution kernels to increase the number of channels output. In addition, batch normalization, dropout and other techniques can be added between the convolution layers to improve the performance and generalization capability of the model.
More specifically, the PH-stirring speed sequential feature interaction unit 3223 is configured to perform feature interaction encoding on the stirring speed sequential feature vector and the PH sequential feature vector to obtain a PH-stirring speed interaction feature vector as the PH-stirring speed interaction feature. That is, an inter-feature attention layer is used to perform attention-based feature interactions between the agitation speed timing feature vector and the PH timing feature vector to obtain a PH-agitation speed interaction feature vector, thereby capturing correlations and interactions between the agitation speed timing dynamics feature and the PH timing dynamics feature. It should be appreciated that since the goal of the traditional attention mechanism is to learn an attention weight matrix, a greater weight is given to important features and a lesser weight is given to secondary features, thereby selecting more critical information to the current task goal. This approach is more focused on weighting the importance of individual features, while ignoring the dependency between features. The attention layer between the features can capture the correlation and the mutual influence between the stirring speed time sequence dynamic change feature and the PH value time sequence dynamic change feature through the feature interaction based on an attention mechanism, learn the dependency relationship between the stirring speed time sequence feature and the PH value time sequence feature, and interact and integrate the time sequence features of the stirring speed time sequence feature and the PH value time sequence feature according to the dependency relationship between the different features, so as to obtain a PH value-stirring speed interaction feature vector.
It should be noted that, in other specific examples of the present application, the stirring speed values and PH values at the plurality of predetermined time points may also be analyzed in other manners to obtain a PH value-stirring speed interaction characteristic, for example: the recorded stirring speed and pH value are arranged into a data set according to time sequence; the trend of the stirring speed and the pH value with time is visualized by drawing a line graph or a scatter graph. The horizontal axis represents time, and the vertical axis represents stirring speed and pH value; statistical methods (e.g., pearson correlation coefficients) or other correlation analysis methods are used to evaluate the correlation between stirring speed and pH. Calculating a correlation coefficient between the stirring speed and the pH value at each time point, and determining the degree of correlation between the stirring speed and the pH value; by a time sequence analysis method (such as autocorrelation function, hysteresis correlation and the like), researching time sequence correlation characteristics between stirring speed and pH value; and extracting interaction characteristics between the stirring speed and the pH value according to the result of the time sequence correlation analysis. For example, indicators of lag correlation coefficients, lag time differences, etc. may be calculated to describe the interaction relationship therebetween; the extracted interaction features are presented using a chart or other visualization tool. For example, the hysteresis correlation coefficient may be plotted against the hysteresis time, or a phase diagram between stirring speed and pH may be plotted; and according to the extracted interaction characteristics, performing interpretation and analysis. How the stirring speed affects the pH change and the dynamic relationship between them is explored.
Specifically, the stirring speed control module 323 is configured to determine, based on the PH-stirring speed interaction characteristic, whether the stirring speed value at the current time point should be increased or decreased. In particular, in one specific example of the present application, the stirring speed control module 323 is configured to: and the PH value-stirring speed interaction characteristic vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the stirring speed value at the current time point should be increased or decreased. That is, classification processing is performed by using time-series cooperative interaction correlation characteristic information between the stirring speed time series characteristic and the PH value time series characteristic, so that the stirring speed value at the current time point is controlled in real time. In this way, the softening parameters can be automatically controlled based on the cooperation of the stirring speed and the pH change, so that the treatment efficiency is improved, the energy consumption is reduced, and the performance of the industrial sewage treatment system is optimized. Specifically, using a plurality of full-connection layers of the classifier to perform full-connection coding on the PH value-stirring speed interaction feature vector so as to obtain a coding classification feature vector; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
A Classifier (Classifier) refers to a machine learning model or algorithm that is used to classify input data into different categories or labels. The classifier is part of supervised learning, which performs classification tasks by learning mappings from input data to output categories.
The fully connected layer (Fully Connected Layer) is one type of layer commonly found in neural networks. In the fully connected layer, each neuron is connected to all neurons of the upper layer, and each connection has a weight. This means that each neuron in the fully connected layer receives inputs from all neurons in the upper layer, and weights these inputs together, and then passes the result to the next layer.
The Softmax classification function is a commonly used activation function for multi-classification problems. It converts each element of the input vector into a probability value between 0 and 1, and the sum of these probability values equals 1. The Softmax function is commonly used at the output layer of a neural network, and is particularly suited for multi-classification problems, because it can map the network output into probability distributions for individual classes. During the training process, the output of the Softmax function may be used to calculate the loss function and update the network parameters through a back propagation algorithm. Notably, the output of the Softmax function does not change the relative magnitude relationship between elements, but rather normalizes them. Thus, the Softmax function does not change the characteristics of the input vector, but simply converts it into a probability distribution form.
In one embodiment of the present application, the energy-saving industrial sewage treatment system 300 further includes: a training module for training the deep neural network model-based time series feature extractor, the inter-feature attention layer and the classifier; wherein, training module includes: the training data acquisition unit is used for acquiring training stirring speed values and training PH values at a plurality of preset time points in a preset time period; the training data parameter time sequence arrangement unit is used for arranging the training stirring speed values and the training PH values of the plurality of preset time points into training stirring speed time sequence input vectors and training PH value time sequence input vectors according to time dimensions; the training data parameter time sequence feature extraction unit is used for respectively carrying out feature extraction on the training stirring speed time sequence input vector and the training PH value time sequence input vector through the time sequence feature extractor based on the deep neural network model so as to obtain a training stirring speed time sequence feature vector and a training PH value time sequence feature vector; a training PH value-stirring speed time sequence feature interaction unit, which is used for carrying out attention-based feature interaction between the training stirring speed time sequence feature vector and the training PH value time sequence feature vector by using the inter-feature attention layer so as to obtain a training PH value-stirring speed interaction feature vector; the training stirring speed real-time control unit is used for enabling the training PH value-stirring speed interaction characteristic vector to pass through a classifier to obtain a classification loss function value; and training the time sequence feature extractor, the inter-feature attention layer and the classifier based on the depth neural network model by using the classification loss function value, wherein in each round of iteration of the training, iteration of a weight matrix is performed based on the training PH value-stirring speed interaction feature vector.
In the technical scheme of the application, when the stirring speed time sequence input vector and the PH value time sequence input vector are processed through a time sequence feature extractor based on a one-dimensional convolution layer to obtain a stirring speed time sequence feature vector and a PH value time sequence feature vector, the stirring speed time sequence feature vector and the PH value time sequence feature vector respectively express local time sequence association features of stirring speed values and PH values, so that when attention-based feature interaction between the stirring speed time sequence feature vector and the PH value time sequence feature vector is performed by using an inter-feature attention layer, dependency relationship features between the stirring speed time sequence feature vector and the PH value time sequence feature vector can be extracted. In this way, when the local time sequence correlation feature expressed by each of the stirring speed time sequence feature vector and the PH value time sequence feature vector is used as a single-dimensional feature representation, the classification dimension correlation related to the local time sequence correlation feature distribution interference expressed by each of the stirring speed time sequence feature vector and the PH value time sequence feature vector is introduced when the dependency relation feature extraction of the attention-based feature interaction is performed, so that the PH value-stirring speed interaction feature vector has the feature representation that the distribution dimension correlation of each of the stirring speed time sequence feature vector and the PH value time sequence feature vector is dense, and the training efficiency of the weight matrix of the classifier is reduced when the PH value-stirring speed interaction feature vector performs classification regression training through the classifier. Based on the above, when the applicant of the present application performs classification regression training on the PH-stirring speed interaction feature vector through a classifier, the applicant performs iteration of a weight matrix based on the PH-stirring speed interaction feature vector, which is specifically expressed as: in each iteration of the training, iterating a weight matrix based on the training PH-stirring speed interaction feature vector, including: performing iteration of a weight matrix according to the following iteration formula based on the training PH value-stirring speed interaction feature vector; wherein, the iterative formula is:
Wherein M is 1 And M 2 The weight matrix of the previous iteration and the current iteration are respectively adopted, wherein, during the first iteration, M is set by adopting different initialization strategies 1 And M 2 (e.g., M 1 Set as a unitary matrix and M 2 Set as the average diagonal matrix of the feature vectors to be classified), V c Is training PH value-stirring speed interaction characteristic vector to be classified, V 1 Is a first eigenvector, V 2 Is the second eigenvector, V 2 T Is the transpose of the second feature vector,and->Respectively represent feature vectors V 1 And V 2 And M is the global mean of b Is a bias matrix, e.g. initially set as a singleBit matrix, max is the maximum function, +.>Representing matrix multiplication +.>Represents matrix addition, +. 2 ' represents the weight matrix after iteration. Like this, can be based on the time sequence collaborative association change of stirring speed and PH value come automatic control softening parameter to improve treatment effeciency and reduce the energy consumption, optimize industrial sewage treatment system's performance, through this kind of mode, can purify industrial sewage more effectively, reduce the pollution to the environment, realize the sustainable utilization of resource.
That is, consider that the PH-stirring speed based interaction feature vector V is in progress c During the dense prediction task of (2), a high resolution representation of the weight matrix is required to be combined with the PH-stirring speed interaction feature vector V c The global context of the model is integrated, so that progressive integration (progressive integrity) is realized based on iterative association representation resource-aware by maximizing the distribution boundary of the weight space in the iterative process, thereby improving the training effect of the weight matrix and improving the training efficiency of the whole model.
It should be noted that, in other specific examples of the present application, it may also be determined that the stirring speed value at the current time point should be increased or decreased based on the PH-stirring speed interaction feature in other manners, for example: firstly, collecting historical data of PH value and corresponding stirring speed value in a period of time; extracting PH value-stirring speed interaction characteristics from the historical data. A correlation index, such as a correlation coefficient or mutual information, of the PH and the stirring speed may be calculated. These indicators may reflect the degree of correlation between the pH and the stirring speed; based on the extracted interaction characteristics, a prediction model is established to predict the stirring speed value at the current time point. Machine learning algorithms such as regression models (e.g., linear regression, decision tree regression) or neural network models (e.g., multi-layer perceptrons, long-term memory networks) may be used; for data to be entered into the model, some preprocessing operations may be required. For example, historical data is normalized or normalized to ensure consistent dimensions for different features; and taking the PH value at the current time point as an input characteristic, and predicting the stirring speed value at the current time point through a prediction model. The model deduces the change trend of the stirring speed at the current time point according to the mode of the PH value-stirring speed interaction characteristic in the historical data; and comparing the stirring speed value of the current time point predicted according to the model with the stirring speed value of the previous time point. If the predicted value is large, it means that the stirring speed at the current time point should be increased; if the predicted value is small, this means that the stirring speed at the current time point should be reduced.
It should be noted that, in other specific examples of the present application, the pre-separated sewage may be further processed through a chemical reactor in other manners, and mixed by stirring after adding soda ash, so as to remove calcium and magnesium hardness ions, so that the water body is softened to generate first suspended floc particles and softened sewage, for example: ensure the clean of the chemical reactor and have proper capacity and stirring equipment. Ensuring that the reactor is tolerant of the chemicals used and the reaction conditions; and determining proper soda ash adding amount according to the water quality condition and the treatment requirement. Sodium carbonate (such as sodium hydroxide) can neutralize acidic substances in water and react with calcium magnesium hardness ions to generate precipitate. Adding a proper amount of sodium carbonate into a chemical reactor; and starting stirring equipment in the chemical reactor, and fully stirring and mixing the sodium carbonate into the pre-separated sewage. The stirring aim is to promote the reaction of sodium carbonate and ions in water and to make the generated precipitate fully contact with the water body; the proper reaction time is determined according to the water quality and the treatment requirements. In a chemical reactor, sewage and sodium carbonate are fully reacted and mixed so as to ensure that calcium magnesium hardness ions react with the sodium carbonate to generate a precipitate; in the reaction process, the precipitate generated by the reaction of the calcium magnesium hardness ions and the sodium carbonate is converged to form first suspended floc particles. The particles can adsorb and precipitate other suspended matters and impurities, so as to further purify the water body; after the reaction is completed, the stirring equipment is stopped, and the first suspended floc particles are precipitated to the bottom of the chemical reactor. Separating the precipitate from the softened sewage by a suitable separation device (such as a precipitation tank, etc.); in the sewage after the softening treatment, most of calcium and magnesium hardness ions are removed, and the water body becomes softer. The softened sewage can be further sent to the next treatment process, such as deep filtration, biological treatment and the like, so as to be further purified and treated.
Specifically, the reinforced coagulation subsystem 330 is configured to introduce the softened sewage into a coagulation air floatation machine, and add a cationic flocculant to generate second suspended floc particles and coagulated air floatation sewage, where the cationic flocculant is a product obtained by performing a free radical polymerization reaction by irradiation with acrylamide and dimethyldiallylammonium chloride. More specifically, cationic flocculants are a common water treatment chemical agent used for coagulation and flocculation in water treatment processes. The polymer compound with positive charges can react with suspended matters, particles and organic matters in water to form larger floccules, thereby facilitating the precipitation and separation of the floccules.
The coagulation air flotation machine is a device commonly used for water treatment and is mainly used for removing suspended matters, turbidity, particulate matters and other pollutants in water. The method combines the principles of coagulation, air floatation and precipitation, and realizes the lifting and separation of pollutants by adding coagulant, injecting gas and forming bubbles. The working principle of the coagulation air floatation machine is that a coagulant is utilized to gather suspended matters into larger agglomerates, and then tiny bubbles are generated by injecting gas, so that pollutants are lifted to the water surface to form scum. Finally, the scum is settled to the bottom through precipitation, and the clear water is taken out from the upper part.
In particular, the zero-valent iron treatment subsystem 350 is configured to perform ultrasonic synergistic reaction on the nano zero-valent iron and the pulsed discharge plasma-photocatalytic reaction sewage under the action of ultrasonic waves, and perform solid-liquid separation treatment in a precipitation zone to obtain separated sewage. Wherein nano zero-valent iron is a material composed of nano-scale iron particles. The pulse discharge plasma-photocatalysis reaction is an advanced technology for sewage treatment. The method combines the pulse discharge plasma technology and the photocatalysis technology, can efficiently degrade pollutants and improve water quality.
Ultrasonic synergistic reaction refers to the use of the action of ultrasonic waves in chemical reactions to enhance the reaction rate, improve the reaction effect, or promote the reaction. The ultrasonic wave produces intense sonic cavitation and intense vortex motion of the liquid by inducing sonic vibration in the liquid, thereby influencing the separation distance of reactant molecules, increasing the mutual collision frequency of reactants and improving the reaction rate and effect. The ultrasonic synergistic reaction has wide application in the fields of chemical synthesis, catalytic reaction, dissolution, extraction and the like. It can be used for accelerating organic synthesis reaction, activating catalyst, dissolving insoluble substance, raising extraction efficiency, etc.
Specifically, the adsorption treatment subsystem 360 is configured to introduce the separated sewage into an adsorption tank, add cellulose aerogel with an iron wire cage framework, separate from a water body under the action of a magnetic field, and desorb and recover heavy metal ions to obtain treated sewage. The iron wire cage skeleton cellulose aerogel is a novel nano porous material and consists of a cellulose-based material and an iron wire cage skeleton. The wire cage skeleton cellulose aerogel has many potential applications including catalyst supports, adsorbents, separation membranes, sensors, and the like. The porous structure and the high specific surface area of the catalyst enable the catalyst to have larger adsorption capacity and reactivity, and can be used for adsorption and catalytic reaction.
As described above, the energy-saving type industrial sewage treatment system 300 according to the embodiment of the present application can be implemented in various wireless terminals, such as a server having an energy-saving type industrial sewage treatment algorithm, and the like. In one possible implementation, the energy efficient industrial wastewater treatment system 300 according to embodiments of the present application may be integrated into a wireless terminal as a software module and/or hardware module. For example, the energy efficient industrial wastewater treatment system 300 may be a software module in the operating system of the wireless terminal, or may be an application developed for the wireless terminal; of course, the energy efficient industrial wastewater treatment system 300 can also be one of a number of hardware modules of the wireless terminal.
Alternatively, in another example, the energy efficient industrial wastewater treatment system 300 and the wireless terminal may be separate devices, and the energy efficient industrial wastewater treatment system 300 may be connected to the wireless terminal through a wired and/or wireless network and transmit interactive information in a agreed data format.
Further, an energy-saving industrial sewage treatment method is also provided.
Fig. 5 is a flow chart of an energy-efficient industrial wastewater treatment method according to an embodiment of the present application. As shown in fig. 5, the energy-saving industrial sewage treatment method according to the embodiment of the present application includes the steps of: s1, filtering industrial sewage through a mechanical grid, and separating the filtered industrial sewage through an oil-water separator to remove impurities, floating oil and suspended matters to obtain pre-separated sewage; s2, the pre-separated sewage passes through a chemical reactor, and is stirred and mixed after soda ash is added, so that calcium and magnesium hardness ions are removed, and the water is softened to generate first suspended floc particles and softened sewage; s3, introducing the softened sewage into a coagulation air floatation machine, and adding a cationic flocculant to generate second suspended floc particles and coagulation air floatation sewage, wherein the cationic flocculant is a product obtained by carrying out free radical polymerization reaction on acrylamide and dimethyl diallyl ammonium chloride through irradiation; s4, introducing the coagulation air floatation sewage into a pulse discharge plasma-photocatalytic reactor for reaction to obtain pulse discharge plasma-photocatalytic reaction sewage; s5, under the ultrasonic action, carrying out ultrasonic synergistic reaction on the nano zero-valent iron and the pulse discharge plasma-photocatalytic reaction sewage, and then carrying out solid-liquid separation treatment in a precipitation area to obtain separated sewage; s6, introducing the separated sewage into an adsorption tank, adding iron wire cage skeleton cellulose aerogel, separating the separated sewage from the water body under the action of a magnetic field, and desorbing and recycling heavy metal ions to obtain the treated sewage.
In summary, the energy-saving industrial sewage treatment method according to the embodiment of the application is explained, and the time sequence collaborative correlation analysis of the stirring speed and the PH in the softening process is performed by collecting the stirring speed value and the PH value in the softening process and introducing a data processing and analyzing algorithm at the rear end, so that the softening parameters are automatically controlled based on the collaboration of the stirring speed and the PH change, the treatment efficiency is improved, the energy consumption is reduced, and the performance of the industrial sewage treatment system is optimized. Therefore, industrial sewage can be purified more effectively, environmental pollution is reduced, and sustainable utilization of resources is realized.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. An energy efficient industrial wastewater treatment system, comprising:
the filtering subsystem is used for filtering the industrial sewage through a mechanical grid, and separating the filtered industrial sewage through an oil-water separator to remove impurities, floating oil and suspended matters so as to obtain pre-separated sewage;
the softening subsystem is used for enabling the pre-separated sewage to pass through the chemical reactor, adding sodium carbonate, stirring and mixing to remove calcium and magnesium hardness ions so as to soften the water body to generate first suspended floc particles and softened sewage;
the reinforced coagulation subsystem is used for introducing the softened sewage into a coagulation air floatation machine, and adding a cationic flocculant to generate second suspended floc particles and coagulation air floatation sewage, wherein the cationic flocculant is a product obtained by performing free radical polymerization reaction on acrylamide and dimethyl diallyl ammonium chloride through irradiation;
the organic matter degradation subsystem is used for introducing the coagulation air floatation sewage into a pulse discharge plasma-photocatalytic reactor for reaction so as to obtain pulse discharge plasma-photocatalytic reaction sewage;
the zero-valent iron treatment subsystem is used for carrying out ultrasonic synergistic reaction on the nanometer zero-valent iron and the pulse discharge plasma-photocatalytic reaction sewage under the ultrasonic action, and then carrying out solid-liquid separation treatment in a precipitation area to obtain separated sewage;
And the adsorption treatment subsystem is used for introducing the separated sewage into an adsorption tank, adding iron wire cage skeleton cellulose aerogel, separating the separated sewage from the water body under the action of a magnetic field, and desorbing and recycling heavy metal ions to obtain the treated sewage.
2. The energy efficient industrial wastewater treatment system of claim 1, wherein the softening subsystem comprises:
the data acquisition module is used for acquiring stirring speed values and PH values at a plurality of preset time points in a preset time period;
the PH value-stirring speed time sequence interaction correlation analysis module is used for performing time sequence correlation analysis on stirring speed values and PH values of the plurality of preset time points to obtain PH value-stirring speed interaction characteristics;
and the stirring speed control module is used for determining whether the stirring speed value at the current time point is increased or decreased based on the PH value-stirring speed interaction characteristic.
3. The energy efficient industrial wastewater treatment system of claim 2, wherein the PH-agitation speed timing cross-correlation analysis module comprises:
the data parameter time sequence arrangement unit is used for arranging the stirring speed values and the PH values of the plurality of preset time points into a stirring speed time sequence input vector and a PH value time sequence input vector according to the time dimension respectively;
The data parameter time sequence feature extraction unit is used for respectively carrying out feature extraction on the stirring speed time sequence input vector and the PH value time sequence input vector through a time sequence feature extractor based on a deep neural network model so as to obtain a stirring speed time sequence feature vector and a PH value time sequence feature vector;
and the PH value-stirring speed time sequence characteristic interaction unit is used for carrying out characteristic interaction association coding on the stirring speed time sequence characteristic vector and the PH value time sequence characteristic vector to obtain a PH value-stirring speed interaction characteristic vector as the PH value-stirring speed interaction characteristic.
4. The energy-efficient industrial wastewater treatment system of claim 3, wherein the deep neural network model-based temporal feature extractor is a one-dimensional convolutional layer-based temporal feature extractor.
5. The energy saving industrial wastewater treatment system of claim 4, wherein the PH-agitation speed timing characteristic interaction unit is configured to: an inter-feature attention layer is used to perform attention-based feature interactions between the agitation speed timing feature vector and the PH timing feature vector to obtain the PH-agitation speed interaction feature vector.
6. The energy efficient industrial wastewater treatment system of claim 5, wherein the agitation speed control module is configured to:
and the PH value-stirring speed interaction characteristic vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the stirring speed value at the current time point should be increased or decreased.
7. The energy efficient industrial wastewater treatment system of claim 6, further comprising: a training module for training the deep neural network model-based time series feature extractor, the inter-feature attention layer and the classifier;
wherein, training module includes:
the training data acquisition unit is used for acquiring training stirring speed values and training PH values at a plurality of preset time points in a preset time period;
the training data parameter time sequence arrangement unit is used for arranging the training stirring speed values and the training PH values of the plurality of preset time points into training stirring speed time sequence input vectors and training PH value time sequence input vectors according to time dimensions;
the training data parameter time sequence feature extraction unit is used for respectively carrying out feature extraction on the training stirring speed time sequence input vector and the training PH value time sequence input vector through the time sequence feature extractor based on the deep neural network model so as to obtain a training stirring speed time sequence feature vector and a training PH value time sequence feature vector;
A training PH value-stirring speed time sequence feature interaction unit, which is used for carrying out attention-based feature interaction between the training stirring speed time sequence feature vector and the training PH value time sequence feature vector by using the inter-feature attention layer so as to obtain a training PH value-stirring speed interaction feature vector;
the training stirring speed real-time control unit is used for enabling the training PH value-stirring speed interaction characteristic vector to pass through a classifier to obtain a classification loss function value; and
training the time sequence feature extractor, the inter-feature attention layer and the classifier based on the depth neural network model by using the classification loss function value, wherein in each round of iteration of the training, iteration of a weight matrix is performed based on the training PH value-stirring speed interaction feature vector.
8. The energy efficient industrial wastewater treatment system of claim 7, wherein in each iteration of the training, iterating a weight matrix based on the training PH-agitation speed interaction feature vector, comprising: performing iteration of a weight matrix according to the following iteration formula based on the training PH value-stirring speed interaction feature vector;
Wherein, the iterative formula is:
wherein M is 1 And M 2 The weight matrix of the last iteration and the current iteration are respectively V c Is training PH value-stirring speed interaction characteristic vector to be classified, V 1 Is a first eigenvector, V 2 Is the second eigenvector, V 2 T Is the transpose of the second feature vector,andrespectively represent feature vectors V 1 And V 2 And M is the global mean of b Is a bias matrix, max is a maximum function, +.>Representing matrix multiplication +.>Represents matrix addition, +. 2 ' represents the weight matrix after iteration.
9. The energy-saving industrial sewage treatment system according to claim 8, wherein the stirring speed real-time control unit comprises:
the full-connection coding subunit is used for carrying out full-connection coding on the PH value-stirring speed interaction feature vector by using a plurality of full-connection layers of the classifier so as to obtain a coding classification feature vector; and
and the classification result generation subunit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
10. An energy-saving industrial sewage treatment method is characterized by comprising the following steps:
filtering industrial sewage through a mechanical grid, and separating the filtered industrial sewage through an oil-water separator to remove impurities, floating oil and suspended matters to obtain pre-separated sewage;
The pre-separated sewage is passed through a chemical reactor, and is stirred and mixed after soda ash is added, so that calcium and magnesium hardness ions are removed to soften water bodies to generate first suspended floc particles and softened sewage;
introducing the softened sewage into a coagulation air floatation machine, and adding a cationic flocculant to generate second suspended floc particles and coagulation air floatation sewage, wherein the cationic flocculant is a product obtained by carrying out free radical polymerization reaction on acrylamide and dimethyl diallyl ammonium chloride through irradiation;
introducing the coagulation air floatation sewage into a pulse discharge plasma-photocatalytic reactor for reaction to obtain pulse discharge plasma-photocatalytic reaction sewage;
under the ultrasonic action, carrying out ultrasonic synergistic reaction on the nano zero-valent iron and the pulse discharge plasma-photocatalytic reaction sewage, and then carrying out solid-liquid separation treatment in a precipitation area to obtain separated sewage;
and (3) introducing the separated sewage into an adsorption tank, adding iron wire cage skeleton cellulose aerogel, separating the separated sewage from the water body under the action of a magnetic field, and desorbing and recovering heavy metal ions to obtain the treated sewage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311492052.0A CN117263464A (en) | 2023-11-09 | 2023-11-09 | Energy-saving industrial sewage treatment system and sewage treatment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311492052.0A CN117263464A (en) | 2023-11-09 | 2023-11-09 | Energy-saving industrial sewage treatment system and sewage treatment method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117263464A true CN117263464A (en) | 2023-12-22 |
Family
ID=89221794
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311492052.0A Withdrawn CN117263464A (en) | 2023-11-09 | 2023-11-09 | Energy-saving industrial sewage treatment system and sewage treatment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117263464A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117884032A (en) * | 2024-03-11 | 2024-04-16 | 山东森杰清洁科技有限公司 | Disinfectant for sewage purification and preparation method thereof |
-
2023
- 2023-11-09 CN CN202311492052.0A patent/CN117263464A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117884032A (en) * | 2024-03-11 | 2024-04-16 | 山东森杰清洁科技有限公司 | Disinfectant for sewage purification and preparation method thereof |
| CN117884032B (en) * | 2024-03-11 | 2024-05-28 | 山东森杰清洁科技有限公司 | Disinfectant for sewage purification and preparation method thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Maarof et al. | Recent trends in removal and recovery of heavy metals from wastewater by electrochemical technologies | |
| da Silva Ribeiro et al. | Removal of boron from mining wastewaters by electrocoagulation method: Modelling experimental data using artificial neural networks | |
| Safeer et al. | A review of artificial intelligence in water purification and wastewater treatment: Recent advancements | |
| Kyzas et al. | Flotation in water and wastewater treatment | |
| Fan et al. | A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence | |
| El-taweel et al. | A review of coagulation explaining its definition, mechanism, coagulant types, and optimization models; RSM, and ANN | |
| Pirdashti et al. | Artificial neural networks: applications in chemical engineering | |
| CN117263464A (en) | Energy-saving industrial sewage treatment system and sewage treatment method | |
| Obi et al. | Multi-characteristic optimization and modeling analysis of electrocoagulation treatment of abattoir wastewater using iron electrode pairs | |
| Allahkarami et al. | Prediction of Co (II) and Ni (II) ions removal from wastewater using artificial neural network and multiple regression models | |
| Rajput et al. | Using machine learning architecture to optimize and model the treatment process for saline water level analysis | |
| CN116116181B (en) | Model optimization-based waste gas and wastewater treatment method and device | |
| Nourouzi et al. | Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction | |
| Obiuto et al. | Simulation-driven strategies for enhancing water treatment processes in chemical engineering: addressing environmental challenges | |
| Qu et al. | Effective aggregation of expert opinions to inform environmental management: An integrated fuzzy group decision-making framework with application to cadmium-contaminated water treatment alternatives evaluation | |
| Esmaeili et al. | Removal of chromium by coagulation-dissolved air flotation system using ferric chloride and poly aluminum chloride (PAC) as coagulants | |
| Uzoh et al. | Electrocoagulation of Pb2+, Co2+, and Mn2+ from simulated wastewater: an algorithmic optimization using hybrid RSM–GA–PSO | |
| Wu et al. | Intelligent Warning of Membrane Fouling Based on Robust Deep Neural Network | |
| Rybarczyk et al. | Simultaneous removal of Al, Cu and Zn ions from aqueous solutions using ion and precipitate flotation methods | |
| Gholami et al. | Applying hybrid genetic and artificial bee colony algorithms to simulate a bio-treatment of synthetic dye-polluted wastewater using a rhamnolipid biosurfactant | |
| Alardhi et al. | Artificial neural network and response surface methodology for modeling reverse osmosis process in wastewater treatment | |
| CN116692975A (en) | Industrial tail water recycling method and system for extracting vanadium from wet stone coal | |
| Kazim et al. | Artificial intelligence application in membrane processes and prediction of fouling for better resource recovery | |
| Behroozpour et al. | Prediction of the continuous cadmium removal efficiency from aqueous solution by the packed-bed column using GMDH and ANFIS models | |
| Kang et al. | Treatment of wet FGD wastewater by a modified chemical precipitation method using a solid powder reagent |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20231222 |