CN112560323B - Industrial wastewater screening method for promoting agglomeration of fine particles - Google Patents
Industrial wastewater screening method for promoting agglomeration of fine particles Download PDFInfo
- Publication number
- CN112560323B CN112560323B CN202011441804.7A CN202011441804A CN112560323B CN 112560323 B CN112560323 B CN 112560323B CN 202011441804 A CN202011441804 A CN 202011441804A CN 112560323 B CN112560323 B CN 112560323B
- Authority
- CN
- China
- Prior art keywords
- industrial wastewater
- fine particles
- model
- main component
- interaction
- 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.)
- Active
Links
- 239000010419 fine particle Substances 0.000 title claims abstract description 155
- 239000010842 industrial wastewater Substances 0.000 title claims abstract description 134
- 238000005054 agglomeration Methods 0.000 title claims abstract description 69
- 230000002776 aggregation Effects 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000001737 promoting effect Effects 0.000 title claims abstract description 37
- 238000012216 screening Methods 0.000 title claims abstract description 33
- 230000003993 interaction Effects 0.000 claims abstract description 110
- 238000004088 simulation Methods 0.000 claims abstract description 43
- 238000005315 distribution function Methods 0.000 claims abstract description 26
- 238000005094 computer simulation Methods 0.000 claims abstract description 25
- 229910004298 SiO 2 Inorganic materials 0.000 claims description 52
- 239000002245 particle Substances 0.000 claims description 43
- 239000000463 material Substances 0.000 claims description 35
- 238000012360 testing method Methods 0.000 claims description 19
- 230000000694 effects Effects 0.000 claims description 17
- 238000005457 optimization Methods 0.000 claims description 17
- 239000013618 particulate matter Substances 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 11
- 229910017053 inorganic salt Inorganic materials 0.000 claims 1
- 239000000126 substance Substances 0.000 abstract description 12
- 238000012795 verification Methods 0.000 abstract description 12
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 28
- 235000013305 food Nutrition 0.000 description 27
- 239000002351 wastewater Substances 0.000 description 27
- 229920002472 Starch Polymers 0.000 description 22
- 235000019698 starch Nutrition 0.000 description 22
- 239000008107 starch Substances 0.000 description 22
- FAPWRFPIFSIZLT-UHFFFAOYSA-M Sodium chloride Chemical compound [Na+].[Cl-] FAPWRFPIFSIZLT-UHFFFAOYSA-M 0.000 description 18
- 238000012545 processing Methods 0.000 description 18
- 239000003795 chemical substances by application Substances 0.000 description 17
- 239000003245 coal Substances 0.000 description 17
- 238000009826 distribution Methods 0.000 description 15
- 239000000428 dust Substances 0.000 description 14
- 238000006477 desulfuration reaction Methods 0.000 description 12
- 230000023556 desulfurization Effects 0.000 description 12
- UXVMQQNJUSDDNG-UHFFFAOYSA-L Calcium chloride Chemical compound [Cl-].[Cl-].[Ca+2] UXVMQQNJUSDDNG-UHFFFAOYSA-L 0.000 description 10
- 229920002401 polyacrylamide Polymers 0.000 description 10
- 238000001556 precipitation Methods 0.000 description 10
- 239000001110 calcium chloride Substances 0.000 description 9
- 229910001628 calcium chloride Inorganic materials 0.000 description 9
- 239000011780 sodium chloride Substances 0.000 description 9
- 230000008859 change Effects 0.000 description 6
- 238000000329 molecular dynamics simulation Methods 0.000 description 6
- 125000004430 oxygen atom Chemical group O* 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 229910052739 hydrogen Inorganic materials 0.000 description 5
- 239000001257 hydrogen Substances 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 238000001000 micrograph Methods 0.000 description 4
- 229920000642 polymer Polymers 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 150000003839 salts Chemical class 0.000 description 4
- 239000011362 coarse particle Substances 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 239000010881 fly ash Substances 0.000 description 3
- 230000001965 increasing effect Effects 0.000 description 3
- 230000000737 periodic effect Effects 0.000 description 3
- 229920006395 saturated elastomer Polymers 0.000 description 3
- 239000011882 ultra-fine particle Substances 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 125000004429 atom Chemical group 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 238000004064 recycling Methods 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 239000004484 Briquette Substances 0.000 description 1
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 208000019693 Lung disease Diseases 0.000 description 1
- 229910002651 NO3 Inorganic materials 0.000 description 1
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 description 1
- QAOWNCQODCNURD-UHFFFAOYSA-L Sulfate Chemical compound [O-]S([O-])(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-L 0.000 description 1
- 238000005411 Van der Waals force Methods 0.000 description 1
- 241000700605 Viruses Species 0.000 description 1
- RHZUVFJBSILHOK-UHFFFAOYSA-N anthracen-1-ylmethanolate Chemical compound C1=CC=C2C=C3C(C[O-])=CC=CC3=CC2=C1 RHZUVFJBSILHOK-UHFFFAOYSA-N 0.000 description 1
- 239000003830 anthracite Substances 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000002802 bituminous coal Substances 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000009833 condensation Methods 0.000 description 1
- 230000005494 condensation Effects 0.000 description 1
- 239000000498 cooling water Substances 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- -1 equipment Substances 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 208000019622 heart disease Diseases 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 230000009878 intermolecular interaction Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000000324 molecular mechanic Methods 0.000 description 1
- 239000012074 organic phase Substances 0.000 description 1
- 230000020477 pH reduction Effects 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 208000023504 respiratory system disease Diseases 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000001845 vibrational spectrum Methods 0.000 description 1
- 229920001285 xanthan gum Polymers 0.000 description 1
- 239000000230 xanthan gum Substances 0.000 description 1
- 229940082509 xanthan gum Drugs 0.000 description 1
- 235000010493 xanthan gum Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/25—Design optimisation, verification or simulation using particle-based methods
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/60—In silico combinatorial chemistry
- G16C20/64—Screening of libraries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- Library & Information Science (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Medicinal Chemistry (AREA)
- Crystallography & Structural Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computing Systems (AREA)
- Separation Of Suspended Particles By Flocculating Agents (AREA)
Abstract
The invention provides an industrial wastewater screening method for promoting agglomeration of fine particles, and belongs to the technical field of agglomeration of fine particles. The method comprises the following steps: s1, respectively constructing a model of fine particles and a model of main components in industrial wastewater based on molecular dynamics simulation software; s2, constructing an interaction model of main components and fine particles in the industrial wastewater; s3, carrying out dynamic simulation on the interaction model in the step S2 to enable the system to reach a simulation equilibrium state; s4, according to a dynamic simulated balance structure, interaction energy and radial distribution function data of main components in the industrial wastewater and the surfaces of the fine particles are obtained, and candidate industrial wastewater for promoting the agglomeration of the fine particles is evaluated and screened out. The invention provides a rapid and accurate screening method which is applied to rapid screening and verification of industrial wastewater in the aspect of chemical agglomeration of fine particles, and can greatly improve the screening efficiency and accuracy of the industrial wastewater for promoting the agglomeration of the fine particles.
Description
Technical Field
The invention relates to the technical field of fine particulate matter agglomeration, in particular to an industrial wastewater screening method for promoting fine particulate matter agglomeration.
Background
The inhalable particulate matter (PM 10) refers to fine particulate matter having an aerodynamic diameter of 10 μm or less, and particularly PM2.5 contained therein has caused serious atmospheric environmental problems, which has attracted high attention in our country. The particle size is small, the specific surface area is large, harmful substances such as viruses and bacteria are easy to adsorb, and the harmful substances can form deposition on alveoli after entering a human body, so that respiratory diseases and heart and lung diseases are caused, and the harm to the human body is great; meanwhile, the residence time of the fine particles in the atmosphere is long, the transmission distance is long, and after the fine particles containing sulfate and nitrate enter cloud water or a precipitation system, the acidification of precipitation is very likely to be promoted, and the atmospheric environment is greatly influenced. The related data show that the main factor of the increase of PM2.5 concentration in the atmosphere in China is a great amount of smoke emission, 70% of the smoke emission comes from coal combustion, and a coal-fired power plant is a main place for coal consumption.
Research shows that most of dust generated in the coal burning process can be captured and removed by dust removing devices such as electrostatic dust collectors, cloth bag dust collectors and wet electrostatic dust collectors, but fine particles in the dust removing devices, especially PM2.5 with smaller particle size, are difficult to remove, if the dust is calculated by the quantity, more than 90% of the fine particles in the total dust are not removed and enter the environment atmosphere, and the dust is huge in quantity and is easier to be absorbed by human bodies, so that the dust is extremely harmful. Therefore, the pretreatment stage is arranged before the dust removing device, so that the agglomeration of fine particles is promoted to grow into larger particles, and the method of cleaning by the traditional dust removing device becomes a new direction of the development of the dust removing technology.
Among the new technologies, the utilization of the chemical agglomeration technology is one of the effective measures for enhancing the removal of fine particles by the existing dust removal device. The principle of the chemical agglomeration technology is that an agglomerating agent is sprayed into a flue to agglomerate and grow fine particles, so that the agglomerating agent is a technical core and a key part of the chemical agglomeration, and the selection of the efficient and economic agglomerating agent has great significance for effectively promoting the agglomeration of the fine particles.
Disclosure of Invention
In view of the above problems in the prior art, the present invention provides an industrial wastewater screening method for promoting agglomeration of fine particles.
In order to achieve the above object, the present invention is specifically achieved by the following techniques:
An industrial wastewater screening method for promoting agglomeration of fine particles, comprising the steps of:
S1, respectively constructing a model of fine particles and a model of main components in industrial wastewater based on molecular dynamics simulation software;
S2, constructing an interaction model of main components and fine particles in the industrial wastewater;
s3, carrying out dynamic simulation on the interaction model in the step S2 to enable the system to reach a simulation equilibrium state;
S4, according to a dynamic simulation balance structure, interaction energy and radial distribution function data of main components in the industrial wastewater and the surfaces of the fine particles are obtained, and candidate industrial wastewater for promoting the agglomeration of the fine particles is evaluated and screened by combining the interaction energy and the radial distribution function data.
Further, in the step S1, the main component in the industrial wastewater is organic matters and/or inorganic salts, and the mass concentration of the main component exceeds 0.001% of the total mass of the industrial wastewater.
Further, in step S1, the model of the main component in the industrial wastewater is an amorphous unit cell model of the main component.
Further, in step S1, the fine particulate matter model is a SiO 2 super cell model.
Further, in step S1, the radial distribution function data isRadial distribution function data of the region.
Further, the model of the fine particulate matter and the model of the main component in the industrial wastewater are subjected to energy minimization treatment, respectively.
Further, the energy minimization process includes:
S11, selecting a main component SiO 2 of fine particles, constructing a SiO 2 super cell model by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization on the SiO 2 super cell model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method to obtain a fine particle model;
S12, measuring the content of each component in the industrial wastewater to obtain the main component in the industrial wastewater, establishing an amorphous unit cell model of the main component by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization on the established amorphous unit cell model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method to obtain the model of the main component in the industrial wastewater.
Further, in step S3, the kinetic simulation is performed using Forcite module of MATERIALS STUDIO software.
Further, the parameters of the kinetic simulation are: the interaction model is dynamically simulated by utilizing a Forcite module in MATERIALS STUDIO software, a regular ensemble is selected under the Compass force field, the temperature is 105 ℃, the time step is set to be 1fs, and the total simulation time is 1000ps.
Further, in step S4, the interaction energy of the main component of the industrial wastewater with the surface of the fine particulate matter is calculated using the following formula;
E( Interaction with each other )＝-(E( particle surface / main component )-E( particle surface )-E( main component ))；
Wherein E ( Interaction with each other ) is the interaction energy of the main component of the industrial wastewater and the surface of the fine particles, E ( particle surface / main component ) is the total energy of the interaction model, E ( particle surface ) is the energy of the fine particle model, and E ( main component ) is the energy of the main component model of the industrial wastewater.
Further, after step S4, the method further includes the following steps:
S5, changing operation parameters of dynamic simulation in the step S3, analyzing interaction energy and RDF under different operation parameters to obtain optimal operation parameters for a simulation test, verifying the promotion effect of candidate industrial wastewater on coal-fired fine particle agglomeration through the simulation test, and screening the industrial wastewater suitable for promoting fine particle agglomeration.
Further, in step S5, the operation parameters include temperature and ph.
The beneficial effects are that:
1. Industrial wastewater generally contains a concentration of organic and inorganic salts that interact with fine particles and collide to adhere the fine particles together. Therefore, the industrial wastewater which can efficiently promote the condensation and growth of the fine particles is quickly screened out, the industrial wastewater is used as an agglomerating agent, the waste is treated by waste, and the treatment cost of the fine particles can be reduced. According to the invention, by using a MATERIALS STUDIO software molecular dynamics simulation module, an interaction model of main components in industrial wastewater and the surface of fine particles is established, interaction energy, radial distribution function and the like are used as main evaluation basis, candidate industrial wastewater suitable for being used as an agglomeration agent for promoting the agglomeration of the fine particles is screened out, and finally, the industrial wastewater which can be practically used as the agglomeration agent of the fine particles is obtained through quick test verification, so that a quick and accurate screening method is provided, and the quick screening method is applied to quick screening and verification of the industrial wastewater in the aspect of chemical agglomeration of the fine particles generated by coal burning, so that the screening efficiency and accuracy of the industrial wastewater for promoting the agglomeration of the fine particles can be greatly improved, and the research cost is greatly saved.
2. The invention provides a treatment mode and a selection direction for recycling the industrial wastewater and selecting the agglomeration agent of the fine particles by researching the feasibility of the industrial wastewater for promoting the agglomeration of the fine particles, is beneficial to improving the recycling rate of the industrial wastewater and reducing the operation cost, and has great scientific research and industrial significance.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an industrial wastewater screening method for promoting agglomeration of fine particulate matter according to an embodiment of the present invention;
FIG. 2 is a flow chart of an industrial wastewater screening method for promoting agglomeration of fine particulate matter according to another embodiment of the present invention;
FIG. 3 is an initial configuration diagram of an interaction model of the main component of the industrial wastewater of example 1 with the surface of fine particles;
FIG. 4 is a diagram showing the configuration of the equilibrium state of the interaction model of the main component of the industrial wastewater with the surface of fine particles in example 1;
FIG. 5 is a graph showing the radial distribution function of the main component and SiO 2 in the industrial wastewater of example 1;
FIG. 6 is a graph showing the change of the particle size distribution of fine particles with time after the addition of the waste water from the food industry in example 1;
FIG. 7 is a scanning electron microscope image of the fine particles of example 1;
FIG. 8 is a quasi-equilibrium configuration diagram of the interaction model of the main component and the surface of fine particles in the industrial wastewater of example 2;
FIG. 9 is a graph showing the radial distribution function of the main component and SiO 2 in the industrial wastewater of example 2;
FIG. 10 is a graph showing the change of the particle size distribution of fine particles with time after the desulfurization industrial wastewater was added in example 2;
FIG. 11 is a pseudo-equilibrium configuration diagram of the interaction model of the main component and the surface of fine particles in the industrial wastewater of example 3;
FIG. 12 is a graph showing the radial distribution function of the main component and SiO 2 in the industrial wastewater of example 3;
FIG. 13 is a graph showing the change of the particle size distribution of fine particles with time after the precipitation treatment of industrial wastewater for food processing in example 3.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. In addition, the terms "comprising," "including," "having," and "containing" are not limiting, as other steps and other ingredients may be added that do not affect the result. Materials, equipment, reagents are commercially available unless otherwise specified.
For a better understanding of the present invention, and not to limit its scope, all numbers expressing quantities, percentages, and other values used in the present invention are to be understood as being modified in all instances by the term "about". Accordingly, unless indicated otherwise, the numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained. Each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
The existing agglomerating agent for promoting the chemical agglomeration of the coal-fired fine particles is usually one or more polymer compound (such as xanthan gum and polyacrylamide) solutions prepared by people, and two or more fine particles are agglomerated together to form large particles by utilizing the bridging effect of the polymer compound. However, the use of the polymer compound increases the processing cost.
Industrial wastewater, such as desulfurization wastewater of flue gas, boiler cooling water, food processing wastewater, sugar-making wastewater, papermaking wastewater and the like, has larger water quantity, but has low pollution, higher centralized treatment cost and is easy to break down when discharged into municipal pipe network. It is found that such industrial wastewater can be used as a chemical agglomeration agent for removing fine particulate matters, and can be applied to a chemical agglomeration process to promote agglomeration. In addition, the agglomerating agent is usually required to be dissolved or dispersed in water in the use process, and industrial wastewater is adopted as the agglomerating agent, so that the waste of water resources can be reduced, and the circulation rate of the water resources can be improved. Therefore, the industrial wastewater is of great social significance as a chemical agglomerating agent for removing the fine particles of the fire coal. However, at present, industrial wastewater is various, the composition of each wastewater component is complex, and great difference exists, if the existing experimental method is utilized to verify one by one so as to screen out a proper agglomerating agent from the wastewater, the time and the labor are wasted, the research cost is huge, and the efficiency is low.
Based on this, referring to fig. 1, the present invention provides an industrial wastewater screening method for promoting agglomeration of fine particles, comprising the steps of:
S1, respectively constructing a model of fine particles and a model of main components in industrial wastewater based on molecular dynamics simulation software.
Wherein, the molecular dynamics simulation software adopts MATERIALS STUDIO software. The main components in the industrial wastewater are organic matters, inorganic salts and the like, the mass concentration of the main components exceeds 0.001 percent of the total mass of the industrial wastewater, and the main components can interact with the fine particles so as to adhere the fine particles together through collision; the model of the main component in the industrial wastewater is an amorphous unit cell model of organic and/or inorganic salts. The fine particles are produced by burning coal, the coal can be honeycomb briquette, anthracite, bituminous coal and the like, and the main component of the fine particles is SiO 2, so SiO 2 is selected to replace the fine particles to simplify the model.
S2, constructing an interaction model of main components and fine particles in the industrial wastewater, and using the interaction model for subsequent molecular dynamics simulation.
The specific operation is as follows: and (3) putting the model of the main component and the model of the fine particles in the industrial wastewater obtained in the step (S1) together by using a layering tool (Build Layer), then converting the model into a Layer structure, constructing a SiO 2 super cell model and a layered interface model of an amorphous unit cell model of the main component, adding vacuum layers with certain thickness on the upper part and the lower part of the amorphous unit cell model of the layered interface model to eliminate periodic influence (when constructing the Layer structure, adding a vacuum Layer of about 3nm between interfaces is needed, otherwise, polymers appear on two sides of the surface structure due to periodic boundary conditions), and obtaining an interaction model.
S3, carrying out Dynamic simulation on the interaction model to enable the system to achieve a simulated equilibrium state.
The kinetic simulation was performed using Forcite module of MATERIALS STUDIO software. The parameters of the kinetic simulation are: the interaction model is dynamically simulated by utilizing a Forcite module in MATERIALS STUDIO software, a regular ensemble (NVT ensemble) is selected under the Compass force field, the temperature is 105 ℃, the time step is set to be 1fs, and the total simulation time is 1000ps.
And (3) completing molecular dynamics simulation according to the parameters and the method, so as to perform structural optimization on the whole lamellar interface model, ensure elimination of systematic relaxation of the lamellar interface model, and obtain a simulated equilibrium conformation and an atomic motion trail of the main components in SiO 2 and industrial wastewater.
S4, obtaining interaction energy and radial distribution function data of main components in the industrial wastewater and the surfaces of the fine particles according to a dynamic simulation process, and evaluating and screening candidate industrial wastewater for promoting the agglomeration of the fine particles by combining the interaction energy and the radial distribution function data.
And (3) counting the atomic motion trail acquired in the step (S3) and calculating the interaction energy. Firstly, calculating the total energy (E ( particle surface / main component )) of the interaction model simulation equilibrium conformation, then, respectively deleting the SiO 2 super cell model and the amorphous unit cell model of the main component in the equilibrium conformation, and respectively calculating and recording the SiO 2 super cell model energy (E ( particle surface )) and the amorphous unit cell model energy (E ( main component )) of the main component under each frame; calculating the interaction energy through an interaction energy calculation formula;
the calculation formula is as follows: e ( Interaction with each other )＝-(E( particle surface / main component )-E( particle surface )-E( main component ));
Wherein E ( Interaction with each other ) is the interaction energy of the main component of the industrial wastewater and the surface of the fine particles, E ( particle surface / main component ) is the total energy of the interaction model, E ( particle surface ) is the energy of the fine particle model, and E ( main component ) is the energy of the main component model of the industrial wastewater. The calculated interaction energy represents an attractive interaction between the inorganic and organic phases, a larger interaction energy indicating a stronger interaction force.
And (3) carrying out statistical analysis on the atomic motion trail acquired in the step (S3) to obtain the interatomic distance reaching the simulated equilibrium conformation, namely a Radial Distribution Function (RDF). The probability distribution of the relative distance between atoms can be judged by counting the positions and the peak heights of the peaks in the RDF curve, so that the interaction strength between various atoms is primarily judged.
The larger the difference between the interaction energy of the main component of the industrial wastewater and the surface of the fine particles and the interaction energy of the water and the surface of the fine particles, and the RDF curve of the interaction of the main component of the industrial wastewater and the surface of the fine particles is thatThe greater the peak value of the area is compared with the peak value of the interaction between water and the surface of the fine particles, the better the effect of the industrial wastewater on promoting the agglomeration of the fine particles is.
Typically, intermolecular interactions include hydrogen bonding and van der Waals interactions, with the hydrogen bonding distance typically beingSelect/>The region (2) is a region for forming hydrogen bonds, and the hydrogen bonds are dominant due to the interaction of the organic matters and the SiO 2, namely, the strength of the hydrogen bonds is higher than that of Van der Waals force, and the effect of promoting agglomeration of fine particles is better.
Preferably, in step S1, since the molecular model built by software is generally unstable, no additional energy effect in the system is determined by experimental budget before simulation, the energy of the initial molecular model needs to be minimized before the interaction model is built, and the process of finding the lowest potential energy is minimized. In the embodiment, MATERIALS STUDIO software Forcite module is adopted for structural optimization to achieve the states of minimizing energy and eliminating internal stress, so that a model for building fine particles and a model for main components in industrial wastewater are closer to a real structure in interaction, and an equilibrium conformation after energy minimization is placed in a periodic box to be used as an initial conformation of molecular dynamics simulation.
Specific operations for energy minimization include:
S11, selecting a main component SiO 2 of fine particles, constructing an 8X 8 SiO 2 super cell model by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization on the SiO 2 super cell model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method (Geometry Optimization) to obtain a model of the fine particles after structural optimization;
S12, measuring the content of each component in the industrial wastewater to obtain the industrial wastewater, wherein the main component is soluble starch, establishing an amorphous unit model of the soluble starch by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization on the established amorphous unit model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method to obtain the model of the main component in the industrial wastewater after structural optimization.
The convergence parameters of the Smart method are as follows: energy 2.0e -5 kcal/mol, force 0.001 kcal/mol-The maximum number of iterations is 5000. The COMPASS force field is selected for molecular mechanics simulation because the force field can simultaneously and accurately simulate the configuration, vibration spectrum, thermodynamics and other properties of various substances in an isolated system and a condensed system in an atomic scale range.
It should be understood that, although the steps S11 and S12 are defined sequentially in the embodiment of the present invention, the sequential definition is only for descriptive purposes, and the two steps are not substantially related. That is, step S11 may be performed first, and then step S12 may be performed; or step S12 is performed first, and then step S11 is performed; or step S11 and step S12 are performed simultaneously.
In order to improve the accuracy of the screening result, referring to fig. 2, it is preferable to further include the steps of:
S5, changing the operation parameters of Dynamic simulation in the step S3, analyzing the interaction energy and RDF under different operation parameters to obtain the optimal operation parameters for a simulation test, verifying the promotion effect of candidate industrial wastewater on coal-fired fine particulate matter agglomeration through the simulation test, and screening the industrial wastewater suitable for promoting fine particulate matter agglomeration. Through the simulation process, comprehensive evaluation is performed by combining the verification experiment result, and the accuracy of the screening result can be improved.
The interaction energy and RDF under different operation parameters are simulated through software to obtain experimental parameters such as optimal temperature, optimal pH and the like, so that the parameter searching process in the process of performing a simulation test can be saved. The specific operation is as follows: simulating under different temperature and pH value conditions, selecting the condition with the largest interaction or the most obvious RDF peak value as the optimal operation parameter for subsequent rapid verification, and verifying the accelerating effect of candidate industrial wastewater on coal fine particle agglomeration by using a simulation test bed under the optimal operation parameter.
Criteria for comprehensive evaluation: firstly, the difference value of interaction energy is larger and the peak value of RDF is larger; secondly, the agglomeration effect is good through quick experiment verification, and the particulate matters below PM10 are obviously reduced.
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. The experimental methods, which do not address specific conditions in the following examples, are generally in accordance with the conditions recommended by the manufacturer.
Example 1
An industrial wastewater screening method for promoting agglomeration of fine particles, comprising the steps of:
S1, respectively constructing an energy minimization model of fine particles and an energy minimization model of soluble starch in food industry wastewater based on MATERIALS STUDIO software; the main component of the food industry wastewater is soluble starch, the mass concentration of the soluble starch exceeds 0.001% of the total mass of the food industry wastewater, the main component of fine particles is SiO 2, siO 2 is selected to replace the fine particles, and an 8X 8 SiO 2 super cell model of the surface of the fine particles is constructed;
the specific operation of energy minimization is:
S11, selecting a main component SiO 2 of fine particles, constructing an 8X 8 SiO 2 super cell model by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization on the SiO 2 super cell model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method (Geometry Optimization), wherein convergence parameters of the Smart method are as follows: energy 2.0e -5 kcal/mol, force 0.001kcal/mol Obtaining a fine particulate matter model after structure optimization by using the maximum iteration number of 5000;
S12, measuring the content of each component in industrial wastewater to obtain the main component of the industrial wastewater, namely soluble starch, establishing an amorphous unit cell model of the soluble starch by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization (Geometry Optimization) on a SiO 2 super unit cell model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method, wherein convergence parameters of the Smart method are as follows: energy 2.0e -5 kcal/mol, force 0.001 kcal/mol- And obtaining a model of the main component in the industrial wastewater after the structure optimization by using the maximum iteration number of 5000.
S2, placing the amorphous unit cell model of the soluble starch obtained in the step S1 and the SiO 2 super unit cell model together by using a layering tool (Build Layer) in MATERIALS STUDIO software, then converting the model into a Layer structure, constructing a layered interface model of the SiO 2 super unit cell model and an amorphous unit cell model of a main component, adding 3nm vacuum layers on the upper part and the lower part of the amorphous unit cell model of the layered interface model to obtain an interaction model for subsequent molecular dynamics simulation, wherein the initial configuration of the interaction model is shown in a graph in FIG. 3, the simulated equilibrium state configuration of the interaction model is shown in a graph in FIG. 4, the upper Layer and the lower Layer in the graphs in FIG. 3 and FIG. 4 are SiO 2 (1 0) surfaces, si and O atoms on the surfaces are respectively saturated with OH and H, the middle Layer is the structure of the soluble starch, and the interaction model contains SiO 2, water molecules and starch chains;
S3, carrying out Dynamic simulation on the interaction model by utilizing a Forcite module to enable the system to reach a simulation equilibrium state, wherein parameters of the Dynamic simulation are as follows: carrying out Dynamic simulation on the interaction model by utilizing a Forcite module in MATERIALS STUDIO software, selecting a regular ensemble (NVT ensemble) under a Compass force field, setting the temperature to 105 ℃, setting the time step to 1fs, and setting the total simulation time to 1000ps; the simulated equilibrium state configuration of the interaction model is shown in fig. 4, and as can be seen from fig. 4, the molecular chains of the soluble starch all move to the surface of the fine particles, interact with the surface of the fine particles, and simultaneously wrap the fine particles inside the molecular chains;
S4, according to the result of Dynamic simulation, the interaction energy (see table 1) and radial distribution function data (see figure 5) of the surfaces of the soluble starch and the SiO 2 are obtained, the difference value of the interaction energy of the main component in the industrial wastewater and the surface of the fine particles and the difference value of the interaction energy of the water and the surface of the fine particles are compared, and the radial distribution function data are combined, so that candidate industrial wastewater for promoting the agglomeration of the fine particles is screened out; wherein, the radial distribution function between the O atoms and the main components of SiO 2 and water is selected by Forcite Analysis tools;
TABLE 1 interaction energy of soluble starch and SiO 2 surface
| E( particle surface / main component ) | E( particle surface ) | E( main component ) | E( Interaction with each other ) | |
| Food processing wastewater | -671101 | -613753 | -56529.2 | 819.393 |
| Water and its preparation method | -672231 | -613754 | -58074 | 403.285 |
According to the calculation in table 1, the interaction energy of the food processing wastewater and the fine particles is significantly greater than the interaction energy of water and the fine particles. As can be seen from FIG. 5, the RDF of the food processing wastewater is shown inThe peak value of the area is obviously enhanced; the results show that the food processing wastewater has a stronger promotion effect on agglomeration of fine particles than water and can be used as candidate industrial wastewater.
S5, by changing the operation parameters of Dynamic simulation in the step S3, wherein the operation parameters comprise the operation temperature and the pH value, namely, simulation is carried out under different temperature and pH value conditions to obtain interaction energy and RDF under different operation parameters, and the optimal operation parameters for simulation test with the maximum interaction energy or the most obvious RDF peak value are selected, wherein in the embodiment, the optimal temperature is 105 ℃, and the pH value is 6-7; and performing simulation tests on a simulation test bench according to the optimal operation parameters to verify the promotion effect of candidate industrial wastewater on the agglomeration of coal-fired fine particles, screening out industrial wastewater suitable for promoting the agglomeration of fine particles, and verifying the result as shown in figures 6-7.
FIG. 6 is a graph showing the change of the particle size distribution of fine particles with time after the addition of the food industry wastewater, and in FIG. 6, starch1 and Starch2 represent samples sampled from different positions of the simulation test stand. As can be seen from fig. 6, in the initial state of the fly ash produced by the coal, the peak values of the particle size distribution peaks of the ultrafine particle (PM 2.5) and the fine particle (PM 10) regions were about 27% and 4%, respectively; when the waste water from food industry is added, the particle size of fine particles is obviously agglomerated and grown, the peak value of the particle size distribution peak of the superfine particle area is obviously reduced, and the particle size distribution peak of the superfine particle area and the particle size distribution peak of the coarse particle area are moved in the direction.
Fig. 7 is a scanning electron microscope image of fine particles, wherein fig. a is a scanning electron microscope image of fine particles in an initial state, and fig. b is a scanning electron microscope image of fine particles after the addition of food industry wastewater, and it can be seen from fig. 7 that the particle size of fine particles is significantly increased after the addition of food industry wastewater, and larger agglomerates are formed by agglomeration, and agglomerates are partially formed.
The above results all indicate that the food industry waste water can effectively promote agglomeration of fine particles generated by fire coal. Meanwhile, the interaction energy, RDF and verification results are combined, compared with water, the food industry waste water can obviously promote fine particle agglomeration generated by fire coal, the possibility of the waste water serving as an agglomerating agent is disclosed, and a new thought is provided for treating fine particle pollution.
Example 2
An industrial wastewater screening method for promoting agglomeration of fine particles, comprising the steps of:
S1, respectively constructing an energy minimization model of fine particles and an energy minimization model of calcium chloride in desulfurization industrial wastewater based on MATERIALS STUDIO software; the main component of the desulfurization industrial wastewater is calcium chloride, the mass concentration of the calcium chloride exceeds 1% of the total mass of the desulfurization industrial wastewater, the main component of fine particles is SiO 2, siO 2 is selected to replace the fine particles, and an 8X 8 SiO 2 super cell model of the surfaces of the fine particles is constructed; the specific operation of energy minimization is the same as in example 1;
S2, using a layering tool (Build Layer) in MATERIALS STUDIO software to put together the amorphous unit cell model of the calcium chloride obtained in the step S1 and the SiO 2 super unit cell model, then converting the model into a Layer structure, constructing a layered interface model of the SiO 2 super unit cell model and the amorphous unit cell model of the main component, and adding 3nm vacuum layers on the upper part and the lower part of the amorphous unit cell model of the layered interface model to obtain an interaction model for subsequent molecular dynamics simulation;
S3, carrying out Dynamic simulation on the interaction model by utilizing a Forcite module to enable the system to reach a simulation equilibrium state, wherein parameters of the Dynamic simulation are as follows: carrying out Dynamic simulation on the interaction model by utilizing a Forcite module in MATERIALS STUDIO software, selecting a regular ensemble (NVT ensemble) under a Compass force field, setting the temperature to 105 ℃, setting the time step to 1fs, and setting the total simulation time to 1000ps; the simulated equilibrium state configuration of the interaction model is shown in fig. 8, the upper layer and the lower layer in fig. 8 are SiO 2 (1) surfaces, si and O atoms on the surfaces are respectively saturated with OH and H, the middle layer is a calcium chloride structure, and the interaction model comprises SiO 2, water molecules and calcium chloride; as can be seen from fig. 8, the calcium chloride moves toward the surface of the fine particles and interacts with the surface of the fine particles;
S4, according to the result of Dynamic simulation, the interaction energy (see table 2) and radial distribution function data (see figure 9) of the surfaces of calcium chloride and SiO 2 are obtained, the difference value of the interaction energy of the main component in the industrial wastewater and the surface of the fine particles and the difference value of the interaction energy of water and the surface of the fine particles are compared, and the radial distribution function data are combined, so that candidate industrial wastewater for promoting the agglomeration of the fine particles is screened out; wherein, the radial distribution function between the O atoms and the main components of SiO 2 and water is selected by Forcite Analysis tools;
TABLE 2 interaction energy of calcium chloride and SiO 2 surfaces
| E( particle surface / main component ) | E( particle surface ) | E( main component ) | E( Interaction with each other ) | |
| Desulfurization of industrial waste water | -709267 | -95102 | -613753 | 412.339 |
| Water and its preparation method | -672231 | -613754 | -58074 | 403.285 |
According to the calculation in Table 2, the interaction energy of the desulfurization processing wastewater and the fine particles is slightly larger than the interaction energy of water and the fine particles. As can be seen from FIG. 9, RDF of desulfurization process wastewater is as followsThe peaks of the regions are not quite obvious; the results show that the desulfurization processing wastewater has a slightly stronger promotion effect on agglomeration of fine particles than water, and can be used as candidate industrial wastewater.
S5, by changing the operation parameters of Dynamic simulation in the step S3, wherein the operation parameters comprise the operation temperature and the pH value, namely, simulation is carried out under different temperature and pH value conditions to obtain interaction energy and RDF under different operation parameters, and the optimal operation parameters for simulation test with the maximum interaction energy or the most obvious RDF peak value are selected, wherein in the embodiment, the optimal temperature is 105 ℃, and the pH value is 6-7; performing a simulation test on a simulation test bed according to the optimal operation parameters to verify the promotion effect of candidate industrial wastewater on the agglomeration of coal-fired fine particles, and screening out industrial wastewater suitable for promoting the agglomeration of fine particles; the verification result is shown in fig. 10.
FIG. 10 is a graph showing the change of the particle size distribution of fine particles with time after the desulfurization industrial wastewater containing calcium chloride with different mass concentrations is added, and as can be seen from FIG. 10, the particle size of fly ash generated by fire coal is obviously agglomerated and grown, the peak value of the particle size distribution peak of the ultrafine particle (PM 2.5) area is obviously reduced, the particle size distribution peak of the coarse particle area moves to the particle size increasing direction according to the sequence of water, 5% CaCl 2 and 1% CaCl 2, which shows that CaCl 2 can effectively promote the agglomeration of fine particles of fire coal, and the effect of promoting the agglomeration of fine particles of 1% CaCl 2 is stronger than that of 5% CaCl 2.
The interaction energy, RDF and verification result are combined, compared with water, desulfurization industrial wastewater can obviously promote fine particle agglomeration generated by fire coal, the possibility of the desulfurization industrial wastewater serving as an agglomerating agent is disclosed, and a new thought is provided for treating fine particle pollution.
Example 3
An industrial wastewater screening method for promoting agglomeration of fine particles, comprising the steps of:
s1, respectively constructing an energy minimization model of fine particles and an energy minimization model of main components in the food processing industrial wastewater after precipitation treatment based on MATERIALS STUDIO software; the main components of the food processing industrial wastewater after the precipitation treatment are starch, PAM and sodium chloride, the mass concentration of each of the main components exceeds 0.001% of the total mass of the food processing industrial wastewater after the precipitation treatment, the main components of fine particles are SiO 2, siO 2 is selected to replace the fine particles, and an 8X 8 SiO 2 super cell model of the surface of the fine particles is constructed; the specific operation of energy minimization is the same as in example 1;
S2, putting together the amorphous unit cell model of the starch, the PAM and the sodium chloride obtained in the step S1 and the SiO 2 super unit cell model by using a layering tool (Build Layer) in MATERIALS STUDIO software, then converting the model into a Layer structure, constructing a layered interface model of the SiO 2 super unit cell model and the amorphous unit cell model of the main component, and adding 3nm vacuum layers on the upper part and the lower part of the amorphous unit cell model of the layered interface model to obtain an interaction model for subsequent molecular dynamics simulation;
S3, carrying out Dynamic simulation on the interaction model by utilizing a Forcite module to enable the system to reach a simulation equilibrium state, wherein parameters of the Dynamic simulation are as follows: carrying out Dynamic simulation on the interaction model by utilizing a Forcite module in MATERIALS STUDIO software, selecting a regular ensemble (NVT ensemble) under a Compass force field, setting the temperature to 105 ℃, setting the time step to 1fs, and setting the total simulation time to 1000ps; the simulated equilibrium state configuration of the interaction model is shown in fig. 11, the upper layer and the lower layer in fig. 11 are SiO 2 (1) surfaces, si and O atoms on the surfaces are respectively saturated with OH and H, the middle layer is a structure of starch, PAM and sodium chloride, and the interaction model comprises SiO 2, water molecules, starch, PAM and sodium chloride; as can be seen from fig. 11, the amorphous unit cell model of starch, PAM and sodium chloride moves toward and interacts with the surface of the fine particles;
S4, according to the result of Dynamic simulation, the interaction energy (see table 3) and radial distribution function data (see figure 12) of the surfaces of starch, PAM, sodium chloride and SiO 2 are obtained, the difference value of the interaction energy of the main component in the industrial wastewater and the surface of the fine particles and the difference value of the interaction energy of water and the surface of the fine particles are compared, and the radial distribution function data are combined, so that candidate industrial wastewater for promoting the agglomeration of the fine particles is screened out; wherein, the radial distribution function between the O atoms and the main components of SiO 2 and water is selected by Forcite Analysis tools;
TABLE 3 interaction energy of starch, PAM, sodium chloride and SiO 2 surfaces
According to the calculation in table 3, the interaction energy of the food processing industrial wastewater after precipitation treatment and the fine particles is significantly larger than the interaction energy of water and the fine particles. As can be seen from FIG. 12, the RDF of the food processing industrial wastewater after the precipitation treatment is as followsThe peak value of the area is obviously enhanced; the above results indicate that the food processing industrial wastewater after precipitation treatment has a stronger promoting effect on agglomeration of fine particles than water and can be used as candidate industrial wastewater.
S5, by changing the operation parameters of Dynamic simulation in the step S3, wherein the operation parameters comprise the operation temperature and the pH value, namely, simulation is carried out under different temperature and pH value conditions to obtain interaction energy and RDF under different operation parameters, and the optimal operation parameters for simulation test with the maximum interaction energy or the most obvious RDF peak value are selected, wherein in the embodiment, the optimal temperature is 105 ℃, and the pH value is 6-7; performing a simulation test on a simulation test bed according to the optimal operation parameters to verify the promotion effect of candidate industrial wastewater on the agglomeration of coal-fired fine particles, and screening out industrial wastewater suitable for promoting the agglomeration of fine particles; the verification result is shown in fig. 13.
Fig. 13 is a graph showing the change of the particle size distribution of fine particles with time after the precipitation treatment of the industrial wastewater for food processing, and as can be seen from fig. 13, the particle size of fly ash generated by fire coal is obviously agglomerated and grown after the addition of starch, PAM and sodium chloride, the peak value of the particle size distribution peak of the ultrafine particle (PM 2.5) area is reduced from 27% to 18%, and the particle size distribution peak of the coarse particle area is increased, which shows that the agglomeration of fine particles of fire coal can be effectively promoted by the starch, PAM and sodium chloride.
The interaction energy, RDF and verification result are combined, compared with water, the precipitated food processing industrial waste water can obviously promote fine particle agglomeration generated by fire coal, the possibility of the precipitated food processing industrial waste water serving as an agglomerating agent is disclosed, and a new thought is provided for treating fine particle pollution.
From a comprehensive analysis of examples 1 to 3, it is understood that the interaction of the food industry wastewater with fine particles in example 1 can be highest among three kinds of industry wastewater, and thatThe most obvious increase of the peak value of the area is the most suitable agglomerating agent for promoting agglomeration of fine particles, and the verification results of all the examples can also prove that the agglomeration effect of the food industry wastewater of the example 1 on the coal-fired fine particles is the most obvious, so that direct evidence is provided for the conclusion, and further the screening method has a guiding effect in practical application.
Although the present disclosure is disclosed above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the scope of the disclosure.
Claims (7)
1. A method for screening industrial wastewater for promoting agglomeration of fine particles, comprising the steps of:
S1, respectively constructing a model of fine particles and a model of main components in industrial wastewater based on molecular dynamics simulation software;
S2, constructing an interaction model of main components and fine particles in the industrial wastewater;
s3, carrying out dynamic simulation on the interaction model in the step S2 to enable the system to reach a simulation equilibrium state;
s4, according to a dynamic simulation balance structure, interaction energy and radial distribution function data of main components in the industrial wastewater and the surfaces of the fine particles are obtained, and candidate industrial wastewater for promoting the agglomeration of the fine particles is evaluated and screened out by combining the interaction energy and the radial distribution function data;
s5, changing operation parameters of dynamic simulation in the step S3, analyzing interaction energy and radial distribution functions under different operation parameters to obtain optimal operation parameters for simulation tests, verifying promotion effects of candidate industrial wastewater on agglomeration of coal-fired fine particles through the simulation tests, and screening industrial wastewater suitable for promoting agglomeration of fine particles;
in the step S1, the model of the fine particles and the model of the main component in the industrial wastewater are respectively subjected to energy minimization treatment; the energy minimization process comprises the following steps:
S11, selecting a main component SiO 2 of fine particles, constructing a SiO 2 super cell model by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization on the SiO 2 super cell model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method to obtain a fine particle model;
S12, measuring the content of each component in the industrial wastewater to obtain the main component in the industrial wastewater, establishing an amorphous unit cell model of the main component by utilizing a Amorphous cell module of MATERIALS STUDIO software, and performing structural optimization on the established amorphous unit cell model by utilizing a MATERIALS STUDIO software Forcite module under a COMPASS force field by adopting a Smart method to obtain the model of the main component in the industrial wastewater.
2. The method according to claim 1, wherein in step S1, the main component of the industrial wastewater is organic and/or inorganic salt, and the mass concentration of the main component exceeds 0.001% of the total mass of the industrial wastewater.
3. The method according to claim 2, wherein in step S1, the model of the main component in the industrial wastewater is an amorphous unit cell model of the main component, and the model of the fine particulate matter is a SiO 2 super unit cell model.
4. The industrial wastewater screening method according to claim 1, wherein in step S1, the radial distribution function data isRadial distribution function data of the region.
5. The industrial wastewater screening method according to claim 1, wherein in step S3, the kinetic simulation is performed by using Forcite module of MATERIALS STUDIO software;
The parameters of the dynamics simulation are as follows: the interaction model is dynamically simulated by utilizing a Forcite module in MATERIALS STUDIO software, a regular ensemble is selected under the Compass force field, the temperature is 105 ℃, the time step is set to be 1fs, and the total simulation time is 1000ps.
6. The method according to any one of claims 1 to 5, wherein in step S4, the interaction energy of the main component of the industrial wastewater with the surface of the fine particulate matter is calculated using the following formula;
E( Interaction with each other )＝-(E( particle surface / main component )-E( particle surface )-E( main component ))；
Wherein E ( Interaction with each other ) is the interaction energy of the main component of the industrial wastewater and the surface of the fine particles, E ( particle surface / main component ) is the total energy of the interaction model, E ( particle surface ) is the energy of the fine particle model, and E ( main component ) is the energy of the main component model of the industrial wastewater.
7. The industrial wastewater screening method according to claim 6, wherein in step S5, the operation parameters include temperature and ph.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011441804.7A CN112560323B (en) | 2020-12-08 | 2020-12-08 | Industrial wastewater screening method for promoting agglomeration of fine particles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011441804.7A CN112560323B (en) | 2020-12-08 | 2020-12-08 | Industrial wastewater screening method for promoting agglomeration of fine particles |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112560323A CN112560323A (en) | 2021-03-26 |
| CN112560323B true CN112560323B (en) | 2024-06-04 |
Family
ID=75062349
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011441804.7A Active CN112560323B (en) | 2020-12-08 | 2020-12-08 | Industrial wastewater screening method for promoting agglomeration of fine particles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112560323B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113192046B (en) * | 2021-05-14 | 2022-03-25 | 中北大学 | Automatic identification method for radial distribution function graph |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4032476A (en) * | 1975-06-11 | 1977-06-28 | The Carborundum Company | Granular activated carbon manufacture from sub-bituminous coal mixed with concentrated inorganic acid without pitch |
| CN103420530A (en) * | 2013-08-13 | 2013-12-04 | 常州大学 | Method for treating degradation-resistant organic pollutants in waste water |
| CN104016530A (en) * | 2014-06-05 | 2014-09-03 | 中国科学院过程工程研究所 | Method for deeply treating, desalting and recycling industrial wastewater with high salt content |
| CN105808832A (en) * | 2016-03-03 | 2016-07-27 | 中国科学院微电子研究所 | Method and system for calculating solvation effect of high-molecular surfactant |
| CN106698582A (en) * | 2017-01-24 | 2017-05-24 | 天津师范大学 | Method for treating industrial wastewater containing heavy metal contaminants by utilizing industrial fly ash and nano iron |
| CN107651794A (en) * | 2016-07-26 | 2018-02-02 | 中国科学院过程工程研究所 | A kind of processing system and processing method of industrial wastewater bio-chemical effluent |
| EP3447030A1 (en) * | 2017-08-21 | 2019-02-27 | Instituto Politécnico de Beja | Process of treatment of agroindustrial waste water by reaction of acidification with addition of strong acids |
| CN109614851A (en) * | 2018-10-26 | 2019-04-12 | 上海市环境监测中心(上海长三角区域空气质量预测预报中心) | The image detecting method of atmosphere pollution particulate matter morphological parameters |
| CN109759233A (en) * | 2019-01-25 | 2019-05-17 | 东南大学 | A kind of system and method for collaboration processing desulfurization wastewater and reinforcing fine particle reunion and removing |
| CN110523176A (en) * | 2019-07-19 | 2019-12-03 | 武汉天空蓝环保科技有限公司 | Integrated multi-pollutant cooperative processing method and system |
| CN110910965A (en) * | 2019-11-15 | 2020-03-24 | 东南大学 | Method for screening rubber particle surface activation technology based on molecular dynamics |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8954479B2 (en) * | 2012-06-13 | 2015-02-10 | International Business Machines Corporation | End-to-end interoperability and workflows from building architecture design to one or more simulations |
-
2020
- 2020-12-08 CN CN202011441804.7A patent/CN112560323B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4032476A (en) * | 1975-06-11 | 1977-06-28 | The Carborundum Company | Granular activated carbon manufacture from sub-bituminous coal mixed with concentrated inorganic acid without pitch |
| CN103420530A (en) * | 2013-08-13 | 2013-12-04 | 常州大学 | Method for treating degradation-resistant organic pollutants in waste water |
| CN104016530A (en) * | 2014-06-05 | 2014-09-03 | 中国科学院过程工程研究所 | Method for deeply treating, desalting and recycling industrial wastewater with high salt content |
| CN105808832A (en) * | 2016-03-03 | 2016-07-27 | 中国科学院微电子研究所 | Method and system for calculating solvation effect of high-molecular surfactant |
| CN107651794A (en) * | 2016-07-26 | 2018-02-02 | 中国科学院过程工程研究所 | A kind of processing system and processing method of industrial wastewater bio-chemical effluent |
| CN106698582A (en) * | 2017-01-24 | 2017-05-24 | 天津师范大学 | Method for treating industrial wastewater containing heavy metal contaminants by utilizing industrial fly ash and nano iron |
| EP3447030A1 (en) * | 2017-08-21 | 2019-02-27 | Instituto Politécnico de Beja | Process of treatment of agroindustrial waste water by reaction of acidification with addition of strong acids |
| CN109614851A (en) * | 2018-10-26 | 2019-04-12 | 上海市环境监测中心(上海长三角区域空气质量预测预报中心) | The image detecting method of atmosphere pollution particulate matter morphological parameters |
| CN109759233A (en) * | 2019-01-25 | 2019-05-17 | 东南大学 | A kind of system and method for collaboration processing desulfurization wastewater and reinforcing fine particle reunion and removing |
| CN110523176A (en) * | 2019-07-19 | 2019-12-03 | 武汉天空蓝环保科技有限公司 | Integrated multi-pollutant cooperative processing method and system |
| CN110910965A (en) * | 2019-11-15 | 2020-03-24 | 东南大学 | Method for screening rubber particle surface activation technology based on molecular dynamics |
Non-Patent Citations (1)
| Title |
|---|
| 赤泥/煤基铁炭材料的制备及其脱除废水Cr(Ⅵ)的性能;仇雅丽;李长明;王德亮;李文松;余剑;高士秋;许光文;;化工学报(07);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112560323A (en) | 2021-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xie et al. | Characteristics of ambient bioaerosols during haze episodes in China: a review | |
| Zhang et al. | Fossil fuel-related emissions were the major source of NH3 pollution in urban cities of northern China in the autumn of 2017 | |
| Meng et al. | Improvement of a global high-resolution ammonia emission inventory for combustion and industrial sources with new data from the residential and transportation sectors | |
| Xu et al. | Regional differences of pollution emissions in China: Contributing factors and mitigation strategies | |
| Gu et al. | Characterization of atmospheric organic carbon and element carbon of PM2. 5 and PM10 at Tianjin, China | |
| Zhang et al. | A refined source apportionment study of atmospheric PM2. 5 during winter heating period in Shijiazhuang, China, using a receptor model coupled with a source-oriented model | |
| Yan et al. | Emission and simulation of primary fine and submicron particles and water-soluble ions from domestic coal combustion in China | |
| CN112560323B (en) | Industrial wastewater screening method for promoting agglomeration of fine particles | |
| Feng et al. | Investigation on air pollution control strategy in Hangzhou for post-G20/pre-Asian-games period (2018–2020) | |
| CN110595972A (en) | Analysis method of PM2.5 concentration value and influence factor | |
| Mohamad et al. | Preliminary assessment of air pollutant sources identification at selected monitoring stations in Klang Valley, Malaysia | |
| Liu et al. | Size-segregated carbonaceous aerosols emission from typical vehicles and potential depositions in the human respiratory system | |
| CN116644689B (en) | Method and system for strong and rapid back calculation of atmospheric pollution source of local scale under complex underlying surface | |
| Wang et al. | Premature mortality associated with exposure to outdoor black carbon and its source contributions in China | |
| Chen et al. | Atmospheric outflow of anthropogenic iron and its deposition to China adjacent seas | |
| Yang et al. | Investigating the spatio-temporal variations of the impact of urbanization on haze pollution using multiple indicators | |
| Zhang et al. | Estimation of secondary PM< sub> 2.5</sub> in China and the United States using a multi-tracer approach | |
| Ji et al. | Contribution of meteorological conditions to inter-annual variations in air quality during the past decade in eastern China | |
| CN111081323A (en) | Graphene multi-stage coarse granulation method based on Tersofff force field | |
| CN115659589A (en) | Metal organic framework screening method for capturing low-concentration carbon dioxide in air | |
| Liu et al. | Characteristics and source apportionment of water-soluble inorganic ions in atmospheric particles in Lvliang, China | |
| Wang et al. | Analysis of new particle formation events and comparisons to simulations of particle number concentrations based on GEOS-Chem–advanced particle microphysics in Beijing, China | |
| Huang et al. | Induced adsorption and agglomeration under bipolar corona discharge to enhance the simultaneous removal of trace mercury and fine particles | |
| Zeng et al. | Fabrication of Sodium Alginate Porous Scaffolds for Heavy Metal Ion Removal from Aqueous Solution | |
| Lin et al. | Overview of the special issue" aerosol source, transport, chemistry, and emission control" for the 10th Asian aerosol conference 2017 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |