A method and system for selecting a coagulant based on molecular simulation
By constructing molecular simulation models of fine particulate matter and agglomerants, and using the CASTEP module to optimize and determine adsorption sites, the problems of long screening cycles and high costs of agglomerants are solved, and efficient and accurate agglomerant selection and performance prediction are achieved.
Patent Information
- Application Number
- CN202310426389.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-04-20
- Publication Date
- 2026-02-27
- Estimated Expiration
- 2043-04-20
AI Technical Summary
Existing chemical agglomeration technologies suffer from long implementation cycles and high costs when screening agglomerating agents, and traditional experimental research methods are highly unpredictable, making it difficult to efficiently screen suitable materials.
Using a molecular simulation-based approach, models of fine particulate matter and agglomerants were constructed. The CASTEP module was then used for structural optimization and adsorption site determination. Binding energies were calculated to select the most efficient agglomerants.
It enables convenient and accurate screening of agglomerating agents, reduces experimental costs, shortens the research and development cycle, and can predict the performance of agglomerating agents, thereby improving agglomeration efficiency.
Smart Images
Figure CN116453607B_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of dust removal, in particular to a method and system for selecting agglomerating agent based on molecular simulation. BACKGROUND
[0002] Coal combustion in the furnace will produce a large amount of fly ash fine particles, which will enter the atmosphere through the chimney and pollute the environment. Coal-fired power plants are considered to be one of the important sources of fly ash fine particles in the atmospheric environment. As the main dust removal unit of coal-fired power plants, the dust removal efficiency of electric precipitation on fly ash particles is as high as 99.5% or more. However, due to the "Greenfiled gap" effect of small particle size, a large amount of fine particles cannot be captured and discharged into the atmosphere.
[0003] Chemical agglomeration technology is a method of capturing fine particles by using various adsorbents. By spraying agglomerating agent into flue gas, physical and chemical reactions occur between fine particles and agglomerating agent, which promotes the agglomeration of fine particles and increases the removal efficiency of fine particles.
[0004] Chemical agglomeration needs a lot of pre-screening work of agglomerating agent when dealing with different types of particles. In industrial applications, there are practical problems such as long implementation period and high execution cost. Due to the complexity of the object, the traditional experimental research method has a lot of randomness. In recent years, with the development of quantum chemistry, statistical mechanics, calculation methods and other related disciplines, and the unprecedented improvement of computer power, theoretical calculation has entered people's field of vision. In the past experiments, a lot of time is often spent on trial and error to find suitable materials. However, by using existing theoretical calculation software, the time cost of trial and error can be effectively reduced, and the performance of the agglomerating agent can be predicted. The commonly used theoretical calculation software includes Gaussian, VASP, Materials Studio, etc. Among them, Materials Studio is suitable for simulating the adsorption of agglomerating agent on fine particles because it is good at calculating macromolecular systems. SUMMARY
[0005] The purpose of the present application is to provide a method and system for selecting agglomerating agent based on molecular simulation. The present application is beneficial to conveniently and accurately screen agglomerating agent, predict the experimental cost of agglomerating agent performance, and shorten the research and development period.
[0006] To achieve the above purpose, the present application provides a method for selecting agglomerating agent based on molecular simulation, comprising the following steps:
[0007] constructing a fine particle surface model and an agglomerating agent molecular model;
[0008] performing structure optimization of the fine particle surface model and the agglomerating agent molecular model by CASTEP module;
[0009] determining an adsorption site of the agglomerating agent on the optimized fine particulate matter surface model;
[0010] placing the optimized agglomerating agent molecular model on the adsorption site and performing overall structure optimization, and calculating binding energy to complete optimization.
[0011] Preferably, the fine particulate matter surface model is constructed using the cut crystal surface and supercell functions; convergence testing should be performed before cutting the crystal cell to determine the K point and E-cut during structure optimization; and the size after cutting the crystal surface and supercell and the thickness of the vacuum layer should be determined in advance when cutting the crystal cell.
[0012] Preferably, the cut metal-nonmetal bond is repaired when cutting the crystal cell.
[0013] Preferably, the method for performing structure optimization of the CASTEP module includes: creating a crystal cell using the AC module; placing the agglomerating agent molecule into the crystal cell for optimization to obtain the lowest energy configuration, thereby completing structure optimization of the CASTEP module.
[0014] Preferably, the method for determining the adsorption site includes: respectively establishing SetA surface structure and Set B agglomerating agent molecular structure and setting the force field; and simulating the adsorption site in the form of SetA and Set B to determine the adsorption site.
[0015] The application also provides an agglomerating agent selection system based on molecular simulation, which includes a construction module, an optimization module, a positioning module, and a combination module.
[0016] The construction module is used to construct a fine particulate matter surface model and an agglomerating agent molecular model.
[0017] The optimization module is used to perform structure optimization of the CASTEP module on the fine particulate matter surface model and the agglomerating agent molecular model.
[0018] The positioning module is used to determine an adsorption site of the agglomerating agent on the optimized fine particulate matter surface model.
[0019] The combination module is used to place the optimized agglomerating agent molecular model on the adsorption site and perform overall structure optimization, and calculate binding energy to complete optimization.
[0020] Preferably, the workflow of the construction module includes: constructing the fine particulate matter surface model using the cut crystal surface and supercell functions; convergence testing should be performed before cutting the crystal cell to determine the K point and E-cut during structure optimization; and the size after cutting the crystal surface and supercell and the thickness of the vacuum layer should be determined in advance when cutting the crystal cell.
[0021] Preferably, the workflow of the optimization module comprises: creating a unit cell by using the AC module; and placing a flocculant molecule into the unit cell for optimization to obtain a lowest-energy configuration, thereby completing the structure optimization of the CASTEP module.
[0022] Compared with the prior art, the application has the following beneficial effects:
[0023] The application can construct an accurate fine particulate matter surface model, can reduce experimental cost and shorten the research and development cycle of the flocculant based on the advantages of computer simulation, and can also predict the selectivity of different component fine particulate matters to the flocculant by changing the components of the surface model. The application can further analyze the interfacial interaction from the interfacial state by means of the model, and further explore the properties of the material. BRIEF DESCRIPTION OF DRAWINGS
[0024] In order to more clearly illustrate the technical solutions of the application, the following briefly introduces the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.
[0025] Figure 1 The method flowchart of the embodiment of the application is shown in the figure;
[0026] Figure 2 The convergence test schematic diagram of the embodiment of the application is shown in the figure;
[0027] Figure 3 The silicon dioxide surface model of the embodiment of the application is shown in the figure;
[0028] Figure 4 The PAM molecule placed in the unit cell of the embodiment of the application is shown in the figure;
[0029] Figure 5 The low-energy configuration schematic diagram of PAM and SiO2 surface of the embodiment of the application is shown in the figure;
[0030] Figure 6 The low-energy configuration schematic diagram of CMC and SiO2 surface of the embodiment of the application is shown in the figure;
[0031] Figure 7 The density of states diagram of the silicon dioxide surface before and after adsorption of the embodiment of the application is shown in the figure;
[0032] Figure 8 The system structure schematic diagram of the embodiment of the application is shown in the figure. DETAILED DESCRIPTION
[0033] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of protection of the present application.
[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.
[0035] Embodiment one
[0036] As shown in the flowchart of the method of the present embodiment, taking SiO2 as an example of the fine particulate matter and PAM and CMC as examples of the agglomerating agent. The steps include: Figure 1 S1. Constructing a fine particulate matter surface model and an agglomerating agent molecular model.
[0037] First, a suitable fine particulate matter surface model is constructed by using the functions of cleaving a crystal face and a supercell. Before cleaving the crystal cell, convergence test needs to be performed to determine the K points and E-cut during structure optimization. When cleaving the crystal cell, the crystal face to be cleaved in advance, the size of the supercell after cleavage, the thickness of the vacuum layer, etc. need to be determined.
[0038] Then, the SiO2 crystal cell is subjected to convergence test, and the Ecut / K points are set from low to high under the condition of fixed K points / Ecut, and the calculated value of the single-point energy is calculated. The single-point energy calculation is to change the Task option to Energy after setting the above-mentioned precision, and then point calculate. The energy value can be found in the document after the calculation is completed.
[0039] Then, all the single-point energies are recorded, and the values of K points / Ecut corresponding to E<0.01eV are taken, as shown in
[0040] The K points and Ecut obtained by the convergence test are used as the parameters of structure optimization to perform structure optimization on the atomic positions and the cell parameters, and the values of the present example are 4*4*3 and 450eV. The surface of the crystal cell model after structure optimization is cleaved, and the supercell is 4*4. Because the surface is a two-dimensional structure, a vacuum layer needs to be added to convert it into a three-dimensional crystal cell. In order to make the upper and lower surfaces not interfere with each other, the thickness of the vacuum layer is taken as 15 angstroms. In order to make the model closer to the characteristics of the surface model in reality, the atoms in the lower layer are fixed, and the silicon dioxide surface model is obtained as shown in Figure 2 . Figure 3
[0041] After the fine particulate matter surface model is constructed, the agglomerating agent molecular model is constructed by using the polymerization function.
[0042] The polymerization degree, the number of molecular chains, and the polymerization rule need to be set in advance when constructing the high molecular flocculant. The obtained polymer structure is optimized. A molecular unit of PAM is constructed, and the head and tail hydrogen atoms are labeled. In order to make the PAM molecules in the periodic structure not interact with each other, and to make the molecular mass close to that of the CMC molecule, the polymerization degree is set to 3, and the chain number is set to 1, as shown in Figure 4 The CMC unit molecule is constructed in the same way.
[0043] S2. The surface model of fine particles and the molecular model of flocculant are subjected to structural optimization by the CASTEP module.
[0044] Since the CASTEP module can only calculate periodic structures, and the flocculant molecules are often not periodic structures, in order to successfully perform the calculation, an AC module is used to create a unit cell, and the flocculant molecules are placed in the unit cell for optimization to obtain the lowest energy configuration. The specific steps include:
[0045] An empty unit cell is created, the PAM molecule is placed in the empty unit cell, the precision is set to “Fine” for structural optimization, the SiO2 surface geometry optimization precision is set to “Fine”, and then the K points and Ecut are adjusted to the results obtained by the convergence test, which are 4*4*3 and 450eV. The optimal configurations of the SiO2 surface and the PAM molecule are obtained respectively. The CMC molecule is obtained in the same way as the optimal configuration of the SiO2 surface.
[0046] S3. The adsorption site of the flocculant is determined on the optimized fine particle surface model.
[0047] The SetA surface structure and the Set B flocculant molecular structure are established respectively, and the corresponding force field is set. The adsorption site simulation is performed in the form of SetA and Set B. The specific steps include:
[0048] Since the classical force field is suitable for the SiO2 surface, the classical force field COMPASS III is directly used. The charges of all atoms under the COMPASS III force field are calculated. The force field and the charge are checked to confirm that they are correct after being modified. The Charge in the Set up tab is changed to “use current”, and the precision is selected as “Fine”. In this embodiment, the Adsorption Locator tool is selected, it is ensured that the surface file is an active document, and the adsorbed molecule is selected in the Adsorbate drop-down list. Then the surface is defined, the surface atoms are selected, and are added to the TartgetAtoms collection for adsorption calculation.
[0049] The SiO2 surface model is set as SetA, and the PAM molecule or the CMC molecule is set as Set B.
[0050] S4. Put the optimized flocculant molecule model on the adsorption site, and optimize the structure as a whole; and calculate the binding energy, complete the optimization.
[0051] The nature of the calculation of the binding energy is also calculated in the form of SetA and Set B. Copy three copies of the overall configuration after the structure optimization. One deletes SetA, and the other deletes SetB. Calculate the single-point energy of the three configurations respectively. The specific steps include:
[0052] Put the PAM molecule on the adsorption site on the surface of SiO2, and optimize the structure. The obtained adsorption configuration is as shown in Figure 5 Copy three copies. One deletes SetA SiO2 surface, and the other deletes Set B PAM molecule. Calculate the single-point energy of the three configurations respectively, and substitute the results into the formula of the binding energy. The binding energy can reflect the strength of the flocculant flocculation ability to a certain extent. The binding energy calculation formula is as follows:
[0053] ΔE = E total - E surface - E molecule
[0054] In the formula, ΔE represents the binding energy; E total represents the single-point energy of the whole; E surface represents the single-point energy of the surface model; E molecule represents the single-point energy of the flocculant molecule. The binding energy of the PAM molecule and the surface of SiO2 is-2.5698eV.
[0055] At the same time, through the same method as described above, the binding energy of the CMC molecule and the surface of SiO2 is-3.9821eV, wherein the adsorption configuration of the CMC molecule and SiO2 is as shown in Figure 6 . It is shown that the CMC flocculant has higher flocculation ability on the surface of SiO2 than the PAM flocculant, which is consistent with the experimental results. The results are displayed by Analysis through the property of CASTEP module, and the density of states diagram before and after the adsorption of silicon dioxide is obtained, as shown in Figure 7 . The adsorption mechanism of the flocculant can be obtained from the density of states diagram.
[0056] Example Two
[0057] As shown in Figure 8As shown, the system structure schematic diagram of the embodiment of the application includes a construction module, an optimization module, a positioning module and a combination module. The construction module is configured to construct a fine particulate matter surface model and a flocculant molecule model. The optimization module is configured to perform structure optimization of the fine particulate matter surface model and the flocculant molecule model by using a CASTEP module. The positioning module is configured to determine an adsorption point of the flocculant on the optimized fine particulate matter surface model by using an Adsorption Locator. The combination module is configured to place the optimized flocculant molecule model on the adsorption point and perform overall structure optimization, and calculate the binding energy to complete the optimization.
[0058] The application will be described in detail below with reference to the embodiment to solve the technical problems in actual life.
[0059] The construction module is used to construct a fine particulate matter surface model and a flocculant molecule model.
[0060] In the embodiment, the construction module uses the functions of cleaving a crystal surface and a supercell to construct a suitable fine particulate matter surface model. Before cleaving the crystal cell, convergence test is performed to determine the K points and E-cut for structure optimization. When cleaving the crystal cell, the crystal surface to be cleaved, the size of the supercell after cleavage, the thickness of the vacuum layer and the like are determined.
[0061] Then, the SiO2 crystal cell is subjected to convergence test. The Ecut / K points are set from low to high under the condition of fixed K points / Ecut, and the calculated value of the single-point energy is calculated. The single-point energy calculation is to change the Task option to Energy after setting the above precision, and then point calculate. The energy value can be found in the document after the calculation is completed.
[0062] Then, all the single-point energies are recorded, and the values of K points / Ecut corresponding to E<0.01eV are taken, such as Figure 2 The K points and Ecut obtained by the convergence test are used as the parameters for structure optimization of the atomic positions and the crystal cell parameters. In this example, the K points and Ecut are 4*4*3 and 450eV. The cleaved surface of the structure-optimized crystal cell model is subjected to supercell 4*4. Because the surface is a two-dimensional structure, a vacuum layer needs to be added to convert it into a three-dimensional crystal cell. In order to make the upper and lower surfaces not interfere with each other, the thickness of the vacuum layer is taken as 15 angstroms. In order to make the model closer to the characteristics of the surface model in reality, the atoms in the lower layer are fixed, and the silicon dioxide surface model is obtained as shown in Figure 3 .
[0063] After the fine particulate matter surface model is constructed, the construction module uses the function of polymerization to construct a flocculant molecule model.
[0064] The polymerization degree, molecular chain number and polymerization rule are set in advance when constructing the polymer flocculant. The obtained polymer structure is optimized. A molecular unit of PAM is constructed, and the head and tail hydrogen atoms are labeled. In order to make the PAM molecules in the periodic structure not interact with each other, and to make the molecular mass close to that of the CMC molecule, the polymerization degree is set to 3, and the chain number is set to 1, as shown in Figure 4 The CMC unit molecule is constructed in the same way.
[0065] The structure optimization of the fine particulate matter surface model and the flocculant molecular model is performed by using the optimization module.
[0066] Since the CASTEP module can only calculate periodic structures, and the flocculant molecules are often not periodic structures, in order to successfully perform the calculation, the AC module is used to create a unit cell, and the flocculant molecules are placed in the unit cell for optimization to obtain the lowest energy configuration. The specific process includes:
[0067] An empty unit cell is created, the PAM molecule is placed in the empty unit cell, the precision is set to “Fine” for structure optimization, the SiO2 surface geometry optimization precision is set to “Fine”, and then the K points and Ecut are adjusted to the results obtained by the convergence test, which are 4*4*3 and 450eV. The optimal configurations of the SiO2 surface and the PAM molecule are obtained respectively. The CMC molecule is obtained in the same way as the optimal configuration of the SiO2 surface.
[0068] Then, the adsorption site of the flocculant on the optimized fine particulate matter surface model is located by the positioning module.
[0069] The SetA surface structure and the Set B flocculant molecular structure are established respectively, and the corresponding force field is set. The adsorption site simulation is performed in the form of SetA and Set B. The specific process includes:
[0070] Since the classical force field is suitable for the SiO2 surface, the classical force field COMPASS III is directly used. The charges of all atoms under the COMPASS III force field are calculated. The force field and the charge are checked to confirm that they are correct. The Charge in the Set up tab is changed to “use current”, and the precision is selected as “Fine”. The AdsorptionLocator tool is selected, and it is ensured that the surface file is an active document. The adsorbed molecule is selected in the Adsorbate drop-down list. Then, the surface is defined, the surface atoms are selected, and they are added to the TartgetAtoms collection for adsorption calculation.
[0071] The SiO2 surface model is set as SetA, and the PAM molecule or the CMC molecule is set as Set B.
[0072] The module combines the optimized agglomerant molecular model with the adsorption sites to perform overall structural optimization; and calculates the binding energy to complete the optimal selection.
[0073] The properties of the binding energy are calculated using SetA and Set B. Three copies of the overall configuration after structural optimization are made. SetA is deleted from one copy, and SetB is deleted from the other. Single-point energies are then calculated for each of the three configurations. The specific process includes:
[0074] PAM molecules were placed at adsorption sites on the SiO2 surface for structural optimization. The resulting adsorption configuration is shown below. Figure 5 Make three copies. In one copy, remove the SiO2 surface from SetA; in the other, remove the PAM molecules from Set B. Calculate the single-point energy for each of the three configurations and substitute the results into the formula for binding energy. Binding energy can reflect the strength of the agglomerating ability of the agglomerant to a certain extent. The formula for calculating binding energy is as follows:
[0075] ΔE=E total -E surface -E molecule
[0076] In the formula, ΔE represents the binding energy; E total E represents the energy at a single point in the whole system. surface E represents the single-point energy of the surface modulus. molecule This represents the single-point energy of the agglomerating agent molecule. The binding energy between PAM molecules and the SiO2 surface is found to be -2.5698 eV.
[0077] Meanwhile, using the same method described above, the binding energy between CMC molecules and the SiO2 surface was found to be -3.9821 eV, where the adsorption configuration of CMC molecules and SiO2 is as follows. Figure 6 As shown, this indicates that CMC agglomerates have a higher agglomerating ability on SiO2 surfaces than PAM agglomerates, consistent with experimental results. The density of states diagrams before and after silica adsorption were obtained using the properties calculated by the CASTEP module and presented through Analysis, as shown below. Figure 7 The adsorption mechanism of this agglomerant can be obtained through the density of states diagram.
[0078] The embodiments described above are merely preferred embodiments of this application and are not intended to limit the scope of this application. Any modifications and improvements made to the technical solutions of this application by those skilled in the art without departing from the spirit of this application shall fall within the protection scope defined by the claims of this application.
Claims
1. A method for selecting agglomerants based on molecular simulation, characterized in that the steps include... include: Construct a surface model of fine particulate matter and a molecular model of agglomerants; The CASTEP module is used to optimize the structure of the fine particulate matter surface model and the agglomerator molecule model. The method includes: creating a unit cell using the AC module; placing the agglomerator molecule into the unit cell for optimization to obtain the configuration with the lowest energy, thus completing the structural optimization of the CASTEP module. The adsorption sites of agglomerates are determined on the optimized surface model of the fine particulate matter by: establishing the surface structure SetA and the agglomerate molecular structure SetB respectively and setting the force field; simulating the adsorption sites in the form of SetA and SetB to determine the adsorption sites; The optimized agglomerator molecule model is placed at the adsorption sites for overall structural optimization. The binding energy is then calculated to complete the selection. The steps include: placing PAM molecules at adsorption sites on the SiO2 surface for structural optimization; copying the resulting adsorption configuration three times, deleting the SiO2 surface from one copy and the PAM molecules from the other copy; calculating the single-point energy for each of the three configurations; and substituting the results into the formula for binding energy. In the formula, Indicates binding energy; Represents the energy at a single point in the whole system; This represents the single-point energy of the surface modulus; This represents the single-point energy of an agglomerating agent molecule.
2. The method for selecting agglomerators based on molecular simulation according to claim 1, characterized in that, The surface model of the fine particles is constructed using the crystal cutting plane and supercell function; before cutting the unit cell, a convergence test should be performed to determine the K-point and E-cut during structure optimization; and when cutting the unit cell, the pre-set crystal cutting plane, the size after the supercell, and the thickness of the vacuum layer should be determined.
3. The method for selecting agglomerators based on molecular simulation according to claim 2, characterized in that, During the cell cutting process, the severed metal-nonmetal bonds are repaired.
4. A molecular simulation-based agglomerator selection system, said system for implementing the method according to any one of claims 1-3, characterized in that, include: Module construction, module optimization, module positioning, and module integration; The building module is used to construct a surface model of fine particulate matter and a molecular model of agglomerants. The optimization module is used to perform structural optimization of the fine particulate matter surface model and the agglomerator molecular model using the CASTEP module. The positioning module is used to determine the adsorption sites of the agglomerant on the optimized surface model of the fine particulate matter; The binding module is used to place the optimized agglomerator molecular model at the adsorption site for overall structural optimization; and to calculate the binding energy to complete the selection.