CN108118396A - A kind of method that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake - Google Patents

A kind of method that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake Download PDF

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CN108118396A
CN108118396A CN201711421597.7A CN201711421597A CN108118396A CN 108118396 A CN108118396 A CN 108118396A CN 201711421597 A CN201711421597 A CN 201711421597A CN 108118396 A CN108118396 A CN 108118396A
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bittern
mrow
salt lake
magnesium sulfate
prepared
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吴健松
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Lingnan Normal University
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    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B29/00—Single crystals or homogeneous polycrystalline material with defined structure characterised by the material or by their shape
    • C30B29/10—Inorganic compounds or compositions
    • C30B29/46—Sulfur-, selenium- or tellurium-containing compounds
    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B29/00—Single crystals or homogeneous polycrystalline material with defined structure characterised by the material or by their shape
    • C30B29/60—Single crystals or homogeneous polycrystalline material with defined structure characterised by the material or by their shape characterised by shape
    • C30B29/62—Whiskers or needles
    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B7/00—Single-crystal growth from solutions using solvents which are liquid at normal temperature, e.g. aqueous solutions
    • C30B7/10—Single-crystal growth from solutions using solvents which are liquid at normal temperature, e.g. aqueous solutions by application of pressure, e.g. hydrothermal processes
    • 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

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  • Engineering & Computer Science (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Materials Engineering (AREA)
  • Metallurgy (AREA)
  • Organic Chemistry (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Inorganic Chemistry (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Compounds Of Alkaline-Earth Elements, Aluminum Or Rare-Earth Metals (AREA)

Abstract

The invention discloses a kind of methods that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake, include the following steps：S1., multigroup experimental data that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake is used for the BP neural network that PSO is trained to optimize, the experimental data to include preparation parameter and experimental result, and the group number of experimental data is more than or equal to 100 groups；S2. the BP neural network of the PSO optimizations in S1. after training can provide optimization preparation parameter, prediction whisker ratio with the physical and chemical properties change of bittern in salt lake；Using the optimization preparation parameter, alkali magnesium sulfate crystal whisker is prepared.Experimental data is realized into digital intellectualization using PSO Optimized BP Neural Networks, whisker can be produced for bittern at any time, optimal experimental program and Accurate Prediction experimental result are provided, greatly reduced and substantial amounts of human and material resources and the wasting of resources are brought due to groping property is tested.

Description

A kind of method that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake
Technical field
The present invention relates to technical field of preparation for inorganic material, and alkali formula is prepared using bittern in salt lake more particularly, to a kind of The method of magnesium sulfate crystal whisker.
Background technology
Alkali magnesium sulfate crystal whisker is that a kind of novel inorganic is fire-retardant, reinforcing fiber materials, in mono-crystalline structures.With composite plastic phase Than there is apparent enhancing, increase firm, fire retardation.It can make product that there is high deformation temperature, the bright and clean beauty of article surface reduces The proportion of product, and asepsis environment-protecting.Small special mono-crystlling fibre structure, particularly suitable for ultra-thin product and micro part Reinforcement increases just.
The method for preparing alkali magnesium sulfate crystal whisker common are hydro-thermal method, when this method is applied to bittern in salt lake, it is necessary to multiple Miscellaneous removal step.《Inorganic chemicals industry》53-55 pages of 12 phase of volume 47 discloses a kind of bittern in salt lake and directly passes through NaOH/ Na2SO4The method that mixed solution prepares alkali magnesium sulfate crystal whisker.
However, in practical applications, alkali magnesium sulfate crystal whisker is prepared from bittern in salt lake, whenever the physical chemistry of bittern Matter changes once, will do a large amount of numerous and diverse groping property and test to adapt to new condition, consume substantial amounts of human and material resources.
The content of the invention
The present invention is the defects of overcoming described in the above-mentioned prior art, provides a kind of utilization bittern in salt lake and prepares alkali magnesium sulfate The method of whisker.
In order to solve the above technical problems, the technical solution adopted by the present invention is：
A kind of method that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake, is included the following steps：
S1., multigroup experimental data that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake is used for the BP that PSO is trained to optimize Neutral net, the experimental data include preparation parameter and experimental result, and the group number of experimental data is more than or equal to 100 groups；
The mathematical model of the PSO is：
vij(t+1)=vij(t)+c1r1j(t)[pij(t)-xij(t)]+c2r2j(t)[gj(t)-xij(t)] (1)
xij(t+1)=xij(t)+vij(t+1) (2)
In formula (1) and formula (2), j is that the jth of particle is tieed up, and i is i-th of particle；t：Represent current evolutionary generation；vij(t) it is hidden Containing i-th of node of layer to the weights between j-th of node；pij(t) particle i passes through the position of j；gij(t) population i is passed through Cross the position of j；xij(t) current particle i is in the position of j；c1And c2All it is the restriction factor of change in displacement, is preset value；R be with Machine function；
PSO Optimized BP Neural Networks are to make (3) formula J values minimum, and J is mean square deviation index；
In formula (3), N is training sample sum,It is the target output value of j-th of neurode of i-th of sample, yj,i It is the real output value of j-th of neurode of i-th of sample, M is the number of neuron；
S2. the BP neural network of the PSO optimizations in S1. after training can become with the physicochemical property of bittern in salt lake Change and provide optimization preparation parameter, prognostic experiment result；Using the optimization preparation parameter, alkali magnesium sulfate crystalline substance is prepared Palpus.
c1And c2All it is the restriction factor of change in displacement, is preset value, usual value is 2.
The process of the BP neural network of training PSO optimizations is as shown in Figure 3.
The preparation experimental implementation that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake can refer to the prior art and obtain, Yi Jixiang Corresponding preparation parameter also can refer to the prior art and obtain.
BP (Back Propagation) neutral net is a kind of Multi-layered Feedforward Networks trained by Back Propagation Algorithm, It is one of current most widely used neural network model, it can learn and store substantial amounts of input-output mode map relation, The math equation of this mapping relations is described without disclosing in advance, there is stronger adaptivity, learning ability and extensive Computation capability.But it is used alone that BP neural network sample size in need is big, convergence rate is slow, generalization ability is weak etc. no Foot, for this purpose, being re-introduced into particle cluster algorithm (Particle SwarmOptimization, PSO) with Optimized BP Neural Network.PSO It is a kind of new evolution algorithm developed in recent years, is the quality that solution is evaluated by fitness, has and realize easy, essence The advantages that degree is high, convergence is fast, the BP neural network abbreviation PSO-BP neutral nets of PSO optimizations, PSO-BP neutral nets error is more Small, accuracy higher.
Experimental data is realized into digital intellectualization using PSO Optimized BP Neural Networks, can be " from complicated and changeable at any time Alkali magnesium sulfate crystal whisker is produced in bittern " problem solving is carried out, after experimental data intelligence, whisker can be produced for bittern at any time and carried For optimal experimental program and Accurate Prediction experimental result, without expending substantial amounts of manpower and materials.
Preferably, the preparation parameter includes magnesium ion concentration, bittern in salt lake volume, Na2CO3-NaHCO3Buffering liquid Product, NaOH/Na2SO4The concentration of mixed base, NaOH/Na2SO4The volume of mixed base, reaction temperature, reaction time.
Alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake, and Na may be employed2CO3-NaHCO3Buffer solution, NaOH/Na2SO4It is mixed Alkali is closed, the experimental implementation and preparation parameter of this method can refer to the prior art and obtain.
Preferably, the experimental result includes whisker ratio.
Preferably, the physicochemical property includes the magnesium ion concentration of bittern in salt lake.
Preferably, the algorithm flow of the BP neural network of the PSO optimizations is：Determine network topology structure, then initially Then BP neural network weight threshold length obtains optimal power threshold value, then calculation error, then weight threshold updates, if Meet termination condition, if then simulation and prediction is obtained as a result, termination condition is unsatisfactory for, back to calculation error, until meeting Termination condition, final simulation and prediction obtain result；The acquisition best initial weights threshold value is obtained using cluster ion algorithm, the ion The flow of group's algorithm is that ion and speed initialization, then particle fitness value calculation, then looks for individual extreme value and group pole It is worth, then speed update and location updating, then ion fitness value calculation, then individual extreme value and group's extreme value, if full Sufficient condition then obtains optimal power threshold value, if being unsatisfactory for condition, back to speed update and location updating, until meeting item Part obtains best initial weights threshold value.
The data group number is more, and the prognostic experiment result of the BP neural network of PSO optimizations is more accurate.
Compared with prior art, the beneficial effects of the invention are as follows：
The present invention's realizes digital intellectualization using PSO Optimized BP Neural Networks by experimental data, can be given birth at any time for bittern It produces whisker and optimal experimental program and Accurate Prediction experimental result is provided, greatly reduce and brought due to groping property is tested largely Human and material resources and the wasting of resources.
Description of the drawings
Fig. 1 is PSO Optimized BP Neural Network algorithm flow schematic diagrames.
Fig. 2 is the preparation parameter training PSO-BP neutral net signals that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake Figure.
Fig. 3 is the training process of PSO-BP neutral nets.
Specific embodiment
The present invention is further illustrated With reference to embodiment.
(1) experiment problem solves explanation：
It is briefly described below by way of table 1.Table 1 gives 5 groups of experimental datas, wherein the I, the II, III group is the reality done It tests and gives preparation parameter and experimental result, it is unknown that the IVth and V group, which has multiple parameters (or result), and unknown-value is equal It is marked with question mark.After I, the II, III group of data is trained BP neural network, training data group is The more the better, and BP neural network is just There is the artificial intelligence of " how to optimize and prepare whisker scheme and make the result optimal ", can instruct the IV, the V group of experiment should be why It does.For example, in the Vth group, it is known that bittern magnesium ion concentration be 3.2mol/L, volume 400.0mL, existing alkali it is dense It spends for 1.0mol/L, bittern temperature is 19 DEG C, it is assumed that prepare the whisker that ratio is 93%, may I ask other preparation parameters should It is how manyAssume again after being tested by the parameter provided, as a result ratio only has 90%, what main cause isTackle parameter What kind of adjustment madeAll these problems can all allow this intelligence " expert " of PSO-BP neutral nets to be answered.
1 PSO-BP Neural Networks Solution examples of table
(2) PSO-BP neural network algorithms flow in embodiment：As shown in Figure 1, the algorithm of the BP neural network of PSO optimizations Flow is：It determines network topology structure, then initial BP neural network weight threshold length, then obtains optimal power threshold value, so Calculation error afterwards, then weight threshold update, if meeting termination condition, if simulation and prediction is obtained as a result, being unsatisfactory for tying Beam condition, then back to calculation error, until meeting termination condition, final simulation and prediction obtains result；Wherein obtain optimal power Value threshold value is obtained using cluster ion algorithm, and the flow of the cluster ion algorithm is ion and speed initialization, and then particle adapts to Angle value calculates, and then looks for individual extreme value and group's extreme value, then speed update and location updating, then ion fitness value meter It calculates, then individual extreme value and group's extreme value, if meeting condition, obtain optimal power threshold value, if being unsatisfactory for condition, return To speed update and location updating, until meeting condition, best initial weights threshold value is obtained.
(3) for whisker ratio using flying-spot microscope observation estimation, result is mass fraction in embodiment；It is tested by XRD It is target product to confirm the whisker being prepared.
Embodiment
The method that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake of the present embodiment, is included the following steps：
S1. alkali magnesium sulfate crystal whisker is prepared, and records data,
(1) Cha Er Han Salt Lake bittern 500mL is taken, filter and discards insoluble matter；Its magnesium ion concentration is measured as 2.4mol/L；
(2) Na of 2.00mol/L is prepared2SO4The NaOH solution of solution and 2.00mol/L, then by bodies such as both solution Product mixing is as precipitating reagent, after isometric mixing, Na2SO4Concentration for 1.00mol/L, the concentration of NaOH is 1.00mol/L； Na2SO4Concentration it is identical with the concentration of NaOH；NaOH/Na2SO4The value of the concentration of mixed base is the concentration of NaOH；
(3) Na is added in bittern2CO3-NaHCO3Buffer solution volume 5mL；
(4) above-mentioned bittern solution 200mL is taken, is placed in the beaker of 1.00L, is then added dropwise to precipitation slowly under stirring Agent 48mL, finishes, and mixed system is placed in the water-bath that water temperature is 15 DEG C and continues to be aged 30h；PH value is 9.20；
(5) filter, while precipitation is washed with water and collects precipitation；(T=80 ± 3 DEG C, t=24h) drying is placed in baking oven, Up to alkali magnesium sulfate crystal whisker, the whisker ratio in product that detects is 92%；
S2. the bittern in salt lake volume of change S1, magnesium ion concentration, Na2CO3-NaHCO3Buffer solution volume, NaOH/Na2SO4 The volume of mixed base, NaOH/Na2SO4One or more of the concentration of mixed base, reaction temperature, reaction time, prepare alkali formula Magnesium sulfate crystal whisker, the above experiment of repetition 100 times, obtains 100 groups of data；
S3. PSO-BP neutral nets, training PSO-BP neutral nets and then by this 100 groups of data are inputted；
S4. when the magnesium ion concentration of bittern in salt lake changes, after inputting reaction condition data, PSO-BP neutral net energy Enough predict whisker ratio, most obtain taking an excellent data group at last, this group of data be exactly it is optimal in chemical technology, with The data of this optimization of Main Basiss produce to enter in production afterwards, prepare alkali magnesium sulfate crystal whisker.
Data of training PSO-BP neutral nets and the results are shown in Table 2 in the present embodiment, will be by the height of whisker ratio It with digital representation, is represented respectively with number 1,2,3,4,5 from low to high, trained purpose is exactly to make real output value infinite close Desired output (desired output is exactly the result actually tested), once achieving the goal, training terminates.Contrast table 2 Middle desired output and real output value, it is seen that the two is sufficiently close to, error very little.5 groups of data are only enumerated in table, other training Data are not shown in the table.
After completing training PSO-BP neutral nets, when the magnesium ion concentration of bittern in salt lake changes, input reaction After condition data, PSO-BP neutral nets can predict whisker ratio, most obtain taking an excellent data group at last, i.e., optimal Preparation parameter is produced according to the preparation parameter, prepares alkali magnesium sulfate crystal whisker, can be greatly reduced due to groping property is tested Bring substantial amounts of human and material resources and the wasting of resources.
2 PSO-BP neural metwork trainings of table
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention Protection domain within.

Claims (4)

  1. A kind of 1. method that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake, which is characterized in that include the following steps：
    S1. multigroup experimental data that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake is used for the BP that PSO is trained to optimize nerves Network, the experimental data include preparation parameter and experimental result, and the group number of experimental data is more than or equal to 100 groups；
    The mathematical model of the PSO is：
    vij(t+1)=vij(t)+c1r1j(t)[pij(t)-xij(t)]+c2r2j(t)[gj(t)-xij(t)] (1)
    xij(t+1)=xij(t)+vij(t+1) (2)
    In formula (1) and formula (2), j is that the jth of particle is tieed up, and i is i-th of particle；t：Represent current evolutionary generation；vij(t) hidden layer I-th of node is to the weights between j-th of node；pij(t) particle i passes through the position of j；gij(t) population i passes through j's Position；xij(t) current particle i is in the position of j；c1And c2All it is the restriction factor of change in displacement, is preset value；R is random letter Number；
    The PSO Optimized BP Neural Networks are that the J values in formula (3) are minimum, and J is mean square deviation index；
    <mrow> <mi>J</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msup> <mrow> <mo>(</mo> <msubsup> <mi>y</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>i</mi> </mrow> <mi>d</mi> </msubsup> <mo>-</mo> <msub> <mi>y</mi> <mrow> <mi>j</mi> <mo>,</mo> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
    In formula (3), N is training sample sum, and M is the number of neuron,It is the mesh of j-th of neurode of i-th of sample Mark output valve, yj,iIt is the real output value of j-th of neurode of i-th of sample；
    S2. in S1. training after the PSO optimization BP neural network can with the physical and chemical properties change of bittern in salt lake and Provide optimization preparation parameter, prognostic experiment result；Using the optimization preparation parameter, alkali magnesium sulfate crystal whisker is prepared.
  2. 2. the method according to claim 1 that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake, which is characterized in that described Preparation parameter includes magnesium ion concentration, bittern in salt lake volume, Na2CO3-NaHCO3Buffer solution volume, NaOH/Na2SO4Mixed base Concentration, NaOH/Na2SO4The volume of mixed base, reaction temperature, reaction time.
  3. 3. the method according to claim 1 that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake, which is characterized in that described Experimental result includes whisker ratio.
  4. 4. the method according to claim 1 that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake, which is characterized in that described Physicochemical property includes the magnesium ion concentration of bittern in salt lake.
CN201711421597.7A 2017-12-25 2017-12-25 A kind of method that alkali magnesium sulfate crystal whisker is prepared using bittern in salt lake Withdrawn CN108118396A (en)

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CN110372017A (en) * 2019-08-30 2019-10-25 河北工业大学 Bittern mixes the separation method that alkaline process prepares basic magnesium carbonate and its natrium potassium salt

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Publication number Priority date Publication date Assignee Title
CN110372017A (en) * 2019-08-30 2019-10-25 河北工业大学 Bittern mixes the separation method that alkaline process prepares basic magnesium carbonate and its natrium potassium salt
CN110372017B (en) * 2019-08-30 2021-11-09 河北工业大学 Separation method for preparing basic magnesium carbonate and sodium-potassium salt thereof by bittern mixed alkali method

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