CN112940433A - Environment-friendly degradable plastic material and preparation method thereof - Google Patents

Environment-friendly degradable plastic material and preparation method thereof Download PDF

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CN112940433A
CN112940433A CN202110352091.5A CN202110352091A CN112940433A CN 112940433 A CN112940433 A CN 112940433A CN 202110352091 A CN202110352091 A CN 202110352091A CN 112940433 A CN112940433 A CN 112940433A
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plastic material
degradable plastic
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黄伟忠
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Dongshen New Material Technology Shenzhen Co ltd
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    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
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    • C08L51/00—Compositions of graft polymers in which the grafted component is obtained by reactions only involving carbon-to-carbon unsaturated bonds; Compositions of derivatives of such polymers
    • C08L51/06—Compositions of graft polymers in which the grafted component is obtained by reactions only involving carbon-to-carbon unsaturated bonds; Compositions of derivatives of such polymers grafted on to homopolymers or copolymers of aliphatic hydrocarbons containing only one carbon-to-carbon double bond
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    • C08L—COMPOSITIONS OF MACROMOLECULAR COMPOUNDS
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Abstract

The invention discloses an environment-friendly degradable plastic material and a preparation method thereof, and relates to the technical field of packaging materials. The environment-friendly degradable plastic material comprises, by mass, 30-50 parts of maleic anhydride grafted polypropylene, 3-10 parts of corn straw fiber, 3-10 parts of wood fiber and wheat straw fiber, 2-5 parts of acetate starch, 3-8 parts of cyano functionalized graphene, 3-10 parts of a plasticizer, 0.5-1 part of an antioxidant, 0.5-0.1 part of a dispersant, 0.2-0.3 part of a cross-linking agent and 0.5-1 part of a flame retardant. The invention can effectively ensure the degradation of the product, simultaneously improve the mechanical effect and the high temperature resistance of the product, effectively improve the degradation efficiency of the product, does not generate pollution components during degradation, is safe and environment-friendly, and has high degradation rate, and the degradation rate of the environment-friendly degradable plastic in 15 days is 92.3-93.9 percent through determination.

Description

Environment-friendly degradable plastic material and preparation method thereof
Technical Field
The invention belongs to the technical field of packaging materials, and particularly relates to an environment-friendly degradable plastic material and a preparation method thereof.
Background
At present, in the existing market of China, plastic products made of polyethylene or polypropylene as main materials are various in types, very wide in application range and large in usage amount, but the phenomenon that the waste plastic products are discarded randomly after being used is serious, the recycling rate is low, the plastic products are difficult to decay and decompose after being discarded in soil for 200 years, and the plastic products can be slowly dissolved and release substances harmful to human bodies when being subjected to high temperature or corrosion. Therefore, the ubiquitous waste plastic bags and bottles are gradually destroying our green home and eroding our life health. In recent years, although the state vigorously develops degradable materials to replace the traditional plastic materials, the toughness and the bearable strength of the degradable plastics are far from the traditional plastics, so that the degradable plastics have the problems of longer degradation period, limited degradation degree and the like,
the use rate of degradable plastics in the market today is therefore still low. The formula and the processing technology of the degradable plastic are both required to be further explored and researched so as to improve the toughness and the bearing strength of the degradable plastic, improve the degradation degree and shorten the degradation period.
The prior art provides an environment-friendly degradable plastic, which comprises the following components in parts by mass: 50-60 parts of polypropylene, 5-10 parts of plant starch, 5-8 parts of acetate starch, 5-10 parts of graphene powder, 10-15 parts of calcium carbonate, 5-8 parts of a plasticizer, 0.8-1 part of an antioxidant and 0.4-0.6 part of an ultraviolet absorber.
However, the degradation period of the degradable plastics in the above patent is not effectively improved.
Furthermore, plastics are light in weight, stable in chemical property, free from corrosion, good in impact resistance and other excellent characteristics to replace most basic materials, and become common raw materials for people's life. In the face of increasingly serious white pollution, people hope to find a plastic substitute which can replace the performance of the existing plastic and does not cause white pollution, and degradable plastic is produced at the same time.
The prior art provides an environment-friendly degradable plastic and a production process thereof, (1) polylactide, polybutylene succinate and polyvinyl chloride are mixed as a base material and are mixed with grafted starch to prepare the plastic, so that the mechanical effect and the high-temperature resistance of a product can be effectively improved while the product is effectively degraded, substances such as sodium bismuthate, polyaluminium chloride and the like are added, the degradation efficiency of the product can be effectively improved, no pollution component is generated during degradation, the environment-friendly degradable plastic is safe and environment-friendly, the degradation rate is high, the 25-day degradation rate of the environment-friendly degradable plastic is 99.3-99.9% as determined by GB/T20197-2006, and the technical problems of low degradation rate of waste plastics and certain pollution in the prior art are solved; (2) the method comprises the steps of putting backing materials into an extrusion cylinder from a feeding port on a feeding pipe on extrusion equipment, enabling the backing materials to enter the extrusion cylinder through the feeding pipe, driving a screw rod by a first motor, driving an annular scraper to ascend and descend in the feeding pipe through a connecting rod by the aid of a screw rod matched with a screw rod connecting block, scraping the residual backing materials on the inner wall of the feeding pipe by the annular scraper, enabling the scraped backing materials to fall into the extrusion cylinder, driving a central wheel to rotate by a second motor, further enabling three planet wheels to rotate in an inner fluted disc, driving the extrusion screws to rotate by spline shafts, enabling the three extrusion screws to rotate along the circumference while rotating in the extrusion cylinder, enabling extruded materials to be extruded from extrusion ports by the three extrusion screws, driving a grain cutting rod to rotate by the aid of the grain cutting rod matched with a cutter holder, enabling the cutting blade to, effectively strike off the remaining bed charge of pan feeding intraductal wall, avoid some bed charges not extruded and cause the extravagant condition at pan feeding intraductal wall, through the structure setting of planet wheel simultaneously, three screw rod of extruding is in the barrel of extruding along the circumference rotation when self rotation is extruded the bed charge, effectively improves the mixed effect of bed charge, has more efficient and extrudes the effect. (3) The degradable plastic master batches fall into the conveying pipe and enter the cooling box through the conveying pipe, the circulating pump is matched with the cooling oil box to circulate cooling oil in the circulating pipe through the connecting pipe, the fourth motor drives the circulating pipe to rotate through the matching of the two gears which are meshed and connected, the circulating pipe is used for stirring and cooling the degradable plastic master batches in the cooling box, the degradable plastic master batches are taken out after cooling, then the master batches are placed in an injection molding machine, high-temperature pressure-maintaining injection molding is adopted to obtain the environment-friendly degradable plastic, the extrusion equipment effectively circulates the cooling oil through the circulating pipe, meanwhile, the circulating pipe rotates, the prepared degradable plastic master batches are effectively and quickly cooled, and the situation that the master batches are mutually bonded in the cooling process is avoided.
And provides an environment-friendly degradable plastic which is prepared from the following raw materials in parts by weight: 40-50 parts of polylactide, 12-14 parts of polybutylene succinate, 4-8 parts of polyvinyl chloride, 4-10 parts of plant fiber, 30-40 parts of grafted starch, 1-3 parts of calcium oxide, 1-3 parts of oligosaccharide, 1-1.6 parts of nano silicon dioxide, 1-2 parts of polyaluminum chloride, 0.6-1.6 parts of sodium bismuthate, 0.4-0.8 part of dispersant, 0.2-0.5 part of cross-linking agent and 1-1.2 parts of plasticizer.
The technical drawback of the above patent is that: the preparation method is complicated, and the mechanical effect and the high temperature resistance of the product are poor.
Moreover, in the production of degradable plastics, harmful gases cannot be effectively treated; in the prior art, a gas collecting hood and a fan are used for sucking and sending the gas into a spray tower plasma photo-oxidation activated carbon device for treatment, wherein harmful cracking gases such as aromatic hydrocarbon, cyclane and the like are easy to cause combustion and explosion of the plasma device, and the tail gas can not be completely and effectively treated, and most of the tail gas is still discharged and can not reach the standard.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) compared with the traditional plastics, the toughness and the bearable strength of the degradable plastics in the prior art have great difference, the degradation period of the degradable plastics is longer, the degradation degree is limited and the like.
(2) The preparation method in the prior art has complex process and high cost.
(3) The existing degradable plastics only pay attention to the degradation rate, but neglect the relevant performance of the product, so that the practical application of the product can not be considered.
Disclosure of Invention
In order to overcome the problems in the related art, the disclosed embodiment of the invention provides an environment-friendly degradable plastic material and a preparation method thereof. The technical scheme is as follows:
according to the first aspect of the disclosed embodiment of the invention, the environment-friendly degradable plastic material is prepared from, by mass, 30-50 parts of maleic anhydride grafted polypropylene, 3-10 parts of corn straw fibers, 3-10 parts of wood fibers and wheat straw fibers, 2-5 parts of acetate starch, 3-8 parts of cyano-functionalized graphene, 3-10 parts of a plasticizer, 0.5-1 part of an antioxidant, 0.5-0.1 part of a dispersant, 0.2-0.3 part of a cross-linking agent and 0.5-1 part of a flame retardant.
In one embodiment of the invention, the flame retardant is HBCD heat stable flame retardant; chemically grafting graphene oxide by using 4-aminophenoxy phthalonitrile to obtain cyano-functionalized graphene;
the method for obtaining the cyano-functionalized graphene comprises the following steps: adding graphene oxide into a thionyl chloride solution, performing ultrasonic stirring, and heating and refluxing at 75 ℃ under the catalytic action of N, N-dimethylformamide for reacting for 18 hours; after the reaction is finished, removing thionyl chloride in the system by utilizing toluene reflux, and drying the obtained black reaction product in vacuum to obtain acyl chlorinated graphene; adding excessive 4-aminophenoxy phthalonitrile and acyl chloride graphene into a toluene solvent, and stirring and refluxing at 28 ℃ for reaction for 48 hours; and washing, centrifugally separating and vacuum drying the reaction product for multiple times to obtain the cyano-functionalized graphene.
According to a second aspect of the disclosed embodiment of the present invention, there is provided a method for preparing an environmentally friendly degradable plastic material, comprising:
step 1, determining design parameters of an environment-friendly degradable plastic material, and setting production parameters of the environment-friendly degradable plastic material;
step 2, designing a three-dimensional model of the environment-friendly degradable plastic material product by using three-dimensional design software; the method comprises the following steps that three-dimensional design software is used for designing a three-dimensional model of an environment-friendly degradable plastic material product, and an environment-friendly degradable plastic material big data analysis platform containing relational database data, sensor data and controller data is constructed based on big data;
analyzing and excavating in an environment-friendly degradable plastic material big data analysis platform under a MapReduce framework to obtain environment-friendly degradable plastic material forming influence factors;
building a neural network model BP by combining the forming influence factors of the environment-friendly degradable plastic material, and generating an initial weight of the neural network model BP;
dynamically improving the weight and the threshold of the neural network model BP to obtain a dynamic neural network model DBP, and generating the weight and the threshold of the dynamic neural network model DBP; obtaining a prediction model AIGA-DBP, and calculating the environment-friendly degradable plastic material forming prediction value according to the prediction model AIGA-DBP;
judging whether the error between the environment-friendly degradable plastic material forming predicted value and the environment-friendly degradable plastic material forming expected value meets the set condition or not; outputting the molding predicted value of the environment-friendly degradable plastic material;
step 3, conveying 30-50 parts of maleic anhydride grafted polypropylene, 3-10 parts of corn straw fiber, 3-10 parts of wood fiber and wheat straw fiber, 2-5 parts of acetate starch, 3-8 parts of cyano functionalized graphene, 3-10 parts of plasticizer, 0.5-1 part of antioxidant, 0.5-0.1 part of dispersant, 0.2-0.3 part of cross-linking agent and 0.5-1 part of flame retardant by using conveying equipment;
step 4, placing the components into a mixer at the temperature of 40-45 ℃ and the rotating speed of 100-1200 r/min for premixing to obtain a premix; placing the premix in a double-screw extruder for mixing, extruding and granulating to obtain granules;
and 5, placing the granules in a blow molding machine for blow molding to obtain the environment-friendly degradable plastic product.
In one embodiment of the invention, the temperatures of all sections of the body of the double-screw extruder are different, wherein the melting temperature of the first section is 175-180 ℃, the melting temperature of the second section is 200-210 ℃, the melting temperature of the third section is 210-230 ℃, the melting temperature of the fourth section is 200-210 ℃, and the extrusion temperature is 170-200 ℃.
Uploading the relational database data, the sensor data and the controller data to a distributed file system (HDFS) through Sqoop, and storing the data in a NoSQL database; mining and analyzing the relational database data, the sensor data and the controller data by using a MapReduce computing framework, writing the analyzed data into a NoSQL database, and displaying the data through Web;
the method comprises the following steps of analyzing and excavating in an environment-friendly degradable plastic material big data analysis platform under a MapReduce framework to obtain environment-friendly degradable plastic material forming influence factors:
using MapReduce computational model to obtain set L of frequent 1 item set1Generating a set C of candidate k-term setsk(k≥2)；
In the Map function processing stage, each Map task calculates the content of each transaction record in the transaction data set processed by the Map task, wherein the content is contained in CkIf a certain item set (containing k items) of the candidate k item set appears in a transaction record for each Map task, the Map function generates and outputs<A certain set of items, 1>The key value pair is given to a Combiner function, processed by the Combiner function and then given to a Reduce function;
during Reduce function processing stage, the Reduce function accumulates CkThe number of occurrences of the item set in (1) is obtained as the support frequency of all the item sets, and all the item sets with the support frequency more than or equal to the set minimum support frequency form a frequent item set LkIf k < maximum number of iterations and is not null, then k + +, noThen, the run is ended.
The method for generating the initial weight of the neural network model BP is that the initial weight is randomly selected between the intervals of [ -1, 1 ];
dynamically improving the weight and the threshold of the neural network model BP, and obtaining a dynamic neural network model DBP comprises the following steps:
adjusting weight w between hidden layer and output layer of neural network model BPkj；
Adjusting wkjIs intended to output a new output of node j
Figure BDA0003002411280000066
Is more than the current output opjCloser to the target value tpjDefining:
Figure BDA0003002411280000061
where α represents closeness, remains unchanged at each training period, and becomes smaller as the number of hidden layer nodes H is adjusted, regardless of the threshold, there are:
Figure BDA0003002411280000062
wherein wkjAnd w* kjWeight before and after updating, ypkFor hidden layer output,. DELTA.wkjIs wkjThe amount of change of (d);
obtaining Δ w according to the formulakjThe solution equation of (c):
Figure BDA0003002411280000063
wherein the content of the first and second substances,
Figure BDA0003002411280000064
according to the least squares and the error sourcesThen solving to obtain delta wkjApproximate solution of (2):
Figure BDA0003002411280000065
for each hidden layer node k connected to an output node j, calculating the weight change Deltaw between k and jkjUpdating the weight value and calculating the square sum error E, and then belonging to [1, H ] at k]Selecting an optimal k from the interval to minimize E;
adjusting weight v between BP input layer and hidden layer of neural network modelik；
Adjustment vikThe method aims to modify the weight to jump out the local minimum point once the neural network algorithm falls into the local minimum point, and judge that the condition that the neural network algorithm falls into the local minimum point is that the change rate Delta E of an error E is 0 and E is>0；
Regardless of the threshold, the change in the weights of the hidden layer node k is solved by the following equation:
Figure BDA0003002411280000071
wherein deltapj＝f-1(ypk+Δypk)-f-1(ypk) M is a natural number, then the hidden layer outputs ypkThe solving formula is as follows:
Figure BDA0003002411280000072
wherein Δ ypkIs ypkThe change amount of (c) is:
Figure BDA0003002411280000073
and solving the constructed matrix equation according to the least squares sum error principle to calculate:
Figure BDA0003002411280000074
calculating the dynamic average change of weight between hidden layer and output layer
Figure BDA0003002411280000075
Figure BDA0003002411280000076
Calculating the dynamic average change of the weight between the input layer and the hidden layer
Figure BDA0003002411280000077
Figure BDA0003002411280000078
And M in the formula is a natural number between 10 and 20, a dynamic average weight of the neural network model BP is obtained, and a dynamic neural network model DBP is obtained according to the dynamic average weight of the neural network model BP.
In an embodiment of the present invention, the step 3 of preparing wood fiber and wheat straw fiber comprises:
(1) peeling and chipping a eucalyptus raw material to obtain wood chips, and feeding the wood chips into a negative pressure mixing bin with the temperature of 60 +/-5 ℃ and the pressure of below-15 kpa for pressure reduction and air exhaust for 5 min; adding excessive cooking agent under negative pressure and maintaining for 30 min; then, the state is restored to the normal temperature and normal pressure, and the redundant cooking agent is discharged;
(2) feeding the wood chips treated in the step (1) into a cooking device, heating for 65min according to a conventional cooking curve, raising the temperature to 125 ℃, preserving the temperature to 135 ℃, cooking, spraying slurry after cooking, washing, selecting and screening according to a conventional method to obtain fine pulp and black liquor, and feeding the black liquor into an alkali recovery system;
the preparation method of the wheat straw fiber is the same as the preparation process of the wood fiber.
In one embodiment of the invention, in the preparation of the environment-friendly degradable plastic material, tail gas generated in the production of the steps 3, 4 and 5 is treated simultaneously; the processing method comprises the following steps:
firstly, enabling the generated tail gas to enter a deslagging box through a closed machine head, precipitating solid impurities in the tail gas in a settling box, and enabling residual steam and harmful gas to enter the next procedure;
and secondly, removing harmful and water vapor after the water is fused, and introducing the residual gas into the next working procedure.
Thirdly, cooling and condensing, performing pall ring absorption and filler absorption to condense gaseous molecules into liquid oily molecular groups, and completely refluxing the liquid oily molecular groups to a waste liquid recovery pipeline through neutralization and absorption; the tail gas at the tail gas emission point is sucked into and treated by a fan through a pipeline, most of the harmful gas is adsorbed and removed by spraying, the residual sucked residual gas enters a dehydration device rotating at a high speed and enters the next electric coke-catching device to remove large harmful molecular groups, and after the residual gas enters plasma adsorption treatment, the residual colorless transparent gas with peculiar smell and the sucked air enter a photo-oxidation device to be decomposed and are discharged into the tail gas emission pipeline through the fan along with the residual air;
and fourthly, cooling, washing and refluxing water, hermetically collecting the water through a pipeline, feeding the water into a separator, removing contained condensed harmful and condensable substances, separating the water by the separator, feeding the residual hot water into a cooler through the pipeline, and recycling the residual hot water by reflux supply equipment.
According to a third aspect of the disclosed embodiment of the invention, a convenient bag made of the environment-friendly degradable plastic material is provided.
According to a fourth aspect of the disclosed embodiment of the invention, a product packaging film prepared by using the environment-friendly degradable plastic material is provided.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
the acetate starch is added into the environment-friendly degradable plastic, so that the degradation period of the plastic is shortened, and the degradation degree is increased; the added graphene improves the toughness of the plastic and prolongs the service life of the degradable plastic product.
The production process of the environment-friendly degradable plastic disclosed by the invention can effectively ensure the degradation of the product, simultaneously improve the mechanical effect and the high-temperature resistance of the product, effectively improve the degradation efficiency of the product, does not generate pollution components during degradation, is safe and environment-friendly, and has high degradation rate, and the degradation rate of the environment-friendly degradable plastic in 15 days is 92.3-93.9% by determination;
according to the invention, a series of films with different mechanical strengths and excellent flexibility are obtained by changing the content, the unidirectional stretching magnification and the thermal crosslinking effect of GN-CN, wherein the high mechanical strength (the tensile strength and the modulus are respectively more than 350MPa and 3.5GPa) of the films; can be applied to different occasions. The preparation method is simple and easy to operate, and is easy to realize industrialization.
Compared with the prior art, the method comprises the steps of firstly constructing a big data analysis platform, then excavating influence factors by using an association rule algorithm, constructing a neural network model BP, dynamically improving the weight and the threshold of the neural network model BP to obtain a dynamic neural network model DBP, then optimizing the dynamic neural network model DBP by using a self-adaptive immune genetic AIGA algorithm to obtain a prediction model AIGA-DBP, and finally calculating a prediction value by using the prediction model AIGA-DBP; the dynamic neural network model DBP in the invention can adapt to various changes caused by different product specifications.
According to the invention, a big data analysis technology is applied, so that the mining of the influence factors is more efficient and accurate, the consideration is more comprehensive, and the prediction accuracy is effectively improved. Can obtain products with good quality.
The tail gas recovery equipment and the method have low use cost, each flue gas outlet is perfectly sealed, the flue gas is easier to be intensively treated, the used cooling water is relieved by the internal treatment of the equipment and directly recycled, the sewage and the waste gas are completely treated, the national standard emission is achieved, the maximum treatment capacity is realized by using the minimum field, and the flue gas treatment is perfected by using the minimum cost.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of a method for obtaining cyano-functionalized graphene according to an embodiment of the present invention.
FIG. 2 is a flow chart of a method for preparing the environmentally friendly degradable plastic material provided by the embodiment of the invention.
FIG. 3 is a flowchart of a method for predicting a molding value of an environmentally friendly degradable plastic material according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for analyzing and mining environmental-friendly degradable plastic material big data analysis platform under a MapReduce framework to obtain environmental-friendly degradable plastic material forming influencing factors according to an embodiment of the present invention.
FIG. 5 is a flow chart of a method for preparing wood fiber and wheat straw fiber according to an embodiment of the present invention.
FIG. 6 is a flow chart of a method for treating exhaust according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The embodiment of the invention provides an environment-friendly degradable plastic material which comprises, by mass, 30-50 parts of maleic anhydride grafted polypropylene, 3-10 parts of corn straw fiber, 3-10 parts of wood fiber and wheat straw fiber, 2-5 parts of acetate starch, 3-8 parts of cyano-functionalized graphene, 3-10 parts of a plasticizer, 0.5-1 part of an antioxidant, 0.5-0.1 part of a dispersant, 0.2-0.3 part of a cross-linking agent and 0.5-1 part of a flame retardant.
Preferably, the flame retardant is HBCD heat stable flame retardant; chemically grafting graphene oxide by using 4-aminophenoxy phthalonitrile to obtain cyano-functionalized graphene;
as shown in fig. 1, the method for obtaining cyano-functionalized graphene comprises:
s101, adding graphene oxide into a thionyl chloride solution, ultrasonically stirring, and heating and refluxing at 75 ℃ for reacting for 18 hours under the catalysis of N, N-dimethylformamide;
s102, after the reaction is finished, removing thionyl chloride in the system by utilizing toluene reflux, and drying the obtained black reaction product in vacuum to obtain acyl chlorinated graphene;
s103, adding excessive 4-aminophenoxy phthalonitrile and acyl chlorinated graphene into a toluene solvent, and stirring and refluxing at 28 ℃ for reaction for 48 hours;
and S104, washing, centrifugally separating and vacuum drying the reaction product for multiple times to obtain the cyano-functionalized graphene.
As shown in fig. 2, an embodiment of the present invention provides a method for preparing an environment-friendly degradable plastic material, including:
s201, determining design parameters of the environment-friendly degradable plastic material, and setting production parameters of the environment-friendly degradable plastic material;
s202, designing a three-dimensional model of the environment-friendly degradable plastic material product by using three-dimensional design software;
s203, conveying 30-50 parts of maleic anhydride grafted polypropylene, 3-10 parts of corn straw fiber, 3-10 parts of wood fiber and wheat straw fiber, 2-5 parts of acetate starch, 3-8 parts of cyano functionalized graphene, 3-10 parts of plasticizer, 0.5-1 part of antioxidant, 0.5-0.1 part of dispersant, 0.2-0.3 part of cross-linking agent and 0.5-1 part of flame retardant by using conveying equipment;
s204, placing the components into a mixer at the temperature of 40-45 ℃ and the rotating speed of 100-1200 r/min for premixing to obtain a premix; placing the premix in a double-screw extruder for mixing, extruding and granulating to obtain granules;
s205, placing the granules in a blow molding machine for blow molding to obtain the environment-friendly degradable plastic product.
As shown in fig. 3, in step S202, a three-dimensional model of an environment-friendly degradable plastic material product is designed by using three-dimensional design software, and the method for predicting the molding value of the environment-friendly degradable plastic material comprises:
s301, in the process of designing a three-dimensional model of an environment-friendly degradable plastic material product by three-dimensional design software, constructing an environment-friendly degradable plastic material big data analysis platform containing relational database data, sensor data and controller data based on big data;
s302, analyzing and excavating in an environment-friendly degradable plastic material big data analysis platform under a MapReduce framework to obtain environment-friendly degradable plastic material forming influence factors;
s303, building a neural network model BP by combining the environment-friendly degradable plastic material forming influence factors, and generating an initial weight of the neural network model BP;
s304, dynamically improving the weight and the threshold of the neural network model BP to obtain a dynamic neural network model DBP, and generating the weight and the threshold of the dynamic neural network model DBP; obtaining a prediction model AIGA-DBP, and calculating the environment-friendly degradable plastic material forming prediction value according to the prediction model AIGA-DBP;
s305, judging whether the error between the environment-friendly degradable plastic material forming predicted value and the environment-friendly degradable plastic material forming expected value meets the set condition; and outputting the predicted molding value of the environment-friendly degradable plastic material.
Preferably, the temperature of each section of the double-screw extruder body is different, the melting temperature of the first section is 175-180 ℃, the melting temperature of the second section is 200-210 ℃, the melting temperature of the third section is 210-230 ℃, the melting temperature of the fourth section is 200-210 ℃, and the extrusion temperature is 170-200 ℃.
Preferably, uploading the relational database data, the sensor data and the controller data to a distributed file system (HDFS) through Sqoop, and storing the data in a NoSQL database; mining and analyzing the relational database data, the sensor data and the controller data by using a MapReduce computing framework, writing the analyzed data into a NoSQL database, and displaying the data through Web;
as shown in fig. 4, the method specifically includes the following steps of analyzing and mining in an environment-friendly degradable plastic material big data analysis platform under a MapReduce framework to obtain environment-friendly degradable plastic material forming influence factors:
s401, obtaining a collection L of frequent 1 item sets by using a MapReduce calculation model1Generating a set C of candidate k-term setsk(k≥2)；
S402, in the Map function processing stage, each Map task calculates that each transaction record in the transaction data set processed by the Map task is contained in CkIf a certain item set (containing k items) of the candidate k item set appears in a transaction record for each Map task, the Map function generates and outputs<A certain set of items, 1>The key value pair is given to a Combiner function, processed by the Combiner function and then given to a Reduce function;
s403, in the Reduce function processing stage, the Reduce function accumulates CkThe number of occurrences of the item set in (1) is obtained as the support frequency of all the item sets, and all the item sets with the support frequency more than or equal to the set minimum support frequency form a frequent item set LkIf k < maximum number of iterations and is not empty, then k + +, is executed, otherwise, the run ends.
Preferably, the method for generating the initial weight of the neural network model BP is to randomly select the initial weight between the intervals [ -1, 1 ];
preferably, dynamically improving the weight and the threshold of the neural network model BP, and obtaining the dynamic neural network model DBP includes:
adjusting weight w between hidden layer and output layer of neural network model BPkj；
Adjusting wkjIs intended to output a new output of node j
Figure BDA0003002411280000136
Is more than the current output opjCloser to the target value tpjDefining:
Figure BDA0003002411280000131
where α represents closeness, remains unchanged at each training period, and becomes smaller as the number of hidden layer nodes H is adjusted, regardless of the threshold, there are:
Figure BDA0003002411280000132
wherein wkjAnd w* kjWeight before and after updating, ypkFor hidden layer output,. DELTA.wkjIs wkjThe amount of change of (d);
obtaining Δ w according to the formulakjThe solution equation of (c):
Figure BDA0003002411280000133
wherein the content of the first and second substances,
Figure BDA0003002411280000134
solving according to the least squares sum error principle to obtain delta wkjApproximate solution of (2):
Figure BDA0003002411280000135
for each hidden layer node k connected to an output node j, calculating the weight change Deltaw between k and jkjUpdating the weight value and calculating the square sum error E, and then belonging to [1, H ] at k]Selecting an optimal k from the interval to minimize E;
adjusting weight v between BP input layer and hidden layer of neural network modelik；
Adjustment vikThe purpose of (1) is to modify once the neural network algorithm falls into a local minimumThe weight can jump out of the minimum point, the condition that the neural network algorithm falls into the local minimum point is judged that the change rate delta E of the error E is 0, and E>0；
Regardless of the threshold, the change in the weights of the hidden layer node k is solved by the following equation:
Figure BDA0003002411280000141
wherein deltapj＝f-1(ypk+Δypk)-f-1(ypk) M is a natural number, then the hidden layer outputs ypkThe solving formula is as follows:
Figure BDA0003002411280000142
wherein Δ ypkIs ypkThe change amount of (c) is:
Figure BDA0003002411280000143
and solving the constructed matrix equation according to the least squares sum error principle to calculate:
Figure BDA0003002411280000144
calculating the dynamic average change of weight between hidden layer and output layer
Figure BDA0003002411280000145
Figure BDA0003002411280000146
Calculating the dynamic average change of the weight between the input layer and the hidden layer
Figure BDA0003002411280000147
Figure BDA0003002411280000148
And M in the formula is a natural number between 10 and 20, a dynamic average weight of the neural network model BP is obtained, and a dynamic neural network model DBP is obtained according to the dynamic average weight of the neural network model BP.
As shown in fig. 5, the preparation method of the wood fiber and the wheat straw fiber comprises the following steps:
s501, peeling and chipping a eucalyptus raw material to obtain wood chips, and conveying the wood chips into a negative-pressure mixing bin with the temperature of 60 +/-5 ℃ and the pressure of below-15 kpa for pressure reduction and air exhaust for 5 min; adding excessive cooking agent under negative pressure and maintaining for 30 min; then, the state is restored to the normal temperature and normal pressure, and the redundant cooking agent is discharged;
s502, feeding the wood chips treated in the step S501 into a cooking device, heating for 65min according to a conventional cooking curve, raising the temperature to 125 ℃ at the highest temperature, preserving the temperature to 135 ℃, cooking, spraying slurry after cooking, washing, selecting and screening according to a conventional method to obtain fine pulp and black liquor, and feeding the black liquor into an alkali recovery system.
The preparation method of the wheat straw fiber is the same as the preparation process of the wood fiber.
As shown in fig. 6, in the preparation of the environment-friendly degradable plastic material, tail gas generated in the production is treated at the same time; the processing method comprises the following steps:
s601, enabling the generated tail gas to enter a slag discharging box through a closed machine head, enabling solid impurities in the tail gas to be precipitated in a precipitation box, and enabling residual steam and harmful gas to enter the next procedure;
and S602, removing harmful and water vapor after the mixture is fused with water, and allowing the residual gas to enter the next process.
S603, cooling and condensing, pall ring and filler absorption, condensing gaseous molecules into liquid oily molecular groups, and completely refluxing the liquid oily molecular groups to a waste liquid recovery pipeline through neutralization and absorption; the tail gas at the tail gas emission point is sucked into and treated by a fan through a pipeline, most of the harmful gas is adsorbed and removed by spraying, the residual sucked residual gas enters a dehydration device rotating at a high speed and enters the next electric coke-catching device to remove large harmful molecular groups, and after the residual gas enters plasma adsorption treatment, the residual colorless transparent gas with peculiar smell and the sucked air enter a photo-oxidation device to be decomposed and are discharged into the tail gas emission pipeline through the fan along with the residual air;
s604, cooling, washing and refluxing the water, hermetically collecting the water through a pipeline, feeding the water into a separator, removing the harmful and condensable substances contained in the water, separating the water by the separator, feeding the residual hot water into a cooler through the pipeline, and recycling the residual hot water by a reflux supply device.
The technical solution of the present invention is further described below with reference to experimental data.
Experiments show that: according to the invention, a series of films with different mechanical strengths and excellent flexibility are obtained by changing the content, the unidirectional stretching magnification and the thermal crosslinking effect of GN-CN, wherein the high mechanical strength (the tensile strength and the modulus are respectively more than 350MPa and 3.5GPa) of the films; can be applied to different occasions. The preparation method is simple and easy to operate, and is easy to realize industrialization.
Compared with the prediction method in the prior art, the method comprises the steps of firstly constructing a big data analysis platform, then excavating influence factors by using a correlation rule algorithm, constructing a neural network model BP, dynamically improving the weight and the threshold of the neural network model BP to obtain a dynamic neural network model DBP, then optimizing the dynamic neural network model DBP by using a self-adaptive immune genetic AIGA algorithm to obtain a prediction model AIGA-DBP, and finally calculating a predicted value by using the prediction model AIGA-DBP; the dynamic neural network model DBP in the invention can adapt to various changes caused by different product specifications.
According to the invention, a big data analysis technology is applied, so that the mining of the influence factors is more efficient and accurate, the consideration is more comprehensive, and the prediction accuracy is effectively improved. Can obtain products with good quality.
The positive effects of the present invention will be further described with reference to the following examples
Example 1
The invention provides an environment-friendly degradable plastic material which comprises, by mass, 30 parts of maleic anhydride grafted polypropylene, 3 parts of corn straw fiber, 3 parts of wood fiber and wheat straw fiber, 2 parts of acetate starch, 3 parts of cyano functionalized graphene, 3 parts of plasticizer, 0.5 part of antioxidant, 0.5 part of dispersant, 0.2 part of cross-linking agent and 0.5 part of flame retardant. The film has high mechanical strength (the tensile strength and the modulus are respectively more than 330MPa and 3.2 GPa).
Example 2
The invention provides an environment-friendly degradable plastic material which comprises, by mass, 50 parts of maleic anhydride grafted polypropylene, 10 parts of corn straw fiber, 10 parts of wood fiber and wheat straw fiber, 5 parts of acetate starch, 8 parts of cyano functionalized graphene, 10 parts of plasticizer, 1 part of antioxidant, 0.1 part of dispersant, 0.3 part of cross-linking agent and 1 part of flame retardant. The film has high mechanical strength (the tensile strength and the modulus are respectively more than 360MPa and 3.6 GPa).
Example 3
The invention discloses an environment-friendly degradable plastic material, which comprises, by mass, 40 parts of maleic anhydride grafted polypropylene, 7 parts of corn straw fiber, 7 parts of wood fiber and wheat straw fiber, 4 parts of acetate starch, 6 parts of cyano-functionalized graphene, 7 parts of plasticizer, 0.75 part of antioxidant, 0.75 part of dispersant, 0.25 part of cross-linking agent and 0.75 part of flame retardant. The film has high mechanical strength (the tensile strength and the modulus are respectively more than 3450MPa and 3.4 GPa).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure should be limited only by the attached claims.

Claims (10)

1. The environment-friendly degradable plastic material is characterized by comprising, by mass, 30-50 parts of maleic anhydride grafted polypropylene, 3-10 parts of corn straw fiber, 3-10 parts of wood fiber and wheat straw fiber, 2-5 parts of acetate starch, 3-8 parts of cyano-functionalized graphene, 3-10 parts of plasticizer, 0.5-1 part of antioxidant, 0.5-0.1 part of dispersant, 0.2-0.3 part of cross-linking agent and 0.5-1 part of flame retardant.
2. The environment-friendly degradable plastic material as claimed in claim 1, wherein the flame retardant is HBCD heat stable flame retardant; chemically grafting graphene oxide by using 4-aminophenoxy phthalonitrile to obtain cyano-functionalized graphene;
the method for obtaining the cyano-functionalized graphene comprises the following steps: adding graphene oxide into a thionyl chloride solution, performing ultrasonic stirring, and heating and refluxing at 75 ℃ under the catalytic action of N, N-dimethylformamide for reacting for 18 hours; after the reaction is finished, removing thionyl chloride in the system by utilizing toluene reflux, and drying the obtained black reaction product in vacuum to obtain acyl chlorinated graphene; adding excessive 4-aminophenoxy phthalonitrile and acyl chloride graphene into a toluene solvent, and stirring and refluxing at 28 ℃ for reaction for 48 hours; and washing, centrifugally separating and vacuum drying the reaction product for multiple times to obtain the cyano-functionalized graphene.
3. The preparation method of the environment-friendly degradable plastic material is characterized by comprising the following steps:
step 1, determining design parameters of an environment-friendly degradable plastic material, and setting production parameters of the environment-friendly degradable plastic material;
step 2, designing a three-dimensional model of the environment-friendly degradable plastic material product by using three-dimensional design software; the method comprises the following steps that three-dimensional design software is used for designing a three-dimensional model of an environment-friendly degradable plastic material product, and an environment-friendly degradable plastic material big data analysis platform containing relational database data, sensor data and controller data is constructed based on big data;
analyzing and excavating in an environment-friendly degradable plastic material big data analysis platform under a MapReduce framework to obtain environment-friendly degradable plastic material forming influence factors;
building a neural network model BP by combining the forming influence factors of the environment-friendly degradable plastic material, and generating an initial weight of the neural network model BP;
dynamically improving the weight and the threshold of the neural network model BP to obtain a dynamic neural network model DBP, and generating the weight and the threshold of the dynamic neural network model DBP; obtaining a prediction model AIGA-DBP, and calculating the environment-friendly degradable plastic material forming prediction value according to the prediction model AIGA-DBP;
judging whether the error between the environment-friendly degradable plastic material forming predicted value and the environment-friendly degradable plastic material forming expected value meets the set condition or not; outputting the molding predicted value of the environment-friendly degradable plastic material;
step 3, conveying 30-50 parts of maleic anhydride grafted polypropylene, 3-10 parts of corn straw fiber, 3-10 parts of wood fiber and wheat straw fiber, 2-5 parts of acetate starch, 3-8 parts of cyano functionalized graphene, 3-10 parts of plasticizer, 0.5-1 part of antioxidant, 0.5-0.1 part of dispersant, 0.2-0.3 part of cross-linking agent and 0.5-1 part of flame retardant by using conveying equipment;
step 4, placing the components into a mixer at the temperature of 40-45 ℃ and the rotating speed of 100-1200 r/min for premixing to obtain a premix; placing the premix in a double-screw extruder for mixing, extruding and granulating to obtain granules;
and 5, placing the granules in a blow molding machine for blow molding to obtain the environment-friendly degradable plastic product.
4. The method for preparing the environment-friendly degradable plastic material as claimed in claim 3, wherein the temperature of each section of the body of the twin-screw extruder is different, the melting temperature of the first section is 175-180 ℃, the melting temperature of the second section is 200-210 ℃, the melting temperature of the third section is 210-230 ℃, the melting temperature of the fourth section is 200-210 ℃, and the extrusion temperature is 170-200 ℃.
5. The method for preparing environment-friendly degradable plastic material according to claim 3, wherein relational database data, sensor data and controller data are uploaded to a distributed file system HDFS through Sqoop and stored in a NoSQL database; mining and analyzing the relational database data, the sensor data and the controller data by using a MapReduce computing framework, writing the analyzed data into a NoSQL database, and displaying the data through Web;
the method comprises the following steps of analyzing and excavating in an environment-friendly degradable plastic material big data analysis platform under a MapReduce framework to obtain environment-friendly degradable plastic material forming influence factors:
using MapReduce computational model to obtain set L of frequent 1 item set1Generating a set C of candidate k-term setsk(k≥2)；
In the Map function processing stage, each Map task calculates the content of each transaction record in the transaction data set processed by the Map task, wherein the content is contained in CkIf a certain item set (containing k items) of the candidate k item set appears in a transaction record for each Map task, the Map function generates and outputs<A certain set of items, 1>The key value pair is given to a Combiner function, processed by the Combiner function and then given to a Reduce function;
during Reduce function processing stage, the Reduce function accumulates CkThe number of occurrences of the item set in (1) is obtained as the support frequency of all the item sets, and all the item sets with the support frequency more than or equal to the set minimum support frequency form a frequent item set LkIf k < maximum number of iterations and is not empty, then k + +, is executed, otherwise, the run ends.
6. The method for preparing environment-friendly degradable plastic material according to claim 3, wherein the method for generating the initial weight of the neural network model BP is to randomly select the initial weight between the intervals of [ -1, 1 ];
dynamically improving the weight and the threshold of the neural network model BP, and obtaining a dynamic neural network model DBP comprises the following steps:
adjusting weight w between hidden layer and output layer of neural network model BPkj；
Adjusting wkjIs intended to output a new output o of node j* pjIs more than the current output opjCloser to the target value tpjDefining:
Figure FDA0003002411270000031
where α represents closeness, remains unchanged at each training period, and becomes smaller as the number of hidden layer nodes H is adjusted, regardless of the threshold, there are:
Figure FDA0003002411270000032
wherein wkjAnd w* kjWeight before and after updating, ypkFor hidden layer output,. DELTA.wkjIs wkjThe amount of change of (d);
obtaining Δ w according to the formulakjThe solution equation of (c):
Figure FDA0003002411270000033
wherein the content of the first and second substances,
Figure FDA0003002411270000041
solving according to the least squares sum error principle to obtain delta wkjApproximate solution of (2):
Figure FDA0003002411270000042
for each hidden layer node k connected to an output node j, calculating the weight change Deltaw between k and jkjUpdating the weight value and calculating the square sum error E, and then belonging to [1, H ] at k]Selecting an optimal k from the interval to minimize E;
adjusting weight v between BP input layer and hidden layer of neural network modelik；
Adjustment vikThe method aims to modify the weight to jump out the local minimum point once the neural network algorithm falls into the local minimum point, and judge that the condition that the neural network algorithm falls into the local minimum point is that the change rate Delta E of an error E is 0 and E is>0；
Regardless of the threshold, the change in the weights of the hidden layer node k is solved by the following equation:
Figure FDA0003002411270000043
wherein deltapj＝f-1(ypk+Δypk)-f-1(ypk) M is a natural number, then the hidden layer outputs ypkThe solving formula is as follows:
Figure FDA0003002411270000044
wherein Δ ypkIs ypkThe change amount of (c) is:
Figure FDA0003002411270000045
and solving the constructed matrix equation according to the least squares sum error principle to calculate:
Figure FDA0003002411270000046
calculating the dynamic average change of weight between hidden layer and output layer
Figure FDA0003002411270000047
Figure FDA0003002411270000048
Calculating the dynamic average change of the weight between the input layer and the hidden layer
Figure FDA0003002411270000049
Figure FDA0003002411270000051
And M in the formula is a natural number between 10 and 20, a dynamic average weight of the neural network model BP is obtained, and a dynamic neural network model DBP is obtained according to the dynamic average weight of the neural network model BP.
7. The method for preparing environment-friendly degradable plastic material according to claim 3, wherein the method for preparing wood fiber and wheat straw fiber in step 3 comprises:
(1) peeling and chipping a eucalyptus raw material to obtain wood chips, and feeding the wood chips into a negative pressure mixing bin with the temperature of 60 +/-5 ℃ and the pressure of below-15 kpa for pressure reduction and air exhaust for 5 min; adding excessive cooking agent under negative pressure and maintaining for 30 min; then, the state is restored to the normal temperature and normal pressure, and the redundant cooking agent is discharged;
(2) feeding the wood chips treated in the step (1) into a cooking device, heating for 65min according to a conventional cooking curve, raising the temperature to 125 ℃, preserving the temperature to 135 ℃, cooking, spraying slurry after cooking, washing, selecting and screening according to a conventional method to obtain fine pulp and black liquor, and feeding the black liquor into an alkali recovery system;
the preparation method of the wheat straw fiber is the same as the preparation process of the wood fiber.
8. The method for preparing the environment-friendly degradable plastic material as claimed in claim 3, wherein in the preparation of the environment-friendly degradable plastic material, tail gas generated in the production in the steps 3, 4 and 5 is treated simultaneously; the processing method comprises the following steps:
firstly, enabling the generated tail gas to enter a deslagging box through a closed machine head, precipitating solid impurities in the tail gas in a settling box, and enabling residual steam and harmful gas to enter the next procedure;
and secondly, removing harmful and water vapor after the water is fused, and introducing the residual gas into the next working procedure.
Thirdly, cooling and condensing, performing pall ring absorption and filler absorption to condense gaseous molecules into liquid oily molecular groups, and completely refluxing the liquid oily molecular groups to a waste liquid recovery pipeline through neutralization and absorption; the tail gas at the tail gas emission point is sucked into and treated by a fan through a pipeline, most of the harmful gas is adsorbed and removed by spraying, the residual sucked residual gas enters a dehydration device rotating at a high speed and enters the next electric coke-catching device to remove large harmful molecular groups, and after the residual gas enters plasma adsorption treatment, the residual colorless transparent gas with peculiar smell and the sucked air enter a photo-oxidation device to be decomposed and are discharged into the tail gas emission pipeline through the fan along with the residual air;
and fourthly, cooling, washing and refluxing water, hermetically collecting the water through a pipeline, feeding the water into a separator, removing contained condensed harmful and condensable substances, separating the water by the separator, feeding the residual hot water into a cooler through the pipeline, and recycling the residual hot water by reflux supply equipment.
9. A convenient bag prepared by the environment-friendly degradable plastic material as claimed in any one of claims 1-2.
10. A product packaging film prepared by using the environment-friendly degradable plastic material as claimed in any one of claims 1-2.
CN202110352091.5A 2021-03-31 2021-03-31 Environment-friendly degradable plastic material and preparation method thereof Pending CN112940433A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117077548A (en) * 2023-10-16 2023-11-17 石家庄长川电气科技有限公司 Artificial intelligent selection method and system for preparation parameters of melt-blown non-woven fabric

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107474517A (en) * 2017-08-29 2017-12-15 四川理工学院 A kind of high-strength high temperature-resistant polymer-based dielectric film and preparation method thereof
CN107522904A (en) * 2016-06-21 2017-12-29 四川鑫达企业集团有限公司 A kind of Starch Based Biodegradable Plastics applied to packaging for foodstuff
CN108586935A (en) * 2018-05-14 2018-09-28 浙江工业大学之江学院 A kind of environment-friendly type degradable plastics
CN109705556A (en) * 2019-01-15 2019-05-03 安徽新利清洁用品有限公司 A kind of environment-friendly degradable plastics and preparation method thereof
CN109847518A (en) * 2019-04-01 2019-06-07 方喜贵 A kind of plastic granulating flue gas processing method and processing equipment
CN109881538A (en) * 2019-02-18 2019-06-14 杭州西红柿环保科技有限公司 A kind of enhanced method for paper pulp molding production
CN111825966A (en) * 2020-07-22 2020-10-27 界首市鸿鑫塑业有限公司 Environment-friendly degradable plastic and production process thereof

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107522904A (en) * 2016-06-21 2017-12-29 四川鑫达企业集团有限公司 A kind of Starch Based Biodegradable Plastics applied to packaging for foodstuff
CN107474517A (en) * 2017-08-29 2017-12-15 四川理工学院 A kind of high-strength high temperature-resistant polymer-based dielectric film and preparation method thereof
CN108586935A (en) * 2018-05-14 2018-09-28 浙江工业大学之江学院 A kind of environment-friendly type degradable plastics
CN109705556A (en) * 2019-01-15 2019-05-03 安徽新利清洁用品有限公司 A kind of environment-friendly degradable plastics and preparation method thereof
CN109881538A (en) * 2019-02-18 2019-06-14 杭州西红柿环保科技有限公司 A kind of enhanced method for paper pulp molding production
CN109847518A (en) * 2019-04-01 2019-06-07 方喜贵 A kind of plastic granulating flue gas processing method and processing equipment
CN111825966A (en) * 2020-07-22 2020-10-27 界首市鸿鑫塑业有限公司 Environment-friendly degradable plastic and production process thereof

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117077548A (en) * 2023-10-16 2023-11-17 石家庄长川电气科技有限公司 Artificial intelligent selection method and system for preparation parameters of melt-blown non-woven fabric
CN117077548B (en) * 2023-10-16 2023-12-15 石家庄长川电气科技有限公司 Artificial intelligent selection method and system for preparation parameters of melt-blown non-woven fabric

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