A method and system for regulating temperature in preparing acetic acid-acrylic emulsion

Abstract

The present invention relates to the technical field of general control or regulation systems, and discloses a temperature regulation method and system for preparing acetoacetic acid propylene emulsion. The present invention first checks the weight and type of acetoacetic acid propylene emulsion raw materials; secondly, plans the path of the raw material pipeline, and uses the ant colony algorithm to obtain the optimal raw material pipeline path; then uses the long short-term memory network to predict the temperature value of the liquid inside the reaction container, and combines the proportional integral differential algorithm to compare with the set temperature value at the prediction time to achieve the purpose of regulating the temperature. The algorithm is accurate and effective in temperature regulation; then, based on image recognition, the processing and preparation are tested for air tightness to ensure the safety and reliability of the preparation process; finally, a database and a monitoring platform are established to store and monitor the parameters and processes required for preparing acetoacetic acid propylene emulsion.

Description

Technical Field

[0001] The present invention relates to the technical field of general control or regulation systems, and in particular to a temperature regulation method and system for preparing acetic acid propylene emulsion. Background Art

[0002] As architectural coatings, latex paint based on vinyl acetate has lower cost than vinyl acetate, and eliminates the post-treatment process of removing emulsifiers, resulting in less pollution. It can obtain latex particles with uniform size and clean surface, which can improve the density, water resistance, scrub resistance and adhesion of the latex coating. The obtained latex particles have good dispersibility and the particle size is larger than that of traditional emulsion polymerization, approaching the micron level. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a method and system for regulating the temperature of an acetic acid propylene emulsion so as to overcome the above-mentioned technical problems existing in the prior art.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention implements a temperature regulating method for preparing acetic acid propylene emulsion through the following technical scheme, and the method comprises the following steps:

[0006] S1, calculating the weight of the initial acetoacetic acid propylene emulsion raw material and identifying the raw material type to obtain the acetoacetic acid propylene emulsion raw material to be processed;

[0007] S2, planning a transmission path of the raw material pipeline, transmitting the to-be-processed acetoacetic acid propylene emulsion raw material to the processing and preparation area through the raw material pipeline, and obtaining the transmitted acetoacetic acid propylene emulsion raw material;

[0008] S3, placing the transferred acetoacetic acid propylene emulsion raw material into a reaction container in a processing and preparation area, adjusting the position of the temperature sensor, and predicting the temperature in the reaction container in the processing and preparation area based on a neural network, using a proportional integral differential algorithm to control the temperature in the reaction container in the processing and preparation area, and controlling the pressure, flow rate and liquid level in the reaction container to obtain the acetoacetic acid propylene emulsion in the preparation process;

[0009] S4, performing air tightness detection on the acetoacetic acid propylene emulsion in the preparation process based on image recognition, locating the leaking point by detecting the leaking point bubble, and performing maintenance at the leaking point to obtain the prepared acetoacetic acid propylene emulsion;

[0010] S5. Establish a database and a monitoring platform, wherein the database stores the parameters and process for preparing the acetoacetic acid propylene emulsion, and the monitoring platform monitors the parameters and process for preparing the acetoacetic acid propylene emulsion in real time.

[0011] The invention firstly identifies the types of acetic acid propylene emulsion raw materials, enables different raw materials to enter different raw material pipelines, and facilitates separate processing, and then checks the weight of the acetic acid propylene emulsion raw materials to obtain the acetic acid propylene emulsion raw materials to be processed; different raw materials enter different raw material pipelines, plan the paths of the raw material pipelines, use the ant colony algorithm to obtain the optimal raw material pipeline path, and transmit the acetic acid propylene emulsion raw materials to the processing and preparation area. The algorithm has a fast convergence speed and can save costs; secondly, the position of the temperature sensor is adjusted, and the temperature value of the liquid inside the reaction container is predicted by using a long short-term memory network, and the predicted temperature value is used to adjust the proportional coefficient, integral time constant and differential time constant, and compared with the set temperature value at the prediction time to achieve the purpose of regulating the temperature, and control the pressure, flow and liquid level in the reaction container. The proportional integral differential algorithm has good regulation effect and high stability, and the long short-term memory network has higher prediction accuracy than other neural networks; based on image recognition, the processing and preparation are tested for air tightness, and the leak point is located by detecting the leak point bubble to ensure the safety and reliability of the preparation process; finally, a database and monitoring platform are established to store and monitor the parameters and processes required for the preparation of acetic acid propylene emulsion, which is convenient for query and management.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11, setting a vehicle to load an initial acetoacetic acid propylene emulsion raw material, attaching a QR code to the initial acetoacetic acid propylene emulsion raw material before passing through a raw material pipeline, and realizing raw material type identification according to the QR code, and different initial acetoacetic acid propylene emulsion raw materials enter different raw material pipelines;

[0014] S12, the initial acetoacetic acid propylene emulsion raw material passes through the raw material pipeline, and passes through the quantity detection device and the weight detection device to check the weight of the initial acetoacetic acid propylene emulsion raw material. After the weight of the initial acetoacetic acid propylene emulsion raw material is checked correctly, the acetoacetic acid propylene emulsion raw material to be processed is obtained. The calculation formula is as follows:

[0015]

[0016] in, Indicates the weight of the initial acetic acid propylene emulsion raw material, Indicates the number of types of initial acetic acid propylene emulsion raw materials, Indicates the weight of each initial acetic acid propylene emulsion raw material, Indicates the quantity of each initial acetic acid propylene emulsion raw material, .

[0017] Preferably, S2 comprises the following steps:

[0018] S21, the raw material pipeline transports the initial acetic acid propylene emulsion raw material into the processing and preparation area, and plans the raw material pipeline; the ant population is set to , At the moment the ant starts from the initial point Move to point ,and At the moment the ant starts from the initial point Move to point , the initial point As the starting point of the raw material pipeline, set The transfer probability of the ant at time is , the transition probability is the probability of changing the direction of the raw material pipeline planning, and the calculation formula is as follows:

[0019]

[0020] in, Indicates the initial point Move to point The pheromone concentration on Indicates the initial point Move to point The pheromone concentration on Indicates the initial point Move to point The heuristic factor on Indicates the initial point Move to point The heuristic factor on represents pheromone factor, represents the expected heuristic factor, Represents the set of points that the ant has not visited;

[0021] S22, the raw material pipeline is laid at the initial point, and the ants are laid according to the transfer probability Visit from largest to smallest For each point in Each point in is a candidate laying point for the raw material pipeline. The pheromone concentration is updated at each point in the equation, and the volatility coefficient is set to and , initial point Move to point Pheromone concentration The update formula is as follows,

[0022]

[0023] in, Indicates the initial point Move to point The updated pheromone concentration on Indicates the initial point Move to point The pheromone concentration increment on ;

[0024] S23, calculate the initial points in sequence Move to The pheromone size of each point in the , find the point with the largest pheromone , That is, the first laying point after the initial point of the raw material pipeline; the ants crawl to the point , calculate arrive The movement probability of the points that have not been reached in the , continue to update the pheromone, and find Move to The point with the largest pheromone at each point , That is, the second laying point after the initial point of the raw material pipeline; repeat S22 and S23, and set the number of iterations to , when the number of points with the largest pheromone found by the ant is equal to When , the iteration is stopped and all the points with the largest pheromones are output; at this time, all the laying points after the initial point of the raw material pipeline are obtained, which are recorded as a laying point set, and all the laying points are connected in sequence to lay the raw material pipeline to complete the planning of the raw material pipeline; the acetic acid-propylene emulsion raw material to be processed is transmitted to the processing and preparation area through the planned raw material pipeline to obtain the acetic acid-propylene emulsion raw material after transmission.

[0025] The invention uses an ant colony algorithm to plan the optimal path for the acetic acid propylene emulsion raw material to enter the processing and preparation area through the raw material pipeline, sets pheromones and heuristic factors, and releases pheromones during the continuous crawling of ants. The position where the total amount of pheromones released by all ants is the highest is the point on the optimal path. The optimal crawling path of the ants is obtained through continuous iteration, which is the optimal path for the raw material pipeline. The algorithm has a fast convergence speed, strong applicability, and saves costs.

[0026] Preferably, S3 comprises the following steps:

[0027] S31, placing the transferred acetic acid-acrylic emulsion raw material into a reaction container in the processing and preparation area according to the experimental steps; placing a temperature sensor in the reaction container, the temperature sensor automatically adjusts the height of the temperature sensor according to the height of the liquid level inside the reaction container, and the temperature sensor is always immersed in the liquid inside the reaction container. The specific steps are as follows:

[0028] S311, set the temperature sensor to have buoyancy detection points, the temperature sensors are recorded as buoyancy detection set from bottom to top ,in Indicates buoyancy detection point; when the processed acetic acid propylene emulsion raw materials are poured into the reaction container in turn, when the buoyancy detection point at the bottom of the temperature sensor No liquid is detected, and the temperature sensor moves downward until the buoyancy detection point at the top of the temperature sensor is reached. Liquid is detected and movement stops; when the buoyancy detection point at the bottom of the temperature sensor Liquid is detected but the buoyancy detection point at the top of the temperature sensor No liquid is detected, and the temperature sensor moves downward until the buoyancy detection point at the top of the temperature sensor is reached. Liquid is detected and movement stops; when the buoyancy detection point at the top of the temperature sensor Liquid is detected and the temperature sensor does not move;

[0029] S312, using the temperature sensor to measure the temperature in the reaction container at different reaction stages, displaying and recording the temperature in real time;

[0030] S32, obtain The temperature data in the reaction vessels in the processing and preparation areas are selected Temperature data as training set samples, The temperature data are used as test set samples and ; Normalize the training set samples to obtain the normalized training set; Set the number of input nodes in the long short-term memory network to 5, the number of output nodes to 1, and the learning rate to ; Input the normalized training set into the long short-term memory network and set the minimum error to , when the absolute value of the difference between the output temperature and the actual temperature is less than or equal to , get the optimal long short-term memory network parameters, and get the trained long short-term memory network model; otherwise, train until the absolute value of the difference between the output temperature and the actual temperature is less than or equal to ;

[0031] S33, input the test set samples into the trained long short-term memory network model, predict the test set samples, and set the error threshold , when the prediction accuracy is less than or equal to , and obtain the final long short-term memory network model, otherwise repeat S32 and S33 until the measurement accuracy is less than or equal to ;

[0032] S34, using the final long short-term memory network model to predict the temperature in the reaction container in the processing and preparation area, and then using the proportional integral differential algorithm to control the temperature in the reaction container in the processing and preparation area, the specific steps are as follows:

[0033] S341, Setting represents the proportionality coefficient, represents the integration time constant, represents the differential time constant; the final long short-term memory network model is used to sample the temperature in the reaction container in the processing and preparation area, and the proportional integral differential algorithm is used to discretize the error temperature value. The calculation formula is as follows:

[0034]

[0035] in, Indicates The predicted sampling temperature value, Indicates The error temperature value of the predicted sampling, Indicates The error temperature value of the predicted sampling, Indicates The error temperature value of the predicted sampling, ;

[0036] S342, Setting Indicates The error temperature value of the predicted sampling, Predicted sampling temperature value The calculation formula is as follows,

[0037]

[0038] Use The predicted sampling temperature value is subtracted from the The predicted sampling temperature value is used to obtain the predicted sampling temperature difference. , the formula is as follows,

[0039]

[0040] Set the first threshold If the absolute value of the difference between the predicted temperature and the predicted sampling temperature is less than or equal to , continue heating to maintain the predicted temperature value; if the predicted temperature value is less than the predicted sampling temperature difference and the absolute value is greater than , increase heating to increase the predicted time set temperature value, if the predicted time set temperature value is greater than the predicted sampling temperature difference and the absolute value is greater than , reduce heating and lower the set temperature value at the prediction time;

[0041] Set the second threshold and the rated pressure of the reaction vessel, when the absolute value of the difference between the current reaction vessel pressure and the rated pressure of the reaction vessel is less than , the current reaction vessel pressure is in a normal state; otherwise, the current reaction vessel pressure is in an abnormal state, and the flow rate and gas discharge are controlled until the absolute value of the difference between the current reaction vessel pressure and the rated pressure of the reaction vessel is less than ; Set the third threshold and the rated flow rate of the reaction vessel, when the absolute value of the difference between the current reaction vessel flow rate and the rated flow rate of the reaction vessel is less than , the current reaction container flow is in a normal state; otherwise, the current reaction container flow is in an abnormal state, and the aceto-acrylic emulsion raw material is adjusted until the absolute value of the difference between the current reaction container flow and the rated flow of the reaction container is less than ; Set the fourth threshold and the rated liquid level of the reaction container, when the absolute value of the difference between the current liquid level of the reaction container and the rated liquid level of the reaction container is less than , the current liquid level of the reaction container is in a normal state; otherwise, the current liquid level of the reaction container is in an abnormal state, and the flow rate and the aceto-acrylic emulsion raw material are controlled until the absolute value of the difference between the current liquid level of the reaction container and the rated liquid level of the reaction container is less than ;

[0042] S343, continuously and in real time regulate the temperature, pressure, flow rate and liquid level in the reaction container in the processing and preparation area to obtain the acetic acid propylene emulsion in the preparation process.

[0043] The invention predicts the temperature in the reaction vessel in the processing and preparation area through the long short-term memory network model, and uses the predicted temperature value to adjust the three adjustable parameters of the proportional coefficient, integral time constant and differential time constant based on the proportional integral differential algorithm, thereby eliminating the residual error of the control system, realizing rapid control of the system, and quickly regulating the temperature in the reaction vessel, so that the control system has good control performance, and the PID algorithm has a simple structure and good robustness, and the long short-term memory network prediction accuracy is high.

[0044] Preferably, S4 comprises the following steps:

[0045] S41. During the processing of the acetic acid-acrylic emulsion in the preparation process, the processing preparation is tested for air tightness, the processing preparation is fully smeared with soap solution to obtain the processing preparation to be processed, the processing equipment image to be processed is obtained, and a processing equipment image set to be processed is formed, and the horizontal dimension of the processing equipment image to be processed is set to , the vertical dimension is , establish a matrix of processing equipment to be processed as follows,

[0046] ,

[0047] in, Indicates the horizontal image of the processing equipment to be processed Dimension, vertical Dimensional pixels;

[0048] Select a column of the processing equipment matrix to be processed as the processing equipment set ,in Indicates the horizontal image of the processing equipment to be processed Dimension, vertical dimensional pixel points, sorting the grayscale values ​​of the pixel points in the processing equipment set by a median filtering method, replacing the grayscale values ​​of the pixel points in the processing equipment set with the median grayscale values ​​of the pixel points in the processing equipment set, and obtaining a denoised processing equipment image;

[0049] S42, forming a denoised processing equipment image set from the denoised processing equipment images, selecting the first Image of processing equipment after frame denoising , No. Image of processing equipment after frame denoising and Image of processing equipment after frame denoising ,Will , and Perform a difference operation and get Frame difference bubble image , No. Frame difference bubble image and Frame difference bubble image ,Will , and Perform AND operation to obtain the bubble image to be processed; , and Perform edge extraction to obtain edge extracted bubble image , and , perform an AND operation on the edge-extracted bubble image and the bubble image to be processed to obtain a bubble contour image , and ,right , and Background modeling obtains a background image, and the background image and A differential operation is performed to obtain a complete bubble contour image, identify the leaking bubbles, and locate the leaking point. The staff goes to the leaking point to perform maintenance and obtain the prepared acetic acid-acrylic emulsion.

[0050] This invention uses the soap bubble method to detect the airtightness of the processing and preparation based on image recognition, improves the traditional three-frame difference method, completes the missing contours of the bubbles, makes the bubble contours clearer, and effectively suppresses interference. The leak point is located by detecting the leak point bubbles, ensuring the safety and reliability of the preparation process.

[0051] Preferably, the S5 comprises the following steps:

[0052] S51, establishing a database, wherein the database stores parameters and processes; the parameters include the start time of the acetoacetic acid emulsion reaction, the end time of the acetoacetic acid emulsion reaction, the acetoacetic acid emulsion reaction time, the acetoacetic acid emulsion reaction temperature value, and the acetoacetic acid emulsion pressure, flow rate and liquid level; the process includes the order of adding acetoacetic acid emulsion raw materials and the acetoacetic acid emulsion production steps;

[0053] S52. Establish a monitoring platform so that the staff can monitor the parameters and process of preparing the acetic acid propylene emulsion in real time. When problems occur in the preparation of the acetic acid propylene emulsion, check the database and monitoring platform to verify the parameters and process of preparing the acetic acid propylene emulsion.

[0054] Turn on the water bath and heat it up, add 12000 g of water into a 50 L glass reactor, and raise the water temperature in the reactor to above 92 degrees Celsius by a thermometer and the temperature adjustment method of the present invention;

[0055] When the temperature is greater than 80 degrees Celsius, turn on the stirrer and slowly add 1050 grams of polyvinyl alcohol into the reactor and stir until the polyvinyl alcohol is completely dissolved;

[0056] Dissolve and stir the reserved 2700 g of water and 45 g of ammonium persulfate, and take out 30% of the mixed solution for later use;

[0057] 10560 g of vinyl acetate, 1200 g of butyl acrylate and 81 g of acrylic acid were added to a dropping tank, the tank was sealed with a rubber stopper, and a stirrer was turned on for mixing for 5 minutes and then turned off;

[0058] Weigh 150 grams of emulsifier and 36 grams of anhydrous sodium acetate and set aside;

[0059] When the polyvinyl alcohol is completely dissolved, the temperature of the liquid in the reaction kettle is lowered to 75-80 degrees Celsius by the temperature adjustment method of the present invention at 10:50 am;

[0060] 150 g of emulsifier and 36 g of anhydrous sodium acetate were added to the reaction kettle and stirred for 5 minutes to fully dissolve;

[0061] Add the reserved 2640 g of vinyl acetate into the reactor, a large amount of foam will appear, and quickly reduce the speed of the stirrer to 115 rpm;

[0062] After reducing the speed of the stirrer, slowly add the 30% ammonium persulfate solution prepared by dissolving and stirring the reserved 2700 g of water and 45 g of ammonium persulfate into the reactor;

[0063] It was observed that the reaction in the reactor was violent, the pressure was so high that the sealing rubber plug was popped open, and the laboratory air was filled with the reacted gas. The temperature was gradually raised to 87 degrees Celsius by the temperature adjustment method of the present invention, and the temperature was lowered, and the rubber plug was resealed to seal the tank body. Finally, the temperature rose to 91 degrees Celsius and then began to fall back; the time was 11:00 am;

[0064] The over-high temperature was an abnormal problem. After contacting Engineer Yang, a small amount of emulsion was taken out from the glass reactor for observation. No particles or other impurities were found, so the test continued.

[0065] Phenomenon: When ammonium persulfate was added, a violent reaction occurred, but it was also observed that the emulsion changed from its original transparent color to milky white, and when the stirring was at the highest level, the emulsion could be clearly observed to reflect blue light on the glass wall;

[0066] At 11:50 a.m., when the temperature is stabilized at 80 degrees Celsius by the temperature regulation method of the present invention, the mixed monomer mentioned above is added dropwise for a set time of 6 hours;

[0067] Add the reserved 2700 g of water and 45 g of ammonium persulfate to dissolve the remaining 70% of the ammonium persulfate solution during stirring, and set the dropping time to 7 hours;

[0068] Continue to observe the emulsion condition and emulsion temperature changes in the reactor, always keep the emulsion temperature at 80-82 degrees Celsius, and increase the mixer speed by 30 rpm every hour;

[0069] The consistency of the emulsion in the reactor increased with time, and at 3:50 p.m., the speed of the stirrer was increased to 235 rpm;

[0070] The consistency continued to increase, and the speed was increased to 351 rpm at 4:20 pm;

[0071] Lasts until 5:50 p.m.

[0072] After the mixed monomers were added dropwise for 6 hours, the remaining ammonium persulfate solution was added to the reactor all at once, and the temperature of the emulsion was raised to above 85 degrees Celsius by the temperature adjustment method of the present invention, and kept warm for 30 minutes;

[0073] After the heat preservation is completed, the temperature of the emulsion is cooled to below 50 degrees Celsius by the temperature adjustment method of the present invention, and the cooling time is 1.5 hours;

[0074] The emulsion was filtered through a 60-mesh filter and then packaged, and the experiment was completed.

[0075] The present invention also discloses a preparation and regulation system for acetic acid-acrylic emulsion, which specifically comprises: a classification weighing module, an automatic production line module, a parameter control module and a leakage point detection module;

[0076] The classification weighing module is used to identify the type of acetoacetic acid propylene emulsion raw materials and check the weight of the acetoacetic acid propylene emulsion raw materials;

[0077] The automated production line module is used to plan the best raw material pipeline path using an ant colony algorithm, with different production lines delivering different acetic acid propylene emulsion raw materials;

[0078] The parameter control module is used to control the temperature, pressure, flow rate and liquid level in the reaction container in real time to achieve automatic monitoring;

[0079] The leak point detection module is used to perform air tightness detection on processing and preparation based on image recognition, and locate the leak point by detecting the leak point bubble.

[0080] The present invention has the following beneficial effects:

[0081] 1 The invention firstly identifies the types of acetic acid propylene emulsion raw materials, realizes that different raw materials enter different raw material pipelines, and is easy to be processed separately, and then checks the weight of the acetic acid propylene emulsion raw materials to obtain the acetic acid propylene emulsion raw materials to be processed; different raw materials enter different raw material pipelines, and the paths of the raw material pipelines are planned. The ant colony algorithm is used to obtain the optimal raw material pipeline path, and the acetic acid propylene emulsion raw materials are transmitted to the processing and preparation area. The algorithm has a fast convergence speed.

[0082] 2. The invention adjusts the position of the temperature sensor, uses a long short-term memory network to predict the temperature value of the liquid inside the reaction container, combines the use of a proportional integral differential algorithm, uses the predicted temperature value to adjust the proportional coefficient, integral time constant and differential time constant, and compares it with the set temperature value at the prediction time to achieve the purpose of regulating the temperature, and controls the pressure, flow and liquid level in the reaction container. The proportional integral differential algorithm has good regulation effect and high stability, and the long short-term memory network has higher prediction accuracy.

[0083] 3. The invention performs air tightness detection on processing and preparation based on image recognition, locates the leakage point by detecting the leakage point bubble, and ensures the safety and reliability of the preparation process; finally, a database and monitoring platform are established to store and monitor the parameters and processes required for the preparation of acetic acid propylene emulsion, which is convenient for query and management.

[0084] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0086] Figure 1A schematic diagram of the process of temperature regulation of a temperature regulation system for preparing an acetic acid propylene emulsion provided by the present invention. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0089] This embodiment discloses a method for adjusting the temperature of a acetic acid-acrylic acid emulsion, which specifically includes the following contents:

[0090] S1, calculating the weight of the initial acetoacetic acid emulsion raw material and identifying the raw material type to obtain the acetoacetic acid emulsion raw material to be processed; Figure 1 As shown in the first step;

[0091] The S1 comprises the following steps:

[0092] S11, setting a vehicle to load an initial acetoacetic acid propylene emulsion raw material, attaching a QR code to the initial acetoacetic acid propylene emulsion raw material before passing through a raw material pipeline, and realizing raw material type identification according to the QR code, and different initial acetoacetic acid propylene emulsion raw materials enter different raw material pipelines;

[0093] S12, the initial acetoacetic acid propylene emulsion raw material passes through the raw material pipeline, and passes through the quantity detection device and the weight detection device to check the weight of the initial acetoacetic acid propylene emulsion raw material. After the weight of the initial acetoacetic acid propylene emulsion raw material is checked correctly, the acetoacetic acid propylene emulsion raw material to be processed is obtained. The calculation formula is as follows:

[0094]

[0095] in, Indicates the weight of the initial acetic acid propylene emulsion raw material, Indicates the number of types of initial acetic acid propylene emulsion raw materials, Indicates the weight of each initial acetic acid propylene emulsion raw material, Indicates the quantity of each initial acetic acid propylene emulsion raw material, ;

[0096] S2, planning the transmission path of the raw material pipeline, transmitting the to-be-processed acetoacetic acid emulsion raw material to the processing and preparation area through the raw material pipeline, and obtaining the transmitted acetoacetic acid emulsion raw material; Figure 1 As shown in the second step;

[0097] The S2 comprises the following steps:

[0098] S21, the raw material pipeline transports the initial acetic acid propylene emulsion raw material into the processing and preparation area, and plans the raw material pipeline; the ant population is set to , At the moment the ant starts from the initial point Move to point ,and At the moment the ant starts from the initial point Move to point , the initial point As the starting point of the raw material pipeline, set The transfer probability of the ant at time is , the transition probability is the probability of changing the direction of the raw material pipeline planning, and the calculation formula is as follows:

[0099]

[0100] in, Indicates the initial point Move to point The pheromone concentration on Indicates the initial point Move to point The pheromone concentration on Indicates the initial point Move to point The heuristic factor on Indicates the initial point Move to point The heuristic factor on represents pheromone factor, represents the expected heuristic factor, Represents the set of points that the ant has not visited;

[0101] S22, the raw material pipeline is laid at the initial point, and the ants are laid according to the transfer probability Visit from largest to smallest For each point in Each point in is a candidate laying point for the raw material pipeline. The pheromone concentration is updated at each point in the equation, and the volatility coefficient is set to and , initial point Move to point Pheromone concentration The update formula is as follows,

[0102]

[0103] in, Indicates the initial point Move to point The updated pheromone concentration on Indicates the initial point Move to point The pheromone concentration increment on ;

[0104] S23, calculate the initial points in sequence Move to The pheromone size of each point in the , find the point with the largest pheromone , That is, the first laying point after the initial point of the raw material pipeline; the ants crawl to the point , calculate arrive The movement probability of the points that have not been reached in the , continue to update the pheromone, and find Move to The point with the largest pheromone at each point , That is, the second laying point after the initial point of the raw material pipeline; repeat S22 and S23, and set the number of iterations to , when the number of points with the largest pheromone found by the ant is equal to When , the iteration is stopped, and all the points with the largest pheromones are output; at this time, all the laying points after the initial point of the raw material pipeline are obtained, which are recorded as a laying point set, and all the laying points are connected in sequence to lay the raw material pipeline, and the planning of the raw material pipeline is completed; the acetoacetic acid emulsion raw material to be processed is transmitted to the processing and preparation area through the planned raw material pipeline to obtain the acetoacetic acid emulsion raw material after transmission;

[0105] S3, placing the transferred acetoacetic acid propylene emulsion raw material into a reaction container in a processing and preparation area, adjusting the position of the temperature sensor, and predicting the temperature in the reaction container in the processing and preparation area based on a neural network, using a proportional integral differential algorithm to control the temperature in the reaction container in the processing and preparation area, and controlling the pressure, flow rate and liquid level in the reaction container to obtain the acetoacetic acid propylene emulsion in the preparation process; Figure 1 As shown in the third step;

[0106] The S3 comprises the following steps:

[0107] S31, placing the transferred acetic acid-acrylic emulsion raw material into a reaction container in the processing and preparation area according to the experimental steps; placing a temperature sensor in the reaction container, the temperature sensor automatically adjusts the height of the temperature sensor according to the height of the liquid level inside the reaction container, and the temperature sensor is always immersed in the liquid inside the reaction container. The specific steps are as follows:

[0108] S311, set the temperature sensor to have buoyancy detection points, the temperature sensors are recorded as buoyancy detection set from bottom to top ,in Indicates buoyancy detection point; when the processed acetic acid propylene emulsion raw materials are poured into the reaction container in turn, when the buoyancy detection point at the bottom of the temperature sensor No liquid is detected, and the temperature sensor moves downward until the buoyancy detection point at the top of the temperature sensor is reached. Liquid is detected and movement stops; when the buoyancy detection point at the bottom of the temperature sensor Liquid is detected but the buoyancy detection point at the top of the temperature sensor No liquid is detected, and the temperature sensor moves downward until the buoyancy detection point at the top of the temperature sensor is reached. Liquid is detected and movement stops; when the buoyancy detection point at the top of the temperature sensor Liquid is detected and the temperature sensor does not move;

[0109] S312, using the temperature sensor to measure the temperature in the reaction container at different reaction stages, displaying and recording the temperature in real time;

[0110] S32, obtain The temperature data in the reaction vessels in the processing and preparation areas are selected Temperature data as training set samples, The temperature data are used as test set samples and ; Normalize the training set samples to obtain the normalized training set; Set the number of input nodes in the long short-term memory network to 5, the number of output nodes to 1, and the learning rate to ; Input the normalized training set into the long short-term memory network and set the minimum error to , when the absolute value of the difference between the output temperature and the actual temperature is less than or equal to , get the optimal long short-term memory network parameters, and get the trained long short-term memory network model; otherwise, train until the absolute value of the difference between the output temperature and the actual temperature is less than or equal to ;

[0111] S33, input the test set samples into the trained long short-term memory network model, predict the test set samples, and set the error threshold , when the prediction accuracy is less than or equal to , and obtain the final long short-term memory network model, otherwise repeat S32 and S33 until the measurement accuracy is less than or equal to ;

[0112] S34, using the final long short-term memory network model to predict the temperature in the reaction container in the processing and preparation area, and then using the PID (proportional integral differential) algorithm to control the temperature in the reaction container in the processing and preparation area, the specific steps are as follows:

[0113] S341, Setting represents the proportionality coefficient, represents the integration time constant, represents the differential time constant; the final long short-term memory network model is used to sample the temperature in the reaction container in the processing and preparation area, and the proportional integral differential algorithm is used to discretize the error temperature value. The calculation formula is as follows:

[0114]

[0115] in, Indicates The predicted sampling temperature value, Indicates The error temperature value of the predicted sampling, Indicates The error temperature value of the predicted sampling, Indicates The error temperature value of the predicted sampling, ;

[0116] S342, Setting Indicates The error temperature value of the predicted sampling, Predicted sampling temperature value The calculation formula is as follows,

[0117]

[0118] Use The predicted sampling temperature value is subtracted from the The predicted sampling temperature value is the predicted sampling temperature difference. , the formula is as follows,

[0119]

[0120] Set the first threshold If the absolute value of the difference between the predicted temperature and the predicted sampling temperature is less than or equal to , continue heating to maintain the predicted temperature value; if the predicted temperature value is less than the predicted sampling temperature difference and the absolute value is greater than , increase heating to increase the predicted time set temperature value, if the predicted time set temperature value is greater than the predicted sampling temperature difference and the absolute value is greater than , reduce heating and lower the set temperature value at the prediction time;

[0121] Set the second threshold and the rated pressure of the reaction vessel, when the absolute value of the difference between the current reaction vessel pressure and the rated pressure of the reaction vessel is less than , the current reaction vessel pressure is in a normal state; otherwise, the current reaction vessel pressure is in an abnormal state, and the flow rate and gas discharge are controlled until the absolute value of the difference between the current reaction vessel pressure and the rated pressure of the reaction vessel is less than ; Set the third threshold and the rated flow rate of the reaction vessel, when the absolute value of the difference between the current reaction vessel flow rate and the rated flow rate of the reaction vessel is less than , the current reaction container flow is in a normal state; otherwise, the current reaction container flow is in an abnormal state, and the aceto-acrylic emulsion raw material is adjusted until the absolute value of the difference between the current reaction container flow and the rated flow of the reaction container is less than ; Set the fourth threshold and the rated liquid level of the reaction container, when the absolute value of the difference between the current liquid level of the reaction container and the rated liquid level of the reaction container is less than , the current liquid level of the reaction container is in a normal state; otherwise, the current liquid level of the reaction container is in an abnormal state, and the flow rate and the aceto-acrylic emulsion raw material are controlled until the absolute value of the difference between the current liquid level of the reaction container and the rated liquid level of the reaction container is less than ;

[0122] S343, continuously and in real time regulating the temperature, pressure, flow rate and liquid level in the reaction container in the processing and preparation area to obtain the acetopropyl acetate emulsion in the preparation process;

[0123] S4, for the acetic acid propylene emulsion in the preparation process, perform air tightness detection on the processing and preparation based on image recognition, locate the leak point by detecting the leak point bubble, and perform maintenance at the leak point to obtain the prepared acetic acid propylene emulsion; Figure 1 As shown in the fourth step;

[0124] The S4 comprises the following steps:

[0125] S41. During the processing of the acetic acid-acrylic emulsion in the preparation process, the processing preparation is tested for air tightness, the processing preparation is fully smeared with soap solution to obtain the processing preparation to be processed, the processing equipment image to be processed is obtained, and a processing equipment image set to be processed is formed, and the horizontal dimension of the processing equipment image to be processed is set to , the vertical dimension is , establish a matrix of processing equipment to be processed as follows,

[0126] ,

[0127] in, Indicates the horizontal image of the processing equipment to be processed Dimension, vertical Dimensional pixels;

[0128] Select a column of the processing equipment matrix to be processed as the processing equipment set ,in Indicates the horizontal image of the processing equipment to be processed Dimension, vertical dimensional pixel points, sorting the grayscale values ​​of the pixel points in the processing equipment set by a median filtering method, replacing the grayscale values ​​of the pixel points in the processing equipment set with the median grayscale values ​​of the pixel points in the processing equipment set, and obtaining a denoised processing equipment image;

[0129] S42, forming a denoised processing equipment image set from the denoised processing equipment images, selecting the first Image of processing equipment after frame denoising , No. Image of processing equipment after frame denoising and Image of processing equipment after frame denoising ,Will , and Perform a difference operation and get Frame difference bubble image , No. Frame difference bubble image and Frame difference bubble image ,Will , and Perform AND operation to obtain the bubble image to be processed; , and Perform edge extraction to obtain edge extracted bubble image , and , perform an AND operation on the edge-extracted bubble image and the bubble image to be processed to obtain a bubble contour image , and ,right , and Background modeling obtains a background image, and the background image and Perform differential calculation to obtain a complete bubble contour image, identify the leaking bubble, and locate the leaking point. The staff will go to the leaking point to perform maintenance and obtain the prepared acetic acid propylene emulsion;

[0130] S5, establish a database and a monitoring platform, the database stores the parameters and processes for preparing the acetoacetic acid propylene emulsion, and the monitoring platform monitors the parameters and processes for preparing the acetoacetic acid propylene emulsion in real time; Figure 1 As shown in the fifth step;

[0131] The S5 comprises the following steps:

[0132] S51, establishing a database, wherein the database stores parameters and processes; the parameters include the start time of the acetoacetic acid emulsion reaction, the end time of the acetoacetic acid emulsion reaction, the acetoacetic acid emulsion reaction time, the acetoacetic acid emulsion reaction temperature value, and the acetoacetic acid emulsion pressure, flow rate and liquid level; the process includes the order of adding acetoacetic acid emulsion raw materials and the acetoacetic acid emulsion production steps;

[0133] S52. Establish a monitoring platform so that the staff can monitor the parameters and process of preparing the acetic acid propylene emulsion in real time. When problems occur in the preparation of the acetic acid propylene emulsion, check the database and monitoring platform to verify the parameters and process of preparing the acetic acid propylene emulsion.

[0134] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0135] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for adjusting the temperature of an acetic acid propylene emulsion, characterized in that: The steps include: S1. Calculate the weight of the initial acetoacetic acid propylene emulsion raw material and identify the raw material type to obtain the acetoacetic acid propylene emulsion raw material to be processed; specifically comprising: Before the initial acetoacetic acid propylene emulsion raw material passes through the raw material pipeline, the raw material type is identified and the weight of the initial acetoacetic acid propylene emulsion raw material is checked to obtain the acetoacetic acid propylene emulsion raw material to be processed; S2, planning the transmission path of the raw material pipeline, different production lines transmit different acetoacetic acid propylene emulsion raw materials, and transmitting the acetoacetic acid propylene emulsion raw materials to be processed to the processing and preparation area through the raw material pipeline to obtain the acetoacetic acid propylene emulsion raw materials after transmission; specifically including: S21, the raw material pipeline transports the initial acetic acid propylene emulsion raw material into the processing and preparation area, and plans the raw material pipeline; the ant population is set to , At this moment, some ants move from the initial point Move to point ,and At this moment, some ants move from the initial point Move to point , the initial point As the starting point of the raw material pipeline, set The transfer probability of the ant at time is , the transition probability is the probability of changing the direction of the raw material pipeline planning, and the calculation formula is as follows: ， in, Indicates the initial point Move to point The pheromone concentration on Indicates the initial point Move to point The pheromone concentration on Indicates the initial point Move to point The heuristic factor on Indicates the initial point Move to point The heuristic factor on represents pheromone factor, represents the expected heuristic factor, Represents the set of points that the ant has not visited; S22, the raw material pipeline is laid at the initial point, and the ants are laid according to the transfer probability Visit from largest to smallest For each point in Each point in is a candidate laying point for the raw material pipeline. The pheromone concentration is updated at each point in the equation, and the volatility coefficient is set to and , initial point Move to point Pheromone concentration The update formula is as follows: ， in, Indicates the initial point Move to point The updated pheromone concentration on Indicates the initial point Move to point The increase in pheromone concentration on S23, calculate the initial points in sequence Move to The pheromone size of each point in the , find the point with the largest pheromone , That is, the first laying point after the initial point of the raw material pipeline; the ants crawl to the point , calculate arrive The movement probability of the points that have not been reached in the , continue to update the pheromone, and find Move to The point with the largest pheromone at each point , That is, the second laying point after the initial point of the raw material pipeline; repeat S22 and S23, and set the number of iterations to , when the number of points with the largest pheromone found by the ant is equal to When , the iteration stops and all points with the largest pheromone are output; S3, placing the transferred acetoacetic acid propylene emulsion raw material into a reaction container in a processing and preparation area, adjusting the position of the temperature sensor, and predicting the temperature in the reaction container in the processing and preparation area based on a neural network, using a proportional integral differential algorithm to control the temperature in the reaction container in the processing and preparation area, and controlling the pressure, flow rate and liquid level in the reaction container to obtain the acetoacetic acid propylene emulsion in the preparation process; specifically comprising: S31, placing the transferred acetic acid-propylene emulsion raw material into the reaction container in the processing and preparation area according to the experimental steps, and adjusting the position of the temperature sensor; S32, obtain The temperature data in the reaction vessels in the processing and preparation areas are selected Temperature data as training set samples, The temperature data are used as test set samples and ; Normalize the training set samples to obtain the normalized training set; Set the number of input nodes in the long short-term memory network to 5, the number of output nodes to 1, and the learning rate to ; Input the normalized training set into the long short-term memory network and set the minimum error to , when the absolute value of the difference between the output temperature and the actual temperature is less than or equal to , get the optimal long short-term memory network parameters, and get the trained long short-term memory network model; otherwise, train until the absolute value of the difference between the output temperature and the actual temperature is less than or equal to ; S33, input the test set samples into the trained long short-term memory network model, predict the test set samples, and set the error threshold , when the prediction accuracy is less than or equal to , and obtain the final long short-term memory network model, otherwise repeat S32 and S33 until the measurement accuracy is less than or equal to ; S34, using the final long short-term memory network model to predict the temperature in the reaction container in the processing and preparation area, and then using a proportional integral differential algorithm to control the temperature in the reaction container in the processing and preparation area; S4, during the preparation process, the processing equipment of the acetoacetic acid propylene emulsion is subjected to air tightness detection based on image recognition, the leaking point is located by detecting the leaking point bubble, and the leaking point is inspected and repaired to obtain the prepared acetoacetic acid propylene emulsion; S5. Establish a database and a monitoring platform, wherein the database stores the parameters and process for preparing the acetoacetic acid propylene emulsion, and the monitoring platform monitors the parameters and process for preparing the acetoacetic acid propylene emulsion in real time.

2. The temperature regulating method for preparing acetic acid propylene emulsion according to claim 1, characterized in that: All laying points after the initial point of the raw material pipeline are obtained, recorded as a laying point set, and the laying points in the laying point set are connected in sequence to lay the raw material pipeline, thereby completing the planning of the raw material pipeline; the acetic acid propylene emulsion raw material to be processed is transmitted to the processing and preparation area through the planned raw material pipeline to obtain the acetic acid propylene emulsion raw material after transmission.

3. The temperature regulating method for preparing acetic acid propylene emulsion according to claim 2, characterized in that: The S34 specifically includes: S341, using the final long short-term memory network model to sample the temperature in the reaction container in the processing and preparation area, and calculating the predicted sampling temperature difference; setting a first threshold , if the absolute value of the difference between the set temperature value at the prediction time and the predicted sampling temperature is less than or equal to , continue heating to maintain the predicted temperature value; if the predicted temperature value is less than the predicted sampling temperature difference and the absolute value is greater than , increase heating to increase the predicted time set temperature value, if the predicted time set temperature value is greater than the predicted sampling temperature difference and the absolute value is greater than , reduce heating and lower the set temperature value at the prediction time; S342, continuously and in real time regulate the temperature, pressure, flow rate and liquid level in the reaction container in the processing and preparation area to obtain the acetic acid propylene emulsion in the preparation process.

4. The temperature regulating method for preparing acetic acid propylene emulsion according to claim 3, characterized in that: The S4 specifically includes: S41. During the preparation process, the processing equipment of the acetic acid-acrylic emulsion is tested for air tightness, and the processing equipment is fully coated with soap solution to obtain the processing equipment to be processed, and an image of the processing equipment to be processed is obtained to form a set of images of the processing equipment to be processed, and the horizontal dimension of the image of the processing equipment to be processed is set to , the vertical dimension is , establish a matrix of processing equipment to be processed as follows, ， in, Indicates the horizontal image of the processing equipment to be processed Dimension, vertical Dimensional pixels; Select a column of the processing equipment matrix to be processed as the processing equipment set ,in Indicates the horizontal image of the processing equipment to be processed Dimension, vertical dimensional pixels, sort the grayscale values ​​of the pixels in the processing equipment set by using the median filtering method, and replace the grayscale values ​​of the pixels in the processing equipment set with the median grayscale values ​​of the pixels in the processing equipment set to obtain a denoised processing equipment image; S42, forming a denoised processing equipment image set from the denoised processing equipment images, selecting the first Image of processing equipment after frame denoising , No. Image of processing equipment after frame denoising and Image of processing equipment after frame denoising ,Will , and Perform a difference operation and get Frame difference bubble image , No. Frame difference bubble image and Frame difference bubble image ,Will , and Perform AND operation to obtain the bubble image to be processed; , and Perform edge extraction to obtain edge extracted bubble image , and , perform an AND operation on the edge-extracted bubble image and the bubble image to be processed to obtain a bubble contour image , and ,right , and Background modeling obtains a background image, and the background image and A differential operation is performed to obtain a complete bubble contour image, identify the leaking bubbles, and locate the leaking point. The staff goes to the leaking point to perform maintenance and obtain the prepared acetic acid-acrylic emulsion.

5. The temperature regulating method for preparing acetic acid propylene emulsion according to claim 4, characterized in that: The S5 specifically includes: S51. Establish a database to store the parameters and process for preparing the acetic acid propylene emulsion; establish a monitoring platform to monitor the parameters and process for preparing the acetic acid propylene emulsion in real time.

6. A temperature regulating system for preparing acetoacrylic acid emulsion, used to implement a temperature regulating method for preparing acetoacrylic acid emulsion as claimed in any one of claims 1 to 5, characterized in that: Specifically include: Classification weighing module, automated production line module, parameter control module and leak point detection module; The classification weighing module is used to identify the type of acetoacetic acid propylene emulsion raw materials and check the weight of the acetoacetic acid propylene emulsion raw materials; The automated production line module is used to plan the best raw material pipeline path using an ant colony algorithm, with different production lines delivering different acetic acid propylene emulsion raw materials; The parameter control module is used to control the temperature, pressure, flow rate and liquid level in the reaction container in real time to achieve automatic monitoring; The leak point detection module is used to perform air tightness detection on processing equipment based on image recognition, and locate the leak point by detecting the leak point bubble.

Images