A preparation process of high-strength tile adhesive

Abstract

The present application relates to the technical field of new wall materials, and specifically to a preparation process of a high-strength tile adhesive, which process includes: weighing the ingredients for preparing the tile adhesive, mixing and stirring them evenly; subjecting the prepared mixture to a aging treatment, and improving the objective function of the model predictive control algorithm based on the temperature distribution of the infrared thermal imaging data of the aging material at each acquisition moment during the gradient cooling, and using the prediction results to control the cooling gradient of the material in the cooling tank; drying and dehydrating the material after standing, and obtaining a powder; weighing silicate cement, silica powder, bisphenol A, talcum powder and fine aggregate according to the formula ratio, and stirring the obtained powder until uniform, to obtain a tile adhesive for a new wall material. The present application aims to control the cooling rate in the process of preparing the tile adhesive, and improve the durability and reliability of the tile adhesive for the new wall material.

Description

Technical Field

[0001] The present application relates to the technical field of novel wall materials, and in particular to a preparation process of a high-strength tile adhesive. Background Art

[0002] High-strength tile adhesives are an important development direction for the bonding technology of building decoration materials. With the widespread application of new wall materials such as ceramic slabs in building decoration, higher requirements are placed on the performance of adhesives. Traditional cement mortar can no longer meet the bonding needs of new wall materials. Therefore, it is particularly important to develop tile adhesives with stronger bonding, better construction performance and environmental protection characteristics. High-strength tile adhesives improve the bonding strength of new wall materials, as well as water resistance, freeze-thaw resistance, aging resistance and other properties. They are of great significance for improving the quality of building decoration, reducing environmental impact and promoting the sustainable development of the industry.

[0003] During the process of gradient cooling of the materials for preparing tile adhesives in the cooling tank, if the cooling in the cooling tank is too slow, the preparation efficiency will be affected; however, if the cooling in the cooling tank is too fast, the temperature distribution inside the material will be uneven, causing changes in thermal stress, density and crystallization behavior, and further affecting the microstructure and physical properties of the material, such as hardness and elastic modulus, etc. In addition, it may also cause changes in chemical activity, differences in physical properties, moisture migration and uneven thermal expansion, and ultimately affect the overall dimensional stability and performance of the material. In the prior art, it is impossible to solve the impact of uneven temperature distribution of different substances in the material caused by too fast a cooling rate, resulting in thermal stress caused by rapid temperature changes, affecting the durability and reliability of the tile adhesive. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a preparation process of a high-strength tile adhesive to solve the existing problems.

[0005] The preparation process of a high-strength tile adhesive in this application adopts the following technical solution:

[0006] An embodiment of the present application provides a preparation process of a high-strength tile adhesive, the process comprising the following steps:

[0007] S1, ingredients: weigh silicate cement, latex powder, silica powder, bisphenol A, glass powder, talcum powder, fine aggregate, reinforcing agent, waterproofing agent and filler; add appropriate amount of water to mix and stir evenly;

[0008] S2, aging: aging the mixture prepared in S1;

[0009] S3, gradient cooling: placing the matured material in a cooling tank for gradient cooling, cooling to room temperature 20°C and then letting it stand; wherein the control method of the cooling gradient is specifically as follows:

[0010] Obtain infrared thermal imaging data of the material at each acquisition moment when the material is cooled by gradient;

[0011] Based on infrared thermal imaging data, the temperature gradient changes of the same material during the cooling process, as well as the position characteristics and temperature distribution of different materials, are reflected to determine the temperature difference centrality factor;

[0012] The objective function of the model predictive control algorithm is improved based on the temperature difference centrality factor of all acquisition moments, and the prediction results are used to control the temperature gradient of the material in the cooling tank.

[0013] S4, drying: drying and removing water from the material after standing in S3 to obtain powder;

[0014] S5, stirring: weigh silicate cement, silica powder, bisphenol A, talcum powder and fine aggregate according to the formula ratio, add the powder obtained in S4 and stir until uniform, so as to obtain a tile adhesive for a new wall material.

[0015] Preferably, the weight ratio of silicate cement, latex powder, silica powder, bisphenol A, glass powder, talcum powder, fine aggregate, reinforcing agent, waterproofing agent and filler in S1 is 85:18:8:5.5:7:2:9:8:6:12.

[0016] Preferably, the silicate cement is a mixture of white silicate cement, pozzolanic silicate cement and slag silicate cement, and the mass percentage of white silicate cement is not less than 92.7%.

[0017] Preferably, the fine aggregate is quartz sand with a fineness modulus of 2.0.

[0018] Preferably, the aging temperature in S2 is 55-60° C., and the aging time is 15-30 min.

[0019] Preferably, the method for determining the temperature difference centrality factor includes:

[0020] A clustering algorithm is used to obtain several clusters of infrared thermal imaging data and their corresponding cluster centers;

[0021] Based on the distance and position distribution between data points in the cluster, the homogeneous temperature grade gradient effect of the cluster is determined;

[0022] The temperature difference centrality factor is obtained based on the proximity relationship between cluster centers, the difference in connection paths, and the difference in homogeneous temperature grade gradient effects between the cluster clusters to which the cluster centers belong.

[0023] Preferably, the method for determining the homogeneous temperature grade gradient effect includes:

[0024] All data points in the cluster are evenly divided into several levels according to the infrared thermal radiation values, which are recorded as infrared thermal radiation levels respectively;

[0025] Calculate the coordinate standard deviation of all data points at each infrared thermal radiation level in the cluster; the coordinate standard deviation is the square root of the cumulative sum of the squares of the standard deviations between the horizontal and vertical coordinates of all the data points;

[0026] For any data point at each infrared heat radiation level in the cluster, calculate the distance between the data point and the cluster center of the cluster to which it belongs, and record it as the center distance of the data point; calculate the minimum value of the distance between the data point and the data points on the boundary of the cluster to which it belongs, and record it as the boundary distance of the data point;

[0027] Negatively fuse the product of the center distance and the boundary distance with the coordinate standard deviation to obtain the grade gradient effect of any data point;

[0028] The level gradient effects of all data points at each infrared thermal radiation level in the cluster are summed, and the average value of the summed results at all infrared thermal radiation levels in the cluster is taken as the homogeneous temperature level gradient effect of the cluster.

[0029] Preferably, the method for determining the temperature difference centrality factor includes:

[0030] All cluster centers are regarded as nodes respectively. For any node, undirected edges are established between any node and the first preset number of nodes closest to any node. The weight of the undirected edge is the difference of homogeneous temperature grade gradient effect between the two connected nodes.

[0031] Obtain all paths of any node in the undirected edge, and record them as the node sequence of any node; calculate the average value of the infrared thermal imaging radiation value in the cluster where any node is located; calculate the standard deviation of the average value of the infrared thermal imaging radiation value of all nodes in each node sequence, and record it as the radiation confusion factor of each node sequence;

[0032] For each node connected to any node through an undirected edge, the difference between the homogeneous temperature grade gradient effect of the any node and each node is calculated, and the difference between all nodes connected to the any node through an undirected edge is averaged, multiplied by the proximity centrality of the any node, and the result is recorded as the homogeneous thermal stress difference of the any node;

[0033] For each node sequence of any node, the product of the homogeneous thermal stress difference of any node, the length of each node sequence and the radiation confusion factor of each node sequence is calculated as the temperature difference coefficient of each node sequence;

[0034] The product of all node sequences of all nodes in the infrared thermal imaging data is averaged to obtain the temperature difference centrality factor of the infrared thermal imaging data.

[0035] Preferably, the method for controlling the temperature drop gradient of the material in the cooling tank comprises:

[0036] Set the expected temperature change; calculate the actual temperature change between two adjacent acquisition moments during the acquisition process;

[0037] Calculating the ratio of the actual temperature change to the preset collection time interval; calculating the ratio of the expected temperature change to the preset collection time interval; calculating the difference between the two ratios, recorded as the first difference;

[0038] Get the temperature difference centrality factor at each acquisition moment;

[0039] Calculate the difference between the target temperature at the end of the cooling process and the actual temperature of the material at each sampling moment, and record it as the second difference;

[0040] Determine the objective function of each acquisition moment based on the first difference, the second difference and the temperature difference centrality factor at each acquisition moment; wherein the first difference, the second difference and the temperature difference centrality factor at each acquisition moment are all positively correlated with the objective function;

[0041] The model predictive control algorithm is used to output a control signal for the infrared thermal imaging data at each acquisition moment in the cooling tank, and the control signal is used to regulate the temperature reduction gradient of the material in the cooling tank.

[0042] Preferably, the moisture content of the material after drying and dehydration in S4 is less than 5.5%.

[0043] In the above scheme, the present application realizes the regulation of the cooling gradient during the preparation of high-strength tile adhesives for new wall materials. Compared with the prior art, which does not take into account the impact of uneven temperature distribution of different substances in the material caused by too fast cooling speed, resulting in poor quality of tile adhesives for new wall materials, the present application proposes a preparation process for high-strength tile adhesives. Aiming at the problems of thermal stress and uneven temperature distribution in the material preparation process, a homogeneous temperature grade gradient effect is constructed based on chameleon clustering and distance to further reflect the uniformity of temperature distribution and thermal stress in the clusters, so as to identify areas with higher thermal stress that may exist inside the material; in view of the uneven temperature distribution during the cooling process and the difference in thermal stress between nodes, a temperature difference centrality factor is constructed based on an undirected graph to reflect the position importance of the node in the network and the relationship between the nodes. The differences in the gradient effects of homogeneous temperature grades and the uneven temperature distribution in the node sequence are used to quantify the contribution of the thermal stress differences of the nodes in a specific sequence to the overall temperature distribution unevenness; for the precise control of the cooling gradient in the cooling tank to reduce thermal stress and improve material durability, the temperature difference centrality factor is used as a weight factor in the objective function to improve the MPC algorithm. By emphasizing the attention to the uneven temperature distribution, the MPC algorithm can more accurately control the cooling gradient, make the actual cooling gradient closer to the expected value, and make the material temperature close to the target temperature at the end of cooling, which solves the problem of uneven temperature distribution of different substances in the material caused by the cooling rate in the process of preparing high-strength tile adhesives for new wall materials in the existing technology, thereby reducing the thermal stress caused by rapid temperature changes and improving the durability and reliability of the material. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A flow chart of a preparation process of a high-strength tile adhesive provided in this application;

[0046] Figure 2 A flow chart of the steps of controlling the temperature drop gradient in the process of preparing the tile adhesive provided in this application;

[0047] Figure 3 Schematic diagram of material temperature and infrared thermal imaging data collection provided for this application;

[0048] Figure 4A flow chart of the steps for determining the temperature difference centrality factor provided for this application. DETAILED DESCRIPTION

[0049] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the preparation process of a high-strength tile adhesive proposed in the present application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0050] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0051] The specific scheme of the preparation process of a high-strength tile adhesive provided by the present application is described in detail below with reference to the accompanying drawings.

[0052] An embodiment of the present application provides a process for preparing a high-strength tile adhesive.

[0053] Specifically, the following high-strength tile adhesive preparation process is provided, please refer to Figure 1 , the process comprises the following steps:

[0054] S1, ingredients: weigh silicate cement, latex powder, silica powder, bisphenol A, glass powder, talcum powder, fine aggregate, reinforcing agent, waterproofing agent and filler in a weight ratio of 85:18:8:5.5:7:2:9:8:6:12. Add an appropriate amount of water, the amount of water added is about 1.5 times the total mass, mix and stir evenly.

[0055] The silicate cement is a mixture of white silicate cement, pozzolanic silicate cement and slag silicate cement, and the mass percentage of white silicate cement is not less than 92.7%; the latex powder includes modified starch, seaweed extract, xanthan gum and guar gum, the viscosity of the latex powder is 40000mPa.s, and the modified starch is starch grafted with potassium methacrylate; the fine aggregate is quartz sand with a fineness modulus of 2.0; the reinforcing agent is a combination of any two or more of gypsum powder, montmorillonite saponite, molecular sieve and activated white clay, and the montmorillonite saponite is (Ca 0.25 (Mg,Fe 2 +)3(Si,Al)4O 10(OH)2·4(H2O)); the waterproofing agent is a mixture of sodium tripolyphosphate and silicone waterproofing agent, and the mass fraction of sodium tripolyphosphate is 19.0%; the fillers are pearl powder, blast furnace gas ash, nano titanium dioxide, and silicon dioxide, and the mass ratio is 0.02:17:0.01:200, and the fineness is 200 mesh.

[0056] S2, aging: the mixture prepared in S1 is aging treated at a aging temperature of 55-60° C. and a aging time of 15-30 min.

[0057] In the first embodiment of the present application, the aging temperature is 55° C. and the aging time is 20 minutes.

[0058] In the second embodiment of the present application, the aging temperature is 60° C. and the aging time is 10 minutes.

[0059] In the third embodiment of the present application, the aging temperature is 57° C. and the aging time is 15 minutes.

[0060] S3, gradient cooling: Place the matured material in a cooling tank for cooling. The material is cooled according to the gradient cooling method. The material cooling gradient is 0.8-1.5℃ / min. After cooling to room temperature 20℃, let it stand.

[0061] This application aims at the changes in the process of preparing tile adhesives, and further optimizes the control of the gradient cooling process to achieve uniform distribution of material temperature. In this application, the flow chart of the steps of cooling gradient control in the process of preparing tile adhesives is as shown in the attached figure. Figure 2 As shown, specifically:

[0062] The first major step is to obtain infrared thermal imaging data at each acquisition moment when the material is subjected to gradient cooling.

[0063] Place the matured material in a cooling trough, place a thermistor at the bottom of the cooling trough to collect the temperature of the material in the cooling trough, fix the infrared thermal imaging device on a stable bracket or tripod to avoid image jitter affecting the measurement accuracy, and calibrate the lens of the infrared thermal imaging device to ensure that it is accurately aimed at the material in the cooling trough. Use the infrared thermal imaging device to collect infrared thermal imaging data of the material in the cooling trough. The collection interval of material temperature and infrared thermal imaging data is 1 minute, which can be set by the implementer according to the actual situation.

[0064] In this application, the material temperature and infrared thermal imaging data collection schematic diagram is as shown in the attached Figure 3 shown. Figure 3 The number 1 is a cooling tank, 2 is an infrared imaging device, and 3 is a thermistor.

[0065] In the infrared thermal imaging data of the cooling tank, the numerical value of each data point represents the infrared thermal radiation value of the area where the data point is located. The infrared thermal radiation values ​​of all data points are mapped to the range of 0 to 1 through normalization processing to simplify the subsequent calculation process and adapt to different analysis needs.

[0066] The second major step is to determine the temperature difference centrality factor based on infrared thermal imaging data to reflect the temperature gradient changes of the same material during the cooling process, as well as the position characteristics and temperature distribution between different materials.

[0067] Gradient cooling is crucial in the preparation process of tile adhesives, mainly affecting their strength and shrinkage. It reduces the thermal stress caused by rapid temperature changes by slowly lowering the temperature, thereby reducing the microscopic cracks and defects inside the material, enhancing the overall strength, and avoiding uneven shrinkage caused by too fast cooling, reducing internal stress and cracks, and improving the durability of the material. Because materials with different chemical compositions and physical states have significant differences in thermodynamic behavior, the heat capacity and thermal conductivity of each component such as silicate cement, latex powder, and silica powder directly affect the heat dissipation rate, and the thermal stability, phase change characteristics, thermal expansion of the material, and environmental factors such as the thermal conductivity of the surrounding medium will affect the cooling process. Therefore, when formulating the cooling gradient, it is necessary to ensure that the material can achieve the required performance during the cooling process, and avoid unnecessary thermal stress and structural damage caused by different temperature reduction rates of different materials.

[0068] First, infrared thermal imaging data is used as input, and the distance between the data points is used as the distance metric. In this embodiment, the threshold parameter and the neighborhood size are set to 5. A clustering algorithm is used to output several clusters and their corresponding cluster centers. It is assumed that G clusters are output after clustering. Each cluster can represent a set of data points with similar thermal radiation intensity. These clusters can reveal the uniformity of temperature distribution and the consistency of cooling rate during the cooling process of the material, and characterize the potential thermal stress and crack area, as well as the heat exchange between the material and the environmental medium. Among them, the chameleon clustering algorithm is a well-known technology and will not be repeated.

[0069] In this application, the flow chart of the steps for determining the temperature difference centrality factor is as shown in the attached Figure 4 As shown, specifically:

[0070] In the first small step, the homogeneous temperature grade gradient effect of the clusters is determined based on the distance and location distribution between the data points within the clusters.

[0071] The infrared thermal radiation values ​​of all data points have been normalized to within the range of 0 to 1. The normalized infrared thermal radiation values ​​are evenly divided into ten levels, which are respectively recorded as infrared thermal radiation levels, that is, the range of each infrared thermal radiation level is 0.1.

[0072] The coordinates of all level data points in the infrared thermal imaging data are extracted and recorded, and the coordinate standard deviation of all data points at each infrared thermal radiation level in the cluster is calculated; the coordinate standard deviation is the square root of the cumulative sum of the squares of the standard deviations between the horizontal and vertical coordinates of all the data points.

[0073] In this embodiment, taking all data points at the jth infrared thermal radiation level in the gth cluster as an example, the standard deviation of the coordinates of all data points at the jth infrared thermal radiation level is calculated and recorded as σ g,j It should be noted that the two-dimensional coordinates of the data point in the image take the lower left corner of the infrared thermal imaging data as the origin, the horizontal axis is the boundary of the bottom row of data in the infrared thermal imaging data, and the vertical axis is the boundary of the leftmost row of data in the infrared thermal imaging data. The standard deviation of the data point coordinates is the square root of the cumulative sum of the squares of the standard deviations between the horizontal and vertical axis coordinates of all data points at the same level.

[0074] For any data point at each infrared thermal radiation level in the cluster, calculate the distance between the data point and the cluster center of the cluster to which it belongs, and record it as the center distance of the data point; calculate the minimum value of the distance between the data point and the data points on the boundary of the cluster to which it belongs, and record it as the boundary distance of the data point; negatively fuse the product of the center distance and the boundary distance with the coordinate standard deviation to obtain the level gradient effect of the data point; sum the level gradient effects of all data points at each infrared thermal radiation level in the cluster, and take the average value of the summed results at all infrared thermal radiation levels in the cluster as the homogeneous temperature level gradient effect of the cluster.

[0075] In this embodiment, the hth data point under the jth infrared thermal radiation level is taken as an example: suppose there are H data points under the jth infrared thermal radiation level, calculate the distance between the coordinates of the hth data point under the jth infrared thermal radiation level and the center of its cluster, record it as D1, and take the coordinates of the hth data point under the jth infrared thermal radiation level as the center, traverse the distance between it and the data points on the boundary of the cluster where it is located, find the shortest distance between the center and the data points on the boundary, record it as D2, for each infrared thermal radiation level j, perform the following operations: for each data point under level j, calculate the product of D1 and D2 for each data point, attenuate the result of the product through the negative power of the exponential function e, and multiply the result of the exponential function by σ under the infrared thermal radiation level g,j, the product result is recorded as the grade gradient effect of each data point under the jth infrared thermal radiation level; the grade gradient effects of all data points under the jth infrared thermal radiation level are summed to obtain the sum under the jth infrared thermal radiation level, and then the sums under all infrared thermal radiation levels are averaged to obtain the homogeneous temperature grade gradient effect Eg of the gth cluster g The distance set in this embodiment is the Manhattan distance, which is a well-known technique and will not be described in detail.

[0076] It should be understood that the obtained homogeneous temperature gradient effect characteristic value Eg g It is used to evaluate the uniformity of temperature distribution and thermal stress of a specific cluster g during the cooling process; the calculation result of the exponential function is the weight coefficient, which is significant in that it highlights the position of the data points in the infrared thermal radiation level j. The greater the distance between the selected data point and the cluster center, the farther the point is from the cluster center, and the greater the shortest distance between the selected data point and the boundary data point, the farther the point is from the cluster boundary. Due to the irregular shape of the cluster, the product of the distance between the selected data point and the cluster center and the shortest distance to the boundary data point of cluster g provides a comprehensive measure of the relative position of the data point relative to the cluster center and the boundary. The larger the value, the more likely the selected data point is to be located deep inside the cluster, both far from the cluster center and the boundary. The smaller the weight coefficient; multiplying the exponential function by the coordinate standard deviation and summing them can reflect the average temperature distribution and thermal stability of the entire cluster during the cooling process. The larger the value, the more uniform the temperature distribution within the cluster, the smaller the thermal stress, and the stronger the level gradient effect Eg. g The bigger.

[0077] The second small step is to obtain the temperature difference centrality factor based on the proximity relationship between cluster centers, the difference in connection paths, and the difference in homogeneous temperature grade gradient effects between the cluster clusters to which the cluster centers belong.

[0078] Extract the cluster centers of all clusters output by the clustering algorithm, treat all cluster centers as nodes, calculate the Euclidean distance between each node and the remaining nodes, and compare each node with the previous node with the shortest Euclidean distance. Undirected edges are established between nodes. The weight of each undirected edge is the difference in the homogeneous temperature grade gradient effect between the two connected nodes. The closeness centrality Cc of the g-th node is calculated. g The calculation method of the closeness centrality is a well-known technique and will not be described in detail.

[0079] In one embodiment of the present application, the weight of each undirected edge is the difference in homogeneous temperature grade gradient effects between the two connected nodes, which can be expressed by the weight of each undirected edge being the absolute value of the difference in homogeneous temperature grade gradient effects between the two connected nodes.

[0080] Then, all paths of any node in the undirected edge are obtained and recorded as the node sequence of any node; the average value of the infrared thermal imaging radiation value in the cluster where any node is located is calculated; the standard deviation of the average value of the infrared thermal imaging radiation value of all nodes on each node sequence is calculated, which is recorded as the radiation confusion factor of each node sequence; for each node connected to any node through an undirected edge, the difference between the homogeneous temperature grade gradient effect of any node and each node is calculated, and the difference between all nodes connected to any node through undirected edges is averaged, multiplied by the proximity centrality of any node, and the result is recorded as the homogeneous thermal stress difference of any node.

[0081] In this embodiment, take the g-th node as an example: starting from the g-th node, obtain all the paths of the g-th node in the undirected edge, respectively recorded as the node sequence of the g-th node, assuming that there are a total of P starting from the g-th node g node sequences, the length of the pth node sequence is C g p . Calculate the average value of the infrared thermal radiation value of each cluster where the node is located, and calculate the standard deviation of the average value of the infrared thermal radiation value of all nodes in the p-th node sequence, which is recorded as the radiation confusion factor of the p-th node sequence.

[0082] For node i connected to the gth node through an undirected edge, calculate its homogeneous temperature grade gradient effect Eg g and Eg i The absolute value of the difference between the g-th node and all nodes connected to the g-th node through undirected edges is averaged to obtain the closeness centrality Cc to the g-th node. g Multiply them and record the result as the homogeneous thermal stress difference at the g-th node.

[0083] For each node sequence of any node, the product of the homogeneous thermal stress difference of any node, the length of each node sequence and the radiation confusion factor of each node sequence is calculated as the temperature difference coefficient of each node sequence; the product of all node sequences of all nodes in the infrared thermal imaging data is averaged to obtain the temperature difference centrality factor of the infrared thermal imaging data.

[0084] In this embodiment, the temperature difference centrality factor of the infrared thermal imaging data is specifically as follows: for the node sequence p of the g-th node, the product of the homogeneous thermal stress difference of the g-th node, the length of the node sequence p and the radiation confusion factor of the node sequence p is calculated as the temperature difference coefficient of the node sequence p of the g-th node, and the product of all node sequences of all nodes in the infrared thermal imaging data is averaged to obtain the temperature difference centrality factor B of the infrared thermal imaging data.

[0085] It should be understood that proximity centrality indicates the positional importance of the g-th node in the entire network. A node with high proximity centrality means that it is more central in the network and has closer connections with other nodes. Therefore, it has a greater impact on the propagation of information in the network and the relationship between nodes. The larger its value, the greater the difference in homogeneous thermal stress of the g-th node; Eg g and Eg i The absolute value of the difference represents the difference between the homogeneous temperature grade gradient of the g-th node and the node i connected to it. The larger its value, the more significant the difference between the g-th node and its neighbors in temperature distribution uniformity and thermal stress, and the greater the homogeneous thermal stress difference of the g-th node. The product of the three combines the thermal stress difference of the nodes, the length of the node sequence, and the unevenness of the temperature distribution in the node sequence, reflecting the contribution of the thermal stress difference of the g-th node in a specific sequence to the overall temperature distribution unevenness. The larger its value, the stronger the overall temperature distribution unevenness of the node sequence starting from the g-th node, and the larger the temperature difference centrality factor.

[0086] The third major step is to improve the objective function of the model predictive control algorithm based on the temperature difference centrality factor of all acquisition moments, and use the prediction results to control the cooling gradient of the material in the cooling tank.

[0087] For each moment of infrared thermal imaging data collection, the actual temperature change of the material collected between adjacent moments is ΔT t , the expected temperature change is ΔT ref,t , ΔT ref,t In this embodiment, the value is 1.5°C. At the acquisition time t, the temperature difference centrality factor B at the corresponding time is calculated based on the infrared thermal imaging data. t , the actual temperature of the material is T t , the target temperature at the end of the cooling process is T final , T final In this embodiment, the value is 20° C., and the implementer can set it according to the actual situation.

[0088] The actual material temperature and temperature difference centrality factor recorded at all data collection moments are used as input, and the target temperature at the end of the material cooling process is used as input. The temperature gradient in the cooling tank is regulated using the model predictive control MPC algorithm, and the temperature difference centrality factor B is used as an important weight factor in the objective function to emphasize the attention to the uneven temperature distribution. The model predictive control MPC algorithm is a well-known technology and will not be described in detail. Specifically, the process of temperature gradient control in this application is as follows:

[0089] Set the expected temperature change; calculate the actual temperature change between two adjacent collection moments during the collection process; calculate the ratio of the actual temperature change to the preset collection time interval; calculate the ratio of the expected temperature change to the preset collection time interval; calculate the difference between the two ratios, recorded as the first difference; calculate the difference between the target temperature at the end of the cooling process and the actual temperature of the material at each collection moment, recorded as the second difference; based on the first difference, the second difference and the temperature difference centrality factor at each collection moment, determine the objective function of each collection moment; wherein the first difference, the second difference and the temperature difference centrality factor at each collection moment are all positively correlated with the objective function; adopt a model predictive control algorithm to output a control signal for the infrared thermal imaging data at each collection moment in the cooling tank, and use the control signal to regulate the cooling gradient of the material in the cooling tank.

[0090] In one embodiment of the present application, the square of the difference between the two ratios is taken as the first difference, and the square of the difference between the target temperature at the end of the cooling process and the actual temperature of the material at each sampling moment is taken as the second difference. In other embodiments of the present application, other suitable methods for calculating the difference can also be used for calculation.

[0091] In one embodiment of the present application, the objective function of the improved MPC algorithm is specifically designed as follows: calculating the product of the first difference, the second difference and the temperature difference centrality factor at each acquisition moment, and constructing the objective function with the sum of the products of all acquisition moments.

[0092] It should be understood that in the designed objective function, the first difference makes the actual cooling gradient as close as possible to the reference cooling gradient through positive feedback regulation, and multiplies it with the temperature difference centrality factor at the acquisition time to consider the influence of thermal stress and temperature distribution unevenness on the control process. The temperature difference centrality factor at the acquisition time mainly reflects the uniformity of temperature distribution and the magnitude of thermal stress. The larger its value is, the more uneven the temperature distribution is and the greater the thermal stress is. At this time, the cooling gradient is reduced to approach the target cooling gradient to eliminate the influence of uneven temperature distribution and large thermal stress of different substances in the material caused by too fast cooling. By taking the second difference as part of the objective function, the controller is encouraged to make the temperature of the material close to the target temperature at the end of the cooling process.

[0093] Finally, the MPC algorithm outputs a control signal at each temperature acquisition moment to adjust the output of the cooling system to control the temperature gradient of the material. Through a smooth and uniform cooling process, the thermal stress caused by rapid temperature changes is reduced, thereby improving the durability and reliability of the material.

[0094] S4, drying: drying and dehydrating the material after standing in S3, and the moisture content after drying is less than 5.5%; the dried material is pulverized to obtain powder with a mesh number less than 400 meshes.

[0095] S5, stirring: weigh silicate cement, silica powder, bisphenol A, talcum powder and fine aggregate respectively according to the formula ratio, mix the silicate cement, silica powder and fine aggregate first, add talcum powder after mixing, continue stirring, add the powder obtained in S4 thereto, stir until uniform, then add bisphenol A, and treat uniformly to obtain the tile adhesive for the novel wall material of the present application.

[0096] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0097] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0098] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0099] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A preparation process of a high-strength tile adhesive, characterized in that: The process The following steps are involved: S1, ingredients: weigh silicate cement, latex powder, silica powder, bisphenol A, glass powder, talcum powder, fine aggregate, reinforcing agent, waterproofing agent and filler; add appropriate amount of water to mix and stir evenly; The weight ratio of silicate cement, latex powder, silica powder, bisphenol A, glass powder, talcum powder, fine aggregate, reinforcing agent, waterproofing agent and filler in S1 is 85:18:8:5.5:7:2:9:8:6:12; S2, aging: aging the mixture prepared in S1; S3, gradient cooling: placing the matured material in a cooling tank for gradient cooling, cooling to room temperature 20°C and then letting it stand; wherein the control method of the cooling gradient is specifically as follows: Obtain infrared thermal imaging data of the material at each acquisition moment when the material is cooled by gradient; Based on infrared thermal imaging data, the temperature gradient changes of the same material during the cooling process, as well as the position characteristics and temperature distribution of different materials, are reflected to determine the temperature difference centrality factor; The objective function of the model predictive control algorithm is improved based on the temperature difference centrality factor of all acquisition moments, and the prediction results are used to control the temperature gradient of the material in the cooling tank. S4, drying: drying and removing water from the material after standing in S3 to obtain powder; S5, stirring: Portland cement, silica powder, bisphenol A, talcum powder and fine aggregate are weighed respectively according to the formula ratio, and the powder obtained in S4 is added and stirred until uniform, thereby obtaining a tile adhesive for a new wall material; The method for determining the temperature difference centrality factor includes: A clustering algorithm is used to obtain several clusters of infrared thermal imaging data and their corresponding cluster centers; Based on the distance and position distribution between data points in the cluster, the homogeneous temperature grade gradient effect of the cluster is determined; Based on the proximity relationship between cluster centers, the difference in connection paths, and the difference in homogeneous temperature grade gradient effects between clusters to which cluster centers belong, the temperature difference centrality factor is obtained; The method for determining the temperature difference centrality factor includes: All cluster centers are regarded as nodes respectively. For any node, undirected edges are established between any node and the first preset number of nodes closest to any node. The weight of the undirected edge is the difference of homogeneous temperature grade gradient effect between the two connected nodes. Get the closeness centrality of any node; Obtain all paths of any node in the undirected edge, and record them as the node sequence of any node; calculate the average value of the infrared thermal imaging radiation value in the cluster where any node is located; calculate the standard deviation of the average value of the infrared thermal imaging radiation value of all nodes in each node sequence, and record it as the radiation confusion factor of each node sequence; For each node connected to any node through an undirected edge, the difference between the homogeneous temperature grade gradient effect of the any node and each node is calculated, and the difference between all nodes connected to the any node through an undirected edge is averaged, multiplied by the proximity centrality of the any node, and the result is recorded as the homogeneous thermal stress difference of the any node; For each node sequence of any node, the product of the homogeneous thermal stress difference of any node, the length of each node sequence and the radiation confusion factor of each node sequence is calculated as the temperature difference coefficient of each node sequence; The products of all node sequences of all nodes in the infrared thermal imaging data are averaged to obtain the temperature difference centrality factor of the infrared thermal imaging data; The method for controlling the temperature drop gradient of the material in the cooling tank comprises: Set the expected temperature change; calculate the actual temperature change between two adjacent acquisition moments during the acquisition process; Calculate the ratio of the actual temperature change to the preset collection time interval; calculate the ratio of the expected temperature change to the preset collection time interval; calculate the difference between the two ratios, recorded as the first difference; calculate the difference between the target temperature at the end of the cooling process and the actual temperature of the material at each collection moment, recorded as the second difference; Determine the objective function of each acquisition moment based on the first difference, the second difference and the temperature difference centrality factor at each acquisition moment; wherein the first difference, the second difference and the temperature difference centrality factor at each acquisition moment are all positively correlated with the objective function; The model predictive control algorithm is used to output a control signal for the infrared thermal imaging data at each acquisition moment in the cooling tank, and the control signal is used to regulate the temperature reduction gradient of the material in the cooling tank.

2. The preparation process of a high-strength tile adhesive according to claim 1, characterized in that: The silicate cement is a mixture of white silicate cement, pozzolanic silicate cement and slag silicate cement, and the mass percentage of white silicate cement is not less than 92.7%.

3. The preparation process of a high-strength tile adhesive according to claim 1, characterized in that: The fine aggregate is quartz sand with a fineness modulus of 2.

0.

4. The preparation process of a high-strength tile adhesive according to claim 1, characterized in that: The aging temperature in S2 is 55-60° C., and the aging time is 15-30 min.

5. The preparation process of a high-strength tile adhesive according to claim 1, characterized in that: The method for determining the homogeneous temperature grade gradient effect includes: All data points in the cluster are evenly divided into several levels according to the infrared thermal radiation values, which are recorded as infrared thermal radiation levels respectively; Calculate the coordinate standard deviation of all data points at each infrared thermal radiation level in the cluster; the coordinate standard deviation is the square root of the cumulative sum of the squares of the standard deviations between the horizontal and vertical coordinates of all the data points; For any data point at each infrared heat radiation level in the cluster, calculate the distance between the data point and the cluster center of the cluster to which it belongs, and record it as the center distance of the data point; calculate the minimum value of the distance between the data point and the data points on the boundary of the cluster to which it belongs, and record it as the boundary distance of the data point; Negatively fuse the product of the center distance and the boundary distance with the coordinate standard deviation to obtain the grade gradient effect of any data point; The level gradient effects of all data points at each infrared thermal radiation level in the cluster are summed, and the average value of the summed results at all infrared thermal radiation levels in the cluster is taken as the homogeneous temperature level gradient effect of the cluster.

6. The process for preparing a high-strength tile adhesive according to claim 1, characterized in that: The moisture content of the material after drying and dehydration in S4 is less than 5.5%.

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