Novel building light sand and preparation technology

Abstract

The invention relates to the technical field of building engineering, and discloses novel building light sand and a preparation technology thereof.The novel building light sand comprises a raw material composed of acid vitreous blazed rock, and the acid vitreous blazed rock contains more than 70% of silicon oxide, more than 12% of aluminum oxide and more than 2% of mineral crystal water; the acid vitreous blazed rock needs to be treated through the working procedures of mining, coarse crushing, fine crushing, classified screening and dust removal, and the diameter of sand grains of the treated blazed rock is 0.5-8 mm. According to the invention, natural volcanic acid vitreous blazed rocks are selected as raw materials, so that the materials are turned into treasures from low cost, the maximum utilization of resources is increased, meanwhile, an efficient and energy-saving industrial induction cooker is designed as main core equipment for producing light sand, various light sand with different particle sizes and different volume weights per hectare can be produced, full-intelligent control is realized in the whole production process, and the production cost is reduced. The stability and the control of product quality are ensured, the green, low-carbon, energy-saving and environment-friendly requirements are met, and the development of related industrial policies is met.

Description

Technical Field

[0001] The invention relates to the technical field of construction engineering, in particular to a novel lightweight building sand and a preparation technology thereof. Background Art

[0002] The construction industry has entered a comprehensive green and low-carbon period, with comprehensive improvements in both building materials and construction technology. However, traditional building materials and construction technology cannot meet the requirements of green and low-carbon buildings. Various resource conditions have restricted the rapid development of the green and low-carbon construction industry. Green, energy-saving and environmentally friendly building materials have become the mainstream of new building materials, and innovation in basic building materials will inevitably become a development trend.

[0003] Lightweight sand aggregate is a new type of basic material that replaces traditional natural sand and artificial sand. It is widely used in the fields of lightweight concrete, lightweight concrete prefabricated parts, lightweight mortar, and prefabricated buildings.

[0004] Traditional building materials, such as natural sand and ordinary cement, have high energy consumption and great environmental damage in their production process. Excessive exploitation of natural sand has led to damage to river ecology and depletion of resources; the production of ordinary cement releases a large amount of carbon dioxide, exacerbating global warming. Traditional construction technology also has defects, with serious waste of resources and high energy consumption during construction. In addition, the thermal insulation performance of the completed buildings is poor, which increases long-term energy consumption. These problems make it difficult for traditional building materials and construction technologies to meet the requirements of green and low-carbon buildings for low energy consumption and high environmental protection. At the same time, resource conditions such as the shortage of high-quality natural sand resources and tight energy supply in some areas have further hindered the rapid progress of the green and low-carbon construction industry. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a new type of lightweight sand for construction and its preparation technology, which solves the problems of high energy consumption and environmental damage in the production of traditional building materials, large waste of construction technology resources, and limited resource conditions, which makes it difficult to meet the requirements of green and low-carbon buildings and hinders industrial development.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a new type of lightweight building sand, including raw materials composed of acidic glassy obsidian, wherein the acidic glassy obsidian has a silicon oxide content greater than 70%, an aluminum oxide content greater than 12%, and mineral crystal water greater than 2%.

[0007] Preferably, before being used to prepare the new lightweight sand for construction, the acidic glassy obsidian needs to be processed through mining, coarse crushing, fine grinding, graded screening and dust removal processes, and the obsidian sand particles obtained after the processing have a diameter of 0.5-8 mm.

[0008] A novel preparation technology of lightweight building sand comprises the following steps: S1: Raw material pretreatment and selection Acidic glassy obsidian with a silicon oxide content greater than 70%, an aluminum oxide content greater than 12%, and mineral crystal water greater than 2% is selected, and after mining, coarse crushing, fine grinding, graded screening, and dust removal treatment, obsidian sand particles with a diameter of 0.5-8 mm are obtained; S2: Unloading process The treated obsidian sand particles are metered and put into the furnace in a uniform annular distribution, and are floated through the high-temperature inner furnace jacket for 2-3 seconds under the action of the rising hot air flow in the furnace jacket; S3: One-time heating Heat the obsidian sand to 900℃ - 1000℃ using a 3-4 stage induction cooker; S4: Secondary heating The obsidian sand is heated to 1000℃ - 1200℃ by 3-4 induction cookers and infrared auxiliary heating; S5: Forming process The heated obsidian is collected from the lower discharge port and cooled and hardened into shape. During the production process, the working temperature of the vertical industrial induction furnace is 0 - 1250 ° C, and the sand discharge amount is 10 - 50 kg per minute.

[0009] Preferably, the 3-4 section induction cooker and the 2-section induction cooker are components of a vertical industrial multi-stage induction cooker, each section of which is 1500 mm long, a cube with an outer diameter of 800-1200 mm, and a cylinder with an inner diameter of 400-800 mm.

[0010] Preferably, the vertical industrial multi-stage induction cooker consists of an outer cover, a thermal insulation layer, an electromagnetic heating device, a high-temperature resistant metal inner sheath, an inner lining pipe, a material metering and conveying device, a material unloading device, a sand material collecting and cooling device, and a control system.

[0011] Preferably, during the S3 primary heating and S4 secondary heating processes, an infrared temperature measuring device is used to perform contactless temperature measurement on the inside of the equipment, and detection points are set at the same distance according to the length of the internal channel of the induction cooker, and self-adjusting temperature control is performed according to the difference between the temperature change state of different points and the preset temperature target.

[0012] Preferably, during the S3 primary heating and S4 secondary heating processes, a gas circulation device is provided to monitor the gas composition and temperature distribution in the furnace in real time through sensors, and to intelligently adjust the gas circulation path and flow rate. At the same time, the circulating gas is used to take away part of the waste heat to preheat the obsidian sand particles that are about to enter the furnace. On the other hand, the gas atmosphere in the furnace is controlled to reduce impurity pollution during the process.

[0013] Preferably, in the S5 molding process, after the obsidian sand grains are cooled and hardened to form, the lightweight sand is subjected to ultrasonic strengthening treatment, and the formed lightweight sand is placed in an ultrasonic processing device and treated at a frequency of 20-40kHz and a power of 100-300W for 5-10 minutes.

[0014] Preferably, the steps S1-S5 are performed using an automated continuous production process, which is completed by a fully automated production line consisting of a raw material storage silo, a lifting conveyor, a metering silo, a material distributor, an electromagnetic heating furnace, a collecting and cooling hopper, a pneumatic conveyor, a finished product silo, an automatic packaging and palletizing machine, a gas station and a central control console.

[0015] Preferably, the S1-S5 steps utilize a generative adversarial network to construct a lightweight sand quality prediction and defect detection model. The generator generates simulated lightweight sand samples by learning a large amount of characteristic data of normal lightweight sand products; the discriminator distinguishes between real samples and generated samples. During the training process, the two compete with each other and are continuously optimized. When a new lightweight sand product is produced, the discriminator can quickly determine whether it has quality defects and predict product quality parameters, including particle size distribution and bulk density deviation. For products that may have defects, the algorithm can trace back to abnormal parameters in the preparation process.

[0016] The present invention provides a new type of lightweight building sand and its preparation technology. It has the following beneficial effects: 1. The present invention uses natural volcanic acidic glassy obsidian as raw material, turning cheap into valuable, and maximizing the utilization of resources. At the same time, it designs an efficient and energy-saving industrial induction cooker as the main core equipment for light sand production. It can produce various light sands with different particle sizes and different hectares of bulk density, and realizes full intelligent control in the whole production process to ensure the stability of product quality, and achieve green, low-carbon, energy-saving and environmental protection requirements, which complies with the development of relevant industrial policies.

[0017] 2. The light sand quality prediction and defect detection model constructed by the present invention using a generative adversarial network can simulate the production situation in advance to optimize the process during the light sand production process through adversarial optimization of the generator and the discriminator; after the product is produced, it can quickly and accurately judge quality defects, predict key parameters such as particle size distribution and bulk density deviation, and promptly discover unqualified products. It can also trace back to abnormal parameters in the preparation process, providing a basis for process adjustment, thereby improving product quality, reducing production costs, improving production efficiency, and enhancing the reliability and stability of the entire production process.

[0018] 3. The present invention, through the establishment of a self-adjusting temperature control algorithm, can adjust the heating power of the induction cooker in real time and accurately according to the difference between the temperature change at different points and the preset temperature target, thus avoiding energy waste. When the temperature is close to the preset target, the heating power is automatically reduced to reduce unnecessary energy consumption, thus achieving efficient use of energy while ensuring production quality, and conforming to the production concept of energy conservation and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a structural diagram of a high temperature electromagnetic cooker used in the present invention; Figure 2 This is a flow chart of the preparation method of the novel lightweight building sand; Figure 3 This is a diagram of a lightweight sand quality prediction and defect detection model constructed using a generative adversarial network in the present invention.

[0020] Among them, 1. distribution hopper; 2. induction cooker bracket; 3. S301 lining pipe; 4. induction cooker partition; 5. induction cooker heating element; 6. thermal insulation layer; 7. induction cooker cover; 8. material collection hopper; 9. material lifting conveyor and light sand airflow conveying device; 10. intelligent control system. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the specification 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.

[0022] Example: Please see attached Figure 1 - Attachment Figure 3 The embodiment of the present invention provides a new type of lightweight sand for construction, including a raw material composed of acidic glassy obsidian, wherein the acidic glassy obsidian has a silicon oxide content greater than 70%, an aluminum oxide content greater than 12%, and mineral crystal water greater than 2%.

[0023] Before being used to prepare the new lightweight sand for construction, the acidic glassy obsidian needs to be processed through mining, coarse crushing, fine grinding, graded screening and dust removal processes, and the obsidian sand particles obtained after the processing have a diameter of 0.5-8 mm.

[0024] A novel preparation technology of lightweight building sand comprises the following steps: S1: Raw material pretreatment and selection Acidic glassy obsidian with a silicon oxide content greater than 70%, an aluminum oxide content greater than 12%, and mineral crystal water greater than 2% is selected, and after mining, coarse crushing, fine grinding, graded screening, and dust removal treatment, obsidian sand particles with a diameter of 0.5-8 mm are obtained; S2: Unloading process The treated obsidian sand particles are metered and put into the furnace in a uniform annular distribution, and are floated through the high-temperature inner furnace jacket for 2-3 seconds under the action of the rising hot air flow in the furnace jacket; S3: One-time heating Heat the obsidian sand to 900℃ - 1000℃ using a 3-4 stage induction cooker; S4: Secondary heating The obsidian sand is heated to 1000℃ - 1200℃ by 3-4 induction cookers and infrared auxiliary heating; S5: Forming process The heated obsidian is collected from the lower discharge port and cooled and hardened into shape. During the production process, the working temperature of the vertical industrial induction furnace is 0 - 1250 ° C, and the sand discharge amount is 10 - 50 kg per minute.

[0025] The 3-4 section induction cooker and the 2-section induction cooker are components of a vertical industrial multi-stage induction cooker. Each section of the vertical industrial multi-stage induction cooker is 1500mm long, has an outer diameter of 800-1200mm and is a cube, and has an inner diameter of 400-800mm and is a cylinder.

[0026] The vertical industrial multi-stage induction cooker consists of an outer cover, a thermal insulation layer, an electromagnetic heating device, a high-temperature resistant metal inner jacket, an inner lining pipe, a material metering and conveying device, a material unloading device, a sand material collecting and cooling device and a control system.

[0027] During the S3 primary heating and S4 secondary heating processes, an infrared temperature measuring device is used to perform non-contact temperature measurement inside the equipment, and detection points are set at the same distance according to the length of the internal channel of the induction cooker. Self-adjusting temperature control is performed according to the difference between the temperature change state of different points and the preset temperature target. For the temperature self-adjustment process in the process, the following algorithm is established: Self-adjusting temperature control algorithm: In steps S3 and S4, an infrared temperature measuring device is used to measure the temperature inside the equipment without contact, and self-adjusting temperature control is performed according to the temperature difference. This process can use the proportional-integral-differential (PID) control algorithm, which is a commonly used feedback control algorithm that can adjust the control quantity (such as the power of the induction cooker) according to the deviation between the set value (preset temperature target) and the actual measured value (temperature at the detection point); Control volume formula:

[0028] in, is the control quantity at the current moment (such as the adjusted heating power of the induction cooker); is the proportional coefficient, which determines the response intensity of the control quantity to the deviation; is the deviation between the current set value and the measured value, that is, ( is the preset temperature target, The actual measured temperature at the detection point); is the integral coefficient, which is used to eliminate the steady-state error of the system; It is the differential coefficient, which can adjust the control amount in advance according to the change rate of the deviation to make the system respond more quickly and stably; In practical applications, it is necessary to determine the The value of .

[0029] During the S3 primary heating and S4 secondary heating processes, a gas circulation device is provided to monitor the gas composition and temperature distribution in the furnace in real time through sensors, and to intelligently adjust the gas circulation path and flow rate. At the same time, the circulating gas is used to take away part of the waste heat to preheat the obsidian sand particles that are about to enter the furnace. On the other hand, the gas atmosphere in the furnace is controlled to reduce impurity pollution during the process.

[0030] In the S5 molding process, after the obsidian sand grains are cooled and hardened to form, the lightweight sand is subjected to ultrasonic strengthening treatment, and the formed lightweight sand is placed in an ultrasonic treatment device and treated at a frequency of 20-40kHz and a power of 100-300W for 5-10 minutes.

[0031] The steps S1-S5 are performed by an automated continuous production process, which is completed by a fully automated production line consisting of a raw material storage bin, a lifting conveyor, a metering bin, a material distributor, an electromagnetic heating furnace, a collecting and cooling hopper, a pneumatic conveyor, a finished product bin, an automatic packaging and stacking machine, a gas station and a central control console. The production process is as follows: Mining → Coarse crushing → Fine crushing → Classification and screening → Measuring and packaging → Transportation to factory → Lifting and conveying → Measuring and feeding → High temperature processing → Collection and cooling → Transport to finished product silo → Measuring and packaging The entire production process of lightweight sand adopts automated continuous production technology.

[0032] The steps S1-S5 use a generative adversarial network to construct a lightweight sand quality prediction and defect detection model. The generator generates simulated lightweight sand samples by learning a large amount of feature data of normal lightweight sand products; the discriminator distinguishes between real samples and generated samples. During the training process, the two compete with each other and are continuously optimized. When a new lightweight sand product is produced, the discriminator can quickly determine whether it has quality defects and predict product quality parameters, including particle size distribution and bulk density deviation. For products that may have defects, the algorithm can trace back to abnormal parameters in the preparation process. The following algorithm is established in the process: Generator Network The goal of the generator is to learn the characteristic distribution of normal light sand products and generate simulated light sand samples that are as realistic as possible, including using multi-layer perceptron (MLP) and convolutional neural network (CNN) as the architecture of the generator.

[0033] Algorithm structure The input is a random noise vector , which obeys the Gaussian distribution and is transformed through a series of fully connected layers or convolutional layers, and finally outputs a simulated light sand sample .

[0034] Formula Hypothesis Generator It is a neural network with multiple hidden layers. For the input random noise vector , after the The transformation of the hidden layer can be expressed as:

[0035] in, , It is The weight matrix of the layer, It is The bias vector of the layer, is the activation function (such as ReLU, LeakyReLU, etc.). The final generated simulation sample is:

[0036] Discriminator network The task of the discriminator is to distinguish whether the input sample is a real light sand sample. Or a simulated sample generated by the generator Here, MLP or CNN is used as the discriminator architecture.

[0037] Algorithm structure The input can be a real sample or a simulated sample, which is extracted and classified through a series of fully connected layers or convolutional layers, and finally outputs a probability value. , which represents the probability that the input sample is a true sample.

[0038] Formula Hypothesis Discriminator It is also a neural network with multiple hidden layers. , after the The transformation of the hidden layer can be expressed as:

[0039] in, , It is The weight matrix of the layer, It is The bias vector of the layer, is the activation function. The final output probability value is:

[0040] in, It is the sigmoid activation function, which is used to map the output value to the [0,1] interval Optimization algorithm during training The training process of GAN is a process in which the generator and the discriminator compete with each other, and the performance is continuously improved by alternately optimizing the loss functions of the two.

[0041] Loss function of the discriminator The goal of the discriminator is to maximize the probability of correctly classifying real samples and simulated samples. Its loss function can be expressed as:

[0042] in, is the distribution of a real light sand sample, is the distribution of random noise.

[0043] The loss function of the generator The goal of the generator is to minimize the probability that the discriminator judges the samples it generates as fake samples. Its loss function can be expressed as:

[0044] Optimization Algorithm A variant of stochastic gradient descent (SGD), such as the Adam algorithm, is usually used to update the parameters of the generator and discriminator. The update formula of the Adam algorithm is as follows: For parameters (can be the weights and biases of the generator or discriminator), in the At iteration: Compute the gradient:

[0045] Compute the first moment estimate:

[0046] Compute the second moment estimate:

[0047] Modified first moment estimate:

[0048] Modified second moment estimate:

[0049] Update parameters:

[0050] in, is the learning rate; and is an exponential decay rate (usually , ), is a small constant (such as , used to prevent division by zero errors; Quality parameter prediction and defect tracing When the discriminator determines that a new light sand product may have defects, the abnormal parameters that may cause defects can be traced back by analyzing the sensitivity of the generator and discriminator to different parameters during the training process and combining the parameter records during the preparation process. This part uses the feature importance analysis method SHAP value to quantify the impact of each preparation parameter on product quality.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A new type of lightweight sand for construction, characterized in that: The invention comprises raw materials composed of acidic glassy obsidian, wherein the silicon oxide content of the acidic glassy obsidian is greater than 70%, the aluminum oxide content is greater than 12%, and the mineral crystal water is greater than 2%.

2. A novel lightweight sand for construction according to claim 1, characterized in that: Before being used to prepare the new lightweight sand for construction, the acidic glassy obsidian needs to be processed through mining, coarse crushing, fine grinding, graded screening and dust removal processes, and the obsidian sand particles obtained after the processing have a diameter of 0.5-8 mm.

3. The preparation technology of a new type of lightweight sand for construction according to claim 1 is characterized in that: The following steps are involved: S1: Raw material pretreatment and selection Acidic glassy obsidian with a silicon oxide content greater than 70%, an aluminum oxide content greater than 12%, and mineral crystal water greater than 2% is selected, and after mining, coarse crushing, fine grinding, graded screening and dust removal treatment, obsidian sand particles with a diameter of 0.5-8mm are obtained; S2: Unloading process The treated obsidian sand particles are metered and put into the furnace in a uniform annular distribution, and are floated through the high-temperature inner furnace jacket for 2-3 seconds under the action of the rising hot air flow in the furnace jacket; S3: One-time heating Heat the obsidian sand to 900℃ - 1000℃ using a 3-4 stage induction cooker; S4: Secondary heating The obsidian sand is heated to 1000℃ - 1200℃ by 3-4 induction cookers and infrared auxiliary heating; S5: Forming process The heated obsidian is collected from the lower discharge port and cooled and hardened into shape. During the production process, the working temperature of the vertical industrial induction furnace is 0 - 1250 ° C, and the sand discharge amount is 10 - 50 kg per minute.

4. A novel preparation technology for lightweight building sand according to claim 3, characterized in that: The 3-4 section induction cooker and the 2-section induction cooker are components of a vertical industrial multi-stage induction cooker. Each section of the vertical industrial multi-stage induction cooker is 1500mm long, has an outer diameter of 800-1200mm and is a cube, and has an inner diameter of 400-800mm and is a cylinder.

5. A novel preparation technology for lightweight building sand according to claim 4, characterized in that: The vertical industrial multi-stage induction cooker consists of an outer cover, a thermal insulation layer, an electromagnetic heating device, a high-temperature resistant metal inner jacket, an inner lining pipe, a material metering and conveying device, a material unloading device, a sand material collecting and cooling device and a control system.

6. The preparation technology of a novel lightweight sand for construction according to claim 3 is characterized in that: During the S3 primary heating and S4 secondary heating processes, an infrared temperature measuring device is used to perform contactless temperature measurement on the inside of the equipment, and detection points are set at the same distance according to the length of the internal channel of the induction cooker. Self-adjusting temperature control is performed according to the difference between the temperature change state of different points and the preset temperature target.

7. The preparation technology of a novel lightweight sand for construction according to claim 3 is characterized in that: During the S3 primary heating and S4 secondary heating processes, a gas circulation device is provided to monitor the gas composition and temperature distribution in the furnace in real time through sensors, and to intelligently adjust the gas circulation path and flow rate. At the same time, the circulating gas is used to take away part of the waste heat to preheat the obsidian sand particles that are about to enter the furnace. On the other hand, the gas atmosphere in the furnace is controlled to reduce impurity pollution during the process.

8. The preparation technology of a novel lightweight sand for construction according to claim 3 is characterized in that: In the S5 molding process, after the obsidian sand grains are cooled and hardened to form, the lightweight sand is subjected to ultrasonic strengthening treatment, and the formed lightweight sand is placed in an ultrasonic treatment device and treated at a frequency of 20-40kHz and a power of 100-300W for 5-10 minutes.

9. The preparation technology of a novel lightweight building sand according to claim 3 is characterized in that: The steps S1-S5 are performed by an automated continuous production process, which is completed by a fully automated production line consisting of a raw material storage silo, a lifting conveyor, a metering silo, a material distributor, an electromagnetic heating furnace, a collecting and cooling hopper, a pneumatic conveyor, a finished product silo, an automatic packaging and stacking machine, a gas station and a central control console.

10. The preparation technology of a novel lightweight sand for construction according to claim 3 is characterized in that: The S1-S5 steps utilize a generative adversarial network to construct a lightweight sand quality prediction and defect detection model. The generator generates simulated lightweight sand samples by learning a large amount of characteristic data of normal lightweight sand products; the discriminator distinguishes between real samples and generated samples. During the training process, the two compete with each other and are continuously optimized. When a new lightweight sand product is produced, the discriminator can quickly determine whether it has quality defects and predict product quality parameters, including particle size distribution and bulk density deviation. For products that may have defects, the algorithm can trace back to abnormal parameters in the preparation process.

Images