An intelligent control method and system for a refractory batching production line

Abstract

The present invention relates to the field of intelligent control technology, and discloses an intelligent control method and system for a refractory batching production line. The method comprises obtaining raw material composition data, production process data and equipment operation status data of the refractory batching production line; constructing a batching model based on power consumption and production volume, combining the raw material composition data, the production process data and the equipment operation status data, obtaining the batching ratio, and then obtaining the material input amount; processing the equipment operation status data based on a preset operation energy consumption model, obtaining the equipment operation energy consumption, and obtaining the maximum energy saving rate according to the equipment operation energy consumption, and then obtaining the actual equipment operation energy consumption; generating equipment production instructions based on the material input amount and the actual equipment operation energy consumption, and controlling the production line equipment to produce refractory materials through the equipment production instructions. The method can optimize the batching accuracy and improve the energy consumption efficiency of the production line.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent control method and system for a refractory material batching production line. Background Art

[0002] As a high-temperature material, refractory materials are widely used in various industrial production. The production of refractory materials usually involves multiple production stages, including batching, mixing, firing, cooling, etc. In the batching process, raw materials of different components need to be mixed in a specific proportion according to the production process requirements to obtain the required performance and effects. Batching is a key link in the entire refractory production process and has an important impact on product quality and production efficiency. In this context, realizing the automation and intelligent control of the batching link has become the key to improving the production efficiency and quality of refractory production lines.

[0003] There are a series of problems in the current batching production, which restrict the efficiency and stability of the production line. First, there are errors in the control of raw material composition and proportion. Although production personnel constantly try to adjust the batching ratio, they still cannot ensure the accuracy of the batching process. This may lead to mismatched ingredients, which in turn affects the performance and durability of refractory materials. In addition, energy consumption and energy efficiency need to be improved. The batching process involves high-temperature mixing, stirring and heating, which requires a lot of energy input. How to optimize the schedule of the batching process, reduce equipment downtime, idle running and energy loss, and improve energy efficiency is a key issue.

[0004] In summary, the existing refractory production lines mainly have the problem of being unable to ensure the accuracy and energy consumption of the batching process, and the energy efficiency needs to be improved. Summary of the invention

[0005] The present invention provides an intelligent control method and system for a refractory material batching production line, which can optimize batching accuracy and improve the energy consumption efficiency of the production line.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides an intelligent control method for a refractory batching production line, comprising: Obtain the raw material composition data, production process data and equipment operation status data of the refractory material batching production line, wherein the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operating energy consumption and production capacity; Building a batching model based on power consumption and production volume, combining the raw material composition data, the production process data and the equipment operation status data to obtain the batching ratio, and then obtain the material input amount; The equipment operation status data is processed based on a preset operation energy consumption model to obtain equipment operation energy consumption, and a maximum energy saving rate is obtained according to the equipment operation energy consumption, and then the actual operation energy consumption of the equipment is obtained, wherein the preset operation energy consumption model is constructed by operation energy consumption and production capacity; Equipment production instructions are generated based on the material input amount and the actual energy consumption of the equipment operation, and the production line equipment is controlled by the equipment production instructions to produce refractory materials.

[0007] Preferably, the raw material composition data also includes standard material ratio and material consumption coefficient; The equipment operation status data also includes equipment production capacity, equipment maximum production capacity, equipment minimum production capacity, material unit power consumption, equipment power consumption, equipment actual operation energy consumption and equipment minimum operation energy consumption; The production process data also includes batching parameters and error correction coefficients.

[0008] Preferably, the batching model is expressed as follows: ; in, Indicates the proportion of material k at time t; Indicates the power consumption of material k; Indicates the standard ratio of material k at time t; represents the production capacity of equipment i at time t; represents the unit power consumption of device i; Represents the production of the system at time t.

[0009] Preferably, the preset operation energy consumption model is expressed as follows: ; In the formula, represents the operating energy consumption of device i at time t; represents the production capacity of equipment i at time t; represents the maximum production capacity of equipment i; represents the power consumption of device i at time t; Indicates the consumption coefficient of material k; Indicates the standard ratio of material k at time t; Indicates the power consumption of material k; represents the error correction coefficient; Represents the production of the system at time t.

[0010] Preferably, the maximum energy saving rate is obtained by: ; In the formula, represents the operating energy consumption of device i at time t; represents the minimum operating energy consumption of device i at time t; represents the production capacity of equipment i at time t; represents the maximum production capacity of equipment i; represents the minimum production capacity of equipment i; Indicates the maximum energy saving rate. Preferably, the material delivery amount is obtained by: Based on the standard ratio, ingredient ratio and production volume, the material input volume is obtained, which is expressed as follows: ; In the formula, Indicates the standard ratio of material k at time t; represents the production of the system at time t; Indicates the proportion of material k at time t; Represents the material delivery amount of material k at time t.

[0011] Preferably, the actual operating energy consumption of the device is obtained by: According to the minimum operating energy consumption of device i at time t, the maximum energy saving rate and the operating energy consumption of device i at time t, the actual operating energy consumption of the device is obtained, which is expressed as follows: ; In the formula, represents the actual operating energy consumption of device i at time t; represents the minimum operating energy consumption of device i at time t; Indicates the maximum energy saving rate; Represents the operating energy consumption of device i at time t.

[0012] In a second aspect, the present invention provides an intelligent control system for a refractory batching production line, comprising: The data acquisition module is used to acquire the raw material composition data, production process data and equipment operation status data of the refractory material batching production line, wherein the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operation energy consumption and production capacity; A proportion calculation module is used to construct a batching model based on power consumption and production volume, and to obtain a batching ratio and a material input amount by combining the raw material composition data, the production process data and the equipment operation status data; An energy-saving calculation module, used for processing the equipment operation status data based on a preset operation energy consumption model to obtain equipment operation energy consumption, and according to the equipment operation energy consumption, obtaining a maximum energy-saving rate, and then obtaining the actual operation energy consumption of the equipment, wherein the preset operation energy consumption model is constructed by operation energy consumption and production capacity; The instruction generation module is used to generate equipment production instructions based on the material input amount and the actual energy consumption of the equipment, and control the production line equipment to produce refractory materials through the equipment production instructions.

[0013] Ratio calculation module In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent control method for the refractory material batching production line described in any one of the above is implemented.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the intelligent control method of the refractory material batching production line described in any one of the above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: the embodiment of the present invention provides an intelligent control method and system for a refractory batching production line. The method includes: obtaining raw material composition data, production process data and equipment operation status data of the refractory batching production line, wherein the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operation energy consumption and production capacity; constructing a batching model based on the power consumption and production volume, combining the raw material composition data, the production process data and the equipment operation status data, obtaining the batching ratio, and then obtaining the material input amount; processing the equipment operation status data based on a preset operation energy consumption model to obtain the equipment operation energy consumption, and according to the equipment operation energy consumption, obtaining the maximum energy saving rate, and then obtaining the actual operation energy consumption of the equipment, wherein the preset operation energy consumption model is constructed by the operation energy consumption and production capacity; generating equipment production instructions based on the material input amount and the actual operation energy consumption of the equipment, and controlling the production line equipment to produce refractory materials through the equipment production instructions.

[0016] In the present invention, the method can calculate the batching ratio and equipment operation energy consumption by integrating raw material composition data, production process data and equipment operation status data, and further calculate the maximum energy saving rate based on the equipment operation energy consumption, thereby obtaining the actual equipment operation energy consumption. Generate an adaptive equipment production instruction based on the batching ratio and the maximum energy saving rate. During the production process, the execution of the equipment production instruction enables the production line equipment to put raw materials according to the optimized batching ratio, and adjust the equipment operation status according to the maximum energy saving rate, thereby ensuring that the production line achieves the lowest energy consumption while producing efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of an intelligent control method for a refractory material batching production line provided by a first embodiment of the present invention; Figure 2 It is a structural schematic diagram of an intelligent control system for a refractory material batching production line provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] Reference Figure 1 The first embodiment of the present invention provides an intelligent control method for a refractory batching production line, comprising the following steps: S11, obtaining raw material composition data, production process data and equipment operation status data of the refractory material batching production line, wherein the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operation energy consumption and production capacity.

[0020] S12, constructing a batching model based on power consumption and production volume, combining the raw material composition data, the production process data and the equipment operation status data to obtain the batching ratio, and then obtain the material input amount.

[0021] S13, processing the equipment operation status data based on a preset operation energy consumption model to obtain the equipment operation energy consumption, obtaining the maximum energy saving rate based on the equipment operation energy consumption, and then obtaining the actual operation energy consumption of the equipment, wherein the preset operation energy consumption model is constructed through operation energy consumption and production capacity.

[0022] S14, generating equipment production instructions based on the material input amount and the actual energy consumption of the equipment, and controlling the production line equipment to produce refractory materials through the equipment production instructions.

[0023] It is worth mentioning that refractory materials, as a high-temperature material, are widely used in various industrial production. The production of refractory materials usually involves multiple production stages, including batching, mixing, firing, cooling, etc. In the batching process, raw materials of different components need to be mixed in a specific proportion according to the production process requirements to obtain the required performance and effects. Batching is a key link in the entire refractory production process and has an important impact on product quality and production efficiency. In this context, realizing the automation and intelligent control of the batching link has become the key to improving the production efficiency and quality of refractory production lines.

[0024] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0025] In step S11, raw material composition data, production process data and equipment operation status data of the refractory material batching production line are obtained.

[0026] Preferably, the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operation energy consumption and production capacity; Preferably, the raw material composition data also includes standard material ratio and material consumption coefficient; The equipment operation status data also includes equipment production capacity, equipment maximum production capacity, equipment minimum production capacity, material unit power consumption, equipment power consumption, equipment actual operation energy consumption and equipment minimum operation energy consumption; The production process data also includes batching parameters and error correction coefficients. Specifically, the acquisition of raw material composition data, production process data and equipment operation status data of the refractory batching production line needs to rely on industrial automation systems and high-precision measurement equipment.

[0027] First of all, for the raw material composition data, it mainly includes the power consumption of different materials, the standard material ratio and the material consumption coefficient. In a feasible way, the power consumption of the material can be obtained by real-time measurement of the electric energy meter or power meter during the production process. Combined with the material usage, the average power consumption per unit material can be calculated. The standard material ratio can be determined based on the production formula of refractory materials, through process design and quality standards. This part of the data is stored in the process database and continuously optimized through laboratory analysis results. As for the material consumption coefficient, it needs to be obtained through long-term production operation data statistics and experimental measurements, reflecting the actual consumption ratio of the material in the production process.

[0028] Specifically, the acquisition of equipment operation status data also relies on automated detection and data acquisition systems. These data include the equipment's production capacity, maximum production capacity, and minimum production capacity, as well as the unit power consumption of materials, equipment power consumption, actual equipment operating energy consumption, and minimum equipment operating energy consumption. In a feasible way, the equipment production capacity is usually combined with sensors and scheduling systems to detect the production speed and output of the equipment in real time, and is obtained through statistical analysis of historical task data. The maximum production capacity and the minimum production capacity are obtained through the data provided by the manufacturer or the equipment manual, respectively, and combined with experimental verification to ensure accuracy. The unit power consumption of materials needs to be recorded by the online energy consumption monitoring system when the equipment processes unit raw materials, and a database is formed in combination with different production conditions. The power consumption of the equipment is directly measured in real time by the electric energy meter installed on the equipment, while the actual operating energy consumption is automatically calculated by the control system based on the comprehensive equipment operating time, task volume and power consumption data. The minimum operating energy consumption of the equipment needs to be obtained through experimental data under low-load operating conditions, which is a reflection of the optimal energy consumption state of the equipment.

[0029] Specifically, the production process data mainly includes batching parameters, production volume and error correction coefficient. The acquisition of these data relies on the actual operation of the production process design and the production line. The batching parameters are derived from the process standards, including specific raw material types, proportions and operating conditions. These data will be dynamically adjusted and transmitted to the control system according to production needs when the production task is issued. The production volume is recorded in real time by the material flow meter or the metering equipment at the output end of the production line. The automation system will store the data in the central control system as an important basis for production management. The error correction coefficient is obtained by statistical analysis of the deviation between the actual production data and the target value. It is usually optimized by combining historical data and real-time analysis to correct the error more accurately in subsequent production.

[0030] Ultimately, this data is collected, transmitted, and stored in real time through the Industrial Internet of Things and central control systems (such as SCADA). The integrity and accuracy of the data rely on the high-precision measurement and real-time feedback mechanism of the sensors, and the data is analyzed and integrated after storage to provide basic support for intelligent control. This data collection and integration method can reflect the real-time status of the production line and ensure the accuracy and efficiency of ingredient ratio calculation, energy consumption optimization, and production instruction generation.

[0031] In step S12, a batching model is constructed based on power consumption and production volume, and the batching ratio is obtained by combining the raw material composition data, the production process data and the equipment operation status data, thereby obtaining the material input amount.

[0032] Preferably, the batching model is expressed as follows: ; in, Indicates the proportion of material k at time t; Indicates the power consumption of material k; Indicates the standard ratio of material k at time t; represents the production capacity of equipment i at time t; represents the unit power consumption of device i; Represents the production volume of the system at time t.

[0033] Specifically, the method calculates the ingredient ratio at time t by integrating raw material composition data, production process data and equipment operation status data. Specifically, in the formula, the numerator part It represents the weighted sum of the power consumption and standard ratio of all different materials in the system. is the power consumption of material k, reflecting the energy consumption of each unit of material in the production process; is the standard ratio of material k at time t, indicating the required ratio of each raw material. By multiplying the power consumption and the standard ratio and summing them, the total energy consumption demand and ratio relationship of each material in the production process can be determined. It is a comprehensive expression of the current operating capacity of the equipment and the system production volume. is the production capacity of equipment i at time t, representing the actual production output capacity of the equipment at that moment; is the unit power consumption of equipment i, reflecting the energy consumption required for each unit of production. The product of the two represents the energy consumption level of equipment i at time t. After adding up the energy consumption of all equipment i, multiplying it by the production volume of the system at time t, the total production energy consumption of the entire system at that time can be obtained. Finally, by dividing the numerator (the sum of the power consumption of different materials and the standard ratio) by the denominator (the comprehensive result of the system production energy consumption), the ingredient ratio at time t is calculated. This ratio reflects the distribution of the required material ratio in the production process to meet the system energy consumption requirements and production process requirements. The entire calculation process is based on the real-time data of equipment status and production energy consumption, which ensures the accuracy and rationality of the ingredient ratio and provides key data support for the optimization of subsequent production processes. In step S13, the equipment operation status data is processed based on the preset operation energy consumption model to obtain the equipment operation energy consumption, and the maximum energy saving rate is obtained according to the equipment operation energy consumption, and then the actual equipment operation energy consumption is obtained, wherein the preset operation energy consumption model is constructed by operation energy consumption and production capacity.

[0034] Preferably, the preset operation energy consumption model is expressed as follows: ; In the formula, represents the operating energy consumption of device i at time t; represents the production capacity of equipment i at time t; represents the maximum production capacity of equipment i; represents the power consumption of device i at time t; Indicates the consumption coefficient of material k; Indicates the standard ratio of material k at time t; Indicates the power consumption of material k; represents the error correction coefficient; represents the production volume of the system at time t. Specifically, the method calculates the operating energy consumption of the equipment by combining the equipment operating status data. In the formula, the first term reflects the actual energy consumption level of device i at time t. Here, is the production capacity of equipment i at time t, indicating the current output level of the equipment; is the maximum production capacity of device i, which is used to normalize the current production status of the device so that the actual production capacity of the device can be compared with the maximum production capacity, thereby measuring the energy efficiency status of the device. is the power consumption of device i at time t, which represents the power consumption of the device per unit time. By multiplying these three, we can get the proportion of the energy actually consumed by the device in the state of maximum production capacity, thus reflecting the energy consumption of the device under the current load. This reflects the impact of raw material consumption on equipment energy consumption. is the consumption coefficient of material k, which indicates the consumption characteristics of the material in the production process; The standard ratio of material k at time t ensures that the proportion of raw materials meets the process requirements; is the power consumption of material k, reflecting the energy consumption required for unit material during processing. By multiplying and summing these parameters, the total energy consumption caused by all material consumption can be obtained, thus making a more comprehensive assessment of the overall energy consumption. The influence of error correction coefficient and system production volume is further considered. Error correction coefficient is used to correct energy consumption deviation in the production process to ensure more accurate calculation results; is the production volume of the system at time t, reflecting the output level of the entire production line at that moment. Multiplying these two parameters can effectively correct the energy consumption deviation caused by the error, making the equipment energy consumption calculation more in line with the actual situation.

[0035] Preferably, the maximum energy saving rate is obtained by: ; In the formula, represents the operating energy consumption of device i at time t; represents the minimum operating energy consumption of device i at time t; represents the production capacity of equipment i at time t; represents the maximum production capacity of equipment i; represents the minimum production capacity of equipment i; Indicates the maximum energy saving rate.

[0036] Specifically, the method calculates the maximum energy saving rate based on the operating energy consumption of the equipment. The core of this step is to quantify the potential for energy saving optimization by comparing the difference between the actual operating energy consumption of the equipment and the minimum operating energy consumption, thereby providing a basis for equipment energy consumption regulation.

[0037] Specifically, in the formula, the numerator part represents the sum of the actual operating energy consumption of all devices at time t. represents the operating energy consumption of equipment i at time t, which is the total energy consumed by the equipment under the current production capacity and load state. The numerator reflects the total energy consumption of the entire production line under the current operating state. The denominator represents the sum of the theoretical minimum energy consumption of the equipment under ideal optimization conditions. Here, is the minimum operating energy consumption of device i at time t, reflecting the energy consumption level of the device in the optimal operating state. Based on the normalized relationship between the current production capacity of the equipment and the minimum production capacity and the maximum production capacity, the minimum energy consumption is corrected, so as to compare the energy consumption of the equipment under the actual production state with the optimal state. is the production capacity of equipment i at time t, is the maximum production capacity of equipment i, Represents the minimum production capacity of device i. This correction term can associate the current load deviation of the device with the minimum energy consumption, ensuring that the calculation result is closer to the actual working condition. Finally, by comparing the actual operating energy consumption with the theoretical minimum energy consumption, the calculation result of the energy saving rate reflects the degree of optimization of the current energy consumption state of the device. The formula represents the energy saving potential of the current total energy consumption relative to the minimum energy consumption. The closer the value of P is to 1, the closer the operating state of the device is to the optimal energy consumption level, and the smaller the energy saving potential; conversely, the smaller P is, the greater the energy saving potential is, and there is a large room for optimization.

[0038] In summary, by comprehensively calculating the equipment operation status data, the method can accurately calculate the operating energy consumption of equipment i at time t. The calculation method comprehensively considers multiple factors such as equipment production capacity, material consumption characteristics, and production errors, thereby providing accurate data support for subsequent energy consumption optimization and energy saving rate calculation. This method helps to identify high-energy consumption equipment and provides a reliable basis for improving the energy efficiency of production lines.

[0039] In step S14, an equipment production instruction is generated based on the material input amount and the actual energy consumption of the equipment, and the production line equipment is controlled by the equipment production instruction to produce refractory materials.

[0040] Preferably, the material input amount is calculated according to the raw material composition data, the production process data and the ingredient ratio, including: The material input quantity is calculated by the following formula: ; In the formula, is the standard ratio of material k at time t; is the production of the system at time t; is the batching ratio of material k at time t; is the material input amount of material k at time t.

[0041] Preferably, the actual operating energy consumption of the equipment is calculated according to the equipment operating status data, the production process data and the maximum energy saving rate, including: The actual energy consumption of the equipment is calculated using the following formula: ; In the formula, is the actual operating energy consumption of device i at time t; is the minimum operating energy consumption of device i at time t; is the maximum energy saving rate; is the operating energy consumption of device i at time t.

[0042] Specifically, the process is divided into two core parts: calculation of material input quantity and calculation of actual equipment operating energy consumption, and generation of production instructions based on these results.

[0043] First, the material input amount is calculated based on the raw material composition data, production process data and ingredient ratio. The calculation of material input amount is to ensure that the production line can put the correct proportion of materials as required to meet the process ratio and production needs. In the formula, is the material delivery amount of material k at time t, which is determined by three key parameters: is the ratio of ingredients at time t, is the production of the system at time t, is the standard ratio of material k at time t. By multiplying the ratio of ingredients, production volume and standard ratio, the actual amount of each material can be accurately calculated, thereby ensuring the accuracy of raw material supply and avoiding material waste and ratio errors.

[0044] Secondly, the actual operating energy consumption of the equipment is calculated based on the equipment operating status data, production process data and maximum energy saving rate. In the formula, is the actual operating energy consumption of device i at time t, reflecting the result of device energy consumption after energy saving rate optimization. The core idea of ​​the formula is to make energy consumption correction based on the gap between the minimum operating energy consumption and the current operating energy consumption of the device, combined with the maximum energy saving rate. Specifically, is the minimum operating energy consumption of device i at time t, representing the energy consumption level of the device in the optimal state; is the operating energy consumption of device i at time t, which represents the energy consumption of the device under the existing load conditions. By introducing the maximum energy saving rate, the actual energy consumption of the device can be adjusted to a state close to the minimum operating energy consumption. It indicates the amount of energy consumption that can be reduced after the energy saving rate is optimized, thereby obtaining the equipment operating energy consumption after energy saving optimization.

[0045] Finally, based on the calculated material delivery amount and the actual energy consumption of the equipment, equipment production instructions are generated and sent to the production line equipment. It is worth noting that the production instructions mainly include two aspects: one is to control the automatic feeding system to accurately deliver various materials according to the calculated material delivery amount; the other is to adjust the operating parameters of the equipment so that the equipment can operate at the optimal energy consumption state while meeting production needs. This process ensures that the production line equipment achieves the minimum energy consumption target while operating efficiently through real-time data feedback and automated execution, thereby achieving the effect of energy saving and consumption reduction, improving production efficiency and optimizing resource utilization.

[0046] In summary, step S15 generates and executes production instructions by calculating the material input amount and the actual energy consumption of the equipment, making the production process more intelligent and precise. This method can effectively improve the batching accuracy of refractory production, optimize the energy consumption of equipment, achieve efficient production and energy saving goals, and provide an efficient, stable and controllable operation plan for the production line.

[0047] The following describes the working process of the present invention by taking a relatively common scenario as an example. The working process is as follows: Assume that a refractory production line is in batch production. The production line needs to mix different raw materials in proportion and complete the production task while the equipment is running. First, the system obtains raw material composition data (including material power consumption, standard ratio and consumption coefficient), production process data (including batching parameters, production volume and error correction coefficient) and equipment operation status data (including equipment production capacity, maximum production capacity, minimum production capacity, unit power consumption, actual operating energy consumption and minimum operating energy consumption) through the automated data acquisition module. These data are collected in real time through sensors, flow meters, power monitoring devices and historical databases, and stored in the system central control platform.

[0048] After data collection is completed, the system calculates the proportion of ingredients based on raw material composition data, production process data and equipment operation status data. The calculation of the proportion of ingredients comprehensively considers the power consumption of the material, the standard proportion and the real-time production needs of the production line to ensure that all kinds of materials are proportioned according to the best proportion, meet the production process requirements and improve resource utilization. Accurate calculation of the proportion of ingredients is the key to ensuring efficient operation of the production line and stable production quality.

[0049] Next, the system uses the equipment operating status data to further calculate the equipment's operating energy consumption. The equipment's operating energy consumption is dynamically calculated based on the equipment's current production capacity, minimum operating energy consumption, and unit energy consumption level. By comprehensively analyzing the equipment's load status and energy consumption, the system can accurately assess the equipment's actual energy consumption under current conditions, providing data support for subsequent energy-saving optimization.

[0050] After obtaining the equipment's operating energy consumption, the system calculates the maximum energy saving rate based on the gap between the equipment's current energy consumption and the minimum energy consumption. The maximum energy saving rate reflects the deviation between the equipment's operating state and the theoretical optimal energy consumption state. By calculating the maximum energy saving rate, the system can quantify the potential energy saving space in the production process, provide a clear goal for optimizing equipment operating parameters, and ensure that production equipment operates efficiently in energy-saving mode.

[0051] Finally, the system generates specific equipment production instructions based on raw material composition data, production process data, equipment operation status data, maximum energy saving rate and ingredient ratio. Equipment production instructions include material delivery amount, equipment operation parameters and energy saving control scheme. These instructions are sent to production line equipment through the central control system. The automated feeding equipment accurately delivers raw materials according to the instructions, and the production equipment optimizes the operation status according to the energy saving control scheme to ensure that the equipment completes the production task at the optimal energy consumption level.

[0052] For example, suppose a refractory production line needs to produce a specific refractory material, and its batching process involves multiple raw materials (such as alumina, magnesium oxide, silicon dioxide, etc.), and the batching ratio and equipment energy consumption need to be dynamically adjusted according to the production task. The following is a detailed example, including the data calculation process and results.

[0053] Assume that the raw material composition data, production process data and equipment operation status data of the production line are as follows: (1) Raw material composition data: Material A (alumina): power consumption =5kW / kg, standard ratio ==0.3; Material B (magnesium oxide): power consumption =3kW / kg, standard ratio == 0.2; Material C (silicon dioxide): power consumption =2kW / kg, standard ratio == 0.5.

[0054] (2) Production process data: Production volume Q = 1000 kg.

[0055] (3) Equipment operation status data: Equipment 1 (Mixer): Production capacity = 500 kg / h, specific power consumption = 10 kW / kg; Equipment 2 (heating furnace): production capacity = 800 kg / h, specific power consumption = 8 kW / kg.

[0056] Substituting the above data into the formula in the embodiment of this scheme for calculation, we get: (1) Material input quantity: Material A: 0.1316 kg; Material B: 0.0526 kg; Material C: 0.0877 kg.

[0057] (2) Equipment energy consumption adjustment: Equipment 1: 3070.04 kW; Device 2: 3930.06 kW.

[0058] Based on the calculation results, equipment production instructions are generated. Through these instructions, the production line equipment is controlled to operate according to the optimized ingredient ratio and energy consumption, ensuring that the production process is efficient and energy-saving.

[0059] In summary, the present invention provides an intelligent control method for a refractory batching production line, comprising: Obtain raw material composition data, production process data and equipment operation status data of the refractory material batching production line, wherein the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operation energy consumption and production capacity; construct a batching model based on the power consumption and production volume, and combine the raw material composition data, the production process data and the equipment operation status data to obtain the batching ratio, and then obtain the material input quantity; process the equipment operation status data based on a preset operation energy consumption model to obtain the equipment operation energy consumption, and obtain the maximum energy saving rate based on the equipment operation energy consumption, and then obtain the actual operation energy consumption of the equipment, wherein the preset operation energy consumption model is constructed through operation energy consumption and production capacity; generate equipment production instructions based on the material input quantity and the actual operation energy consumption of the equipment, and control the production line equipment to produce refractory materials through the equipment production instructions.

[0060] In the present invention, the method can calculate the proportion of ingredients and the energy consumption of equipment operation by integrating raw material composition data, production process data and equipment operation status data, and further calculate the maximum energy saving rate based on the equipment operation energy consumption, thereby obtaining the actual equipment operation energy consumption. Generate adaptive equipment production instructions based on the proportion of ingredients and the maximum energy saving rate. During the production process, the execution of the equipment production instructions can enable the production line equipment to put raw materials according to the optimized proportion of ingredients, and adjust the equipment operation status according to the maximum energy saving rate, thereby ensuring that the production line achieves the lowest energy consumption while producing efficiently. Figure 2 The second embodiment of the present invention provides an intelligent control system for a refractory batching production line, comprising: The data acquisition module is used to acquire the raw material composition data, production process data and equipment operation status data of the refractory material batching production line, wherein the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operation energy consumption and production capacity; A proportion calculation module is used to construct a batching model based on power consumption and production volume, and to obtain a batching ratio and a material input amount by combining the raw material composition data, the production process data and the equipment operation status data; An energy-saving calculation module, used for processing the equipment operation status data based on a preset operation energy consumption model to obtain equipment operation energy consumption, and according to the equipment operation energy consumption, obtaining a maximum energy-saving rate, and then obtaining the actual operation energy consumption of the equipment, wherein the preset operation energy consumption model is constructed by operation energy consumption and production capacity; The instruction generation module is used to generate equipment production instructions based on the material input amount and the actual energy consumption of the equipment, and control the production line equipment to produce refractory materials through the equipment production instructions.

[0061] Ratio calculation module In an optional implementation, the ratio calculation module is specifically used to: The batching model is expressed as follows: ; in, Indicates the proportion of material k at time t; Indicates the power consumption of material k; Indicates the standard ratio of material k at time t; represents the production capacity of equipment i at time t; represents the unit power consumption of device i; Indicates the production volume of the system at time t. In an optional implementation, the energy-saving calculation module is specifically used for: The preset operation energy consumption model is expressed as follows: ; In the formula, represents the operating energy consumption of device i at time t; represents the production capacity of equipment i at time t; represents the maximum production capacity of equipment i; represents the power consumption of device i at time t; Indicates the consumption coefficient of material k; Indicates the standard ratio of material k at time t; Indicates the power consumption of material k; represents the error correction coefficient; Indicates the production volume of the system at time t. In an optional implementation, the energy-saving calculation module is specifically used for: The maximum energy saving rate is calculated by the following formula: ; In the formula, is the operating energy consumption of device i at time t; is the minimum operating energy consumption of device i at time t; is the production capacity of equipment i at time t; is the maximum production capacity of equipment i; represents the minimum production capacity of equipment i; For maximum energy saving rate.

[0062] In an optional implementation, the energy-saving calculation module is specifically used to: The actual energy consumption of the equipment is calculated using the following formula: ; In the formula, is the actual operating energy consumption of device i at time t; is the minimum operating energy consumption of device i at time t; is the maximum energy saving rate; is the operating energy consumption of device i at time t.

[0063] It should be noted that the intelligent control system for a refractory material batching production line provided in an embodiment of the present invention is used to execute all the process steps of the intelligent control method for a refractory material batching production line in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0064] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a program for an intelligent control method for a refractory batching production line. When the processor executes the computer program, the steps in the above-mentioned intelligent control method for a refractory batching production line are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented, such as the instruction generation module.

[0065] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0066] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0067] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0068] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0069] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0070] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0071] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent control method for a refractory batching production line, characterized in that: include: Obtain the raw material composition data, production process data and equipment operation status data of the refractory material batching production line, wherein the raw material composition data includes the power consumption of different materials, the production process data includes the production volume, and the equipment operation status data includes the operating energy consumption and production capacity; Building a batching model based on power consumption and production volume, combining the raw material composition data, the production process data and the equipment operation status data to obtain the batching ratio, and then obtain the material input amount; The equipment operation status data is processed based on a preset operation energy consumption model to obtain equipment operation energy consumption, and a maximum energy saving rate is obtained according to the equipment operation energy consumption, and then the actual operation energy consumption of the equipment is obtained, wherein the preset operation energy consumption model is constructed by operation energy consumption and production capacity; Equipment production instructions are generated based on the material input amount and the actual energy consumption of the equipment operation, and the production line equipment is controlled by the equipment production instructions to produce refractory materials.

2. The intelligent control method for a refractory material batching production line according to claim 1, characterized in that: The raw material composition data also includes material standard ratio and material consumption coefficient; The equipment operation status data also includes equipment production capacity, equipment maximum production capacity, equipment minimum production capacity, material unit power consumption, equipment power consumption, equipment actual operation energy consumption and equipment minimum operation energy consumption; The production process data also includes batching parameters and error correction coefficients.

3. The intelligent control method for a refractory batching production line according to claim 2, characterized in that: The batching model is expressed as follows: ； in, Indicates the proportion of material k at time t; Indicates the power consumption of material k; Indicates the standard ratio of material k at time t; represents the production capacity of equipment i at time t; represents the unit power consumption of device i; Represents the production of the system at time t.

4. The intelligent control method for a refractory material batching production line according to claim 2, characterized in that: The preset operation energy consumption model is expressed as follows: ； In the formula, represents the operating energy consumption of device i at time t; represents the production capacity of equipment i at time t; represents the maximum production capacity of equipment i; represents the power consumption of device i at time t; Indicates the consumption coefficient of material k; Indicates the standard ratio of material k at time t; Indicates the power consumption of material k; represents the error correction coefficient; Represents the production of the system at time t.

5. The intelligent control method for a refractory material batching production line according to claim 2, characterized in that: The maximum energy saving rate is obtained by: ； In the formula, represents the operating energy consumption of device i at time t; represents the minimum operating energy consumption of device i at time t; represents the production capacity of equipment i at time t; represents the maximum production capacity of equipment i; represents the minimum production capacity of equipment i; Indicates the maximum energy saving rate.

6. The intelligent control method for a refractory batching production line according to claim 1, characterized in that: The material delivery amount is obtained by the following method: Based on the standard ratio, ingredient ratio and production volume, the material input volume is obtained, which is expressed as follows: ； In the formula, Indicates the standard ratio of material k at time t; represents the production of the system at time t; Indicates the proportion of material k at time t; Represents the amount of material k put in at time t.

7. The intelligent control method for a refractory material batching production line according to claim 1, characterized in that: The actual operating energy consumption of the equipment is obtained by: According to the minimum operating energy consumption of device i at time t, the maximum energy saving rate and the operating energy consumption of device i at time t, the actual operating energy consumption of the device is obtained, which is expressed as follows: ； In the formula, represents the actual operating energy consumption of device i at time t; represents the minimum operating energy consumption of device i at time t; Indicates the maximum energy saving rate; Represents the operating energy consumption of device i at time t.

8. Data acquisition module, used to obtain raw material composition data, production process data and equipment operation status data of the refractory batching production line, including: Raw material composition data includes the power consumption of different materials, production process data includes production volume, and equipment operation status data includes operating energy consumption and production capacity; A proportion calculation module is used to construct a batching model based on power consumption and production volume, and to obtain a batching ratio and a material input amount by combining the raw material composition data, the production process data and the equipment operation status data; An energy-saving calculation module, used for processing the equipment operation status data based on a preset operation energy consumption model to obtain equipment operation energy consumption, and according to the equipment operation energy consumption, obtaining a maximum energy-saving rate, and then obtaining the actual operation energy consumption of the equipment, wherein the preset operation energy consumption model is constructed by operation energy consumption and production capacity; The instruction generation module is used to generate equipment production instructions based on the material input amount and the actual energy consumption of the equipment, and control the production line equipment to produce refractory materials through the equipment production instructions.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An intelligent control device for a refractory material batching production line, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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