Electronic device for realizing prediction and control system for industrial process and control method thereof

Abstract

To provide an electronic device and control method for implementing a temperature prediction and a control system.SOLUTION: An electronic device 100 includes: a communication interface; a memory in which a first learned neural network model and a second learned neural network model are stored; and one or more processors for receiving process information including input fuel information for a cement production device via a communication interface, inputting the preprocessed process information into a second learned neural network model to obtain first predicted temperature information output by the first learned neural network model and error information related to measured temperatures in a first preheating chamber in the cement production device, identifying predicted calorific value information for first recycled fuel among the input fuels based on the acquired error information and input fuel information, inputting the updated input fuel information into the first learned neural network model to acquire second predicted temperature information for the first preheating chamber, and providing guide information including the acquired second predicted temperature information.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to an electronic device and a control method thereof for implementing a prediction and control system for an industrial process, and more particularly to an electronic device and a control method thereof for predicting the calorific value of recycled fuel and the temperature of a preheating chamber in a cement manufacturing apparatus using a trained neural network model, and controlling the input fuel for cement based on the prediction. [Background technology]

[0002] Recently, technological developments have been made to reduce carbon emissions and unnecessary energy consumption in industrial processes by optimizing indicators such as electricity and coal consumption in order to make the processes more environmentally friendly.

[0003] In the case of the cement process, the characteristics of the fuel used place even more emphasis on the need to reduce carbon emissions and unnecessary energy consumption.The cement process consists of the following steps: a mining process to excavate limestone, the raw material for cement; a coarse crushing process to roughly crush the mined limestone; a mixing process to mix the coarsely crushed limestone to reduce quality variations; a raw material grinding process to finely crush the mixed limestone into powder with natural resources (e.g., clay, silica, iron ore, etc.) or recycled resources (coal ash, slag, foundry sand, desulfurized gypsum, etc.); a preheating process in which fine powdered raw materials supplied from a raw material storage facility are fed into a preheating chamber to produce calcium oxide needed for the firing process; a firing process in which the produced calcium oxide is heated to a predetermined temperature to produce clinker through a chemical reaction; a cooling process in which the produced clinker is cooled; and a grinding process in which gypsum is added to the clinker and further crushed to complete cement.

[0004] In particular, the preheating and calcination processes are carried out in a preheating chamber and a calcination furnace, respectively, within a cement manufacturing apparatus. To produce high-quality clinker, the preheating chamber must maintain an appropriate process temperature. If the temperature is lower than the appropriate process temperature, the chemical reaction may not proceed well, while if the temperature is higher than the appropriate process temperature, unnecessary fuel consumption and increased air pollution gas emissions may occur. Therefore, a method for maintaining the appropriate process temperature is needed.

[0005] In order to reduce carbon emissions, there has been active development of processes for producing cement using recycled fuels (e.g., waste synthetic resins). However, when using recycled fuels, the components of the recycled fuel are not constant, and the calorific value varies depending on factors such as the moisture content. This makes it difficult to identify the calorific value of the recycled fuel, which makes it difficult to maintain the appropriate process temperature. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent Publication No. 10-2022-0147028 Summary of the Invention [Problem to be solved by the invention]

[0007] The present disclosure is intended to solve the above-mentioned problems, and provides an electronic device and a control method thereof that predicts the calorific value of recycled fuel using a trained neural network model, predicts the temperature of a preheating chamber using the predicted calorific value of the recycled fuel, and provides a user with guide information for maintaining an appropriate process temperature based on the predicted temperature. [Means for solving the problem]

[0008] An electronic device for implementing a temperature prediction and control system according to one embodiment of the present disclosure may include a communication interface, a memory storing a first trained neural network model and a second trained neural network model, and one or more processors that pre-process the received process information, including input fuel information of a cement manufacturing apparatus, when the process information is received via the communication interface.

[0009] The one or more processors can input the preprocessed process information into the second trained neural network model and obtain first predicted temperature information output from the first trained neural network model and error information regarding the measured temperature of a first preheating chamber in the cement manufacturing apparatus.

[0010] The one or more processors may identify predicted calorific value information of a first regenerated fuel among the input fuels based on the acquired error information and the input fuel information.

[0011] The one or more processors may input updated fuel input information based on the identified predicted heat quantity information into the first trained neural network model to obtain second predicted temperature information of the first preheating chamber.

[0012] The one or more processors may provide guide information including the obtained second predicted temperature information.

[0013] A method for controlling an electronic device for implementing a temperature prediction and control system according to an embodiment of the present disclosure may include, when process information including input fuel information of a cement manufacturing apparatus is received, performing pre-processing of the received process information.

[0014] The control method may include inputting the preprocessed process information into a second trained neural network model, and obtaining first predicted temperature information output from the first trained neural network model and error information regarding the measured temperature of a first preheating chamber in the cement manufacturing apparatus.

[0015] The control method may include identifying predicted calorific value information of a first regenerated fuel among the input fuels based on the acquired error information and the input fuel information.

[0016] The control method may include inputting updated fuel input information based on the identified predicted heat quantity information into the first trained neural network model to obtain second predicted temperature information of the first preheating chamber.

[0017] The control method may include providing guide information including the acquired second predicted temperature information.

[0018] A non-transitory computer-readable recording medium according to one embodiment of the present disclosure stores computer instructions that, when executed by a processor of an electronic device for implementing a temperature prediction and control system, cause the electronic device to perform operations, the operations may include, when process information including input fuel information for a cement manufacturing apparatus is received, pre-processing the received process information.

[0019] The operations may include inputting the pre-processed process information into a second trained neural network model and obtaining first predicted temperature information output from the first trained neural network model and error information related to a measured temperature of a first preheating chamber in the cement manufacturing apparatus.

[0020] The operation may include identifying predicted calorific value information of a first regenerated fuel among the input fuels based on the acquired error information and the input fuel information.

[0021] The operations may include inputting updated fuel input information based on the identified predicted heat quantity information into the first trained neural network model to obtain second predicted temperature information of the first preheating chamber.

[0022] The operations may include providing guide information including the obtained second predicted temperature information. [Effects of the Invention]

[0023] According to the present disclosure, an artificial intelligence model trained on data relating to the manufacturing process can be used to predict the calorific value of recycled fuel that can reduce carbon emissions, and the predicted calorific value of the recycled fuel can be used to accurately predict the temperature of the preheating chamber. According to the present disclosure, guide information for maintaining an appropriate process temperature can be provided to the user based on the accurately predicted temperature. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a diagram for schematically explaining a control method for an electronic device according to an embodiment; [Figure 2] FIG. 1 is a block diagram illustrating a configuration of an electronic device according to an embodiment. [Figure 3] 1 is a flowchart illustrating a control method for an electronic device according to an embodiment. [Figure 4] FIG. 2 is a diagram illustrating a first trained neural network model and its training method according to one embodiment. [Figure 5] FIG. 10 is a diagram illustrating a second trained neural network model and its training method according to one embodiment. [Figure 6] 10A and 10B are diagrams illustrating a method for providing guide information according to an embodiment. [Figure 7] 10A and 10B are diagrams illustrating a method for obtaining and providing guide information according to an embodiment. [Figure 8a] 1 is a diagram illustrating a method for providing a UI according to an embodiment. [Figure 8b] Same as above. [Figure 9a] 1 is a diagram illustrating a method for providing a UI according to an embodiment. [Figure 9b] Same as above. [Figure 9c] Same as above. [Figure 10] 10A and 10B are diagrams illustrating a method for identifying and transmitting control information to a control engine according to an embodiment. [Figure 11]FIG. 2 is a block diagram showing a detailed configuration of an electronic device according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0025] The present disclosure will now be described in detail with reference to the accompanying drawings.

[0026] The terms used in this specification will be briefly explained to specifically explain the present disclosure.

[0027] The terms used in the embodiments of the present disclosure are currently commonly used and commonly selected as much as possible in consideration of the functions of the present disclosure, but these may change depending on the intentions of engineers in the field, precedents, the emergence of new technologies, etc. In addition, in certain cases, the applicant may arbitrarily select terms, and in such cases, the meanings thereof will be described in detail in the relevant description section of the disclosure. Therefore, the terms used in the present disclosure should be defined based on the meanings of the terms and the overall content of the present disclosure, rather than simply by the names of the terms.

[0028] In this specification, the terms "have," "may have," "include," or "may include" refer to the presence of a given feature (e.g., a value, function, operation, or component such as a part) and do not exclude the presence of additional features.

[0029] The phrase "at least one of A and / or B" should be understood to refer to either "A" or "B" or "A and B."

[0030] As used herein, expressions such as "first," "second," "first," or "second" may modify various components regardless of order and / or importance, and are used only to distinguish one component from other components, and do not limit the component in question.

[0031] When a component (e.g., a first component) is referred to as being "operatively or communicatively coupled with / to" or "connected to" another component (e.g., a second component), it should be understood that the component may be directly coupled to the other component or may be coupled through another component (e.g., a third component).

[0032] The singular expression includes the plural expression unless the context clearly dictates otherwise. In this application, the terms "comprise" or "comprise" and the like are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the presence or possible addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0033] In this disclosure, a "module" or "unit" performs at least one function or operation and may be embodied in hardware or software, or a combination of hardware and software. Furthermore, multiple "modules" or multiple "units" may be integrated into at least one module and embodied in at least one processor (not shown), except for "modules" or "units" that must be embodied in specific hardware.

[0034] An electronic device according to an embodiment of the present disclosure may include an artificial intelligence model (or artificial neural network model or learning network model) configured with at least one neural network layer. The artificial neural network may include a deep neural network (DNN), such as, but not limited to, a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network.

[0035] In addition, in this specification, a "parameter" refers to a value used in the calculation process of each layer of a neural network, and may include, for example, a weight used when applying an input value to a predetermined calculation formula. The parameter may also be expressed in the form of a matrix. The parameter is a value set as a result of training, and can be updated as needed via separate training data.

[0036] FIG. 1 is a diagram for schematically explaining a control method for an electronic device according to an embodiment.

[0037] Referring to FIG. 1, first, according to one embodiment, a calcination process is performed in a kiln included in the cement manufacturing apparatus 1. For the cement manufacturing process to be successful, the temperature of the kiln in which the calcination process is performed must be within a predetermined range (1500°C to 2000°C according to one embodiment). The cement manufacturing apparatus 1 may include multiple preheating chambers. Among these, if the temperature of the first preheating chamber 10 connected to the pyro rotor and the calcination furnace is also within a predetermined range, the process can be performed efficiently.

[0038] The cement manufacturing apparatus 1 may include multiple fuel inlets. For example, the cement manufacturing apparatus may include an auxiliary fuel inlet, a main fuel inlet, and a recycled fuel inlet 20. By supplying recycled fuel to the cement manufacturing apparatus 1, carbon emissions are reduced and energy emissions are decreased. However, due to the characteristics of recycled fuel, it is difficult to measure the accurate calorific value, which makes it difficult to control the temperatures of the preheating chamber and the kiln.

[0039] Below, various embodiments will be described that predict the calorific value of the recycled fuel input into the cement manufacturing apparatus, predict the temperature of the first preheating chamber 10 connected to the pyro rotor and the calciner based on this, and provide guide information for increasing the proportion of recycled fuel while maintaining the temperature of the first preheating chamber within a predetermined range.

[0040] FIG. 2 is a block diagram showing the configuration of an electronic device according to an embodiment.

[0041] Referring to FIG. 2, electronic device 100 may include a communication interface 110, a memory 120, and one or more processors 130.

[0042] According to an embodiment, the electronic device 100 may be embodied as a device that processes data and communicates with external devices, such as a server, but is not limited thereto. For example, the electronic device 100 may be embodied as various devices such as a smart TV, a tablet, a monitor, a smartphone, a desktop computer, a laptop computer, etc. The electronic device 100 according to an embodiment of the present disclosure is not limited to the above-mentioned devices, and the electronic device 100 may be embodied as an electronic device 100 having two or more functions of the above-mentioned devices.

[0043] The electronic device 100 may communicate with an external device and an external server in various ways. According to an embodiment, a single communication module may be implemented for communication with the external device and the external server. For example, the electronic device 100 may communicate with the external device using a Bluetooth module and may also communicate with the external server using a Bluetooth module.

[0044] In other embodiments, the communication modules for communicating with the external device and the external server may be implemented separately. For example, the electronic device 100 may communicate with the external device using a Bluetooth module and with the external server using an Ethernet modem or a Wi-Fi module.

[0045] According to an embodiment, the external device (not shown) may be embodied as a cement manufacturing device, but is not limited thereto.

[0046] The communication interface 110 can input and output various types of data. For example, the communication interface 110 can transmit and receive various types of data to and from an external device (e.g., a source device), an external storage medium (e.g., a USB memory), and an external server (e.g., a web hard drive) via communication methods such as AP-based Wi-Fi (Wireless LAN network), Bluetooth, Zigbee, wired / wireless LAN (Local Area Network), WAN (Wide Area Network), Ethernet, IEEE 1394, HDMI (High-Definition Multimedia Interface), USB (Universal Serial Bus), MHL (Mobile High-Definition Link), AES / EBU (Audio Engineering Society / European Broadcasting Union), optical, coaxial, etc.

[0047] For example, the communication interface 110 may include a Bluetooth Low Energy (BLE) module. BLE refers to a Bluetooth technology capable of transmitting and receiving low-power, low-volume data in the 2.4 GHz frequency band with a reach radius of approximately 10 m. However, without being limited thereto, the communication interface 110 may also include a Wi-Fi communication module. That is, the communication interface 110 may include at least one of a Bluetooth Low Energy (BLE) module and a Wi-Fi communication module.

[0048] According to one example, the communication interface 110 can use different communication modules to communicate with external devices such as a remote control device and an external server. For example, the communication interface 110 can use at least one of an Ethernet module or a Wi-Fi module to communicate with an external server, and a Bluetooth module to communicate with an external device such as a remote control device. However, this is merely one embodiment, and the communication interface 110 can use at least one of various communication modules when communicating with multiple external devices or external servers.

[0049] The memory 120 may store data required for various embodiments. Depending on the purpose of data storage, the memory 120 may be implemented as a memory embedded in the electronic device 100 or as a memory removable from the electronic device 100. For example, data for operating the electronic device 100 may be stored in a memory embedded in the electronic device 100, and data for extended functions of the electronic device 100 may be stored in a memory removable from the electronic device 100.

[0050] The memory embedded in the electronic device 100 may be implemented as at least one of a volatile memory (e.g., a dynamic RAM (DRAM), a static RAM (SRAM), or a synchronous dynamic RAM (SDRAM)), a non-volatile memory (e.g., a one-time programmable ROM (OTPROM), a programmable ROM (PROM), an erasable and programmable ROM (EPROM), an electrically erasable and programmable ROM (EEPROM), a mask ROM, a flash ROM, a flash memory (e.g., a NAND flash or a NOR flash), a hard drive, or a solid state drive (SSD)). The memory removable from the electronic device 100 may be implemented as at least one of a memory card (e.g., a compact flash (CF), a secure digital (SD), a micro-SD (micro secure digital), a mini-SD (mini secure digital), an extreme digital (xD), an MMC (multi-media card), a cellular phone (MMC ... The storage device may be implemented in the form of an external memory (e.g., a card), an external memory connectable to a USB port (e.g., a USB memory), or the like.

[0051] According to one embodiment, memory 120 may store a first trained neural network model and a second trained neural network model, which are described in more detail with reference to Figures 4 and 5.

[0052] One or more processors 130 (hereinafter, processors) are electrically connected to the communication interface 110 and the memory 120 and control the overall operation of the electronic device 100. The processor 130 may be comprised of one or more processors. Specifically, the processor 130 may perform the operations of the electronic device 100 according to various embodiments of the present disclosure by executing at least one instruction stored in the memory 120.

[0053] According to an embodiment, the processor 130 may be implemented as a digital signal processor (DSP) for processing digital video signals, a microprocessor, a graphics processing unit (GPU), an artificial intelligence (AI) processor, a neural processing unit (NPU), or a time controller (TCON). However, without being limited thereto, the processor 130 may include or be defined as one or more of a central processing unit (CPU), a microcontroller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), a communication processor (CP), or an ARM processor. The processor 130 may also be implemented as a system on chip (SoC) or large scale integration (LSI) having a built-in processing algorithm, or may be implemented in the form of an application specific integrated circuit (ASIC) or field programmable gate array (FPGA).

[0054] According to an embodiment, the processor 130 may be implemented as a digital signal processor (DSP), a microprocessor, or a time controller (TCON). However, without being limited thereto, the processor 130 may include or be defined as one or more of a central processing unit (CPU), a microcontroller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), a communication processor (CP), or an ARM processor. The processor 130 may also be implemented as a system on chip (SoC) or large scale integration (LSI) having a built-in processing algorithm, or may be implemented in the form of a field programmable gate array (FPGA).

[0055] According to one embodiment, the processor 130 may receive process information including input fuel information of the cement manufacturing apparatus 1 via the communication interface 110. Here, the process information is information related to the general environment of the cement manufacturing apparatus 1 in which the cement process is performed, and according to one example, the process information may include input fuel information input into the cement manufacturing apparatus 1 for cement production or process status information corresponding to the cement manufacturing apparatus 1.

[0056] For example, the input fuel information may include information regarding the input amount corresponding to each of multiple types of fuel input for cement production and calorific value information corresponding to each of the multiple types of input fuel. The multiple types of fuel may include, for example, a bituminous coal-type main fuel, a bituminous coal-type auxiliary fuel, and multiple types of recycled fuel. Here, the main fuel and the auxiliary fuel are the same type of fuel, but the main fuel is input via a first fuel input unit provided on one side of the kiln in the cement production apparatus 1, and the auxiliary fuel is input via a second fuel input unit provided on the other side of the kiln. The auxiliary fuel is a fuel that supplements the main fuel.

[0057] The recycled fuel is a fuel used for environmentally friendly processes and carbon emission reduction, and has a relatively low carbon content compared to the main and auxiliary fuels of bituminous coal, which can reduce carbon emissions and unnecessary energy consumption. For example, the recycled fuel may include at least one of waste synthetic resin, waste tires, waste oil, and sewage sludge.

[0058] The process status information corresponding to the cement manufacturing apparatus 1 may include, for example, process environment information related to the cement manufacturing apparatus 1, and as an example, the process status information may include at least one of the oxygen concentration, carbon dioxide concentration, and nitrogen oxide concentration in the exhaust gas discharged from the inlet of the kiln in the cement manufacturing apparatus 1. Alternatively, the process status information may include, but is not limited to, the temperature of the air input into the pyro rotor and kiln after the cooling process has progressed, pressure information of the fan in the cooler, speed information of the conveyor belt, speed information and current information of the pyro rotor and kiln, and information on the raw materials (e.g., lime powder) input into the cement manufacturing apparatus 1.

[0059] As an example, the processor 130 may receive process information, including input fuel information, of the cement manufacturing apparatus 1 from an external server (not shown) that stores process information of the cement manufacturing apparatus 1, via the communication interface 110. Alternatively, the processor 130 may receive process information from a sensor provided in the cement manufacturing apparatus 1 via the communication interface 110, but is not limited to this.

[0060] According to one embodiment, when process information is received, the processor 130 may perform pre-processing of the received process information. For example, the processor 130 may identify outliers from among multiple data included in the received process information using a predetermined algorithm, and obtain process information from which the identified outliers have been removed. Alternatively, if process information corresponding to a predetermined time period during a process is not received, the processor 130 may perform pre-processing by receiving the process information that was not received from the communication interface 110. However, the present invention is not limited to this.

[0061] According to one embodiment, the processor 130 can input the preprocessed process information into a second trained neural network model and obtain first predicted temperature information output from the first trained neural network model and error information regarding the measured temperature of the first preheating chamber in the cement manufacturing apparatus 1.

[0062] According to one example, the second trained neural network model may be a neural network model trained to receive process information including input fuel information and process state information, and output error information related to the predicted temperature information output from the first trained neural network model and the measured temperature information of the first preheating chamber. The processor 130 may input the preprocessed process information including the input fuel information and process state information to the second trained neural network model to obtain the error information.

[0063] Here, the error information between the predicted temperature information output from the first trained neural network model and the measured temperature information of the first preheating chamber refers to an error caused by changes in the calorific value of waste synthetic resin, one of the multiple recycled fuel types. In other words, in the case of waste synthetic resin, it is difficult to accurately identify the calorific value due to the characteristics of the fuel. Since the calorific value of the waste synthetic resin is not accurately identified, an error occurs between the predicted temperature and the measured temperature of the first preheating chamber. In this disclosure, we attempt to accurately predict the temperature of the first preheating chamber by accurately identifying the calorific value of waste synthetic resin, one of the recycled fuels.

[0064] In addition, according to an example, the multiple types of temperature information including the first predicted temperature information and the second predicted temperature information may include information related to the temperature value.

[0065] According to one embodiment, processor 130 can identify predicted calorific value information of a first recycled fuel among the input fuels based on the acquired error information and input fuel information. Here, for example, the first recycled fuel may be waste synthetic resin, and processor 130 can identify predicted calorific value information of the first recycled fuel based on the acquired error information and information regarding the input amount of the first recycled fuel included in the input fuel information. For example, processor 130 can identify the predicted calorific value of the waste synthetic resin using the following equation (1):

[0066]

number

[0067] Here, A means the predicted calorific value of the waste synthetic resin, B means the calorific value of the existing waste synthetic resin for predicting the temperature of the existing first preheating chamber, C means error information regarding the temperature of the first preheating chamber output through the second trained neural network model, and D means the input amount of waste synthetic resin. As an example, assume that the calorific value of the existing waste synthetic resin is 10, the magnitude of the error is 50, and the input amount is 100. The processor 130 can determine the magnitude of the predicted calorific value of the waste synthetic resin as 10.5 (10 + (50 / 100)) through equation (1). Through this, the processor 130 can determine the accurate calorific value of the waste synthetic resin.

[0068] According to one embodiment, processor 130 may input updated input fuel information based on the identified predicted calorific value information into a first trained neural network model to obtain second predicted temperature information for the first preheating chamber. According to one example, processor 130 may input input fuel information updated with predicted calorific value information for the identified waste synthetic resin into a first trained neural network model to obtain second predicted temperature information for the first preheating chamber from the first trained neural network model. Here, the first predicted temperature information is predicted temperature information for the first preheating chamber corresponding to the input fuel information before the calorific value information for the first recycled fuel, i.e., the waste synthetic resin, is updated, and the second predicted temperature information is predicted temperature information for the first preheating chamber corresponding to the input fuel information after the calorific value information for the first recycled fuel, i.e., the waste synthetic resin, is updated.

[0069] According to one embodiment, processor 130 can provide guide information including the acquired second predicted temperature information. According to one example, processor 130 can acquire guide information that guides the input amount of at least one of the main fuel and the auxiliary fuel to be reduced when the acquired second predicted temperature information is identified as exceeding a target temperature value. Alternatively, according to one example, processor 130 can acquire guide information that guides the input amount of the first regenerated fuel to be increased when the acquired second predicted temperature information is identified as being less than a target temperature value. Processor 130 can provide the acquired guide information.

[0070] FIG. 3 is a flowchart illustrating a method for controlling an electronic device according to an embodiment.

[0071] According to one embodiment, first, in the control method, when process information including input fuel information of the cement manufacturing apparatus 1 is received (S310: Y), the received process information can be pre-processed (S320). According to one example, the processor 130 can identify outlier data from a plurality of data included in the received process information using a predetermined algorithm, and can obtain process information from which the identified outlier data has been removed.

[0072] Next, according to one embodiment, the control method inputs the preprocessed process information into a second trained neural network model, and obtains first predicted temperature information output from the first trained neural network model and error information regarding the measured temperature of the first preheating chamber in the cement manufacturing apparatus 1 (S330).

[0073] Next, according to one embodiment, the control method can identify predicted calorific value information of the first recycled fuel among the input fuels based on the acquired error information and input fuel information (S340). According to one example, the processor 130 can identify predicted calorific value information of the first recycled fuel based on error information regarding the temperature of the first preheating chamber output from the second trained neural network model, information regarding the input amount of the first recycled fuel corresponding to waste synthetic resin among the multiple types of input fuels, and calorific value information regarding the existing waste synthetic resin.

[0074] Next, according to one embodiment, the control method can input the updated input fuel information based on the identified predicted heat quantity information into the first trained neural network model to obtain second predicted temperature information of the first pre-heating chamber (S350). According to one example, the processor 130 can update the input fuel information based on the identified predicted heat quantity information using Equation (1), and input the updated input fuel information into the first trained neural network model to obtain second predicted temperature information of the first pre-heating chamber.

[0075] Next, according to one embodiment, the control method may provide guide information including the acquired second predicted temperature information (S360). According to one example, when the acquired second predicted temperature information is identified as exceeding the target temperature value, the processor 130 may obtain guide information that guides the input amount of at least one of the main fuel and the auxiliary fuel to be reduced. Alternatively, according to one example, when the acquired second predicted temperature information is identified as being below the target temperature value, the processor 130 may obtain guide information that guides the input amount of the first regenerated fuel to be increased. The processor 130 may provide the obtained guide information.

[0076] However, without being limited thereto, according to one embodiment, the processor 130 may provide guide information regarding the input amount of different types of fuel other than the main fuel, auxiliary fuel, and recycled fuel. Alternatively, according to one embodiment, the processor 130 may also provide guide information regarding the pressure of a cooler fan included in the cement manufacturing apparatus 1.

[0077] According to the above example, the electronic device 100 can predict the calorific value of the recycled fuel using a trained neural network model and can predict the temperature of the first preheating chamber based on the predicted calorific value. As a result, the electronic device 100 can provide guidance information to the user based on the accurately predicted preheating chamber temperature, thereby enabling an environmentally friendly and efficient process.

[0078] FIG. 4 is a diagram illustrating a first trained neural network model and a training method thereof according to one embodiment.

[0079] The artificial neural network (or neural network model) including the first trained neural network model and the second trained neural network model according to one embodiment of the present disclosure may include a deep neural network (DNN), such as a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network, but is not limited to the aforementioned examples.

[0080] According to one embodiment, a first trained neural network model may be stored in memory 120. The first trained neural network model may be trained to output predicted temperature information for the first preheating chamber when input fuel information including information on input amounts corresponding to each of different types of input fuels, including a first regenerative fuel, and calorific value information corresponding to each of the input fuels is input.

[0081] 4, in one embodiment, a data set including input fuel information 40 including fuel input amount information and calorific value information corresponding to each of the main fuel, auxiliary fuel, and multiple regenerative fuels is input as training data to a first neural network model 400, and the neural network model can be trained. In this case, the data set may include temperature information 41 of the first preheating chamber as a label, which is output based on (or caused by) the input fuel.

[0082] For example, a data set including fuel input amount information and calorific value information corresponding to each of the main fuel, auxiliary fuel, and multiple regenerated fuels at time n may be input as training data to the first neural network model, and the neural network model may be trained. In this case, the data set may include temperature information of the first preheating chamber at time n+1, measured based on the fuel input at time n, as a label, and training may be performed.

[0083] FIG. 5 is a diagram illustrating a second trained neural network model and its training method according to one embodiment.

[0084] According to one embodiment, a second trained neural network model may be stored in memory 120. According to one example, the second trained neural network model may be trained to receive process information including input fuel information and process status information, and output error information related to predicted temperature information output from the first trained neural network model and measured temperature information of the first pre-heating chamber. Here, the measured temperature information of the first pre-heating chamber is information related to the temperature of the first pre-heating chamber measured via an actual sensor.

[0085] Referring to FIG. 5 , in one embodiment, a dataset including process information 50 including input fuel information and process status information is input as training data to a second neural network model 500, and the second neural network model 500 can be trained. Here, the input fuel information may include, for example, fuel input amount information and calorific value information corresponding to each of the main fuel, auxiliary fuel, and multiple recycled fuels. The process status information may include, for example, process environment information related to the cement manufacturing apparatus 1. For example, the process status information may include at least one of the oxygen concentration, carbon dioxide concentration, and nitrogen oxide concentration in the exhaust gas discharged from the inlet of the kiln in the cement manufacturing apparatus 1. Alternatively, the process status information may include the temperature of air input into the pyro-rotor and kiln after the cooling process has progressed, pressure information of a fan in the cooler, speed information of the conveyor belt, speed information and current information of the pyro-rotor and kiln, and information on the raw material (e.g., lime powder) input into the cement manufacturing apparatus 1.

[0086] The data set (set) may include, in its label, a difference between the predicted temperature information 51 of the first preheating chamber output from the first trained neural network model and the measured temperature information 52 of the first preheating chamber. That is, the second neural network model 500 may perform training using the predicted temperature information output from the first trained neural network model as training data. In one example, the process information may include temperature history information of the first preheating chamber and input fuel history information, and the measured temperature information 52 of the first preheating chamber may be obtained based on the temperature history information of the first preheating chamber included in the process information.

[0087] FIG. 6 is a diagram illustrating a method for providing guide information according to an embodiment.

[0088] Referring to FIG. 6, first, according to one embodiment, the control method may receive a target temperature value for a first preheating chamber in the cement manufacturing apparatus via a user interface (not shown) (S610).

[0089] Next, according to one embodiment, the control method can identify the input amounts corresponding to each of the different types of input fuel for the temperature value of the first preheating chamber to reach the target temperature value based on the acquired second predicted temperature information and the received target temperature value (S620), and obtain guide information corresponding to the identified input amounts (S630).

[0090] According to one example, memory 120 may already store information regarding the change in temperature of the first preheating chamber due to a change in unit input amount for each of a plurality of types of input fuel, including main fuel and auxiliary fuel. According to one example, processor 130 may acquire information regarding the change in temperature of the first preheating chamber due to a change in unit input amount through process information including process history information using a predetermined algorithm. Processor 130 may identify the amount of main fuel or auxiliary fuel to be input based on the information stored in memory 120 and the difference between the second predicted temperature information and the target temperature value, and acquire guide information including information regarding the identified input amount. Alternatively, processor 130 may identify the amount of first regenerated fuel to be input based on the information stored in memory 120 and the difference between the second predicted temperature information and the target temperature value, and acquire guide information including information regarding the identified input amount.

[0091] For example, when the second predicted temperature information exceeds a target temperature value, the processor 130 may acquire guide information for changing the amount of at least one of the bituminous coal-type main fuel and auxiliary fuel fed so that the temperature value of the first preheating chamber reaches the target temperature value. Alternatively, when the second predicted temperature information is below the target temperature value, the processor 130 may acquire guide information for increasing the amount of the first recycled fuel fed. When the processor 130 identifies that the second predicted temperature information is below the target temperature value based on the information stored in the memory 120, it may acquire guide information for increasing the amount of waste synthetic resin fed corresponding to the first recycled fuel.

[0092] However, without being limited thereto, the processor 130 may also obtain guide information corresponding to the magnitude of the temperature information predicted using the first and second trained neural network models, as will be described in detail with reference to FIG.

[0093] Next, according to one embodiment, the control method may provide a UI including the obtained guide information (S640). A specific method for providing the UI will be described in detail with reference to Figures 8, 9a, and 9b.

[0094] FIG. 7 is a diagram illustrating a method for obtaining and providing guide information according to an embodiment.

[0095] 7, according to one embodiment, the control method may first identify sub-fuel input information in which the input amount corresponding to each input fuel included in the input fuel information has been changed (S710). Here, the sub-fuel input information may be process information in which the input fuel ratio has been changed by a predetermined value, but is not limited thereto. According to one example, the sub-fuel input information may be process information in which the process state (e.g., the magnitude of pressure within the cement manufacturing apparatus) has been changed. For example, the processor 130 may identify sub-fuel input information in which the input amount of the main fuel or auxiliary fuel has been reduced by a predetermined value.

[0096] Next, according to one embodiment, the control method may input the sub-fuel input information into the first trained neural network model to obtain second sub-predicted temperature information (S720).

[0097] Next, according to one embodiment, the control method may acquire guidance information regarding the input fuel using the acquired second predicted temperature information and second sub-predicted temperature information (S730). Here, the guidance information refers to information regarding a recommended input ratio corresponding to each input fuel for increasing the ratio of recycled fuel input while the temperature of the first preheating chamber maintains a target temperature value. According to one example, the processor 130 may acquire the guidance information regarding the input fuel using a predetermined algorithm through a dataset including the acquired second predicted temperature information and process information corresponding to the second predicted temperature information, and a dataset including the acquired second sub-predicted temperature information and process information corresponding to the second predicted temperature information.

[0098] However, without being limited thereto, according to one embodiment, the processor 130 may obtain guidance information regarding the input fuel based on information regarding the change in temperature of the first preheating chamber due to a change in the unit input amount corresponding to each of multiple types of input fuel, including the main fuel and auxiliary fuel, stored in the memory 120.

[0099] Alternatively, according to one embodiment, the processor 130 may identify guidance information regarding the input fuel based on at least one of the temperature history information of the first preheating chamber and the process environment information regarding the cement manufacturing apparatus 1, and provide the identified guidance information. According to one example, the processor 130 may identify the guidance information regarding the input fuel based on at least one of the acquired second predicted temperature information, the second sub-predicted temperature information, the temperature history information of the first preheating chamber, and the process environment information regarding the cement manufacturing apparatus 1. Here, the process environment information may include at least one of the oxygen concentration, the carbon monoxide concentration, the carbon dioxide concentration, and the nitrogen oxide concentration in the exhaust gas discharged from the inlet of the calciner in the cement manufacturing apparatus 1.

[0100] According to one example, once the current temperature value of the first preheating chamber is identified based on the temperature history information of the first preheating chamber, processor 130 can identify guidance information based on a difference between the current temperature value of the first preheating chamber and the target temperature value. For example, if the difference between the current temperature value of the first preheating chamber and the target temperature value is equal to or greater than a threshold, processor 130 can identify guidance information that minimizes the time it takes for the temperature value of the first preheating chamber to reach the target temperature value while maintaining the temperature of the first preheating chamber at the target temperature value.

[0101] Alternatively, in one example, the processor 130 can identify the current temperature value of the first preheating chamber and the temperature value of the first preheating chamber at a first point in time a predetermined time before the present based on the temperature history information of the first preheating chamber, and provide guidance information based thereon.

[0102] For example, processor 130 may identify the current temperature value of the first preheating chamber and the temperature value of the first preheating chamber one minute prior to the present (or a past temperature value of the first preheating chamber), and compare the identified current temperature value of the first preheating chamber with the past temperature value of the first preheating chamber to obtain trend information regarding the temperature of the first preheating chamber. If the time it takes for the temperature value of the first preheating chamber to reach the target temperature value is less than a threshold based on the obtained trend information, processor 130 may identify guidance information that increases the time it takes for the temperature value of the first preheating chamber to reach the target temperature value while the temperature of the first preheating chamber is maintained at the target temperature value. Alternatively, if the time it takes for the temperature value of the first preheating chamber to reach the target temperature value is equal to or greater than a threshold based on the obtained trend information, processor 130 may identify guidance information that decreases the time it takes for the temperature value of the first preheating chamber to reach the target temperature value while the temperature of the first preheating chamber is maintained at the target temperature value.

[0103] Alternatively, according to one example, the processor 130 may identify guidance information using process environment information related to the cement manufacturing apparatus 1. For example, when the processor 130 identifies that the concentration of carbon monoxide (CO) in the exhaust gas discharged from the inlet of the kiln is equal to or greater than a predetermined value, the processor 130 may update the guidance information so that the amount of recycled fuel to be input while the temperature of the first preheating chamber is maintained at a target temperature value is reduced from the previously identified amount of recycled fuel. That is, when the firing condition of the kiln is poor, such as when the concentration of carbon monoxide is equal to or greater than a predetermined value, the processor 130 may provide the user with guidance information to reduce the amount of recycled fuel to be input, thereby guiding the amount of recycled fuel to be input in consideration of the firing condition of the kiln.

[0104] However, in this case, if the processor 130 determines that the magnitude of the current applied to the main motor included in the kiln is equal to or greater than a predetermined value based on the process environment information, it may provide guidance information to maintain the previously determined amount of recycled fuel input. This is because if the current value of the main motor of the kiln is equal to or greater than a predetermined value, it means that the kiln is in good condition.

[0105] Alternatively, for example, the processor 130 may identify guidance information using the concentration of oxygen in the exhaust gas included in the process environment information. Specifically, the processor 130 may identify trend information of the concentration of oxygen in the exhaust gas based on the process environment information, and if it is determined that the concentration of oxygen in the exhaust gas is decreasing over time, it may provide guidance information to reduce the amount of regenerative fuel to be input. In other words, since a decrease in the concentration of oxygen in the exhaust gas means that the temperature of the preheating chamber is increasing, the processor 130 may identify the amount of regenerative fuel to be input based on the concentration of oxygen in the exhaust gas.

[0106] However, without being limited thereto, according to one embodiment, processor 130 may identify guidance information using a predetermined algorithm. For example, processor 130 may provide a function for feedback control in which an output maintains a reference value based on an error between a control variable and a reference input. Processor 130 may also identify guidance information regarding the input fuel using, as input values, the target temperature value of the first preheating chamber, the current temperature value, and information for identifying the temperature value of the first preheating chamber (which may include, for example, information regarding control variables corresponding to different types of cement manufacturing apparatus 1, including process information such as information regarding the amounts of main fuel, auxiliary fuel, and recycled fuel input; concentrations of oxygen, carbon dioxide, and nitrogen oxides in the exhaust gas at the inlet of the kiln; temperature information of the air input to the pyro-rotor and kiln after cooling; fan pressure in the cooler; conveyor belt speed; and speed information of the pyro-rotor and kiln).

[0107] Next, according to one embodiment, the control method may provide guide information including the obtained guidance information (S740).

[0108] 8a and 8b are diagrams illustrating a method for providing a UI according to an embodiment.

[0109] 8a-8b, according to one embodiment, processor 130 may provide UI 800. According to one example, electronic device 100 may include a display (not shown), and processor 130 may provide UI 800 via the display (not shown).

[0110] According to one embodiment, processor 130 may identify temperature history information of the first preheating chamber based on information stored in memory 120, and provide UI800 including graph information corresponding to the temperature history of the first preheating chamber based on the identified temperature history information. In this case, according to one example, processor 130 may provide UI800 including graph information for comparing the acquired second predicted temperature information with the temperature history information of the first preheating chamber.

[0111] It should be noted that, according to one embodiment, the processor 130 may also provide the obtained guidance information along with graph information for comparing the second predicted temperature information with the temperature history information of the first preheating chamber.

[0112] In this case, according to one example, processor 130 may also provide effect information 904 corresponding to a predicted temperature change in the preheat chamber based on the guidance information, where effect information 904 includes a contribution value to the temperature change corresponding to each of a plurality of types of fuel.

[0113] According to one example, processor 130 may also provide UI 810 for inputting a target temperature value for the first preheating chamber. Here, the guidance information refers to information regarding recommended input rates for each input fuel for increasing the proportion of recycled fuel input while maintaining the temperature of the first preheating chamber at the target temperature value. Processor 130 may provide UI 810 for inputting a target temperature value for the first preheating chamber from the user, along with UI 800 including graph information for comparing acquired second predicted temperature information with temperature history information for the first preheating chamber. Processor 130 may receive information regarding the target temperature value from the user based on the provided UI 810 and perform an operation based on the received information.

[0114] Note that, according to one embodiment, processor 130 may provide a UI for providing historical information corresponding to each of a plurality of types of input fuel and a UI 820 for receiving user input corresponding to maximum and minimum values ​​for each input amount of main or auxiliary fuel. According to one example, processor 130 may provide the above-described UI 820 via a display (not shown), and, when a corresponding user input is received, may provide a UI corresponding to FIG. 8b. This will be described in more detail with reference to FIG. 8c.

[0115] 8b, in one embodiment, processor 130 may provide a UI for providing historical information corresponding to each of a plurality of types of input fuel and a UI 820 for receiving user input corresponding to maximum and minimum values ​​for each input amount of main fuel or auxiliary fuel. When UI 820 is provided and user input is received, processor 130 may provide a UI 823 corresponding to historical information corresponding to each of a plurality of types of input fuel and a UI 822 corresponding to user input corresponding to maximum and minimum values ​​for each input amount of main fuel or auxiliary fuel. In this case, when processor 130 receives user input corresponding to maximum and minimum values ​​for each input amount of main fuel or auxiliary fuel via UI 822 corresponding to user input corresponding to maximum and minimum values ​​for each input amount of main fuel or auxiliary fuel, processor 130 may input information corresponding to the received user input into a trained neural network model, thereby obtaining guidance information reflecting the received user input.

[0116] According to one embodiment, the process information may include process history information including temperature history information of the first preheating chamber and history information of the input fuel. According to one example, the processor 130 can identify history information regarding the input amount corresponding to each of a plurality of types of input fuel based on the information stored in the memory 120, and based on this, provide a UI 823 including graph information corresponding to the input history of at least one of the main fuel or the auxiliary fuel.

[0117] In one embodiment, a UI823 corresponding to historical information corresponding to each of multiple types of input fuel and a UI822 corresponding to user input corresponding to maximum and minimum values ​​for each input amount of main fuel or auxiliary fuel can be provided, but this is not limited to this, and it is of course possible to provide both the above-mentioned UI822 and 823.

[0118] 9a, 9b, and 9c are diagrams illustrating a method for providing a UI according to an embodiment.

[0119] Referring to FIG. 9A, according to one embodiment, the processor 130 may provide a UI 900 including guidance information. For example, the UI 900 may include input amount information 901 corresponding to each of a plurality of types of input fuel currently being input. The "MCOAL," "PCOAL," "MRDF," and "PRDF" shown in FIG. 9A are examples of the plurality of types of input fuel, but are not limited thereto. Alternatively, according to one example, the UI 900 may include acquired guidance information 903. The guidance information 903 refers to information regarding recommended input ratios corresponding to each input fuel for increasing the proportion of recycled fuel input while maintaining the temperature of the first preheating chamber within a predetermined range. Furthermore, according to one example, the UI 900 may also provide change information 902 corresponding to a difference between the input amount information 901 corresponding to each of a plurality of types of input fuel currently being input and the acquired guidance information 903.

[0120] 9b, according to one embodiment, processor 130 may provide UI 910 including information 911 regarding an improved preheating chamber temperature improvement value when fuel is added based on the guidance information and an improved preheating chamber temperature improvement value after a predetermined time (e.g., 5 minutes) corresponding to the currently added fuel regardless of the guidance information, based on the acquired guidance information. In this case, UI 910 may be provided that includes the magnitude of the current preheating chamber temperature and the magnitude of the preheating chamber temperature predicted through a neural network model. Note that the improved preheating chamber temperature improvement value after a predetermined time (e.g., 5 minutes) corresponding to the currently added fuel refers to a value for the predicted preheating chamber temperature after the predetermined time based on the currently added fuel regardless of the guidance information, and the improved preheating chamber temperature improvement value when fuel is added based on the guidance information refers to a value for the predicted preheating chamber temperature after the predetermined time for the fuel added based on the guidance information.

[0121] In addition, according to one example, when fuel is added based on the guidance information, the processor 130 may provide, as "effect" information, information regarding the improved preheating chamber temperature improvement value and the difference value of the preheating chamber temperature improvement value after a predetermined time (e.g., 5 minutes) corresponding to the currently added fuel, regardless of the guidance information.

[0122] FIG. 9c is an example of a user interface for providing a control method according to an embodiment of the present disclosure.

[0123] 9c shows an example of a UI element for displaying temperature information of the preheating chamber. The temperature information can be provided in the form of a graph reflecting time information, as in 921, and can be provided in a form that makes it easy to distinguish between actual measurement information and predicted information.

[0124] 9c shows an example of a UI element for providing a function for setting the fuel input amount. A user can set a minimum and / or maximum value for the fuel input amount via 922. In this case, the fuel may include multiple types, and a user can set the input amount of each of the supplemental fuel and / or the regenerative fuel via 922.

[0125] 923 in FIG. 9c is an example of a UI element for providing fuel input amount information. The fuel input amount information may be provided in a graph format reflecting time information, and may be provided in a format that makes it easy to distinguish between the actual value and the guide value. In this case, the fuel may include multiple types, and the user may check the input amount of each of the auxiliary fuel and / or the recycled fuel via 923.

[0126] 9c shows an example of a UI element for providing a function for setting the temperature of the preheating chamber, 924. The user can set the target temperature of the preheating chamber via 924.

[0127] 9c shows an example of a UI element for providing guidance information, which may include first information on the current fuel input amount, second information on a recommended fuel input amount for increasing the proportion of recycled fuel input while maintaining the temperature of the preheating chamber within a predetermined range, and change information corresponding to a difference between the first information and the second information.

[0128] In this case, the fuel may include multiple types, and "MCOAL," "PCOAL," "MRDF," and "PRDF" shown in 925 may be examples of multiple types of input fuel, but are not limited thereto. Also, 925 may include information regarding the degree to which each of the multiple types of fuel contributes to a temperature change.

[0129] 926 in Fig. 9c is an example of a UI element for providing predicted temperature information. 926 may include information about the predicted temperature value of the preheating chamber after a predetermined time (5 minutes in the example of Fig. 9c) corresponding to the currently added fuel, and if fuel is added based on the guidance information, information about the predicted temperature value of the preheating chamber after the predetermined time and / or information about the temperature change amount.

[0130] FIG. 10 is a diagram illustrating a method for identifying and transmitting control information to a control engine according to one embodiment.

[0131] 10 , according to one embodiment, when a user input corresponding to the acquired guide information is received, the control method can identify control information corresponding to the received user input (S1010). Here, the control information refers to a control signal for controlling a control engine, and the control engine refers to an engine for controlling the cement manufacturing apparatus 1. According to one example, the electronic device 100 may further include a user interface (not shown), and the processor 130 can receive the user input corresponding to the acquired guide information via the user interface (not shown). Then, the processor 130 can identify the control information corresponding to the received user input based on the information stored in the memory 120.

[0132] Next, according to one embodiment, the control method can transmit the identified control information to a control engine (S1020). According to one example, the processor 130 can transmit the identified control information to the control engine via the communication interface 110.

[0133] According to the above example, the predicted temperature of the first preheating chamber is identified taking into account the calorific value of the recycled fuel, and based on this, guide information for efficient operation of the cement manufacturing apparatus 1 can be provided to the user. This makes it easier to implement environmentally friendly and carbon-reducing processes.

[0134] FIG. 11 is a block diagram showing a detailed configuration of an electronic device according to an embodiment.

[0135] 11, the electronic device 100′ may include a communication interface 110, a memory 120, one or more processors 130, a microphone 140, a speaker 150, a display 160, a user interface 170, and at least one sensor 180. A detailed description of the components shown in FIG. 11 that overlap with the components shown in FIG. 2 will be omitted.

[0136] The microphone 140 refers to a module that captures sound and converts it into an electrical signal, and may be a condenser microphone, ribbon microphone, moving coil microphone, piezoelectric element microphone, carbon microphone, or MEMS (Micro Electro Mechanical System) microphone. It may also be implemented in an omnidirectional, bidirectional, unidirectional, subcardioid, supercardioid, or hypercardioid type.

[0137] Various embodiments are possible in which the electronic device 100 ′ performs an action in response to a user voice signal received via the microphone 140 .

[0138] As an example, the electronic device 100′ can control the display 160 based on a user voice signal received via the microphone 140. For example, when a user voice signal to display the A content is received, the electronic device 100′ can control the display 160 to display the A content.

[0139] As another example, the electronic device 100' may control an external display device connected to the electronic device 100' based on a user voice signal received through the microphone 140. Specifically, the electronic device 100' may provide a control signal for controlling the external display device so that an operation corresponding to the user voice signal is performed on the external display device, and may transmit the provided control signal to the external display device. Here, the electronic device 100' may store a remote control application for controlling the external display device. Also, the electronic device 100' may transmit the provided control signal to the external display device using at least one communication method of Bluetooth, Wi-Fi, or infrared. For example, when a user voice signal for displaying content A is received, the electronic device 100' may transmit a control signal to the external display device to control the content A to be displayed on the external display device. Here, the electronic device 100' refers to various terminal devices on which a remote control application can be installed, such as a smartphone or an AI speaker.

[0140] As yet another example, the electronic device 100′ may use a remote control device to control an external display device connected to the electronic device 100′ based on a user voice signal received via the microphone 140. Specifically, the electronic device 100′ may transmit a control signal to the remote control device to control the external display device so that an operation corresponding to the user voice signal is performed on the external display device. The remote control device may also transmit the control signal received from the electronic device 100′ to the external display device. For example, when a user voice signal for displaying content A is received, the electronic device 100′ may transmit a control signal to the remote control device to control the external display device so that content A is displayed on the external display device, and the remote control device may transmit the received control signal to the external display device.

[0141] The speaker 150 consists of a tweeter for reproducing high frequencies, a midrange for reproducing mid-range frequencies, a woofer for reproducing low frequencies, a subwoofer for reproducing extremely low frequencies, an enclosure for controlling resonance, and a crossover network that divides the electrical signal frequencies input to the speaker into bands.

[0142] The speaker 150 can output an audio signal to the outside of the electronic device 100′. The speaker 150 can output multimedia playback, recording playback, various notification sounds, voice messages, etc. The electronic device 100′ may include an audio output device such as the speaker 150, or may include an output device such as an audio output terminal. In particular, the speaker 150 can provide acquired information, information processed or produced based on the acquired information, a response result or an operation result to a user's voice, etc. in the form of voice.

[0143] The display 160 may be implemented as a display including self-emitting elements or a display including non-self-emitting elements and a backlight. For example, the display may be implemented as various types of displays, such as a liquid crystal display (LCD), an organic light-emitting diode (OLED) display, light-emitting diodes (LED), micro LEDs, mini LEDs, a plasma display panel (PDP), a quantum dot (QD) display, or quantum dot light-emitting diodes (QLED). The display 160 may also include a driving circuit and a backlight unit, each implemented as an a-si TFT, a low temperature polysilicon (LTPS) TFT, or an organic TFT (OTFT). The display 160 may also be implemented as a touch screen combined with a touch sensor, a flexible display, a rollable display, a 3D display, a display in which multiple display modules are physically connected, or the like. The processor 130 may control the display 160 to output the output image acquired according to the various embodiments described above. Here, the output image may be a high-resolution image of 4K or 8K or higher.

[0144] According to another embodiment, the electronic device 100′ may not include the display 160. The electronic device 100′ may be connected to an external display device and may transmit images or content stored in the electronic device 100′ to the external display device. Specifically, the electronic device 100′ may transmit images or content to the external display device along with a control signal for controlling the external display device to display the images or content.

[0145] Here, the external display device may be connected to the electronic device 100' via the communication interface 110 or an input / output interface (not shown). For example, the electronic device 100' may not include a display like an STB (Set Top Box). Alternatively, the electronic device 100' may include only a small display capable of displaying simple information such as text information. Here, the electronic device 100' may transmit images or content to the external display device via the communication interface 110 in a wired or wireless manner, or may transmit the image or content to the external display device via the input / output interface (not shown).

[0146] The user interface 170 is a configuration for allowing the electronic device 100′ to interact with a user. For example, the user interface 170 may include at least one of a touch sensor, a motion sensor, a button, a jog dial, a switch, a microphone, and a speaker, but is not limited to these.

[0147] The at least one sensor 180 (hereinafter, "sensor") may include multiple sensors of various types. The sensor 180 may measure a physical quantity or sense the operating state of the electronic device 100' and convert the measured or sensed information into an electrical signal. The sensor 180 may include a camera, and the camera may include a lens that focuses visible light or other optical signals reflected and received by an object onto the image sensor and an image sensor that can sense the visible light or other optical signals. Here, the image sensor may include a 2D pixel array divided into a plurality of pixels.

[0148] According to the above example, the electronic device 100' can predict the calorific value of the recycled fuel using a trained neural network model and can predict the temperature of the first preheating chamber based on the predicted calorific value. As a result, the electronic device 100' can provide guidance information to the user based on the accurately predicted preheating chamber temperature, thereby enabling an environmentally friendly and efficient process.

[0149] The methods according to the various embodiments of the present disclosure may be implemented in the form of an application that can be installed on a conventional electronic device. Alternatively, the methods according to the various embodiments of the present disclosure may be implemented using a deep learning-based trained neural network (or a deep trained neural network), i.e., a trained network model. The methods according to the various embodiments of the present disclosure may be implemented only as a software upgrade or hardware upgrade for a conventional electronic device. The various embodiments of the present disclosure may also be implemented via an embedded server provided in the electronic device or an external server of the electronic device.

[0150] According to an embodiment of the present disclosure, the various embodiments described above may be embodied as software including instructions stored in a machine-readable storage medium (e.g., a computer). The machine is a device capable of retrieving the stored instructions from the storage medium and operating in accordance with the retrieved instructions, and may include a display device (e.g., display device (A)) according to the disclosed embodiments. When instructions are executed by a processor, the processor may perform the function corresponding to the instructions directly or by using other components under the control of the processor. The instructions may include code provided or executed by a compiler or interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory" merely means that the storage medium does not contain a signal and is tangible, and does not distinguish between data being stored in the storage medium semi-permanently or temporarily.

[0151] According to one embodiment, the methods according to the various embodiments described above may be provided as part of a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)) or online via an application store (e.g., the Play Store™). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily provided in a storage medium such as a memory on a manufacturer's server, an application store server, or a relay server.

[0152] Furthermore, each of the components (e.g., modules or programs) according to the various embodiments described above may be composed of one or more entities, and some of the subcomponents described above may be omitted, or other subcomponents may be further included in the various embodiments. Alternatively or additionally, some components (e.g., modules or programs) may be integrated into a single entity and perform the same or similar functions as those performed by the respective components before being integrated. According to various embodiments, operations performed by modules, programs, or other components may be performed sequentially, in parallel, iteratively, or heuristically, and at least some operations may be performed in a different order, omitted, or other operations may be added.

[0153] Although preferred embodiments of the present disclosure have been illustrated and described above, the present disclosure is not limited to the specific embodiments described above, and various modifications may be made by a person having ordinary skill in the technical field to which the disclosure pertains without departing from the gist of the present disclosure as claimed in the claims, and such modifications should not be understood individually from the technical ideas and perspectives of the present disclosure. [Explanation of symbols]

[0154] 100 Electronic equipment 110 Communication Interface 120 memory 130 One or more processors

Claims

1. An electronic device for implementing a temperature prediction and control system, comprising: a communication interface; a memory in which the first trained neural network model and the second trained neural network model are stored; When process information including input fuel information of the cement manufacturing apparatus is received via the communication interface, preprocessing of the received process information is performed; inputting the preprocessed process information into the second trained neural network model, and obtaining first predicted temperature information output from the first trained neural network model and error information relating to the measured temperature of a first preheating chamber in the cement manufacturing apparatus; Identifying predicted calorific value information of a first recycled fuel, which is at least one of waste synthetic resin, waste tires, waste oil, and sewage sludge, among the input fuels based on the acquired error information and the input fuel information; inputting updated fuel input information based on the identified predicted heat quantity information into the first trained neural network model to obtain second predicted temperature information of the first preheating chamber; one or more processors for providing guide information including the obtained second predicted temperature information; the first trained neural network model; When input fuel information including information on input amounts corresponding to different types of input fuels including the first recycled fuel and calorific value information corresponding to each input fuel is input, the system is trained to output predicted temperature information of the first preheating chamber, the second trained neural network model An electronic device that is trained to output error information regarding the predicted temperature information output from the first trained neural network model and the measured temperature information of the first preheating chamber when the preprocessed process information including the input fuel information and process status information is input.

2. The device further includes a user interface that is at least one of a touch sensor, a motion sensor, a button, a jog dial, a switch, a microphone, and a speaker; The input fuel information is information about input amounts corresponding to different types of input fuels, including at least one of the first regenerated fuel, the main fuel, and the auxiliary fuel, and information about calorific values ​​corresponding to each input fuel; The one or more processors: receiving a target temperature value for a first preheat chamber in the cement manufacturing apparatus via the user interface; Identifying, based on the acquired second predicted temperature information and the received target temperature value, the amounts of the different types of fuel to be added so that the temperature value of the first preheating chamber reaches the target temperature value; Obtaining guide information corresponding to the identified input amount; The electronic device of claim 1 , wherein the obtained guide information is provided via a display.

3. The one or more processors: Identifying sub-fuel input information whose input amount corresponding to each input fuel included in the input fuel information has been changed; The sub-fuel input information is input to the first trained neural network model to obtain second sub-predicted temperature information; obtaining guidance information regarding input fuel using the obtained second predicted temperature information and the second sub-predicted temperature information; The electronic device according to claim 2 , wherein guide information including the acquired guidance information is provided.

4. Process information in which the pretreatment was performed: temperature history information of the first preheating chamber and history information of the input fuel; The one or more processors: The electronic device of claim 2 , further comprising a display that displays the received temperature history information of the first preheating chamber, the input fuel history information, and the acquired second predicted temperature information.

5. The one or more processors: The electronic device according to claim 1 , wherein the predicted calorific value information of the first recycled fuel is identified based on the acquired error information and information about the input amount of the first recycled fuel included in the input fuel information.

6. Further comprising a user interface, which is at least one of a touch sensor, a motion sensor, a button, a jog dial, a switch, a microphone, and a speaker; The one or more processors: When a user input corresponding to the obtained guide information is received via the user interface, identifying control information corresponding to the received user input; The electronic device of claim 1 , further comprising: a communication interface configured to transmit the identified control information to a control engine.

7. A method for controlling an electronic device to implement a temperature prediction and control system, comprising: When process information including input fuel information of the cement manufacturing apparatus is received, pre-processing the received process information; inputting the pre-processed process information into a second trained neural network model, and obtaining first predicted temperature information output from the first trained neural network model and error information related to a measured temperature of a first preheating chamber in the cement manufacturing apparatus; identifying predicted calorific value information of a first recycled fuel, which is at least one of waste synthetic resin, waste tires, waste oil, and sewage sludge, among the input fuels based on the acquired error information and the input fuel information; inputting updated fuel input information based on the identified predicted heat quantity information into the first trained neural network model to obtain second predicted temperature information of the first preheating chamber; providing guide information including the acquired second predicted temperature information; the first trained neural network model; When input fuel information including information on input amounts corresponding to different types of input fuels including the first recycled fuel and calorific value information corresponding to each input fuel is input, the system is trained to output predicted temperature information of the first preheating chamber, the second trained neural network model A control method in which, when the pre-processed process information including the input fuel information and process status information is input, the method is trained to output error information regarding the predicted temperature information output from the first trained neural network model and the measured temperature information of the first pre-heating chamber.

8. The input fuel information is information about input amounts corresponding to different types of input fuels, including at least one of the first regenerated fuel, the main fuel, and the auxiliary fuel, and information about calorific values ​​corresponding to each input fuel; The step of providing the guide information includes: receiving a target temperature value for a first preheat chamber within the cement manufacturing apparatus; identifying, based on the acquired second predicted temperature information and the received target temperature value, amounts of the different types of fuel to be input so that the temperature value of the first preheating chamber reaches the target temperature value; and obtaining guide information corresponding to the identified input amount; The control method includes: The control method according to claim 7 , further comprising: providing the obtained guide information via a display.

9. The step of acquiring guide information includes: Identifying sub-fuel input information in which the input amount corresponding to each input fuel included in the input fuel information has been changed; inputting the sub-fuel input information into the first trained neural network model to obtain second sub-predicted temperature information; obtaining guidance information regarding input fuel using the obtained second predicted temperature information and the second sub-predicted temperature information; The control method according to claim 8 , further comprising: providing guide information including the acquired guidance information.

10. Process information in which the pretreatment was performed: temperature history information of the first preheating chamber and history information of the input fuel; The step of providing the acquired guide information through a display includes: The control method according to claim 8 , further comprising providing the received first preheating chamber temperature history information, the input fuel history information, and the obtained second predicted temperature information via a display.

11. The step of identifying predicted heat quantity information comprises: The control method according to claim 7 , further comprising identifying predicted calorific value information of the first regenerated fuel based on the acquired error information and information on the input amount of the first regenerated fuel included in the input fuel information.

12. when a user input corresponding to the obtained guide information is received, identifying control information corresponding to the received user input; The control method of claim 7, further comprising: transmitting the identified control information to a control engine.

13. 1. A non-transitory computer-readable recording medium storing computer instructions that, when executed by a processor of an electronic device for implementing a temperature prediction and control system, cause the electronic device to perform operations, the operations including: When process information including input fuel information of the cement manufacturing apparatus is received, pre-processing the received process information; inputting the pre-processed process information into a second trained neural network model, and obtaining first predicted temperature information output from the first trained neural network model and error information related to a measured temperature of a first preheating chamber in the cement manufacturing apparatus; identifying predicted calorific value information of a first recycled fuel, which is at least one of waste synthetic resin, waste tires, waste oil, and sewage sludge, among the input fuels based on the acquired error information and the input fuel information; inputting updated fuel input information based on the identified predicted heat quantity information into the first trained neural network model to obtain second predicted temperature information of the first preheating chamber; providing guide information including the acquired second predicted temperature information; the first trained neural network model; When input fuel information including information on input amounts corresponding to different types of input fuels including the first recycled fuel and calorific value information corresponding to each input fuel is input, the system is trained to output predicted temperature information of the first preheating chamber, the second trained neural network model A computer-readable recording medium that is trained to output error information regarding the predicted temperature information output from the first trained neural network model and the measured temperature information of the first preheating chamber when the preprocessed process information including the input fuel information and process status information is input.

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