Energy optimization management method for cement production

Abstract

The invention provides an energy optimization management method for cement production, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: obtaining and collecting multiple types of historical energy consumption data; storing the historical energy consumption data and historical working condition data of cement production by using a time domain alignment strategy based on a time sequence optimization structure; the historical energy consumption data, the historical working condition data and the equipment characteristic data are combined, the equipment characteristic data serve as input variables, an energy consumption prediction model is trained, and change modes of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production working conditions are recognized through the energy consumption prediction model; a multi-target energy efficiency evaluation function is constructed based on the energy consumption prediction model, energy consumption interval clustering is carried out on full-cycle data, and different energy consumption levels are delimited; in combination with real-time working condition data and real-time energy consumption data which are collected in real time, a corresponding energy consumption level is determined, and then production parameters are dynamically adjusted through a multivariable regression algorithm. According to the invention, automatic energy optimization management in the cement production process can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent manufacturing, and in particular to an energy optimization management method for cement production. Background Art

[0002] Energy consumption accounts for one of the largest costs in cement production. With rising global energy costs and increasingly stringent environmental regulations, the cement industry must confront the challenge of inefficient energy management. Therefore, implementing optimized energy management not only helps reduce production costs but also minimizes energy waste and improves energy efficiency, ensuring cement manufacturers remain competitive in the fiercely competitive market. Furthermore, optimized energy management helps reduce carbon emissions, complying with increasingly stringent environmental regulations and ultimately enhancing a company's sustainable development capabilities.

[0003] By precisely monitoring and optimizing energy consumption throughout the production process, cement manufacturers can rationally allocate resources, avoid energy waste and equipment overload, and reduce overall production energy consumption. Leveraging advanced predictive models and intelligent management systems, companies can fine-tune control over each production process, ensuring that each link operates at optimal energy efficiency. This improves the efficiency of the entire production system, reduces the company's environmental burden, and promotes the cement industry's transition to a green, low-carbon future. Summary of the Invention

[0004] The present application provides an energy optimization management method for cement production, which can realize automated energy optimization management during the cement production process.

[0005] In a first aspect of the present application, a method for optimizing energy management in cement production is provided, the method comprising:

[0006] Based on the cement production process, appropriate energy monitoring equipment is deployed at key production links to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting multiple types of historical energy consumption data;

[0007] Based on the time series optimization structure, the historical energy consumption data and the historical operating condition data of cement production are stored in a time domain alignment strategy;

[0008] Based on the historical energy consumption data and the historical operating condition data, combined with equipment characteristic data of the cement production equipment, the equipment characteristic data is used as an input variable to train an energy consumption prediction model, and the energy consumption prediction model is used to identify the change patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different production conditions;

[0009] Based on the energy consumption prediction model, a multi-objective energy efficiency evaluation function is constructed, a benchmark energy consumption vector is introduced to construct a dynamic evaluation system for equipment energy efficiency, energy consumption intervals are clustered for full-cycle data, and different energy consumption levels are defined;

[0010] By combining the real-time operating condition data and real-time energy consumption data collected in real time, the corresponding energy consumption level is determined, and then the production parameters are dynamically adjusted through a multivariate regression algorithm.

[0011] On the basis of the above technical solution, preferably, based on the historical energy consumption data and the historical operating condition data, combined with the equipment characteristic data of the cement production equipment, the equipment characteristic data is used as an input variable to train an energy consumption prediction model, and before identifying the change patterns of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production conditions through the energy consumption prediction model, the method further includes:

[0012] Constructing an input variable vector by using the historical energy consumption data, the historical operating condition data and the equipment characteristic data;

[0013] Construct an energy consumption prediction model input variable system covering all energy consumption links in the plant;

[0014] During the training process, the energy consumption per unit of clinker, grinding power consumption, and vertical mill power consumption were used as target variables to construct a multi-objective regression model structure. An adaptive learning rate optimization algorithm with an early stopping mechanism was used for training to avoid overfitting.

[0015] Deploy the trained energy consumption prediction model. Based on the historical energy consumption data, historical operating condition data, and equipment characteristic data at any moment, the model outputs the predicted values of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption corresponding to any moment through a forward reasoning process, thereby identifying the change pattern of energy consumption indicators under given operating conditions.

[0016] On the basis of the above technical solution, preferably, the method of inputting historical energy consumption data, historical operating condition data and equipment characteristic data at any moment and outputting the corresponding unit clinker energy consumption, grinding power consumption and vertical mill power consumption prediction values through a forward reasoning process, thereby identifying the change pattern of energy consumption indicators under given operating conditions, specifically includes:

[0017] The historical energy consumption data, historical operating condition data, and equipment characteristic data at the time to be predicted are normalized and constructed into an input variable vector. The dimension of the input variable vector remains consistent with the training phase, and the sliding window structure of the input variable in the time dimension is retained;

[0018] During forward reasoning, the input variable vector passes through several convolutional and pooling layers of the convolutional neural network in sequence to extract the trend characteristics of changes within the local time segment. The convolution kernel slides in the time dimension to extract the first-order derivative and second-order derivative approximate expression, capturing the local slope changes of energy consumption and operating condition fluctuations.

[0019] The output of the convolutional neural network is input as a feature sequence into the long short-term memory network. The long short-term memory network learns the temporal dependency between the past and current moments of different dimensions in the input variable vector through its gating mechanism. The forget gate controls the degree of historical information retention, the input gate controls the writing of new states, and the output gate generates a hidden vector of the current state. The hidden vector represents a fusion representation of global features and historical dependency features.

[0020] The hidden vector is mapped to the unit clinker energy consumption prediction value, the grinding power consumption prediction value and the vertical mill power consumption prediction value through a set of parallel fully connected layers, forming a set of one-to-one corresponding regression outputs.

[0021] On the basis of the above technical solution, preferably, the multi-objective energy efficiency evaluation function is constructed based on the energy consumption prediction model, the benchmark energy consumption vector is introduced to construct the equipment energy efficiency dynamic evaluation system, the energy consumption interval clustering of the full cycle data is performed, and different energy consumption levels are delineated, specifically including:

[0022] Extracting the unit clinker energy consumption prediction value, the grinding power consumption prediction value and the vertical mill power consumption prediction value from the energy consumption prediction model to form a three-dimensional predicted energy consumption index vector;

[0023] Based on statistical analysis of historical data or optimal operating conditions under standardized production conditions, a unit clinker energy consumption benchmark value, a grinding power consumption benchmark value, and a vertical mill power consumption benchmark value are defined. The unit clinker energy consumption benchmark value, the grinding power consumption benchmark value, and the vertical mill power consumption benchmark value constitute a benchmark energy consumption vector;

[0024] The multi-objective energy efficiency evaluation function is defined by performing weighted distance calculation on the difference between the predicted energy consumption index vector and the benchmark energy consumption vector. Specifically, the energy efficiency evaluation function is defined in the following form:

[0025]

[0026] Among them, E t is the energy efficiency deviation value at time t, C t is the predicted value of energy consumption per unit clinker, M t is the predicted value of grinding power consumption, V tis the predicted power consumption value of the vertical mill, C0 is the reference value of the energy consumption per unit clinker, M0 is the reference value of the grinding power consumption, V0 is the reference value of the vertical mill power consumption, w1 is the weight coefficient corresponding to the predicted power consumption per unit clinker and the reference value of the energy consumption per unit clinker, w2 is the weight coefficient corresponding to the predicted power consumption per unit clinker and the reference value of the grinding power consumption, and w3 is the weight coefficient corresponding to the predicted power consumption per unit clinker and the reference value of the vertical mill power consumption;

[0027] The energy efficiency deviation measurement values in the entire cycle are clustered unsupervisedly, using a K-means variant clustering algorithm, and the energy efficiency deviation measurement values at each time point are marked as the corresponding energy consumption level according to the clustering results.

[0028] On the basis of the above technical solution, preferably, the real-time operating condition data collected in real time is combined with the real-time operating condition data to dynamically adjust the production parameters through a multivariate regression algorithm, specifically including:

[0029] According to the energy monitoring equipment deployed in the cement production system, the real-time operating condition data and the real-time energy consumption data of each key energy consumption node are obtained;

[0030] Aligning the real-time operating condition data and the real-time energy consumption data with each other in a unified time stamp manner to construct a real-time input variable vector in a unified format;

[0031] After the real-time input variable vector is constructed, the deployed energy consumption prediction model is called to perform forward reasoning to calculate the actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value, and the actual vertical mill power consumption prediction value at the current moment;

[0032] The actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value, and the actual vertical mill power consumption prediction value are combined into an energy consumption index vector, which is substituted into the multi-objective energy efficiency evaluation function constructed in the historical modeling stage to obtain the actual energy efficiency deviation measurement value at the current moment;

[0033] Determine the energy consumption level label corresponding to the actual energy efficiency deviation measurement value through a preset energy consumption interval clustering model;

[0034] When it is determined that the energy consumption level label is at a preset level, a mapping relationship is established with the real-time operating condition data as an independent variable and the energy efficiency deviation measurement value as a dependent variable to form an operating condition energy consumption function with reverse control;

[0035] By numerically solving the gradient descent direction of the operating condition energy consumption function, combining the real-time operating condition data with the partial derivative information in the energy efficiency function, the optimal direction and amplitude of adjusting each variable while maintaining equipment stability are calculated to form a production parameter adjustment vector.

[0036] On the basis of the above technical solution, preferably, the storing of the historical energy consumption data and the historical operating condition data of cement production using a time domain alignment strategy based on a time series optimization structure specifically includes:

[0037] Constructing a time series data model, wherein the time series data model uses the timestamp as the primary index, sets a unique identifier for each energy monitoring device, and encapsulates the parameter data of each energy monitoring device at each time granularity into a four-tuple structure consisting of the timestamp, the energy monitoring device identifier, the parameter type, and the parameter value, thereby forming a multidimensional data set based on the time axis in a unified format;

[0038] Performing time normalization processing on the data stream generated by each of the energy monitoring devices, specifically including time interpolation processing, sampling frequency normalization processing and data window reconstruction processing;

[0039] The data stream after time normalization is time-sliced according to a fixed time window length. Each time window constitutes a complete operating condition snapshot. The operating condition snapshot covers the historical energy consumption parameters and historical operating condition parameters collected by the energy monitoring equipment, realizing synchronous encapsulation under the time axis.

[0040] On the basis of the above technical solutions, preferably, according to the cement production process, adaptive energy monitoring equipment is deployed in key production links to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby obtaining and collecting multiple types of historical energy consumption data, specifically including:

[0041] Based on the complete cement production line process diagram, process node identification and energy consumption correlation analysis are carried out to clarify the energy consumption characteristics of each subsystem in the clinker calcination section, raw meal preparation section, cement grinding section, waste heat recovery section, and auxiliary facilities section. All deployed energy monitoring equipment is connected to the edge collection terminal;

[0042] Obtain historical energy consumption data uniformly collected and transmitted in real time by the edge acquisition terminal for the energy monitoring devices of different formats.

[0043] In a second aspect of the present application, a device for energy optimization management of cement production is provided, the device being configured to execute any one of the above-described methods for energy optimization management of cement production. The device comprises an acquisition module, a processing module, and an output module, wherein:

[0044] The acquisition module is used to deploy adaptive energy monitoring equipment at key production links according to the cement production process, to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting multiple types of historical energy consumption data;

[0045] The processing module is used to store the historical energy consumption data and the historical operating condition data of cement production in a time domain alignment strategy based on a time series optimization structure;

[0046] The processing module is configured to train an energy consumption prediction model based on the historical energy consumption data and the historical operating condition data, in combination with equipment characteristic data of the cement production equipment, using the equipment characteristic data as input variables, and identify, through the energy consumption prediction model, change patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different production conditions;

[0047] The processing module is used to construct a multi-objective energy efficiency evaluation function based on the energy consumption prediction model, introduce a benchmark energy consumption vector to construct a dynamic evaluation system for equipment energy efficiency, cluster energy consumption intervals for full-cycle data, and define different energy consumption levels;

[0048] The output module is used to combine the real-time operating condition data and real-time energy consumption data collected in real time to determine the corresponding energy consumption level, and then dynamically adjust the production parameters through a multivariate regression algorithm.

[0049] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0050] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0051] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0052] 1. This application obtains multi-dimensional historical energy consumption data by deploying energy monitoring equipment in key links of cement production, and realizes unified storage and management of energy consumption and operating condition data based on time series optimization structure, further combines equipment characteristic data to train energy consumption prediction model, and accurately identifies the energy consumption behavior change pattern under different operating conditions; constructs multi-objective energy efficiency evaluation function and energy consumption interval clustering model, dynamically divides energy efficiency levels, calculates the current energy efficiency status in real time based on real-time collected data, and calculates the optimal adjustment direction and amplitude of production parameters through multivariate regression algorithm under the judgment of low energy efficiency level, thereby realizing automatic optimization and adjustment of production parameters, constructing a closed-loop adaptive energy optimization control system for the entire cement production process, and realizing high-frequency, precise and dynamic automated energy optimization management.

[0053] 2. This application constructs an energy consumption prediction model that integrates convolutional neural networks and long short-term memory networks. Based on the input of historical energy consumption data, historical operating condition data and equipment characteristic data, it automatically extracts local trends and time-dependent features to achieve high-precision prediction of unit clinker energy consumption, grinding power consumption and vertical mill power consumption, and effectively identifies energy consumption change patterns under different production conditions, thereby improving the dynamic perception ability of cement production energy consumption behavior and the accuracy of model-driven energy efficiency analysis, providing precise support for subsequent intelligent energy efficiency evaluation and optimization control.

[0054] 3. This application constructs a multi-objective energy efficiency evaluation function and a benchmark energy consumption vector to quantify the degree of deviation of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different working conditions, and uses the K-means variant clustering algorithm to partition the full-cycle energy efficiency deviation measurement values to achieve automatic division of energy consumption levels, thereby establishing a dynamic equipment energy efficiency evaluation system that can respond in real time, effectively improving the energy efficiency recognition accuracy, operating status judgment capability and adaptability of energy-saving optimization strategies in the cement production process, and providing a data basis and logical support for hierarchical management and refined scheduling.

[0055] 4. This application collects the operating data and energy consumption data of each key node of cement production in real time, combines the trained energy consumption prediction model and energy efficiency evaluation function, dynamically calculates the current energy efficiency deviation, and constructs an operating condition energy consumption function based on the energy consumption level judgment result. It uses multivariable regression and gradient numerical solution methods to accurately calculate the optimal adjustment direction and amplitude of production parameters, thereby realizing real-time optimization and control of production process parameters, effectively improving equipment operation efficiency, reducing unit energy consumption, and enhancing the adaptability and refinement of energy consumption regulation, and building a closed-loop dynamic energy-saving optimization mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of an energy optimization management method for cement production disclosed in an embodiment of the present application;

[0057] Figure 2 This is a module diagram of an energy optimization management device for cement production disclosed in an embodiment of the present application;

[0058] Figure 3 This is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0059] Description of the accompanying drawings: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0061] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0062] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0063] Faced with the dual challenges of rising energy costs and intensifying environmental pressures, cement manufacturers must implement refined energy optimization management to improve energy utilization, reduce production costs, and minimize carbon emissions. By accurately monitoring and dynamically optimizing energy consumption across all production processes, combined with intelligent forecasting models and management systems, resources can be rationally allocated, equipment loads can be balanced, and energy efficiency can be maximized. This enhances market competitiveness and sustainable development capabilities, driving the cement industry's green and low-carbon transformation.

[0064] This embodiment discloses an energy optimization management method for cement production, referring to Figure 1 , including the following steps S110-S150:

[0065] S110, based on the cement production process, deploys adaptive energy monitoring equipment at key production links to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting multiple types of historical energy consumption data.

[0066] The energy optimization management method for cement production disclosed in the embodiments of this application is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and personal computers (PCs), and can also be a background server running the energy optimization management method for cement production. The server can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0067] In one possible implementation, according to the cement production process, adaptive energy monitoring equipment is deployed at key production links to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby obtaining and collecting multiple types of historical energy consumption data, specifically including: identifying process nodes and analyzing the correlation between energy consumption based on a complete cement production line process diagram, clarifying the energy consumption composition characteristics of each subsystem in the clinker calcination section, raw meal preparation section, cement grinding section, waste heat recovery section and auxiliary facilities section, and connecting all deployed energy monitoring equipment to the edge collection terminal; obtaining historical energy consumption data uniformly collected and transmitted in real time by the edge collection terminal for energy monitoring equipment of different formats.

[0068] Specifically, it is first necessary to conduct process node identification and energy consumption correlation analysis based on a complete cement production line process diagram. By systematically analyzing the process flow and energy transfer paths of the clinker calcination section, raw meal preparation section, cement grinding section, waste heat recovery section, and auxiliary facilities section, the core process units and main energy consumption sources of each subsystem are identified, including the rotary kiln main drive system, vertical mill main motor, grate cooler fan system, raw meal grinding system, coal powder preparation system, and waste heat boiler steam generation device. Combined with historical operating data and process equipment technical parameters, an energy consumption structure model for each process subsystem is constructed, and appropriate energy monitoring equipment layout points are determined to ensure that the monitoring coverage can reflect the spatial distribution of energy consumption behavior and energy flow patterns.

[0069] After completing the planning of monitoring points, deploy appropriate energy monitoring equipment based on the energy consumption parameter types and data accuracy requirements of each subsystem. For power consumption parameters, a combination of three-phase smart meters and current transformers that support Modbus RTU or IEC 61850 communication protocols is used to achieve high-precision acquisition of equipment current, voltage, active power, and power factor. For thermal energy parameters such as gas or steam, a vortex flowmeter or turbine flowmeter with a temperature and pressure compensation mechanism is used in combination with thermocouples and differential pressure transmitters to form a thermal energy metering node. For air volume and material flow, piezoresistive pressure sensors, electromagnetic flowmeters, and speed sensors are deployed to construct a flow acquisition channel for gas-solid two-phase media. All energy monitoring equipment should have real-time communication capabilities and high-reliability insulation structures, and be able to adapt to the dust, high temperature, and strong interference electromagnetic environment of the cement plant.

[0070] Once deployed, all energy monitoring devices must be connected to edge acquisition terminals via RS485 bus, Ethernet, or 4G / 5G wireless communication. These terminals should possess multi-protocol parsing capabilities, end-side buffering mechanisms, data resuming capabilities, and preprocessing capabilities. They should be able to perform unified protocol conversion, timestamp synchronization, anomaly elimination, and hierarchical caching on data collected by heterogeneous energy monitoring devices, and then encapsulate them into structured data packets in a specified format. These edge acquisition terminals transmit the processed, structured historical energy consumption data to the central energy management system in real time, while retaining local redundant copies to prevent data loss and ensure the integrity, continuity, and low latency of data collection.

[0071] Through the above implementation steps, a complete energy monitoring equipment deployment system and a stable historical energy consumption data acquisition path can be formed, realizing accurate monitoring and data collection of key energy consumption nodes in the entire cement production process, and providing basic data support for the subsequent construction of a time series database, training of energy consumption prediction models, and dynamic energy efficiency optimization.

[0072] S120 , storing historical energy consumption data and historical operating condition data of cement production using a time domain alignment strategy based on a time series optimization structure.

[0073] In one possible implementation, historical energy consumption data and historical operating condition data of cement production are stored using a time domain alignment strategy based on a time series optimization structure, specifically including: constructing a time series data model, the time series data model uses a timestamp as the primary index, and by setting a unique identifier for each energy monitoring device, encapsulating the parameter data of each energy monitoring device at each time granularity into a four-tuple structure of timestamp, energy monitoring device identifier, parameter type and parameter value, forming a multidimensional data set based on the time axis in a unified format; performing time standardization processing on the data stream generated by each energy monitoring device, specifically including time interpolation processing, sampling frequency normalization processing and data window reconstruction processing; performing time slicing processing on the data stream after time standardization according to a fixed time window length, and each time window constitutes a complete operating condition snapshot, and the operating condition snapshot covers the historical energy consumption parameters and historical operating condition parameters collected by the energy monitoring device, realizing synchronous encapsulation under the time axis.

[0074] Specifically, a time series data model is first constructed. The time series data model uses the timestamp as the primary index, sets a unique energy monitoring device identifier for each energy monitoring device, and represents the parameter value collected at each time granularity as a quadruple of timestamp, energy monitoring device identifier, parameter type and parameter value through a unified data encapsulation structure. A two-dimensional time-device data table structure is constructed based on the quadruple, and all energy monitoring device data are vertically spliced in the time axis direction to form a data primary key structure that crosses devices and parameter types, thereby realizing the orderly organization of multi-dimensional energy consumption parameters under a unified time reference.

[0075] Subsequently, the data streams collected by each energy monitoring device are subjected to time normalization processing, which includes three consecutive steps: time interpolation processing, sampling frequency normalization processing, and data window reconstruction processing. In the time interpolation processing stage, for missing time points due to network fluctuations, communication delays, or device interval sampling, linear interpolation or cubic spline interpolation methods are used to estimate the missing values in the time dimension, thereby ensuring the integrity of the parameter set corresponding to each time point. In the sampling frequency normalization processing stage, according to the benchmark sampling period set by the system (such as 10 seconds or 30 seconds), data sources with different sampling frequencies are uniformly processed. High-frequency data is downsampled using the average resampling method, and low-frequency data is padded using the interpolation method, thereby achieving sampling alignment of data of different frequencies at a unified time step. In the data window reconstruction processing stage, the data stream is divided into sliding or fixed time windows in combination with the standardized data stream, ensuring that each time window contains the complete sampling values of all energy monitoring devices in that time period, forming a data snapshot that can be input into the model.

[0076] Finally, the time-normalized data is time-sliced according to a fixed time window length. Each slice constitutes a complete operating condition snapshot, encompassing the historical energy consumption parameters and historical operating condition parameters collected by all energy monitoring devices within the current time window, ensuring that the parameter data within the same time window is strictly aligned on the time axis. All operating condition snapshots are continuously stored in a time series database, forming a structured data system with horizontal device distribution dimensions and vertical time continuity dimensions. This provides a consistent, high-precision time series data foundation for subsequent energy consumption forecasting modeling, energy efficiency evaluation calculations, and dynamic control strategies.

[0077] S130, based on historical energy consumption data and historical operating condition data, combined with equipment characteristic data of cement production equipment, using the equipment characteristic data as input variables, trains an energy consumption prediction model, and uses the energy consumption prediction model to identify the change patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different production conditions.

[0078] In one possible implementation, based on historical energy consumption data and historical operating condition data, combined with equipment characteristic data of cement production equipment, the energy consumption prediction model is trained with the equipment characteristic data as input variables. Before the energy consumption prediction model is used to identify the change patterns of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production conditions, the method further includes: constructing an input variable vector using historical energy consumption data, historical operating condition data and equipment characteristic data; constructing an energy consumption prediction model input variable system covering the energy consumption links of the entire plant; during the training process, unit clinker energy consumption, grinding power consumption and vertical mill power consumption are used as target variables to construct a multi-objective regression model structure, and an adaptive learning rate optimization algorithm with an early stopping mechanism is used for training to avoid overfitting; the trained energy consumption prediction model is deployed, and based on the input of historical energy consumption data, historical operating condition data and equipment characteristic data at any moment, the predicted values of unit clinker energy consumption, grinding power consumption and vertical mill power consumption corresponding to any moment are output through a forward reasoning process, thereby identifying the change patterns of energy consumption indicators under given operating conditions.

[0079] Specifically, historical energy consumption data, historical operating condition data, and equipment characteristic data must first be structurally unified and formatted to construct an input variable vector. This input variable vector uses timestamps as the index and aggregates the corresponding energy consumption parameter values, operating condition parameter values, and static equipment characteristic parameters at each time point. The energy consumption parameter values include the electrical energy, current, gas flow, and thermal energy values collected by each energy monitoring device. The operating condition parameter values include material flow, air pressure, temperature, vibration, and speed. The equipment characteristic parameters include the equipment's rated power, design load, maintenance cycle, start-stop frequency, and actual operating life. After time alignment and feature normalization, these are uniformly encoded into a fixed-dimensional input variable vector to form a complete and intact training sample infrastructure.

[0080] Based on these input variable vectors, an input variable system for the energy consumption prediction model was constructed, covering all energy consumption aspects of the plant. This system, based on the logical division of cement production, divides the input variable vectors into functional subsets for clinker calcination, raw meal preparation, cement grinding, and auxiliary facilities. This ensures that the model can independently model and jointly analyze the differences in energy consumption behavior between different production stages. A feature screening mechanism was established for each variable subset. Low-correlation input items were eliminated based on the Pearson correlation coefficient and mutual information index, while retaining high-impact variables to improve the model's generalization and stability.

[0081] During the model training phase, a multi-objective regression model structure was constructed using unit clinker energy consumption, grinding power consumption, and vertical mill power consumption as independent target variables. A combined model structure was adopted that integrated a convolutional neural network with a long-short-term memory network. The convolutional neural network was used to extract the local trend characteristics of the input variable vector within the time dimension, and the long-short-term memory network was used to capture the temporal dependencies and nonlinear interactions between variables. During the training process, a multi-objective loss function was introduced to simultaneously perform error backpropagation and weighted optimization on the three types of target variables. To prevent overfitting during training, an adaptive learning rate optimization algorithm with an early stopping mechanism was adopted. The training process was automatically terminated when the validation set loss function did not significantly decrease within several rounds, and the gradient update rate was dynamically adjusted to improve convergence efficiency and generalization performance.

[0082] After the training is completed, the energy consumption prediction model is deployed to the industrial data processing system, and forward reasoning is performed based on the real-time input of historical energy consumption data, historical operating data, and equipment characteristic data. The forward reasoning process inputs the current moment input variable vector into the trained model structure, and after sequential processing by the convolution layer and the memory network structure, it finally outputs the unit clinker energy consumption prediction value, grinding power consumption prediction value, and vertical mill power consumption prediction value corresponding to the current moment. The three output values constitute the current energy consumption index prediction vector. By comparing and analyzing the evolution trend of the energy consumption index prediction vector at different time nodes, the change pattern of each energy consumption index under the current production working conditions can be identified, thereby providing a quantitative basis for subsequent energy efficiency evaluation, energy consumption level determination, and dynamic parameter adjustment strategy.

[0083] In one possible implementation, based on the historical energy consumption data, historical operating condition data and equipment characteristic data at any moment of input, the predicted values of unit clinker energy consumption, grinding power consumption and vertical mill power consumption corresponding to that moment are output through a forward reasoning process, so as to identify the change pattern of energy consumption indicators under given operating conditions. Specifically, the following steps are performed: first, the historical energy consumption data, historical operating condition data and equipment characteristic data at the moment to be predicted are standardized and constructed into an input variable vector. The dimension of the input variable vector remains consistent with that in the training phase, and the sliding window structure of the input variable in the time dimension is retained; in the forward reasoning process, the input variable vector passes through several convolutional layers and pooling layers of the convolutional neural network in turn to extract the change trend within the local time segment. Features, the convolution kernel slides in the time dimension to extract the approximate expression of the first-order derivative and the second-order derivative, and captures the local slope changes of energy consumption and working condition fluctuations; the output of the convolutional neural network is input into the long short-term memory network as a feature sequence. The long short-term memory network learns the time dependency between different dimensions in the input variable vector between the past and the current moment through its gating mechanism. The forgetting gate controls the degree of retention of historical information, the input gate controls the writing of new states, and the output gate generates the hidden vector of the current state. The hidden vector represents the fusion representation of global features and historical dependency features; the hidden vector is mapped to the unit clinker energy consumption prediction value, grinding power consumption prediction value and vertical mill power consumption prediction value through a group of parallel fully connected layers, forming a group of one-to-one corresponding regression outputs.

[0084] Specifically, the historical energy consumption data, historical operating condition data, and equipment characteristic data at the time to be predicted are first standardized. The Z-Score method is used for numerical normalization. The specific calculation formula is:

[0085]

[0086] where x i,j represents the jth original variable of the i-th sample, μ j and σ j are the mean and standard deviation of the jth variable, x′ i,j is the normalized data value. The normalized data is organized into input variable vectors Where T represents the number of time steps in the sliding window, and D represents the dimension of the input variable corresponding to each time step. The dimension of the input variable vector is consistent with that in the training phase to ensure the compatibility of the model structure and retain the window structure of the time dimension for time series feature extraction.

[0087] Then enter the forward reasoning process, input variable vector X t It is sent to the convolutional neural network structure for feature extraction. In the convolution layer, the input feature tensor is The convolution kernel is The convolution output at each position is:

[0088]

[0089] Where k is the time step length of the convolution kernel, f(·) is a nonlinear activation function such as ReLU, and b c By stacking multiple convolutional layers and pooling layers, we can extract local slope features such as first-order derivatives and second-order derivatives within the time window, thereby enhancing the model's ability to perceive energy consumption parameter fluctuation trends.

[0090] The feature sequence output by the convolutional neural network is fed into the long short-term memory network for temporal dependency modeling. Let the convolution output at time t be x t , then the update process of the long short-term memory network is as follows:

[0091] Forget Gate:

[0092] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0093] Input Gate:

[0094] i t =σ(W i ·[h t-1 ,x t ]+b i ),

[0095] Unit status update:

[0096]

[0097] Output gate and hidden vector:

[0098] o t =σ(W o ·[h t-1 ,x t ]+b o ),h t =o t ⊙tanh(c t )

[0099] Where W f ,W i ,W c ,W o are the gating parameter matrices, b f ,b i ,b c ,b ois the bias vector, σ(·) is the sigmoid function, ⊙ represents the Hadamard product, h t It is the final output hidden vector, which is used to integrate the global features and time-dependent features between the current moment and the historical moment.

[0100] The hidden vector h t are simultaneously input into three parallel fully connected layer structures and mapped into the unit clinker energy consumption prediction values Grinding power consumption prediction Vertical mill power consumption prediction value The calculation formula is:

[0101]

[0102] Where W C ,W M ,W V is the regression weight matrix, b C ,b M ,b V is a bias term, and its output values are the regression prediction results of the three core energy consumption indicators under the current input conditions. This output constitutes the energy consumption indicator prediction vector at any given moment. By dynamically comparing this prediction vector with the prediction vectors at other moments in the historical interval, we can identify the changing patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under given operating conditions, assisting in the subsequent decision-making of optimized control strategies.

[0103] In this application's energy optimization management method for cement production, energy consumption indicators selected as unit clinker energy consumption, grinding power consumption, and vertical mill power consumption are core evaluation dimensions, demonstrating rigorous process-specificity and efficient energy efficiency identification. These three energy consumption indicators cover the three most critical, concentrated energy consumption links in cement production, which are most susceptible to changes in production parameters. They accurately reflect the dynamic characteristics and structural distribution of energy use, and serve as the foundation for building a precise energy efficiency evaluation system and implementing intelligent energy-saving control.

[0104] Unit clinker energy consumption is a highly representative indicator that reflects the overall thermal and electrical energy consumption intensity of the clinker calcining stage. Its value is directly related to fuel supply, rotary kiln thermal efficiency, grate cooler waste heat recovery efficiency, and raw fuel composition stability. It is a core parameter for measuring the energy efficiency level of thermal systems. Grinding power consumption reflects the level of electricity consumption corresponding to each ton of cement output in the entire grinding system. It covers the power consumption performance of cement mills, powder selectors, conveying fans, and grading systems. It is a key indicator for evaluating the energy efficiency control capabilities of finished product processing. Vertical mill power consumption focuses more on the vertical roller mill system widely used in modern cement production lines. Its energy consumption changes are not only closely related to the ventilation inside the mill, material particle size, lamination pressure, and operating strategy, but also have an important impact on the electrical load stability of the system operation. It is the basis for optimizing the operation mode of the mill system and improving grinding efficiency.

[0105] Compared with the coarse-grained management method in the prior art that only uses total power consumption, system average energy consumption or overall comprehensive energy consumption as the basis for evaluation, this application can achieve sensitive capture of energy consumption response to changes in production behavior by decomposing the energy consumption structure and extracting key sub-item indicators with physical interpretability, so that the energy consumption assessment model has stronger refined identification capabilities and process correspondence. At the same time, the multi-objective prediction system composed of unit clinker energy consumption, grinding power consumption and vertical mill power consumption as indicators with sub-item independence and interrelationships can support the construction of multi-dimensional energy efficiency evaluation functions, realize dynamic grading and regional optimization and regulation, and significantly improve the energy efficiency identification accuracy, energy-saving optimization response speed and adaptability of control strategies compared with the prior art, thereby achieving a systematic improvement in the operating efficiency of the cement production system and a scientific reconstruction of the energy use structure while ensuring stable product quality.

[0106] S140, based on the energy consumption prediction model, constructs a multi-objective energy efficiency evaluation function, introduces a benchmark energy consumption vector to build a dynamic evaluation system for equipment energy efficiency, clusters energy consumption intervals for the full cycle data, and defines different energy consumption levels.

[0107] First, the unit clinker energy consumption prediction value, grinding power consumption prediction value and vertical mill power consumption prediction value at each moment are extracted from the energy consumption prediction model. The three prediction values constitute a set of three-dimensional prediction energy consumption index vectors, denoted as (C t ,M t ,V t ), where C t 、M t 、V t They represent the predicted value of unit clinker energy consumption, grinding power consumption and vertical mill power consumption at the current time t respectively. The predicted energy consumption index vector serves as the core input data for the subsequent energy efficiency evaluation function calculation.

[0108] Subsequently, based on statistical analysis of historical energy consumption data or optimal operating data obtained under standardized production conditions, a baseline value for unit clinker energy consumption (C0), a baseline value for grinding power consumption (M0), and a baseline value for vertical mill power consumption (V0) are set. These three values form a baseline energy consumption vector (C0, M0, V0). This baseline energy consumption vector represents the energy consumption characteristics of the equipment system at its optimal energy efficiency state and serves as a reference for comparing the energy consumption index vectors predicted at each subsequent moment, providing a benchmark standard for deviation analysis.

[0109] Based on the above-mentioned predicted energy consumption index vector and benchmark energy consumption vector, a multi-objective energy efficiency evaluation function is further constructed, and the energy efficiency deviation measurement value E at each moment is calculated using the weighted Euclidean distance form. t , which is used to quantify the degree of deviation between the current system operating state and the optimal energy efficiency state. The calculation formula is:

[0110]

[0111] Among them, w1, w2, and w3 represent the importance weights of the three dimensions of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption, respectively. The weight values are set according to their proportion in the total energy consumption or their sensitivity to process stability, ensuring that the evaluation function has a stronger response ability to high-impact dimensions. By calculating the energy efficiency deviation measurement value at each time point, a complete energy efficiency deviation time series {E t}, used for energy efficiency status evaluation throughout the entire cycle.

[0112] Finally, the energy efficiency deviation time series {E t The K-means variant clustering algorithm is used for unsupervised cluster analysis. The K-means variant algorithm divides the energy efficiency deviation values at all times into several energy consumption level intervals by initializing, iteratively optimizing the cluster centers and minimizing the intra-class square error. Each cluster center represents a typical energy efficiency level area. The system divides the E t The value is classified into the category of the nearest cluster center and labeled with a corresponding energy consumption grade label, such as "Excellent," "Medium," "Poor," or a multi-level structure. This energy consumption grade label serves as a real-time basis for the dynamic evaluation system of equipment energy efficiency, supporting subsequent control optimization mechanisms triggered by energy consumption grades, and achieving continuous tracking and intelligent regulation of energy efficiency levels at all stages of cement production.

[0113] S150 combines the real-time working condition data and real-time energy consumption data collected in real time to determine the corresponding energy consumption level, and then dynamically adjusts the production parameters through a multivariate regression algorithm.

[0114] In one possible implementation, the real-time working condition data and the real-time working condition data collected in real time are combined to dynamically adjust the production parameters through a multivariate regression algorithm, specifically including: obtaining the real-time working condition data and the real-time energy consumption data of each key energy consumption node according to the energy monitoring equipment deployed in the cement production system; aligning the real-time working condition data and the real-time energy consumption data with the timestamps to construct a real-time input variable vector in a unified format; after the real-time input variable vector is constructed, calling the deployed energy consumption prediction model for forward reasoning to calculate the actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value and the actual vertical mill power consumption prediction value at the current moment; and aligning the actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value and the actual The predicted value of the mill's power consumption constitutes an energy consumption index vector, which is substituted into the multi-objective energy efficiency evaluation function constructed based on the historical modeling stage to obtain the actual energy efficiency deviation measurement value at the current moment; the energy consumption level label corresponding to the actual energy efficiency deviation measurement value is determined through a preset energy consumption interval clustering model; when it is determined that the energy consumption level label is at the preset level, a mapping relationship is established with the real-time operating data as the independent variable and the energy efficiency deviation measurement value as the dependent variable to form an operating condition energy consumption function with reverse control; by numerically solving the operating condition energy consumption function in the direction of gradient descent, combining the real-time operating condition data with the partial derivative information in the energy efficiency function, the optimal direction and amplitude of adjustment of each variable under the premise of maintaining equipment stability are calculated to form a production parameter adjustment vector.

[0115] Specifically, the system first collects real-time operating data and energy consumption data for each key energy-consuming node based on the energy monitoring equipment deployed in the cement production system. The real-time operating data includes, but is not limited to, parameters such as raw material feed rate, main drive motor speed, mill ventilation pressure, grate cooler air volume and outlet temperature, and classifier wind speed. The real-time energy consumption data includes parameters such as active power, electrical energy per unit time, fuel flow, steam production, and waste heat recovery efficiency. Both types of data are collected at high frequency by energy monitoring equipment distributed at each energy-consuming node and transmitted to the edge acquisition terminal.

[0116] The real-time operating condition data and the real-time energy consumption data are then timestamped. A time synchronization mechanism and time interpolation methods are used to synchronize and correct data streams with different frequencies or communication delays. A real-time input variable vector in a unified format is constructed with a fixed time step (e.g., 10 seconds). This input variable vector uses each time step as an index dimension and aggregates the standardized values of all current operating condition parameters and energy consumption parameters to form a multidimensional structured vector that serves as the input basis for subsequent model reasoning and control optimization.

[0117] After the real-time input variable vector is constructed, the energy consumption prediction model deployed in the training phase is called to perform forward reasoning on the vector, and the actual unit clinker energy consumption prediction value, actual grinding power consumption prediction value and actual vertical mill power consumption prediction value at the current moment are output in sequence. The three prediction results constitute the energy consumption index vector (C t ,M t ,V t The energy consumption index vector is then input into the multi-objective energy efficiency evaluation function constructed in the historical stage to calculate the actual energy efficiency deviation metric value E at the current moment. t , which is used to quantify the degree of difference between the current real-time operating state and the baseline energy consumption state.

[0118] Based on the energy consumption deviation metric E t , further clustering labels are mapped to the value through the preset energy consumption interval clustering model to obtain its corresponding energy consumption level label. If the energy consumption level label is within the preset critical value range of "medium" or "poor", the system immediately enters the dynamic parameter adjustment stage. In this stage, the current real-time working condition data is used as the independent variable and the current actual energy efficiency deviation measurement value is used as the dependent variable. A mapping relationship is formed through regression modeling, that is, the reverse working condition energy consumption function is controlled. The construction form is:

[0119] E t =f(X t )

[0120] where X t is the current operating condition variable vector, and f(·) is the energy efficiency response function.

[0121] By numerically solving the energy consumption function of the above working condition in the direction of gradient descent, the objective function E is calculated. t For each operating variable x i The partial derivative of Combined with the current input variable value and gradient direction, under the conditions of equipment stability, operation constraints and safety boundary conditions, the optimal adjustment range and direction of each key variable are determined to construct the production parameter adjustment vector ΔX t =(δx1,δx2,…,δx n The production parameter adjustment vector is then transmitted to the industrial control system interface and parameter correction operations are performed, completing an adaptive closed-loop optimization control based on the real-time energy efficiency level, thereby dynamically keeping the production conditions running within the optimal energy efficiency level.

[0122] This embodiment also discloses an energy optimization management device for cement production, referring to Figure 2 , comprising an acquisition module 201, a processing module 202 and an output module 203, the device is used to execute any one of the above-mentioned energy optimization management methods for cement production, wherein:

[0123] The acquisition module 201 is used to deploy adaptive energy monitoring equipment at key production links according to the cement production process, to achieve coverage and collection of key energy consumption nodes throughout the plant, and thus to acquire and collect multiple types of historical energy consumption data.

[0124] The processing module 202 is used to store the historical energy consumption data and the historical operating condition data of cement production using a time domain alignment strategy based on a time series optimization structure.

[0125] Processing module 202 is used to train an energy consumption prediction model based on historical energy consumption data and historical operating condition data, combined with equipment characteristic data of cement production equipment, using the equipment characteristic data as input variables, and identify the change patterns of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production conditions through the energy consumption prediction model.

[0126] The processing module 202 is used to construct a multi-objective energy efficiency evaluation function based on the energy consumption prediction model, introduce a benchmark energy consumption vector to construct a dynamic evaluation system for equipment energy efficiency, cluster energy consumption intervals for full-cycle data, and define different energy consumption levels.

[0127] The output module 203 is used to combine the real-time operating condition data and the real-time energy consumption data collected in real time to determine the corresponding energy consumption level, and then dynamically adjust the production parameters through a multivariate regression algorithm.

[0128] In a possible implementation, the processing module 202 is configured to construct an input variable vector using historical energy consumption data, historical operating condition data, and equipment characteristic data.

[0129] The acquisition module 201 is used to construct an input variable system of an energy consumption prediction model covering energy consumption links of the entire plant.

[0130] The processing module 202 is used to construct a multi-objective regression model structure using unit clinker energy consumption, grinding power consumption and vertical mill power consumption as target variables during training, and adopt an adaptive learning rate optimization algorithm with an early stopping mechanism for training to avoid overfitting.

[0131] Processing module 202 is used to deploy the trained energy consumption prediction model. Based on the historical energy consumption data, historical operating condition data and equipment characteristic data at any time, it outputs the corresponding unit clinker energy consumption, grinding power consumption and vertical mill power consumption predicted values at any time through a forward reasoning process, thereby identifying the change pattern of energy consumption indicators under given operating conditions.

[0132] In one possible implementation, the processing module 202 is used to standardize the historical energy consumption data, historical operating condition data, and equipment characteristic data at the time to be predicted, and construct them into an input variable vector. The dimension of the input variable vector remains consistent with that in the training phase, and the sliding window structure of the input variable in the time dimension is retained.

[0133] Processing module 202 is used to input variable vectors through several convolutional layers and pooling layers of the convolutional neural network in the forward reasoning process to extract the change trend characteristics within the local time segment. The convolution kernel slides in the time dimension to extract the approximate expression of the first-order derivative and the second-order derivative to capture the local slope changes of energy consumption and operating condition fluctuations.

[0134] Output module 203 is used to input the output of the convolutional neural network as a feature sequence into the long short-term memory network. The long short-term memory network learns the time dependency between different dimensions in the input variable vector between the past and the current moment through its gating mechanism. The forgetting gate controls the degree of retention of historical information, the input gate controls the writing of new states, and the output gate generates a hidden vector of the current state. The hidden vector represents the fusion representation of global features and historical dependency features.

[0135] The output module 203 is used to map the hidden vector to the unit clinker energy consumption prediction value, the grinding power consumption prediction value and the vertical mill power consumption prediction value through a set of parallel fully connected layers, forming a set of one-to-one corresponding regression outputs.

[0136] In a possible implementation, the acquisition module 201 is configured to extract a unit clinker energy consumption prediction value, a grinding power consumption prediction value, and a vertical mill power consumption prediction value from the energy consumption prediction model to form a three-dimensional predicted energy consumption index vector.

[0137] Processing module 202 is used to define the unit clinker energy consumption baseline value, the grinding power consumption baseline value and the vertical mill power consumption baseline value based on statistical analysis of historical data or the optimal operating state under standardized production conditions. The unit clinker energy consumption baseline value, the grinding power consumption baseline value and the vertical mill power consumption baseline value constitute a baseline energy consumption vector.

[0138] The processing module 202 is used for the multi-objective energy efficiency evaluation function. The energy efficiency evaluation function is defined in the following form by performing weighted distance calculation on the difference between the predicted energy consumption index vector and the reference energy consumption vector:

[0139]

[0140] Among them, E t is the energy efficiency deviation value at time t, C t is the predicted value of energy consumption per unit clinker, M t is the predicted value of grinding power consumption, V tis the predicted value of vertical mill power consumption, C0 is the reference value of unit clinker energy consumption, M0 is the reference value of grinding power consumption, V0 is the reference value of vertical mill power consumption, w1 is the weight coefficient corresponding to the predicted value of unit clinker energy consumption and the reference value of unit clinker energy consumption, w2 is the weight coefficient corresponding to the predicted value of grinding power consumption and the reference value of grinding power consumption, and w3 is the weight coefficient corresponding to the predicted value of vertical mill power consumption and the reference value of vertical mill power consumption.

[0141] The processing module 202 is configured to perform unsupervised clustering on the energy efficiency deviation measurement values in the entire cycle using a K-means variant clustering algorithm, and mark the energy efficiency deviation measurement value at each time point as a corresponding energy consumption level according to the clustering result.

[0142] In a possible implementation, the acquisition module 201 is configured to acquire real-time operating condition data and real-time energy consumption data of each key energy consumption node based on energy monitoring equipment deployed in the cement production system.

[0143] The processing module 202 is used to uniformly align the timestamps of the real-time operating condition data and the real-time energy consumption data to construct a real-time input variable vector in a unified format.

[0144] The processing module 202 is used to call the deployed energy consumption prediction model for forward reasoning after the real-time input variable vector is constructed, and calculate the actual unit clinker energy consumption prediction value, actual grinding power consumption prediction value and actual vertical mill power consumption prediction value at the current moment.

[0145] Processing module 202 is used to form an energy consumption index vector by combining the actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value and the actual vertical mill power consumption prediction value, and substitute it into the multi-objective energy efficiency evaluation function constructed based on the historical modeling stage to obtain the actual energy efficiency deviation measurement value at the current moment.

[0146] The processing module 202 is configured to determine an energy consumption level label corresponding to an actual energy efficiency deviation measurement value through a preset energy consumption interval clustering model.

[0147] The processing module 202 is used to establish a mapping relationship with the real-time operating condition data as the independent variable and the energy efficiency deviation measurement value as the dependent variable when it is determined that the energy consumption level label is at the preset level, so as to form an operating condition energy consumption function with reverse control.

[0148] Processing module 202 is used to numerically solve the gradient descent direction of the operating condition energy consumption function, combine real-time operating condition data with partial derivative information in the energy efficiency function, calculate the optimal direction and amplitude of adjustment of each variable while maintaining equipment stability, and form a production parameter adjustment vector.

[0149] In one possible embodiment, the processing module 202 is used to construct a time series data model. The time series data model uses the timestamp as the primary index. By setting a unique identifier for each energy monitoring device, the parameter data of each energy monitoring device at each time granularity is encapsulated into a four-tuple structure of timestamp, energy monitoring device identifier, parameter type and parameter value, forming a multidimensional data set based on the time axis in a unified format.

[0150] The processing module 202 is used to perform time normalization processing on the data stream generated by each energy monitoring device, specifically including time interpolation processing, sampling frequency normalization processing and data window reconstruction processing.

[0151] The processing module 202 is used to time-slice the data stream after time normalization according to a fixed time window length. Each time window constitutes a complete operating condition snapshot. The operating condition snapshot covers the historical energy consumption parameters and historical operating condition parameters collected by the energy monitoring equipment, realizing synchronous encapsulation under the time axis.

[0152] In one possible implementation, the processing module 202 is used to perform process node identification and energy consumption correlation analysis based on a complete cement production line process diagram, clarify the energy consumption composition characteristics of each subsystem in the clinker calcination section, raw meal preparation section, cement grinding section, waste heat recovery section and auxiliary facilities section, and all deployed energy monitoring equipment is connected to the edge collection terminal.

[0153] The acquisition module 201 is used to acquire historical energy consumption data uniformly collected and transmitted in real time by the edge acquisition terminal for energy monitoring devices of different standards.

[0154] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0155] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0156] The communication bus 302 is used to implement the connection and communication between these components.

[0157] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0158] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0159] The processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and circuits to connect various parts of the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and calling data stored in the memory 305, the processor 301 performs various server functions and processes data. Optionally, the processor 301 may be implemented using at least one hardware form selected from digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 301 and may be implemented separately on a single chip.

[0160] The memory 305 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. The memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface 303 module, and an application program for an energy optimization management method for cement production.

[0161] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for an energy optimization management method for cement production. When executed by one or more processors 301, the electronic device executes one or more methods as in the above-mentioned embodiments.

[0162] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0163] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0165] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0166] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0167] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0168] The present application also discloses a computer-readable storage medium storing instructions, which, when executed by one or more processors 301, enable an electronic device to execute one or more of the methods described in the above embodiments.

[0169] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An energy optimization management method for cement production, characterized in that: The method comprises: Based on the cement production process, appropriate energy monitoring equipment is deployed at key production links to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting multiple types of historical energy consumption data; Based on the time series optimization structure, the historical energy consumption data and the historical operating condition data of cement production are stored in a time domain alignment strategy; Based on the historical energy consumption data and the historical operating condition data, combined with equipment characteristic data of the cement production equipment, the equipment characteristic data is used as an input variable to train an energy consumption prediction model, and the energy consumption prediction model is used to identify the change patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different production conditions; Based on the energy consumption prediction model, a multi-objective energy efficiency evaluation function is constructed, a benchmark energy consumption vector is introduced to construct a dynamic evaluation system for equipment energy efficiency, energy consumption intervals are clustered for full-cycle data, and different energy consumption levels are defined; By combining the real-time operating condition data and real-time energy consumption data collected in real time, the corresponding energy consumption level is determined, and then the production parameters are dynamically adjusted through a multivariate regression algorithm.

2. The energy optimization management method for cement production according to claim 1, characterized in that: Based on the historical energy consumption data and the historical operating condition data, combined with equipment characteristic data of the cement production equipment, the energy consumption prediction model is trained using the equipment characteristic data as an input variable. Before identifying the change patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different production conditions through the energy consumption prediction model, the method further includes: Constructing an input variable vector by using the historical energy consumption data, the historical operating condition data and the equipment characteristic data; Construct an energy consumption prediction model input variable system covering all energy consumption links in the plant; During the training process, the energy consumption per unit of clinker, grinding power consumption, and vertical mill power consumption were used as target variables to construct a multi-objective regression model structure. An adaptive learning rate optimization algorithm with an early stopping mechanism was used for training to avoid overfitting. Deploy the trained energy consumption prediction model. Based on the historical energy consumption data, historical operating condition data, and equipment characteristic data at any moment, the model outputs the predicted values of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption corresponding to any moment through a forward reasoning process, thereby identifying the change pattern of energy consumption indicators under given operating conditions.

3. The energy optimization management method for cement production according to claim 2, characterized in that: The method outputs the corresponding unit clinker energy consumption, grinding power consumption and vertical mill power consumption prediction values according to the historical energy consumption data, historical operating condition data and equipment characteristic data at any moment through the forward reasoning process, thereby identifying the change pattern of energy consumption indicators under given operating conditions, specifically including: The historical energy consumption data, historical operating condition data, and equipment characteristic data at the time to be predicted are normalized and constructed into an input variable vector. The dimension of the input variable vector remains consistent with the training phase, and the sliding window structure of the input variable in the time dimension is retained; During forward reasoning, the input variable vector passes through several convolutional and pooling layers of the convolutional neural network in sequence to extract the trend characteristics of changes within the local time segment. The convolution kernel slides in the time dimension to extract the first-order derivative and second-order derivative approximate expression, capturing the local slope changes of energy consumption and operating condition fluctuations. The output of the convolutional neural network is input as a feature sequence into the long short-term memory network. The long short-term memory network learns the temporal dependency between the past and current moments of different dimensions in the input variable vector through its gating mechanism. The forget gate controls the degree of historical information retention, the input gate controls the writing of new states, and the output gate generates a hidden vector of the current state. The hidden vector represents a fusion representation of global features and historical dependency features. The hidden vector is mapped to the unit clinker energy consumption prediction value, the grinding power consumption prediction value and the vertical mill power consumption prediction value through a set of parallel fully connected layers, forming a set of one-to-one corresponding regression outputs.

4. The energy optimization management method for cement production according to claim 1, characterized in that: The multi-objective energy efficiency evaluation function is constructed based on the energy consumption prediction model, the benchmark energy consumption vector is introduced to construct the equipment energy efficiency dynamic evaluation system, the energy consumption interval is clustered for the full cycle data, and different energy consumption levels are defined, specifically including: Extracting the unit clinker energy consumption prediction value, the grinding power consumption prediction value and the vertical mill power consumption prediction value from the energy consumption prediction model to form a three-dimensional predicted energy consumption index vector; Based on statistical analysis of historical data or optimal operating conditions under standardized production conditions, a unit clinker energy consumption benchmark value, a grinding power consumption benchmark value, and a vertical mill power consumption benchmark value are defined. The unit clinker energy consumption benchmark value, the grinding power consumption benchmark value, and the vertical mill power consumption benchmark value constitute a benchmark energy consumption vector; The multi-objective energy efficiency evaluation function is defined by performing weighted distance calculation on the difference between the predicted energy consumption index vector and the benchmark energy consumption vector. Specifically, the energy efficiency evaluation function is defined in the following form: Among them, E t is the energy efficiency deviation value at time t, C t is the predicted value of energy consumption per unit clinker, M t is the predicted value of grinding power consumption, V t is the predicted power consumption value of the vertical mill, C0 is the reference value of the energy consumption per unit clinker, M0 is the reference value of the grinding power consumption, V0 is the reference value of the vertical mill power consumption, w1 is the weight coefficient corresponding to the predicted power consumption per unit clinker and the reference value of the energy consumption per unit clinker, w2 is the weight coefficient corresponding to the predicted power consumption per unit clinker and the reference value of the grinding power consumption, and w3 is the weight coefficient corresponding to the predicted power consumption per unit clinker and the reference value of the vertical mill power consumption; The energy efficiency deviation measurement values in the entire cycle are clustered unsupervisedly, using a K-means variant clustering algorithm, and the energy efficiency deviation measurement values at each time point are marked as the corresponding energy consumption level according to the clustering results.

5. The energy optimization management method for cement production according to claim 1, characterized in that: The real-time operating data collected in real time is combined with the real-time operating data to dynamically adjust the production parameters through a multivariate regression algorithm, specifically including: According to the energy monitoring equipment deployed in the cement production system, the real-time operating condition data and the real-time energy consumption data of each key energy consumption node are obtained; Aligning the real-time operating condition data and the real-time energy consumption data with each other in a unified time stamp manner to construct a real-time input variable vector in a unified format; After the real-time input variable vector is constructed, the deployed energy consumption prediction model is called to perform forward reasoning to calculate the actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value, and the actual vertical mill power consumption prediction value at the current moment; The actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value, and the actual vertical mill power consumption prediction value are combined into an energy consumption index vector, which is substituted into the multi-objective energy efficiency evaluation function constructed in the historical modeling stage to obtain the actual energy efficiency deviation measurement value at the current moment; Determine the energy consumption level label corresponding to the actual energy efficiency deviation measurement value through a preset energy consumption interval clustering model; When it is determined that the energy consumption level label is at a preset level, a mapping relationship is established with the real-time operating condition data as an independent variable and the energy efficiency deviation measurement value as a dependent variable to form an operating condition energy consumption function with reverse control; By numerically solving the gradient descent direction of the operating condition energy consumption function, combining the real-time operating condition data with the partial derivative information in the energy efficiency function, the optimal direction and amplitude of adjusting each variable while maintaining equipment stability are calculated to form a production parameter adjustment vector.

6. The energy optimization management method for cement production according to claim 1, characterized in that: The storing of the historical energy consumption data and the historical operating condition data of cement production using a time domain alignment strategy based on a time series optimization structure specifically includes: Constructing a time series data model, wherein the time series data model uses the timestamp as the primary index, sets a unique identifier for each energy monitoring device, and encapsulates the parameter data of each energy monitoring device at each time granularity into a four-tuple structure consisting of the timestamp, the energy monitoring device identifier, the parameter type, and the parameter value, thereby forming a multidimensional data set based on the time axis in a unified format; Performing time normalization processing on the data stream generated by each of the energy monitoring devices, specifically including time interpolation processing, sampling frequency normalization processing and data window reconstruction processing; The data stream after time normalization is time-sliced according to a fixed time window length. Each time window constitutes a complete operating condition snapshot. The operating condition snapshot covers the historical energy consumption parameters and historical operating condition parameters collected by the energy monitoring equipment, realizing synchronous encapsulation under the time axis.

7. The energy optimization management method for cement production according to claim 1, characterized in that: According to the cement production process, appropriate energy monitoring equipment is deployed at key production links to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby obtaining and collecting multiple types of historical energy consumption data, including: Based on the complete cement production line process diagram, process node identification and energy consumption correlation analysis are carried out to clarify the energy consumption characteristics of each subsystem in the clinker calcination section, raw meal preparation section, cement grinding section, waste heat recovery section, and auxiliary facilities section. All deployed energy monitoring equipment is connected to the edge collection terminal; Obtain historical energy consumption data uniformly collected and transmitted in real time by the edge acquisition terminal for the energy monitoring devices of different formats.

8. An energy optimization management device for cement production, characterized in that: The device is used to execute the energy optimization management method for cement production according to any one of claims 1 to 7, and the device comprises an acquisition module (201), a processing module (202), and an output module (203), wherein: The acquisition module (201) is used to deploy adaptive energy monitoring equipment at key production links according to the cement production process, to achieve coverage and acquisition of key energy consumption nodes throughout the plant, thereby acquiring and collecting multiple types of historical energy consumption data; The processing module (202) is used to store the historical energy consumption data and the historical operating condition data of cement production using a time domain alignment strategy based on a time series optimization structure; The processing module (202) is used to train an energy consumption prediction model based on the historical energy consumption data and the historical operating condition data, in combination with equipment characteristic data of cement production equipment, using the equipment characteristic data as input variables, and identifying the change patterns of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production conditions through the energy consumption prediction model; The processing module (202) is used to construct a multi-objective energy efficiency evaluation function based on the energy consumption prediction model, introduce a benchmark energy consumption vector to construct a dynamic evaluation system for equipment energy efficiency, cluster energy consumption intervals for full-cycle data, and define different energy consumption levels; The output module (203) is used to combine the real-time working condition data and the real-time energy consumption data collected in real time to determine the corresponding energy consumption level, and then dynamically adjust the production parameters through a multivariate regression algorithm.

9. An electronic device, characterized in that: The electronic device comprises a processor (301), a communication bus (302), a user interface (303), a network interface (304) and a memory (305), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are both used to communicate with other devices, the communication bus (302) is used to realize connection and communication between components in the electronic device, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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