Method for energy optimization management of a cement production

Abstract

The application provides an energy optimization management method for cement production, and relates to the technical field of intelligent manufacturing. The method comprises the following steps: acquiring historical energy consumption data of multiple types; storing the historical energy consumption data and historical working condition data of cement production based on time sequence optimization structure and time domain alignment strategy; combining the historical energy consumption data, historical working condition data and equipment characteristic data, taking the equipment characteristic data as an input variable, training an energy consumption prediction model, identifying the change mode of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production conditions through the energy consumption prediction model; constructing a multi-objective energy efficiency evaluation function based on the energy consumption prediction model, performing energy consumption interval clustering on full cycle data, and dividing different energy consumption levels; combining 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 adjusting production parameters through a multivariate regression algorithm. The application can realize automatic energy optimization management in the cement production process.

Description

TECHNICAL FIELD

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

[0002] In the cement production process, energy consumption is one of the largest parts of production costs. With the continuous rise of global energy costs and the increasingly stringent environmental regulations, the cement industry must face the challenges brought by low energy management efficiency. Therefore, implementing energy optimization management not only helps to reduce production costs, but also reduces energy waste, improves energy utilization, and ensures that cement production enterprises maintain competitiveness in the fierce market competition. At the same time, optimizing energy management can also help reduce carbon emissions and meet increasingly stringent environmental requirements, thereby improving the sustainable development ability of enterprises.

[0003] By precisely monitoring and optimizing various energy consumptions in the production process, cement production enterprises can achieve rational allocation of resources, avoid energy waste and excessive load of equipment, and reduce overall production energy consumption. With the help of advanced prediction models and intelligent management systems, enterprises can fine-tune control of different production links to ensure that each link operates in the best energy efficiency state, thereby improving the operating efficiency of the entire production system, reducing the environmental burden of enterprises, and promoting the green and low-carbon transformation of the cement industry. SUMMARY

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

[0005] In a first aspect of the present application, an energy optimization management method for cement production is provided, which comprises:

[0006] According to the cement production process, deploy appropriate energy monitoring devices at key production links to cover and collect key energy consumption nodes in the whole plant, thereby obtaining multiple types of historical energy consumption data;

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

[0008] On the basis of the historical energy consumption data and the historical working condition data, combine the equipment characteristic data of the cement production equipment, use the equipment characteristic data as input variables, train an energy consumption prediction model, and identify the change pattern of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production conditions through the energy consumption prediction model;

[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 interval clustering is performed on the full-cycle data, and different energy consumption levels are defined.

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

[0011] Based on the above technical solutions, preferably, in addition to the historical energy consumption data and the historical operating condition data, and combining the equipment characteristic data of cement production equipment, using the equipment characteristic data as input variables to train an energy consumption prediction model, before identifying the variation patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different production operating conditions through the energy consumption prediction model, the method further includes:

[0012] Construct an input variable vector using the historical energy consumption data, the historical operating condition data, and the equipment feature data;

[0013] Construct an input variable system for an energy consumption prediction model that covers all energy consumption stages of the plant;

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

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

[0016] Based on the above technical solutions, preferably, the step of outputting the predicted values ​​of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption at any given moment through a forward reasoning process, based on historical energy consumption data, historical operating condition data, and equipment characteristic data at any input moment, 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 feature data of the time to be predicted are standardized and constructed into an input variable vector. The dimension of the input variable vector is kept consistent with that of the training phase, and the sliding window structure of the input variables in the time dimension is retained.

[0018] During the forward inference process, the input variable vector passes through several convolutional and pooling layers of the convolutional neural network in sequence to extract the trend features of change within a local time segment. The convolutional kernel slides along the time dimension to extract the first and second derivative approximate expressions, 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 dependence of different dimensions in the input variable vector between the past and the present time through its gating mechanism. The forget 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.

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

[0021] Based on the above technical solutions, preferably, the step of constructing a multi-objective energy efficiency evaluation function based on the energy consumption prediction model, introducing a benchmark energy consumption vector to construct a dynamic evaluation system for equipment energy efficiency, performing energy consumption interval clustering on the full-cycle data, and delineating different energy consumption levels specifically includes:

[0022] The predicted energy consumption per unit of clinker, the predicted power consumption of grinding, and the predicted power consumption of vertical mill are extracted from the energy consumption prediction model to form a three-dimensional predicted energy consumption index vector.

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

[0024] The multi-objective energy efficiency evaluation function calculates a weighted distance between the predicted energy consumption index vector and the baseline energy consumption vector, and is specifically defined in the following form:

[0025]

[0026] Among them, E t C is the energy efficiency deviation metric at time t. t M represents the predicted energy consumption per unit of clinker. t V is the predicted grinding power consumption value. tHere, C0 is the predicted energy consumption of the vertical mill, M0 is the benchmark energy consumption per unit of clinker, V0 is the benchmark energy consumption of the vertical mill, w1 is the weighting coefficient corresponding to the predicted energy consumption per unit of clinker and the benchmark energy consumption per unit of clinker, w2 is the weighting coefficient corresponding to the predicted energy consumption of the grinding and the benchmark energy consumption of the grinding, and w3 is the weighting coefficient corresponding to the predicted energy consumption of the vertical mill and the benchmark energy consumption of the vertical mill.

[0027] The energy efficiency deviation metric values ​​over the entire period are clustered using a variant of the K-means clustering algorithm in an unsupervised manner, and the energy efficiency deviation metric values ​​at each time point are labeled as the corresponding energy consumption level according to the clustering results.

[0028] Based on the above technical solutions, preferably, the step of dynamically adjusting production parameters by combining real-time collected operating condition data with multivariate regression algorithm specifically includes:

[0029] Based on the energy monitoring equipment deployed in the cement production system, the real-time operating condition data and real-time energy consumption data of each of the key energy consumption nodes are obtained.

[0030] The real-time operating condition data and the real-time energy consumption data are timestamped to construct a real-time input variable vector with a unified format.

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

[0032] The predicted energy consumption per unit of clinker, the predicted power consumption of actual grinding, and the predicted power consumption of actual vertical mill are combined to form an energy consumption index vector. This vector is then substituted into a 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.

[0033] The energy consumption level label corresponding to the actual energy efficiency deviation value is determined by a preset energy consumption interval clustering model.

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

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

[0036] Based on the above technical solutions, preferably, the storage of historical energy consumption data and historical operating condition data of cement production using a time-domain alignment strategy based on a time-series optimized structure specifically includes:

[0037] A time series data model is constructed, which uses timestamps as the main 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.

[0038] Time standardization processing is performed on the data streams generated by each of the energy monitoring devices, specifically including time interpolation processing, sampling frequency normalization processing, and data window reconstruction processing.

[0039] The time-standardized data stream is sliced ​​according to a fixed time window length. Each time window constitutes a complete operating condition snapshot, which 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] Based on the above technical solutions, preferably, the deployment of suitable energy monitoring equipment at key production stages according to the cement production process flow enables coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting various types of historical energy consumption data, specifically including:

[0041] Based on the complete cement production line process diagram, process nodes are identified and energy consumption correlation analysis is performed to 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. All deployed energy monitoring equipment is connected to the edge acquisition terminal.

[0042] The edge acquisition terminal acquires and transmits historical energy consumption data in real time from energy monitoring devices of different types.

[0043] A second aspect of this application provides an energy optimization management device for cement production, the device being used to execute an energy optimization management method for cement production as described in any of the above-described methods, the device comprising an acquisition module, a processing module, and an output module, wherein:

[0044] The acquisition module is used to deploy appropriate energy monitoring equipment at key production stages according to the cement production process, so as to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting various 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 based on a time-series optimized structure and a time-domain alignment strategy.

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

[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, perform energy consumption interval clustering on full-cycle data, and delineate different energy consumption levels.

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

[0049] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein 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 to cause the electronic device to perform the method as described in any of the foregoing.

[0050] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the preceding descriptions.

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

[0052] 1. This application acquires multidimensional historical energy consumption data by deploying energy monitoring equipment in key stages of cement production, and achieves unified storage and management of energy consumption and operating condition data based on time series optimization structure. Furthermore, it trains an energy consumption prediction model by combining equipment characteristic data to accurately identify energy consumption behavior change patterns under different operating conditions; it constructs a multi-objective energy efficiency evaluation function and an energy consumption interval clustering model to dynamically classify energy efficiency levels, calculates the current energy efficiency status in real time by combining real-time collected data, and calculates the optimal adjustment direction and magnitude of production parameters through multivariate regression algorithm under low energy efficiency level judgment, 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, accurate, 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 feature data, it automatically extracts local trend and time-series dependent features to achieve high-precision prediction of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption. It effectively identifies energy consumption change patterns under different production operating conditions, thereby improving the dynamic perception capability of cement production energy consumption behavior and the accuracy of model-driven energy efficiency analysis, providing precise support for subsequent intelligent energy efficiency assessment and optimization control.

[0054] 3. This application quantifies the deviation of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different operating conditions by constructing a multi-objective energy efficiency evaluation function and a benchmark energy consumption vector. It also uses a K-means variant clustering algorithm to partition the full-cycle energy efficiency deviation measurement value, thereby realizing the automatic classification of energy consumption levels. This establishes a dynamic evaluation system for equipment energy efficiency that can respond in real time, effectively improving the accuracy of energy efficiency identification, the ability to judge operating status, and the adaptability of energy-saving optimization strategies in the cement production process. This provides a data foundation and logical support for achieving hierarchical control and refined scheduling.

[0055] 4. This application collects real-time operating condition data and energy consumption data at key nodes in cement production, combines a trained energy consumption prediction model and energy efficiency evaluation function, dynamically calculates the current degree of energy efficiency deviation, constructs an operating condition energy consumption function based on the energy consumption level judgment results, and uses multivariate regression and gradient numerical solution methods to accurately calculate the optimal adjustment direction and magnitude of production parameters, thereby realizing real-time optimization control of production process parameters, effectively improving equipment operating efficiency, reducing unit energy consumption, and enhancing the adaptability and refinement of energy consumption regulation, and constructing a closed-loop dynamic energy-saving optimization mechanism. Attached Figure Description

[0056] Figure 1 This is a schematic flowchart of an energy optimization management method for cement production disclosed in an embodiment of this application;

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

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

[0059] Explanation of reference numerals in the attached drawings: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0060] 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 with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

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

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

[0063] Faced with the dual challenges of rising energy costs and increasing environmental pressures, cement producers must implement refined energy optimization management to improve energy efficiency, reduce production costs, and decrease carbon emissions. By precisely monitoring and dynamically optimizing energy consumption at each stage of production, combined with intelligent predictive models and management systems, rational resource allocation, balanced equipment load, and maximized energy efficiency can be achieved. This will enhance the company's market competitiveness and sustainable development capabilities, driving the cement industry towards a green and low-carbon transformation.

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

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

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

[0067] In one possible implementation, based on the cement production process, suitable energy monitoring equipment is deployed at key production stages to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring various types of historical energy consumption data. Specifically, this includes: identifying process nodes and analyzing energy consumption correlations based on a complete cement production line process diagram; clarifying 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; connecting all deployed energy monitoring equipment to an edge acquisition terminal; and acquiring historical energy consumption data uniformly collected and transmitted in real time by the edge acquisition terminal from energy monitoring equipment of different types.

[0068] Specifically, the first step is to identify process nodes and conduct energy consumption correlation analysis based on a complete cement production line process diagram. Through systematic analysis of 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 mill system, pulverized coal preparation system, and waste heat boiler steam generator. Combined with historical operating data and process equipment technical parameters, an energy consumption structure model for each process subsystem is constructed, and suitable energy monitoring equipment deployment locations are determined to ensure that the monitoring coverage can reflect the spatial distribution and energy flow patterns of energy consumption behavior.

[0069] After completing the planning of monitoring points, appropriate energy monitoring equipment is deployed according to the energy consumption parameter types and data accuracy requirements of each subsystem. For electrical energy consumption parameters, a combination of three-phase smart meters and current transformers supporting 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, vortex flow meters or turbine flow meters with temperature and pressure compensation mechanisms are used in combination with thermocouples and differential pressure transmitters to form thermal energy metering nodes. For air volume and material flow, piezoresistive pressure sensors, electromagnetic flow meters, and velocity 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 highly reliable insulation structures, and be able to adapt to the dust, high temperature, and strong electromagnetic interference environment of cement plants.

[0070] After deployment, all energy monitoring devices must connect to the edge acquisition terminal via RS485 bus, Ethernet, or 4G / 5G wireless communication. The edge acquisition terminal should possess multi-protocol parsing capabilities, an end-side buffering mechanism, data breakpoint resume functionality, and preprocessing capabilities. It should be able to perform unified protocol conversion, timestamp synchronization, anomaly removal, and hierarchical caching operations on data collected by heterogeneous energy monitoring devices, and encapsulate it into structured data packets in a specified format. The edge acquisition terminal will transmit the processed structured historical energy consumption data to the central energy management system in real time, while maintaining a local redundant copy to prevent data loss, ensuring the integrity, continuity, and low latency of data acquisition.

[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, enabling precise monitoring and data collection of key energy consumption nodes throughout the cement production process. This provides basic data support for subsequent construction of time series databases, training of energy consumption prediction models, and dynamic energy efficiency optimization.

[0072] S120 stores historical energy consumption data and historical operating condition data of cement production using a time-domain alignment strategy based on a time-series optimized 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 optimized structure. Specifically, this includes: constructing a time-series data model, using timestamps as the primary index; encapsulating the parameter data of each energy monitoring device at each time granularity into a four-tuple structure containing timestamp, energy monitoring device identifier, parameter type, and parameter value, forming a multi-dimensional data set based on a time axis in a unified format; performing time standardization processing on the data streams generated by each energy monitoring device, specifically including time interpolation, sampling frequency normalization, and data window reconstruction; and performing time slicing processing on the time-standardized data streams according to a fixed time window length, with each time window constituting a complete operating condition snapshot. This snapshot covers historical energy consumption parameters and historical operating condition parameters collected by the energy monitoring devices, achieving synchronous encapsulation under the time axis.

[0074] Specifically, a time series data model is first constructed, which uses timestamps as the primary index. A unique energy monitoring device identifier is set for each energy monitoring device. Through a unified data encapsulation structure, the parameter values ​​collected at each time granularity are represented as a quadruple of timestamp, energy monitoring device identifier, parameter type, and parameter value. A two-dimensional time-device data table structure is constructed based on this quadruple. All energy monitoring device data are vertically spliced ​​along the time axis to form a cross-device, cross-parameter type data primary key structure, realizing the orderly organization of multi-dimensional energy consumption parameters under a unified time benchmark.

[0075] Subsequently, time standardization processing is performed on the data streams collected by each energy monitoring device. This processing includes three consecutive steps: time interpolation, sampling frequency normalization, and data window reconstruction. In the time interpolation stage, for missing time points due to network fluctuations, communication delays, or intermittent sampling by devices, linear interpolation or cubic spline interpolation methods are used to estimate the missing values ​​in the time dimension, thus ensuring the integrity of the parameter set corresponding to each time point. In the sampling frequency normalization stage, based on the system-set baseline sampling period (e.g., 10 seconds or 30 seconds), data sources with different sampling frequencies are processed uniformly. High-frequency data is downsampled using an average resampling method, and low-frequency data is filled using interpolation, thereby achieving sampling alignment of different frequency data at a unified time step. In the data window reconstruction stage, combined with the standardized data stream, the data stream is divided according to a sliding or fixed time window method, ensuring that each time window contains complete sampling values ​​from all energy monitoring devices within that time period, forming a data snapshot that can be input for modeling.

[0076] Finally, the time-standardized data is sliced ​​according to a fixed time window length. Each slice constitutes a complete operating condition snapshot, which covers all 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 are strictly aligned on the time axis. All operating condition snapshots are continuously stored in a time series database, forming a structured data system with both horizontal device distribution dimensions and vertical time continuity dimensions. This provides a consistent and high-precision time series data foundation for subsequent energy consumption prediction modeling, energy efficiency assessment 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, uses equipment characteristic data as input variables to train an energy consumption prediction model, and identifies the variation patterns of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production conditions through the energy consumption prediction model.

[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, and using the equipment characteristic data as input variables, an energy consumption prediction model is trained. Before identifying the changing 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 from historical energy consumption data, historical operating condition data, and equipment characteristic data; constructing an input variable system for the energy consumption prediction model covering all energy consumption links in the plant; during the training process, using unit clinker energy consumption, grinding power consumption, and vertical mill power consumption as target variables respectively, constructing a multi-objective regression model structure, and using an adaptive learning rate optimization algorithm with an early stop mechanism for training to avoid overfitting; deploying the trained energy consumption prediction model, and based on the historical energy consumption data, historical operating condition data, and equipment characteristic data at any given time, outputting the predicted values ​​of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption at any given time through a forward inference process, thereby identifying the changing patterns of energy consumption indicators under given operating conditions.

[0079] Specifically, the first step is to unify and integrate the historical energy consumption data, historical operating condition data, and equipment characteristic data to construct an input variable vector. This input variable vector is indexed by timestamps, aggregating 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 electrical energy, current, gas flow rate, and heat energy values ​​collected by various energy monitoring devices. The operating condition parameter values ​​include material flow rate, gas pressure, temperature, vibration, and rotational speed. The equipment characteristic parameters include the equipment's rated power, design load, maintenance cycle, start-up and shutdown frequency, and actual operating years. After time alignment and feature normalization, these are uniformly encoded into a fixed-dimensional input variable vector to form a complete and complete training sample foundation structure.

[0080] Subsequently, based on the aforementioned input variable vector, an input variable system for an energy consumption prediction model covering all energy consumption stages of the plant is constructed. This input variable system, according to the division of cement production processes, divides the input variable vector into subsets for clinker calcination, raw meal preparation, cement grinding, and auxiliary facilities, ensuring that the model can independently model and jointly analyze the differences in energy consumption behavior between different processes. A feature filtering mechanism is set for different variable subsets, using Pearson correlation coefficients and mutual information indices to eliminate low-correlation input terms while retaining high-impact variables to improve the model's generalization ability and stability.

[0081] During the model training phase, unit clinker energy consumption, grinding power consumption, and vertical mill power consumption were used as independent objective variables to construct a multi-objective regression model structure. A combined model structure integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks was adopted. The CNNs were used to extract local trend features of the input variable vectors over time, while the LSTM networks were used to capture temporal dependencies and nonlinear interactions between variables. During training, a multi-objective loss function was introduced to simultaneously perform backpropagation and weighted optimization on the three types of objective variables. To prevent overfitting during training, an adaptive learning rate optimization algorithm with an early stopping mechanism was employed. 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 training, the energy consumption prediction model is deployed to the industrial data processing system, and forward inference is performed by combining real-time input historical energy consumption data, historical operating condition data, and equipment characteristic data. The forward inference process involves inputting the current input variable vector into the trained model structure, processing it sequentially through convolutional layers and a memory network structure, and finally outputting the predicted unit clinker energy consumption, grinding power consumption, and vertical mill power consumption for the current moment. These three output values ​​constitute the current energy consumption index prediction vector. By comparing and analyzing the evolution trend of this energy consumption index prediction vector at different time points, the change patterns of each energy consumption index under the current production operating conditions can be identified, thus providing a quantitative basis for subsequent energy efficiency assessment, energy consumption level determination, and dynamic parameter adjustment strategies.

[0083] In one possible implementation, based on historical energy consumption data, historical operating condition data, and equipment characteristic data at any given moment, a forward inference process is used to output predicted values ​​for unit clinker energy consumption, grinding power consumption, and vertical mill power consumption at that moment, thereby identifying the changing patterns of energy consumption indicators under given operating conditions. Specifically, this includes: first, standardizing the historical energy consumption data, historical operating condition data, and equipment characteristic data for the moment to be predicted to construct an input variable vector. The dimension of the input variable vector remains consistent with that of the training phase, and the sliding window structure of the input variables in the time dimension is retained. During the forward inference process, the input variable vector sequentially passes through several convolutional and pooling layers of a convolutional neural network to extract the changing trends within local time segments. The convolutional kernel slides along the time dimension to extract approximate expressions of the first and second derivatives, capturing local slope changes in 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 dependence of different dimensions in the input variable vector between past and present times through its gating mechanism. The forget 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 dependent features. The hidden vector is mapped to the predicted unit clinker energy consumption, grinding power consumption, and vertical mill power consumption through a set of parallel fully connected layers, forming a set of one-to-one regression outputs.

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

[0085]

[0086] Where x i,j Let μ represent the j-th original variable of the i-th sample. j With σ j Let x′ be the mean and standard deviation of the j-th variable, respectively. i,j These are the normalized data values. The normalized data is then organized into an input variable vector. 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 this input variable vector is consistent with that of the training phase to ensure model structure compatibility and to retain the window structure of the time dimension for temporal feature extraction.

[0087] Then the forward inference process begins, with the input variable vector X. t The input feature tensor is fed into a convolutional neural network structure for feature extraction. In the convolutional layer, let the input feature tensor be... 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 This is the bias term. By stacking multiple convolutional and pooling layers, local slope features such as the first and second derivatives within the time window are extracted to enhance the model's ability to perceive the fluctuation trend of energy consumption parameters.

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

[0091] Forgotten 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] Cell 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] Among them W f W i W c W o These are the gating parameter matrices, b f ,b i ,b c ,b oLet h be the bias vector, σ(·) be the sigmoid function, ⊙ denote the Hadamard product, and h t The final output hidden vector is used to integrate global features and temporal dependency features between the current time step and historical time steps.

[0100] The hidden vector h t The data is simultaneously input into three parallel fully connected layer structures, each mapped to a predicted unit clinker energy consumption value. Predicted power consumption for grinding Predicted power consumption of vertical mill The calculation formula is as follows:

[0101]

[0102] Among them W C W M W V For the regression weight matrix, b C ,b M ,b V The output values ​​are the regression prediction results of the three core energy consumption indicators under the current input conditions, which are the bias terms. This output constitutes the energy consumption indicator prediction vector at any given time. By dynamically comparing this prediction vector with the prediction vectors at other times in the historical interval, the variation patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under given operating conditions can be identified, assisting in the subsequent decision-making of optimization control strategies.

[0103] In the energy optimization management method for cement production proposed in this application, the energy consumption indicators selected are unit clinker energy consumption, grinding power consumption, and vertical mill power consumption as core evaluation dimensions, which have rigorous process targeting and efficient energy efficiency identification capabilities. These three energy consumption indicators cover the three most critical, concentrated, and easily affected energy consumption links in the cement production process, and can accurately reflect the dynamic characteristics and structural distribution of energy use. They are the foundation for constructing a precise energy efficiency evaluation system and carrying out intelligent energy-saving control.

[0104] Unit clinker energy consumption, as an indicator reflecting the overall thermal and electrical energy consumption intensity of the clinker calcination section, is highly representative. Its value is directly related to fuel supply, rotary kiln thermal efficiency, grate cooler waste heat recovery efficiency, and raw material and fuel composition stability, making it a core parameter for measuring the energy efficiency level of thermal systems. Grinding power consumption reflects the power consumption level corresponding to each ton of cement output in the entire grinding system, covering the power consumption performance of cement mills, classifiers, conveying fans, and grading systems. It is a key indicator for evaluating the energy efficiency control capability of finished product processing. Vertical roller mill power consumption focuses more on the vertical roller mill system widely used in modern cement production lines. Its energy consumption variation is not only closely related to mill ventilation, material particle size, lamination pressure, and operating strategies, but also has a significant impact on the electrical load stability of the system operation, forming the basis for optimizing the mill system operation mode and improving grinding efficiency.

[0105] Compared to existing coarse-grained management methods that rely solely on total power consumption, system average energy consumption, or overall comprehensive energy consumption for evaluation, this application decomposes the energy consumption structure and extracts key sub-indicators with physical interpretability. This enables sensitive capture of changes in production behavior in energy consumption response, giving the energy consumption assessment model a more refined identification capability and process correspondence. Furthermore, the multi-objective prediction system, comprised of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption as indicators with independent components yet interrelated relationships, supports the construction of multi-dimensional energy efficiency evaluation functions. This allows for dynamic level classification and regional optimization control, significantly improving energy efficiency identification accuracy, energy-saving optimization response speed, and control strategy adaptability compared to existing technologies. Ultimately, this ensures a systematic improvement in the operating efficiency of the cement production system and a scientific restructuring of the energy use structure while maintaining stable product quality.

[0106] S140 constructs a multi-objective energy efficiency evaluation function based on an energy consumption prediction model, introduces a benchmark energy consumption vector to construct a dynamic evaluation system for equipment energy efficiency, performs energy consumption interval clustering on full-cycle data, and delineates different energy consumption levels.

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

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

[0109] Based on the predicted energy consumption index vector and the baseline energy consumption vector constructed above, a multi-objective energy efficiency evaluation function is further constructed, and the energy efficiency deviation metric E at each time step is calculated using a weighted Euclidean distance. t This is used to quantify the deviation between the current system operating state and the optimal energy efficiency state. The calculation formula is:

[0110]

[0111] Here, w1, w2, and w3 represent the importance weights of the three dimensions: unit clinker energy consumption, grinding power consumption, and vertical mill power consumption, respectively. The weight values ​​are set based on their proportion in total energy consumption or their sensitivity to process stability, ensuring that the evaluation function has a stronger responsiveness to high-impact dimensions. By calculating the energy efficiency deviation metric at each time point, a complete energy efficiency deviation time series {E} can be formed. t} is used for energy efficiency status assessment throughout the entire life cycle.

[0112] Finally, the energy efficiency deviation time series {E t Unsupervised clustering analysis was performed using a variant of the K-means clustering algorithm. This variant algorithm divides the energy efficiency deviation from the metric at all time points into several energy consumption level intervals by initializing and iteratively optimizing cluster centers and minimizing the within-cluster squared error. Each cluster center represents a typical energy efficiency level region. The system then calculates the energy efficiency deviation at each time point... t The value is assigned to the category of the nearest cluster center and labeled with the corresponding energy consumption level tag, such as "Excellent," "Medium," "Poor," or a multi-level structure. This energy consumption level tag can serve as a real-time judgment basis for the dynamic evaluation system of equipment energy efficiency, supporting subsequent control optimization mechanisms triggered by energy consumption levels, and realizing continuous tracking and intelligent control of energy efficiency levels at each stage of cement production.

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

[0114] In one possible implementation, production parameters are dynamically adjusted using a multivariate regression algorithm by combining real-time collected operating condition data and real-time energy consumption data. Specifically, this includes: acquiring real-time operating condition data and real-time energy consumption data for each key energy consumption node based on energy monitoring equipment deployed in the cement production system; aligning the real-time operating condition data and real-time energy consumption data with unified timestamps to construct a unified format real-time input variable vector; after the real-time input variable vector is constructed, calling the deployed energy consumption prediction model for forward inference to calculate the predicted actual unit clinker energy consumption, actual grinding power consumption, and actual vertical mill power consumption at the current moment; and then comparing the predicted actual unit clinker energy consumption, actual grinding power consumption, and actual vertical mill power consumption with the actual unit clinker energy consumption and actual grinding power consumption. The predicted power consumption of the vertical mill is used to form an energy consumption index vector. This vector is then substituted into a multi-objective energy efficiency evaluation function built based on historical modeling to obtain the actual energy efficiency deviation metric at the current moment. A preset energy consumption interval clustering model is used to determine the energy consumption level label corresponding to the actual energy efficiency deviation metric. When the energy consumption level label is determined to be at a preset level, a mapping relationship is established using real-time operating data as the independent variable and the energy efficiency deviation metric as the dependent variable, forming a control-inverse operating energy consumption function. By numerically solving the gradient descent direction of the operating energy consumption function, and combining real-time operating data with the partial derivative information in the energy efficiency function, the optimal direction and magnitude of adjustment for each independent variable are calculated while maintaining equipment stability, forming a production parameter adjustment vector.

[0115] Specifically, the real-time operating condition data and real-time energy consumption data of each key energy consumption node are first obtained based on the energy monitoring equipment already deployed in the cement production system. The real-time operating condition 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 rate, steam production, and waste heat recovery thermal efficiency. Both types of data are collected at high frequency by energy monitoring equipment distributed at each energy consumption node and transmitted to the edge acquisition terminal.

[0116] Subsequently, the real-time operating condition data and the real-time energy consumption data are timestamped and aligned. A time synchronization mechanism and time interpolation method are used to synchronize and correct data streams with different frequencies or communication delays, constructing a unified format real-time input variable vector at fixed time steps (e.g., 10 seconds). This input variable vector, indexed by each time step, aggregates the standardized values ​​of all current operating condition parameters and energy consumption parameters, forming a multi-dimensional structured vector as the input basis for subsequent model inference and control optimization.

[0117] After the real-time input variable vector is constructed, the energy consumption prediction model deployed during the training phase is invoked to perform forward inference processing on the vector, sequentially outputting the predicted value of actual unit clinker energy consumption, actual grinding power consumption, and actual vertical mill power consumption at the current moment. These three prediction results constitute the energy consumption index vector (C) at the current moment. t M t V t The energy consumption index vector is then input into a multi-objective energy efficiency evaluation function constructed from historical data to calculate the actual energy efficiency deviation metric E at the current moment. t It 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 The system further uses a pre-defined energy consumption interval clustering model to map the numerical value to its corresponding energy consumption level label. If the energy consumption level label falls within a pre-defined critical range such as "medium" or "poor," the system immediately enters the dynamic parameter tuning phase. In this phase, the current real-time operating 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, i.e., a control function for the reverse operating condition energy consumption, constructed as follows:

[0119] E t =f(X) t )

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

[0121] By numerically solving the energy consumption function under the above operating conditions in the gradient descent direction, the objective function E is calculated. t Regarding the variable x for each operating condition i partial derivatives By combining the current values ​​of the input variables and the gradient direction, and under the conditions of equipment stability, operational constraints, and safety boundaries, the optimal adjustment magnitude and direction for each key variable are determined, and a production parameter adjustment vector ΔX is constructed. t =(δx1,δx2,…,δx) n The production parameter adjustment vector is then transmitted to the industrial control system interface and a parameter correction operation is performed to complete an adaptive closed-loop optimization control based on the real-time energy efficiency level, thereby dynamically maintaining the production operation within the optimal energy efficiency level.

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

[0123] The acquisition module 201 is used to deploy suitable energy monitoring equipment in key production links according to the cement production process, so as to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting various types of historical energy consumption data.

[0124] Processing module 202 is used to store historical energy consumption data and historical operating condition data of cement production based on time-series optimized structure and time-domain alignment strategy.

[0125] The 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 equipment characteristic data as input variables. The energy consumption prediction model identifies the variation patterns of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production operating conditions.

[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, perform energy consumption interval clustering on the full-cycle data, and delineate different energy consumption levels.

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

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

[0129] The acquisition module 201 is used to construct the input variable system for the energy consumption prediction model covering all energy consumption links in the plant.

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

[0131] The 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 given time, it outputs the predicted values ​​of unit clinker energy consumption, grinding power consumption and vertical mill power consumption at any given time through a forward inference 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 feature data of the time to be predicted, and construct an input variable vector. The dimension of the input variable vector is kept consistent with that of the training phase, and the sliding window structure of the input variables in the time dimension is retained.

[0133] The processing module 202 is used to extract the trend features of changes within a local time segment by passing the input variable vector through several convolutional and pooling layers of the convolutional neural network in sequence during the forward inference process. The convolutional kernel slides in the time dimension to extract the first and second derivative approximate expressions to capture the local slope changes of energy consumption and operating condition fluctuations.

[0134] The 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 temporal dependence of different dimensions in the input variable vector between the past and the present time through its gating mechanism. The forget gate controls the degree of retention of historical information, the input gate controls the writing of the new state, and the output gate generates the hidden vector of the current state. The hidden vector represents the fusion representation of global features and historical dependent features.

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

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

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

[0138] Processing module 202 is used for multi-objective energy efficiency evaluation functions. The energy efficiency evaluation function is defined by calculating a weighted distance between the predicted energy consumption index vector and the baseline energy consumption vector, specifically in the following form:

[0139]

[0140] Among them, E t C is the energy efficiency deviation metric at time t. t M represents the predicted energy consumption per unit of clinker. t V is the predicted power consumption for grinding. tHere, C0 is the predicted energy consumption of the vertical mill, M0 is the benchmark energy consumption per unit of clinker, V0 is the benchmark energy consumption of the grinding mill, w1 is the weighting coefficient corresponding to the predicted energy consumption per unit of clinker and the benchmark energy consumption per unit of clinker, w2 is the weighting coefficient corresponding to the predicted energy consumption of the grinding mill and the benchmark energy consumption of the grinding mill, and w3 is the weighting coefficient corresponding to the predicted energy consumption of the vertical mill and the benchmark energy consumption of the vertical mill.

[0141] The processing module 202 is used to perform unsupervised clustering of the energy efficiency deviation metric values ​​over the entire cycle using a K-means variant clustering algorithm, and to label the energy efficiency deviation metric values ​​at each time point as the corresponding energy consumption level according to the clustering results.

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

[0143] The processing module 202 is used to align the real-time operating condition data and real-time energy consumption data with timestamps 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 to perform forward inference after the real-time input variable vector is constructed, and to calculate the predicted value of the actual unit clinker energy consumption, the predicted value of the actual grinding power consumption, and the predicted value of the actual vertical mill power consumption at the current moment.

[0145] The processing module 202 is used to combine the actual unit clinker energy consumption prediction value, the actual grinding power consumption prediction value and the actual vertical mill power consumption prediction value into an energy consumption index vector, 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 used to determine the energy consumption level label corresponding to the 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 real-time operating condition data as the independent variable and energy efficiency deviation measurement as the dependent variable when the energy consumption level label is determined to be at a preset level, so as to form an operating condition energy consumption function with reverse control.

[0148] The processing module 202 is used to calculate the optimal direction and magnitude of adjusting each variable while maintaining equipment stability by numerically solving the gradient descent direction of the operating condition energy consumption function, and combining real-time operating condition data with the partial derivative information in the energy efficiency function, thereby forming a production parameter adjustment vector.

[0149] In one possible implementation, the processing module 202 is used to construct a time series data model. The time series data model uses timestamps as the main 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 standardization processing on the data streams generated by various energy monitoring devices, specifically including time interpolation processing, sampling frequency normalization processing, and data window reconstruction processing.

[0151] The processing module 202 is used to perform time-slicing processing on the time-standardized data stream 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 identify process nodes and perform energy consumption correlation analysis based on the 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 facility section, and connect all deployed energy monitoring equipment to the edge acquisition terminal.

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

[0154] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical 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 apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

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

[0156] The communication bus 302 is used to enable communication between these components.

[0157] The user interface 303 may include a display screen and a 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 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 301.

[0160] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, 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 touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. As a computer storage medium, the memory 305 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 to obtain the user input data; while 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 performs one or more methods as described in the above embodiments.

[0162] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0163] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0165] The units described as separate components may or may not be physically separate. The 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0167] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory 305 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 305 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0168] This application also discloses a computer-readable storage medium storing instructions. When executed by one or more processors 301, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0169] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. An energy optimization management method for cement production, characterized in that, The method includes: Based on the cement production process, suitable energy monitoring equipment is deployed in key production stages to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting various types of historical energy consumption data. Based on a time-series optimized structure, the historical energy consumption data and historical operating condition data of cement production are stored using a time-domain alignment strategy. Based on the historical energy consumption data and the historical operating condition data, combined with the equipment characteristic data of cement production equipment, and using the equipment characteristic data as input variables, an energy consumption prediction model is trained. The energy consumption prediction model identifies the variation patterns of unit clinker energy consumption, grinding power consumption and vertical mill power consumption under different production operating conditions. The energy consumption prediction model adopts a combined model structure that integrates convolutional neural networks and long short-term memory networks. 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 interval clustering is performed on the full-cycle data, and different energy consumption levels are defined. By combining real-time operating data and real-time energy consumption data, the corresponding energy consumption level is determined, and then production parameters are dynamically adjusted through a multivariate regression algorithm. When the energy consumption level label is determined to be at a preset level, a mapping relationship is established with the real-time operating condition data as the independent variable and the energy efficiency deviation measurement value as the dependent variable, forming an operating condition energy consumption function that controls the reverse. By numerically solving the gradient descent direction of the operating condition energy consumption function, and combining the real-time operating condition data with the partial derivative information in the energy efficiency function, the optimal direction and magnitude of adjusting each variable under the premise of maintaining equipment stability are calculated, forming a production parameter adjustment vector. The process involves constructing a multi-objective energy efficiency evaluation function based on the energy consumption prediction model, introducing a benchmark energy consumption vector to build a dynamic evaluation system for equipment energy efficiency, clustering energy consumption intervals across the entire lifecycle data, and defining different energy consumption levels. Specifically, this includes: The predicted energy consumption per unit of clinker, the predicted power consumption of grinding, and the predicted power consumption of vertical mill are extracted from the energy consumption prediction model to form a three-dimensional predicted energy consumption index vector. Based on historical data statistical analysis or the optimal operating state under standardized production conditions, a benchmark value for energy consumption per unit clinker, a benchmark value for power consumption during grinding, and a benchmark value for power consumption during vertical mill are defined. The benchmark value for energy consumption per unit clinker, the benchmark value for power consumption during grinding, and the benchmark value for power consumption during vertical mill constitute a benchmark energy consumption vector. The multi-objective energy efficiency evaluation function calculates a weighted distance between the predicted energy consumption index vector and the baseline energy consumption vector, and is specifically defined in the following form: ； in, For a moment The energy efficiency deviation from the measurement value, The predicted energy consumption per unit of clinker. The predicted power consumption for grinding is... The predicted power consumption of the vertical mill is... The unit clinker energy consumption benchmark value is... The grinding power consumption reference value is... The power consumption benchmark value for the vertical mill is... The weighting coefficients corresponding to the predicted unit clinker energy consumption and the benchmark unit clinker energy consumption are... The weighting coefficients corresponding to the predicted grinding power consumption value and the benchmark grinding power consumption value are... The weighting coefficients corresponding to the predicted power consumption value of the vertical mill and the benchmark power consumption value of the vertical mill; The energy efficiency deviation metric values ​​over the entire period are clustered using a variant of the K-means clustering algorithm in an unsupervised manner, and the energy efficiency deviation metric values ​​at each time point are labeled as the corresponding energy consumption level according to the clustering results.

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, and combined with the equipment characteristic data of cement production equipment, using the equipment characteristic data as input variables, an energy consumption prediction model is trained. Before identifying the variation patterns of unit clinker energy consumption, grinding power consumption, and vertical mill power consumption under different production operating conditions through the energy prediction model, the method further includes: Construct an input variable vector using the historical energy consumption data, the historical operating condition data, and the equipment feature data; Construct an input variable system for an energy consumption prediction model that covers all energy consumption stages of the plant; During the training process, the unit clinker energy consumption, grinding power consumption and vertical mill power consumption were used as target variables to construct a multi-objective regression model structure, and an adaptive learning rate optimization algorithm with an early stopping mechanism was used for training to avoid overfitting. The deployed and trained energy consumption prediction model, based on historical energy consumption data, historical operating condition data and equipment characteristic data at any given moment, outputs the predicted values ​​of unit clinker energy consumption, grinding power consumption and vertical mill power consumption at any given moment through a forward inference 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 process involves using historical energy consumption data, historical operating condition data, and equipment characteristic data at any given moment to output predicted values ​​for unit clinker energy consumption, grinding power consumption, and vertical mill power consumption at that moment through a forward inference process. This process identifies the changing patterns of energy consumption indicators under given operating conditions, specifically including: The historical energy consumption data, historical operating condition data, and equipment feature data of the time to be predicted are standardized and constructed into an input variable vector. The dimension of the input variable vector is kept consistent with that of the training phase, and the sliding window structure of the input variables in the time dimension is retained. During the forward inference process, the input variable vector passes through several convolutional and pooling layers of the convolutional neural network in sequence to extract the trend features of change within a local time segment. The convolutional kernel slides along the time dimension to extract the first and second derivative approximate expressions, 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 dependence of different dimensions in the input variable vector between the past and the present time through its gating mechanism. The forget 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 predicted unit clinker energy consumption, the predicted grinding power consumption, and the predicted vertical mill power consumption 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 process of dynamically adjusting production parameters by combining real-time collected operating condition data and real-time energy consumption data using a multivariate regression algorithm specifically includes: Based on the energy monitoring equipment deployed in the cement production system, the real-time operating condition data and real-time energy consumption data of each of the key energy consumption nodes are obtained. The real-time operating condition data and the real-time energy consumption data are timestamped to construct a real-time input variable vector with a unified format. After the real-time input variable vector is constructed, the deployed energy consumption prediction model is called to perform forward inference to calculate the predicted value of actual unit clinker energy consumption, actual grinding power consumption, and actual vertical mill power consumption at the current moment. The predicted energy consumption per unit of clinker, the predicted power consumption of actual grinding, and the predicted power consumption of actual vertical mill are combined to form an energy consumption index vector. This vector is then substituted into a 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 value is determined by a preset energy consumption interval clustering model.

5. The energy optimization management method for cement production according to claim 1, characterized in that, The time-series optimized structure stores the historical energy consumption data and historical operating condition data of cement production using a time-domain alignment strategy, specifically including: A time series data model is constructed, which uses timestamps as the main 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. Time standardization processing is performed on the data streams generated by each of the energy monitoring devices, specifically including time interpolation processing, sampling frequency normalization processing, and data window reconstruction processing. The time-standardized data stream is 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 data and historical operating condition data collected by the energy monitoring equipment, realizing synchronous encapsulation under the time axis.

6. The energy optimization management method for cement production according to claim 1, characterized in that, According to the cement production process, suitable energy monitoring equipment is deployed at key production stages to achieve coverage and data collection of key energy consumption nodes throughout the plant, thereby acquiring various types of historical energy consumption data, specifically including: Based on the complete cement production line process diagram, process nodes are identified and energy consumption correlation analysis is performed to 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. All deployed energy monitoring equipment is connected to the edge acquisition terminal. The edge acquisition terminal acquires and transmits historical energy consumption data in real time from energy monitoring devices of different types.

7. An energy optimization management device for cement production, characterized in that, The device is used to execute an energy optimization management method for cement production as described in any one of claims 1-6, the device comprising an acquisition module (201), a processing module (202), and an output module (203), wherein: The acquisition module (201) is used to deploy suitable energy monitoring equipment in key production links according to the cement production process, so as to achieve coverage and collection of key energy consumption nodes throughout the plant, thereby acquiring and collecting various 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 based on the time series optimized structure and the time domain alignment strategy. 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, combined with the equipment characteristic data of cement production equipment, using the equipment characteristic data as input variables, and to 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. 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, perform energy consumption interval clustering on full-cycle data, and delineate different energy consumption levels. The output module (203) is used to combine real-time operating condition data and real-time energy consumption data to determine the corresponding energy consumption level, and then dynamically adjust the production parameters through a multivariate regression algorithm.

8. An electronic device, characterized in that, The device includes a processor (301), a communication bus (302), a user interface (303), a network interface (304), and a memory (305). 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 the connection and communication between the components within the electronic device. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device performs the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-6.

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