Prediction and control method for free calcium in cement kiln

Abstract

The invention discloses a method for predicting and controlling free calcium of a cement kiln, which belongs to the technical field of production quality detection and control of the cement kiln, and is characterized in that core process characteristics and multi-source quality data are acquired through an APC expert optimization control system and a quality management system, and a data preprocessing technology based on a process mechanism is adopted; dividing the features into 10 time delay groups according to process time delay characteristics, performing time alignment processing, performing smoothing processing on the data by using a unilateral Gaussian filter, and constructing a 60-minute statistical window to generate a training sample; constructing a prediction model by adopting a sequential Transform deep learning algorithm, and capturing a long-term dependency relationship among process parameters through a multi-head self-attention mechanism; and deploying the trained model to realize real-time prediction of the content of free calcium once every five minutes, generating an intelligent control strategy according to a prediction result, and executing the intelligent control strategy through an APC system. The prediction precision of the free calcium content is improved, compared with a traditional method, the prediction error is reduced, and the cement product quality is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of cement kiln production quality detection and control technology. Specifically, this invention relates to a method for predicting and controlling free calcium in cement kilns. Background Technology

[0002] In cement production, the free calcium (f-CaO) content is a crucial indicator of clinker calcination quality. Excessive free calcium content leads to poor cement stability, affecting the volume stability and durability of concrete; conversely, insufficient content indicates incomplete calcination, hindering cement strength development. Therefore, precise control of free calcium content is essential for improving cement product quality.

[0003] Chinese Patent 112365935A discloses a soft measurement method for free calcium in cement based on a multi-scale deep network. The specific method involves: analyzing the cement production process, selecting process variables related to the f-CaO content in cement clinker, and determining the auxiliary variables required for the soft measurement model; using the Laida criterion to mark outliers in each auxiliary variable, and replacing outliers and missing values ​​with the mean of the auxiliary variable; performing three-level wavelet packet decomposition on the auxiliary variables and extracting real-time features; feeding the extracted real-time features into an LSTM model and training the model, and correcting the model parameters using an error backpropagation algorithm; and using the trained LSTM model to predict the f-CaO content.

[0004] Existing technologies include several cement quality prediction techniques based on statistical methods and traditional machine learning algorithms. However, these methods typically rely on only one or a few process parameters, resulting in limited prediction accuracy. Some studies have employed neural networks for cement production process quality prediction, but these often utilize traditional feedforward neural network or recurrent neural network structures, which suffer from the gradient vanishing problem when processing long sequences. In recent years, although some research has begun to use recurrent neural networks such as LSTM for time series prediction, limitations remain in capturing long-term dependencies and achieving high parallel computing efficiency, making it difficult to meet the real-time and accuracy requirements of industrial production. Summary of the Invention

[0005] The present invention aims to provide a method for predicting and controlling free calcium in cement kilns, so as to achieve the technical objective of predicting free calcium content and accurately controlling the free calcium content in clinker.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides a method for predicting and controlling free calcium in cement kilns:

[0008] Step 1: The computer collects process parameter data, raw meal composition data, and clinker quality data in real time during the cement kiln production process;

[0009] Step 2: Perform data preprocessing on the collected data;

[0010] Step 3: Train the free calcium prediction model using a temporal Transformer deep learning algorithm based on a multi-head self-attention mechanism;

[0011] Step 4: Deploy the trained model to the production line and predict the free calcium content at regular intervals;

[0012] Step 5: The production line generates and executes a control strategy based on the free calcium prediction results.

[0013] In step one, the computer collects process parameter data during the cement kiln production process through the expert optimization control system, and collects raw material composition data and clinker quality data through the quality management system.

[0014] In step two, the computer sequentially performs data merging, time alignment, data matching, and data filtering on the collected process parameter data, raw material composition data, and clinker quality data to generate sample data for model training.

[0015] Sample data generation uses a 60-minute statistical window, which statistically analyzes the characteristic aggregate values ​​of process parameter data and raw material composition data within the window interval before the free calcium detection time.

[0016] In step two, the computer standardizes the data using the Z-score standardization method.

[0017] In step three, the temporalized Transformer neural network structure includes an input embedding layer, a position encoding layer, a multi-head self-attention layer, a feedforward neural network layer, layer normalization and residual connections, and a regression output layer.

[0018] The multi-head self-attention layer contains 8 attention heads.

[0019] The computer periodically performs incremental learning on the free calcium prediction model using new sample data.

[0020] When three consecutive free calcium prediction results show abnormal free calcium content in clinker, the model triggers an early warning signal, and at the same time, the production line experts optimize the control system to execute corresponding control actions.

[0021] The technical effects of this invention are as follows:

[0022] (1) This invention improves the prediction accuracy of free calcium in clinker. By using the key feature values ​​of 9 process parameters and the time-series Transformer deep learning algorithm, the prediction accuracy of free calcium content is significantly improved. Compared with traditional methods, the prediction error is reduced, and compared with LSTM model, the prediction accuracy is improved.

[0023] (2) This invention enhances the ability to model long-term dependencies. The self-attention mechanism of the Transformer architecture can effectively capture long-term dependencies in time series data, overcoming the gradient vanishing problem of traditional RNN models.

[0024] (3) This invention improves computational efficiency. The parallelization of the Transformer model significantly improves training and inference efficiency, and the training time is shortened compared to the LSTM model.

[0025] (4) The present invention realizes real-time control of cement kiln and predicts free calcium content every 5 minutes. Compared with the traditional detection cycle of 1-2 hours, it greatly improves the real-time control.

[0026] (5) This invention enhances system stability by improving the model’s adaptability to different working conditions and the stability of prediction through multi-head attention mechanism and multi-parameter comprehensive analysis.

[0027] (6) The present invention reduces energy consumption costs, and the precise prediction and control avoids over-calcination, effectively reducing energy consumption and improving economic benefits.

[0028] (7) The present invention reduces reliance on manual labor. Automated predictive control reduces reliance on operator experience and improves the objectivity and consistency of control. Attached Figure Description

[0029] This manual includes the following figures, which illustrate the following:

[0030] Figure 1 This is a flowchart of a method for predicting and controlling free calcium in cement kilns according to the present invention;

[0031] Figure 2 This is a schematic diagram of the time alignment of process parameters for a method for predicting and controlling free calcium in cement kilns according to the present invention.

[0032] Figure 3 This is a schematic diagram of a time-series Transformer neural network structure for predicting and controlling free calcium in cement kilns according to the present invention.

[0033] Figure 4 This is a diagram of the architecture of a real-time predictive control system for a method of predicting and controlling free calcium in cement kilns according to the present invention.

[0034] Figure 5 This is a flowchart illustrating the control strategy decision-making process for a method of predicting and controlling free calcium in cement kilns, as described in this invention. Detailed Implementation

[0035] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, in order to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention, and to facilitate its implementation.

[0036] This invention provides a method for predicting and controlling free calcium in cement kilns:

[0037] Step 1: The computer collects process parameter data, raw meal composition data, and clinker quality data in real time during the cement kiln production process;

[0038] Step 2: Perform data preprocessing on the collected data;

[0039] Step 3: Train the free calcium prediction model using a temporal Transformer deep learning algorithm based on a multi-head self-attention mechanism;

[0040] Step 4: Deploy the trained model to the production line and predict the free calcium content at regular intervals;

[0041] Step 5: The production line generates and executes a control strategy based on the free calcium prediction results.

[0042] In step one, the computer collects process parameter data during the cement kiln production process through the expert optimization control system, and collects raw material composition data and clinker quality data through the quality management system.

[0043] In step two, the computer sequentially performs data merging, time alignment, data matching, and data filtering on the collected process parameter data, raw meal composition data, and clinker quality data to generate sample data for model training. The sample data generation uses a 60-minute statistical window, statistically analyzing the aggregated characteristic values ​​of the process parameter data and raw meal composition data within the window period before the free calcium detection time.

[0044] In step two, the computer standardizes the data using the Z-score standardization method.

[0045] In step three, the temporalized Transformer neural network structure includes an input embedding layer, a position encoding layer, a multi-head self-attention layer, a feedforward neural network layer, layer normalization and residual connections, and a regression output layer. The multi-head self-attention layer contains eight attention heads.

[0046] The computer periodically performs incremental learning on the free calcium prediction model using new sample data. When three consecutive free calcium predictions indicate abnormal free calcium content in the clinker, the model issues an early warning signal, and production line experts simultaneously optimize the control system to execute corresponding control actions.

[0047] The present invention provides a method for predicting and controlling free calcium in cement kilns.

[0048] The computer collects process parameter data, raw meal composition data, and clinker quality data in real time during the cement kiln production process. Specifically, the computer collects process parameter data during the cement kiln production process through an Expert Optimization Control System (APC). The process parameter data used for model training consists of the characteristic values ​​of nine process parameters, including the kiln feed rate feedback value, decomposer scale pressure, kiln head scale pressure, grate cooler layer pressure, kiln head hood negative pressure, decomposer layer 5 temperature, secondary air temperature, tertiary air temperature, and kiln main transmission current. The computer collects raw meal composition data and clinker quality data through a quality management system. The raw meal composition data includes raw meal CAO content, raw meal SiO2 content, raw meal Fe2O3 content, raw meal Al2O3 content, raw meal SM (the ratio of SiO2 content to the sum of Al2O3 and Fe2O3), raw meal IM (the ratio of Al2O3 content to Fe2O3 content), and raw meal KH (cement raw meal saturation ratio). In this invention, the clinker quality data refers to the free calcium content in the clinker. The data acquisition frequency of this invention is once every 10 seconds, and the established historical database contains 6 months of production data.

[0049] The computer performs data preprocessing on the collected data, including data merging, time alignment, data matching, data filtering, and sample generation. Data merging involves combining multiple CSV files obtained from the expert optimization control system and merging raw material and clinker data from the quality management system separately. Time alignment involves dividing the raw materials into 10 groups based on the time lag characteristics of the process, according to the time lag of the raw materials arriving at each process stage: kiln feed group, coal control group, high-temperature blower group, preheater group, kiln body group, kiln head group, grate cooler stage 1 group, grate cooler stage 2 group, and auxiliary parameter group. In an embodiment of the present invention, the time delay of the kiln feeding group is 0s, the time delay of the coal control group is 90s, the time delay of the high-temperature fan group is 120s, the time delay of the decomposition furnace group is 150s, the time delay of the preheater group is 180s, the time delay of the kiln body group is 195s, the time delay of the kiln head group is 1695s, the time delay of the first stage of the grate cooler group is 2595s, and the time delay of the second stage of the grate cooler group is 3495s. The process parameter data is then aligned with these 10 groups. Figure 2As shown. Data matching involves matching raw material data and clinker data to the aforementioned groups according to the production process logic. Specifically, raw material composition data is matched to the time closest to the kiln feeding group, and clinker quality data is matched to the time closest to the grate cooler stage 2 group. Data filtering involves using a one-sided Gaussian filter to perform online smoothing on the time-aligned data. In this invention, the window size for the kiln main transmission current is 120 seconds, the window size for the secondary and tertiary air temperatures is 20 seconds, the window size for the five layers of the decomposer temperature is 30 seconds, and the window size for the decomposer scale pressure, kiln head scale pressure, grate cooler layer pressure, and kiln head hood negative pressure is 20 seconds. Finally, samples are generated based on a 60-minute statistical window to generate training sample data.

[0050] A temporal Transformer deep learning algorithm based on a multi-head self-attention mechanism was used to train the free calcium prediction model. The temporal Transformer neural network structure based on the multi-head self-attention mechanism included an input embedding layer, a position encoding layer, a multi-head self-attention layer, a feedforward neural network layer, layer normalization and residual connections, and a regression output layer. The input embedding layer mapped the temporal data of the feature values ​​of the nine process parameters and the feature values ​​of the seven raw material components to a linear transformation. dimensional feature space, =128. The position coding layer adds position information to the time-series data, using sine and cosine position coding, with the formula:

[0051]

[0052]

[0053] Where PE is the position code; pos is the position index of the element in the sequence; 2i is the even-numbered dimension index of the position code; 2i+1 is the odd-numbered dimension index of the position code; 1000 is a fixed constant used to scale the frequency; and 128 is the total dimension of the position code.

[0054] The multi-head self-attention layer, comprising an 8-head self-attention mechanism, is used to capture long-term dependencies between temporal features. The calculation formula for the multi-head self-attention mechanism is as follows:

[0055]

[0056]

[0057]

[0058] Where Q is the query, used as the input for retrieving information; K is the key, used to match the query (Q) and calculate the attention weight coefficients; V is the value, the information finally weighted by the attention weights, and the source of the attention mechanism's output; MultiHead(Q,K,V) is the final output of the multi-head self-attention mechanism. Concat is the concatenation operation, concatenating the attention outputs of multiple heads into a tensor; head1, head2, ..., head are the attention outputs of each head; W 0 Output a weight matrix and perform a linear transformation on the concatenated result of multiple heads. W i Q W i K W i V Let QK be the linear transformation matrix of the query, key, and value corresponding to the i-th attention head. T The original matrix of attention scores is obtained by multiplying the query matrix by the transpose of the key matrix; d k Let K be the dimension of the key (K); softmax is the normalization function.

[0059] The feedforward neural network layer consists of two fully connected layers with 512 hidden layers. Normalization and residual connections are implemented by adding residual connections and layer normalization after each sub-layer. The regression output layer outputs the predicted free calcium content through a fully connected layer. The first layer (128→512) uses the ReLU activation function; the second layer (512→128) uses linear activation; and the first layer (128→1) is the output layer, used to output the predicted free calcium content.

[0060] Model training utilizes training sample data, which includes at least 1000 sets of feature values ​​for process parameters and raw material composition, along with corresponding labeled data on the free calcium content of the clinker. The model training employs the AdamW optimizer with a learning rate of 0.0001, a cosine annealing learning rate scheduling strategy, a batch size of 64, and 200-800 training epochs. A Dropout mechanism is used to prevent overfitting, with a Dropout rate of 0.1.

[0061] Model validation employs cross-validation to evaluate model performance and ensure that prediction accuracy meets industrial application requirements; the mean absolute error (MAE) is required to be ≤0.15%, and the root mean square error (RMSE) ≤0.25%. In the embodiments of this invention, the mean absolute error after model validation is 0.12%, and the root mean square error is 0.19%.

[0062] The trained model is deployed to the production line, and the free calcium content is predicted every 5 minutes. Specifically, the model trained in the training center is deployed to the inference server at the edge of the production line. Every 5 minutes, the required data within the previous 60 minutes is obtained. After time alignment, data matching, and Gaussian filtering according to the data preprocessing workflow, the data is input into the model for inference prediction of the free calcium content in clinker.

[0063] The model predictions are combined with process mechanisms and production operation experience to generate control strategies, thereby recommending optimal parameters. These recommendations are then pushed to the Expert Optimization Control System (APC) for execution. Specifically, based on the deviation between the predicted free calcium content in the clinker and the target threshold, two operational scenarios are considered: when the predicted free calcium content is high, control actions are executed to increase the kiln head temperature; when the predicted free calcium content is low, control actions are executed to decrease the kiln head temperature. Simultaneously, the coordinated adjustment of secondary air volume and kiln speed is considered. In an embodiment of this invention, when the predicted free calcium content is >1.2%, the coal feed rate to the decomposer and kiln head is increased by adjusting the pressure of the decomposer scale and kiln head scale; when the predicted free calcium content is <0.8%, the coal feed rate to the decomposer and kiln head is decreased by adjusting the pressure of the decomposer scale and kiln head scale. When three consecutive free calcium predictions show an abnormal free calcium content in the clinker, i.e., a predicted free calcium value greater than 1.2% or less than 0.8%, the production line expert optimization control system issues a warning signal to alert the operators. At the same time, the corresponding control actions are sent to the DCS equipment for execution.

[0064] The beneficial effects of the present invention are described in detail below.

[0065] This invention integrates nine process parameters from the Expert Optimization Control System (APC) with seven raw material composition data and one clinker free calcium data from the Quality Management System through multi-source data integration and time-series alignment. The data is then divided into ten parameter groups based on the time lag characteristics of the cement kiln process, achieving precise time-series matching of the entire process. This solves the problem of data mismatch between traditional data acquisition methods and actual production processes caused by process lags.

[0066] This invention employs a single-sided Gaussian filter to perform differentiated filtering on different process parameters, effectively eliminating random noise in the production process; at the same time, it generates training samples based on a 60-minute statistical window, which not only preserves the integrity of the time series features but also avoids interference from short-term fluctuations on the model.

[0067] This invention applies a multi-head self-attention mechanism to the prediction of free calcium in cement kilns using a time-series Transformer model based on multi-head self-attention. By using 8 attention heads to capture the long-term time-series correlation between 9 process parameters and 7 raw material composition data in parallel, this invention is better suited to the long-cycle and multi-variable coupling characteristics of cement kiln production, and the prediction accuracy is greatly improved.

[0068] This invention achieves real-time regulation and ensures stable production through closed-loop control and early warning. By employing a dynamic control strategy, it reduces energy consumption and quality fluctuations. Based on the free calcium prediction value output by the model every 5 minutes, the system can automatically generate differentiated control strategies. This closed-loop control can correct production deviations in real time, achieving dual optimization of quality and energy consumption.

[0069] This invention replaces offline detection and improves real-time performance. Traditional clinker free calcium detection requires offline sampling and laboratory analysis, while this solution achieves real-time prediction every 5 minutes, shortening the detection cycle and solving the problem of untimely control caused by detection lag.

[0070] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution; or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. A method for cement kiln free calcium prediction and control, characterized by: Step one: a computer collects process parameter data, raw material composition data and clinker quality data in the production process of a cement kiln in real time; Step two: the collected data is preprocessed; Step three: a time-series Transformer deep learning algorithm based on multi-head self-attention mechanism is used to train a free calcium prediction model; Step four: the trained model is deployed to the production line, and free calcium content prediction is performed every certain period of time; Step five: the production line generates control strategies based on the free calcium prediction results and executes them.

2. A method for cement kiln free calcium prediction and control as claimed in claim 1 wherein: In step one, the computer collects process parameter data in the production process of a cement kiln through an expert optimization control system, and collects raw material composition data and clinker quality data through a quality management system.

3. A method for cement kiln free calcium prediction and control as claimed in claim 1 wherein: In step two, the computer sequentially performs data merging, time alignment, data matching and data filtering on the collected process parameter data, raw material composition data and clinker quality data to generate sample data for model training.

4. A method for cement kiln free calcium prediction and control as claimed in claim 3 wherein: The sample data generation uses a 60-minute statistical window to calculate the characteristic aggregate values of process parameter data and raw material composition data in the window interval before the free calcium detection time.

5. A method for cement kiln free calcium prediction and control as claimed in claim 1 wherein: In step two, the computer standardizes the data by the Z-score standardization method.

6. A method for cement kiln free calcium prediction and control as claimed in claim 1 wherein: In step three, the time-series Transformer neural network structure includes an input embedding layer, a position encoding layer, a multi-head self-attention layer, a feedforward neural network layer, layer normalization and residual connection, and a regression output layer.

7. A method for cement kiln free calcium prediction and control as claimed in claim 6 wherein: The multi-head self-attention layer contains 8 attention heads.

8. A method for cement kiln free calcium prediction and control as claimed in claim 1 wherein: The computer periodically performs incremental learning on the free calcium prediction model with new sample data.

9. A method for cement kiln free calcium prediction and control as claimed in claim 1 wherein: When the free calcium prediction results of three consecutive times show that the free calcium content in the clinker is abnormal, the model sends out a warning signal, and the production line expert optimization control system performs corresponding control actions.

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