Intelligent operation judgment system for cement kiln equipment

Abstract

The invention discloses an intelligent operation judgment system for cement kiln equipment. The intelligent operation judgment system comprises a knowledge base unit, a data acquisition unit, a data processing unit, an intelligent analysis unit, a decision execution unit, a feedback optimization unit and a man-machine cooperation unit, the knowledge base unit stores historical fault cases, process rules and expert experience and supports root cause reasoning; the data acquisition unit acquires equipment operation full-dimension data in real time through sensor acquisition, visual perception acquisition and process parameter acquisition; and the data processing unit is used for performing low-delay data processing and lightweight model reasoning, preprocessing the data, performing real-time anomaly detection and executing a preset process rule. The system is reasonable in design, through real-time data acquisition, intelligent analysis and predictive maintenance, the production process is optimized, the energy consumption is reduced, the fault downtime is shortened, and through'sensing-transmission-analysis-decision 'closed-loop management, the production efficiency and safety are remarkably improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation of cement kilns, and in particular to an intelligent operation judgment system for cement kiln equipment. Background Art

[0002] Cement kilns are core equipment in cement production, primarily used to calcine cement clinker. Their technological development and application span a wide range of fields, including building materials, metallurgy, and chemicals.

[0003] Currently, traditional cement kiln control systems rely heavily on manual experience or simple automation. For example, adjustments to key parameters like kiln temperature and air volume rely primarily on visual inspection of flame shape (e.g., "black flame" length) and clinker granulation. Differences in operation between different shifts lead to energy consumption fluctuations of up to 8%-10%. Raw material yields rely primarily on sampling and XRF analysis at regular intervals. This delayed feedback leads to delayed batching adjustments, and clinker f-CaO levels often exceed the specified limit by over 15%, hindering optimization of operating parameters. Furthermore, equipment maintenance is typically scheduled, such as a fixed eight-month replacement cycle for refractory bricks (with an actual remaining lifespan of up to 30%). This increases maintenance costs and makes it difficult to predict equipment failures and optimize operating parameters. This makes equipment prone to failures and unplanned downtime. For example, kiln support wheel bearing overheating can take an average of 12 hours to resolve, impacting production schedules. The cement industry is energy-intensive, requiring optimization of the combustion process to reduce energy consumption and carbon emissions to meet environmental requirements. In order to solve the above problems, we proposed an intelligent operation judgment system for cement kiln equipment. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems existing in the prior art and to propose an intelligent operation judgment system for cement kiln equipment. Through real-time data collection, intelligent analysis and predictive maintenance, it optimizes the production process, reduces energy consumption, and reduces downtime due to faults. Through the "perception-transmission-analysis-decision-making" closed-loop management, it significantly improves production efficiency and safety and reduces maintenance costs.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An intelligent operation judgment system for cement kiln equipment, comprising a knowledge base unit, a data acquisition unit, a data processing unit, an intelligent analysis unit, a decision execution unit, a feedback optimization unit, and a human-machine collaboration unit;

[0007] The knowledge base unit stores historical failure cases, process rules and expert experience to support root cause reasoning;

[0008] The data acquisition unit collects full-dimensional data of equipment operation in real time through sensor acquisition, visual perception acquisition and process parameter acquisition;

[0009] The data processing unit performs low-latency data processing and lightweight model reasoning, pre-processes data, performs real-time anomaly detection and executes preset process rules;

[0010] The intelligent analysis unit performs complex model training, simulation optimization and global decision-making, dynamically updates the kiln body 3D simulation model, simulates thermodynamic and mechanical stress states, and performs fault prediction and generates optimal control strategies;

[0011] The decision execution unit dynamically generates executable optimization instructions, performs multi-objective optimization, balances production, energy consumption, and emission constraints, generates interpretable reports, and implements a manual confirmation mechanism;

[0012] The feedback optimization unit performs closed-loop iteration of system performance, performs online learning, and performs simulation verification;

[0013] The human-machine collaboration unit is designed with an intelligent interface to allow manual intervention and feedback to the feedback optimization unit.

[0014] Preferably, the data acquisition unit includes a high-temperature-resistant and dust-proof optical fiber sensor, an infrared thermal imager, a thermocouple, a cylinder scanner, a vibration sensor, a gas analyzer, and a laser particle size analyzer.

[0015] Preferably, the data acquisition unit also includes an inspection robot equipped with infrared thermal imaging and laser radar.

[0016] Preferably, the human-machine collaboration unit can perform AR remote assistance.

[0017] Preferably, the knowledge base unit converts the operation strategies of industry experts into a rule engine to achieve operation standardization.

[0018] Preferably, the cement kiln equipment intelligent operation judgment system transmits data through LoRa and Wi-Fi wireless communication technologies.

[0019] Preferably, the intelligent operation judgment system for cement kiln equipment performs data storage, analysis and visualization based on a cloud platform, supports multi-terminal access via the Web and mobile APP, and provides real-time temperature distribution, trend analysis, and alarm management functions.

[0020] Preferably, the data transmission in the system adopts AES-256 encryption algorithm, and industrial firewalls and intrusion detection systems are deployed.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] This intelligent operation judgment system for cement kiln equipment can collect full-dimensional data on equipment operation in real time, monitor the data for real-time anomalies and execute preset rules. It can also perform complex model training, build a 3D simulation model of the kiln body, simulate thermodynamic and mechanical stress states, predict component life and failures, store historical failure cases, process rules and expert experience, support root cause reasoning, balance constraints such as output, energy consumption, and emissions, perform multi-objective optimization, and perform simulation verification. It can test the safety and economy of new strategies in digital twins and then put them into practical application, realizing a full-chain closed loop from "data-driven monitoring" to "intelligent decision-making and control", providing core support for the efficient, safe and green operation of cement kilns. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a structural diagram of a cement kiln equipment intelligent operation judgment system proposed by the present invention;

[0024] Figure 2 This is a flow chart of an intelligent operation judgment system for cement kiln equipment proposed by the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0026] Reference Figure 1-Figure 2 , an intelligent operation judgment system for cement kiln equipment, including a knowledge base unit, a data acquisition unit, a data processing unit, an intelligent analysis unit, a decision execution unit, a feedback optimization unit and a human-machine collaboration unit;

[0027] The knowledge base unit stores historical failure cases, process rules and expert experience, supports root cause reasoning, establishes industry standards such as data interfaces and model evaluation, promotes system interconnection, and can convert the operating strategies of industry experts into rule engines to achieve operational standardization.

[0028] The data acquisition unit collects data from all dimensions of equipment operation in real time through sensor acquisition, visual perception acquisition, and process parameter acquisition. Sensor acquisition includes high-temperature and dust-resistant fiber optic sensors, infrared thermal imagers, thermocouples, drum scanners, vibration sensors, gas analyzers, laser particle size analyzers, and more. Visual perception acquisition uses industrial cameras to capture the flame shape, kiln lining status, and material distribution within the kiln. Process parameter acquisition includes obtaining control parameters such as kiln speed, feed rate, and air volume from DCS / SCADA. Real-time data collection includes kiln temperature, pressure, rotation speed, gas composition (such as CO and NOx), vibration, refractory brick erosion, and raw material fineness.

[0029] Specifically, infrared thermal imagers at the kiln head monitor refractory brick erosion, gas analyzers at the kiln tail monitor SO2 and NOx emissions, drum scanners monitor temperature distribution, vibration sensors monitor bearing health, and laser particle size analyzers measure raw meal fineness. These systems synchronize data at the second level. Real-time monitoring and automatic adjustment of emission data ensure 100% compliance with NOx and dust emission standards, meeting environmental regulations. Vibration and oil analysis can predict equipment failures, facilitating operation and maintenance.

[0030] The data collection unit also includes an inspection robot equipped with infrared thermal imaging and lidar, which can automatically identify hidden dangers of kiln deformation and pipeline leakage, saving labor costs. The data collection unit can also deploy temperature and humidity sensors, dust concentration meters, and weather stations to perceive the impact of the external environment on kiln operation, such as changes in the moisture content of raw materials on rainy days.

[0031] The data processing unit performs low-latency data processing and lightweight model reasoning, pre-processes the data, performs real-time anomaly detection and executes preset process rules. Specifically, the data processing unit deploys an edge computing gateway on the device side, combines industrial camera visual data (such as kiln lining status, material flow) with sensor data, performs data cleaning, time series alignment and feature extraction, reduces data transmission volume, and performs real-time analysis of high-frequency data such as vibration and temperature to reduce data return delay and improve data reliability. Perform real-time anomaly detection and trigger instant alarms based on TinyML's lightweight model (such as a compressed version of LSTM). Execute the preset rules of the knowledge base unit, such as automatically adjusting the fan speed when the temperature exceeds the limit. Automatically identify abnormal patterns (such as ring formation and air leakage) and reduce manual intervention, which can improve efficiency and reduce unplanned downtime.

[0032] The intelligent analysis unit conducts complex model training, simulation optimization, and global decision-making, dynamically updates the 3D simulation model of the kiln body, simulates thermodynamic and mechanical stress states, and predicts faults and generates optimal control strategies. Specifically, the intelligent analysis unit combines digital twin technology to establish a three-dimensional dynamic model and thermodynamic simulation system for the kiln temperature field, airflow field, and material movement. Using data transmitted by the data processing unit and the three-dimensional modeling established by the intelligent analysis unit, it constructs a real-time digital mirror of equipment such as the kiln, preheater, and grate cooler, achieving synchronization between physical entities and virtual models. It also simulates different operating conditions in a virtual environment, verifies optimization strategies in advance, and uses deep learning and machine learning for anomaly detection and fault prediction. For example, Transformer or LSTM networks are used to analyze equipment runtime data to identify early anomalies (such as kiln shell deformation and refractory material wear). Graph neural networks (GNNs) are used to build equipment correlation maps to locate the source of faults (such as preheater blockage leading to abnormal kiln tail temperature). Physical degradation models and deep learning are integrated to predict the life of key components (such as kiln tires and rollers) and achieve precise maintenance scheduling.

[0033] Among them, the exception handling logic is: the data processing unit detects a transient abnormality (such as a sudden temperature rise), triggering an emergency kiln speed reduction or shutdown protection, and then the intelligent analysis unit identifies potential faults (such as refractory material wear), and pushes maintenance recommendations and adjusts long-term production plans.

[0034] The intelligent analysis unit can also perform real-time threshold alarms. For example, when the bearing temperature exceeds the set threshold (such as 85°C) or the vibration intensity is greater than 11.2mm / s, the edge gateway directly triggers a local sound and light alarm with a response time of less than 200ms.

[0035] The decision execution unit dynamically generates executable optimization instructions, performs multi-objective optimization, balances constraints such as output, energy consumption, and emissions, generates explainable reports, and implements a manual confirmation mechanism. The decision execution unit sets multi-level thresholds and automatically determines and adjusts equipment parameters based on the industry standards of the knowledge base unit and the analysis results of the intelligent analysis unit. XAI generates visual diagrams and natural language explanations of the causes of faults. After the operator reviews the AI suggestions, they are sent to the DCS system to avoid blind automation. Exception information can also be pushed via SMS and APP.

[0036] The feedback optimization unit iterates system performance in a closed loop, conducts online learning, and performs simulation verification. The model is updated incrementally based on actual control results and new data, enabling rapid adaptation to different production lines and fuel types. For example, the combustion process is optimized by using deep reinforcement learning (DRL) to dynamically adjust parameters such as pulverized coal fineness and primary air volume to achieve a balance between low NOx emissions and high thermal efficiency.

[0037] The Human-Machine Collaboration Unit features an intelligent interface, allowing for manual intervention and providing feedback to the Feedback Optimization Unit. This HAI interface displays the real-time kiln temperature field and material movement trajectory on a large screen in the central control room. Key parameters are prominently highlighted (e.g., red alert temperature >1450°C) and intelligent operational suggestions are provided. For example, when the system detects "negative pressure fluctuations at the kiln head exceeding ±100 Pa for 5 minutes," an action prompt (e.g., "Recommend checking the kiln tail seal for air leaks") automatically pops up, reducing manual error. Manual intervention and feedback to the Feedback Optimization Unit are also permitted. Control commands are executed after manual confirmation, and execution data is fed back to the system, triggering model retraining. The unit can also automatically switch to manual decision-making mode when AI confidence is low, mitigating the risk of operational errors. The HAI automatically generates maintenance plans, such as "Replace a roller bearing in 3 days," and pushes them to maintenance personnel via an app, achieving work order accuracy exceeding 95%. The HAI also enables AR remote assistance. Maintenance personnel use AR glasses to access a 3D model of the equipment and historical fault records, allowing remote expert guidance on repairs.

[0038] The intelligent operation judgment system of cement kiln equipment transmits data through wireless communication technologies such as LoRa and Wi-Fi. It has low power consumption and long-distance transmission characteristics and can adapt to the high temperature and complex environment of cement kilns.

[0039] The intelligent operation judgment system for cement kiln equipment performs data storage, analysis and visualization based on a cloud platform. It supports multi-terminal access such as the Web and mobile APP, and provides functions such as real-time temperature distribution, trend analysis, and alarm management.

[0040] The system uses the AES-256 encryption algorithm for data transmission to ensure secure transmission of sensor data from the edge to the cloud. Industrial firewalls and intrusion detection systems (IDS) are deployed to defend against malicious attacks against the DCS system.

[0041] The following example scenario further illustrates the intelligent operation judgment system for cement kiln equipment:

[0042] Example scenario 1: Kiln status monitoring

[0043] Data acquisition unit: The thermal imaging system displays the kiln head flame shape, material movement trajectory, ring formation position and refractory material erosion in real time, and supports temperature point query and video recording.

[0044] Data processing unit: Analyzes the combustion uniformity in the kiln, avoids over-burning or under-burning problems, and optimizes the thermal system.

[0045] Intelligent Analysis Unit: Utilizes 3D modeling technology to analyze kiln wall temperature distribution, predict changes in refractory thickness and potential shedding risks, implement preventive maintenance, integrate vibration, sound, current and other data, and use AI algorithms to determine equipment operating status (such as bearing wear and motor abnormalities), reduce unplanned downtime, and detect CO, NOx and other components in kiln exhaust gas in real time to ensure environmental compliance, and link with government regulatory platforms.

[0046] Decision-making execution unit: Set thresholds for temperature, vibration, etc., automatically alarm and push notifications to management personnel when abnormalities occur, and support remote parameter adjustment.

[0047] Feedback optimization unit: Builds temperature field models and combustion optimization algorithms based on historical data, reducing energy consumption by 10%-15% and improving clinker quality stability.

[0048] Example Scenario 2: Handling Abnormal Kiln Tail Temperature

[0049] Data acquisition unit: The infrared thermal imager detects an abnormal increase in the temperature at the kiln tail, and the vibration sensor simultaneously alarms.

[0050] Data processing unit: Preliminary judgment is local overheating, triggering the fan to speed up and cool down.

[0051] Intelligent Analysis Unit: Digital twin simulation revealed that a clogged preheater caused exhaust gas to stagnate, and GNN traced the source to confirm the root cause.

[0052] Decision-making execution unit: recommends reducing the feed rate and arranging a shutdown and cleaning plan, and XAI generates a blockage location diagram.

[0053] Human-machine collaboration unit: The operator confirms and executes the production reduction instruction, and the maintenance team receives the work order.

[0054] Feedback optimization unit: After cleaning, the data is fed back and the model updates the congestion prediction threshold.

[0055] Example scenario three: Kiln lining abnormality diagnosis and repair

[0056] Data acquisition unit: The infrared thermal imager detected an abnormal temperature increase in a certain area of the kiln body (1480℃, normal 1420±20℃), and the vibration sensor simultaneously captured the periodic impact signal.

[0057] Data processing unit: calculates the temperature gradient (ΔT=60℃ / 10min) and vibration kurtosis value (>3.5), marks it as "suspected kiln lining peeling", and triggers data encryption upload.

[0058] Intelligent analysis unit: The digital twin model simulates the impact of kiln lining shedding on cylinder stress. The knowledge graph associates the causal chain of "thinning of the kiln lining → increased cylinder temperature → uneven force on the support rollers" and determines it to be a Level III fault.

[0059] Decision-making execution unit: Automatically issues instructions to reduce the kiln speed by 3%, increase the cooling air volume in the area, and simultaneously pushes the work order to the inspection robot. It completes on-site infrared scanning verification within 30 minutes and initiates the kiln lining re-hanging procedure after confirmation.

[0060] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A cement kiln equipment intelligent operation judgment system, characterized in that: It includes knowledge base unit, data acquisition unit, data processing unit, intelligent analysis unit, decision execution unit, feedback optimization unit and human-machine collaboration unit; The knowledge base unit stores historical failure cases, process rules and expert experience to support root cause reasoning; The data acquisition unit collects full-dimensional data of equipment operation in real time through sensor acquisition, visual perception acquisition and process parameter acquisition; The data processing unit performs low-latency data processing and lightweight model reasoning, pre-processes data, performs real-time anomaly detection and executes preset process rules; The intelligent analysis unit performs complex model training, simulation optimization and global decision-making, dynamically updates the kiln body 3D simulation model, simulates thermodynamic and mechanical stress states, and performs fault prediction and generates optimal control strategies; The decision execution unit dynamically generates executable optimization instructions, performs multi-objective optimization, balances production, energy consumption, and emission constraints, generates interpretable reports, and implements a manual confirmation mechanism; The feedback optimization unit performs closed-loop iteration of system performance, performs online learning, and performs simulation verification; The human-machine collaboration unit is designed with an intelligent interface to allow manual intervention and feedback to the feedback optimization unit.

2. The intelligent operation judgment system for cement kiln equipment according to claim 1 is characterized in that: The data acquisition unit includes a high-temperature-resistant and dust-proof optical fiber sensor, an infrared thermal imager, a thermocouple, a cylinder scanner, a vibration sensor, a gas analyzer, and a laser particle size analyzer.

3. The intelligent operation judgment system for cement kiln equipment according to claim 1 is characterized in that: The data acquisition unit also includes an inspection robot equipped with infrared thermal imaging and laser radar.

4. The intelligent operation judgment system for cement kiln equipment according to claim 1 is characterized in that: The human-machine collaboration unit can perform AR remote assistance.

5. The intelligent operation judgment system for cement kiln equipment according to claim 1 is characterized in that: The knowledge base unit converts the operation strategies of industry experts into a rule engine to achieve operation standardization.

6. The intelligent operation judgment system for cement kiln equipment according to claim 1 is characterized in that: The cement kiln equipment intelligent operation judgment system transmits data through LoRa and Wi-Fi wireless communication technologies.

7. The intelligent operation judgment system for cement kiln equipment according to claim 1 is characterized in that: The intelligent operation judgment system for cement kiln equipment performs data storage, analysis and visualization based on a cloud platform, supports multi-terminal access via the Web and mobile APP, and provides real-time temperature distribution, trend analysis, and alarm management functions.

8. The intelligent operation judgment system for cement kiln equipment according to claim 1 is characterized in that: The data transmission in the system adopts AES-256 encryption algorithm, and industrial firewalls and intrusion detection systems are deployed.

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