Cement energy efficiency and equipment health optimization system based on digital twinning
By constructing a cross-timescale twin model and a reverse twin repair mechanism, an energy efficiency and equipment health optimization system is built, which solves the problem of unified modeling and optimization control under multiple timescales in cement production, achieves a dynamic balance between energy efficiency and equipment health, and improves the overall energy efficiency and equipment life of the production process.
Patent Information
- Application Number
- CN202511300939.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-12
- Publication Date
- 2026-01-30
AI Technical Summary
Existing technologies struggle to achieve unified modeling and optimization control across multiple time scales in cement production. They lack coordination between energy efficiency and equipment health, and model continuity is insufficient in the absence of data, leading to conflicts between energy efficiency optimization and equipment health maintenance.
By employing cross-timescale twin models, inverse twin repair mechanisms, and game-theoretic optimization strategies, an energy efficiency and equipment health optimization system is constructed. Through data acquisition and processing, cross-timescale twin model construction, missing signal inversion repair, and optimized control, a dynamic balance between energy efficiency and equipment health is achieved.
Maintaining model continuity and consistency across different time scales improves overall energy efficiency and equipment lifespan in the production process, reduces specific heat and specific power consumption, and enhances the safety and stability of system operation.
Smart Images
Figure CN121433129A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of cement production process control and optimization technology, and in particular to a cement energy efficiency and equipment health optimization system based on digital twins. Background Technology
[0002] Currently, cement production, as a typical high-energy-consuming and high-equipment-load industry, involves complex production processes and numerous steps. Energy efficiency and equipment health directly impact a company's economic benefits and production safety. Existing technologies often employ data analysis methods based on a single time scale for monitoring and controlling the production process. For example, real-time data collection of operating parameters such as temperature, pressure, and current is combined with empirical models for energy consumption assessment and equipment status judgment. However, these methods often only provide localized optimization for short-term or single-stage processes, failing to reflect the dynamic coupling relationships across different time scales and easily leading to conflicts between energy efficiency indicators and equipment health indicators.
[0003] Existing technologies for handling sensor anomalies and data gaps typically rely on simple data interpolation or experience-based compensation methods. These methods lack accuracy under complex operating conditions, potentially leading to accumulated deviations in key process parameters or equipment operating status, thus reducing the model's reliability and usability. Regarding the relationship between energy efficiency optimization and equipment health maintenance, existing methods often employ a single-objective optimization approach, either prioritizing energy reduction while neglecting equipment wear and tear, or excessively focusing on equipment lifespan at the expense of energy efficiency. A systematic approach that balances both is lacking.
[0004] Existing technologies struggle to achieve unified modeling and optimization control across multiple time scales, lack an effective mechanism for establishing a coordinated relationship between energy efficiency and equipment health, and lack reliable means to maintain model continuity in the event of data loss.
[0005] Therefore, how to provide a cement energy efficiency and equipment health optimization system based on digital twins is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0006] One objective of this invention is to propose a cement energy efficiency and equipment health optimization system based on digital twins. This invention fully utilizes cross-timescale twin models, inverse twin repair mechanisms, and game-theoretic optimization strategies. It details the acquisition and processing of multi-timescale data during cement production, the construction and updating of cross-timescale twin models, the inversion and repair of missing signals, the solution to the balance between energy efficiency and equipment health, and the generation and execution of optimization control strategies. This invention achieves a dynamic balance between energy efficiency optimization and equipment health maintenance, solving the problems of inaccurate single-objective optimization and data missing compensation in existing technologies. It maintains model continuity and consistency across different timescales from seconds to months, and improves overall energy efficiency, equipment lifespan, and operational safety of the production process through rolling optimization and closed-loop control.
[0007] A cement energy efficiency and equipment health optimization system based on digital twins according to an embodiment of the present invention includes the following modules:
[0008] The data acquisition and processing module is used to collect and process multi-timescale process parameters and equipment parameters during the cement production process;
[0009] The cross-timescale twin model building module is used to build cross-timescale twin models and realize data interaction and constraints through energy conservation constraints and cross-layer couplers.
[0010] The reverse twin repair module is used to invert the missing signal by utilizing process parameters and equipment parameters and based on the energy conservation constraints of the cross-timescale twin model when sensor data is missing or abnormal.
[0011] The game theory optimization module is used to construct the game relationship between the energy efficiency optimization party and the equipment health optimization party, and solve for the equilibrium solution;
[0012] The optimization control execution module is used to generate and issue optimization control strategies based on the equilibrium solution, and to monitor the running data in real time during the execution process and update the state of the twin model across time scales.
[0013] Optionally, modules can be integrated using the following methods:
[0014] S1. Collect process parameters and equipment parameters at multiple time scales during cement production, and process the process parameters and equipment parameters to obtain the processed process parameters and equipment parameters.
[0015] S2. Construct cross-timescale twin models, including fast-layer twins, medium-speed twins, and slow-speed twins. Fast-layer twins describe dynamic processes at the second and minute levels, medium-speed twins describe hourly energy efficiency and kiln lining evolution processes, and slow-speed twins describe day-level and month-level equipment degradation processes. Energy conservation constraints are introduced, and data interaction and constraints between different-level twin models are realized through cross-layer couplers.
[0016] S3. When sensor data is missing or abnormal, a reverse twin repair mechanism is adopted. Using process parameters and equipment parameters, the missing signal is inverted based on energy conservation constraints to obtain the corresponding temperature, vibration or energy consumption parameters.
[0017] S4. Based on the cross-timescale twin model, construct the game relationship between the energy efficiency optimization party and the equipment health optimization party. The goal of the energy efficiency optimization party is to minimize specific heat consumption and specific power consumption, while the goal of the equipment health optimization party is to maximize equipment life and health index. The optimal balance between energy efficiency and equipment health is obtained by solving the equilibrium solution.
[0018] S5. Determine the optimal control strategy based on the equilibrium solution. The optimal control strategy includes the set values of rotary kiln speed, pulverized coal injection rate, ventilation air volume ratio, classifier speed and mill circulating load. The optimal control strategy is issued and executed, and the data is monitored in real time during the execution process to update the state of the twin model across time scales, forming rolling optimization and closed-loop control.
[0019] Optionally, the process parameters include combustion flame images, kiln head temperature, kiln tail temperature, energy efficiency trend data, and kiln lining thickness data. The equipment parameters include mill current fluctuation data, equipment vibration data, refractory brick wear data, and bearing maintenance record data. The second-level and minute-level data include combustion flame images, kiln head temperature, kiln tail temperature, and mill current fluctuation data. The hour-level data includes energy efficiency trend data and kiln lining thickness data. The day-to-month-level data includes equipment vibration data, refractory brick wear data, and bearing maintenance record data.
[0020] Optionally, the processing of process parameters and equipment parameters includes cleaning, time alignment, and anomaly detection of process parameters and equipment parameters.
[0021] Optionally, S2 specifically includes:
[0022] S21. Based on process parameters and equipment parameters, determine the scope and time scale of the modeling object, divide the main process links and key equipment in the cement production process into modeling units, set the sampling period from seconds to minutes for the fast layer, the sampling period from hours for the medium-speed layer, and the sampling period from days to months for the slow layer, and establish the mapping relationship from data to time level.
[0023] S22. Construct a rapid layer twin, using transient mechanism units that take combustion process, gas-solid heat exchange and milling transient load as objects, take second-level and minute-level data as input, set mass conservation constraints and energy conservation constraints as hard constraints, and output kiln head and kiln tail temperatures, flue gas oxygen content, main air volume, instantaneous electric power and key process status, and generate hour-level statistics through time aggregation calculation;
[0024] S23. Construct a medium-speed layer twin, adopt hourly computing units with energy efficiency balance and kiln skin evolution as the objects, receive hourly statistics and combine them with hourly data for state update, use mass conservation constraints and energy conservation constraints as consistency constraints during the state update process, output hourly specific heat consumption, specific power consumption, kiln skin thickness and heat loss evaluation results, and generate long-term statistics through time accumulation and trend extraction.
[0025] S24. Construct a slow-speed layer twin, adopt a day-to-month degradation calculation unit with equipment degradation and remaining life assessment as the object, receive long-term statistics and integrate day-to-month data for state update, maintain consistency with the output of the medium-speed layer in terms of quality and energy balance during the state update process, form the health index of key equipment and the remaining life estimate, and give constraints on the operating boundary and efficiency parameters of the medium-speed layer and the fast-speed layer.
[0026] S25. Establish a cross-layer coupler to complete bottom-up data aggregation and top-down constraint pushdown, specify the time synchronization and data verification order, verify the consistency of the three levels in mass conservation and energy conservation, and perform state rollback or parameter reset when deviations are exceeded.
[0027] Optionally, S3 specifically includes:
[0028] S31. Read process parameters and equipment parameters, classify them according to measurement point location, time scale and equipment unit, generate a list of missing or abnormal signals, and mark the corresponding time period and the modeling unit to which they belong.
[0029] S32. Invoke the energy conservation constraints and mass conservation constraints, combine them with the available observables and related signals at the same moment, and derive the initial estimate of the missing quantity based on the boundary energy flow and material flow budget relationship to form candidate reconstruction values;
[0030] S33. Perform consistency checks on the candidate reconstructed values across time scales using the twin model, where:
[0031] Short-term forward extrapolation was performed using fast-layer twins to verify the stability of the transient equilibrium.
[0032] Does the energy efficiency trend of the mid-speed twin comparison conform to historical trajectories?
[0033] In the slow layer twin check, the changes in degradation indicators are consistent with the existing degradation records. If the pre-stored tolerance is not met at any level, the candidate reconstruction value is narrowed and the derivation is repeated until the three-level consistency requirements are met simultaneously.
[0034] S34. Perform multi-evidence integration on the candidate reconstructed values that have passed the consistency check, generate an evidence priority list based on sensor calibration records and operational reliability, prioritize the use of relevant quantities from high-confidence sources for cross-verification, and output the reconstruction results with upper and lower limits and confidence levels.
[0035] S35. Write the corresponding temperature, vibration and energy consumption values from the reconstruction results, along with the timestamp, credibility level and audit mark, into the corresponding positions of the cross-timescale twin model to update the status.
[0036] Optionally, S4 specifically includes:
[0037] S41. Based on the predicted state of the cross-timescale twin model, the energy efficiency optimization party and the equipment health optimization party are established as two independent decision-making parties. The objectives of the energy efficiency optimization party are determined to be minimizing specific heat consumption and specific power consumption, and the objectives of the equipment health optimization party are to maximize equipment life and health index. The common constraint set of emissions, quality and safety is also determined.
[0038] S42. Based on process parameters and equipment parameters, determine the set of adjustable control parameters involved in decision-making, their value range and variation step size, and establish a constraint priority list and timing rules for conflict handling. The fast layer twin corresponds to short-cycle control parameters, the medium-speed layer twin corresponds to medium-cycle control parameters, and the slow layer twin corresponds to long-term degradation suppression related constraints.
[0039] S43. Establish a game theory relationship between energy efficiency optimizers and equipment health optimizers, and adopt a decision-making mechanism of mutual concession, guardrail, and arbitration:
[0040] Set up health protection barriers and energy efficiency protection barriers as insurmountable boundaries;
[0041] According to the predetermined transfer rules, a sequence of bids and counterbids is generated within the common constraint set, and the arbitration process adjudicates each round of bids based on the credibility ranking of twin predictions and long-term impact assessment.
[0042] S44. Without breaching the health protection barrier and the energy efficiency protection barrier, the set of adjustable control parameters is updated alternately in the order of health first and then energy efficiency. The impact of each update on the energy efficiency target and the health target is verified using a cross-timescale twin model to form a stable combination. The stable combination is then determined as the equilibrium solution.
[0043] Optionally, S5 specifically includes:
[0044] S51. Read the equilibrium solution and combine it with real-time acquired data to determine the overall framework of the optimization control strategy. The optimization control strategy is constrained by energy efficiency targets and equipment health targets, and specifies the set value range, upper limit of change amplitude and upper limit of change rate of rotary kiln speed, coal injection volume, ventilation air volume ratio, classifier speed and mill circulating load.
[0045] S52. Under the overall framework of the optimized control strategy, the equilibrium solution is transformed into a specific sequence of optimized control instructions, clarifying the target value, allowable range, adjustment step size and sampling period of each control parameter, and corresponding to fast layer twin, medium-speed layer twin and slow layer twin according to the hierarchical relationship of the cross-time scale twin model.
[0046] S53. Issue and execute the control command sequence in the optimized control strategy, collect running data in real time during the execution process, and compare it with the prediction results of the cross-timescale twin model. When the actual operation deviates from the prediction, trigger the amplitude limiting, gradual change or suspension mechanism according to the optimized control strategy.
[0047] S54. When the operating data deviates continuously from the prediction results or the health monitoring quantity approaches the threshold, rollback or freeze measures are executed according to the emergency rules in the optimized control strategy, and the equilibrium solution is called to regenerate an alternative optimized control strategy.
[0048] S55. After the optimization control strategy for this cycle is completed or an emergency response is triggered, record the target value, actual value, deviation and response measures of each control parameter, and write the final operating data into the cross-timescale twin model to update the state and form the optimization control strategy for the next cycle.
[0049] The beneficial effects of this invention are:
[0050] This invention achieves unified modeling and dynamic coupling across different time scales, from seconds to months, by constructing cross-timescale twin models. The fast-layer twin model captures fluctuations in combustion processes and instantaneous energy efficiency, the medium-speed twin model depicts hourly energy efficiency changes and kiln lining evolution, and the slow-speed twin model reflects equipment degradation patterns from days to months. By establishing constraints and data interaction between multiple levels through cross-layer couplers, this invention effectively addresses the deficiency in existing technologies where single-timescale modeling cannot fully represent the complexity of cement production.
[0051] In the event of missing data or sensor malfunctions, this invention employs a reverse twin repair mechanism. By combining energy conservation and mass conservation constraints to invert the missing signal, and generating repaired data with a clear credibility level through a multi-evidence integration mechanism, it not only improves the accuracy and robustness of data completion but also ensures the continuity and integrity of the twin model across time scales. This avoids deviations in optimization results caused by incomplete data and enhances the applicability of the digital twin model under complex working conditions.
[0052] This invention establishes a game theory relationship between energy efficiency optimization and equipment health optimization, and finds the optimal balance point between the two through equilibrium solutions. The resulting optimized control strategy can simultaneously consider energy efficiency indicators and equipment lifespan, achieving dynamic and rolling closed-loop optimized control. Compared with existing technologies, this invention can not only reduce specific heat consumption and specific power consumption, but also significantly extend the service life of key equipment, improve the safety and stability of system operation, thereby achieving overall optimization and sustainable development of the cement production process. Attached Figure Description
[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0054] Figure 1 This is a schematic diagram of the structure of a cement energy efficiency and equipment health optimization system based on digital twin proposed in this invention;
[0055] Figure 2 This is a schematic diagram of the process for optimizing cement energy efficiency and equipment health based on digital twins, as proposed in this invention. Detailed Implementation
[0056] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.
[0057] refer to Figure 1 A cement energy efficiency and equipment health optimization system based on digital twins includes the following modules:
[0058] The data acquisition and processing module is used to collect and process multi-timescale process parameters and equipment parameters during the cement production process;
[0059] The cross-timescale twin model building module is used to build cross-timescale twin models and realize data interaction and constraints through energy conservation constraints and cross-layer couplers.
[0060] The reverse twin repair module is used to invert the missing signal by utilizing process parameters and equipment parameters and based on the energy conservation constraints of the cross-timescale twin model when sensor data is missing or abnormal.
[0061] The game theory optimization module is used to construct the game relationship between the energy efficiency optimization party and the equipment health optimization party, and solve for the equilibrium solution;
[0062] The optimization control execution module is used to generate and issue optimization control strategies based on the equilibrium solution, and to monitor the running data in real time during the execution process and update the state of the twin model across time scales.
[0063] refer to Figure 2 A method for optimizing cement energy efficiency and equipment health based on digital twins includes the following steps:
[0064] S1. Collect process parameters and equipment parameters at multiple time scales during cement production, and process the process parameters and equipment parameters to obtain the processed process parameters and equipment parameters.
[0065] S2. Construct cross-timescale twin models, including fast-layer twins, medium-speed twins, and slow-speed twins. Fast-layer twins describe dynamic processes at the second and minute levels, medium-speed twins describe hourly energy efficiency and kiln lining evolution processes, and slow-speed twins describe day-level and month-level equipment degradation processes. Energy conservation constraints are introduced, and data interaction and constraints between different-level twin models are realized through cross-layer couplers.
[0066] S3. When sensor data is missing or abnormal, a reverse twin repair mechanism is adopted. Using process parameters and equipment parameters, the missing signal is inverted based on energy conservation constraints to obtain the corresponding temperature, vibration or energy consumption parameters.
[0067] S4. Based on the cross-timescale twin model, construct the game relationship between the energy efficiency optimization party and the equipment health optimization party. The goal of the energy efficiency optimization party is to minimize specific heat consumption and specific power consumption, while the goal of the equipment health optimization party is to maximize equipment life and health index. The optimal balance between energy efficiency and equipment health is obtained by solving the equilibrium solution.
[0068] S5. Determine the optimal control strategy based on the equilibrium solution. The optimal control strategy includes the set values of rotary kiln speed, pulverized coal injection rate, ventilation air volume ratio, classifier speed and mill circulating load. The optimal control strategy is issued and executed, and the data is monitored in real time during the execution process to update the state of the twin model across time scales, forming rolling optimization and closed-loop control.
[0069] In this embodiment, the process parameters include combustion flame images, kiln head temperature, kiln tail temperature, energy efficiency trend data, and kiln lining thickness data. The equipment parameters include mill current fluctuation data, equipment vibration data, refractory brick wear data, and bearing maintenance record data. The second-level and minute-level data include combustion flame images, kiln head temperature, kiln tail temperature, and mill current fluctuation data. The hour-level data includes energy efficiency trend data and kiln lining thickness data. The day-to-month-level data includes equipment vibration data, refractory brick wear data, and bearing maintenance record data.
[0070] In this embodiment, the processing of process parameters and equipment parameters includes cleaning, time alignment, and anomaly detection of process parameters and equipment parameters.
[0071] In this embodiment, S2 specifically includes:
[0072] S21. Based on process parameters and equipment parameters, determine the scope and time scale of the modeling object, divide the main process links and key equipment in the cement production process into modeling units, set the sampling period from seconds to minutes for the fast layer, the sampling period from hours for the medium-speed layer, and the sampling period from days to months for the slow layer, and establish the mapping relationship from data to time level.
[0073] S22. Construct a rapid layer twin, using transient mechanism units that take combustion process, gas-solid heat exchange and milling transient load as objects, take second-level and minute-level data as input, set mass conservation constraints and energy conservation constraints as hard constraints, and output kiln head and kiln tail temperatures, flue gas oxygen content, main air volume, instantaneous electric power and key process status, and generate hour-level statistics through time aggregation calculation;
[0074] S23. Construct a medium-speed layer twin, adopt hourly computing units with energy efficiency balance and kiln skin evolution as the objects, receive hourly statistics and combine them with hourly data for state update, use mass conservation constraints and energy conservation constraints as consistency constraints during the state update process, output hourly specific heat consumption, specific power consumption, kiln skin thickness and heat loss evaluation results, and generate long-term statistics through time accumulation and trend extraction.
[0075] S24. Construct a slow-speed layer twin, adopt a day-to-month degradation calculation unit with equipment degradation and remaining life assessment as the object, receive long-term statistics and integrate day-to-month data for state update, maintain consistency with the output of the medium-speed layer in terms of quality and energy balance during the state update process, form the health index of key equipment and the remaining life estimate, and give constraints on the operating boundary and efficiency parameters of the medium-speed layer and the fast-speed layer.
[0076] S25. Establish a cross-layer coupler to complete bottom-up data aggregation and top-down constraint pushdown, specify the time synchronization and data verification order, verify the consistency of the three levels in mass conservation and energy conservation, and perform state rollback or parameter reset when deviations are exceeded.
[0077] In this embodiment, S3 specifically includes:
[0078] S31. Read process parameters and equipment parameters, classify them according to measurement point location, time scale and equipment unit, generate a list of missing or abnormal signals, and mark the corresponding time period and the modeling unit to which they belong.
[0079] S32. Invoke the energy conservation constraints and mass conservation constraints, combine them with the available observables and related signals at the same moment, and derive the initial estimate of the missing quantity based on the boundary energy flow and material flow budget relationship to form candidate reconstruction values;
[0080] S33. Perform consistency checks on the candidate reconstructed values across time scales using the twin model, where:
[0081] Short-term forward extrapolation was performed using fast-layer twins to verify the stability of the transient equilibrium.
[0082] Does the energy efficiency trend of the mid-speed twin comparison conform to historical trajectories?
[0083] In the slow layer twin check, the changes in degradation indicators are consistent with the existing degradation records. If the pre-stored tolerance is not met at any level, the candidate reconstruction value is narrowed and the derivation is repeated until the three-level consistency requirements are met simultaneously.
[0084] S34. Perform multi-evidence integration on the candidate reconstructed values that have passed the consistency check, generate an evidence priority list based on sensor calibration records and operational reliability, prioritize the use of relevant quantities from high-confidence sources for cross-verification, and output the reconstruction results with upper and lower limits and confidence levels.
[0085] S35. Write the corresponding temperature, vibration and energy consumption values from the reconstruction results, along with the timestamp, credibility level and audit mark, into the corresponding positions of the cross-timescale twin model to update the status.
[0086] In this embodiment, S4 specifically includes:
[0087] S41. Based on the predicted state of the cross-timescale twin model, the energy efficiency optimization party and the equipment health optimization party are established as two independent decision-making parties. The objectives of the energy efficiency optimization party are determined to be minimizing specific heat consumption and specific power consumption, and the objectives of the equipment health optimization party are to maximize equipment life and health index. The common constraint set of emissions, quality and safety is also determined.
[0088] S42. Based on process parameters and equipment parameters, determine the set of adjustable control parameters involved in decision-making, their value range and variation step size, and establish a constraint priority list and timing rules for conflict handling. The fast layer twin corresponds to short-cycle control parameters, the medium-speed layer twin corresponds to medium-cycle control parameters, and the slow layer twin corresponds to long-term degradation suppression related constraints.
[0089] S43. Establish a game theory relationship between energy efficiency optimizers and equipment health optimizers, and adopt a decision-making mechanism of mutual concession, guardrail, and arbitration:
[0090] Set up health protection barriers and energy efficiency protection barriers as insurmountable boundaries;
[0091] According to the predetermined transfer rules, a sequence of bids and counterbids is generated within the common constraint set, and the arbitration process adjudicates each round of bids based on the credibility ranking of twin predictions and long-term impact assessment.
[0092] S44. Without breaching the health protection barrier and the energy efficiency protection barrier, the set of adjustable control parameters is updated alternately in the order of health first and then energy efficiency. The impact of each update on the energy efficiency target and the health target is verified using a cross-timescale twin model to form a stable combination. The stable combination is then determined as the equilibrium solution.
[0093] In this embodiment, S5 specifically includes:
[0094] S51. Read the equilibrium solution and combine it with real-time acquired data to determine the overall framework of the optimization control strategy. The optimization control strategy is constrained by energy efficiency targets and equipment health targets, and specifies the set value range, upper limit of change amplitude and upper limit of change rate of rotary kiln speed, coal injection volume, ventilation air volume ratio, classifier speed and mill circulating load.
[0095] S52. Under the overall framework of the optimized control strategy, the equilibrium solution is transformed into a specific sequence of optimized control instructions, clarifying the target value, allowable range, adjustment step size and sampling period of each control parameter, and corresponding to fast layer twin, medium-speed layer twin and slow layer twin according to the hierarchical relationship of the cross-time scale twin model.
[0096] S53. Issue and execute the control command sequence in the optimized control strategy, collect running data in real time during the execution process, and compare it with the prediction results of the cross-timescale twin model. When the actual operation deviates from the prediction, trigger the amplitude limiting, gradual change or suspension mechanism according to the optimized control strategy.
[0097] S54. When the operating data deviates continuously from the prediction results or the health monitoring quantity approaches the threshold, rollback or freeze measures are executed according to the emergency rules in the optimized control strategy, and the equilibrium solution is called to regenerate an alternative optimized control strategy.
[0098] S55. After the optimization control strategy for this cycle is completed or an emergency response is triggered, record the target value, actual value, deviation and response measures of each control parameter, and write the final operating data into the cross-timescale twin model to update the state and form the optimization control strategy for the next cycle.
[0099] Example 1:
[0100] To verify the feasibility of this invention in practice, it was applied to a cement production enterprise with an annual clinker production capacity of 2 million tons. The enterprise has long faced problems of high specific heat consumption and frequent maintenance of key equipment. The production line uses a five-stage preheater, decomposer, grate cooler, and closed-circuit cement mill system. During year-round operation, it suffers from large fluctuations in energy consumption, frequent kiln lining peeling, and insufficient lifespan of the mill's main motor bearings. Statistics for 2024 show that the plant's average specific heat consumption for clinker was approximately 3090 kJ / kg, and its specific electricity consumption was approximately 95 kWh / t cement, still lagging behind industry advanced levels. Furthermore, the mill bearings need to be replaced every 12 months, and the grate cooler fan requires a major overhaul every 8 months, severely impacting production continuity and equipment reliability.
[0101] Prior to the application of the method of this invention, factories mainly relied on operators to manually adjust equipment based on single-point signals (such as kiln tail temperature, coal feed rate, and mill current). Optimization primarily focused on short-term operational stability, failing to consider long-term equipment health management. For example, operators reduced coal consumption by increasing secondary air volume, but this often led to increased kiln lining spalling and shortened refractory brick lifespan. The lack of unified modeling across time scales created a conflict between energy efficiency optimization and equipment health maintenance, making it difficult to achieve systemic improvement.
[0102] After applying this invention, a real-time acquisition system was deployed to obtain multi-level process and equipment data ranging from seconds to months. After cleaning, time alignment, and anomaly detection, the data was input into a cross-timescale twin model. The fast-speed twin is used to characterize the transient combustion process, receiving second-level flame images and temperature fluctuations at the kiln head and tail. The medium-speed twin is used to track hourly energy efficiency and kiln lining thickness changes. The slow-speed twin combines daily to monthly vibration trends, refractory brick wear, and maintenance records to characterize equipment degradation patterns.
[0103] During a test run in May 2025, the thermocouple signal at the kiln tail was interrupted for approximately 40 minutes due to a sensor malfunction. Using traditional linear interpolation, the deviation could reach ±20℃, easily leading to errors in thermal regime judgment. By invoking a reverse twin repair mechanism, utilizing energy and mass conservation constraints to invert the kiln tail temperature, and combining this with multi-level consistency checks, the deviation between the repaired data and subsequent manual verification results was controlled within ±6℃, effectively maintaining model continuity.
[0104] The energy efficiency optimization strategy aims to reduce specific heat and specific power consumption, while the equipment health optimization strategy aims to extend the life of refractory bricks and reduce mill vibration. The system constructs a game theory relationship on a cross-timescale twin model and generates an optimized control strategy that balances energy efficiency and health through equilibrium solution calculation. In this case, the control strategy corresponding to the equilibrium solution includes adjusting the rotary kiln speed from 3.1 rpm to 3.0 rpm, adjusting the pulverized coal injection rate from 8.2 t / h to 8.0 t / h, increasing the proportion of secondary air in the ventilation air volume ratio by 2%, reducing the classifier speed by 3%, and adjusting the mill circulating load from 270% to 260%.
[0105] The optimized control strategy was issued to the control system for execution. During operation, real-time monitoring data was compared with the cross-timescale twin model, revealing a significant reduction in kiln lining fluctuations and a more stable mill current curve. After two months of continuous operation verification, this invention not only improved energy efficiency but also effectively extended equipment lifespan, demonstrating the advantages of combining the cross-timescale twin model with the reverse repair mechanism.
[0106] Table 1 Comparison results between traditional methods and the method of this invention
[0107]
[0108] As shown in Table 1, the method of this invention achieves significant improvements in both energy efficiency and equipment health. Regarding energy efficiency, the average specific heat consumption under the traditional method is 3090 kJ / kg clinker, while the method of this invention reduces it to 3004 kJ / kg, a decrease of 2.8%. Simultaneously, the specific electricity consumption decreases from 95 kWh / t cement to 91.7 kWh / t, a reduction of 3.5%. These results demonstrate that this invention, through a cross-timescale twin model and optimized control strategy, effectively reduces energy consumption, making the production process more economical.
[0109] This invention demonstrates significant advantages in critical data repair and process stability. Traditional methods can only compensate for missing kiln tail temperature through empirical interpolation, resulting in a deviation of ±20℃. In contrast, the method of this invention, utilizing a reverse twin repair mechanism, controls the deviation to ±6℃, improving accuracy by approximately 70%. The kiln lining thickness fluctuation range is reduced from ±16mm to ±12mm, a decrease of 25%, indicating that while ensuring energy efficiency optimization, this invention effectively suppresses drastic fluctuations in kiln lining thickness, thereby avoiding the risk of premature refractory brick failure.
[0110] The method of this invention has also achieved positive results in terms of equipment health and lifespan. The temperature rise of the mill main bearing decreased from 44℃ to 41℃, a reduction of approximately 6.8%, which not only improved the equipment operating conditions but also reduced the probability of failure. Furthermore, the replacement cycle of refractory bricks was extended from 10 months to 12 months, an extension of 20%, demonstrating that the goals of equipment health optimization have been achieved. This invention not only reduces energy consumption but also significantly improves the stability and lifespan of equipment operation, successfully solving the problem of balancing energy efficiency optimization and equipment protection in traditional methods.
[0111] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A cement energy efficiency and equipment health optimization system based on digital twin, characterized in that, Includes the following modules: The data acquisition and processing module is used to collect and process multi-timescale process parameters and equipment parameters during the cement production process; The cross-timescale twin model building module is used to build cross-timescale twin models and realize data interaction and constraints through energy conservation constraints and cross-layer couplers. The reverse twin repair module is used to invert the missing signal by utilizing process parameters and equipment parameters and based on the energy conservation constraints of the cross-timescale twin model when sensor data is missing or abnormal. The game theory optimization module is used to construct the game relationship between the energy efficiency optimization party and the equipment health optimization party, and solve for the equilibrium solution; The optimization control execution module is used to generate and issue optimization control strategies based on the equilibrium solution, and to monitor the running data in real time during the execution process and update the state of the twin model across time scales.
2. The cement energy efficiency and equipment health optimization method based on digital twinning applied to the cement energy efficiency and equipment health optimization system based on digital twinning in claim 1, characterized in that, Includes the following steps: S1. Collect process parameters and equipment parameters at multiple time scales during cement production, and process the process parameters and equipment parameters to obtain the processed process parameters and equipment parameters. S2. Construct cross-timescale twin models, including fast-layer twins, medium-speed twins, and slow-speed twins. Fast-layer twins describe dynamic processes at the second and minute levels, medium-speed twins describe hourly energy efficiency and kiln lining evolution processes, and slow-speed twins describe day-level and month-level equipment degradation processes. Energy conservation constraints are introduced, and data interaction and constraints between different-level twin models are realized through cross-layer couplers. S3. When sensor data is missing or abnormal, a reverse twin repair mechanism is adopted. Using process parameters and equipment parameters, the missing signal is inverted based on energy conservation constraints to obtain the corresponding temperature, vibration or energy consumption parameters. S4. Based on the cross-timescale twin model, construct the game relationship between the energy efficiency optimization party and the equipment health optimization party. The goal of the energy efficiency optimization party is to minimize specific heat consumption and specific power consumption, while the goal of the equipment health optimization party is to maximize equipment life and health index. The optimal balance between energy efficiency and equipment health is obtained by solving the equilibrium solution. S5. Determine the optimal control strategy based on the equilibrium solution. The optimal control strategy includes the set values of rotary kiln speed, pulverized coal injection rate, ventilation air volume ratio, classifier speed and mill circulating load. The optimal control strategy is issued and executed, and the data is monitored in real time during the execution process to update the state of the twin model across time scales, forming rolling optimization and closed-loop control.
3. The cement energy efficiency and equipment health optimization method based on digital twinning of claim 2, wherein, The process parameters include combustion flame images, kiln head temperature, kiln tail temperature, energy efficiency trend data, and kiln lining thickness data. The equipment parameters include mill current fluctuation data, equipment vibration data, refractory brick wear data, and bearing maintenance record data. The second-level and minute-level data include combustion flame images, kiln head temperature, kiln tail temperature, and mill current fluctuation data. The hour-level data includes energy efficiency trend data and kiln lining thickness data. The day-to-month-level data includes equipment vibration data, refractory brick wear data, and bearing maintenance record data.
4. The cement energy efficiency and equipment health optimization method based on digital twinning of claim 2, wherein, The processing of process parameters and equipment parameters includes cleaning, time alignment, and anomaly detection of process parameters and equipment parameters.
5. The cement energy efficiency and equipment health optimization method based on digital twin according to claim 2, characterized in that, S2 specifically includes: S21, based on process parameters and equipment parameters, determine the range and time scale of the modeling object, divide the main process links and key equipment in the cement production process into modeling units, set the second to minute sampling period of the fast layer, the hour sampling period of the medium speed layer, and the day to month sampling period of the slow layer, and establish the mapping relationship of data to time hierarchy; S22, construct a fast layer twin, adopt a transient mechanism unit taking the combustion process, gas-solid heat exchange and grinding transient load as the object, take second and minute level data as input, set mass conservation constraint and energy conservation constraint as hard constraint, output kiln head and kiln tail temperature, flue gas oxygen content, main air volume, instantaneous electric power and key process state, and generate hour level statistics through time aggregation operation; S23, construct a medium speed layer twin, adopt a hour level calculation unit taking energy efficiency balance and kiln skin evolution as the object, receive hour level statistics and combine hour level data for state updating, take mass conservation constraint and energy conservation constraint as consistency constraint in the state updating process, output hour level specific heat consumption, specific power consumption, kiln skin thickness and heat loss evaluation results, and generate long-term statistics through time accumulation and trend extraction; S24, construct a slow layer twin, adopt a day to month level degradation calculation unit taking equipment degradation and remaining life evaluation as the object, receive long-term statistics and fuse day to month level data for state updating, maintain consistency with medium speed layer output in mass and energy balance during state updating, form key equipment health index and remaining life estimation, and give operation boundary and efficiency parameter constraint of medium speed layer and fast layer; S25, establish a cross-layer coupler, complete bottom-up data aggregation and top-down constraint pushdown, specify time synchronization and data checking sequence, check the consistency of the three levels in mass conservation and energy conservation, and perform state rollback or parameter reset when there is a difference.
6. The cement energy efficiency and equipment health optimization method based on digital twinning of claim 2, wherein, The S3 specifically comprises: S31, read process parameters and equipment parameters, classify according to measurement point position, time scale and equipment unit, generate a list of missing or abnormal signals, and mark the corresponding time period and the modeling unit to which it belongs; S32, call energy conservation constraint and mass conservation constraint, combine available observations and related signals at the same time, derive the initial estimated value of the missing amount according to the boundary energy flow and material flow balance relationship, and form the candidate reconstruction value; S33, perform consistency check of cross-time scale twin model on the candidate reconstruction value, wherein: In the fast layer twin, short period forward deduction is performed to verify whether the transient balance is stable, In the medium speed layer twin, whether the energy efficiency trend is compatible with the historical track is compared, In the slow layer twin, whether the change of degradation index is consistent with the existing degradation record is checked, and if any layer does not meet the pre-stored tolerance, the candidate reconstruction value is interval contracted and repeated derivation until the three levels meet the consistency requirements at the same time; S34, integrate multiple evidences on the candidate reconstruction value passed by consistency check, generate evidence priority list according to sensor calibration record and operation reliability, preferentially adopt related quantities from high reliability sources for cross verification, and output the reconstruction result with upper and lower limit boundary and confidence level; S35, write the numerical values corresponding to temperature, vibration and energy consumption in the reconstruction result into the corresponding positions of the cross-time-scale twin model together with the time stamp, credibility level and audit identifier to update the state.
7. The cement energy efficiency and equipment health optimization method based on digital twin of claim 2, wherein, The S4 specifically comprises: S41, based on the predicted state of the cross-time-scale twin model, establish the energy efficiency optimization party and the equipment health optimization party as two independent decision-making parties, respectively determine the target of the energy efficiency optimization party as minimizing specific heat consumption and specific power consumption, the target of the equipment health optimization party as maximizing equipment life and health index, and determine the common constraint set of emissions, quality and safety; S42, determine the adjustable control parameter set participating in decision-making, value range and variation step according to the process parameters and equipment parameters, establish the constraint priority list and timing rules for conflict processing, the fast layer twin corresponds to the short-period control parameter, the medium-speed layer twin corresponds to the medium-period control parameter, and the slow layer twin corresponds to the long-term degradation inhibition related constraint; S43, construct the game relationship between the energy efficiency optimization party and the equipment health optimization party, and adopt the decision-making mechanism of mutual concession-guardrail-arbitration: Set the health protection guardrail and the energy efficiency protection guardrail as an insurmountable boundary; According to the predetermined concession rule, generate a bidding and counter-bidding sequence in the common constraint set, and by the arbitration process, sort and evaluate the long-term impact of each round of bidding according to the credibility of the twin prediction; S44, under the condition of not breaking through the health protection guardrail and the energy efficiency protection guardrail, alternately update the adjustable control parameter set according to the turn order of health first and energy efficiency second, use the cross-time-scale twin model to verify the influence of each update on the energy efficiency target and the health target, form a stable combination, and determine the stable combination as the equilibrium solution.
8. The cement energy efficiency and equipment health optimization method based on digital twinning of claim 2, wherein, The S5 specifically comprises: S51, read the equilibrium solution, combine the real-time acquisition data to determine the overall framework of the optimization control strategy, and the optimization control strategy takes the energy efficiency target and the equipment health target as constraint conditions, and specifies the set value range, the upper limit of the change amplitude and the upper limit of the change rate of the rotary kiln speed, the coal injection amount, the ventilation air volume ratio, the speed of the classifier and the circulating load of the mill; S52, under the overall framework of the optimization control strategy, convert the equilibrium solution into a specific optimization control instruction sequence, and specify the target value, the allowed interval, the adjustment step and the sampling period of each control parameter, and correspond to the fast layer twin, the medium-speed layer twin and the slow layer twin according to the hierarchical relationship of the cross-time-scale twin model; S53, issue and execute the control instruction sequence in the optimization control strategy, collect the running data in real time during the execution, and compare with the prediction result of the cross-time-scale twin model, when the actual running deviates from the prediction, trigger the limiting, slow-changing or suspension mechanism according to the optimization control strategy; S54, when the running data and the prediction result continuously deviate or the health monitoring quantity approaches the threshold value, execute the rollback or freezing measures according to the emergency rules in the optimization control strategy, and call the equilibrium solution to generate an alternative optimization control strategy; S55, after the execution of the optimization control strategy in the current period is completed or the emergency treatment is triggered, the target value, the actual value, the deviation and the treatment measure of each control parameter are recorded, and the final operation data is written into the cross-time scale twin model to update the state, forming the optimization control strategy of the next period.