Coal proportion control method based on energy consumption dynamic balance

Abstract

The invention relates to the technical field of industrial production, in particular to a coal proportion control method based on energy consumption dynamic balance, which comprises the following steps of: acquiring physical and chemical characteristic data of different coal types in real time through a sensor network, and constructing a multivariable energy consumption dynamic balance model in combination with production working conditions and equipment operation parameters; on the basis of the model, a genetic algorithm is used for optimizing the coal proportion, it is ensured that the production requirement is met under the lowest energy consumption, and equipment safety and environmental protection standards are considered; in the production process, energy consumption data are monitored in real time through a closed-loop control system, and the proportion is dynamically adjusted to eliminate energy consumption deviation; in addition, according to environmental data such as temperature, humidity and pressure of a production site, coal combustion characteristics are dynamically adjusted, combustion efficiency is improved, and energy consumption is reduced; the method also supports long-term storage and analysis of energy consumption data, periodically adjusts an energy consumption model and a matching strategy, and achieves the continuous improvement of energy efficiency. The method is suitable for efficient coal utilization and energy consumption optimization under complex working conditions.

Description

Technical Field

[0001] The invention relates to the technical field of industrial production, and in particular to a coal proportion control method based on dynamic balance of energy consumption. Background Art

[0002] As an important global energy resource, coal is widely used in industrial fields such as electricity, steel, and chemicals. With the continuous growth of energy demand and increasingly stringent environmental protection standards, how to efficiently use coal, reduce energy consumption and emissions in the production process has become a key issue that needs to be urgently addressed in all walks of life. In actual production, the combustion characteristics of different types of coal, the operating parameters of equipment, and the fluctuation of production conditions will have a direct impact on energy consumption.

[0003] There are multiple technical problems in the existing technology in terms of coal ratio control. First, traditional coal ratio methods are mostly based on static or empirical data, which cannot adapt to complex and changeable production environments, resulting in large fluctuations in energy consumption and difficulty in achieving the optimal ratio. Secondly, the existing system lacks real-time monitoring and feedback mechanisms for production conditions and equipment operating status, and cannot adjust the ratio scheme in time according to actual energy consumption changes, resulting in the inability to continuously optimize energy efficiency. In addition, the existing technology has poor adaptability to environmental parameters. In a production environment where temperature, humidity and other conditions change greatly, the coal combustion characteristics cannot be effectively adjusted, affecting the overall energy consumption control and the equipment's operating stability. Summary of the invention

[0004] The invention provides a coal proportion control method based on dynamic balance of energy consumption.

[0005] The coal proportion control method based on dynamic balance of energy consumption includes the following steps:

[0006] S1, coal characteristic data collection: collect coal characteristic data through the sensor network, including coal calorific value, ash, volatile matter, sulfur content and moisture. The collected coal characteristic data is transmitted to the control center in real time and compared with historical coal characteristic data to generate a coal characteristic database;

[0007] S2, energy consumption model construction: Based on the collected coal characteristic data, combined with the production conditions and the actual operating parameters of the equipment, an energy consumption dynamic balance model is constructed to dynamically predict the energy consumption changes in different production stages;

[0008] S3, coal ratio optimization: Based on the prediction results of the energy consumption dynamic balance model, the ratio of different coal types is optimized in real time through genetic algorithms;

[0009] S4, real-time feedback control: in the coal ratio and actual production process, real-time monitoring of energy consumption changes and production conditions, and dynamic adjustment of coal ratio according to energy consumption deviations in the production process;

[0010] S5, energy consumption data storage and analysis: store the energy consumption data and equipment operating parameters generated during each batching and production process, and analyze them in association with historical data for long-term energy consumption trend assessment and optimization of coal batching strategies;

[0011] S6, Energy efficiency optimization feedback: Based on the stored and analyzed energy consumption data, the energy consumption dynamic balance model and coal ratio optimization plan are regularly adjusted to achieve continuous improvement of energy efficiency;

[0012] S7, Adaptation to the production environment: Dynamically adjust the combustion characteristics of coal according to the actual working conditions of the production site to further improve energy efficiency and reduce energy consumption.

[0013] Optionally, the S1 specifically includes:

[0014] S11, sensor type selection and deployment: according to the physical and chemical characteristics of different types of coal, select and deploy appropriate sensor networks, including calorific value sensors, ash sensors, volatile matter analyzers, sulfur content detectors and moisture measurement sensors;

[0015] S12, Calibration and self-check of sensor network: Before formally collecting data, all deployed sensors are calibrated and self-checked to ensure their data collection accuracy in different working environments;

[0016] S13, real-time collection of coal characteristic data: through the sensor network, real-time collection of coal characteristic data, including calorific value, ash content, volatile matter, sulfur content and moisture;

[0017] S14, data transmission and preliminary filtering: the collected coal characteristic data is transmitted to the control center in real time through the sensor network;

[0018] S15, historical data comparison and generation of characteristic data: In the control center, the collected real-time coal characteristic data is compared and analyzed with the historical coal characteristic data. The change trend and similarity of the characteristic parameters of each type of coal are analyzed through the feature matching algorithm to generate a coal characteristic database that reflects the changes of different types of coal under different time periods and conditions.

[0019] Optionally, the S2 specifically includes:

[0020] S21, production condition data collection: through the condition sensors installed in the production equipment, the production condition data, including temperature, pressure, flow rate and combustion gas composition, are collected in real time. These condition parameters are used in combination with the coal characteristic data to ensure that the model can accurately reflect the energy consumption of coal under different production conditions;

[0021] S22, equipment operation parameter collection: extracting equipment operation parameters from the equipment operation status, the equipment operation parameters including equipment power, load condition, operation speed and cooling efficiency;

[0022] S23, multivariate data fusion and preprocessing: multi-dimensional data fusion of coal characteristic data, production condition data and equipment operation parameters, and preprocessing of coal characteristic data, production condition data and equipment operation parameters by using standardization and normalization methods;

[0023] S24, energy consumption dynamic balance model construction: based on nonlinear regression algorithm, energy consumption dynamic balance model is constructed;

[0024] S25, dynamic prediction of energy consumption: After the model is built, real-time production condition data and equipment operating parameters are input, and combined with the current coal characteristic data, dynamic prediction of energy consumption changes in different production stages is carried out.

[0025] Optionally, the S3 specifically includes:

[0026] S31, optimization target setting: setting the optimization target according to the prediction results of the energy consumption dynamic balance model;

[0027] S32, constraint definition: define the constraint conditions in the optimization algorithm according to the actual production situation;

[0028] S33, genetic algorithm application: according to the actual optimization objectives and constraints, the genetic algorithm is used to optimize the coal ratio and output the optimal coal ratio scheme;

[0029] S34, verification and adjustment of the proportioning results: the proportioning scheme output by the genetic algorithm is simulated and verified to confirm the feasibility of the scheme in actual production.

[0030] Optionally, the S4 specifically includes:

[0031] S41, real-time collection of energy consumption data: During the coal proportioning and production process, sensors are used to collect equipment energy consumption data, combustion parameters (such as temperature, pressure, gas composition) and equipment operating status in real time;

[0032] S42, energy consumption deviation calculation: comparing the energy consumption data collected in real time with the target energy consumption predicted by the energy consumption dynamic balance model, and calculating the energy consumption deviation;

[0033] S43, setting of coal ratio adjustment rules: setting of rules for dynamically adjusting coal ratio according to the size of energy consumption deviation;

[0034] S44, dynamic ratio adjustment: adjust the coal ratio in real time according to the set adjustment rules;

[0035] S45, closed-loop control feedback mechanism: The adjusted coal ratio plan is fed back to the production equipment in real time through the preset closed-loop control system and continuously monitored;

[0036] S46, abnormal situation handling: When the energy consumption deviation exceeds the set threshold range or a production abnormality occurs (such as equipment failure or change in coal quality), the alarm mechanism is activated and the proportioning scheme is adjusted according to the pre-set emergency plan.

[0037] Optionally, the S5 specifically includes:

[0038] S51, Energy consumption data storage format design: During the production process, each coal ratio and the energy consumption data and equipment operation parameters generated by actual production are stored in a standardized data format;

[0039] S52, real-time data storage and backup: energy consumption data and equipment operating parameters are stored in the database in real time, and regular automatic backup is performed;

[0040] S53, historical data correlation analysis: in the database, energy consumption data and equipment operation parameters are correlated with historical data for analysis;

[0041] S54, long-term energy consumption trend assessment: Generate an energy consumption trend assessment report based on the correlation analysis results of historical data;

[0042] S55, coal ratio strategy optimization: based on the long-term energy consumption trend assessment results, optimize the coal ratio plan;

[0043] S56, continuous monitoring of energy efficiency optimization: After the optimized coal ratio plan and equipment adjustment suggestions are put into production, continuously monitor the changes in energy consumption in actual production, verify the effectiveness of the optimization measures by comparing with historical data, and make further adjustments based on the feedback results.

[0044] Optionally, the S6 includes:

[0045] S61, periodic evaluation of energy consumption data: regularly extract the latest energy consumption data and production operation parameters from the database for periodic evaluation;

[0046] S62, Analysis of factors affecting energy efficiency: Based on the results of periodic evaluation, regression analysis and correlation analysis methods are used to analyze the key factors affecting energy efficiency, including changes in coal characteristics, fluctuations in production conditions, equipment operating status, etc. The goal of the analysis is to clarify the reasons for the current high energy consumption and determine the parameters that need to be optimized;

[0047] S63, regular adjustment of the energy consumption dynamic balance model: regularly update and adjust the energy consumption dynamic balance model based on the results of energy efficiency evaluation and influencing factor analysis.

[0048] Optionally, S6 further includes:

[0049] S64, Coal ratio optimization scheme adjustment: Based on the updated energy consumption dynamic balance model, the coal ratio scheme is optimized and adjusted;

[0050] S65, adjusted energy efficiency optimization verification: apply the adjusted coal ratio scheme to actual production and continuously track the energy consumption performance in production;

[0051] S66, feedback closed-loop optimization mechanism: Based on real-time monitoring and feedback of energy consumption data, the optimized energy consumption dynamic balance model and coal ratio scheme are incorporated into the closed-loop control system.

[0052] Optionally, the S7 includes:

[0053] S71, real-time collection of production environment parameters: through the environmental sensor network installed at the production site, real-time collection of environmental data under production conditions, including temperature, humidity, pressure, oxygen content and combustion gas composition;

[0054] S72, Correlation analysis between environmental parameters and combustion characteristics: Based on the real-time collected environmental data, the correlation analysis method is applied to determine the impact of changes in environmental data on coal combustion characteristics;

[0055] S73, setting of dynamic adjustment rules for combustion characteristics: setting of dynamic adjustment rules for combustion characteristics according to the correlation analysis results.

[0056] Optionally, the S7 further includes:

[0057] S74, real-time combustion characteristic adjustment: adjusting the combustion characteristics of coal in real time according to the set dynamic adjustment rules;

[0058] S75, Feedback control of environmental parameter changes: Combine the real-time combustion characteristic adjustment scheme with the closed-loop control system, and verify the effect of each combustion adjustment by continuously monitoring the changes in environmental data. If the verification results show that the adjustment strategy does not achieve the expected effect, the environmental influencing factors will be automatically re-evaluated and the combustion strategy will be adjusted;

[0059] S76, Balance optimization between combustion characteristics and production needs: In the dynamic adjustment process of environmental data, current production needs are taken into consideration to ensure that the production schedule or equipment operation is not affected when the combustion characteristics are adjusted.

[0060] Beneficial effects of the present invention:

[0061] The present invention uses multiple sensor networks to accurately collect coal characteristic data, production conditions and equipment operating parameters, and realizes real-time monitoring of coal characteristics and production environment. Through data fusion and preprocessing, combined with nonlinear regression algorithms, a multivariate energy consumption dynamic balance model is constructed, and the coal ratio is dynamically optimized through genetic algorithms. On this basis, the system can adjust the coal ratio strategy in real time to minimize energy consumption and ensure equipment safety and compliance with environmental protection standards. Compared with traditional coal ratio methods, the present invention provides more accurate and dynamic energy consumption prediction and ratio optimization, and improves overall production efficiency.

[0062] The present invention combines the feedback mechanism of real-time energy consumption data. Through real-time monitoring and deviation calculation, it can dynamically adjust the coal ratio according to the difference between the actual energy consumption and the model prediction value, ensuring the balance between energy consumption and production demand in the production process. At the same time, based on the real-time regulation of the closed-loop control system, it can quickly respond to energy consumption deviations or production anomalies, and realize timely adjustment of coal ratio and combustion characteristics. This closed-loop feedback mechanism significantly improves the response speed and regulation accuracy in the production process, ensuring that the system is always in an energy-efficient optimization state during operation.

[0063] The present invention can collect environmental parameters (such as temperature, humidity, pressure, etc.) of the production site in real time, and automatically adjust the combustion characteristics of coal to adapt to environmental changes, further improve combustion efficiency and reduce energy consumption. Combined with the long-term storage and analysis of energy consumption data, the system can continuously optimize the ratio strategy and energy consumption dynamic balance model based on historical data and trend analysis to achieve continuous improvement of energy efficiency. In addition, through adaptive adjustment rules and feedback control, the balance between combustion characteristic adjustment and production demand is ensured, thereby ensuring efficient operation under different production environments, making the present invention applicable to a variety of complex working conditions, and improving the robustness and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0066] Figure 2 Schematic diagram of S4 process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0068] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0069] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0070] like Figure 1-Figure 2 As shown, the coal ratio control method based on dynamic balance of energy consumption includes the following steps:

[0071] S1, coal characteristic data collection: collect coal characteristic data through the sensor network, including coal calorific value, ash, volatile matter, sulfur content and moisture. The collected coal characteristic data is transmitted to the control center in real time and compared with historical coal characteristic data to generate a coal characteristic database;

[0072] S2, energy consumption model construction: Based on the collected coal characteristic data, combined with the production conditions and the actual operating parameters of the equipment, an energy consumption dynamic balance model is constructed to dynamically predict the energy consumption changes in different production stages;

[0073] S3, coal ratio optimization: Based on the prediction results of the energy consumption dynamic balance model, the ratio of different types of coal is optimized in real time through genetic algorithms to ensure that the coal after ratio matching can meet production requirements with the lowest energy consumption while taking into account the safety and environmental protection standards of the equipment;

[0074] S4, real-time feedback control: In the coal ratio and actual production process, real-time monitoring of energy consumption changes and production conditions, dynamic adjustment of coal ratio according to energy consumption deviation in the production process, to ensure the balance between energy consumption and production demand;

[0075] S5, energy consumption data storage and analysis: store the energy consumption data and equipment operating parameters generated during each batching and production process, and analyze them in association with historical data for long-term energy consumption trend assessment and optimization of coal batching strategies;

[0076] S6, Energy efficiency optimization feedback: Based on the stored and analyzed energy consumption data, the energy consumption dynamic balance model and coal ratio optimization plan are regularly adjusted to achieve continuous improvement of energy efficiency;

[0077] S7, production environment adaptation: Dynamically adjust the combustion characteristics of coal according to the actual working conditions of the production site (such as temperature, humidity, pressure and other environmental parameters) to further improve energy efficiency and reduce energy consumption.

[0078] S1 specifically includes:

[0079] S11, sensor type selection and deployment: According to the physical and chemical characteristics of different types of coal, select and deploy appropriate sensor networks, including calorific value sensors, ash sensors, volatile matter analyzers, sulfur content detectors and moisture measurement sensors. Sensor selection takes into account the measurement accuracy of each parameter and the adaptability to the on-site environment;

[0080] S12, Calibration and self-checking of sensor networks: Before formally collecting data, all deployed sensors are calibrated and self-checked to ensure their data collection accuracy in different working environments. The calibration method includes calibration with standard samples, and regular self-checking and error correction during the production process;

[0081] S13, real-time collection of coal characteristic data: through the sensor network, real-time collection of coal characteristic data, including calorific value, ash content, volatile matter, sulfur content and moisture content, and the collection frequency and transmission rate of each sensor should be dynamically adjusted according to production needs to ensure the real-time nature of the data;

[0082] S14, data transmission and preliminary filtering: the collected coal characteristic data is transmitted to the control center in real time through the sensor network, and preliminary filtering is performed during the transmission process to remove abnormal data and noise to ensure the reliability of the data;

[0083] S15, historical data comparison and generation of characteristic data: In the control center, the collected real-time coal characteristic data is compared and analyzed with the historical coal characteristic data. The change trend and similarity of the characteristic parameters of each type of coal are analyzed through the feature matching algorithm to generate a coal characteristic database that reflects the changes of different types of coal under different time periods and conditions.

[0084] S2 specifically includes:

[0085] S21, production condition data collection: through the condition sensors installed in the production equipment, the production condition data, including temperature, pressure, flow rate and combustion gas composition, are collected in real time. These condition parameters are used in combination with the coal characteristic data to ensure that the model can accurately reflect the energy consumption of coal under different production conditions;

[0086] S22, equipment operation parameter collection: extract equipment operation parameters from the equipment operation status, including equipment power, load condition, operation speed and cooling efficiency. By real-time monitoring of these parameters, the operation status changes of the equipment under different working conditions are captured to provide support for energy consumption prediction;

[0087] S23, multivariate data fusion and preprocessing: multi-dimensional data fusion of coal characteristic data, production condition data and equipment operation parameters, and preprocessing of coal characteristic data, production condition data and equipment operation parameters using standardization and normalization methods to eliminate dimensional differences between different data sources and improve data consistency and model accuracy. The preprocessed data will be used for model training and dynamic prediction;

[0088] S24, energy consumption dynamic balance model construction: Based on the nonlinear regression algorithm, the energy consumption dynamic balance model is constructed, including:

[0089] (1) Variable selection: Based on the preprocessed data, select the key variables that affect energy consumption, including coal characteristic data (such as calorific value, ash content, etc.), production condition data (such as temperature, pressure) and equipment operating parameters (such as load rate);

[0090] (2) Determine the model structure: Determine the structure of the multivariable energy consumption dynamic balance model and use the nonlinear regression algorithm to construct the energy consumption dynamic balance model. The model expression can be expressed as:

[0091] ;

[0092] in, Indicates energy consumption, Represents coal characteristic data, including calorific value, ash, volatile matter, sulfur content and moisture. Indicates production condition data, such as temperature and pressure, Indicates the equipment operating parameters. represents the combustion gas composition, function It represents a nonlinear regression model that can dynamically predict energy consumption by fitting historical data and current data;

[0093] (3) Model training and validation: Use historical data to train the model, use cross-validation to evaluate the model’s prediction accuracy, and adjust the model based on the validation results until the required accuracy is achieved;

[0094] S25, dynamic prediction of energy consumption: after the model is built, real-time production condition data and equipment operating parameters are input, combined with current coal characteristic data, to dynamically predict energy consumption changes in different production stages;

[0095] By constructing a multivariable energy consumption dynamic balance model, accurate prediction of energy consumption in the production process can be achieved, ensuring dynamic regulation and optimization of the ratio in different production stages.

[0096] S3 specifically includes:

[0097] S31, optimization target setting: according to the prediction results of the energy consumption dynamic balance model, set the optimization target, which mainly includes:

[0098] (1) Minimization of energy consumption: ensuring that the energy consumption in the production process is minimized after coal proportioning;

[0099] (2) Meeting production needs: The coal ratio needs to meet the basic needs of the production process under the premise of minimum energy consumption;

[0100] (3) Equipment safety: ensuring that the coal mix does not cause equipment overload or safety hazards;

[0101] (3) Compliance with environmental standards: The optimized ratio must meet the environmental emission standards of the country or region;

[0102] S32, constraint definition: According to the actual production situation, define the constraint conditions in the optimization algorithm, including:

[0103] (1) Coal type characteristic constraints: The characteristic data of different coal types (calorific value, ash content, volatile matter, etc.) are used as the constraints for the proportion;

[0104] (2) Equipment operating constraints: Consider the equipment’s operating capabilities, such as maximum processing capacity and load limits;

[0105] (3) Environmental emission constraints: The ratio needs to be within the environmental emission limit range, such as the emission standards of sulfur dioxide and nitrogen oxides;

[0106] S33, application of genetic algorithm: according to the actual optimization objectives and constraints, the genetic algorithm is used to optimize the coal ratio and output the optimal coal ratio scheme. The specific steps include:

[0107] (1) Initial population generation: A group of initial populations is randomly generated based on the characteristic data of different coal types, and each individual represents a coal ratio scheme;

[0108] (2) Definition of fitness function: The fitness function is used to evaluate the quality of an individual, with the goal of minimizing energy consumption, expressed as:

[0109] ;

[0110] in, is the fitness function, Indicates energy consumption, Characteristic data of different types of coal, is the production condition parameter, is the device operating parameter, Combustion gas composition;

[0111] (3) Selection operation: According to the evaluation of the fitness function, individuals with higher fitness are selected to enter the next generation, using strategies such as roulette selection or tournament selection;

[0112] (4) Crossover operation: Through the crossover operation, some genes of two matching individuals are exchanged to generate new individuals, thereby generating a new matching scheme;

[0113] (5) Mutation operation: introducing a small amount of mutation in the matching individuals, increasing the diversity of the solution space and avoiding local optimal solutions by randomly changing certain parts of the matching parameters;

[0114] (6) Iteration and convergence: Through multiple iterations, the genetic algorithm gradually evolves the optimal coal ratio scheme. When the value of the fitness function no longer changes significantly or reaches the set convergence condition, the optimal coal ratio scheme is output;

[0115] S34, verification and adjustment of the ratio results: the ratio scheme output by the genetic algorithm is simulated and verified to confirm the feasibility of the scheme in actual production. If the simulation results meet the production needs and environmental protection requirements, the ratio scheme will be put into actual production and adjusted according to the real-time feedback data during the production process;

[0116] Through genetic algorithms, real-time optimization of coal ratio is achieved to meet production needs while minimizing energy consumption, ensuring safe operation of equipment and complying with environmental emission standards.

[0117] S4 specifically includes:

[0118] S41, real-time collection of energy consumption data: During the coal proportioning and production process, sensors are used to collect equipment energy consumption data, combustion parameters (such as temperature, pressure, gas composition) and equipment operating status in real time;

[0119] S42, energy consumption deviation calculation: compare the energy consumption data collected in real time with the target energy consumption predicted by the energy consumption dynamic balance model, and calculate the energy consumption deviation. The calculation formula of the energy consumption deviation is as follows:

[0120] ,in, is the energy consumption deviation, For the energy consumption data collected in real time, Based on the target energy consumption predicted by the dynamic balance model of energy consumption, determine whether the coal ratio needs to be adjusted according to the deviation size and trend;

[0121] S43, setting of coal ratio adjustment rules: according to the size of energy consumption deviation, set the rules for dynamically adjusting the coal ratio. The adjustment rules include:

[0122] (1) Fine-tuning rule: When the energy consumption deviation is small, the system stability is maintained by fine-tuning the proportion of coal types in the current proportioning scheme;

[0123] (2) Substantial adjustment of rules: When the energy consumption deviation is large, a new coal ratio optimization calculation is immediately started, and the optimal ratio scheme is recalculated using a nonlinear optimization algorithm;

[0124] S44, dynamic ratio adjustment: adjust the coal ratio in real time according to the set adjustment rules. For example, when making fine adjustments, increase the proportion of high calorific value coal to reduce energy consumption, or when making large adjustments, recalculate the optimal ratio to maximize energy efficiency. The adjusted ratio plan is immediately put into production to correct the energy consumption deviation as soon as possible.

[0125] S45, closed-loop control feedback mechanism: The adjusted coal ratio scheme is fed back to the production equipment in real time through the preset closed-loop control system, and continuous monitoring is carried out to ensure that the energy consumption deviation is gradually reduced until it is eliminated. The closed-loop control system is based on the interaction between energy consumption data, equipment status and ratio adjustment to ensure the accuracy and immediacy of real-time regulation;

[0126] S46, abnormal situation handling: When the energy consumption deviation exceeds the set threshold range or production abnormalities occur (such as equipment failure or change in coal quality), the alarm mechanism is activated and the proportioning scheme is adjusted according to the pre-set emergency plan to ensure safe operation of the system;

[0127] Through real-time feedback control, energy consumption changes are monitored in real time during the coal blending process, energy consumption deviations are calculated, and the coal blending plan is dynamically adjusted according to the deviations to ensure a balance between production demand and energy consumption. The closed-loop control system further improves the response speed and accuracy of real-time control to ensure that the coal blending is always in the optimal state.

[0128] S5 specifically includes:

[0129] S51, Energy consumption data storage format design: During the production process, each coal ratio and the energy consumption data and equipment operating parameters generated by actual production are stored in a standardized data format. The data format includes:

[0130] (1) Coal characteristic data: calorific value, ash content, volatile matter, sulfur content, moisture content, etc.;

[0131] (2) Production condition data: such as temperature, pressure, combustion gas composition, etc.;

[0132] (3) Equipment operating parameters: equipment power, operating speed, load conditions, etc.;

[0133] (4) Timestamp: Mark the time of each data record to ensure that the data is stored in chronological order for subsequent time series analysis;

[0134] S52, real-time data storage and backup: energy consumption data and equipment operating parameters are stored in the database in real time, and regular automatic backup is performed;

[0135] S53, historical data correlation analysis: In the database, energy consumption data and equipment operating parameters are correlated with historical data. Time series analysis algorithms and regression analysis are used to mine historical data to identify long-term trends in energy consumption. Correlation analysis includes:

[0136] Time series analysis: Analyze the changing trends of energy consumption data in different periods and evaluate the fluctuation patterns of energy consumption;

[0137] Regression analysis: Analyze the relationship between production conditions, equipment operating parameters and energy consumption, and identify key influencing factors;

[0138] S54, long-term energy consumption trend assessment: Generate an energy consumption trend assessment report based on the correlation analysis results of historical data. The report includes the following contents:

[0139] (1) Energy consumption trend: Identify the upward or downward trend of energy consumption and analyze possible causes (such as equipment aging, changes in coal quality, etc.);

[0140] (2) Trends in key parameter changes: Identify trends in production conditions or equipment operating parameters that have a significant impact on energy consumption;

[0141] S55, Coal Proportion Strategy Optimization: Based on the long-term energy consumption trend assessment results, the coal proportion scheme is optimized. According to the results of the energy consumption dynamic balance model, historical data analysis and trend assessment, the specific parameters of the coal proportion are adjusted. The optimization scheme includes:

[0142] (1) Adjustment of proportion parameters: Redefine the proportion parameters of each type of coal based on its historical performance and actual production needs to ensure continuous optimization of energy efficiency;

[0143] (2) Equipment operation optimization suggestions: Based on the long-term trends of equipment operating parameters, make suggestions for equipment maintenance or operation adjustments to ensure that the equipment operates in the best condition, thereby reducing energy consumption;

[0144] S56, continuous monitoring of energy efficiency optimization: After the optimized coal ratio scheme and equipment adjustment suggestions are put into production, the energy consumption changes in actual production are continuously monitored, and the effectiveness of the optimization measures is verified by comparison with historical data, and further adjustments are made based on the feedback results;

[0145] The system can effectively store, correlate and analyze energy consumption data, and evaluate long-term trends, and optimize coal blending strategies based on these results. In-depth analysis of historical data and evaluation of long-term trends provide a basis for continuous optimization of the system, ensuring continuous improvement of energy efficiency and efficient production operation.

[0146] S6 includes:

[0147] S61, Periodic evaluation of energy consumption data: Regularly extract the latest energy consumption data and production operation parameters from the database and conduct periodic evaluation. By analyzing the energy consumption data for the most recent period, evaluate whether the current energy consumption performance meets the expected target, and identify abnormal fluctuations in energy consumption and potential improvement opportunities;

[0148] S62, Analysis of factors affecting energy efficiency: Based on the results of periodic evaluation, regression analysis and correlation analysis methods are used to analyze the key factors affecting energy efficiency, including changes in coal characteristics, fluctuations in production conditions, equipment operating status, etc. The goal of the analysis is to clarify the reasons for the current high energy consumption and determine the parameters that need to be optimized;

[0149] S63, regular adjustment of the energy consumption dynamic balance model: based on the results of energy efficiency evaluation and influencing factor analysis, the energy consumption dynamic balance model is regularly updated and adjusted. The specific adjustment steps include:

[0150] (1) Parameter update: Recalibrate the parameters in the model based on the latest data, such as coal characteristics and weights of equipment operating parameters;

[0151] (2) Model structure optimization: Optimize the model structure based on the analysis results, which may involve introducing new variables or adjusting the model's calculation formula to ensure that the model can more accurately predict future energy consumption changes.

[0152] The S6 also includes:

[0153] S64, Coal ratio optimization plan adjustment: Based on the updated energy consumption dynamic balance model, the coal ratio plan is optimized and adjusted. The specific optimization steps include:

[0154] (1) Optimization objective function update: Based on the prediction results of the new model, redefine the objective function of minimizing energy consumption to make it more consistent with the current production situation;

[0155] (2) Optimization algorithm iteration: Use genetic algorithms or other nonlinear optimization algorithms to iteratively calculate the new objective function to obtain the optimized coal ratio scheme;

[0156] S65, adjusted energy efficiency optimization verification: apply the adjusted coal ratio scheme to actual production, and continuously track the energy consumption performance in production to verify whether the actual energy consumption is consistent with the new forecast results, and ensure that the adjusted model and ratio strategy effectively improve energy efficiency. If it is found that the actual performance deviates from the forecast, further optimize the model and ratio scheme;

[0157] S66, feedback closed-loop optimization mechanism: Based on real-time monitoring and feedback of energy consumption data, the optimized energy consumption dynamic balance model and coal ratio scheme are incorporated into the closed-loop control system. The closed-loop control system will automatically trigger periodic adjustments according to changes in real-time energy consumption data, forming a continuous optimization feedback mechanism to ensure continuous improvement of energy efficiency;

[0158] Energy efficiency optimization feedback enables dynamic adjustment of models and proportioning schemes based on periodic evaluation. By updating the dynamic balance model of energy consumption and optimizing the coal proportioning strategy, energy efficiency can be continuously optimized according to the latest data, and a closed-loop feedback mechanism is used to ensure continuous improvement of energy efficiency and stability of production.

[0159] S7 includes:

[0160] S71, real-time collection of production environment parameters: through the environmental sensor network installed at the production site, real-time collection of environmental data under production conditions, including temperature, humidity, pressure, oxygen content and combustion gas composition, the collected data is transmitted to the control center through the sensor network for subsequent combustion characteristics adjustment;

[0161] S72, Correlation analysis between environmental parameters and combustion characteristics: Based on the real-time collected environmental data, the correlation analysis method is applied to determine the impact of environmental data changes on coal combustion characteristics. The contents of the correlation analysis include:

[0162] (1) Temperature effect: Analyze the changes in coal combustion rate and combustion efficiency at different temperatures;

[0163] (2) Humidity effect: evaluate the effect of humidity changes on coal moisture content and combustion performance;

[0164] (3) Pressure influence: Analyze the combustion rate and energy consumption level of coal under different pressure conditions;

[0165] S73, setting of dynamic adjustment rules for combustion characteristics: according to the correlation analysis results, dynamic adjustment rules for combustion characteristics are set, and the rules include:

[0166] (1) Adjustment of temperature changes: When the ambient temperature rises, reduce the proportion of coal with high volatile content to prevent excessive combustion speed; when the temperature drops, increase the proportion of coal with high calorific value to ensure combustion efficiency;

[0167] (2) Adjustment for humidity changes: When the humidity is high, coal with low moisture content should be used first to improve combustion performance;

[0168] (3) Adjustment of pressure changes: When the pressure is high, reduce the coal supply rate to avoid excessive combustion and increase in energy consumption.

[0169] The S7 also includes:

[0170] S74, real-time combustion characteristic adjustment: according to the set dynamic adjustment rules, the combustion characteristics of coal are adjusted in real time. During the production process, according to the changes in environmental parameters, the proportion, combustion rate and coal supply rate of different types of coal are automatically adjusted to ensure the optimal combustion efficiency and energy consumption under different working conditions;

[0171] S75, Feedback control of environmental parameter changes: Combine the real-time combustion characteristic adjustment scheme with the closed-loop control system, and verify the effect of each combustion adjustment by continuously monitoring the changes in environmental data. When the verification results show that the adjustment strategy does not achieve the expected effect, the environmental impact factors will be automatically re-evaluated and the combustion strategy will be adjusted to ensure energy efficiency improvement in the production process;

[0172] S76, Balance optimization between combustion characteristics and production needs: During the dynamic adjustment of environmental data, current production needs are taken into consideration to ensure that the production schedule or equipment operation is not affected when the combustion characteristics are adjusted. For example, when the environmental pressure is low, the adjusted combustion rate needs to match the production needs to ensure that energy consumption is minimized while maintaining production efficiency.

[0173] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0174] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A coal proportion control method based on dynamic balance of energy consumption, characterized in that: The following steps are involved: S1, coal characteristic data collection: collect coal characteristic data through the sensor network, including coal calorific value, ash, volatile matter, sulfur content and moisture. The collected coal characteristic data is transmitted to the control center in real time and compared with historical coal characteristic data to generate a coal characteristic database; S2, energy consumption model construction: Based on the collected coal characteristic data, combined with the production conditions and the actual operating parameters of the equipment, an energy consumption dynamic balance model is constructed to dynamically predict the energy consumption changes in different production stages; S3, coal ratio optimization: Based on the prediction results of the energy consumption dynamic balance model, the ratio of different coal types is optimized in real time through genetic algorithms; S4, real-time feedback control: in the coal ratio and actual production process, real-time monitoring of energy consumption changes and production conditions, and dynamic adjustment of coal ratio according to energy consumption deviations in the production process; S5, energy consumption data storage and analysis: store the energy consumption data and equipment operating parameters generated during each batching and production process, and analyze them in association with historical data for long-term energy consumption trend assessment and optimization of coal batching strategies; S6, Energy efficiency optimization feedback: Based on the stored and analyzed energy consumption data, the energy consumption dynamic balance model and coal ratio optimization plan are regularly adjusted to achieve continuous improvement of energy efficiency; S7, Adaptation to the production environment: Dynamically adjust the combustion characteristics of coal according to the actual working conditions of the production site to further improve energy efficiency and reduce energy consumption.

2. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 1, characterized in that: The S1 specifically includes: S11, sensor type selection and deployment: According to the physical and chemical characteristics of different types of coal, select and deploy sensor networks, including calorific value sensors, ash sensors, volatile matter analyzers, sulfur content detectors and moisture measurement sensors; S12, Calibration and self-checking of sensor networks: Calibrate and self-check the deployed sensors; S13, real-time collection of coal characteristic data: through the sensor network, real-time collection of coal characteristic data, including calorific value, ash content, volatile matter, sulfur content and moisture; S14, data transmission and preliminary filtering: the collected coal characteristic data is transmitted to the control center in real time through the sensor network; S15, historical data comparison and generation of characteristic data: the collected real-time coal characteristic data is compared and analyzed with the historical coal characteristic data, and the change trend and similarity of the characteristic parameters of each type of coal are analyzed through the feature matching algorithm to generate a coal characteristic database to reflect the changes of different types of coal under different time periods and conditions.

3. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 2, characterized in that: The S2 specifically includes: S21, production condition data collection: through the condition sensors installed in the production equipment, the production condition data, including temperature, pressure, flow rate and combustion gas composition, are collected in real time; S22, equipment operation parameter collection: extracting equipment operation parameters from the equipment operation status, the equipment operation parameters including equipment power, load condition, operation speed and cooling efficiency; S23, multivariate data fusion and preprocessing: multi-dimensional data fusion of coal characteristic data, production condition data and equipment operation parameters, and preprocessing of coal characteristic data, production condition data and equipment operation parameters by using standardization and normalization methods; S24, energy consumption dynamic balance model construction: based on nonlinear regression algorithm, energy consumption dynamic balance model is constructed; S25, dynamic prediction of energy consumption: After the model is built, real-time production condition data and equipment operating parameters are input, and combined with the current coal characteristic data, dynamic prediction of energy consumption changes in different production stages is carried out.

4. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 3 is characterized in that: The S3 specifically includes: S31, optimization target setting: setting the optimization target according to the prediction results of the energy consumption dynamic balance model; S32, constraint definition: define the constraint conditions in the optimization algorithm according to the actual production situation; S33, genetic algorithm application: according to the actual optimization objectives and constraints, the genetic algorithm is used to optimize the coal ratio and output the optimal coal ratio scheme; S34, verification and adjustment of the proportioning results: the proportioning scheme output by the genetic algorithm is simulated and verified to confirm the feasibility of the scheme in actual production.

5. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 4, characterized in that: The S4 specifically includes: S41, real-time collection of energy consumption data: During the coal proportioning and production process, the energy consumption data, combustion parameters and equipment operation status of the equipment are collected in real time through sensors; S42, energy consumption deviation calculation: comparing the energy consumption data collected in real time with the target energy consumption predicted by the energy consumption dynamic balance model, and calculating the energy consumption deviation; S43, setting of coal ratio adjustment rules: setting of rules for dynamically adjusting coal ratio according to the size of energy consumption deviation; S44, dynamic ratio adjustment: adjust the coal ratio in real time according to the set adjustment rules; S45, closed-loop control feedback mechanism: The adjusted coal ratio plan is fed back to the production equipment in real time through the preset closed-loop control system and continuously monitored; S46, abnormal situation handling: When the energy consumption deviation exceeds the set threshold range or production abnormalities occur, the alarm mechanism is activated and the ratio scheme is adjusted according to the pre-set emergency plan.

6. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 5, characterized in that: The S5 specifically includes: S51, Energy consumption data storage format design: During the production process, each coal ratio and the energy consumption data and equipment operation parameters generated by actual production are stored in a standardized data format; S52, real-time data storage and backup: energy consumption data and equipment operating parameters are stored in the database in real time, and regular automatic backup is performed; S53, historical data correlation analysis: in the database, energy consumption data and equipment operation parameters are correlated with historical data for analysis; S54, long-term energy consumption trend assessment: Generate an energy consumption trend assessment report based on the correlation analysis results of historical data; S55, coal ratio strategy optimization: based on the long-term energy consumption trend assessment results, optimize the coal ratio plan; S56, continuous monitoring of energy efficiency optimization: After the optimized coal ratio plan and equipment adjustment suggestions are put into production, the energy consumption changes in actual production are continuously monitored.

7. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 6, characterized in that: The S6 includes: S61, periodic evaluation of energy consumption data: regularly extract the latest energy consumption data and production operation parameters from the database for periodic evaluation; S62, Analysis of factors affecting energy efficiency: Based on the results of periodic assessment, regression analysis and correlation analysis methods are used to analyze the factors affecting energy efficiency; S63, regular adjustment of the energy consumption dynamic balance model: regularly update and adjust the energy consumption dynamic balance model based on the results of energy efficiency evaluation and influencing factor analysis.

8. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 7, characterized in that: The S6 further includes: S64, Coal ratio optimization scheme adjustment: Based on the updated energy consumption dynamic balance model, the coal ratio scheme is optimized and adjusted; S65, adjusted energy efficiency optimization verification: apply the adjusted coal ratio scheme to actual production and continuously track the energy consumption performance in production; S66, feedback closed-loop optimization mechanism: Based on real-time monitoring and feedback of energy consumption data, the optimized energy consumption dynamic balance model and coal ratio scheme are incorporated into the closed-loop control system.

9. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 8, characterized in that: The S7 includes: S71, real-time collection of production environment parameters: through the environmental sensor network installed at the production site, real-time collection of environmental data under production conditions, including temperature, humidity, pressure, oxygen content and combustion gas composition; S72, Correlation analysis between environmental parameters and combustion characteristics: Based on the real-time collected environmental data, the correlation analysis method is applied to determine the impact of changes in environmental data on coal combustion characteristics; S73, setting of dynamic adjustment rules for combustion characteristics: setting of dynamic adjustment rules for combustion characteristics according to the correlation analysis results.

10. The method for controlling coal proportion based on dynamic balance of energy consumption according to claim 9, characterized in that: The S7 further comprises: S74, real-time combustion characteristic adjustment: adjusting the combustion characteristics of coal in real time according to the set dynamic adjustment rules; S75, Feedback control of environmental parameter changes: Combine the real-time combustion characteristic adjustment scheme with the closed-loop control system, and verify the effect of each combustion adjustment by continuously monitoring the changes in environmental data. If the verification results show that the adjustment strategy does not achieve the expected effect, the environmental influencing factors will be automatically re-evaluated and the combustion strategy will be adjusted; S76, Balance optimization between combustion characteristics and production needs: In the dynamic adjustment process of environmental data, current production needs are taken into consideration to ensure that the production schedule or equipment operation is not affected when the combustion characteristics are adjusted.

Images