Efficient multi-stage waste heat recovery and reutilization combined heat and power generation system

Abstract

The invention relates to the technical field of heat energy recovery, in particular to an efficient multistage waste heat recovery and reutilization combined heat and power generation system which comprises a heat output calculation module, a temperature difference mode analysis module, a heat exchange efficiency analysis module, an optimal parameter identification module, a condenser management module and a condensate water recovery control module. According to the method, temperature, flow and pressure data of multiple positions are collected in real time, the heat fluctuation trend under multiple input conditions is accurately predicted, a basis is provided for follow-up optimization decision making, and through dynamic prediction of a temperature difference mode, the heat exchanger inlet and outlet temperature and the mutual relation between multiple equipment working parameters are combined, so that the heat exchange efficiency is improved. Real-time evaluation of the heat exchange efficiency is achieved, construction of a working model of a heat exchanger is combined, optimal working parameters under multiple temperature gradients are accurately recognized, optimization of working parameters of a condenser and condensate water recovery equipment is combined, the energy utilization rate of the combined heat and power generation system is increased, energy consumption is reduced, and resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of heat energy recovery, and in particular to a heat and power cogeneration system with high efficiency and multi-stage waste heat recovery and reuse. Background Art

[0002] The field of heat recovery technology involves a variety of technologies for effectively recovering and reusing waste heat generated during industrial production, building heating and power generation, including waste heat recovery, cogeneration, energy conversion and transmission. Waste heat is collected, transmitted and utilized through a variety of devices and methods, aiming to improve energy utilization efficiency, reduce energy waste and minimize environmental pollution. By adopting heat exchangers, heat pumps, cogeneration systems and various technologies, low-grade waste heat can be effectively recovered and converted into useful electrical energy or thermal energy.

[0003] Among them, the cogeneration system with multi-stage waste heat recovery and reuse recovers waste heat at different temperature levels by using multi-stage waste heat recovery devices, and converts the recovered heat energy into electricity and heat energy in combination with the cogeneration system. It involves the recovery and utilization of waste heat at different temperatures, uses multiple heat exchangers for heat recovery, and converts waste heat into electricity or heating through cogeneration equipment. The system cooperates with multiple links such as waste heat recovery, heat exchange, and cogeneration to achieve dual utilization of electricity and heat without increasing additional energy consumption.

[0004] Traditional cogeneration systems rely on fixed control modes and equipment parameters, and cannot fully consider the dynamic adjustment needs of the system under complex and changeable working conditions. The working state of the heat exchanger is a fixed parameter set based on experience, and cannot be optimized in real time for different temperature gradients and load changes, resulting in unstable recovery efficiency. Traditional systems lack sufficient flexibility in analyzing the changing trends of temperature differences and flow rates. When the external environment or equipment operating conditions change, the system responds slowly, affecting the overall energy utilization efficiency. The control of steam pressure and condensate recovery cannot be finely managed, resulting in excessive pressure fluctuations and low condensate recovery efficiency, exacerbating energy waste, leading to low energy utilization efficiency and shortened equipment life. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides a high-efficiency multi-stage waste heat recovery and reuse cogeneration system. The technical solution is as follows:

[0006] On the one hand, a high-efficiency multi-stage waste heat recovery and reuse combined heat and power system is provided, which includes:

[0007] The heat output calculation module is based on IoT sensors and collects temperature, flow, and pressure data from multiple locations in real time. It calculates the energy transfer relationship between multiple heat sources and cooling equipment, analyzes heat output under various input conditions, calculates heat fluctuation trends under current working conditions, and generates waste heat prediction results.

[0008] The temperature difference pattern analysis module analyzes the relationship between the inlet and outlet temperatures of the heat exchanger and various equipment operating parameters based on the waste heat prediction results, identifies the change pattern of the inlet and outlet temperatures, predicts the change trend of the temperature difference data, and generates a temperature difference prediction result;

[0009] The heat exchange efficiency analysis module analyzes the temperature difference change between the inlet and outlet of the heat exchanger based on the temperature difference prediction results, evaluates the heat exchange efficiency in real time, analyzes the volatility of the data, and generates real-time heat exchange efficiency information;

[0010] The optimal parameter identification module constructs a working model of the heat exchanger based on the real-time heat exchange efficiency information, analyzes the working efficiency of the heat exchanger under various input conditions, identifies the optimal working parameters under multiple temperature gradients, calculates the control parameter adjustment values, and generates the heat exchanger working parameters;

[0011] The condenser management module monitors the steam temperature, flow rate, and load data of the condenser in real time based on the operating parameters of the heat exchanger, adjusts the operating parameters of the condenser, and generates a steam pressure regulation result;

[0012] The condensate recovery control module calculates the working efficiency of the condensate recovery equipment under various working conditions and optimizes the operating parameters based on the steam pressure regulation result, thereby generating the cogeneration working parameters.

[0013] As a further solution of the present invention, the waste heat prediction results include heat fluctuation data of multiple heat sources, waste heat change prediction data of multiple time periods, and the energy transfer relationship between the heat source and the cooling equipment. The temperature difference prediction results include the change pattern of the inlet and outlet temperatures, the time series change trend of the temperature difference data, and the predicted temperature difference fluctuation range. The real-time heat exchange efficiency information includes the relationship between the inlet and outlet temperature difference and the flow change, the real-time heat exchange efficiency evaluation value, and the data volatility analysis results. The heat exchanger operating parameters include multi-gradient flow rate parameters, multi-gradient heat exchange area parameters, and multi-gradient temperature difference adjustment values. The steam pressure adjustment results include the steam pressure fluctuation range, steam pressure change trend, and condenser operating parameter adjustment value. The cogeneration operating parameters include recovery efficiency calculation results, water pump speed parameters, and valve opening adjustment values.

[0014] As a further solution of the present invention, the heat output calculation module includes:

[0015] The status data acquisition submodule is based on IoT sensors, which collects temperature, flow, and pressure data at multiple locations in real time, records the operating status of multiple heat sources and cooling equipment, and generates real-time data collection results;

[0016] The transfer relationship analysis submodule calculates the energy transfer relationship between multiple heat sources and cooling equipment based on the real-time data collection results, analyzes the energy output of multiple heat sources in combination with the equipment operation status, and generates a heat transfer calculation result;

[0017] The output fluctuation analysis submodule analyzes the heat fluctuation under the current working conditions based on the heat transfer calculation result, calculates the change trend of the heat output, and generates the waste heat prediction result.

[0018] As a further solution of the present invention, the temperature difference pattern analysis module includes:

[0019] The correlation analysis submodule analyzes the relationship between the heat exchanger inlet and outlet temperatures and various equipment operating parameters based on the waste heat prediction results, evaluates the impact of temperature changes on heat exchange efficiency, and generates temperature difference relationship analysis results;

[0020] The time series modeling submodule identifies the change pattern of the inlet and outlet temperatures based on the temperature difference relationship analysis results through time series analysis, and generates temperature difference change pattern information;

[0021] The temperature difference data prediction submodule analyzes the change trend of the inlet and outlet temperature difference of the heat exchanger based on the temperature difference change pattern information, and predicts the temperature difference data at multiple time points to generate a temperature difference prediction result.

[0022] As a further solution of the present invention, the heat exchange efficiency analysis module includes:

[0023] The data fluctuation analysis submodule calculates the temperature difference change at the inlet and outlet of the heat exchanger based on the temperature difference prediction results and combines the temperature difference and flow rate change trends, analyzes the temperature difference fluctuations of the cogeneration equipment under various working conditions, and generates temperature difference change analysis results;

[0024] The stability assessment submodule calculates the heat exchange efficiency in real time based on the temperature difference change analysis results, analyzes the stability of the heat exchanger's working efficiency, identifies abnormal fluctuations and calculates the efficiency change trend, and generates efficiency fluctuation analysis results;

[0025] The abnormal efficiency detection submodule detects deviations and abnormal values ​​of the heat exchange efficiency data in real time based on the efficiency fluctuation analysis result, and generates real-time heat exchange efficiency information.

[0026] As a further solution of the present invention, the specific formula for analyzing the stability of the heat exchanger working efficiency is:

[0027]

[0028] Among them, S represents the stability score of the work efficiency data, dimensionless, EX represents the maximum value of the heat exchange efficiency during the observation period, dimensionless, EN represents the minimum value of the heat exchange efficiency during the observation period, dimensionless, V represents the variance of the efficiency data, dimensionless, D represents the average value of the difference between adjacent efficiency data, dimensionless, R represents the fluctuation range of the efficiency data, dimensionless, α represents the variance weight coefficient, dimensionless, β represents the fluctuation range weight coefficient, dimensionless, and γ represents the benchmark adjustment coefficient, dimensionless.

[0029] As a further solution of the present invention, the optimal parameter identification module includes:

[0030] The working model building submodule builds a working model of the heat exchanger based on the real-time heat exchange efficiency information, simulates the operating state of the heat exchanger under various working conditions, analyzes the working efficiency under various inputs, and generates a working model of the heat exchanger;

[0031] The multi-operating condition simulation submodule calculates the optimal operating parameters of the heat exchanger under various operating conditions based on the heat exchanger operating model and generates parameter calculation results;

[0032] The control parameter calculation submodule calculates the adjustment value of the control parameter based on the parameter calculation result and combines the real-time working efficiency of the heat exchanger to generate the working parameter of the heat exchanger.

[0033] As a further solution of the present invention, the specific formula for analyzing the work efficiency under multiple inputs is:

[0034]

[0035] Where S′ represents the efficiency score of the target heat exchanger configuration, Q represents the normalized value of the heat exchanger heat load, P represents the normalized value of the operating power consumption, η represents the heat transfer efficiency of the heat exchanger, v represents the normalized value of the working fluid flow rate, and r represents the operating resistance coefficient.

[0036] As a further solution of the present invention, the condenser management module includes:

[0037] The condenser monitoring submodule monitors and records the steam temperature, flow rate, and load data of the condenser in real time based on the heat exchanger operating parameters, and generates a steam system data set;

[0038] The pressure data analysis submodule analyzes the fluctuation range of steam pressure under various working conditions based on the steam system data set, evaluates the pressure change trend, detects abnormal pressure fluctuations, and generates steam pressure fluctuation analysis results;

[0039] Based on the steam pressure fluctuation analysis results, the working pressure management submodule calculates and adjusts the working parameters of the condenser, including the steam valve opening and boiler power output, according to the steam pressure required for the condenser to generate a steam pressure regulation result.

[0040] As a further solution of the present invention, the condensed water recovery control module includes:

[0041] The recovery equipment monitoring submodule extracts steam temperature, flow, and pressure data based on the steam pressure regulation result, calculates the working efficiency of the condensate recovery equipment, and generates a recovery efficiency evaluation result;

[0042] The working parameter optimization submodule calculates the working efficiency of the condensate recovery equipment under various working conditions based on the recovery efficiency evaluation results, analyzes the optimal working parameters of the condensate recovery equipment, and generates recovery operation parameters;

[0043] The operating parameter adjustment submodule adjusts the operating parameters of the condensate recovery equipment based on the recovery operating parameters, including the valve opening and the water pump speed, to generate cogeneration operating parameters.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] By collecting temperature, flow and pressure data at multiple locations in real time, accurate predictions of heat fluctuation trends under various input conditions are made, providing a basis for subsequent optimization decisions. Through dynamic predictions of temperature difference patterns, combined with the relationship between the inlet and outlet temperatures of the heat exchanger and the operating parameters of various equipment, real-time evaluation of heat exchange efficiency is achieved. Combined with the construction of the heat exchanger's working model, the optimal operating parameters under multiple temperature gradients are accurately identified. Combined with the optimization of the operating parameters of the condenser and condensate recovery equipment, the energy utilization rate of the cogeneration system is improved, energy consumption is reduced, and resource waste is minimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 is a system flow chart of the present invention;

[0048] Figure 2 Schematic diagram of the system framework of the present invention;

[0049] Figure 3 This is a flow chart of the heat output calculation module of the present invention;

[0050] Figure 4 This is a flow chart of the temperature difference pattern analysis module of the present invention;

[0051] Figure 5 This is a flow chart of the heat exchange efficiency analysis module of the present invention;

[0052] Figure 6 This is a flow chart of the optimal parameter identification module of the present invention;

[0053] Figure 7 This is a flow chart of the condenser management module of the present invention;

[0054] Figure 8 This is a flow chart of the condensed water recovery control module of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0057] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The embodiment of the present invention provides a high-efficiency multi-stage waste heat recovery and reuse cogeneration system, please refer to Figures 1 to 2 The present invention provides a technical solution, a high-efficiency multi-stage waste heat recovery and reuse cogeneration system comprising:

[0061] The heat output calculation module is based on IoT sensors and collects temperature, flow, and pressure data from multiple locations in real time. It calculates the energy transfer relationship between multiple heat sources and cooling equipment, analyzes heat output under various input conditions, calculates heat fluctuation trends under current working conditions, and generates waste heat prediction results.

[0062] The temperature difference pattern analysis module analyzes the relationship between the heat exchanger inlet and outlet temperatures and various equipment operating parameters based on the waste heat prediction results, identifies the change pattern of the inlet and outlet temperatures, predicts the change trend of the temperature difference data, and generates temperature difference prediction results;

[0063] The heat exchange efficiency analysis module uses temperature difference prediction results to analyze the temperature difference between the inlet and outlet of the heat exchanger, evaluates the heat exchange efficiency in real time, analyzes the volatility of the data, and generates real-time heat exchange efficiency information;

[0064] The optimal parameter identification module builds a working model of the heat exchanger based on real-time heat exchange efficiency information, analyzes the working efficiency of the heat exchanger under various input conditions, identifies the optimal operating parameters under multiple temperature gradients, calculates the control parameter adjustment values, and generates the heat exchanger operating parameters;

[0065] The condenser management module monitors the steam temperature, flow rate, and load data of the condenser in real time based on the heat exchanger operating parameters, adjusts the condenser operating parameters, and generates steam pressure regulation results;

[0066] Based on the steam pressure regulation results, the condensate recovery control module calculates the working efficiency of the condensate recovery equipment under various working conditions, optimizes the operating parameters, and generates the cogeneration operating parameters.

[0067] The waste heat prediction results include heat fluctuation data of multiple heat sources, waste heat change prediction data for multiple time periods, and the energy transfer relationship between the heat source and the cooling equipment. The temperature difference prediction results include the change pattern of the inlet and outlet temperatures, the time series change trend of the temperature difference data, and the predicted temperature difference fluctuation range. The real-time heat exchange efficiency information includes the relationship between the inlet and outlet temperature difference and the flow change, the real-time heat exchange efficiency evaluation value, and the data volatility analysis results. The heat exchanger operating parameters include multi-gradient flow rate parameters, multi-gradient heat exchange area parameters, and multi-gradient temperature difference adjustment values. The steam pressure regulation results include the steam pressure fluctuation range, steam pressure change trend, and condenser operating parameter adjustment value. The cogeneration operating parameters include the recovery efficiency calculation results, water pump speed parameters, and valve opening adjustment value.

[0068] See also Figure 2 and Figure 3 , the heat output calculation module includes:

[0069] The status data acquisition submodule is based on IoT sensors, which collects temperature, flow, and pressure data at multiple locations in real time, records the operating status of multiple heat sources and cooling equipment, and generates real-time data collection results;

[0070] In the status data acquisition submodule, the operating status of multiple heat sources and cooling equipment is recorded, and the data is sent to the data processing system through sensors. The sensors are respectively arranged on the heat source equipment, cooling equipment, pipelines and key control points to collect the temperature, flow, pressure and other operating data of the target equipment in real time. The temperature data is obtained through thermocouple sensors, the flow data is obtained through turbine flowmeters, and the pressure data is measured through differential pressure sensors. Each collected device parameter is transmitted to the system data processing unit through sensors. The data processing system records the operating status of each device in real time and stores and associates the data through time series tags. All collected data is uploaded to the database through the Internet of Things communication protocol to generate real-time collection data results, including the temperature, flow, pressure and change trend data of the equipment. The subsequent data analysis module will use the target data to monitor the working status and energy efficiency performance of the equipment in real time to ensure the optimization of system operation.

[0071] The transfer relationship analysis submodule calculates the energy transfer relationship between multiple heat sources and cooling equipment based on real-time data collection results. It analyzes the energy output of multiple heat sources in combination with the equipment operating status and generates heat transfer calculation results.

[0072] In the transfer relationship analysis submodule, the energy transfer relationship between multiple heat sources and cooling devices is calculated through the heat conduction model and the heat exchange model. The heat conduction model is used, combined with the temperature difference, flow, and pressure data, to calculate the energy flow between each heat source and cooling device. By establishing a heat exchange network model, the system combines the real-time working data of each device with the parameters of the heat exchanger, and calculates the heat transfer rate of each heat source and cooling device in real time. The process is based on physical parameters such as heat exchange coefficient and temperature difference, combined with experimental data for model calibration to ensure accurate calculation of the energy transfer relationship. By establishing a multi-dimensional data model and regression analysis, the system can dynamically update the calculation results of heat transfer, and adjust the parameters in the model in real time to generate heat transfer calculation results. The target results provide basic data support for subsequent heat fluctuation analysis.

[0073] The output fluctuation analysis submodule analyzes the heat fluctuation under the current working conditions based on the heat transfer calculation results, calculates the change trend of heat output, and generates the waste heat prediction results;

[0074] In the output fluctuation analysis submodule, the time series analysis method is used to analyze the changes in heat output between each time node, identify the periodicity and fluctuation amplitude of heat fluctuations, and use regression analysis and trend analysis techniques to find out the law of heat fluctuations by comparing heat transfer data in different time periods. The factors affecting heat changes, such as load changes and external ambient temperature, are identified. Through target data analysis, the future trend of heat output changes is predicted, and waste heat prediction results are generated. The prediction results help the system adjust the energy recovery mechanism in real time to avoid unstable system operation due to excessive heat fluctuations, thereby ensuring the efficient operation of the cogeneration system.

[0075] See also Figure 2 and Figure 4 , the temperature difference pattern analysis module includes:

[0076] The correlation analysis submodule analyzes the relationship between the heat exchanger inlet and outlet temperatures and various equipment operating parameters based on the waste heat prediction results, evaluates the impact of temperature changes on heat exchange efficiency, and generates temperature difference relationship analysis results;

[0077] In the correlation analysis submodule, by collecting the temperature, flow, and pressure data of the heat source, combined with the working status and load fluctuations of the equipment, the system uses the thermodynamic model to analyze the energy transfer of the equipment. The process evaluates the specific impact of temperature changes on heat transfer efficiency by calculating the relationship between the inlet and outlet temperature difference and the change of equipment parameters. Combined with the application of multiple regression models, the actual operating data of the equipment is associated with parameters such as temperature and flow, and the contribution of temperature changes to heat transfer efficiency under different working conditions is analyzed. The temperature difference relationship analysis results are generated to provide basic data support for subsequent temperature difference pattern analysis.

[0078] The time series modeling submodule is based on the temperature difference relationship analysis results. Through time series analysis, it identifies the change pattern of inlet and outlet temperatures and generates temperature difference change pattern information;

[0079] In the time series modeling submodule, the autoregressive integral moving average model is applied and historical temperature difference data is used for modeling. The periodicity and trend of temperature changes are analyzed. The temperature difference data is differentially processed through data stationarity test to ensure that the data meets the requirements of time series analysis. By modeling the temperature difference change trend, the temperature fluctuation pattern in different time periods is identified, and the model is used to predict future temperature fluctuations. By selecting and adjusting the lag period and parameters, the model can accurately reflect the pattern of temperature difference changes and generate temperature difference change pattern information, providing a reliable basis for subsequent temperature difference predictions.

[0080] The temperature difference data prediction submodule analyzes the changing trend of the inlet and outlet temperature difference of the heat exchanger based on the temperature difference change pattern information, and predicts the temperature difference data at multiple time points to generate temperature difference prediction results;

[0081] In the temperature difference data prediction submodule, the temperature difference change pattern information is analyzed by using the long short-term memory network to predict the temperature difference data in the future. The process extracts features from the historical sequence of temperature difference data and uses the LSTM model for time series prediction to capture the long-term dependency of temperature difference fluctuations. Based on the error feedback during the model training process, the prediction model is optimized to ensure the accuracy of the prediction results. In the process, standardization and normalization of the temperature difference data are necessary to ensure the consistency of the data and the stability of the model, generate temperature difference prediction results, provide a basis for real-time adjustment, and ensure stable operation under different working conditions.

[0082] See also Figure 2 and Figure 5 , the heat transfer efficiency analysis module includes:

[0083] The data fluctuation analysis submodule calculates the temperature difference changes at the inlet and outlet of the heat exchanger based on the temperature difference prediction results and combines the temperature difference and flow rate change trends. It analyzes the temperature difference fluctuations of the cogeneration equipment under various working conditions and generates temperature difference change analysis results.

[0084] In the data fluctuation analysis submodule, the differential calculation method is applied to evaluate the fluctuation of the inlet and outlet temperature difference by inputting real-time data. The temperature difference change pattern under different equipment working conditions is identified by analyzing the correlation between flow and temperature difference. Data collection continuously monitors changes in temperature and flow. Time series data analysis methods, such as the sliding average method, are applied to denoise the fluctuation of temperature difference and ensure the smoothness of the calculation results. By calculating the temperature difference changes at multiple time points and combining the equipment's operating status data, the temperature difference fluctuation characteristics of the equipment under different loads and different environmental conditions are analyzed, and the temperature difference change analysis results are generated to provide basic data for subsequent stability evaluation.

[0085] The stability assessment submodule calculates the heat transfer efficiency in real time based on the temperature difference change analysis results, analyzes the stability of the heat exchanger's working efficiency, identifies abnormal fluctuations, calculates the efficiency change trend, and generates efficiency fluctuation analysis results;

[0086] The specific formula for analyzing the stability of the heat exchanger's working efficiency is:

[0087]

[0088] Among them, S represents the stability score of the work efficiency data, dimensionless, EX represents the maximum value of the heat exchange efficiency during the observation period, dimensionless, EN represents the minimum value of the heat exchange efficiency during the observation period, dimensionless, V represents the variance of the efficiency data, dimensionless, D represents the average value of the difference between adjacent efficiency data, dimensionless, R represents the fluctuation range of the efficiency data, dimensionless, α represents the variance weight coefficient, dimensionless, β represents the fluctuation range weight coefficient, dimensionless, and γ represents the benchmark adjustment coefficient, dimensionless.

[0089] formula:

[0090]

[0091] Detailed explanation of the formula and the process of formula calculation and derivation:

[0092] The formula is used to calculate the stability of the heat exchanger's operating efficiency data and evaluate the degree of fluctuation in the efficiency data. The results are used to determine the operating status of the heat exchanger.

[0093] Parameter meaning and setting value:

[0094] EX represents the maximum heat transfer efficiency during the observation period, which is assumed to be 0.85;

[0095] EN represents the minimum heat transfer efficiency during the observation period, which is assumed to be 0.72;

[0096] V represents the variance of efficiency data, which is assumed to be 0.0025;

[0097] D represents the efficiency change gradient, which is assumed to be 0.008;

[0098] R represents the efficiency fluctuation range, which is assumed to be 0.13;

[0099] α represents the variance weight coefficient, which is assumed to be 0.6;

[0100] β represents the volatility range weight coefficient, which is assumed to be 0.3;

[0101] γ represents the baseline adjustment coefficient, which is assumed to be 0.1;

[0102] Substitute the parameters into the formula for calculation:

[0103]

[0104] The result S=0.0338 indicates that the volatility score of the heat exchanger working efficiency data is 0.0338. The value shows that the heat exchanger is in good operating condition and the efficiency fluctuation is within the controllable range.

[0105] The abnormal efficiency detection submodule detects deviations and abnormal values ​​of heat exchange efficiency data in real time based on the efficiency fluctuation analysis results, and generates real-time heat exchange efficiency information;

[0106] In the abnormal efficiency detection submodule, by setting a tolerance range and applying abnormal detection methods such as the standard deviation method or Z-score detection based on the real-time monitored efficiency data, efficiency data that exceeds the normal fluctuation range is identified, the detected abnormal values ​​are isolated, their deviations are recorded and alarms are generated. The data will be compared according to the set standard range. If the efficiency fluctuation exceeds the set threshold, the monitoring parameters will be adjusted in real time to ensure that it is within the normal operating range. The real-time heat exchange efficiency information generated provides a reference for equipment adjustment and ensures the stability and efficiency of the equipment in long-term operation.

[0107] See also Figure 2 and Figure 6 , the optimal parameter identification module includes:

[0108] The working model construction submodule builds a working model of the heat exchanger based on real-time heat exchange efficiency information, simulates the operating status of the heat exchanger under various working conditions, analyzes the working efficiency under various inputs, and generates a working model of the heat exchanger;

[0109] The specific formula for analyzing work efficiency under various inputs is:

[0110]

[0111] Where S′ represents the efficiency score of the target heat exchanger configuration, Q represents the normalized value of the heat exchanger heat load, P represents the normalized value of the operating power consumption, η represents the heat transfer efficiency of the heat exchanger, v represents the normalized value of the working fluid flow rate, and r represents the operating resistance coefficient.

[0112] formula:

[0113]

[0114] Detailed explanation of the formula and the process of formula calculation and derivation:

[0115] The formula is used to calculate the efficiency score of the heat exchanger under different input configurations. The results are used to evaluate the advantages and disadvantages of the operating configuration and optimize the selection of the thermal system.

[0116] Parameter meaning and setting value:

[0117] Q is the normalized value of the heat exchanger's heat load, derived from the real-time detection of the heat exchanger's heat transfer by the monitoring system. The original heat load value is assumed to be 198kW, the rated heat load of the equipment is 260kW, and the normalized value is 0.76, reflecting the ratio of the equipment's current heat transfer power to the rated level.

[0118] P is the normalized value of operating power consumption. The current and voltage are recorded by the PLC system and combined with the power factor calculation. The monitoring value is set to 12kW. The maximum design power consumption of the equipment is 20kW. The normalized value is 0.60, indicating that the energy consumption is at 60% of the load level.

[0119] η is the heat transfer efficiency of the heat exchanger, which is calculated from the ratio of the temperature difference between the two ends of the heat exchanger to the heat transfer capacity. The inlet and outlet temperature difference measured by the thermal test system is set to 24K, the theoretical maximum temperature difference of the system is 28.2K, and the normalized value is 0.85;

[0120] v is the normalized value of the working fluid flow rate, obtained by the flow meter on the pipeline side. The set flow rate is 1.28 m / s, the design recommended flow rate is 1.60 m / s, and the normalized value is 0.80, which is used to reflect the degree of use of the flow rate under working conditions relative to the design standard;

[0121] r is the operating resistance coefficient. The pressure drop sensor is set to measure a pressure difference of 18.8 kPa across the heat exchanger. The standard pressure drop is 15 kPa, and the normalized value is 1.25, indicating that the flow resistance intensity of the current system operation is higher than the standard state.

[0122] Substitute the parameters into the formula for calculation:

[0123]

[0124] 1.2667·0.85·0.64=0.6899;

[0125] The result of 0.6899 indicates that the overall efficiency score of the heat exchanger under this configuration is 0.6899. The closer this value is to 1, the better the effective heat transfer capacity and flow smoothness of the system per unit energy consumption. The score is used for horizontal comparison between heat exchange configuration schemes and to set classification thresholds for operation scheduling and parameter optimization strategy formulation.

[0126] The result 87.96 indicates that the target configuration heat exchanger has an operating efficiency score of 87.96 points. By comparing the S' values ​​under different operating conditions, the maximum value is identified and the corresponding parameter configuration combination is marked.

[0127] The multi-operating condition simulation submodule calculates the optimal operating parameters of the heat exchanger under various operating conditions based on the heat exchanger operating model and generates parameter calculation results;

[0128] In the multi-operating condition simulation submodule, by introducing a variety of operating condition data and applying Monte Carlo simulation methods, numerical optimization methods, etc., different operating conditions such as loads and temperature differences are calculated. Different operating conditions are simulated for different temperature, flow and pressure conditions, and the working status of the heat exchanger under each operating condition is analyzed. Through numerical optimization methods, the optimal operating parameters of the heat exchanger under various operating conditions, such as key control parameters such as flow and temperature difference, are calculated, and fine-tuned in combination with the real-time operating data of the equipment. The simulation results are optimized through gradual iteration to ensure that the heat exchanger can achieve optimal efficiency under different operating conditions, and generate parameter calculation results, which provide a basis for the subsequent adjustment of control parameters.

[0129] The control parameter calculation submodule calculates the adjustment value of the control parameter based on the parameter calculation results and combines the real-time working efficiency of the heat exchanger to generate the heat exchanger working parameters;

[0130] In the control parameter calculation submodule, by comparing the current real-time working efficiency with the optimal working efficiency, the PID control algorithm, fuzzy control algorithm, etc. are used to dynamically calculate the required control parameter adjustment values. Based on the equipment operating status and real-time data, the relationship between parameters such as temperature and flow and the heat exchange efficiency is analyzed, and various control parameters such as the opening of the temperature control valve and flow regulation are adjusted to achieve the optimal heat exchange effect. During the calculation process, real-time optimization is performed in combination with historical data, and thresholds are set to determine whether the control parameters need to be adjusted. After each adjustment, the heat exchange efficiency will be re-evaluated based on the feedback data to ensure that the equipment operates in the optimal state, generate the heat exchanger working parameters, and provide specific operational guidance for actual equipment adjustment.

[0131] See also Figure 2 and Figure 7 , the condenser management module includes:

[0132] The condenser monitoring submodule monitors and records the condenser's steam temperature, flow rate, and load data in real time based on the heat exchanger's operating parameters, generating a steam system data set.

[0133] In the condenser monitoring submodule, based on the heat exchanger operating parameters, the steam temperature, flow rate, and load data of the condenser are monitored and recorded in real time. By installing temperature, pressure, and flow sensors at multiple key locations on the condenser, real-time data is continuously acquired. During the monitoring process, the temperature sensor uses thermocouple technology to record the steam temperature in real time, the flow sensor uses electromagnetic flowmeter technology to record the water flow of the condenser, and the pressure sensor uses piezoelectric or strain sensors to monitor the steam pressure in real time. All collected data is transmitted to the data center in real time via a wireless network, encrypted and stored using data storage management technology, and each data point is timestamped to ensure that data changes in different time periods can be accurately compared during subsequent analysis. All real-time data will be synthesized into a steam data set, providing a reliable basis for subsequent analysis and decision-making.

[0134] The pressure data analysis submodule analyzes the fluctuation range of steam pressure under various working conditions based on the steam system data set, evaluates the pressure change trend, detects abnormal pressure fluctuations, and generates steam pressure fluctuation analysis results;

[0135] In the pressure data analysis submodule, by applying statistical analysis methods such as standard deviation and variance, the fluctuation of steam pressure under different operating conditions is analyzed. By calculating the average value and fluctuation range of historical data, abnormal fluctuations outside the normal fluctuation range can be identified. Trend analysis models such as linear regression analysis are used to evaluate the long-term trend of pressure fluctuations and identify potential problems with pressure changes. In the fluctuation analysis process, outlier detection methods such as the Z-score algorithm are also applied to mark and isolate data that does not conform to the expected fluctuation pattern. Through target steps, the pressure fluctuation is monitored in real time and its changing trend is evaluated to generate steam pressure fluctuation analysis results, providing a decision basis for subsequent pressure adjustments.

[0136] The working pressure management submodule calculates and adjusts the condenser's operating parameters, including steam valve opening and boiler power output, based on the steam pressure fluctuation analysis results and the steam pressure required for the condenser to generate steam pressure regulation results.

[0137] In the working pressure management submodule, the deviation between the current steam pressure and the required steam pressure is calculated, and the PID control algorithm is used to adjust the steam valve opening and boiler power in real time. The control process involves automatically adjusting the steam valve opening according to the real-time feedback of pressure data and steam flow to ensure that the steam pressure is within the set reasonable range. According to the relationship between boiler power and steam temperature, the output power of the boiler is dynamically adjusted to optimize the steam production process. By calculating the adjusted working parameters and combining them with real-time feedback data, the steam pressure adjustment result is generated to ensure that the condenser operates stably at the required pressure.

[0138] See also Figure 2 and Figure 8 , the condensate recovery control module includes:

[0139] The recovery equipment monitoring submodule extracts steam temperature, flow, and pressure data based on the steam pressure regulation results, calculates the working efficiency of the condensate recovery equipment, and generates a recovery efficiency evaluation result;

[0140] In the recovery equipment monitoring submodule, real-time data is collected from sensors at key locations in the steam to obtain instant information on temperature, flow, and pressure. The target data is used to calculate the condensate recovery efficiency. A thermodynamics-based heat calculation model and fluid mechanics formulas are used, combined with the condenser's operating status and steam characteristics, to evaluate the efficiency of the condensate recovery equipment. During data processing, the condensate recovery efficiency is analyzed by comparing the temperature, flow, and pressure under different operating conditions, and a recovery efficiency evaluation result is generated to provide accurate benchmark data for subsequent operations.

[0141] The working parameter optimization submodule calculates the working efficiency of the condensate recovery equipment under various working conditions based on the recovery efficiency evaluation results, analyzes the optimal working parameters of the condensate recovery equipment, and generates recovery operation parameters;

[0142] In the working parameter optimization submodule, through multi-operating condition simulation, combined with parameters such as temperature, flow, and pressure, the recovery efficiency under different working conditions is evaluated using numerical optimization methods. According to the actual operating data of the condensate recovery equipment, the operating parameters are optimized using genetic algorithms or particle swarm optimization algorithms. The optimal working parameters under different working conditions are calculated, including key adjustment items such as valve opening and pump speed. Through comparative analysis of multiple working conditions, the working parameters that can maximize the recovery efficiency are identified, and the recovery operating parameters are generated to provide an optimization basis for equipment adjustment.

[0143] The operating parameter adjustment submodule adjusts the operating parameters of the condensate recovery equipment based on the recovery operating parameters, including the valve opening and the water pump speed, to generate the cogeneration operating parameters;

[0144] In the operating parameter adjustment submodule, the valve opening and pump speed are automatically adjusted through the PID control algorithm. The process detects the current working status of the recovery equipment to determine whether there is a deviation from the optimal working parameters. According to the recovery operating parameters, the valve opening and pump speed adjustment amount are automatically calculated, and the target parameters are adjusted in real time to ensure that the equipment operates in the optimal state. The process continuously monitors equipment feedback and corrects the control strategy through real-time feedback to ensure that the generated cogeneration operating parameters can always enable the equipment to operate efficiently and stably.

[0145] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0146] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0147] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0148] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0149] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0150] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0151] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0152] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0153] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0154] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0155] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A high-efficiency multi-stage waste heat recovery and reuse cogeneration system, characterized in that: The system comprises: The heat output calculation module is based on IoT sensors and collects temperature, flow, and pressure data from multiple locations in real time. It calculates the energy transfer relationship between multiple heat sources and cooling equipment, analyzes heat output under various input conditions, calculates heat fluctuation trends under current working conditions, and generates waste heat prediction results. The temperature difference pattern analysis module analyzes the relationship between the inlet and outlet temperatures of the heat exchanger and various equipment operating parameters based on the waste heat prediction results, identifies the change pattern of the inlet and outlet temperatures, predicts the change trend of the temperature difference data, and generates a temperature difference prediction result; The heat exchange efficiency analysis module analyzes the temperature difference change between the inlet and outlet of the heat exchanger based on the temperature difference prediction results, evaluates the heat exchange efficiency in real time, analyzes the volatility of the data, and generates real-time heat exchange efficiency information; The optimal parameter identification module constructs a working model of the heat exchanger based on the real-time heat exchange efficiency information, analyzes the working efficiency of the heat exchanger under various input conditions, identifies the optimal working parameters under multiple temperature gradients, calculates the control parameter adjustment values, and generates the heat exchanger working parameters; The condenser management module monitors the steam temperature, flow rate, and load data of the condenser in real time based on the operating parameters of the heat exchanger, adjusts the operating parameters of the condenser, and generates a steam pressure regulation result; The condensate recovery control module calculates the working efficiency of the condensate recovery equipment under various working conditions and optimizes the operating parameters based on the steam pressure regulation result, thereby generating the cogeneration working parameters.

2. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 1 is characterized in that: The waste heat prediction results include heat fluctuation data of multiple heat sources, waste heat change prediction data of multiple time periods, and the energy transfer relationship between the heat source and the cooling equipment. The temperature difference prediction results include the change pattern of the inlet and outlet temperatures, the time series change trend of the temperature difference data, and the predicted temperature difference fluctuation range. The real-time heat exchange efficiency information includes the relationship between the inlet and outlet temperature difference and the flow change, the real-time heat exchange efficiency evaluation value, and the data volatility analysis results. The heat exchanger operating parameters include multi-gradient flow rate parameters, multi-gradient heat exchange area parameters, and multi-gradient temperature difference adjustment values. The steam pressure regulation results include the steam pressure fluctuation range, steam pressure change trend, and condenser operating parameter adjustment value. The cogeneration operating parameters include recovery efficiency calculation results, water pump speed parameters, and valve opening adjustment values.

3. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 1 is characterized in that: The heat output calculation module includes: The status data acquisition submodule is based on IoT sensors, which collects temperature, flow, and pressure data at multiple locations in real time, records the operating status of multiple heat sources and cooling equipment, and generates real-time data collection results; The transfer relationship analysis submodule calculates the energy transfer relationship between multiple heat sources and cooling equipment based on the real-time data collection results, analyzes the energy output of multiple heat sources in combination with the equipment operation status, and generates a heat transfer calculation result; The output fluctuation analysis submodule analyzes the heat fluctuation under the current working conditions based on the heat transfer calculation result, calculates the change trend of the heat output, and generates the waste heat prediction result.

4. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 1 is characterized in that: The temperature difference pattern analysis module includes: The correlation analysis submodule analyzes the relationship between the heat exchanger inlet and outlet temperatures and various equipment operating parameters based on the waste heat prediction results, evaluates the impact of temperature changes on heat exchange efficiency, and generates temperature difference relationship analysis results; The time series modeling submodule identifies the change pattern of the inlet and outlet temperatures based on the temperature difference relationship analysis results through time series analysis, and generates temperature difference change pattern information; The temperature difference data prediction submodule analyzes the change trend of the inlet and outlet temperature difference of the heat exchanger based on the temperature difference change pattern information, and predicts the temperature difference data at multiple time points to generate a temperature difference prediction result.

5. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 1 is characterized in that: The heat exchange efficiency analysis module includes: The data fluctuation analysis submodule calculates the temperature difference change at the inlet and outlet of the heat exchanger based on the temperature difference prediction results and combines the temperature difference and flow rate change trends, analyzes the temperature difference fluctuations of the cogeneration equipment under various working conditions, and generates temperature difference change analysis results; The stability assessment submodule calculates the heat exchange efficiency in real time based on the temperature difference change analysis results, analyzes the stability of the heat exchanger's working efficiency, identifies abnormal fluctuations and calculates the efficiency change trend, and generates efficiency fluctuation analysis results; The abnormal efficiency detection submodule detects deviations and abnormal values ​​of the heat exchange efficiency data in real time based on the efficiency fluctuation analysis result, and generates real-time heat exchange efficiency information.

6. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 5 is characterized in that: The specific formula for analyzing the stability of the heat exchanger working efficiency is: Among them, S represents the stability score of the work efficiency data, dimensionless, EX represents the maximum value of the heat exchange efficiency during the observation period, dimensionless, EN represents the minimum value of the heat exchange efficiency during the observation period, dimensionless, V represents the variance of the efficiency data, dimensionless, D represents the average value of the difference between adjacent efficiency data, dimensionless, R represents the fluctuation range of the efficiency data, dimensionless, α represents the variance weight coefficient, dimensionless, β represents the fluctuation range weight coefficient, dimensionless, and γ represents the benchmark adjustment coefficient, dimensionless.

7. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 1 is characterized in that: The optimal parameter identification module includes: The working model building submodule builds a working model of the heat exchanger based on the real-time heat exchange efficiency information, simulates the operating state of the heat exchanger under various working conditions, analyzes the working efficiency under various inputs, and generates a working model of the heat exchanger; The multi-operating condition simulation submodule calculates the optimal operating parameters of the heat exchanger under various operating conditions based on the heat exchanger operating model and generates parameter calculation results; The control parameter calculation submodule calculates the adjustment value of the control parameter based on the parameter calculation result and combines the real-time working efficiency of the heat exchanger to generate the working parameter of the heat exchanger.

8. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 7 is characterized in that: The specific formula for analyzing the work efficiency under multiple inputs is: Where S′ represents the efficiency score of the target heat exchanger configuration, Q represents the normalized value of the heat exchanger heat load, P represents the normalized value of the operating power consumption, η represents the heat transfer efficiency of the heat exchanger, v represents the normalized value of the working fluid flow rate, and r represents the operating resistance coefficient.

9. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 1 is characterized in that: The condenser management module includes: The condenser monitoring submodule monitors and records the steam temperature, flow rate, and load data of the condenser in real time based on the heat exchanger operating parameters, and generates a steam system data set; The pressure data analysis submodule analyzes the fluctuation range of steam pressure under various working conditions based on the steam system data set, evaluates the pressure change trend, detects abnormal pressure fluctuations, and generates steam pressure fluctuation analysis results; Based on the steam pressure fluctuation analysis results, the working pressure management submodule calculates and adjusts the working parameters of the condenser, including the steam valve opening and boiler power output, according to the steam pressure required for the condenser to generate a steam pressure regulation result.

10. The high-efficiency multi-stage waste heat recovery and reuse cogeneration system according to claim 1 is characterized in that: The condensed water recovery control module includes: The recovery equipment monitoring submodule extracts steam temperature, flow, and pressure data based on the steam pressure regulation result, calculates the working efficiency of the condensate recovery equipment, and generates a recovery efficiency evaluation result; The working parameter optimization submodule calculates the working efficiency of the condensate recovery equipment under various working conditions based on the recovery efficiency evaluation results, analyzes the optimal working parameters of the condensate recovery equipment, and generates recovery operation parameters; The operating parameter adjustment submodule adjusts the operating parameters of the condensate recovery equipment, including the valve opening and the water pump speed, based on the recovery operating parameters, to generate cogeneration operating parameters.

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