An automatic control system for drying waste heat recovery

Abstract

The application discloses a kind of automatic control systems of drying waste heat recovery, including heat energy utilization rate calculation module, flow control module, airflow analysis module and adjustment module;Heat energy utilization rate calculation module combines the current heat energy utilization rate calculated by preset heat transfer model, obtains the matching deviation of heat exchanger operation and dryer state;Flow control module extracts wind speed, pressure distribution data from air pipe system, adopts airflow simulation algorithm to analyze the uniformity of airflow distribution, determines the deviation area and adjustment amplitude of air volume adjustment;Airflow analysis module executes accurate adjustment to deviation area through the automation control module of air volume regulating valve, obtains adjusted wind speed and pressure data, judges whether airflow distribution reaches preset uniformity standard;Adjustment module obtains flow, temperature monitoring data of condensate water pipeline, combines heat recovery rate calculation formula of waste heat recovery device, analyzes the linkage efficiency of condensate water management and waste heat recovery, determines optimization requirement.

Description

An automated control system for waste heat recovery during drying Technical Field

[0001] This invention relates to the field of industrial equipment control technology, and in particular to an automated control system for waste heat recovery during drying. Background Technology

[0002] In industrial drying processes, the coordinated operation of heat exchangers, duct systems, and waste heat recovery devices is crucial for achieving efficient thermal energy utilization. Heat exchangers transfer heat from the heat source to the drying medium, duct systems distribute the hot drying medium evenly throughout the drying area, and waste heat recovery devices recover and reuse the waste heat generated during the drying process to reduce energy consumption. However, most current solutions have significant shortcomings in system integration and automation control, often focusing only on optimizing the performance of individual devices while neglecting the dynamic coordination of the overall system, leading to energy waste and equipment instability.

[0003] In actual production, the operating status of a dryer changes dynamically due to factors such as material type, moisture content, and drying speed. However, traditional heat exchanger control methods typically operate on preset parameters, making real-time adjustments impossible based on the dryer's actual operating conditions. This control method leads to the heat exchanger providing excessive heat in some situations, resulting in energy waste, while in other situations, it provides insufficient heat, affecting the drying effect.

[0004] In industrial drying processes, duct systems are responsible for evenly delivering the hot drying medium to each drying zone. However, existing duct systems rely on rudimentary adjustment methods, typically relying on manual damper adjustments or simple fan speed control to regulate airflow. This approach fails to precisely control airflow and pressure within the duct system, resulting in uneven airflow distribution. In some areas, excessive airflow leads to energy waste, while in other areas, insufficient airflow affects drying efficiency.

[0005] The generation of condensate is unavoidable in industrial drying processes. Condensate is typically at a high temperature and contains a significant amount of heat energy. However, existing waste heat recovery devices and condensate pipelines lack sufficient coordination and effective monitoring and control mechanisms. The flow rate of the condensate pipeline cannot be optimized in real time according to the actual needs of the waste heat recovery device, resulting in low waste heat recovery efficiency. These shortcomings make the system poorly adaptable to complex operating conditions and unable to meet the demands of efficient and stable industrial processes. Summary of the Invention

[0006] In order to solve the above-mentioned technical problems, the present invention provides an automated control system for waste heat recovery from drying.

[0007] The technical solution of this invention is implemented as follows:

[0008] An automated control system for waste heat recovery from drying includes: a heat energy utilization rate calculation module, a flow control module, an airflow analysis module, and an adjustment module;

[0009] The thermal energy utilization rate calculation module calculates the current thermal energy utilization rate in conjunction with the preset heat transfer model, and obtains the matching deviation between the heat exchanger operation and the dryer status.

[0010] The flow control module extracts wind speed and pressure distribution data from the duct system, uses airflow simulation algorithms to analyze the uniformity of airflow distribution, and determines the deviation area and adjustment range of airflow regulation.

[0011] The airflow analysis module performs precise adjustments for deviation areas through the automatic control module of the airflow regulating valve, obtains the adjusted wind speed and pressure data, and determines whether the airflow distribution meets the preset uniformity standard.

[0012] The adjustment module acquires flow and temperature monitoring data of the condensate pipeline, combines it with the heat recovery rate calculation formula of the waste heat recovery device, analyzes the linkage efficiency between condensate management and waste heat recovery, and determines optimization requirements. Based on the optimization requirements, it adjusts the opening of the condensate flow control valve and judges whether the preset recovery efficiency target has been achieved by monitoring the change trend of the heat recovery rate in real time.

[0013] Furthermore, the thermal energy utilization calculation module uses a preset heat transfer model to process the distribution characteristics of heat flow parameters, and combines the inlet and outlet parameters to determine the numerical result of thermal energy utilization. If the numerical result of thermal energy utilization exceeds the preset threshold, the material characteristic data is adjusted to obtain a new distribution of heat flow parameters and to determine the trend of change in the operating status.

[0014] Based on the changing trend of the operating status, obtain dynamic data of the matching deviation and determine the fluctuation range of the deviation;

[0015] The dynamic data of matching deviation is processed by random forest algorithm. Combined with the real-time acquisition results of temperature and humidity data, the predicted value of the operating status is obtained. If the predicted value of the operating status is inconsistent with the actual sensor data, the deviation fluctuation range is classified to determine the abnormal point of thermal energy utilization.

[0016] Based on the anomalies in thermal energy utilization, the transfer model is adjusted using real-time collected heat flow parameters to obtain optimized matching deviation results.

[0017] Furthermore, the thermal energy utilization calculation module determines whether the heat exchanger is deviating from the optimal state based on the matching deviation. If the deviation exceeds a preset threshold, the operating parameters are updated by adjusting the control algorithm of the heat exchanger flow rate, and a dynamic adjustment command is generated.

[0018] The thermal energy utilization calculation module analyzes the deviation of the heat exchanger operation from the optimal state through matching deviation analysis to obtain the deviation judgment result; if the deviation judgment result exceeds the preset threshold, the updated operating parameters are calculated through flow control to obtain parameter adjustment data;

[0019] A control algorithm is used to process parameter adjustment data and generate dynamic adjustment commands.

[0020] Adjust the heat exchanger operation according to the dynamic adjustment command and obtain new operating status data;

[0021] By comparing the new operating status data with the optimal status, the trend of the matching deviation is determined. If the trend shows that the deviation still exceeds the preset threshold, the deviation judgment results are classified to determine the abnormal operating range.

[0022] Adjust the flow control strategy based on the abnormal operating range to obtain optimized operating parameters.

[0023] Furthermore, the flow control module collects wind speed data and pressure distribution data through the duct system sensors to obtain initial distribution information;

[0024] The initial distribution information is processed using an airflow simulation algorithm to generate airflow distribution data.

[0025] By analyzing the uniformity of airflow distribution data, the deviation area and adjustment range data are determined. If the deviation area exceeds the preset threshold, the adjusted airflow value is calculated using the airflow adjustment formula to obtain the airflow adjustment parameters.

[0026] Generate adjustment instructions based on air volume adjustment parameters, and obtain new air speed data and pressure distribution data;

[0027] By regenerating airflow distribution data using new wind speed and pressure distribution data, the trend of uniformity change is determined. If the trend of uniformity change still shows that the deviation area exceeds the preset threshold, the support vector machine algorithm is used to classify the deviation area, determine the optimization adjustment strategy, and obtain the final airflow adjustment parameters.

[0028] Furthermore, the airflow analysis module performs precise adjustments to the deviation area through the automatic control module of the airflow regulating valve, and obtains the adjusted wind speed and pressure data;

[0029] Based on the acquired wind speed and pressure data, airflow distribution information is generated. It is determined whether the airflow distribution meets the uniformity standard defined by the preset threshold. If the airflow distribution exceeds the preset threshold, the airflow adjustment value of the deviation area is calculated by the control module to obtain new adjustment parameters.

[0030] The air volume regulating valve performs secondary adjustments based on the regulating parameters to obtain updated air velocity and pressure data.

[0031] By regenerating airflow distribution information using updated wind speed and pressure data, the uniformity standard is assessed. If the uniformity standard is still not met, a support vector machine algorithm is used to classify the deviation areas and determine the optimized adjustment parameters.

[0032] Based on the optimized adjustment parameters, the control module drives the air volume regulating valve to perform the final adjustment, and obtains the final air speed and pressure data.

[0033] Furthermore, the adjustment module acquires the flow rate and temperature data of the condensate pipeline, performs real-time monitoring through a preset sensor module, and obtains initial monitoring results;

[0034] Based on the initial monitoring results, the flow rate and temperature data are processed using the heat recovery rate calculation formula to obtain the heat recovery value of waste heat recovery.

[0035] Based on the relationship between heat recovery value and recovery rate value, the linkage efficiency between condensate management and waste heat recovery is calculated, and the efficiency distribution information is determined. If the efficiency distribution information is lower than the preset threshold, the deviation between flow data and temperature data is analyzed by calculation formula to obtain adjustment parameters.

[0036] The control module of the waste heat recovery device is driven by adjusting parameters to perform regulation and obtain updated flow and temperature data.

[0037] By recalculating the linkage efficiency using the updated traffic and temperature data, the achievement of optimization requirements is assessed. If the optimization requirements are not achieved, a random forest algorithm is used to classify the efficiency distribution information, determine the final adjustment parameters, and execute the adjustment to obtain the final data.

[0038] Furthermore, the adjustment module obtains the changing trend data of the heat recovery rate through real-time monitoring to determine the fluctuation range of the changing trend;

[0039] Based on the fluctuation range of the trend, the adjustment parameters of the valve opening are calculated using a preset flow control model to obtain the adjustment parameter values;

[0040] The valve opening is updated by adjusting the parameter values ​​to drive the control and regulation module, and the adjusted flow control data is obtained.

[0041] The adjusted flow control data is processed to obtain a new heat recovery rate value. If the new heat recovery rate value is lower than the preset threshold, the deviation between the trend and the recovery efficiency is analyzed by the support vector machine algorithm to determine the optimization adjustment parameters.

[0042] Based on the optimized and adjusted parameters, the control adjustment is re-executed to obtain the final heat recovery rate data and determine the achievement of the efficiency target;

[0043] By comparing the final heat recovery rate data with the preset threshold, the stability information of the system operation is determined.

[0044] Furthermore, the automated control system for waste heat recovery during drying also includes a global optimization module:

[0045] The global optimization module integrates heat exchanger operating parameters, air volume adjustment data, and condensate management results, and calculates the comprehensive index of system efficiency and equipment stability through a dynamic coordination algorithm to generate a global optimization scheme for automated control.

[0046] The global optimization module obtains heat exchanger parameters, air volume data, and condensate results through the data integration module to generate initial operating status data.

[0047] The initial operating status data is processed by a dynamic coordination algorithm to calculate the comprehensive index of system efficiency and equipment stability. If the comprehensive index value is lower than the preset threshold, the deviation between the air volume data and equipment stability is analyzed by the support vector machine algorithm to determine the control parameters of the adjustment process.

[0048] The adjustment process is updated based on the control parameters. The adjusted heat exchanger parameters and condensate results are obtained, and new operating status data are generated.

[0049] The new operating status data is processed by a dynamic coordination algorithm to calculate the updated system efficiency and comprehensive indicators, and obtain the preliminary results of the optimization scheme. If the updated system efficiency reaches the preset threshold, the stability status of the equipment is determined by logical judgment, and the control parameters of the global optimization scheme are generated.

[0050] Adjust the operating status according to the control parameters of the global optimization scheme, obtain the final comprehensive index value, and judge the achievement of the optimization scheme.

[0051] Furthermore, the automated control system for waste heat recovery during drying also includes a fault diagnosis module:

[0052] The fault diagnosis module uses a global optimization scheme to update the operating instructions of each subsystem, verifies the improvement in system efficiency through real-time feedback data, and judges whether the equipment stability meets industrial requirements.

[0053] The fault diagnosis module generates subsystem instructions through a global optimization scheme, obtains adjustment data for the running instructions of each subsystem, and obtains the initial running state.

[0054] A real-time feedback mechanism is used to process adjustment data, extract key features from the feedback data, and determine the changing trend of system operation.

[0055] The system efficiency is calculated by the trend of change, the value of the improvement is obtained, and it is determined whether the preset threshold is reached. If the improvement is lower than the preset threshold, the deviation between the feedback data and the equipment stability is analyzed by the support vector machine algorithm to obtain the correction parameters.

[0056] Update the running instructions based on the corrected parameters, obtain the adjusted feedback data, and determine the new system efficiency value;

[0057] The system processes new system efficiency values ​​and equipment stability through logical judgments. If the preset threshold is met, the final operation instruction adjustment plan is generated.

[0058] The system updates subsystem instructions based on the final operating instructions, obtains real-time feedback data, and determines the stability status of the equipment.

[0059] Furthermore, the automated control system for waste heat recovery during drying also includes a data acquisition module, which acquires temperature, humidity, and material characteristic data during the operation of the dryer, and collects heat flow parameters at the inlet and outlet of the heat exchanger in real time through sensors.

[0060] Compared with the prior art, the present invention has the following advantages:

[0061] Dynamic adjustment of heat exchanger

[0062] 1. This invention uses a heat energy utilization rate calculation module, combined with a heat transfer model and real-time data, to dynamically adjust the operating parameters of the heat exchanger, optimize the heat energy utilization efficiency, analyze the operating status, accurately locate abnormal points and adjust the model, and ensure that the heat exchanger is always in a high-efficiency operating state under different operating conditions, avoiding poor drying effect or energy waste due to insufficient or excessive heat energy supply.

[0063] 2. Using airflow simulation algorithms, accurately analyze the wind speed and pressure distribution in the duct system, automatically identify areas with uneven airflow distribution, and make precise adjustments through airflow regulating valves. If deviations still exist after adjustment, further optimize the adjustment strategy to ensure uniform airflow distribution, improve drying efficiency, and avoid energy waste caused by excessive or insufficient local airflow.

[0064] 3. By adjusting the module to monitor the flow and temperature data of the condensate pipeline in real time, and combining the heat recovery rate calculation formula, the opening of the condensate flow control valve is dynamically adjusted to optimize the waste heat recovery efficiency. By monitoring the change trend of the heat recovery rate in real time, it is determined whether the optimization requirements have been met, and the final adjustment parameters are determined to ensure the efficient linkage between the waste heat recovery device and the condensate pipeline, thereby improving the overall energy utilization efficiency of the system.

[0065] 3. By integrating the operating parameters of heat exchangers, duct systems and waste heat recovery devices through the global optimization module, and calculating the comprehensive indicators of system efficiency and equipment stability through dynamic coordination algorithms, a global optimization scheme is generated to ensure that the system operates efficiently and stably under complex working conditions and meets the needs of industrial production. Attached Figure Description

[0066] Figure 1 is a system framework diagram of an automated control system for waste heat recovery from drying according to the present invention; Detailed Implementation

[0067] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Embodiments

[0068] As shown in Figure 1, this embodiment provides an automated control system for waste heat recovery from drying, including: a thermal energy utilization rate calculation module, a flow control module, an airflow analysis module, and an adjustment module;

[0069] The thermal energy utilization rate calculation module calculates the current thermal energy utilization rate in conjunction with the preset heat transfer model, and obtains the matching deviation between the heat exchanger operation and the dryer status.

[0070] The flow control module extracts wind speed and pressure distribution data from the duct system, uses airflow simulation algorithms to analyze the uniformity of airflow distribution, and determines the deviation area and adjustment range of airflow regulation.

[0071] The airflow analysis module performs precise adjustments for deviation areas through the automatic control module of the airflow regulating valve, obtains the adjusted wind speed and pressure data, and determines whether the airflow distribution meets the preset uniformity standard.

[0072] The adjustment module acquires flow and temperature monitoring data of the condensate pipeline, combines it with the heat recovery rate calculation formula of the waste heat recovery device, analyzes the linkage efficiency between condensate management and waste heat recovery, and determines optimization requirements. Based on the optimization requirements, it adjusts the opening of the condensate flow control valve and judges whether the preset recovery efficiency target has been achieved by monitoring the change trend of the heat recovery rate in real time.

[0073] Furthermore, the thermal energy utilization calculation module uses a preset heat transfer model to process the distribution characteristics of heat flow parameters, and combines the inlet and outlet parameters to determine the numerical result of thermal energy utilization. If the numerical result of thermal energy utilization exceeds the preset threshold, the material characteristic data is adjusted to obtain a new distribution of heat flow parameters and to determine the trend of change in the operating status.

[0074] Based on the changing trend of the operating status, obtain dynamic data of the matching deviation and determine the fluctuation range of the deviation;

[0075] The dynamic data of matching deviation is processed by random forest algorithm. Combined with the real-time acquisition results of temperature and humidity data, the predicted value of the operating status is obtained. If the predicted value of the operating status is inconsistent with the actual sensor data, the deviation fluctuation range is classified to determine the abnormal point of thermal energy utilization.

[0076] Based on the anomalies in thermal energy utilization, the transfer model is adjusted using real-time collected heat flow parameters to obtain optimized matching deviation results.

[0077] In one embodiment, the current thermal energy utilization rate, such as 75%, is calculated based on the collected heat flow parameters and a preset heat transfer model, such as the Fourier heat conduction equation.

[0078] Using thermal energy utilization rate values, combined with dryer operating status data, such as fan speed of 1500 rpm and heating power of 10 kW, the matching deviation between heat exchanger operation and dryer status is determined, such as a deviation value of 5%.

[0079] If the matching deviation exceeds the preset threshold, such as 3%, then the material characteristic data is adjusted, such as adjusting the material moisture content to 12%, to obtain a new heat flux parameter distribution, such as an inlet heat flux density of 480W / m² and an outlet heat flux density of 310W / m².

[0080] Based on the new heat flux parameter distribution, combined with the inlet and outlet parameters, the trend of the operating status was determined, and the thermal energy utilization rate was increased to 78%.

[0081] By analyzing the changing trends of the operating status, the random forest algorithm is used to process the dynamic data of the matching deviation, such as the deviation fluctuation range of 2% to 6%, to obtain the predicted value of the operating status, such as the predicted thermal energy utilization rate of 77%.

[0082] If the predicted value of the operating status is inconsistent with the real-time data collected by the sensor, such as the actual thermal energy utilization rate of 76%, the deviation fluctuation range is classified by the support vector machine algorithm. If the classification result is "slightly abnormal", the abnormal point of thermal energy utilization is determined, such as the abnormal point located at the outlet temperature of 85℃.

[0083] Based on the anomalies in thermal energy utilization, the heat transfer model is adjusted using real-time collected heat flow parameters, such as adjusting the heat transfer coefficient, to obtain optimized matching deviation results, reducing the deviation value to 2%.

[0084] Furthermore, the optimized matching deviation results are obtained through the data integration module. Combined with air volume data, such as air volume of 2000 m³ / h, and condensate water results, such as condensate water volume of 50 L / h, new operating status data are generated, such as system efficiency of 80% and equipment stability of 95%.

[0085] Furthermore, the thermal energy utilization calculation module determines whether the heat exchanger is deviating from the optimal state based on the matching deviation. If the deviation exceeds a preset threshold, the operating parameters are updated by adjusting the control algorithm of the heat exchanger flow rate, and a dynamic adjustment command is generated.

[0086] The thermal energy utilization calculation module analyzes the deviation of the heat exchanger operation from the optimal state through matching deviation analysis to obtain the deviation judgment result; if the deviation judgment result exceeds the preset threshold, the updated operating parameters are calculated through flow control to obtain parameter adjustment data;

[0087] A control algorithm is used to process parameter adjustment data and generate dynamic adjustment commands.

[0088] Adjust the heat exchanger operation according to the dynamic adjustment command and obtain new operating status data;

[0089] By comparing the new operating status data with the optimal status, the trend of the matching deviation is determined. If the trend shows that the deviation still exceeds the preset threshold, the deviation judgment results are classified to determine the abnormal operating range.

[0090] Adjust the flow control strategy based on the abnormal operating range to obtain optimized operating parameters.

[0091] In one embodiment, based on initial operating state data, a dynamic coordination algorithm is used to calculate the matching deviation between the current operating state and the optimal state. For example, by calculating the difference between the current heat exchange efficiency and the preset optimal efficiency, the deviation value is obtained as 5%.

[0092] If the deviation exceeds the preset threshold of 3%, the heat exchanger parameters will be processed through the flow control algorithm, for example, the flow rate will be adjusted from 1000m³ / h to 1200m³ / h, and parameter adjustment data will be generated.

[0093] The PID control algorithm is used to process parameter adjustment data and generate dynamic adjustment commands, such as adjusting the opening of the flow control valve from 50% to 60%.

[0094] Adjust the heat exchanger operation according to the dynamic adjustment command and obtain new operating status data, such as collecting the adjusted inlet temperature as 80℃ and outlet temperature as 60℃.

[0095] By comparing the new operating status data with the optimal state, the changing trend of the matching deviation is calculated. For example, if the deviation value decreases from 5% to 4%, the deviation change value is determined to be 1%.

[0096] If the deviation change value still exceeds the preset threshold of 3%, the support vector machine algorithm is used to classify the deviation change value. For example, the deviation is divided into three intervals: low, medium and high, and the abnormal operation interval is determined as the high deviation interval.

[0097] Adjust the flow control strategy according to the abnormal operating range, for example, further adjust the flow rate from 1200m³ / h to 1300m³ / h to generate optimized operating parameters;

[0098] Furthermore, the optimized operating parameters are processed through a global optimization scheme to generate subsystem instructions, such as adjusting the fan speed from 1500 rpm to 1600 rpm, and obtaining the adjusted operating status data.

[0099] Furthermore, the flow control module collects wind speed data and pressure distribution data through the duct system sensors to obtain initial distribution information;

[0100] The initial distribution information is processed using an airflow simulation algorithm to generate airflow distribution data.

[0101] By analyzing the uniformity of airflow distribution data, the deviation area and adjustment range data are determined. If the deviation area exceeds the preset threshold, the adjusted airflow value is calculated using the airflow adjustment formula to obtain the airflow adjustment parameters.

[0102] Generate adjustment instructions based on air volume adjustment parameters, and obtain new air speed data and pressure distribution data;

[0103] By regenerating airflow distribution data using new wind speed and pressure distribution data, the trend of uniformity change is determined. If the trend of uniformity change still shows that the deviation area exceeds the preset threshold, the support vector machine algorithm is used to classify the deviation area, determine the optimization adjustment strategy, and obtain the final airflow adjustment parameters.

[0104] In one embodiment, wind speed data (m / s) and static pressure data (Pa) are collected by sensors in the duct system at a sampling interval of 0.1 seconds to form an initial distribution information matrix.

[0105] Using computational fluid dynamics and CFD simulation algorithms, the initial data is discretized into a three-dimensional grid based on the k-ε turbulence model to generate airflow distribution data containing velocity vector field and pressure gradient field;

[0106] The standard deviation algorithm is used to calculate the deviation of the wind speed at each monitoring point from the average value. If the wind speed deviation in the area exceeds ±15% or the pressure difference is greater than 50Pa, the air volume adjustment formula Q=K·√ΔP is triggered, where K is the valve characteristic coefficient. The adjusted air volume value is calculated and the adjustment parameters are output.

[0107] The adjustment parameters are converted into 4-20mA electrical signals by the PID controller to drive the damper actuator, and the sensor data after adjustment is collected synchronously.

[0108] The sliding window method is used to perform trend analysis on the new airflow distribution data. If the deviation still exceeds the threshold after three consecutive iterations, the data is input into a support vector machine and an SVM classifier. The radial basis function kernel is used to divide the deviation region level and output the optimization strategy parameters.

[0109] Furthermore, the control module adjusts the opening of the air valve to ±5% accuracy according to the optimized parameters, acquires the adjusted data in real time, and reconstructs the airflow distribution model;

[0110] Finally, Monte Carlo simulation was used to evaluate the airflow uniformity index and determine whether the design standard of wind speed fluctuation ≤10% in each region was met.

[0111] Furthermore, the airflow analysis module performs precise adjustments to the deviation area through the automatic control module of the airflow regulating valve, and obtains the adjusted wind speed and pressure data;

[0112] Based on the acquired wind speed and pressure data, airflow distribution information is generated. It is determined whether the airflow distribution meets the uniformity standard defined by the preset threshold. If the airflow distribution exceeds the preset threshold, the airflow adjustment value of the deviation area is calculated by the control module to obtain new adjustment parameters.

[0113] The air volume regulating valve performs secondary adjustments based on the regulating parameters to obtain updated air velocity and pressure data.

[0114] By regenerating airflow distribution information using updated wind speed and pressure data, the uniformity standard is assessed. If the uniformity standard is still not met, a support vector machine algorithm is used to classify the deviation areas and determine the optimized adjustment parameters.

[0115] Based on the optimized adjustment parameters, the control module drives the air volume regulating valve to perform the final adjustment, and obtains the final air speed and pressure data.

[0116] In one embodiment, the deviation area is precisely adjusted by the automatic control module of the air volume regulating valve, for example, adjusting the valve opening from 50% to 65%, and obtaining the adjusted air velocity data, such as 2.5 m / s and pressure data, such as 150 Pa.

[0117] Airflow distribution information is generated based on wind speed and pressure data, and the standard deviation is used to determine whether the airflow uniformity is lower than a preset threshold, such as ±0.3m / s.

[0118] If the airflow distribution exceeds the threshold, the control module calculates the airflow adjustment value for the deviation area based on the PID algorithm and generates new adjustment parameters, such as valve opening of 70%.

[0119] The air volume regulating valve is used to perform a secondary adjustment based on the new parameters to obtain updated air velocity data of 2.8 m / s and pressure data of 160 Pa.

[0120] The standard deviation of airflow distribution is recalculated by updating the data to determine whether the uniformity standard of ±0.2 m / s is met.

[0121] If the target is not met, a support vector machine algorithm is used to perform cluster analysis on the deviation area, such as dividing it into high / low speed zones, and determining the optimized adjustment parameters, such as the valve opening of the zoned valves at 75% and 60%;

[0122] Based on the optimized parameters, the air volume regulating valve is driven to perform the final adjustment, and the final air velocity (3.0 m / s) and pressure data (165 Pa) are obtained.

[0123] Furthermore, airflow distribution information is generated from the final data. If it still does not meet the standard, such as a local wind speed deviation of ±0.4m / s, then the ambient temperature (25℃) and humidity (60%RH) are collected. The influence weight of temperature and humidity on airflow is analyzed using a BP neural network, and compensation and adjustment parameters are output, such as valve opening correction +5%.

[0124] Furthermore, the adjustment module acquires the flow rate and temperature data of the condensate pipeline, performs real-time monitoring through a preset sensor module, and obtains initial monitoring results;

[0125] Based on the initial monitoring results, the flow rate and temperature data are processed using the heat recovery rate calculation formula to obtain the heat recovery value of waste heat recovery.

[0126] Based on the relationship between heat recovery value and recovery rate value, the linkage efficiency between condensate management and waste heat recovery is calculated, and the efficiency distribution information is determined. If the efficiency distribution information is lower than the preset threshold, the deviation between flow data and temperature data is analyzed by calculation formula to obtain adjustment parameters.

[0127] The control module of the waste heat recovery device is driven by adjusting parameters to perform regulation and obtain updated flow and temperature data.

[0128] By recalculating the linkage efficiency using the updated traffic and temperature data, the achievement of optimization requirements is assessed. If the optimization requirements are not achieved, a random forest algorithm is used to classify the efficiency distribution information, determine the final adjustment parameters, and execute the adjustment to obtain the final data.

[0129] In one embodiment, flow data, such as 2.5 m³ / h, and temperature data, such as 65°C, are collected in real time by an electromagnetic flow meter and a PT100 temperature sensor installed on the condensate pipe to form an initial monitoring dataset.

[0130] The heat recovery rate is calculated using the formula Q=ρ×c×q×ΔT, where ρ is density, c is specific heat capacity, q is flow rate, and ΔT is temperature difference. For example, the heat recovery amount is 12.8kW.

[0131] The calculation results were compared with the preset recovery rate standard, such as ≥80%, and the linkage efficiency was found to be 72% using the efficiency formula η=Q_actual / Q_theoretical×100%.

[0132] If the efficiency is below the threshold, the optimization coefficients for flow rate and temperature are calculated based on the deviation analysis model. For example, if the flow rate needs to be increased by 15%, the temperature needs to be reduced by 3℃.

[0133] The speed of the variable frequency pump of the waste heat recovery device was adjusted from 1200 rpm to 1380 rpm by the PID controller. The updated data was obtained: flow rate 2.88 m³ / h, temperature 62℃.

[0134] If the efficiency value is recalculated to 78% but is still below the threshold, a random forest algorithm is used with 100 decision trees. The input features include historical flow, temperature, and efficiency value classification efficiency distribution. The output is the final adjustment parameters, such as flow correction +8% and temperature correction -1℃.

[0135] Further, after performing a second adjustment, the final data was obtained: flow rate 3.11 m³ / h, temperature 61℃. Substituting these values ​​into the efficiency formula, we verified whether the target value, such as 85%, was achieved.

[0136] Furthermore, the adjustment module obtains the changing trend data of the heat recovery rate through real-time monitoring to determine the fluctuation range of the changing trend;

[0137] Based on the fluctuation range of the trend, the adjustment parameters of the valve opening are calculated using a preset flow control model to obtain the adjustment parameter values;

[0138] The valve opening is updated by adjusting the parameter values ​​to drive the control and regulation module, and the adjusted flow control data is obtained.

[0139] The adjusted flow control data is processed to obtain a new heat recovery rate value. If the new heat recovery rate value is lower than the preset threshold, the deviation between the trend and the recovery efficiency is analyzed by the support vector machine algorithm to determine the optimization adjustment parameters.

[0140] Based on the optimized and adjusted parameters, the control adjustment is re-executed to obtain the final heat recovery rate data and determine the achievement of the efficiency target;

[0141] By comparing the final heat recovery rate data with the preset threshold, the stability information of the system operation is determined.

[0142] In one embodiment, the flow rate of the condensate pipe is 500 liters per hour and the temperature is 45 degrees Celsius, obtained by a preset sensor module, to obtain the initial monitoring results;

[0143] The heat recovery rate was calculated using the formula Q=ρ×c×ΔT×V, where ρ is the density of water, c is the specific heat capacity of water, ΔT is the temperature difference, and V is the flow rate. After processing the flow rate and temperature data from the initial monitoring results, the heat recovery value was found to be 1200 kJ.

[0144] Based on the relationship between the heat recovery value and the preset recovery efficiency target of 80%, the current heat recovery rate is calculated to be 75%, and the recovery rate value is determined.

[0145] If the recovery rate is lower than the preset threshold, the fluctuation range of the heat recovery rate is obtained by acquiring the trend data of the change trend, which is ±5%.

[0146] Using a preset flow control model y=k×x+b, where k is the slope and b is the intercept, the fluctuation range of the changing trend is processed, and the opening adjustment parameter of the condensate flow control valve is calculated. The adjustment parameter value is an increase of 10% in the opening.

[0147] By adjusting the parameter values, the control module is driven to update the valve opening, and the adjusted flow control data is obtained as 550 liters per hour.

[0148] Process the adjusted flow control data, calculate the new heat recovery rate to be 78%, and obtain the new recovery rate value;

[0149] If the new recovery rate is still lower than the preset threshold, the deviation between the trend and the recovery efficiency is analyzed using the support vector machine algorithm, and the optimization adjustment parameter is determined to be an increase of 15% in the opening degree.

[0150] Based on the optimized and adjusted parameters, the control and regulation module is re-driven to adjust the valve opening, and the final heat recovery rate data is 82%, which is used to determine the achievement of the efficiency target.

[0151] Furthermore, the automated control system for waste heat recovery during drying also includes a global optimization module:

[0152] The global optimization module integrates heat exchanger operating parameters, air volume adjustment data, and condensate management results, and calculates the comprehensive index of system efficiency and equipment stability through a dynamic coordination algorithm to generate a global optimization scheme for automated control.

[0153] The global optimization module obtains heat exchanger parameters, air volume data, and condensate results through the data integration module to generate initial operating status data.

[0154] The initial operating status data is processed by a dynamic coordination algorithm to calculate the comprehensive index of system efficiency and equipment stability. If the comprehensive index value is lower than the preset threshold, the deviation between the air volume data and equipment stability is analyzed by the support vector machine algorithm to determine the control parameters of the adjustment process.

[0155] The adjustment process is updated based on the control parameters. The adjusted heat exchanger parameters and condensate results are obtained, and new operating status data are generated.

[0156] The new operating status data is processed by a dynamic coordination algorithm to calculate the updated system efficiency and comprehensive indicators, and obtain the preliminary results of the optimization scheme. If the updated system efficiency reaches the preset threshold, the stability status of the equipment is determined by logical judgment, and the control parameters of the global optimization scheme are generated.

[0157] Adjust the operating status according to the control parameters of the global optimization scheme, obtain the final comprehensive index value, and judge the achievement of the optimization scheme.

[0158] In one embodiment, the heat exchanger operating parameters, such as a temperature setpoint of 60°C, air volume adjustment data, such as a fan speed of 1200 rpm, and condensate management results, such as a condensate flow rate of 5 L / min, are obtained through the data integration module to generate initial operating status data.

[0159] The initial operating status data was processed using a dynamic coordination algorithm to calculate a comprehensive index of system efficiency and equipment stability, resulting in a comprehensive index value of 0.85.

[0160] If the comprehensive index value is lower than the preset threshold of 0.9, the deviation between the air volume data and the equipment stability is analyzed by the support vector machine algorithm to determine the control parameters of the adjustment process, such as adjusting the fan speed to 1300 rpm.

[0161] Based on the control parameter update and adjustment process, obtain the adjusted heat exchanger parameters, such as a temperature setpoint of 65℃ and condensate results, such as a condensate flow rate of 6L / min, and generate new operating status data.

[0162] The new operating status data is processed by a dynamic coordination algorithm to calculate the updated system efficiency and comprehensive index, and to obtain the preliminary results of the optimization scheme, such as a comprehensive index value of 0.92.

[0163] If the updated system efficiency reaches the preset threshold of 0.9, the system will analyze the stability of the equipment through logical judgment and generate control parameters for a global optimization scheme, such as keeping the temperature setpoint at 65℃ and the fan speed at 1300rpm.

[0164] Furthermore, the operating status is adjusted according to the control parameters of the global optimization scheme, and the final comprehensive index value is 0.93, which is used to determine the achievement of the optimization scheme.

[0165] The system generates subsystem instructions through a global optimization scheme, obtains adjustment data for the operation instructions of each subsystem, such as adjusting the condensate flow rate to 6.5L / min, and obtains the initial operating state.

[0166] A real-time feedback mechanism is used to process adjustment data, extract key features such as temperature fluctuation range of ±1℃ from the feedback data, determine the changing trend of system operation, and judge the stability status of equipment.

[0167] Furthermore, the automated control system for waste heat recovery during drying also includes a fault diagnosis module:

[0168] The fault diagnosis module uses a global optimization scheme to update the operating instructions of each subsystem, verifies the improvement in system efficiency through real-time feedback data, and judges whether the equipment stability meets industrial requirements.

[0169] The fault diagnosis module generates subsystem instructions through a global optimization scheme, obtains adjustment data for the running instructions of each subsystem, and obtains the initial running state.

[0170] A real-time feedback mechanism is used to process adjustment data, extract key features from the feedback data, and determine the changing trend of system operation.

[0171] The system efficiency is calculated by the trend of change, the value of the improvement is obtained, and it is determined whether the preset threshold is reached. If the improvement is lower than the preset threshold, the deviation between the feedback data and the equipment stability is analyzed by the support vector machine algorithm to obtain the correction parameters.

[0172] Update the running instructions based on the corrected parameters, obtain the adjusted feedback data, and determine the new system efficiency value;

[0173] The system processes new system efficiency values ​​and equipment stability through logical judgments. If the preset threshold is met, the final operation instruction adjustment plan is generated.

[0174] In one embodiment, the operation instructions of each subsystem are generated through a global optimization scheme. A multi-objective optimization algorithm, such as NSGA-II, is used to calculate the optimal parameter combination. The initial operating state data is set as temperature 30°C, pressure 0.8MPa, and flow rate 200m³ / h.

[0175] The initial operating status data is processed using a real-time feedback mechanism. Key features in the feedback data, such as the standard deviation of temperature fluctuation of 0.5℃ and the pressure change rate of 0.02MPa / s, are extracted using the Kalman filter algorithm to determine that the trend of system operation is linearly increasing.

[0176] Based on the changing trend, the system efficiency was calculated. Using the weighted average method, the overall energy consumption ratio was 0.85 and the output rate was 92%, resulting in an improvement of 5.3%.

[0177] If the improvement is less than 6% of the preset threshold, the support vector machine algorithm with RBF kernel function and penalty coefficient C=1.0 is used to analyze the deviation between the feedback data and the equipment stability, and the correction parameters are obtained as temperature adjustment +1.2℃ and flow rate adjustment -15m³ / h.

[0178] The operating instructions are updated based on the corrected parameters. The setpoint is adjusted using a PID control algorithm. The adjusted feedback data is obtained, and a new system efficiency value of 6.8% is determined.

[0179] The system processes new system efficiency values ​​and equipment stability through logical judgment. If the preset threshold of 6% is met, the final operation instruction adjustment plan is generated, setting the temperature to 31.2℃, the pressure to 0.82MPa, and the flow rate to 185m³ / h.

[0180] Furthermore, the subsystem instructions are updated according to the final operation instructions, and data is collected in real time using the OPC-UA protocol to determine the equipment stability status as vibration amplitude of 0.05mm and noise of 65dB.

[0181] The heat exchanger parameters, heat transfer coefficient 1200W / m²·K, air volume data 3000m³ / h, and condensate results, pH value 6.5, are obtained through the data integration module to generate comprehensive operating status data.

[0182] Using a dynamic coordination algorithm based on fuzzy logic control, the comprehensive operating status data is processed, and the comprehensive index of system efficiency (7.1%) and equipment stability (0.92) is calculated, indicating that the optimization scheme is qualified.

[0183] The system updates subsystem instructions based on the final operating instructions, obtains real-time feedback data, and determines the stability status of the equipment.

[0184] Furthermore, the automated control system for waste heat recovery during drying also includes a data acquisition module. This module acquires temperature, humidity, and material characteristic data during the dryer's operation, and uses sensors to collect real-time heat flow parameters at the heat exchanger inlet and outlet. (Example)

[0185] As shown in Figure 1, this embodiment provides an automated control system for waste heat recovery from drying, including: a thermal energy utilization rate calculation module, a flow control module, an airflow analysis module, and an adjustment module;

[0186] The thermal energy utilization rate calculation module calculates the current thermal energy utilization rate in conjunction with the preset heat transfer model, and obtains the matching deviation between the heat exchanger operation and the dryer status.

[0187] The flow control module extracts wind speed and pressure distribution data from the duct system, uses airflow simulation algorithms to analyze the uniformity of airflow distribution, and determines the deviation area and adjustment range of airflow regulation.

[0188] The airflow analysis module performs precise adjustments for deviation areas through the automatic control module of the airflow regulating valve, obtains the adjusted wind speed and pressure data, and determines whether the airflow distribution meets the preset uniformity standard.

[0189] The adjustment module acquires flow and temperature monitoring data of the condensate pipeline, combines it with the heat recovery rate calculation formula of the waste heat recovery device, analyzes the linkage efficiency between condensate management and waste heat recovery, and determines optimization requirements. Based on the optimization requirements, it adjusts the opening of the condensate flow control valve and judges whether the preset recovery efficiency target has been achieved by monitoring the change trend of the heat recovery rate in real time.

[0190] Furthermore, the thermal energy utilization calculation module uses a preset heat transfer model to process the distribution characteristics of heat flow parameters, and combines the inlet and outlet parameters to determine the numerical result of thermal energy utilization. If the numerical result of thermal energy utilization exceeds the preset threshold, the material characteristic data is adjusted to obtain a new distribution of heat flow parameters and to determine the trend of change in the operating status.

[0191] Based on the changing trend of the operating status, obtain dynamic data of the matching deviation and determine the fluctuation range of the deviation;

[0192] The dynamic data of matching deviation is processed by random forest algorithm. Combined with the real-time acquisition results of temperature and humidity data, the predicted value of the operating status is obtained. If the predicted value of the operating status is inconsistent with the actual sensor data, the deviation fluctuation range is classified to determine the abnormal point of thermal energy utilization.

[0193] Based on the anomalies in thermal energy utilization, the transfer model is adjusted using real-time collected heat flow parameters to obtain optimized matching deviation results.

[0194] Furthermore, the thermal energy utilization calculation module determines whether the heat exchanger is deviating from the optimal state based on the matching deviation. If the deviation exceeds a preset threshold, the operating parameters are updated by adjusting the control algorithm of the heat exchanger flow rate, and a dynamic adjustment command is generated.

[0195] The thermal energy utilization calculation module analyzes the deviation of the heat exchanger operation from the optimal state through matching deviation analysis to obtain the deviation judgment result; if the deviation judgment result exceeds the preset threshold, the updated operating parameters are calculated through flow control to obtain parameter adjustment data;

[0196] A control algorithm is used to process parameter adjustment data and generate dynamic adjustment commands.

[0197] Adjust the heat exchanger operation according to the dynamic adjustment command and obtain new operating status data;

[0198] By comparing the new operating status data with the optimal status, the trend of the matching deviation is determined. If the trend shows that the deviation still exceeds the preset threshold, the deviation judgment results are classified to determine the abnormal operating range.

[0199] Adjust the flow control strategy based on the abnormal operating range to obtain optimized operating parameters.

[0200] Furthermore, the flow control module collects wind speed data and pressure distribution data through the duct system sensors to obtain initial distribution information;

[0201] The initial distribution information is processed using an airflow simulation algorithm to generate airflow distribution data.

[0202] By analyzing the uniformity of airflow distribution data, the deviation area and adjustment range data are determined. If the deviation area exceeds the preset threshold, the adjusted airflow value is calculated using the airflow adjustment formula to obtain the airflow adjustment parameters.

[0203] Generate adjustment instructions based on air volume adjustment parameters, and obtain new air speed data and pressure distribution data;

[0204] By regenerating airflow distribution data using new wind speed and pressure distribution data, the trend of uniformity change is determined. If the trend of uniformity change still shows that the deviation area exceeds the preset threshold, the support vector machine algorithm is used to classify the deviation area, determine the optimization adjustment strategy, and obtain the final airflow adjustment parameters.

[0205] Furthermore, the airflow analysis module performs precise adjustments to the deviation area through the automatic control module of the airflow regulating valve, and obtains the adjusted wind speed and pressure data;

[0206] Based on the acquired wind speed and pressure data, airflow distribution information is generated. It is determined whether the airflow distribution meets the uniformity standard defined by the preset threshold. If the airflow distribution exceeds the preset threshold, the airflow adjustment value of the deviation area is calculated by the control module to obtain new adjustment parameters.

[0207] The air volume regulating valve performs secondary adjustments based on the regulating parameters to obtain updated air velocity and pressure data.

[0208] By regenerating airflow distribution information using updated wind speed and pressure data, the uniformity standard is assessed. If the uniformity standard is still not met, a support vector machine algorithm is used to classify the deviation areas and determine the optimized adjustment parameters.

[0209] Based on the optimized adjustment parameters, the control module drives the air volume regulating valve to perform the final adjustment, and obtains the final air speed and pressure data.

[0210] Furthermore, the adjustment module acquires the flow rate and temperature data of the condensate pipeline, performs real-time monitoring through a preset sensor module, and obtains initial monitoring results;

[0211] Based on the initial monitoring results, the flow rate and temperature data are processed using the heat recovery rate calculation formula to obtain the heat recovery value of waste heat recovery.

[0212] Based on the relationship between heat recovery value and recovery rate value, the linkage efficiency between condensate management and waste heat recovery is calculated, and the efficiency distribution information is determined. If the efficiency distribution information is lower than the preset threshold, the deviation between flow data and temperature data is analyzed by calculation formula to obtain adjustment parameters.

[0213] The control module of the waste heat recovery device is driven by adjusting parameters to perform regulation and obtain updated flow and temperature data.

[0214] By recalculating the linkage efficiency using the updated traffic and temperature data, the achievement of optimization requirements is assessed. If the optimization requirements are not achieved, a random forest algorithm is used to classify the efficiency distribution information, determine the final adjustment parameters, and execute the adjustment to obtain the final data.

[0215] Furthermore, the adjustment module obtains the changing trend data of the heat recovery rate through real-time monitoring to determine the fluctuation range of the changing trend;

[0216] Based on the fluctuation range of the trend, the adjustment parameters of the valve opening are calculated using a preset flow control model to obtain the adjustment parameter values;

[0217] The valve opening is updated by adjusting the parameter values ​​to drive the control and regulation module, and the adjusted flow control data is obtained.

[0218] The adjusted flow control data is processed to obtain a new heat recovery rate value. If the new heat recovery rate value is lower than the preset threshold, the deviation between the trend and the recovery efficiency is analyzed by the support vector machine algorithm to determine the optimization adjustment parameters.

[0219] Based on the optimized and adjusted parameters, the control adjustment is re-executed to obtain the final heat recovery rate data and determine the achievement of the efficiency target;

[0220] By comparing the final heat recovery rate data with the preset threshold, the stability information of the system operation is determined.

[0221] Furthermore, the automated control system for waste heat recovery during drying also includes a global optimization module:

[0222] The global optimization module integrates heat exchanger operating parameters, air volume adjustment data, and condensate management results, and calculates the comprehensive index of system efficiency and equipment stability through a dynamic coordination algorithm to generate a global optimization scheme for automated control.

[0223] The global optimization module obtains heat exchanger parameters, air volume data, and condensate results through the data integration module to generate initial operating status data.

[0224] The initial operating status data is processed by a dynamic coordination algorithm to calculate the comprehensive index of system efficiency and equipment stability. If the comprehensive index value is lower than the preset threshold, the deviation between the air volume data and equipment stability is analyzed by the support vector machine algorithm to determine the control parameters of the adjustment process.

[0225] The adjustment process is updated based on the control parameters. The adjusted heat exchanger parameters and condensate results are obtained, and new operating status data are generated.

[0226] The new operating status data is processed by a dynamic coordination algorithm to calculate the updated system efficiency and comprehensive indicators, and obtain the preliminary results of the optimization scheme. If the updated system efficiency reaches the preset threshold, the stability status of the equipment is determined by logical judgment, and the control parameters of the global optimization scheme are generated.

[0227] Adjust the operating status according to the control parameters of the global optimization scheme, obtain the final comprehensive index value, and judge the achievement of the optimization scheme.

[0228] Furthermore, the automated control system for waste heat recovery during drying also includes a fault diagnosis module:

[0229] The fault diagnosis module uses a global optimization scheme to update the operating instructions of each subsystem, verifies the improvement in system efficiency through real-time feedback data, and judges whether the equipment stability meets industrial requirements.

[0230] The fault diagnosis module generates subsystem instructions through a global optimization scheme, obtains adjustment data for the running instructions of each subsystem, and obtains the initial running state.

[0231] A real-time feedback mechanism is used to process adjustment data, extract key features from the feedback data, and determine the changing trend of system operation.

[0232] The system efficiency is calculated by the trend of change, the value of the improvement is obtained, and it is determined whether the preset threshold is reached. If the improvement is lower than the preset threshold, the deviation between the feedback data and the equipment stability is analyzed by the support vector machine algorithm to obtain the correction parameters.

[0233] Update the running instructions based on the corrected parameters, obtain the adjusted feedback data, and determine the new system efficiency value;

[0234] The system processes new system efficiency values ​​and equipment stability through logical judgments. If the preset threshold is met, the final operation instruction adjustment plan is generated.

[0235] The system updates subsystem instructions based on the final operating instructions, obtains real-time feedback data, and determines the stability status of the equipment.

[0236] Furthermore, the fault diagnosis module extracts abnormal fluctuation data of system operation from the verification results, analyzes potential instability factors in combination with the preset fault diagnosis model, and generates further dynamic coordination and adjustment strategies.

[0237] The fault diagnosis module obtains abnormal fluctuation data through verification results, processes the abnormal fluctuation data using a preset fault diagnosis model, and obtains potential instability factors.

[0238] Based on potential instability factors, the system operation variation characteristics are extracted, and the correlation between variation characteristics and instability is analyzed using the random forest algorithm to identify key characteristics with strong correlation.

[0239] A dynamically coordinated parameter adjustment table is generated based on key characteristics. The control commands for system operation are updated through the parameter adjustment table, and the adjusted abnormal fluctuation data is obtained.

[0240] New instability indicators are extracted from the adjusted abnormal fluctuation data. If the indicator exceeds the preset threshold, the deviation between the indicator and the system operation is analyzed by logistic regression algorithm to obtain the correction coefficient.

[0241] Update the dynamically coordinated parameter adjustment table according to the correction coefficient, obtain the adjusted system operation data, and determine whether the new instability index meets the preset threshold.

[0242] The final adjustment strategy is generated by adjusting the system operation data, and the control instructions are updated using the final adjustment strategy to determine the stable state of the system operation.

[0243] In one embodiment, abnormal fluctuation data of system operation is extracted by verification results. For example, abnormal points with temperature fluctuations exceeding ±2°C are identified from real-time data collected by temperature sensors. These abnormal fluctuation data are processed using a preset fault diagnosis model to obtain potential instability factors, such as internal scaling of the heat exchanger or uneven airflow distribution.

[0244] Based on potential instability factors, the system operation variation characteristics are extracted. For example, by analyzing the heat exchanger efficiency decline trend and air volume fluctuation frequency, the random forest algorithm is used to analyze the correlation between these variation characteristics and instability, and to identify key characteristics with strong correlation, such as air volume fluctuation frequency exceeding 0.5Hz.

[0245] A dynamically coordinated parameter adjustment table is generated for key features. For example, the air volume control parameter is adjusted to a fluctuation range of ±10%. The control command of the system is updated through the parameter adjustment table, and abnormal fluctuation data after adjustment is obtained.

[0246] New instability indicators are extracted from the adjusted abnormal fluctuation data. For example, the temperature fluctuation range is reduced to ±1℃. If the indicator exceeds the preset threshold, the deviation between the indicator and the system operation is analyzed by logistic regression algorithm to obtain the correction coefficient. For example, the air volume control parameter needs to be further adjusted to ±5%.

[0247] The parameter adjustment table for dynamic coordination is updated based on the correction coefficient. The control commands for system operation are adjusted using the updated parameter adjustment table, and the adjusted system operation data is obtained.

[0248] New instability indicators are calculated based on the adjusted system operating data. For example, if the temperature fluctuation range is stable within ±0.5℃, and the new instability indicators meet the preset threshold, the stability state of the system operation is determined by logical judgment.

[0249] The final adjustment strategy is generated based on the stability of the system operation. For example, the air volume control parameter is fixed to a fluctuation range of ±5%. The control command is updated through the final adjustment strategy to obtain the optimized system operation data.

[0250] The data integration module obtains heat exchanger parameters, air volume data, and condensate results from the optimized system operation data. For example, the heat exchanger efficiency is improved to 95%, and the air volume fluctuation frequency is stabilized at 0.2Hz. The dynamic coordination algorithm processes these data to calculate the comprehensive index of system efficiency and equipment stability, and obtains the comprehensive index value, such as the comprehensive efficiency reaching 90%.

[0251] If the comprehensive index value reaches the preset threshold, the stability status of the equipment is determined through logical judgment, and the control parameters of the global optimization scheme are generated. For example, the heat exchanger cleaning cycle is adjusted to once every 30 days, and the achievement of the optimization scheme is judged.

[0252] Furthermore, the automated control system for waste heat recovery during drying also includes a data acquisition module, which acquires temperature, humidity, and material characteristic data during the operation of the dryer, and collects heat flow parameters at the inlet and outlet of the heat exchanger in real time through sensors.

[0253] The specific embodiments of the invention have been described in detail above, but these are merely examples. The invention is not limited to the specific embodiments described above. Those skilled in the art should understand that the embodiments and descriptions in the specification are only illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An automated control system for waste heat recovery during drying, characterized in that, include: The module includes a thermal energy utilization calculation module, a flow control module, an airflow analysis module, and a regulation module. The thermal energy utilization rate calculation module calculates the current thermal energy utilization rate based on a preset heat transfer model, obtaining the matching deviation between the heat exchanger operation and the dryer status. The flow control module extracts wind speed and pressure distribution data from the duct system, uses an airflow simulation algorithm to analyze the uniformity of airflow distribution, and determines the deviation area and adjustment range of airflow adjustment. The airflow analysis module performs precise adjustments for the deviation area through the automatic control module of the airflow regulating valve, obtains the adjusted wind speed and pressure data, and judges whether the airflow distribution meets the preset uniformity standard. The adjustment module obtains the flow and temperature monitoring data of the condensate pipeline, combines it with the heat recovery rate calculation formula of the waste heat recovery device, analyzes the linkage efficiency of condensate management and waste heat recovery, and determines the optimization requirements. Based on the optimization requirements, the opening of the condensate flow control valve is adjusted, and the results are verified through actual measurement. The system monitors the changing trend of heat recovery rate in real time to determine whether the preset recovery efficiency target has been achieved. The heat energy utilization calculation module uses a preset heat transfer model to process the distribution characteristics of heat flux parameters. Combining inlet and outlet parameters, it determines the numerical result of heat energy utilization rate. If the numerical result of heat energy utilization rate exceeds the preset threshold, the system adjusts the material characteristic data to obtain a new distribution of heat flux parameters and judges the changing trend of the operating status. Based on the changing trend of the operating status, the system obtains dynamic data of matching deviation and determines the fluctuation range of the deviation. The system processes the dynamic data of matching deviation using a random forest algorithm and combines the real-time acquisition results of temperature and humidity data to obtain the predicted value of the operating status. If the predicted value of the operating status is inconsistent with the actual sensor data, the deviation fluctuation range is classified to determine the abnormal points of heat energy utilization. Based on the anomalies in thermal energy utilization, the transfer model is adjusted using real-time collected heat flow parameters to obtain optimized matching deviation results.

2. The automated control system for waste heat recovery from drying according to claim 1, characterized in that: The thermal energy utilization calculation module determines whether the heat exchanger operation deviates from the optimal state based on the matching deviation. If the deviation exceeds a preset threshold, the operating parameters are updated by adjusting the heat exchanger flow control algorithm, generating a dynamic adjustment command. The thermal energy utilization calculation module analyzes the degree of deviation between the heat exchanger operation and the optimal state through matching deviation analysis to obtain the deviation judgment result. If the deviation judgment result exceeds the preset threshold, the updated operating parameters are calculated through flow control to obtain parameter adjustment data. The parameter adjustment data is processed by the control algorithm to generate a dynamic adjustment command. The heat exchanger operation is adjusted according to the dynamic adjustment command to obtain new operating status data. By comparing the new operating status data with the optimal state, the changing trend of the matching deviation is determined. If the changing trend shows that the deviation still exceeds the preset threshold, the deviation judgment result is classified to determine the abnormal operating range. Adjust the flow control strategy based on the abnormal operating range to obtain optimized operating parameters.

3. The automated control system for waste heat recovery from drying according to claim 1, characterized in that: The flow control module collects wind speed and pressure distribution data from sensors in the duct system to obtain initial distribution information. It then processes this initial distribution information using an airflow simulation algorithm to generate airflow distribution data. By analyzing the uniformity of the airflow distribution data, it identifies deviation areas and adjustment ranges. If a deviation area exceeds a preset threshold, the adjusted airflow value is calculated using an airflow adjustment formula to obtain airflow adjustment parameters. Based on these parameters, an adjustment command is generated to acquire new wind speed and pressure distribution data. Airflow distribution data is then generated again using the new data to determine the uniformity trend. If the trend still indicates that a deviation area exceeds a preset threshold, a support vector machine algorithm is used to classify the deviation areas, determine an optimized adjustment strategy, and obtain the final airflow adjustment parameters.

4. The automated control system for waste heat recovery from drying according to claim 1, characterized in that: The airflow analysis module performs precise adjustments to the deviation area through the automated control module of the airflow regulating valve, acquiring adjusted wind speed and pressure data. Based on the acquired wind speed and pressure data, it generates airflow distribution information and determines whether the airflow distribution meets the uniformity standard defined by a preset threshold. If the airflow distribution exceeds the preset threshold, the control module calculates the airflow adjustment value for the deviation area to obtain new adjustment parameters. The airflow regulating valve performs a secondary adjustment based on the adjustment parameters, acquiring updated wind speed and pressure data. Airflow distribution information is regenerated using the updated wind speed and pressure data to determine the achievement of the uniformity standard. If the uniformity standard is still not achieved, a support vector machine algorithm is used to classify the deviation area and determine optimized adjustment parameters. Based on the optimized adjustment parameters, the control module drives the airflow regulating valve to perform the final adjustment, acquiring the final wind speed and pressure data.

5. The automated control system for waste heat recovery from drying according to claim 1, characterized in that: The adjustment module acquires flow and temperature data from the condensate pipeline and performs real-time monitoring through a preset sensor module to obtain initial monitoring results. Based on these initial monitoring results, the flow and temperature data are processed using a heat recovery rate calculation formula to obtain the heat recovery value for waste heat recovery. According to the relationship between the heat recovery value and the recovery rate value, the linkage efficiency between condensate management and waste heat recovery is calculated to determine the efficiency distribution information. If the efficiency distribution information is lower than a preset threshold, the deviation between the flow and temperature data is analyzed using a calculation formula to obtain adjustment parameters. These adjustment parameters drive the control module of the waste heat recovery device to perform adjustment, acquiring updated flow and temperature data. Using the updated flow and temperature data, the linkage efficiency is recalculated to determine the achievement of optimization requirements. If the optimization requirements are not achieved, a random forest algorithm is used to classify the efficiency distribution information, determine the final adjustment parameters, and execute the adjustment to obtain the final data.

6. The automated control system for waste heat recovery from drying according to claim 5, characterized in that: The adjustment module obtains the changing trend data of the heat recovery rate by real-time monitoring, determines the fluctuation range of the changing trend, and calculates the adjustment parameters of the valve opening based on the fluctuation range of the changing trend using a preset flow control model to obtain the adjustment parameter value. The system updates valve opening by adjusting parameter values ​​to drive the control and regulation module, thereby obtaining adjusted flow control data. A real-time monitoring module processes this data to obtain a new heat recovery rate value. If the new heat recovery rate value is lower than a preset threshold, a support vector machine algorithm is used to analyze the deviation between the trend and the recovery efficiency, determining optimized adjustment parameters. Based on these optimized parameters, control and regulation are re-executed to obtain the final heat recovery rate data, assessing the achievement of the efficiency target. Finally, by comparing the final heat recovery rate data with the preset threshold, the system's stability is determined.

7. The automated control system for waste heat recovery from drying according to claim 1, characterized in that, It also includes a global optimization module: the global optimization module integrates heat exchanger operating parameters, air volume adjustment data, and condensate management results, and calculates the comprehensive index of system efficiency and equipment stability through a dynamic coordination algorithm to generate a global optimization scheme for automated control; The global optimization module acquires heat exchanger parameters, airflow data, and condensate results through the data integration module to generate initial operating status data. A dynamic coordination algorithm is used to process the initial operating status data, calculating a comprehensive index of system efficiency and equipment stability. If the comprehensive index value is lower than a preset threshold, a support vector machine algorithm is used to analyze the deviation between the airflow data and equipment stability, determining the control parameters for the adjustment process. The adjustment process is updated based on the control parameters, acquiring the adjusted heat exchanger parameters and condensate results to generate new operating status data. The new operating status data is then processed by the dynamic coordination algorithm to calculate the updated system efficiency and comprehensive index, obtaining preliminary results for the optimization scheme. If the updated system efficiency reaches a preset threshold, logical judgment is used to determine the equipment stability status, generating the control parameters for the global optimization scheme. Adjust the operating status according to the control parameters of the global optimization scheme, obtain the final comprehensive index value, and judge the achievement of the optimization scheme.

8. The automated control system for waste heat recovery from drying according to claim 1, characterized in that, It also includes a fault diagnosis module: the fault diagnosis module uses a global optimization scheme to update the operating instructions of each subsystem, verifies the improvement of system efficiency through real-time feedback data, and judges whether the equipment stability meets industrial requirements; The fault diagnosis module generates subsystem instructions through a global optimization scheme, obtains adjustment data of the operation instructions of each subsystem, and obtains the initial operating state; it processes the adjustment data using a real-time feedback mechanism, extracts key features from the feedback data, and determines the changing trend of system operation; it calculates system efficiency through the changing trend, obtains the value of the improvement, and determines whether it has reached a preset threshold. If the improvement is lower than the preset threshold, it analyzes the deviation between the feedback data and the equipment stability through a support vector machine algorithm to obtain correction parameters. The system updates the operating instructions based on the corrected parameters, obtains the adjusted feedback data, and determines the new system efficiency value. It then processes the new system efficiency value and equipment stability through logical judgment. If the preset threshold is met, it generates the final operating instruction adjustment plan. Based on the final operating instruction adjustment plan, it updates the subsystem instructions, obtains real-time feedback data, and determines the equipment stability status.

9. An automated control system for waste heat recovery from drying according to claim 8, characterized in that, It also includes a data acquisition module, which acquires temperature, humidity and material characteristic data during the operation of the dryer, and collects heat flow parameters at the inlet and outlet of the heat exchanger in real time through sensors; the fault diagnosis module extracts abnormal fluctuation data of system operation from the verification results, analyzes potential instability factors in combination with the preset fault diagnosis model, and generates further dynamic coordination and adjustment strategies. The fault diagnosis module obtains abnormal fluctuation data through verification results, processes the abnormal fluctuation data using a preset fault diagnosis model, and obtains potential instability factors; extracts the changing characteristics of system operation based on the potential instability factors, analyzes the correlation between the changing characteristics and instability using the random forest algorithm, and determines the key characteristics with strong correlation; generates a dynamically coordinated parameter adjustment table for the key characteristics, updates the control commands of system operation through the parameter adjustment table, and obtains the adjusted abnormal fluctuation data. New instability indicators are extracted from the adjusted abnormal fluctuation data. If the indicator exceeds the preset threshold, the deviation between the indicator and the system operation is analyzed by logistic regression algorithm to obtain the correction coefficient. The dynamically coordinated parameter adjustment table is updated according to the correction coefficient, the adjusted system operation data is obtained, and it is determined whether the new instability indicator meets the preset threshold. The final adjustment strategy is generated by adjusting the system operation data, and the control instructions are updated using the final adjustment strategy to determine the stability state of the system operation.

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