Compression heat zero gas consumption dryer intelligent control system

Abstract

The present invention discloses an intelligent control system for a compressed heat zero gas consumption dryer, and relates to the technical field of compressed air drying. The present invention comprises a data sensing module, an intelligent decision-making module and an execution module; the data sensing module collects compressed air humidity, temperature, pressure and flow data in real time, and can dynamically adjust the collection frequency according to the working conditions, so as to provide a data basis for subsequent decision-making; the present invention uses an integrated data sensing module to monitor the humidity, temperature, pressure and flow of compressed air in real time, and dynamically adjusts the collection frequency according to the working conditions, so as to provide an accurate data basis for intelligent decision-making; the intelligent decision-making module uses advanced data processing technology and VAE model to repair and complete the data, so as to ensure the accuracy and integrity of the data; in this way, the system can accurately control the drying process, reduce energy waste, achieve a drying effect with zero gas consumption, and thus significantly reduce the operating cost.

Description

Technical Field

[0001] The invention relates to the technical field of compressed air drying, in particular to an intelligent control system for a compression heat zero air consumption dryer. Background Art

[0002] Compressed air plays an important role in industrial production, but its humidity is often too high and needs to be processed by a dryer to meet process requirements. Although traditional compression heat dryers can reduce the humidity of compressed air, they have problems with high energy consumption and high operating costs. Especially in adsorption dryers, the lack of intelligent control often leads to low adsorbent efficiency and energy waste. In addition, when processing compressed air, traditional dryers are often unable to dynamically adjust operating parameters according to real-time operating conditions, resulting in unstable drying effects and difficulty in adapting to changing industrial needs;

[0003] Existing compressed air drying technology mainly relies on manual operation and simple automatic control, lacking comprehensive analysis and intelligent decision-making of various parameters in the drying process. This results in the dryer being unable to respond promptly and accurately when faced with complex and changeable working conditions, thus affecting drying efficiency and quality. At the same time, due to the lack of effective data collection and processing mechanisms, existing technologies make it difficult to achieve real-time monitoring and fault prediction of the operating status of the dryer, increasing maintenance costs and production risks. In addition, traditional dryers have a relatively simple valve control strategy and cannot be flexibly adjusted according to specific working conditions, limiting the performance of the dryer.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to solve the problems of high energy consumption, high operating cost, lack of intelligent control and inability to dynamically adjust operating parameters according to real-time working conditions in traditional compressed air drying technology, and to propose an intelligent control system for a compressed heat zero air consumption dryer.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The intelligent control system of the compression heat zero gas consumption dryer includes:

[0008] The data sensing module collects compressed air humidity, temperature, pressure and flow data in real time through humidity sensors distributed in the air intake, air outlet and regeneration pipes, thermistor temperature sensors surrounding the hot zone, and pressure flow sensors integrated in the air intake and regeneration pipes. It also dynamically adjusts the collection frequency according to the working conditions to provide a data basis for subsequent decision-making;

[0009] The intelligent decision-making module receives data from the data perception module through the data processing unit, removes outliers and standardizes them, and introduces the VAE model to repair and complete the data to ensure data quality; the working condition analysis subunit uses the processed data combined with the working condition model library to determine the working condition, and displays the working condition evolution through the visualization function; the valve strategy generation unit uses intelligent algorithms to generate the optimal strategy for valve switching time and size for different stages according to different working conditions, and at the same time considers external factors to correct the strategy;

[0010] The execution module uses electromagnetic valves to perform valve switching and opening adjustment, buffers the impact at the moment of valve opening and closing according to the pipeline pressure and flow, and realizes closed-loop optimization control buffering effect through sensor monitoring and PID algorithm.

[0011] Furthermore, the execution steps of the data processing unit in the intelligent decision-making module are as follows:

[0012] Receive and organize the data from the data perception module, remove outliers, perform data standardization, and ensure data quality; at the same time, establish a data repair mechanism to supplement the data. The process is as follows:

[0013] Collect historical normal data covering all working conditions from the dryer's historical operation records, including humidity, temperature, pressure, and flow information. After cleaning up the erroneous data, normalize the values ​​to the range of 0-1 to facilitate model processing;

[0014] VAE model training: Data is converted into latent space vectors by the encoder and then restored by the decoder. The restoration accuracy is guaranteed by minimizing the mean square error, and the KL divergence regularization term is combined to enable the model to grasp the internal structure of the data and learn the data distribution pattern.

[0015] When new data is accessed, outliers and missing values ​​can be locked according to simple rules or statistical methods such as 3-sigma. Abnormal humidity and missing temperature data can be locked in time. Complete and reasonable data can be generated through the encoder and decoder process based on known partial data.

[0016] The repaired or completed data is embedded into the original set, and its rationality is verified by comparing it with adjacent data and referring to physical properties.

[0017] Furthermore, the execution steps of the working condition analysis subunit in the intelligent decision-making module are as follows:

[0018] Based on the processed data and the built-in dryer operating model library, the current operating conditions are identified, including normal operation, abnormal air intake, and heat exchange imbalance, and the anchoring direction is generated for the valve control strategy.

[0019] At the same time, a visualization function for the evolution of working conditions is added; time series analysis and dynamic Bayesian network technology are used to draw trend graphs and state transition graphs of the evolution of working conditions over time in real time, and present them to operators intuitively through a visualization interface.

[0020] Furthermore, the execution steps of the valve strategy generation unit in the intelligent decision-making module are as follows:

[0021] According to different working conditions, intelligent algorithms (such as fuzzy control and neural network algorithms) are used to generate the optimal strategies for valve switching time and size in real time; the strategies include adsorption stage strategy, regeneration stage strategy and cold blowing stage strategy;

[0022] The adsorption phase strategy is as follows:

[0023] Monitor the intake air humidity. When the humidity sensor monitoring value H is within the preset normal humidity range and the flow sensor monitoring value F is within the stable flow range The time when the intake valve switches to closed and prepares for regeneration Set to the preset standard adsorption time ;

[0024] Intake valve opening Set as ,in It is the proportional coefficient obtained through equipment debugging; the outlet valve opening Fixed to full open, ;

[0025] When the inlet humidity H≥Hmax and the flow rate F≥Fmax, first determine the basic adsorption time extension coefficient , through the formula: , where a and b are empirical coefficients, obtained by fitting the equipment characteristics with past operating data; at this time, the adsorption time Then , with the help of real-time monitoring of humidity and flow rate, dynamic adjustment , control the adsorption end point; intake valve opening According to the flow expansion, set ,and ; At the same time, set the upper limit of pressure , when the pressure sensor monitors the value And the upper limit of pressure When the absolute value of the difference is less than the preset threshold, the feedback mechanism is proportional to Close the intake valve, ;

[0026] Exhaust valve opening Adjust with the air intake to maintain air pressure balance. ;

[0027] When the intake air humidity H<Hmin and the flow rate F<Fmin, define the adsorption time shortening coefficient , using the formula , c, d are empirical constants; adsorption time That is ;

[0028] Intake valve opening Set as , ; Exhaust valve opening Set as ;

[0029] The regeneration phase strategy is as follows:

[0030] Based on the regeneration time provided by the adsorbent manufacturer and measured by the equipment , combined with a temperature sensor to monitor the regeneration temperature ,when Rise to the ideal regeneration temperature range Start timing and reach The time when the regeneration valve switches to close and the cold blow valve opens That is ;

[0031] Regeneration valve opening Set as , is the regeneration air flow rate, The best matching coefficient obtained after debugging; the opening of the cold blow valve Initial setting , to prevent cold air flow interference;

[0032] When the regeneration temperature < The regeneration air flow When it is lower than the standard, set the temperature compensation coefficient and regeneration air flow compensation coefficient , , , Adapt constants for the device; regeneration time Adjust to , dynamically extended according to real-time feedback of temperature and flow;

[0033] Regeneration valve opening Increase to , speed up the flow of high-temperature air; check related equipment, trigger early warning when a fault occurs, adjust the auxiliary valve, and stabilize the regeneration airflow;

[0034] When the regeneration temperature ≥ The regeneration air flow When it is greater than the standard, set the overheat shortening coefficient , , is the empirical coefficient; regeneration time becomes , quickly shorten the regeneration time at high temperature; accurately control the switch according to the real-time temperature; close the regeneration valve to , mitigate thermal shock; check cooling links and activate emergency plans as needed; monitor equipment temperature fields to prevent local overheating;

[0035] The cold blowing phase strategy is as follows:

[0036] According to the thermodynamic characteristics of the adsorbent and the heat dissipation design of the equipment, the standard cold blowing time is obtained. , combined with a temperature sensor to monitor the adsorbent temperature ,when Reduce to the preset adsorption range The time for the cold blow valve to switch closed and the air intake valve to open That is ;

[0037] Cold blow valve opening Set as , is the cooling air flow rate, Ensure that sufficient cold air enters; the opening of the intake valve and regeneration valve , prevent hot air from mixing in;

[0038] When the adsorbent temperature ≥ When the cooling airflow is lower than the preset threshold, the formula Calculation of insufficient cooling extension factor , Adapt constants for the equipment; cold blowing time Adjust to , flexibly control according to the temperature drop;

[0039] Increase the cold blow valve opening to ; Troubleshoot cooling failures, optimize heat exchanger valves, and improve cooling efficiency;

[0040] When the adsorbent temperature < When the cooling exceeds the threshold, the formula Get the early termination coefficient , is the empirical coefficient; when When the threshold is reached, the cold blowing ends immediately, the cold blowing valve closes, and the intake valve opens for a certain period of time. in advance, ; Close the cold blow valve to ; Check the temperature monitoring and control system to prevent misoperation;

[0041] During the operation of the compression heat zero gas consumption dryer, in addition to the humidity, temperature, and flow parameters directly monitored inside the equipment, external factors will also interfere with its operating conditions, thereby affecting the valve switching time and opening, and corrections will be made to the adjustments at different stages.

[0042] Furthermore, the specific operation steps of the valve strategy generation unit for correcting the adjustment at different stages are as follows: wherein the external factor correction includes:

[0043] Ambient temperature correction: Assume the standard ambient temperature is , ambient temperature correction factor The calculation is as follows:

[0044] in and They are the upper and lower limits of the ambient temperature allowed by the equipment;

[0045] Altitude correction: Assume the device's calibrated altitude is , the actual altitude is , Altitude correction factor for: ,in, They are the maximum and minimum values ​​of the applicable altitude range of the equipment;

[0046] Grid voltage fluctuation correction: If the standard grid voltage is , grid voltage fluctuation correction factor It is expressed as: ;

[0047] in and They are respectively the upper and lower limits of normal fluctuation of the grid voltage;

[0048] Will get , and Normalization is substituted into the following formula: ,in They are the weight coefficients corresponding to ambient temperature, altitude, and grid voltage fluctuation;

[0049] Based on comprehensive correction value Determine the correction index when hour, ;when hour, ,in It is an empirical constant used to amplify or reduce the correction amplitude and adjust the sensitivity requirements according to the equipment working conditions;

[0050] The obtained correction index The application is in different operation stages, including:

[0051] The corrected adsorption time and corrected intake valve opening during the adsorption phase are: ,in , are the calculated adsorption time and intake valve opening respectively;

[0052] The corrected regeneration time and corrected regeneration valve opening during the regeneration phase are: ,in , are respectively the calculated regeneration time and the regeneration valve opening;

[0053] The corrected cold blowing time and the corrected cold blowing valve opening in the cold blowing stage are: in , They are the calculated cold blowing time and cold blowing valve opening respectively.

[0054] Furthermore, the specific operation steps of the execution module are as follows:

[0055] Solenoid valve assembly: Use solenoid valves to receive instructions from the intelligent decision-making module, execute valve switching actions, and follow the set switching time;

[0056] At the same time, with the high-precision characteristics of electromagnetic drive, the valve opening can be adjusted with an accuracy of 0.1%, which is suitable for airflow control under various working conditions; and an automatic calibration mechanism is established, including:

[0057] A sensor network is integrated inside the electromagnetic valve to monitor the valve core position, electromagnetic coil current intensity, and temperature parameters in real time. During the operation interval or before and after each valve action, the actual parameters are automatically compared with the preset ideal operation curve based on the built-in algorithm. Once a deviation is found, the self-calibration program is immediately started to dynamically adjust the electromagnetic force output and valve core displacement compensation to ensure that the valve opening accuracy is continuously maintained within 0.1%;

[0058] Buffer adjustment mechanism: When the valve is opened or closed, the buffer mechanism is automatically activated according to the pipeline pressure and flow rate to buffer the impact of water hammer effect and reduce equipment vibration and mechanical wear;

[0059] Pressure and vibration sensors are embedded in the buffer adjustment mechanism to monitor the actual pressure fluctuations in the pipeline and the vibration amplitude of the equipment during the buffering process in real time, analyze the data, and use the PID algorithm to dynamically adjust the key parameters of the buffer mechanism, including the damping coefficient and buffer stroke duration, to form a closed-loop optimization control and ensure that the buffering effect is always in the best state.

[0060] Furthermore, the specific operation steps of the data perception module are as follows:

[0061] Humidity sensor: distributed in the air inlet, outlet and regeneration pipelines, it can capture the changes in compressed air humidity in real time, provide humidity data for judging the moisture absorption degree of the adsorbent, planning the adsorption and regeneration switching nodes, and make the valve switching timing match the actual working conditions;

[0062] Thermistor temperature sensor: placed around the intake heat exchanger and regeneration heater hot zone to monitor temperature fluctuations, assist in controlling the start and end temperatures of the regeneration and cold blowing stages, and set the corresponding valve switching time accordingly to ensure reasonable use of heat;

[0063] Pressure flow sensor: integrated in the air intake and regeneration pipelines, it measures pressure and flow simultaneously, dynamically grasps the flow state of compressed air, adjusts the valve opening accordingly, maintains air flow stability, and avoids drying abnormalities caused by abnormal flow;

[0064] At the same time, the sensor dynamically adjusts the collection frequency according to the working conditions.

[0065] Furthermore, the specific operation steps of dynamically adjusting the acquisition frequency of the sensor according to the working conditions in the data perception module are as follows:

[0066] The intelligent decision-making module obtains the current working condition determined by the intelligent decision-making module. When the working condition is stable and the parameters do not fluctuate abnormally, a low-frequency acquisition instruction is issued to each sensor. If an abnormal working condition signal is detected, the high-frequency or ultra-high-frequency acquisition strategy is adjusted. The low-frequency acquisition frequency is once every 10-15 minutes, the high-frequency acquisition frequency is once every 1-5 minutes, and the ultra-high-frequency acquisition frequency is once every 5-20 seconds.

[0067] At the same time, an event-driven mechanism is set up for equipment operation nodes and potential fault hazards, including:

[0068] When the adsorbent saturation reaches the threshold, the humidity sensor detects an abnormal change in the humidity drop slope and enters the extremely fast collection state, feeding back humidity data every 1-2 seconds to help the control system determine the adsorption-regeneration switching node;

[0069] At the moment the regeneration process starts, the thermistor temperature sensor starts high-frequency acquisition to monitor the heating process and ensure accurate control of the regeneration temperature.

[0070] When the pressure flow sensor detects a sudden change in flow exceeding the safety threshold, or the temperature sensor detects a risk of local overheating, the sensor switches to ultra-fast data collection, sends back data in real time, and cooperates with the control system to implement emergency regulation and control to promptly resolve equipment operation crises.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] (1) The present invention monitors the humidity, temperature, pressure and flow of compressed air in real time through an integrated data sensing module, dynamically adjusts the collection frequency according to the working conditions, and provides an accurate data basis for intelligent decision-making; the intelligent decision-making module uses advanced data processing technology and VAE model to repair and complete the data, ensuring the accuracy and integrity of the data; in this way, the system can accurately control the drying process, reduce energy waste, achieve a zero-gas-consumption drying effect, and thus significantly reduce operating costs;

[0073] (2) In the present invention, the intelligent decision-making module uses the working condition analysis subunit and the valve strategy generation unit, combines the processed data and the working condition model library, accurately determines the current working condition, and generates the optimal strategy for the valve switching time and size for different working conditions. This intelligent control method improves the stability of the drying process, enables the dryer to adapt to the changing industrial needs and working condition changes, and ensures the consistency and reliability of the compressed air drying quality;

[0074] (3) In the present invention, the execution module uses a high-speed electromagnetic valve to accurately perform valve switching and opening adjustment, achieving precise control of airflow regulation; the closed-loop optimization control buffer effect is achieved through sensor monitoring and PID algorithm, further improving the response speed and control accuracy of the system. In addition, the data perception module dynamically adjusts the acquisition frequency according to the working conditions, and the setting of the event-driven mechanism enables the system to respond to potential fault hazards in a timely manner, improves the intelligence level and operational convenience of the system, and reduces the need for manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0076] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0077] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0079] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations.

[0080] like Figure 1 As shown, the intelligent control system of the compression heat zero gas consumption dryer includes a data perception module, an intelligent decision module and an execution module;

[0081] The data sensing module collects compressed air humidity, temperature, pressure and flow data in real time through humidity sensors distributed in the air intake, air outlet and regeneration pipes, thermistor temperature sensors surrounding key hot areas, and pressure flow sensors integrated in the air intake and regeneration pipes. It can also dynamically adjust the collection frequency according to the working conditions, providing a data basis for subsequent decision-making;

[0082] Humidity sensor: distributed in the air intake, air outlet and regeneration pipelines, capturing subtle changes in compressed air humidity in real time, providing humidity data for judging the degree of moisture absorption of the adsorbent and planning the adsorption and regeneration switching nodes, so that the valve switching timing is more in line with the actual working conditions; thermistor temperature sensor: placed around the key hot areas of the air intake heat exchanger and regeneration heater, monitoring temperature fluctuations, assisting in controlling the start and end temperatures of the regeneration and cold blowing stages, and setting the corresponding valve switching time accordingly to ensure the rational use of heat; pressure flow sensor: integrated in the air intake and regeneration pipelines, synchronously measuring pressure and flow, dynamically grasping the flow state of compressed air in the system, and flexibly adjusting the valve opening accordingly to maintain airflow stability and avoid drying abnormalities caused by abnormal flow; dynamically adjusting the acquisition frequency of the sensor according to the working conditions, the process is as follows:

[0083] The intelligent decision-making module obtains the current working condition determined by the intelligent decision-making module. When the working condition is stable and the parameters are stable, a low-frequency acquisition instruction is issued to each sensor. If an abnormal working condition signal is detected, the acquisition strategy is immediately adjusted to a high-frequency or even ultra-high-frequency acquisition strategy. The low-frequency acquisition frequency is once every 10-15 minutes, the high-frequency acquisition frequency is once every 1-5 minutes, and the ultra-high-frequency acquisition frequency is once every 5-20 seconds.

[0084] In addition to dynamically adjusting the frequency according to fluctuations in operating conditions, an event-driven mechanism is also set up for key operating nodes of the equipment and potential fault hazards; when the adsorbent is approaching saturation, the humidity sensor detects an abnormal change in the slope of the humidity decrease and immediately enters the ultra-fast collection state, feeding back humidity data every 1-2 seconds to help the control system accurately grasp the adsorption-regeneration switching node; at the moment the regeneration link is started, the thermistor temperature sensor starts high-frequency collection, closely monitors the heating process, and ensures accurate control of the regeneration temperature; once the pressure flow sensor detects a sudden change in flow exceeding the safety threshold, or the temperature sensor detects a risk of local overheating, the relevant sensors quickly switch to ultra-fast collection, intensively send back data, and cooperate with the control system to implement emergency regulation and control to promptly resolve equipment operation crises.

[0085] The intelligent decision-making module includes a data processing unit, a working condition analysis subunit and a valve strategy generation unit;

[0086] Data processing unit: receives data from the data perception module, removes outliers and standardizes them, introduces VAE models to repair and complete data, and ensures data quality; working condition analysis submodule: determines working conditions based on the processed data combined with the working condition model library, and displays the evolution of working conditions through visualization functions; valve strategy generation unit: uses intelligent algorithms to generate optimal strategies for valve switching time and size for different working conditions (adsorption, regeneration, and cold blowing stages), and considers external factors (ambient temperature, altitude, and grid voltage fluctuations) to correct strategies;

[0087] The data processing unit receives and organizes the data from the data perception module, removes outliers, performs data standardization, ensures data quality, and provides a reliable basis for subsequent analysis; at the same time, a data repair mechanism is established to supplement the data. The process is as follows:

[0088] Historical normal data covering various working conditions are collected from a large number of past operation records of the dryer, including humidity, temperature, pressure, and flow information. After cleaning up obviously erroneous data, the values ​​are normalized to the range of 0-1 to facilitate model processing; when conditions permit, some data can be accurately labeled according to working conditions; VAE model training: data is converted into latent space vectors by the encoder and then restored by the decoder; the restoration accuracy is guaranteed by minimizing the mean square error, and combined with the KL divergence regularization term, the model is prompted to grasp the internal structure of the data and learn the data distribution pattern; new data is accessed, and simple rules or statistical methods such as 3-sigma are used to quickly lock in abnormal values ​​and missing values; situations such as sudden changes in humidity and missing temperature data can be detected immediately; complete and reasonable data are generated based on known partial data through the encoder and decoder process; the repaired or supplemented data is embedded in the original set, and its rationality is verified by comparing adjacent data and referring to physical properties; for example, the supplemented pressure data must be consistent with the actual flow and pipeline resistance to ensure data consistency and accuracy for accurate analysis of subsequent working conditions.

[0089] The working condition analysis subunit, based on the processed data and the built-in dryer working condition model library, determines the current working condition, covering normal operation, abnormal air intake, heat exchange imbalance and other modes, and generates anchoring directions for the valve control strategy. At the same time, a working condition evolution visualization function is added. The trend chart and state transition chart of the working condition evolution over time are drawn in real time using time series analysis and dynamic Bayesian network technology, and are presented to the operator intuitively through a visual interface.

[0090] The valve strategy generation unit uses intelligent algorithms (such as fuzzy control and neural network algorithms) to generate the optimal strategy for valve switching time and size in real time according to different working conditions; the strategies include adsorption stage strategy, regeneration stage strategy and cold blowing stage strategy; the adsorption stage strategy is as follows:

[0091] Monitor the intake air humidity. When the humidity sensor monitoring value H is within the preset normal humidity range and the flow sensor monitoring value F is within the stable flow range The time when the intake valve switches to closed and prepares for regeneration Set to the preset standard adsorption time , standard adsorption time Through the statistical analysis of experiments and historical operation data, it is found that the saturated adsorption demand of the adsorbent under normal working conditions is met; the intake valve opening Set as ,in It is the proportional coefficient obtained through equipment debugging, the purpose of which is to make the intake valve opening match the current intake flow , to ensure stable air intake; outlet valve opening Fixed to full open, , maintain the system in a zero back pressure or extremely low back pressure state, which is conducive to the smooth discharge of compressed air;

[0092] When the inlet humidity H is greater than or equal to Hmax and the flow rate F is greater than or equal to Fmax, first determine the basic adsorption time extension coefficient , through the formula: , where a and b are empirical coefficients, obtained by fitting the equipment characteristics with past operating data; at this time, the adsorption time Then , with the help of real-time monitoring of humidity and flow rate, dynamic adjustment , accurately control the adsorption end point; intake valve opening According to the flow expansion, set ,and , ensure that enough humid air enters; at the same time set the upper limit of pressure , when the pressure sensor monitors the value And the upper limit of pressure When the absolute value of the difference is less than the preset threshold, the feedback mechanism is proportional to Close the intake valve, , to ensure system safety; outlet valve opening Fine-tune with the air intake to maintain air pressure balance. ;

[0093] When the intake air humidity H is less than Hmin and the flow rate F is less than Fmin, the adsorption time reduction coefficient is defined , using the formula , c, d are empirical constants; adsorption time That is , flexibly adjusted according to real-time monitoring; intake valve opening Set as , , adapted to low flow conditions; outlet valve opening Set as ;

[0094] The regeneration phase strategy is as follows: Based on the regeneration time provided by the adsorbent manufacturer and the actual equipment measurement , combined with a temperature sensor to monitor the regeneration temperature ,when Rise to the ideal regeneration temperature range Start timing and reach The time when the regeneration valve switches to close and the cold blow valve opens That is , accurately control the regeneration cycle; regeneration valve opening Set as , is the regeneration air flow rate, The best matching coefficient is obtained after debugging to ensure uniform heat supply; the cold blow valve opening Initial setting , to prevent cold air flow interference;

[0095] When the regeneration temperature < The regeneration air flow When it is lower than the standard, set the temperature compensation coefficient and regeneration air flow compensation coefficient , , , Adapt constants for the device; regeneration time Adjust to , dynamically extended according to real-time feedback of temperature and flow; regeneration valve opening Increase to , speed up the flow of high-temperature air; check related equipment, trigger early warning when a fault occurs, adjust the auxiliary valve, and stabilize the regeneration airflow;

[0096] When the regeneration temperature ≥ The regeneration air flow When it is greater than the standard, set the overheat shortening coefficient , , is the empirical coefficient; regeneration time becomes , quickly shorten the regeneration time at high temperature; accurately control the switch according to the real-time temperature; close the regeneration valve to , mitigate thermal shock; check cooling links and activate emergency plans as needed; closely monitor the temperature field of equipment to prevent local overheating;

[0097] The cold blowing phase strategy is as follows:

[0098] According to the thermodynamic characteristics of the adsorbent and the heat dissipation design of the equipment, the standard cold blowing time is obtained. , combined with a temperature sensor to monitor the adsorbent temperature ,when Reduce to the appropriate adsorption range The time for the cold blow valve to switch closed and the air intake valve to open That is ; Cold blow valve opening Set as , is the cooling air flow rate, Ensure that sufficient cold air enters; the opening of the intake valve and regeneration valve , prevent hot air from mixing in;

[0099] When the adsorbent temperature ≥ When the cooling airflow is lower than the preset threshold, the formula Calculation of insufficient cooling extension factor , Adapt constants for the equipment; cold blowing time Adjust to , flexibly control according to the temperature drop; increase the cold blow valve opening to ; Troubleshoot cooling failures, optimize heat exchanger valves, and improve cooling efficiency;

[0100] When the adsorbent temperature < When the cooling exceeds the threshold, the formula Get the early termination coefficient , is the empirical coefficient; when When the threshold is reached, the cold blowing ends immediately, the cold blowing valve closes, and the intake valve opens for a certain period of time. in advance, ; Close the cold blow valve to ; Check the temperature monitoring and control system to prevent misoperation;

[0101] During the operation of the compression heat zero gas consumption dryer, in addition to the humidity, temperature, and flow parameters directly monitored inside the equipment, many external factors will also interfere with its working conditions, thereby affecting the valve switching time and opening. The correction process for the adjustment at different stages is as follows: The external factor correction includes:

[0102] Ambient temperature correction: Ambient temperature is an external factor that cannot be ignored. If it is too low, the heat dissipation will be accelerated, which will affect the heat exchange efficiency inside the dryer; if it is too high, it may increase the heat dissipation burden of the equipment and interfere with the normal operation of each stage. , ambient temperature correction factor The calculation is as follows:

[0103] in and They are the upper and lower limits of the ambient temperature allowed by the equipment, which quantifies the degree to which the current ambient temperature deviates from the standard value.

[0104] Altitude correction: Altitude will change the atmospheric pressure, which will affect the properties of compressed air and the operating pressure of the equipment. Assume that the equipment is calibrated at an altitude of , the actual altitude is , Altitude correction factor for: ,in, These are the maximum and minimum values ​​of the applicable altitude range of the equipment. The air is thin and the pressure is low in high altitude areas. The working conditions of the equipment when processing compressed air are different from those in low altitude areas. This coefficient can capture such differences.

[0105] Correction of grid voltage fluctuation: The operation of the dryer depends on electricity to drive valves, heaters and other components. Fluctuations will cause the actual power of the equipment to change. If the standard grid voltage is , grid voltage fluctuation correction factor It is expressed as: ;

[0106] in and They are respectively the upper and lower limits of the normal fluctuation of the grid voltage. When the voltage is unstable, the heating power of the heater and the stability of the valve drive will be affected. This coefficient is used to measure the degree of influence of voltage fluctuation;

[0107] Will get , and Normalization is substituted into the following formula: ,in They are the weight coefficients corresponding to ambient temperature, altitude, and grid voltage fluctuation. The weight distribution is based on the importance of each factor on the operation of the equipment, and is obtained through a large number of experiments and statistical analysis of actual operation data;

[0108] Based on comprehensive correction value Determine the correction index when hour, ;when hour, ,in It is an empirical constant used to enlarge or reduce the correction amplitude and adapt the sensitivity requirements of the equipment working condition. The application is in different operation stages, including:

[0109] The corrected adsorption time and corrected intake valve opening during the adsorption phase are: ,in , are the calculated adsorption time and intake valve opening respectively;

[0110] The corrected regeneration time and corrected regeneration valve opening during the regeneration phase are: ,in , are respectively the calculated regeneration time and the regeneration valve opening;

[0111] The corrected cold blowing time and the corrected cold blowing valve opening in the cold blowing stage are: ,in , They are the calculated cold blowing time and cold blowing valve opening respectively.

[0112] The execution module uses a high-speed electromagnetic valve to accurately perform valve switching and opening adjustment, buffers the impact at the moment of valve opening and closing according to pipeline pressure and flow, and achieves closed-loop optimization control buffering effect through sensor monitoring and PID algorithm;

[0113] Solenoid valve assembly: Use solenoid valves with extremely fast response speed, receive instructions from the intelligent decision-making module, execute valve switching actions with millisecond precision, strictly follow the set switching time, and have extremely small errors; at the same time, relying on the high-precision characteristics of electromagnetic drive, achieve fine adjustment of valve opening with an accuracy of 0.1%, which is suitable for airflow control under various working conditions; at the same time, establish an automatic calibration mechanism, including: integrating a micro sensor network inside the solenoid valve to monitor the valve core position, electromagnetic coil current intensity, and temperature parameters in real time; in the operating interval or before and after each valve action, automatically compare the actual parameters with the preset ideal operating curve based on the built-in algorithm. Once a deviation is found, immediately start the self-calibration program to dynamically adjust the electromagnetic force output and valve core displacement compensation to ensure that the valve opening accuracy is continuously maintained within 0.1%, solve the accuracy drift problem caused by long-term use and changes in ambient temperature and humidity, and reduce the frequency of manual calibration;

[0114] Buffer adjustment mechanism: When the valve is opened and closed, the buffer mechanism is automatically activated according to the pipeline pressure and flow, which effectively buffers the impact of water hammer effect, reduces equipment vibration and mechanical wear, extends the service life of the valve, and maintains the smooth operation of the system; and embeds pressure and vibration sensors in the buffer adjustment mechanism to monitor the actual pressure fluctuation of the pipeline and the vibration amplitude of the equipment during the buffering process in real time, analyze the data, and dynamically adjust the key parameters of the buffer mechanism, including the damping coefficient and the buffer stroke duration, with the help of PID algorithm, to form a closed-loop optimization control to ensure that the buffering effect is always in the best state.

[0115] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Compression heat zero gas consumption dryer intelligent control system, characterized in that: include: The data sensing module collects compressed air humidity, temperature, pressure and flow data in real time through humidity sensors distributed in the air intake, air outlet and regeneration pipes, thermistor temperature sensors surrounding the hot zone, and pressure flow sensors integrated in the air intake and regeneration pipes. It also dynamically adjusts the collection frequency according to the working conditions to provide a data basis for subsequent decision-making; The intelligent decision-making module receives data from the data perception module through the data processing unit, removes outliers and standardizes them, and introduces the VAE model to repair and complete the data to ensure data quality; the working condition analysis subunit uses the processed data combined with the working condition model library to determine the working condition, and displays the evolution of the working condition through the visualization function; the valve strategy generation unit uses intelligent algorithms to generate the optimal strategy for valve switching time and size for different stages according to different working conditions, and at the same time considers external factors to correct the strategy; The execution steps of the valve strategy generation unit in the intelligent decision-making module are as follows: According to different working conditions, intelligent algorithms are used to generate the optimal strategies for valve switching time and size in real time; the strategies include adsorption stage strategy, regeneration stage strategy and cold blowing stage strategy; The adsorption phase strategy is as follows: Monitor the intake air humidity. When the humidity sensor monitoring value H is within the preset normal humidity range and the flow sensor monitoring value F is within the stable flow range The time when the intake valve switches to closed and prepares for regeneration Set to the preset standard adsorption time ; Intake valve opening Set as ,in It is the proportional coefficient obtained through equipment debugging; the outlet valve opening Fixed to full open, ; When the inlet humidity H≥Hmax and the flow rate F≥Fmax, first determine the basic adsorption time extension coefficient , through the formula: , where a and b are empirical coefficients, obtained by fitting the equipment characteristics with past operating data; at this time, the adsorption time Then , with the help of real-time monitoring of humidity and flow rate, dynamic adjustment , control the adsorption end point; intake valve opening According to the flow expansion, set ,and ; At the same time, set the upper limit of pressure , when the pressure sensor monitors the value And the upper limit of pressure When the absolute value of the difference is less than the preset threshold, the feedback mechanism is proportional to Close the intake valve, ; Exhaust valve opening Adjust with the air intake to maintain air pressure balance. ; When the intake air humidity H<Hmin and the flow rate F<Fmin, define the adsorption time shortening coefficient , using the formula , c and d are empirical constants; adsorption time That is ; Intake valve opening Set as , ; Exhaust valve opening Set as ; The regeneration phase strategy is as follows: Based on the regeneration time provided by the adsorbent manufacturer and measured by the equipment , combined with temperature sensor to monitor regeneration temperature ,when Rise to the ideal regeneration temperature range Start timing and reach The time when the regeneration valve switches to close and the cold blow valve opens That is ; Regeneration valve opening Set as , is the regeneration air flow rate, The best matching coefficient obtained after debugging; the opening of the cold blow valve Initial setting , to prevent cold air flow interference; When the regeneration temperature < The regeneration air flow When it is lower than the standard, set the temperature compensation coefficient and regeneration air flow compensation coefficient , , , Adapt constants for the device; regeneration time Adjust to , dynamically extended according to real-time feedback of temperature and flow; Regeneration valve opening Increase to , speed up the inflow of high-temperature air; Check related equipment, trigger warnings when failures occur, adjust auxiliary valves, and stabilize regeneration airflow; When the regeneration temperature ≥ The regeneration air flow When it is greater than the standard, set the overheat shortening coefficient , , is the empirical coefficient; regeneration time becomes , quickly shorten the regeneration time at high temperature; accurately control the switch according to the real-time temperature; close the regeneration valve to , mitigate thermal shock; check cooling links and activate emergency plans as needed; monitor equipment temperature fields to prevent local overheating; The cold blowing phase strategy is as follows: According to the thermodynamic characteristics of the adsorbent and the heat dissipation design of the equipment, the standard cold blowing time is obtained. , combined with a temperature sensor to monitor the adsorbent temperature ,when Reduce to the preset adsorption range The time for the cold blow valve to switch closed and the air intake valve to open That is ; Cold blow valve opening Set as , is the cooling air flow rate, Ensure that sufficient cold air enters; the opening of the intake valve and regeneration valve , prevent hot air from mixing in; When the adsorbent temperature ≥ When the cooling airflow is lower than the preset threshold, the formula Calculation of insufficient cooling extension factor , Adapt constants for the equipment; cold blowing time Adjust to , flexibly control according to the temperature drop; Increase the cold blow valve opening to ; Troubleshoot cooling failures, optimize heat exchanger valves, and improve cooling efficiency; When the adsorbent temperature < When the cooling exceeds the threshold, the formula Get the early termination coefficient , is the empirical coefficient; when When the threshold is reached, the cold blowing ends immediately, the cold blowing valve closes, and the intake valve opens for a certain period of time. in advance, ; Close the cold blow valve to ; Check the temperature monitoring and control system to prevent misoperation; During the operation of the compression heat zero gas consumption dryer, in addition to the humidity, temperature and flow parameters directly monitored inside the equipment, external factors will also interfere with its working conditions, thereby affecting the valve switching time and opening, and corrections should be made for adjustments at different stages; The specific operation steps of the valve strategy generation unit for correcting the adjustment at different stages are as follows: The external factors correction include: Ambient temperature correction: Assume the standard ambient temperature is , ambient temperature correction factor The calculation is as follows: in and They are the upper and lower limits of the ambient temperature allowed by the equipment; Altitude correction: Assume the device's calibrated altitude is , the actual altitude is , Altitude correction factor for: ,in, They are the maximum and minimum values ​​of the applicable altitude range of the equipment; Grid voltage fluctuation correction: If the standard grid voltage is , grid voltage fluctuation correction factor It is expressed as: ; in and They are respectively the upper and lower limits of normal fluctuation of the grid voltage; Will get , and Normalization is substituted into the following formula: ,in They are the weight coefficients corresponding to ambient temperature, altitude, and grid voltage fluctuation; Based on comprehensive correction value Determine the correction index when hour, ;when hour, ,in It is an empirical constant used to amplify or reduce the correction amplitude and adjust the sensitivity requirements according to the equipment working conditions; The obtained correction index The application is in different operation stages, including: The corrected adsorption time and corrected intake valve opening during the adsorption phase are: ,in , are the calculated adsorption time and intake valve opening respectively; The corrected regeneration time and corrected regeneration valve opening during the regeneration phase are: ,in , are respectively the calculated regeneration time and the regeneration valve opening; The corrected cold blowing time and the corrected cold blowing valve opening in the cold blowing stage are: ,in , They are the calculated cold blowing time and cold blowing valve opening; The execution module uses electromagnetic valves to perform valve switching and opening adjustment, buffers the impact at the moment of valve opening and closing according to the pipeline pressure and flow, and realizes closed-loop optimization control buffering effect through sensor monitoring and PID algorithm.

2. The intelligent control system of the compression heat zero gas consumption dryer according to claim 1 is characterized in that: The execution steps of the data processing unit in the intelligent decision-making module are as follows: Receive and organize the data from the data perception module, remove outliers, perform data standardization, and ensure data quality; at the same time, establish a data repair mechanism to supplement the data. The process is as follows: Collect historical normal data covering all working conditions from the dryer's historical operation records, including humidity, temperature, pressure, and flow information. After cleaning up the erroneous data, normalize the values ​​to the range of 0-1 to facilitate model processing; VAE model training: Data is converted into latent space vectors by the encoder and then restored by the decoder. The restoration accuracy is guaranteed by minimizing the mean square error, and the KL divergence regularization term is combined to enable the model to grasp the internal structure of the data and learn the data distribution pattern. When new data is accessed, abnormal values ​​and missing values ​​are locked according to the 3-sigma rule or statistical method; abnormal humidity and missing temperature data can be locked in time; complete and reasonable data are generated through the encoder and decoder process based on the known partial data; The repaired or completed data is embedded into the original set, and its rationality is verified by comparing it with adjacent data and referring to physical properties.

3. The intelligent control system of the compression heat zero gas consumption dryer according to claim 1 is characterized in that: The execution steps of the working condition analysis subunit in the intelligent decision-making module are as follows: Based on the processed data and the built-in dryer operating model library, the current operating conditions are identified, including normal operation, abnormal air intake, and heat exchange imbalance, and the anchoring direction is generated for the valve control strategy. At the same time, a visualization function for the evolution of working conditions is added; time series analysis and dynamic Bayesian network technology are used to draw trend graphs and state transition graphs of the evolution of working conditions over time in real time, and present them to operators intuitively through a visualization interface.

4. The intelligent control system for the compression heat zero gas consumption dryer according to claim 1 is characterized in that: The specific operation steps of the execution module are as follows: Solenoid valve assembly: Use solenoid valves to receive instructions from the intelligent decision-making module, execute valve switching actions, and follow the set switching time; At the same time, with the high-precision characteristics of electromagnetic drive, the valve opening can be adjusted with an accuracy of 0.1%, which is suitable for airflow control under various working conditions; and an automatic calibration mechanism is established, including: A sensor network is integrated inside the electromagnetic valve to monitor the valve core position, electromagnetic coil current intensity, and temperature parameters in real time. During the operation interval or before and after each valve action, the actual parameters are automatically compared with the preset ideal operation curve based on the built-in algorithm. Once a deviation is found, the self-calibration program is immediately started to dynamically adjust the electromagnetic force output and valve core displacement compensation to ensure that the valve opening accuracy is continuously maintained within 0.1%; Buffer adjustment mechanism: When the valve is opened or closed, the buffer mechanism is automatically activated according to the pipeline pressure and flow rate to buffer the impact of water hammer effect and reduce equipment vibration and mechanical wear; Pressure and vibration sensors are embedded in the buffer adjustment mechanism to monitor the actual pressure fluctuations in the pipeline and the vibration amplitude of the equipment during the buffering process in real time, analyze the data, and use the PID algorithm to dynamically adjust the key parameters of the buffer mechanism, including the damping coefficient and buffer stroke duration, to form a closed-loop optimization control and ensure that the buffering effect is always in the best state.

5. The intelligent control system for the compression heat zero gas consumption dryer according to claim 1 is characterized in that: The specific operation steps of the data perception module are as follows: Humidity sensor: distributed in the air inlet, outlet and regeneration pipelines, it can capture the changes in compressed air humidity in real time, provide humidity data for judging the moisture absorption degree of the adsorbent, planning the adsorption and regeneration switching nodes, and make the valve switching timing match the actual working conditions; Thermistor temperature sensor: placed around the intake heat exchanger and regeneration heater hot zone to monitor temperature fluctuations, assist in controlling the start and end temperatures of the regeneration and cold blowing stages, and set the corresponding valve switching time accordingly to ensure reasonable use of heat; Pressure flow sensor: integrated in the air intake and regeneration pipelines, it measures pressure and flow simultaneously, dynamically grasps the flow state of compressed air, adjusts the valve opening accordingly, maintains air flow stability, and avoids drying abnormalities caused by abnormal flow; At the same time, the sensor dynamically adjusts the collection frequency according to the working conditions.

6. The intelligent control system for the compression heat zero gas consumption dryer according to claim 5 is characterized in that: The specific operation steps of dynamically adjusting the acquisition frequency of the sensor according to the working conditions in the data perception module are as follows: The intelligent decision-making module obtains the current working condition determined by the intelligent decision-making module. When the working condition is stable and the parameters do not fluctuate abnormally, a low-frequency acquisition instruction is issued to each sensor. If an abnormal working condition signal is detected, the high-frequency or ultra-high-frequency acquisition strategy is adjusted. The low-frequency acquisition frequency is once every 10-15 minutes, the high-frequency acquisition frequency is once every 1-5 minutes, and the ultra-high-frequency acquisition frequency is once every 5-20 seconds. At the same time, an event-driven mechanism is set up for equipment operation nodes and potential fault hazards, including: When the adsorbent saturation reaches the threshold, the humidity sensor detects an abnormal change in the humidity drop slope and enters the extremely fast collection state, feeding back humidity data every 1-2 seconds to help the control system determine the adsorption-regeneration switching node; At the moment the regeneration process starts, the thermistor temperature sensor starts high-frequency acquisition to monitor the heating process and ensure accurate control of the regeneration temperature. When the pressure flow sensor detects a sudden change in flow exceeding the safety threshold, or the temperature sensor detects a risk of local overheating, the sensor switches to ultra-fast data collection, sends back data in real time, and cooperates with the control system to implement emergency regulation and control to promptly resolve equipment operation crises.

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