An isenthalpic regeneration and temperature and humidity control linkage air conditioning cold and heat offset suppression method

Abstract

The present application relates to a kind of equal enthalpy regeneration and temperature and humidity control linkage's air conditioning cold and heat offset inhibition method. Among them, the method includes: system real-time monitoring solution concentration and based on indoor and outdoor temperature and humidity parameters intelligent selection operating mode. When dehumidifying, salt solution absorbs latent heat to complete dehumidification, and surface cooler independently handles sensible heat;When concentration is lower than threshold value, system linkage load prediction automatically triggers equal enthalpy regeneration, heat source module bidirectional heating regenerative air and dilute solution, and adjusts heat as needed;While dynamically calculating regenerative heat and dehumidification cold, intelligently coordinating heat source output and external cold source cooling, realize cold and heat load real-time matching, significantly reduce cold and heat offset.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning and refrigeration technology, specifically relating to an air conditioning cooling and heating cancellation suppression method that links isenthalpic regeneration with temperature and humidity control. Background Technology

[0002] Currently, in places requiring deep dehumidification (such as pharmaceutical, electronics, and data center industries), solution-based dehumidification air handling units are commonly used to treat the air. The core principle is to utilize the hygroscopic properties of a salt solution to dehumidify the fresh air, and then regenerate the diluted solution for reuse. Common system architectures typically combine the solution dehumidification unit with a surface cooler, regeneration unit, heat source module, and a basic control system.

[0003] In traditional systems, the regeneration process of the solution requires a large amount of heat, while the dehumidification process requires cooling to offset the temperature rise (sensible heat load) caused by dehumidification. These two demands are often mismatched in terms of time and energy, and the control system lacks an effective coordination strategy, resulting in simultaneous cooling and heating within the system and causing huge energy waste. This is the core problem of "heat cancellation".

[0004] Existing systems typically rely on simple concentration thresholds for regeneration startup, lacking predictability. This can easily lead to under-regeneration (affecting dehumidification efficiency) or over-regeneration (wasting heat and water resources). Furthermore, the regeneration heat supply is imprecise, often employing a "one-size-fits-all" heating method that cannot be precisely adjusted according to real-time load, resulting in low efficiency.

[0005] Traditional control systems cannot intelligently identify and select the optimal operating mode (such as total heat recovery, isothermal dehumidification, isenthalpic regeneration, etc.) based on real-time indoor and outdoor temperature and humidity parameters. They operate in a fixed mode under different seasons and weather conditions, and cannot achieve optimal global energy consumption.

[0006] The control of the solution humidification unit (handling latent heat) and the surface cooler (handling sensible heat) is independent and lacks coordination. When outdoor air parameters change abruptly, the two subsystems will experience control disturbances, resulting in large system fluctuations, poor stability, and difficulty in quickly returning to the set state.

[0007] Existing units heavily rely on external cooling and heating sources (such as municipal steam, electricity, and natural gas). Especially under partial load conditions, the utilization efficiency of external energy is very low, resulting in high operating costs.

[0008] Even with the use of efficient components such as solution dehumidification and heat recovery, the performance potential of the entire system cannot be fully realized without a highly compatible intelligent control strategy (such as linkage between concentration threshold and load prediction, and real-time matching of hot and cold loads). Summary of the Invention

[0009] To address the aforementioned problems in the existing technology, this invention provides a method for suppressing and canceling cooling and heating in air conditioning systems that combines isenthalpic regeneration with temperature and humidity control.

[0010] The objective of this invention can be achieved through the following technical solutions:

[0011] A method for suppressing cooling and heating in an air conditioning system that combines isenthalpic regeneration with temperature and humidity control includes:

[0012] S1: The concentration of the salt solution in the solution humidification unit is monitored in real time by an online concentration sensor configured in the control system; the control system automatically identifies and selects a preset operating mode based on the temperature and humidity parameters of indoor and outdoor air through an operating control model;

[0013] S2: Under dehumidification conditions, the control system controls the operating parameters of the solution dehumidification unit and the surface cooler based on the solution dehumidification and sensible heat separation control strategy, so that when the fresh air flows through the surface cooler, the surface cooler undertakes the regulation of the sensible heat load, and when the fresh air flows through the solution dehumidification unit, the salt solution absorbs the latent heat load to complete the dehumidification.

[0014] S3: When the concentration of the salt solution is lower than the set value, the regeneration process is triggered by the concentration threshold trigger and load prediction linkage mechanism. The control system controls the heat source module to heat the regeneration air and the dilute solution to be regenerated in both directions entering the regeneration unit, and adjusts the regeneration heat output of the heat source module as needed according to the solution concentration data monitored online.

[0015] S4: The control system dynamically calculates the heat required for the regeneration process in S3 and the cooling required for the dehumidification process in S2. Based on the calculation results, it coordinates the heat output of the heat source module and the cooling capacity of the external cold source system through an intelligent coordination strategy based on real-time matching of heat and cold loads.

[0016] Specifically, the control system is equipped with a concentration sensor, a temperature and humidity sensor, and a smart IoT gateway to perform online concentration monitoring, intelligent identification of operating modes, isothermal dehumidification control, isenthalpic regeneration control, and dynamic energy matching.

[0017] Specifically, the solution humidification unit is installed inside the box and is used to humidify or dehumidify the fresh air. It is an integrated molded cavity composed of a front liquid baffle section, two-stage heat and humidity exchange sections and a rear liquid baffle section.

[0018] Specifically, the regeneration unit is used to regenerate the dilute solution in the solution humidification unit, and its structure is arranged symmetrically with the solution humidification unit; the surface cooler is located on the air inlet side of the solution humidification unit and is used to control the sensible heat temperature of the air before humidification.

[0019] Specifically, the inputs to the operation control model are the real-time received indoor and outdoor temperature and humidity, salt solution concentration, fresh air volume, exhaust air volume, and the current output capacity of the heat source module and the external cold source system; with minimizing the total system energy consumption as the objective function, the optimal mode is automatically selected from several pre-stored operation modes based on a preset energy consumption database; a rolling optimization algorithm based on load prediction is used to calculate and output the solution flow rate setpoint of the solution humidification unit, the chilled water flow rate setpoint of the surface cooler, the regeneration heat setpoint of the heat source module, and the cooling capacity setpoint of the external cold source system; the input data is refreshed in a fixed time window.

[0020] Specifically, the solution-based dehumidification and sensible-cold separation control strategy includes sensible-latent decoupling, isothermal dehumidification, dynamic compensation, and cold-heat cancellation suppression;

[0021] The decoupling of sensible and latent heat components decomposes the total heat load of fresh air into sensible heat and latent heat components. The latent heat component is independently borne by the solution humidification unit by adjusting the flow rate and concentration of the salt solution, while the sensible heat component is independently borne by the surface cooler by adjusting the flow rate of the chilled water.

[0022] In the dehumidification process, the control system aims to maintain the deviation between the outlet air temperature and the set value by adjusting the cold water flow rate of the surface cooler in real time, so that the fresh air can complete near-isothermal dehumidification in the solution dehumidification unit.

[0023] When the outdoor wet-bulb temperature or the humidity of the fresh air undergoes a step change, the control system first adjusts the salt solution flow rate to suppress latent heat fluctuations, and then iteratively fine-tunes the cooling water flow rate of the surface cooler to compensate for residual sensible heat deviations until both sensible heat and latent heat reach a steady state.

[0024] During the simultaneous change of sensible and latent loads, the control system controls the output capacity of the heat source module according to the reheat demand. The cold energy recovered by the heat source module and the cooling supplied by the external cold source system can be linked for control, so that the energy overlap between the heat supply of the heat source and the dehumidification cooling energy in the same time window is less than the suppression threshold set according to the total system load.

[0025] Specifically, the concentration threshold triggering and load prediction linkage mechanism includes:

[0026] The control system presets a first concentration threshold and a second concentration threshold, with the first concentration threshold being greater than the second concentration threshold; when the salt solution concentration is less than the first concentration threshold, the load prediction algorithm is activated, and when the salt solution concentration is less than the second concentration threshold, the regeneration process is immediately triggered;

[0027] Based on the predicted indoor and outdoor temperature and humidity values, fresh air volume time series data and historical dehumidification load curves within the future time window, the predicted time when the salt solution concentration decays to the second concentration threshold is calculated, and a regeneration start-up pre-instruction is generated.

[0028] Based on the predicted time and the preheating time of the heat source module, the control system starts the heat source module in advance so that the temperature of the regenerated air and dilute solution reaches the target value required for near-isoenthalpic regeneration at the predicted time.

[0029] During the regeneration process, the control system adaptively corrects the first and second concentration thresholds based on the real-time rate of change of salt solution concentration and the prediction error, which are then used to trigger the next regeneration.

[0030] Specifically, the intelligent coordination strategy based on real-time matching of heating and cooling loads includes:

[0031] A dynamic calculation model for cooling and heating loads is established to obtain the cooling load required for the dehumidification process in S2 and the heating load required for the regeneration process in S3 in real time. A multi-objective optimization function is constructed with minimizing the total energy consumption of the system as the primary objective and keeping the cooling and heating offset below a set threshold as the secondary objective. A constrained model predictive control algorithm is used for rolling optimization.

[0032] Calculate the energy matching coefficient and select the coordination mode based on the coefficient value;

[0033] By establishing real-time communication with the external energy system through the smart IoT gateway, the output power of the heat source module and the cooling capacity of the external cold source system are dynamically adjusted according to the selected coordination mode, and the internally recovered cooling capacity and the external cooling supply are staggered in time series.

[0034] Specifically, the heat source module includes a low-temperature heat source branch and a high-temperature heat source branch. The low-temperature heat source branch directly utilizes external low-grade hot water or condensation heat to provide the basic heat required for near-isenthalpic regeneration of the regenerated air and dilute solution. The high-temperature heat source branch intervenes when the heat of the low-temperature heat source branch is insufficient, providing peak heat to meet the high-load regeneration requirements.

[0035] Specifically, the control system compares the regenerated heat demand with the real-time output of the low-temperature heat source branch in real time, and dynamically switches or superimposes the output of the two branches at a frequency of 1Hz according to the principle of prioritizing low temperature and supplementing high temperature, so as to ensure that the regenerated heat is supplied on demand and avoids overheating.

[0036] Specifically, the smart IoT gateway has a built-in MQTT-SSL communication protocol stack, which periodically uploads real-time output, salt solution concentration, and system heating and cooling load data of the heat source module and the external cold source system to the external energy system; it receives real-time data of electricity price, cooling price, and carbon emission factor from the external energy system, and inputs the above data into the operation control model to correct the target weight of the rolling optimization algorithm.

[0037] Specifically, when the smart IoT gateway detects an external energy system failure or communication interruption, it automatically switches to a local emergency strategy: maintaining the operating parameters from the last successful communication and continuing to perform real-time matching of hot and cold loads based on the local clock until communication is restored.

[0038] The beneficial effects of this invention are as follows:

[0039] Through an intelligent coordination strategy based on real-time matching of heating and cooling loads, the system dynamically coordinates internal heat sources and external cold sources, enabling bidirectional interaction and staggered operation of the recovered cooling capacity and external cooling supply, fundamentally avoiding the most serious energy waste problem in traditional systems. Isothermal dehumidification technology optimizes the efficiency of the refrigeration unit, and isenthalpic regeneration technology reduces the temperature of the regeneration heat source, resulting in a dual improvement in the overall energy efficiency ratio (COP). Under partial load conditions, the system can achieve self-sufficiency through heat and moisture recovery technology, significantly reducing or even eliminating the need for external energy supply, and significantly lowering operating costs during low-load periods. A concentration threshold triggering and load prediction linkage mechanism can predict regeneration demand and preheat in advance, avoiding control lag and making the regeneration process more stable and efficient, solving the problems of over-regeneration or under-regeneration. The operation control model can automatically identify and switch to the lowest energy consumption operating mode based on indoor and outdoor environmental parameters, ensuring the unit remains at its optimal operating point under any conditions. The decoupling of latent and sensible heat and the dynamic compensation control strategy can prioritize suppressing latent heat fluctuations and quickly compensate for sensible heat deviations when outdoor parameters change abruptly, greatly improving the system's stability and anti-interference capabilities, and ensuring the stability of supply air parameters. The heat source module can utilize various energy sources such as low-grade waste heat and high-temperature heat pumps. The intelligent coordination strategy can interact well with different forms of external energy systems (power grid, distributed energy stations, etc.), making it applicable to a wider range of scenarios. The built-in intelligent IoT gateway supports remote monitoring, data uploading, and energy efficiency diagnostics, providing a solid foundation for integration into smart building management systems and achieving multi-unit collaborative optimization and demand-side response. Attached Figure Description

[0040] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0041] Figure 1 This is a schematic flowchart of an air conditioning cooling and heating cancellation suppression method that links isenthalpic regeneration with temperature and humidity control according to the present invention.

[0042] Figure 2 This is a schematic diagram of the isenthalpic regeneration structure in this invention. Detailed Implementation

[0043] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of example embodiments to those skilled in the art. Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring aspects of this disclosure. The blocks shown in the drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the drawings are merely illustrative and do not necessarily include all contents and operations / steps, nor do they necessarily have to be performed in the order described. For example, some operations / steps can be broken down, while others can be combined or partially combined. Therefore, the actual execution order may change depending on the actual situation.

[0044] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0045] Please see Figure 1-2 A method for suppressing cooling and heating in air conditioning systems that combines isenthalpic regeneration with temperature and humidity control includes:

[0046] S1: The concentration of the salt solution in the solution humidification unit is monitored in real time by an online concentration sensor configured in the control system; the control system automatically identifies and selects a preset operating mode based on the temperature and humidity parameters of indoor and outdoor air through an operating control model;

[0047] S2: Under dehumidification conditions, the control system controls the operating parameters of the solution dehumidification unit and the surface cooler based on the solution dehumidification and sensible heat separation control strategy, so that when the fresh air flows through the surface cooler, the surface cooler undertakes the regulation of the sensible heat load, and when the fresh air flows through the solution dehumidification unit, the salt solution absorbs the latent heat load to complete the dehumidification.

[0048] S3: When the concentration of the salt solution is lower than the set value, the regeneration process is triggered by the concentration threshold trigger and load prediction linkage mechanism. The control system controls the heat source module to heat the regeneration air and the dilute solution to be regenerated in both directions entering the regeneration unit, and adjusts the regeneration heat output of the heat source module as needed according to the solution concentration data monitored online.

[0049] S4: The control system dynamically calculates the heat required for the regeneration process in S3 and the cooling required for the dehumidification process in S2. Based on the calculation results, it coordinates the heat output of the heat source module and the cooling capacity of the external cold source system through an intelligent coordination strategy based on real-time matching of heat and cold loads.

[0050] Specifically, the control system is equipped with a concentration sensor, a temperature and humidity sensor, and a smart IoT gateway to perform online concentration monitoring, intelligent identification of operating modes, isothermal dehumidification control, isenthalpic regeneration control, and dynamic energy matching.

[0051] Specifically, the solution humidification unit is installed inside the box and is used to humidify or dehumidify the fresh air. It is an integrated molded cavity composed of a front liquid baffle section, two-stage heat and humidity exchange sections and a rear liquid baffle section.

[0052] Specifically, the regeneration unit is used to regenerate the dilute solution in the solution humidification unit, and its structure is arranged symmetrically with the solution humidification unit; the surface cooler is located on the air inlet side of the solution humidification unit and is used to control the sensible heat temperature of the air before humidification.

[0053] Specifically, the inputs to the operation control model are the real-time received indoor and outdoor temperature and humidity, salt solution concentration, fresh air volume, exhaust air volume, and the current output capacity of the heat source module and the external cold source system; with minimizing the total system energy consumption as the objective function, the optimal mode is automatically selected from several pre-stored operation modes based on a preset energy consumption database; a rolling optimization algorithm based on load prediction is used to calculate and output the solution flow rate setpoint of the solution humidification unit, the chilled water flow rate setpoint of the surface cooler, the regeneration heat setpoint of the heat source module, and the cooling capacity setpoint of the external cold source system; the input data is refreshed in a fixed time window.

[0054] Specifically, the solution-based dehumidification and sensible-cold separation control strategy includes sensible-latent decoupling, isothermal dehumidification, dynamic compensation, and cold-heat cancellation suppression;

[0055] The sensible and latent heat decoupling decomposes the total heat load of the fresh air into sensible heat and latent heat components. The latent heat component is independently borne by the solution humidification unit by adjusting the flow rate and concentration of the salt solution; the sensible heat component is independently borne by the surface cooler by adjusting the flow rate of the chilled water.

[0056] In the dehumidification process, the control system aims to maintain the deviation between the outlet air temperature and the set value by adjusting the cold water flow rate of the surface cooler in real time, so that the fresh air can complete near-isothermal dehumidification in the solution dehumidification unit.

[0057] When the outdoor wet-bulb temperature or the humidity of the fresh air undergoes a step change, the control system first adjusts the salt solution flow rate to suppress latent heat fluctuations, and then iteratively fine-tunes the cooling water flow rate of the surface cooler to compensate for residual sensible heat deviations until both sensible heat and latent heat reach a steady state.

[0058] During the simultaneous change of sensible and latent loads, the control system controls the output capacity of the heat source module according to the reheat demand. The cold energy recovered by the heat source module and the cooling supplied by the external cold source system can be linked for control, so that the energy overlap between the heat supply of the heat source and the dehumidification cooling energy in the same time window is less than the suppression threshold set according to the total system load.

[0059] Specifically, the concentration threshold triggering and load prediction linkage mechanism includes:

[0060] The control system presets a first concentration threshold and a second concentration threshold, with the first concentration threshold being greater than the second concentration threshold; when the salt solution concentration is less than the first concentration threshold, the load prediction algorithm is activated, and when the salt solution concentration is less than the second concentration threshold, the regeneration process is immediately triggered;

[0061] Based on the predicted indoor and outdoor temperature and humidity values, fresh air volume time series data and historical dehumidification load curves within the future time window, the predicted time when the salt solution concentration decays to the second concentration threshold is calculated, and a regeneration start-up pre-instruction is generated.

[0062] Based on the predicted time and the preheating time of the heat source module, the control system starts the heat source module in advance so that the temperature of the regenerated air and dilute solution reaches the target value required for near-isoenthalpic regeneration at the predicted time.

[0063] During the regeneration process, the control system adaptively corrects the first and second concentration thresholds based on the real-time rate of change of salt solution concentration and the prediction error, which are then used to trigger the next regeneration.

[0064] Specifically, the intelligent coordination strategy based on real-time matching of heating and cooling loads includes:

[0065] A dynamic calculation model for cooling and heating loads is established to obtain the cooling load required for the dehumidification process in S2 and the heating load required for the regeneration process in S3 in real time. A multi-objective optimization function is constructed with minimizing the total energy consumption of the system as the primary objective and keeping the cooling and heating offset below a set threshold as the secondary objective. A constrained model predictive control algorithm is used for rolling optimization.

[0066] Calculate the energy matching coefficient and select the coordination mode based on the coefficient value;

[0067] By establishing real-time communication with the external energy system through the smart IoT gateway, the output power of the heat source module and the cooling capacity of the external cold source system are dynamically adjusted according to the selected coordination mode, and the internally recovered cooling capacity and the external cooling supply are staggered in time series.

[0068] Specifically, the heat source module includes a low-temperature heat source branch and a high-temperature heat source branch. The low-temperature heat source branch directly utilizes external low-grade hot water or condensation heat to provide the basic heat required for near-isenthalpic regeneration of the regenerated air and dilute solution. The high-temperature heat source branch intervenes when the heat of the low-temperature heat source branch is insufficient, providing peak heat to meet the high-load regeneration requirements.

[0069] Specifically, the control system compares the regenerated heat demand with the real-time output of the low-temperature heat source branch in real time, and dynamically switches or superimposes the output of the two branches at a frequency of 1Hz according to the principle of prioritizing low temperature and supplementing high temperature, so as to ensure that the regenerated heat is supplied on demand and avoids overheating.

[0070] Specifically, the smart IoT gateway has a built-in MQTT-SSL communication protocol stack, which periodically uploads real-time output, salt solution concentration, and system heating and cooling load data of the heat source module and the external cold source system to the external energy system; it receives real-time data of electricity price, cooling price, and carbon emission factor from the external energy system, and inputs the above data into the operation control model to correct the target weight of the rolling optimization algorithm.

[0071] Specifically, when the smart IoT gateway detects an external energy system failure or communication interruption, it automatically switches to a local emergency strategy: maintaining the operating parameters from the last successful communication and continuing to perform real-time matching of hot and cold loads based on the local clock until communication is restored.

[0072] In this embodiment, a cleanroom in a manufacturing plant is used as the application scenario. The scenario requirements are: maintaining a workshop temperature of 23±1°C and a humidity of 45±5% RH throughout the year. The fresh air handling capacity is approximately 20,000 m³ / h. Low energy consumption, stable control, and high reliability are required; the equipment uses one ultra-high efficiency solution-based dehumidification fresh air unit based on the method of this invention.

[0073] System main component configuration:

[0074] Both the solution humidification unit and the regeneration unit adopt an integrated molded cavity with a symmetrical arrangement. The cavity is injection molded from PPH material in one piece, without splicing welds, fundamentally solving the problems of leakage and corrosion;

[0075] The surface cooler uses a high-efficiency copper tube aluminum fin surface cooler, which is located on the air outlet side of the solution humidification unit. The water circuit is equipped with an electric regulating valve and a temperature sensor.

[0076] Heat source module: Employs a staged heating design. The low-temperature heat source branch connects to the rooftop solar collector system (60-75°C hot water). The high-temperature heat source branch uses a variable frequency electric heater (providing hot air up to 90°C).

[0077] Control System and Sensors: An online refractometer is used for the concentration sensor to monitor the salt solution concentration in the solution humidification and regeneration units in real time. High-precision temperature and humidity sensors are installed at the fresh air inlet, supply air outlet, return air inlet, and exhaust air outlet. The intelligent IoT gateway incorporates the MQTT-SSL protocol for communication with the plant's building energy management system and the power grid demand-side response platform.

[0078] Example of setting up a workflow:

[0079] The date is set as May 15th, cloudy, outdoor temperature 25.2°C, humidity 65%RH. The heat load inside the workshop is stable.

[0080] S1: The control system acquires data through sensors: outdoor temperature 25.2°C / 65%RH, indoor temperature 23°C / 45%RH, and the current solution concentration is 42%. Based on the data, the operation control model determines that the current condition is "high humidity" and automatically selects "isothermal dehumidification + total heat recovery" as the optimal operating mode.

[0081] S2: Calculated by the model.

[0082] The current latent heat load of the fresh air is 85kW, and the sensible heat load is 15kW (i.e., cooling is required). The fresh air first flows into the surface cooler for cooling. The control system calculates that 15kW of cooling capacity is needed to cool the air to the supply air temperature of 23°C. The cooled air then passes through the solution humidification unit, where a 42% concentration salt solution absorbs moisture, completing the dehumidification process. This process is approximately isothermal, and the fresh air temperature drops slightly to 22.5°C. The opening of the surface cooler's chilled water valve is automatically adjusted to 45%.

[0083] Dynamic compensation: At 14:00, a sudden rainfall caused a sharp increase in outdoor humidity. The control system prioritized increasing the solution pump frequency to improve solution flow and suppress latent heat load fluctuations; after detecting an upward trend in the supply air temperature, it iteratively fine-tuned the opening of the surface cooler water valve to 50%, and stabilized the supply air parameters back to the set value within 30 seconds.

[0084] S3: After several hours of operation, the solution concentration dropped to 38% (the first concentration threshold is 39%). The control system activated the load prediction algorithm. Based on weather forecasts and scheduling, the model predicted an increase in workshop capacity and a rise in wet load within the next two hours. It was calculated that the concentration would decay to the second concentration threshold of 35% after 65 minutes. 40 minutes before the predicted time (considering the preheating time of the heat source module), the system activated the low-temperature heat source branch to preheat the regeneration air and dilute solution using solar water heating. When the concentration reached 35%, the regeneration process officially started. The control system monitored that the solar water heating could provide 80% of the required heat, with the remaining 20% ​​supplied by the high-temperature heat source branch (electric heating) in pulses at a frequency of 1Hz, ensuring that the regeneration air outlet temperature remained stable at the target value of 40°C, achieving near-isenthalpic regeneration.

[0085] S4: The control system calculates in real time that the current cooling load required for dehumidification is 90kW, and the heat load required for regeneration is 80kW. It calculates the energy matching coefficient K, and the system enters "high-efficiency matching mode." The system prioritizes using the condensation heat recovered during the regeneration process to preheat the fresh air. Simultaneously, it communicates with BEMS via the IoT gateway to obtain the current real-time electricity price, which is 1.2 yuan / kWh during peak hours. The model predictive control algorithm decides to slightly reduce the external cooling load of the surface cooler and extend the regeneration time, ensuring that the output of regenerated heat and the demand for external cooling are completely staggered in time, successfully suppressing the heat-cooling offset within this time window to below 5kW.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for suppressing cold and heat offset in an air conditioner with isenthalpic regeneration and temperature and humidity control linkage, characterized in that, The application relates to a control system for a liquid desiccant air conditioning system, comprising: S1: real-time monitoring of the concentration of a salt solution in a solution humidification unit through an online concentration sensor arranged in a control system; The control system automatically identifies and selects a preset operation mode through an operation control model based on the temperature and humidity parameters of indoor and outdoor air; The input of the operation control model is the real-time received indoor and outdoor temperature and humidity, the salt solution concentration, the fresh air volume, the exhaust air volume and the current output capacity of a heat source module and an external cold source system; the current optimal mode is automatically selected from a plurality of preset operation modes based on a preset energy consumption database, with the total energy consumption of the system as a target function; a rolling optimization algorithm based on load prediction is adopted to calculate and output the solution flow setting value of the solution humidification unit, the cold water flow setting value of a surface cooler, the regeneration heat setting value of the heat source module and the cooling capacity setting value of the external cold source system; the input data is refreshed in a fixed time window; S2: in a dehumidification condition, the control system controls the operation parameters of the solution humidification unit and the surface cooler based on a solution dehumidification and sensible cooling separation control strategy, so that when fresh air flows through the surface cooler, the surface cooler bears the adjustment of the sensible heat load, and when the fresh air flows through the solution humidification unit, the salt solution absorbs the latent heat load to complete dehumidification; The solution dehumidification and sensible cooling separation control strategy comprises sensible and latent decoupling, isothermal dehumidification, dynamic compensation and cold and heat offset suppression; The sensible and latent decoupling decomposes the total heat load of the fresh air into a sensible heat component and a latent heat component, wherein the latent heat component is independently borne by the solution humidification unit by adjusting the salt solution flow and concentration; the sensible heat component is independently borne by the surface cooler by adjusting the cold water flow; The isothermal dehumidification maintains the deviation of the outlet air temperature from the set value as a target in the dehumidification condition, and adjusts the cold water flow of the surface cooler in real time, so that the fresh air completes nearly isothermal dehumidification in the solution humidification unit; The dynamic compensation preferentially adjusts the salt solution flow to suppress the latent heat fluctuation when the outdoor wet-bulb temperature or the fresh air humidity content changes in steps, and then finely adjusts the cold water flow of the surface cooler in an iterative manner to compensate for the residual sensible heat deviation until the sensible heat and the latent heat reach a steady state; The cold and heat offset suppression controls the output capacity of the heat source module according to the reheat demand during the simultaneous change of the sensible and latent loads, and the cold quantity recovered by the heat source module and the cooling capacity of the external cold source system can be controlled in linkage, so that the energy intersection of the heat supply quantity of the heat source and the dehumidification cold quantity in the same time window is less than a suppression threshold set according to the total load of the system; S3: when the concentration of the salt solution is lower than a set value, a concentration threshold triggering and load prediction linkage mechanism triggers a regeneration process, the control system controls the heat source module to bidirectionally heat the regeneration air entering the regeneration unit and the dilute solution to be regenerated, and adjusts the regeneration heat output of the heat source module according to the online monitored solution concentration data; The concentration threshold triggering and load prediction linkage mechanism comprises: The first concentration threshold and the second concentration threshold are preset in the control system, and the first concentration threshold is greater than the second concentration threshold; the load prediction algorithm is started when the salt solution concentration is less than the first concentration threshold, and the regeneration process is triggered immediately when the salt solution concentration is less than the second concentration threshold; Based on the indoor and outdoor temperature and humidity prediction values in the future time window, the fresh air volume time sequence data and the historical dehumidification load curve, the predicted time when the salt solution concentration decays to the second concentration threshold is calculated, and a regeneration start pre-command is generated; According to the predicted time and the preheating time of the heat source module, the control system starts the heat source module in advance, so that the temperature of the regeneration air and the dilute solution reaches the target value required for near-isenthalpic regeneration at the predicted time; During the regeneration process, the control system adaptively corrects the first concentration threshold and the second concentration threshold according to the real-time salt solution concentration change rate and the prediction error, which is used for the next regeneration triggering; S4: The control system dynamically calculates the heat required in the regeneration process in S3 and the cold required in the dehumidification process in S2, and based on the calculation result, the heat output of the heat source module and the cooling capacity of the external cold source system are coordinated through an intelligent coordination strategy based on real-time matching of cold and heat loads; The intelligent coordination strategy based on real-time matching of cold and heat loads includes: A cold and heat load dynamic calculation model is established to obtain the cold load required in the dehumidification process in S2 and the heat load required in the regeneration process in S3 in real time, a multi-objective optimization function is constructed, the minimum total energy consumption of the system is taken as the primary target, and the cold and heat offset is less than the set threshold as the secondary target, and a model predictive control algorithm with constraints is used for rolling optimization; An energy matching degree coefficient is calculated, and the coordination mode is selected according to the value of the coefficient; Real-time communication is established with the external energy system through the intelligent Internet gateway, the output power of the heat source module and the cooling capacity of the external cold source system are dynamically adjusted according to the selected coordination mode, and the internal recovered cold and the external cooling are staggered in time sequence.

2. The method of claim 1, wherein, The control system is configured with a concentration sensor, a temperature and humidity sensor and an intelligent Internet gateway for performing online concentration monitoring, intelligent identification of operation mode, isothermal dehumidification control, isenthalpic regeneration control and dynamic energy matching.

3. The method of claim 1, wherein, The solution humidifying unit is arranged in the box body and used for humidifying or dehumidifying the fresh air, and is an integrated molding cavity composed of a front liquid blocking section, two-stage heat and moisture exchange sections and a rear liquid blocking section.

4. The method of claim 1, wherein, The regeneration unit is used for regenerating the dilute solution in the solution humidifying unit, and is symmetrically arranged with the solution humidifying unit; the surface air cooler is arranged on the air inlet side of the solution humidifying unit and used for controlling the sensible heat temperature of the air before humidification.

5. The method of claim 1, wherein, The heat source module includes a low-temperature heat source branch and a high-temperature heat source branch; the low-temperature heat source branch directly uses external low-grade hot water or condensing heat to provide the basic heat required for near-isenthalpic regeneration of the regeneration air and the dilute solution; and the high-temperature heat source branch is involved when the heat of the low-temperature heat source branch is insufficient to provide peak heat to meet the high-load regeneration demand.

6. The method of claim 1, wherein, The control system compares the regenerative heat demand with the real-time output of the low-temperature heat source branch in real time, dynamically switches or superimposes the outputs of the two branches at a frequency of 1 Hz according to the principle of low-temperature priority and high-temperature supplement, ensures the on-demand supply of regenerative heat, and avoids excessive heating.

7. The method of claim 2, wherein, The intelligent Internet of Things gateway is built-in with an MQTT-SSL communication protocol stack, periodically uploads the real-time output of the heat source module and the external cold source system, the concentration of the salt solution and the system cold and heat load data to the external energy system, receives the real-time data of the electricity price, the cold price and the carbon emission factor issued by the external energy system, and inputs the above data into the operation control model to correct the target weight of the rolling optimization algorithm.

8. The method of claim 2, wherein, When the intelligent Internet of Things gateway detects the failure or communication interruption of the external energy system, it automatically switches to the local emergency strategy: maintains the operation parameters at the time of the last successful communication, and continues to perform real-time matching of the cold and heat load based on the local clock until the communication is restored.

Images