An air treatment method, apparatus and system

Abstract

This invention relates to the field of air handling technology, and in particular to an air handling method, apparatus, and system. The method is applied to a control unit in an air handling system. The control unit is connected to a return air valve, which is located on the return air duct between the return air distribution unit of the HVAC module and the input terminal of the DAC unit in the DAC module. The method provided in this application acquires monitoring data under the current operating state and predicts adsorption efficiency. Then, using a hybrid prediction model and an HVAC energy consumption model, it performs multi-objective optimization based on one or more preset optimization objectives. By adjusting the opening of the return air valve and the flow rate of incoming fresh air, it adaptively adjusts to different operating states, thereby improving air handling efficiency.

Description

Technical Field

[0001] This invention relates to the field of air treatment technology, and in particular to an air treatment method, apparatus and system. Background Technology

[0002] With the increasing severity of global climate change and the acceleration of urbanization, building energy consumption and indoor air quality (IAQ) have become key factors affecting human health, living comfort, and sustainable development. Traditional heating, ventilation, and air conditioning (HVAC) systems, while maintaining indoor temperature and humidity, often rely on introducing large amounts of fresh outdoor air to dilute indoor pollutants (such as CO2 from human respiration and volatile organic compounds (VOCs)). This process consumes a significant amount of energy, accounting for approximately 25%-50% of total building energy consumption, especially under extreme climatic conditions where the cooling / heating load of fresh air handling is even more pronounced. In recent years, the impact of indoor CO2 concentration on human cognitive abilities, work efficiency, and even long-term health has received increasing attention. In closed or poorly ventilated indoor environments, CO2 concentrations can easily exceed standards, and traditional HVAC systems that rely solely on increasing fresh air volume face significant challenges in achieving energy-saving goals. Therefore, how to minimize HVAC system energy consumption while ensuring excellent indoor air quality has become a pressing technical bottleneck in the fields of building energy conservation and healthy buildings.

[0003] Currently, there have been some preliminary explorations into the integration of DACs with HVAC systems, such as placing DAC modules in the fresh air, return air, or exhaust air paths. Integrating DAC modules into HVAC systems, where the CO2 concentration in the return air is typically higher than in the outdoor fresh air, helps improve the capture efficiency of the DAC module. Simultaneously, purifying the return air can increase its reuse rate, directly reducing the fresh air load.

[0004] However, existing DAC and HVAC integration solutions still have many shortcomings. Their control strategies mostly rely on fixed thresholds or preset ratios, lacking real-time sensing and dynamic response capabilities for key parameters such as return air CO2 concentration, temperature, and humidity, making it difficult to optimize the adsorption performance of the DAC module. Simultaneously, existing solutions have limited ability to regulate the adsorbent's operating environment, failing to incorporate online optimization based on factors such as temperature, humidity, and gas flow rate, thus affecting adsorption efficiency and regeneration energy consumption. More importantly, the lack of a model-based predictive collaborative control mechanism between the DAC and HVAC systems leads to their disconnected operation, preventing the achievement of system-level multi-objective dynamic optimization while ensuring air quality, severely restricting the improvement of overall energy efficiency and adaptability. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an air treatment method, apparatus and system to adapt to different working conditions and improve air treatment efficiency.

[0006] In a first aspect, embodiments of the present invention provide an air handling method applied to a control unit in an air handling system. The control unit is connected to a return air valve, which is located on the return air duct between the return air distribution unit of an HVAC module and the input terminal of the DAC unit in a DAC module. The method includes:

[0007] Obtain current monitoring data; the monitoring data should include at least the return air CO2 concentration, indoor CO2 concentration, and outdoor fresh air parameters;

[0008] Input the indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into the preset adsorption performance prediction module, and output the predicted adsorption efficiency.

[0009] Using a hybrid prediction model based on CO2 mass balance and an HVAC module energy consumption model, the opening of the return air valve and the fresh air flow into the HVAC module are dynamically adjusted according to at least one preset optimization target related to indoor air quality and system energy consumption.

[0010] In addition to the first aspect, the monitoring data also includes: temperature and humidity;

[0011] After inputting indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into the adsorption performance prediction module and outputting the predicted adsorption efficiency, the following steps are also included:

[0012] If the temperature and humidity do not meet the preset environmental conditions for the adsorbent to perform, the return air modulation component is controlled to modulate the return air entering the DAC module to other adsorption windows.

[0013] In conjunction with the first aspect, the method also includes:

[0014] Obtain the CO2 concentration at the outlet of the DAC module;

[0015] The CO2 removal efficiency was calculated by combining the return air CO2 concentration and the outlet CO2 concentration.

[0016] The CO2 removal efficiency and the current adsorbent saturation status are fed back to the control terminal.

[0017] In conjunction with the first aspect, the steps for dynamically adjusting the ratio of air entering the DAC module and returning air flowing through the HVAC module, as well as the opening degree of the fresh air valve in the HVAC module, include:

[0018] Construct a hybrid prediction model and an HVAC module energy consumption model;

[0019] Based on the preset constrained optimization problem and constraints, an iterative search algorithm is used to find the optimal solution.

[0020] In conjunction with the first aspect, after inputting indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into the adsorption performance prediction module and outputting the predicted adsorption efficiency, the following steps are also included:

[0021] The system combines current operating parameters and external conditions to determine whether the regeneration trigger condition has been met. The current operating parameters include at least one of the following: predicted adsorption efficiency, CO2 concentration at the outlet of the DAC module, and change in return air CO2 concentration. The external conditions include at least one of the following: expected building occupancy, real-time energy price, and available low-cost or waste heat energy signals.

[0022] If so, in response to the regeneration parameter configuration operation, control the air handling system to execute the regeneration mode.

[0023] In conjunction with the first aspect, the steps for controlling the air handling system to execute the regeneration mode include:

[0024] Close the return air valve to block the return air duct leading to the DAC module;

[0025] Based on the type of adsorbent and the regeneration cost of available renewable energy, a target energy source is selected and regeneration is performed, and a start command is sent to the interface corresponding to the target energy type.

[0026] Following the first aspect, the step of controlling the air handling system to execute the regeneration mode also includes:

[0027] Obtain the CO2 concentration at the regeneration outlet;

[0028] If the CO2 concentration at the regeneration outlet is less than the CO2 concentration threshold, and / or the preset regeneration time is reached, and / or the temperature and humidity of the adsorbent reach the regeneration completion point, a stop regeneration command is sent to the interface.

[0029] The system controls the air handling system to switch from regeneration mode to adsorption mode or standby mode, and resets the adsorbent state of the DAC module to its initial value.

[0030] In conjunction with the first aspect, the steps for dynamically adjusting the opening of the return air valve and the fresh air flow into the HVAC module include:

[0031] If the indoor CO2 concentration is greater than the first threshold or the change in indoor CO2 concentration is continuously greater than the variable threshold, the proportion of return air entering the DAC module will be increased first to improve the CO2 removal capacity. At the same time, the fresh air volume will be reduced to save energy consumption for treating fresh air.

[0032] If the indoor CO2 concentration is less than the second threshold or the difference between the predicted adsorption efficiency and the current adsorption efficiency is less than zero, reduce the proportion of return air entering the DAC module and increase the fresh air volume to seek the optimal total energy consumption of the system.

[0033] Secondly, embodiments of the present invention also provide an air handling device, applied to a control unit in an air handling system. The control unit is connected to a return air valve, which is located on the return air duct between the return air distribution unit of the HVAC module and the input terminal of the DAC unit in the DAC module. The device includes:

[0034] The acquisition module is used to acquire current monitoring data; the monitoring data includes at least the return air CO2 concentration, indoor CO2 concentration, and outdoor fresh air parameters.

[0035] The prediction module is used to input indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into the preset adsorption performance prediction module and output the predicted adsorption efficiency.

[0036] The adjustment module is used to dynamically adjust the opening of the return air valve and the fresh air flow into the HVAC module based on a hybrid prediction model based on CO2 mass balance and an HVAC module energy consumption model, according to at least one preset optimization target related to indoor air quality and system energy consumption.

[0037] Thirdly, embodiments of the present invention also provide an air handling system, comprising:

[0038] A DAC module, including a DAC unit, wherein an adsorbent is provided inside the DAC unit to remove CO2;

[0039] The HVAC module includes a return air splitter unit, the inlet of which is connected to the outlet of the return air filter; the first outlet of the return air splitter unit is connected to the inlet of the mixing unit to supply indoor return air to the mixing unit; the second outlet of the return air splitter unit is connected to the DAC unit in the DAC module through a return air duct, and a return air valve is provided on the return air duct, which is connected to the control unit.

[0040] At least one sensor is used to acquire monitoring data including at least the return air CO2 concentration, total return air volume, indoor air parameters, and outdoor air parameters;

[0041] The control unit includes a processor and a memory; the control unit is used to perform the methods described above.

[0042] The embodiments of the present invention bring the following beneficial effects: The air handling method, apparatus and system provided in this application are applied to the control unit of the air handling system. The control unit is connected to the return air valve, which is located on the return air duct between the return air diversion unit of the HVAC module and the input end of the DAC unit in the DAC module. The method includes: acquiring current monitoring data; the monitoring data includes at least the return air CO2 concentration, indoor CO2 concentration and outdoor fresh air parameters; inputting the indoor CO2 concentration, return air CO2 concentration and outdoor fresh air parameters into a preset adsorption performance prediction module, and outputting the predicted adsorption efficiency; using a hybrid prediction model based on CO2 mass balance and an HVAC module energy consumption model, and according to at least one preset optimization target related to indoor air quality and system energy consumption, dynamically adjusting the opening of the return air valve and the fresh air flow rate into the HVAC module.

[0043] The method provided in this application acquires monitoring data under the current operating state and predicts the adsorption efficiency. Then, it uses a hybrid prediction model and an HVAC energy consumption model to perform multi-objective optimization based on one or more of multiple preset optimization objectives. By adjusting the opening of the return air valve and the flow rate of the incoming fresh air, it makes adaptive adjustments for different operating states to improve air handling efficiency.

[0044] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0045] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0046] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the air treatment method provided in an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the air handling device provided in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of an air handling system provided in an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the control unit provided in an embodiment of the present invention.

[0051] Figure label:

[0052] 10 - Acquisition module, 20 - Prediction module, 30 - Adjustment module;

[0053] 130 - Processor, 131 - Memory, 132 - Bus, 133 - Communication interface. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] To facilitate understanding of this embodiment, the technical terms used in this application will be briefly introduced below.

[0056] Direct Air Capture (DAC) technology, a cutting-edge technology for actively removing CO2 from the air, was initially primarily applied to large-scale carbon removal to address climate change. However, researchers have gradually recognized the potential of DAC technology in small-scale, distributed applications, particularly in improving indoor air quality and enhancing building energy efficiency. Integrating DAC modules with HVAC systems, by capturing CO2 from indoor air, can theoretically significantly reduce the amount of fresh air required to dilute CO2, thereby substantially reducing HVAC energy consumption.

[0057] After introducing the technical terms used in this application, the application scenarios and design concepts of the embodiments of this application will be briefly described below.

[0058] Existing integrated DAC and HVAC systems suffer from rigid control strategies, lacking real-time sensing and dynamic adjustment of return air parameters, making it difficult to optimize adsorption performance. Furthermore, the lack of online optimization of the adsorbent's operating environment and the absence of a collaborative control mechanism between the DAC and HVAC systems prevent multi-objective dynamic optimization, thus limiting system energy efficiency and adaptability.

[0059] Based on this, the embodiments of this application provide an air treatment method, apparatus and system to dynamically adapt to different working conditions and improve air treatment efficiency.

[0060] Example 1

[0061] This application provides an air handling method applied to a control unit in an air handling system. The control unit is connected to a return air valve, which is located on the return air duct between the return air distribution unit of the HVAC module and the input terminal of the DAC unit in the DAC module. Combined with... Figure 1 As shown, the method includes:

[0062] S110, acquire current monitoring data; the monitoring data should include at least the return air CO2 concentration, indoor CO2 concentration, and outdoor fresh air parameters.

[0063] S120 inputs indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into the adsorption performance prediction module and outputs the predicted adsorption efficiency.

[0064] S130 utilizes a hybrid prediction model based on CO2 mass balance and an HVAC module energy consumption model to dynamically adjust the opening of the return air valve and the fresh air flow into the HVAC module according to at least one preset optimization target related to indoor air quality and system energy consumption.

[0065] This invention constructs an air handling system by integrating a DAC module and an HVAC module, and proposes a model-predictive intelligent collaborative control strategy. This effectively solves problems in existing technologies such as low system integration, poor adaptability, crude energy management, and insufficient adsorbent performance management. The method dynamically adjusts the return air treatment ratio and the fresh air valve opening using real-time monitoring data, optimizing CO2 purification efficiency and energy consumption balance. It introduces an online adsorption performance prediction mechanism to enhance the perception and management capabilities of the adsorbent's state, extending its service life. This achieves synergistic optimization of indoor air quality and system energy efficiency, demonstrating significant technical and economic benefits.

[0066] Combination Figure 3 As shown, the HVAC module in this air handling system includes two ducts connected in a mixing unit to mix the fresh air and return air in both ducts. After temperature and humidity regulation by the main AHU fan coil unit, the air is then delivered to the room. Specifically, the first duct is used to introduce outdoor fresh air; when the fresh air valve is open, the outdoor fresh air is introduced, filtered, and then flows into the mixing unit. The second duct is used to introduce indoor return air; this indoor return air is filtered by a return air filter and then split by a return air splitter; part of it flows into the mixing unit, and the other part is directed to the DAC module for CO2 capture.

[0067] The DAC module includes a DAC unit containing an adsorbent for capturing CO2. A return air valve is installed on the duct between the DAC unit and the return air distribution unit. When the return air valve is open, high-CO2-concentration indoor return air enters the DAC unit within the DAC module. The adsorbent in the DAC unit captures CO2 in the airflow, reducing its concentration. The low-CO2-concentration return air is then directed to the mixing unit, where it is thoroughly mixed with fresh air, high-CO2-concentration indoor return air, and low-CO2-concentration return air to ensure the combined CO2 concentration meets requirements. The air is then conditioned and delivered to the room.

[0068] In addition, the DAC module also includes a return air modulation component, which is used to export the return airflow for temperature and humidity modulation before resupplying it to the DAC unit when the temperature and humidity of the indoor return air leading to the DAC unit do not meet the environmental conditions for the adsorbent to perform.

[0069] In step S110, this application simultaneously collects key parameters of return air (rich CO2 source), indoor CO2 concentration (demand side), and outdoor CO2 concentration (energy side), and acquires monitoring data from multiple dimensions to provide a comprehensive data foundation for the collaborative optimization of DAC and HVAC modules. Compared with existing technologies that rely on fixed thresholds or simple feedback (such as only detecting indoor CO2 concentration), it can better capture the characteristics of return air flow (concentration, flow rate) and dynamic changes in indoor and outdoor environments, thereby improving the collaborative efficiency of DAC and HVAC.

[0070] Understandably, to improve the accuracy and validity of the acquired monitoring data, data preprocessing can be performed after step S110. Specific preprocessing methods include, but are not limited to: filtering, validity verification, and unit conversion.

[0071] Step S120 will determine the indoor CO2 concentration (in combination with...) Figure 1 (Data collected by indoor CO2 sensors), return air CO2 concentration (combined with...) Figure 1 The CO2 collection efficiency of the DAC module under the current operating conditions is predicted by inputting the CO2 parameters (collected by the return air CO2 sensor) and the outdoor fresh air parameter concentration (collected by the sensor placed outdoors) into the machine learning model (such as LSTM or gradient boosting tree). This is beneficial for the whole life cycle management of the adsorbent, avoids forced operation under inefficient conditions, and improves dynamic adaptability by adjusting the air volume distribution according to the predicted efficiency.

[0072] Understandably, before step S110, when the air handling system is initially started, all preset parameters, adsorption and energy models are loaded; then sensor calibration and equipment self-test are performed for monitoring: checking whether all sensor readings are within a reasonable range and whether actuators (various valves, main AHU fan coil units, etc.) respond normally; then the initial state setting is entered: the DAC module is usually in standby or adsorption ready state, then all current input parameters are used as initial values ​​and the system is officially running.

[0073] In step S130, the mixing prediction model based on CO2 mass balance is used to achieve precise control over CO2 concentration changes during air treatment. Specifically, this model combines the CO2 concentration in the indoor air (Ci_room), the CO2 concentration in the return air (Ci_RA), and the adsorption efficiency (η_DAC) of the DAC module output by the adsorption performance prediction module to predict the CO2 concentration (Ci_mix) after mixing. This predicted value will be used to ensure that the indoor air quality meets the preset standard (i.e., Ci_mix ≤ Ci_setpoint). The mixing prediction model is used to predict the CO2 concentration in the mixing unit.

[0074] Furthermore, this predictive model is integrated with the energy consumption model of the HVAC module. In this way, the system can comprehensively consider air quality requirements and energy consumption, aiming to minimize total energy consumption, and dynamically adjust the ratio of return air entering the DAC module and circulating in the HVAC module, as well as the fresh air flow rate entering the HVAC module. Through the above dynamic adjustments, the CO2 concentration in the mixed airflow output by the mixing unit meets the requirements of the current operating conditions.

[0075] To illustrate its operation, consider this example: When the monitored indoor CO2 concentration (Ci_room) is close to the setpoint (Ci_setpoint), the bypass return air flow rate (x) is increased by opening the return air bypass valve to enhance the CO2 removal effect. This allows more return air to be sent to the DAC module for CO2 capture. When the indoor CO2 concentration is much lower than the setpoint, the bypass return air flow rate (x) can be reduced to decrease unnecessary DAC operations and save energy consumption.

[0076] This optimized control strategy not only ensures indoor air quality, but also maximizes the overall system's energy efficiency by dynamically adjusting operating parameters.

[0077] In conjunction with the first aspect, the monitoring data also includes: temperature and humidity. Following step S120, the following also includes:

[0078] S121, if the temperature and humidity do not meet the preset environmental conditions for the adsorbent to perform, control the operation of the return air modulation component to modulate the return air entering the DAC module and then pass it into the DAC unit in the DAC module.

[0079] To ensure the adsorbent in the DAC unit of the DAC module (i.e., CO2 capture module) operates under optimal conditions, the system monitors not only the CO2 concentration in the indoor and return air, but also temperature and humidity. After obtaining the predicted adsorption efficiency, the system further checks whether the temperature and humidity of the current return air meet the preset conditions required for efficient adsorbent operation (e.g., a specific temperature and humidity range). If the current temperature or humidity deviates from the adsorbent's optimal operating window (e.g., excessive humidity may reduce adsorbent activity), the next adjustment measure is triggered. Specifically, the system controls the operation of the return air conditioning component to process the return airflow entering the DAC unit of the DAC module, adjusting its temperature and humidity to be more suitable for adsorbent operation before it is introduced into the DAC unit. This conditioning process can be achieved through heating / cooling, humidification / dehumidification, etc., thereby improving adsorption efficiency and avoiding performance degradation due to unsuitable environments.

[0080] This ensures that the DAC module always operates under optimal conditions, maximizing CO2 capture efficiency, regardless of changes in the external environment. Simultaneously, it helps extend the adsorbent's lifespan, reduce energy consumption, and improve the overall system's stability and adaptability.

[0081] In conjunction with the first aspect, the method also includes:

[0082] S210, obtain the CO2 concentration at the outlet of the DAC module.

[0083] S220, combined with the return air CO2 concentration and the outlet CO2 concentration, calculate the CO2 removal efficiency.

[0084] S230 feeds back the CO2 removal efficiency and the current adsorbent saturation status to the control terminal.

[0085] After the DAC module processes the return air, the system detects the CO2 concentration (denoted as Co) at its outlet. This parameter reflects the amount of CO2 remaining in the air after treatment with the adsorbent.

[0086] By combining the return air CO2 concentration (Ci_RA) before entering the DAC module and the outlet CO2 concentration (Co), the CO2 removal efficiency can be calculated. This efficiency value reflects the actual CO2 removal capacity of the DAC module at present.

[0087] Specifically, the CO2 removal efficiency is calculated using the embedded mechanical co-adsorption model with the following formula:

[0088]

[0089]

[0090] in, The single-pass CO2 removal efficiency of the DAC module; This represents the CO2 removal rate of the DAC module per unit time, expressed in kg / h or mol / h. The return air flow rate to the DAC module, in meters. 3 / h; To account for the effective return air CO2 concentration entering the adsorption bed under conditions of bypass or uneven distribution;

[0091] The effective return air CO2 concentration entering the adsorption bed, in kg / m³ 3 or mol / m 3 ; The CO2 removal efficiency of the adsorbent in a brand new or fully regenerated state under reference operating conditions (reference temperature, humidity, flow rate, CO2 concentration) is 0-1. The temperature influence factor (dimensionless, usually between 0 and 1) represents the efficiency correction of the current inlet air temperature relative to the optimal adsorption temperature. This is the humidity influence factor (dimensionless, typically between 0 and 1), representing the impact of the current inlet air humidity RH_DAC_in. The effect varies depending on the adsorbent. Specifically, for humidity-sensitive adsorbents, a certain low humidity range may be more favorable; for solid amines, a certain humidity level (e.g., 10-40% RH) may be beneficial to the reaction, but excessively high humidity (e.g., >60% RH) will increase diffusion resistance; for chemisorption (e.g., the [Abdullatif] model considers the effect of water),... It may be integrated into a more complex model, or represented as an empirical factor; The adsorbent saturation influence factor (dimensionless, usually between 0 and 1) represents the effect of the amount of CO2 currently adsorbed by the adsorbent on its adsorption rate. When the adsorbent is close to saturation, this factor approaches 0.

[0092] Subsequently, the adsorbent state is updated using the following formula:

[0093]

[0094] in, The updated state of the adsorbent. This represents the current state of the adsorbent. This represents the CO2 removal rate of the DAC module per unit time, expressed in kg / h or mol / h. This is the preset control time step.

[0095] As an feasible approach, the calculated CO2 removal efficiency, along with the current saturation state of the adsorbent (estimated by factors such as adsorbent usage time and cumulative adsorption capacity), is fed back to the control terminal. This allows the control terminal to determine whether the adsorbent is nearing saturation, requires regeneration, or needs replacement, and adjust the system's operating strategy accordingly (e.g., increasing regeneration frequency, adjusting airflow distribution). This prevents the adsorbent from continuing to operate in a saturated state, which would lead to a decrease in purification efficiency and helps extend its service life. Furthermore, it provides advance warning of when the adsorbent needs replacement or regeneration, reducing the risk of sudden malfunctions.

[0096] In conjunction with the first aspect, step S130, which dynamically adjusts the ratio of air entering the DAC module and returning air flowing through the HVAC module, and the opening degree of the fresh air valve in the HVAC module, includes:

[0097] S131, Construct a hybrid prediction model and an HVAC module energy consumption model.

[0098] S132, based on the preset constrained optimization problem and constraints, uses an iterative search algorithm to find the optimal solution.

[0099] In this embodiment, the objective function used to find the optimal solution can be:

[0100]

[0101] in, The first preset weighting coefficient, This is the second preset weighting coefficient. This represents the indoor CO2 concentration value. To comprehensively consider the predicted CO2 concentration of the mixed unit (or supply air duct) after taking into account fresh air, untreated return air, and return air treated by DAC, This is the predicted total energy consumption of the system.

[0102] Fresh air volume, unit is m³ 3 / h; for; Total return air volume, in m³ 3 / h; for, This refers to the CO2 concentration value in the return air. The return air flow rate to the DAC module, in meters (m³). 3 / h; The single-pass CO2 removal efficiency of the DAC module;

[0103] Energy consumption for handling fresh and recirculated air in HVAC modules; For the DAC module fan energy consumption, Energy consumption of return air conditioning components. It is preset.

[0104] And apply the following constraints: , as well as ,in, For the preset minimum fresh air volume, Preset maximum fresh air volume; Here, x represents the preset minimum return air treatment ratio, and x represents the return air treatment ratio. This is the preset maximum return air treatment ratio.

[0105] The control unit employs optimization algorithms (such as gradient descent, particle swarm optimization, or simplified iterative search) to optimize the return air handling ratio x. (0-1) and Find the objective function within the feasible region that is within the allowed range. Minimum (target return air treatment ratio) and target fresh air volume By combining these methods, we can obtain the optimal solution.

[0106] Subsequently, control commands are generated based on the obtained optimal solution for adjustment, which will not be elaborated here.

[0107] In conjunction with the first aspect, after step S120, the following is also included:

[0108] S140, combined with current operating parameters and / or external conditions, determine whether the regeneration trigger condition has been met; wherein, the current operating parameters include at least one of the predicted adsorption efficiency, the outlet CO2 concentration of the DAC module, and the change value of the return air CO2 concentration; the external conditions include at least one of the expected building occupancy, the real-time energy price, and the available low-cost or waste heat energy signal.

[0109] S150, in response to the regeneration parameter configuration operation, controls the air handling system to execute the regeneration mode.

[0110] In conjunction with the first aspect, step S150 includes:

[0111] S151, close the return air valve to block the return air duct leading to the DAC module.

[0112] S152, based on the type of adsorbent and the regeneration cost of available regenerative energy, selects the target energy source and performs regeneration, and sends a start command to the interface corresponding to the target energy type.

[0113] When the adsorption performance prediction module predicts that the adsorbent is about to penetrate (i.e., predicts that the adsorption efficiency will drop to the preset threshold), monitors that the CO2 concentration at the DAC outlet rises above the threshold, the change in the return air CO2 concentration is less than the preset change threshold, the building occupancy status reaches a certain level, the real-time energy price is available, or a signal of available low-cost or waste heat energy is received, the regeneration mode is activated. At this time, the return air valve to the DAC module is closed, and the corresponding regeneration command is output according to the adsorbent type and available regeneration energy.

[0114] In this embodiment, the simplified model for a specific adsorbent type is as follows:

[0115]

[0116] in, for; For compression work; Sensible heat required for heating; The heat required for desorption includes the heat of desorption of CO2 and H2O.

[0117] Understandably, the control unit can estimate the total energy input required based on a preset algorithm, according to the target regeneration level and regeneration temperature, and control the power of the heater or humidifier and the regeneration time accordingly; monitor the CO2 concentration and temperature / humidity at the regeneration outlet to determine the degree of regeneration completion.

[0118] In conjunction with the first aspect, after step S150, the following also includes:

[0119] S160, obtain the CO2 concentration at the regeneration outlet.

[0120] S170, if the CO2 concentration at the regeneration outlet is less than the CO2 concentration threshold, and / or, the preset regeneration time is reached, and / or, the temperature and humidity of the adsorbent reach the regeneration completion point, a stop regeneration command is sent to the interface.

[0121] S180 controls the air handling system to switch from regeneration mode to adsorption mode or standby mode, and resets the adsorbent state of the DAC module to the initial value.

[0122] When the CO2 concentration at the regeneration outlet drops to a very low level, or when the preset regeneration time, total energy input, adsorbent temperature, or humidity reaches the regeneration completion point, the regeneration process stops, switches back to standby mode, and resets the adsorption and status of the DAC module to the initial values.

[0123] Furthermore, a brief cooling or purging process can be performed before switching back to standby mode.

[0124] In conjunction with the first aspect, step S130, which involves dynamically adjusting the opening of the return air valve and the fresh air flow into the HVAC module, includes:

[0125] S131, if the indoor CO2 concentration is greater than the first threshold or the change in indoor CO2 concentration is continuously greater than the variable threshold, the proportion of return air entering the DAC module is increased first to improve the CO2 removal capacity. At the same time, the fresh air volume is reduced to save energy consumption for processing fresh air.

[0126] In conjunction with the first aspect, step S130, which involves dynamically adjusting the opening of the return air valve and the fresh air flow into the HVAC module, includes:

[0127] If the indoor CO2 concentration is less than the second threshold or the difference between the predicted adsorption efficiency and the current adsorption efficiency is less than zero, reduce the proportion of return air entering the DAC module and increase the fresh air volume to seek the optimal total energy consumption of the system.

[0128] By intelligently reducing the proportion of return air entering the DAC module and increasing the fresh air volume when the indoor CO2 concentration is below a second threshold or the difference between the predicted adsorption efficiency and the current adsorption efficiency is less than zero, this system can not only respond promptly to changes in indoor air quality but also adaptively adjust based on online predictions of actual adsorption performance. This strategy effectively avoids energy waste caused by excessive system operation, further enhancing the system's energy-saving effect while ensuring indoor air quality. Furthermore, this control logic improves the system's intelligence level, reduces the need for manual intervention, and enhances the overall economic efficiency and stability of operation.

[0129] S132, if the indoor CO2 concentration is less than the second threshold or the difference between the predicted adsorption efficiency and the current adsorption efficiency is less than zero, reduce the proportion of return air entering the DAC module and increase the fresh air volume in order to seek the optimal total energy consumption of the system.

[0130] When the indoor CO2 concentration is below the second threshold or the difference between the predicted and current adsorption efficiencies is less than zero, it indicates that the current indoor CO2 concentration is at a low level, or the predicted adsorption performance of the DAC module shows a decrease in its processing capacity. In this case, by reducing the proportion of return air supplied to the DAC module and correspondingly increasing the fresh air volume, the excessive reliance on the DAC module can be reduced, thus minimizing energy waste caused by the DAC module's operating load, even when the indoor CO2 concentration is low. Simultaneously, increasing the fresh air volume to dilute the indoor CO2 concentration helps achieve optimal control of the overall system energy consumption. When the difference between the predicted and current adsorption efficiencies is less than zero, it indicates that the adsorption performance of the DAC module may be declining. Reducing its processing load in this case can prevent a decrease in treatment effect due to insufficient adsorption efficiency, while simultaneously introducing fresh air helps maintain indoor air quality.

[0131] By dynamically adjusting the return air ratio and the opening of the fresh air valve, the system optimizes energy consumption, improves operational efficiency and intelligence level while ensuring indoor air quality, providing an effective guarantee for environmental control that balances energy saving and comfort.

[0132] For example, in scenario a: during peak office hours, the indoor CO2 concentration is high and the temperature and humidity are suitable.

[0133] Understandably, this is when the requirements for indoor air quality (IAQ) are highest. Due to the high density of people, the CO2 concentration in both the indoor and return air is already exceeding or close to the standard, requiring a rapid response to reduce the CO2 concentration. At this time, the temperature and humidity conditions of the return air are very favorable for the CO2 adsorption function of the DAC module, and the adsorbent itself has sufficient adsorption capacity (low saturation).

[0134] At this point, with ensuring indoor air quality (IAQ) as the primary objective, the adsorption performance is predicted using the adsorption performance prediction module. If the prediction result indicates that the DAC module can efficiently remove CO2 under the current operating conditions, the opening of the return air valve will be increased to enhance the proportion of return air entering the DAC module. This allows a large amount of return air to be processed by the DAC module, maximizing CO2 capture.

[0135] Meanwhile, because the CO2 concentration in the purified return air is significantly reduced, the amount of fresh air introduced is also greatly reduced, thereby significantly reducing the large cooling / heating load required to process the fresh air and achieving energy saving. Even though the DAC module consumes slightly more energy due to the large air volume it processes, the overall energy consumption is expected to decrease compared to the saved fresh air load.

[0136] In scenario b: during lunch break or early after get off work, the indoor CO2 concentration is moderately low, and the adsorbent is at a certain saturation level.

[0137] Understandably, while the indoor CO2 concentration is not severely exceeded at this time, it still needs to be maintained at a good level. At this point, the number of people may decrease or they may be leaving, leading to a reduction in the CO2 production rate. The key issue is that after a period of operation, the adsorbent in the DAC unit has reached a medium-to-high saturation level, resulting in a decrease in its adsorption efficiency. The return air temperature and humidity may fluctuate slightly due to the decrease in the number of people and changes in the air conditioning load.

[0138] At this point, greater emphasis will be placed on energy efficiency balance. Due to the reduced efficiency of the adsorbent, relying solely on the DAC to handle large volumes of return air may no longer be the most economical option. Predicting adsorption performance using an adsorption performance prediction module may allow for the selection of a moderate return air handling ratio, while simultaneously increasing the fresh air volume to synergistically meet indoor air quality (IAQ) requirements. By reducing the opening of the return air valve and increasing the incoming fresh air flow, a balance point can be found that minimizes the sum of the DAC module's operating energy consumption and the optimized fresh air handling energy consumption.

[0139] Meanwhile, due to the high saturation of the adsorbent, the control unit will prioritize "preparing for regeneration" and switch to it in time once more favorable regeneration conditions (such as no occupancy, low electricity price, etc.) appear.

[0140] In scenario c: During off-peak hours at night, indoor CO2 concentrations are low, there are off-peak electricity prices, and renewable energy is available.

[0141] Understandably, at this time, there are few or no people indoors, or even in the building, and the indoor CO2 concentration is already at a very low level, so there is no need to actively reduce it through DAC modules or large amounts of fresh air. At this time, the adsorbent may be close to saturation after a day of operation. The key favorable conditions are: it is a period of low electricity prices, and there may be low-cost renewable energy (such as condensate generated by the HVAC system at night that can be used to regenerate the humidity-sensitive adsorbent, or building energy storage that can provide cheap electricity for thermal regeneration).

[0142] At this point, the indoor air quality (IAQ) requires minimal pressure, while the adsorbent needs regeneration under optimal conditions. Therefore, the DAC module is switched to regeneration mode. In this mode, return air is no longer supplied to the DAC module, and the HVAC module maintains only the minimum necessary fresh air volume. Based on the available low-cost energy type and adsorbent characteristics, the energy input (power, temperature, or humidity) and duration of the regeneration process are precisely controlled, with the goal of achieving full adsorbent regeneration with minimal energy consumption, preparing for efficient CO2 capture in the next operating cycle.

[0143] Secondly, embodiments of this application also provide an air handling device, applied to a control unit in an air handling system. The control unit is connected to a return air valve, which is located on the return air duct between the return air distribution unit of the HVAC module and the input terminal of the DAC unit in the DAC module. Combined with... Figure 2As shown, the device includes: an acquisition module 10, a prediction module 20, and an adjustment module 30.

[0144] The acquisition module 10 is used to acquire the current monitoring data; the monitoring data includes at least the return air CO2 concentration, indoor CO2 concentration, and outdoor fresh air parameters.

[0145] Prediction module 20 is used to input indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into a preset adsorption performance prediction module and output the predicted adsorption efficiency.

[0146] The adjustment module 30 is used to dynamically adjust the opening of the return air valve and the fresh air flow into the HVAC module based on a hybrid prediction model based on CO2 mass balance and an HVAC module energy consumption model, according to at least one preset optimization target related to indoor air quality and system energy consumption.

[0147] Thirdly, embodiments of this application also provide an air handling system, which includes: a DAC module, an HVAC module, at least one sensor (not shown in the figure), and a control unit, the control unit being used to perform the above-described method.

[0148] A DAC module includes a DAC unit, wherein an adsorbent is disposed within the DAC unit to remove CO2.

[0149] The HVAC module includes a return air splitter unit, the inlet of which is connected to the outlet of the return air filter; the first outlet of the return air splitter unit is connected to the inlet of the mixing unit to supply indoor return air to the mixing unit; the second outlet of the return air splitter unit is connected to the DAC unit in the DAC module through a pipeline, and a return air valve is provided on the pipeline, which is connected to the control unit.

[0150] Combination Figure 3 As shown, the inlet of the mixing unit of the HVAC module is also connected to the outlet of the fresh air filter and the outlet of the DAC module; the outlet of the mixing unit is connected in sequence to the main AHU fan coil unit and the supply air duct. In this way, the filtered outdoor fresh air, the filtered high CO2 concentration return air, and the low CO2 concentration treated return air are mixed in the mixing unit, and then supplied to the room after the temperature and humidity are regulated by the main AHU fan coil unit.

[0151] The DAC module includes a DAC unit containing an adsorbent for capturing CO2. A return air valve is installed on the duct between the DAC unit and the return air distribution unit. When the return air valve is open, high-CO2-concentration indoor return air enters the DAC unit within the DAC module. The adsorbent in the DAC unit captures CO2 in the airflow, reducing its concentration. The low-CO2-concentration return air is then directed to the mixing unit, where it is thoroughly mixed with fresh air, high-CO2-concentration indoor return air, and low-CO2-concentration return air to ensure the combined CO2 concentration meets requirements. The air is then conditioned and delivered to the room.

[0152] In addition, the DAC module also includes a return air modulation component, which is used to export the return airflow for temperature and humidity modulation before resupplying it to the DAC unit when the temperature and humidity of the indoor return air leading to the DAC unit do not meet the environmental conditions for the adsorbent to perform.

[0153] At least one sensor is used to acquire monitoring data including at least the return air CO2 concentration, total return air volume, indoor air parameters, and outdoor air parameters; the sensor is connected to the control unit.

[0154] The control unit is used to execute the method described above, in conjunction with Figure 4 As shown, the control unit includes a memory 131 and a processor 130. The memory 131 stores computer programs, and the processor 130 runs the computer programs to make the electronic device perform the methods described above.

[0155] Furthermore, combined Figure 4 The control unit shown also includes a bus 132 and a communication interface 133. The processor 130, the communication interface 133 and the memory 131 are connected via the bus 132.

[0156] The memory 131 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 133 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 132 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0157] Processor 130 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 130 or by instructions in software form. Processor 130 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 131, and processor 130 reads the information in memory 131 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0158] Fourthly, embodiments of this application provide a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the above-described method.

[0159] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0160] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0161] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0163] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An air treatment method, characterized in that, A control unit applied in an air handling system, the control unit being connected to a return air valve, the return air valve being located on the return air duct between the return air splitter unit of an HVAC module and the input terminal of the DAC unit in a DAC module; the method includes: Acquire current monitoring data; the monitoring data includes at least return air CO2 concentration, indoor CO2 concentration, and outdoor fresh air parameters; The indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters are input into a preset adsorption performance prediction module, which outputs the predicted adsorption efficiency. Using a hybrid prediction model based on CO2 mass balance and an HVAC module energy consumption model, the opening degree of the return air valve and the fresh air flow rate into the HVAC module are dynamically adjusted according to at least one preset optimization target related to indoor air quality and system energy consumption.

2. The method according to claim 1, characterized in that, The monitoring data also includes: temperature and humidity; After inputting the indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into the adsorption performance prediction module and outputting the predicted adsorption efficiency, the method further includes: If the temperature and humidity do not meet the preset environmental conditions for the adsorbent to perform, the return air modulation component is controlled to operate so that the return air entering the DAC module is modulated and then fed into the DAC unit in the DAC module.

3. The method according to claim 1, characterized in that, The method further includes: Obtain the outlet CO2 concentration of the DAC module; The CO2 removal efficiency is calculated by combining the return air CO2 concentration and the outlet CO2 concentration. The CO2 removal efficiency and the current adsorbent saturation status are fed back to the control terminal.

4. The method according to claim 1, characterized in that, The steps for dynamically adjusting the opening of the return air valve and the fresh air flow into the HVAC module include: Construct the hybrid prediction model and the HVAC module energy consumption model; Based on the pre-defined constrained optimization problem and constraints, an iterative search algorithm is used to find the optimal solution.

5. The method according to claim 1, characterized in that, After inputting the indoor CO2 concentration, return air CO2 concentration, and outdoor fresh air parameters into the adsorption performance prediction module and outputting the predicted adsorption efficiency, the method further includes: The system combines current operating parameters and external conditions to determine whether the regeneration trigger condition has been met. The current operating parameters include at least one of the predicted adsorption efficiency, the outlet CO2 concentration of the DAC module, and the change in the return air CO2 concentration. The external conditions include at least one of the expected building occupancy, real-time energy prices, and available low-cost or waste heat energy signals. If so, in response to the regeneration parameter configuration operation, the air handling system is controlled to execute the regeneration mode.

6. The method according to claim 5, characterized in that, The steps of controlling the air handling system to execute the regeneration mode include: Close the return air valve to block the return air duct leading to the DAC module; Based on the type of adsorbent and the regeneration cost of available renewable energy, a target energy source is selected and regeneration is performed, and a start command is sent to the interface corresponding to the target energy type.

7. The method according to claim 6, characterized in that, After controlling the air handling system to execute the regeneration mode, the method further includes: Obtain the CO2 concentration at the regeneration outlet; If the CO2 concentration at the regeneration outlet is less than the CO2 concentration threshold, and / or the preset regeneration time is reached, and / or the temperature and humidity of the adsorbent reach the regeneration completion point, a stop regeneration command is sent to the interface. The air handling system is controlled to switch from regeneration mode to adsorption mode or standby mode, and the adsorbent state of the DAC module is reset to the initial value.

8. The method according to claim 1, characterized in that, The steps of dynamically adjusting the ratio of air entering the DAC module and returning air flowing through the HVAC module, and the opening degree of the fresh air valve of the HVAC module, include: If the indoor CO2 concentration is greater than the first threshold or the change in indoor CO2 concentration is continuously greater than the variable threshold, the proportion of return air entering the DAC module will be increased first to improve the CO2 removal capacity. At the same time, the fresh air volume will be reduced to save energy consumption for processing fresh air. If the indoor CO2 concentration is less than the second threshold or the difference between the predicted adsorption efficiency and the current adsorption efficiency is less than zero, the proportion of return air entering the DAC module is reduced and the fresh air volume is increased in order to seek the optimal total energy consumption of the system.

9. An air handling device, characterized in that, A control unit used in an air handling system, the control unit being connected to a return air valve, the return air valve being located on the return air duct between the return air splitter unit of the HVAC module and the input terminal of the DAC unit in the DAC module; the device includes: The acquisition module is used to acquire current monitoring data; the monitoring data includes at least return air CO2 concentration, indoor CO2 concentration, and outdoor fresh air parameters; The prediction module is used to input the indoor CO2 concentration, the return air CO2 concentration, and the outdoor fresh air parameters into a preset adsorption performance prediction module, and output the predicted adsorption efficiency. The adjustment module is used to dynamically adjust the opening of the return air valve and the fresh air flow rate into the HVAC module based on a hybrid prediction model based on CO2 mass balance and an HVAC module energy consumption model, according to at least one preset optimization target related to indoor air quality and system energy consumption.

10. An air handling system, characterized in that, include: A DAC module includes a DAC unit, wherein an adsorbent is disposed within the DAC unit to remove CO2; The HVAC module includes a return air splitter unit, the inlet of which is connected to the outlet of a return air filter; the first outlet of the return air splitter unit is connected to the inlet of a mixing unit to supply indoor return air to the mixing unit; the second outlet of the return air splitter unit is connected to a DAC unit in the DAC module via a return air duct, and a return air valve is provided on the return air duct, which is connected to the control unit. At least one sensor is used to acquire monitoring data including at least the return air CO2 concentration, total return air volume, indoor air parameters, and outdoor air parameters; A control unit, including a processor and a memory; the control unit is configured to perform the method as described in any one of claims 1-8.

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