Single-rotating-wheel dehumidification energy-saving optimization control method based on model prediction

Abstract

A single-rotating-wheel dehumidification energy-saving optimization control method based on model prediction comprises the steps that temperature and humidity data and flow data in a single-rotating-wheel airflow pipeline in an air treatment system are collected through an integrated sensor, so that original time sequence data and a temperature smooth curve of a synchronous timestamp are established; and calculating an equilibrium adsorption curve based on the isotherm parameter set and the temperature smooth curve, and constructing a mechanism framework model for predicting the disturbance of the temperature to the adsorption capacity in combination with a dynamic constant. And carrying out quadratic programming according to the average value and variance of the adsorption capacity samples through a distributed robust mechanism so as to obtain rotating speed distribution and temperature compensation of the single rotating wheel. And on the basis of rotating speed distribution and temperature compensation, in combination with predicted disturbance of the temperature output by the mechanism framework model to the adsorption capacity, an optimal rotating speed and a temperature control sequence of the single rotating wheel are solved in real time by adopting an MPC algorithm. And executing the optimal rotating speed and the temperature control sequence so as to carry out optimization control on single-rotating-wheel adsorption dehumidification, and obtaining model correction parameters for executing feedback.

Description

Technical Field

[0001] This invention belongs to the field of air treatment technology, and more specifically, relates to a model-predictive single-rotor dehumidification energy-saving optimization control method. Background Technology

[0002] In precision manufacturing electronic workshops (such as semiconductor packaging / printed circuit boards), such places need to maintain constant temperature and humidity (e.g., 23±1℃, 45±2% RH) for a long time, but there are a lot of intermittent heat source disturbances, such as welding, hot air drying, UV curing and other staged heat source operations.

[0003] A single-rotor dehumidifier is a heat and moisture treatment device and a key structural component in air handling systems (especially fresh air handling units or dehumidifying air conditioning systems). Its main function is to adsorb and remove moisture from the air, achieving air dehumidification, and it is commonly used in the pre-treatment stage of air conditioning and ventilation systems. The single-rotor primarily utilizes hygroscopic materials (such as silica gel or molecular sieves) rotating on a rotor structure to perform periodic moisture absorption and regeneration operations. The operating logic of an air handling system can be understood as follows: fresh air inlet; pre-filter; surface cooler / heater (optional); single-rotor dehumidification section; downstream cooling coil (fine-tuning); fan; air outlet. The single-rotor section is responsible for active dehumidification, reducing the latent heat load on the subsequent cooling coils.

[0004] Currently, the moisture absorption efficiency of rotary desiccant materials (such as silica gel) is highly sensitive to its own temperature. When the temperature of the moisture absorption section rises sharply from 25°C to 35°C due to heat source disturbance, the unit moisture absorption capacity decreases by more than 40%. If the rotary speed and regeneration air temperature are not pre-adjusted in advance through model prediction, the system's dehumidification capacity will become instantly unbalanced, the RH will rise rapidly, and production abnormalities will occur. Therefore, high-frequency thermal disturbances causing a sudden rise in temperature in the rotary desiccant absorption zone can easily lead to a weakening of dynamic dehumidification capacity. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to overcome the aforementioned deficiencies and propose a model-predictive single-rotor dehumidification energy-saving optimization control method.

[0006] The present invention adopts the following technical solution.

[0007] The first aspect of this invention discloses a model-predictive-based energy-saving optimization control method for a single-rotor dehumidifier, the method comprising: Temperature, humidity, and flow data in a single-rotor airflow duct of an air handling system are collected by integrating sensors to establish raw time-series data with synchronized timestamps and temperature smoothing curves. Based on the isotherm parameter set and the temperature smoothing curve, the equilibrium adsorption curve is calculated, and combined with the kinetic constant, a mechanistic framework model for predicting the effect of temperature on the adsorption amount is constructed. The distributed robust mechanism is used to perform secondary programming based on the mean and variance of the adsorption amount sample to obtain the rotation speed distribution and temperature compensation of a single rotor. Based on the aforementioned rotational speed distribution and temperature compensation, and combined with the temperature prediction perturbation of the adsorption amount output by the aforementioned mechanistic framework model, the MPC algorithm is used to solve the optimal rotational speed and temperature control sequence of a single rotor in real time. The optimal rotation speed and temperature control sequence is executed to optimize the control of single-rotor adsorption dehumidification and obtain the model correction parameters of the execution feedback.

[0008] Furthermore, the step of collecting temperature, humidity, and flow data in the single-rotor airflow duct of the air handling system using integrated sensors to establish original time-series data with synchronized timestamps and temperature smoothing curves includes: Multiple sets of temperature and humidity sensors and heat flow sensors are installed at equal intervals in the circumferential direction in the airflow duct at the inlet of the moisture absorption section to construct an integrated sensor for monitoring the temperature difference and flow rate of the airflow inlet and outlet, and each sensor in the integrated sensor is connected to the edge node. The temperature and humidity sensor and the heat flow sensor are polled according to the set sampling frequency to obtain temperature and humidity data and gas flow data. The temperature and humidity data and gas flow data are synchronized in time through the edge node to obtain the original time-series data with the synchronization timestamp. The temperature and humidity sensor is used to collect temperature and humidity data in the single-rotor airflow duct, the heat flow sensor is used to collect flow data, and the edge node is communicatively connected to the centralized controller.

[0009] Furthermore, the method of collecting temperature, humidity, and flow data in the single-rotor airflow duct of the air handling system through integrated sensors to establish original time-series data with synchronized timestamps and temperature smoothing curves also includes: Multiple sampling points are extracted from the original time-series data according to the set time window, and the mean temperature, average heat flow, and heat flow mutation value of all extracted sampling points are calculated based on the multiple sampling points to construct a feature vector; Based on the feature vector, an autoregressive plus exogenous input model is used to iteratively calculate the discrete predicted value within the first time period in the future using a first-order prediction formula, and the discrete predicted value is then processed by a three-point moving average to obtain the temperature smoothing curve.

[0010] Furthermore, the equilibrium adsorption curve is calculated based on the isotherm parameter set and the temperature smoothing curve, and combined with kinetic constants, a mechanistic framework model for predicting the effect of temperature on adsorption amount perturbation is constructed, including: Obtain the isotherm parameter set and kinetic constants, wherein the isotherm parameter set includes the maximum adsorption capacity, adsorption energy constant, and empirical index; Based on the temperature smoothing curve and isotherm parameter set, the Dubinin-Astakhov isotherm model is invoked to construct discrete curves, and the equilibrium adsorption curve is calculated based on the discrete curves.

[0011] Furthermore, the step of calculating the equilibrium adsorption curve based on the isotherm parameter set and the temperature smoothing curve, and constructing a mechanistic framework model for predicting the effect of temperature on adsorption amount perturbation, also includes: Based on the equilibrium adsorption curve and kinetic constants, when the adsorption amount changes with time in accordance with first-order kinetics, the discrete time in the equilibrium adsorption curve is updated so as to calculate the dynamic adsorption curve based on the updated discrete time. A reference adsorption capacity is obtained, and the instantaneous efficiency decay ratio is calculated based on the dynamic adsorption curve to construct an adsorption efficiency curve. The instantaneous efficiency decay ratio is the ratio between the dynamic adsorption curve and the reference adsorption capacity.

[0012] Furthermore, the step of performing secondary programming based on the sample mean and variance of adsorption amount using a distributed robust mechanism to obtain the rotational speed distribution and temperature compensation for a single rotor includes: The difference between the expected humidity and the actual humidity of multiple sets of air inlets and outlets of a single rotor is obtained, and the multiple sets of the difference are used as historical observation gap airflow rate samples to generate the Chebyshev inequality. The sensitivity coefficient is written into the Chebyshev inequality to obtain the robustness constraint. The robustness constraint is then used to calibrate the sensitivity coefficient based on the trial run dataset, and the speed distribution and temperature compensation of the single rotor are output as the reference operating point.

[0013] Furthermore, based on the aforementioned rotational speed allocation and temperature compensation, and combined with the temperature-related perturbation of the adsorption capacity output by the mechanistic framework model, the optimal rotational speed and temperature control sequence for a single rotor are solved in real time using the MPC algorithm, including: Based on the current humidity error in the single-rotor airflow duct, as well as the minimum values ​​of rotation speed distribution and temperature compensation, and combined with the predicted disturbance and configuration parameters, a discrete model is constructed using the MPC algorithm. The configuration is obtained to obtain the prediction step size, energy consumption weight, balance factor and robust safety constraint. Based on the prediction step size, energy consumption weight, balance factor and robust safety constraint, the discrete model is optimized in combination with temperature threshold and airflow rate threshold to obtain the optimization objective and constraints.

[0014] Furthermore, the method of using the MPC algorithm to solve for the optimal speed and temperature control sequence of a single rotor in real time, based on the speed distribution and temperature compensation, combined with the temperature prediction perturbation output by the mechanistic framework model, also includes: The current temperature error, predicted disturbance, optimization objective and constraints, and configuration parameters of the discrete model are input into the QP solver, in which the optimal rotational speed and temperature control sequence are calculated in the rolling time domain.

[0015] A second aspect of this invention discloses a model-predictive single-rotor dehumidification energy-saving optimization control system, the system comprising: The data acquisition and processing module is used to collect temperature, humidity and flow data in the single-rotor airflow duct of the air handling system through integrated sensors, so as to establish raw time-series data with synchronized timestamps and temperature smoothing curves. The adsorption perturbation quantification module is used to calculate the equilibrium adsorption curve based on the isotherm parameter set and the temperature smoothing curve, and to construct a mechanistic framework model for predicting the effect of temperature on the amount of adsorption by combining kinetic constants. The robustness compensation module is used to perform secondary planning based on the mean and variance of the adsorption amount sample through a distributed robust mechanism to obtain the speed distribution and temperature compensation of a single rotor. The optimal control sequence solving module is used to solve the optimal speed and temperature control sequence of a single rotor in real time using the MPC algorithm, based on the speed distribution and temperature compensation, combined with the temperature prediction perturbation of the adsorption amount output by the mechanistic framework model. The optimized control execution module is used to execute the optimal speed and temperature control sequence to optimize the control of single-rotor adsorption dehumidification and obtain the model correction parameters of the execution feedback.

[0016] A third aspect of the present invention discloses a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method described in the first aspect.

[0017] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0018] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following advantages: (1) This invention integrates temperature and humidity sensors and flow sensors to capture and quantify the impact of intermittent heat source disturbances on the temperature of the moisture absorption section in real time, laying a data foundation for subsequent model prediction input. At the same time, by using a mechanistic framework model to quantify the impact of temperature rise on adsorption efficiency, it can intelligently predict the dynamic adsorption capacity at present and in the future, reducing the problem of weakened dynamic dehumidification capacity caused by the instantaneous temperature rise in the desiccant zone of the rotor due to high-frequency thermal disturbances.

[0019] (2) This invention utilizes the distributed robust mechanism to estimate the worst-case scenario of the gap using the sample mean and variance. Then, it uses quadratic programming to allocate rotational speed and temperature compensation, which can balance safety margin and energy-saving control optimization. Under any distribution, it can cover the real gap with a high confidence level, effectively reducing the risk of system runaway. In addition, in the rolling time domain, a discrete-time system model and optimization objective are constructed using distributed robust compensation and disturbance prediction. The model predictive control (MPC) algorithm is used to solve the optimal impeller speed and regeneration air temperature control sequence in real time, which can further balance humidity stability and energy saving. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the model prediction-based single-rotor dehumidification energy-saving optimization control method provided by the present invention. Figure 2 This is a schematic diagram of the structure of the model prediction-based single-rotor dehumidification energy-saving optimization control system provided by the present invention. Detailed Implementation

[0021] The present application will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be construed as limiting the scope of protection of the present application.

[0022] like Figure 1 As shown, in one embodiment, a model-predictive single-rotor dehumidification energy-saving optimization control method includes the following steps: Step S110: Temperature, humidity and flow data in the single-rotor airflow duct of the air handling system are collected by integrated sensors to establish original time-series data with synchronized timestamps and temperature smoothing curves.

[0023] In some embodiments, the model-predictive-based single-rotor dehumidification energy-saving optimization control method provided by the present invention includes the following steps in step S110: Step S111: Multiple sets of temperature and humidity sensors and heat flow sensors are installed in the airflow duct at the inlet of the moisture absorption section at equal intervals in the circumferential direction to construct an integrated sensor for monitoring the temperature difference and flow rate of the airflow inlet and outlet, and each sensor in the integrated sensor is connected to the edge node.

[0024] Step S112: Poll the temperature and humidity sensor and the heat flow sensor according to the set sampling frequency to obtain temperature and humidity data and gas flow data, and synchronize the temperature and humidity data and gas flow data through the edge node to obtain the original time-series data with the synchronization timestamp. Among them, the temperature and humidity sensor is used to collect temperature and humidity data in the single-rotor airflow duct, the heat flow sensor is used to collect flow data, and the edge node is connected to the centralized controller for communication.

[0025] In some embodiments, the model-predictive-based single-rotor dehumidification energy-saving optimization control method provided by the present invention further includes the following steps in step S110: Step S113: Extract multiple sampling points from the original time series data according to the set time window, and calculate the average temperature, average heat flow and heat flow mutation value of all extracted sampling points based on the multiple sampling points to construct a feature vector.

[0026] Step S114: Based on the feature vector, the autoregressive plus exogenous input model is used to call the first-order prediction formula to iteratively calculate the discrete prediction value in the first time period of the future, and the discrete prediction value is processed by a three-point moving average to obtain the temperature smoothing curve.

[0027] In a specific embodiment, the model-predictive-based energy-saving optimization control method for single-rotor dehumidification provided by the present invention includes steps 1 to 4: Step 1: Heat source disturbance detection and moisture absorption section temperature prediction.

[0028] Real-time capture and quantification of the impact of intermittent heat source disturbances on the temperature of the moisture absorption section provide input for subsequent model predictions.

[0029] Includes the following sub-steps: Sub-step 1.1, sensor and device structure deployment.

[0030] Specifically, based on the plant process diagram, the inlet space dimensions of the moisture absorption section, and the piping layout, four temperature and humidity sensors (e.g., wet-bulb / dry-bulb or capacitive) are placed at equal intervals (ideally every 90°) along the circumference of the airflow duct at the moisture absorption section inlet to obtain the average temperature and humidity of the cross-section. A heat flux sensor (e.g., a combination of a Pitot tube anemometer and thermocouple) is installed 10cm downstream at the same location to monitor the temperature difference and flow velocity at the air inlet and outlet to estimate the heat flux. All sensors are connected to an edge computing node (or a PLC expansion I / O module) via shielded twisted-pair cables. This node has four analog inputs, four digital inputs, and an Ethernet output. This step ensures that the measurement points cover the entire cross-section of the moisture absorption section inlet, obtaining uniform temperature, humidity, and heat flux information, laying the hardware foundation for subsequent predictive modeling.

[0031] The edge nodes communicate with the centralized controller (MPC or DCS) via the Modbus / TCP protocol to upload raw data in real time.

[0032] Sub-step 1.2: Real-time data acquisition and time synchronization.

[0033] Specifically, edge nodes poll each sensor at a 50Hz sampling rate to obtain temperature signals (converted to actual temperature values, in °C after sensor calibration), relative humidity signals (converted to humidity values), and instantaneous heat flow calculated from wind speed and temperature difference signals combined with the heat flow sensor calibration curve. Then, the edge nodes synchronize time according to IEEE 1588 or NTP, packaging the above three sets of data into timestamp records—the original time-series data with synchronized timestamps—and pushing them to the controller database in real time via TCP / IP. This step achieves high-frequency synchronous acquisition of multiple physical quantities, ensuring that the data used by subsequent prediction models has sufficient spatiotemporal resolution and consistency.

[0034] Sub-step 1.3: Feature extraction and data preprocessing.

[0035] Specifically, using a 60-second prediction window, sample data from N (e.g., N=3000) sampling points in the original time-series data are extracted. The average temperature, average heat flux, and heat flux abrupt change value (the difference between adjacent heat flux sample data; a large difference may indicate an abrupt change) of the sample data are calculated. Then, sampling points where the temperature or flux abrupt change exceeds a set threshold (e.g., ±5℃ or ±20%) are imputed, and finally, the key feature vector within the short-time window is output. This step reduces the dimensionality of the massive original data to a 4-dimensional feature vector, preserving both the intensity and fluctuation characteristics of the heat source disturbance while significantly reducing the input dimensionality of the prediction model, thus improving online computation efficiency.

[0036] Sub-step 1.4, short-term temperature trend prediction.

[0037] Specifically, based on the key feature vectors obtained in sub-step 1.3, the first-order prediction formula is called using an autoregressive plus exogenous input model (ARX): In the formula, These are temperature forecasts for the future. The average temperature of the sample data. , These represent the average heat flux and the heat flux mutation value of the sample data, respectively. The temperature autoregression coefficient has a value range of [0.5, 0.9] and is obtained by fitting historical temperature sample data. The weight of the effect of heat flow on temperature, with a value range of [0.1, 0.3]. This is the heat flux abrupt change compensation coefficient, with a value range of [0.05, 0.15].

[0038] Then, starting from the feature vector of the latest second, the discrete predicted values ​​for the next 30 seconds are calculated iteratively using the above formula, i.e. ~ The discrete predicted values ​​within 30 seconds are then converted into a curve of predicted values ​​versus time. A three-point moving average is applied to eliminate peak errors, resulting in the final temperature prediction curve. This step utilizes the key characteristics of heat source disturbance and moisture absorption temperature to quickly generate a short-term temperature trend, providing accurate temperature input for subsequent adsorption efficiency models and quantifying the impact amplitude and duration of heat source disturbance in advance.

[0039] Step S120: Calculate the equilibrium adsorption curve based on the isotherm parameter set and temperature smoothing curve, and construct a mechanistic framework model for predicting the effect of temperature on adsorption amount by combining kinetic constants.

[0040] In some embodiments, the model-predictive single-rotor dehumidification energy-saving optimization control method provided by the present invention includes the following steps in step S120: Step S121: Obtain the isotherm parameter set and kinetic constants. The isotherm parameter set includes the maximum adsorption capacity, adsorption energy constant, and empirical index.

[0041] Step S122: Based on the temperature smoothing curve and isotherm parameter set, the Dubinin-Astakhov isotherm model is called to construct discrete curves, and the equilibrium adsorption curve is calculated based on the discrete curves.

[0042] In some embodiments, the model-predictive-based single-rotor dehumidification energy-saving optimization control method provided by the present invention further includes the following steps in step S120: Step S123: Based on the equilibrium adsorption curve and kinetic constants, when the adsorption amount changes with time in accordance with first-order kinetics, the discrete time in the equilibrium adsorption curve is updated so as to calculate the dynamic adsorption curve based on the updated discrete time.

[0043] Step S124: Obtain the reference adsorption amount and calculate the instantaneous efficiency decay ratio based on the dynamic adsorption curve to construct the adsorption efficiency curve. The instantaneous efficiency decay ratio is the ratio between the dynamic adsorption curve and the reference adsorption amount.

[0044] In a specific embodiment, the single-rotor dehumidification energy-saving optimization control method based on model prediction provided by the present invention includes step 2, dynamic adsorption efficiency evaluation. The impact of temperature rise on adsorption efficiency is quantified using a mechanistic framework model to obtain the dynamic adsorption capacity at the current and future times.

[0045] Includes the following sub-steps: Sub-step 2.1, calibration of adsorption material parameters.

[0046] Specifically, the isotherm parameter set and kinetic constants are obtained. The isotherm parameter set includes the maximum adsorption capacity of the adsorbent material (unit: kg / m²), with a value range of [0.05, 0.15]; the adsorption energy constant (unit: J / mol), with a value range of [5000, 8000]; and the empirical index, dimensionless, with a value range of [1.5, 2.5]. The kinetic constant (unit: s²) is also obtained. -1 (), obtained by experimental calibration, typical values ​​[0.01, 0.05].

[0047] It should be noted that the experimental calibration process for the kinetic constants is as follows: First, the sample quality control is the same as above; it must be in its initial state after drying. Place the sample in a low-humidity environment (e.g., 10% RH) to equilibrate, then quickly transfer it to a high-humidity environment (e.g., 50% RH), recording this moment as t=0. Record the sample weight change every few seconds or tens of seconds (depending on your instrument's speed) until no further weight change is observed after several minutes. Then, plot the adsorption amount as a function of time, with time on the x-axis and the amount of water adsorbed at the corresponding time point on the y-axis. Assuming the adsorption process follows a simplified single-exponential model, the expression is: In the formula, Let t be the amount of water adsorbed at time t. To balance the adsorption amount, is a kinetic constant, representing the rate at which the adsorbent material absorbs water.

[0048] Sub-step 2.2: Calculate the instantaneous equilibrium adsorption curve.

[0049] Specifically, the simplified expression of the Dubinin-Astakhov isotherm model is invoked: In the formula, The equilibrium adsorption amount at time t (unit: kg / m³) 2 ), This represents the maximum adsorption capacity. It is the adsorption energy constant; is the gas constant, a fixed value of 8.314 J / (mol·K); To predict temperature values, It is an empirical index, dimensionless.

[0050] Then, for the future prediction interval (within 30 seconds), the above formula is called once per second to finally obtain the corresponding discrete curve.

[0051] Sub-step 2.3: Couple kinetics to calculate dynamic adsorption amount.

[0052] Specifically, assuming the adsorption process follows first-order kinetics, the discrete-time update formula is: In the formula, This represents the actual adsorption amount at time t. Here is the dynamic constant. To balance the adsorption amount, The time step (e.g., 1 second).

[0053] Then, initialize Then iterate and calculate the next 30 seconds. Finally, a dynamic adsorption curve was constructed.

[0054] Sub-step 2.4: Calculate the instantaneous adsorption efficiency decay ratio.

[0055] Specifically, a reference adsorption capacity is set. ,in, The equilibrium capacity at 25℃ (298K) This value was obtained by averaging multiple sets of equilibrium adsorption values ​​at a temperature of 298 K. Then, the instantaneous efficiency decay ratio was calculated. Finally, for The corresponding curves are smoothed (e.g., by a three-second moving average) to obtain the adsorption efficiency curve.

[0056] Step S130: Based on the mean and variance of the adsorption amount sample, a secondary planning is performed using a distributed robust mechanism to obtain the rotational speed distribution and temperature compensation of a single rotor.

[0057] In some embodiments, the model-predictive-based single-rotor dehumidification energy-saving optimization control method provided by the present invention includes the following steps in step S130: Step S131: Obtain the difference between the expected humidity and the actual humidity of multiple sets of air inlets and outlets of a single rotor, and use multiple sets of differences as historical observation gap airflow rate samples to generate the Chebyshev inequality.

[0058] Step S132: Write the sensitivity coefficient into the Chebyshev inequality to obtain the robustness constraint, and use the robustness constraint to calibrate the sensitivity coefficient according to the trial run dataset, with the speed distribution and temperature compensation of the output single rotor as the reference operating point.

[0059] In a specific embodiment, the single-rotor dehumidification energy-saving optimization control method based on model prediction provided by the present invention includes step 3, which is based on distributed robust constraints and quadratic programming feedforward compensation. Utilizing the distributed robust mechanism, the worst-case scenario of the gap is estimated through the sample mean and variance (based on the Chebyshev inequality), and then quadratic programming is used to allocate rotational speed and temperature compensation, balancing safety margin and energy saving.

[0060] Among them, the worst-case compensation boundary obtained by the Chebyshev inequality ensures that the real gap is covered with 95% confidence under any distribution, effectively reducing the risk of system runaway; the quadratic programming aims to minimize heating and speed increments while meeting the minimum compensation requirements, taking into account energy saving and stable equipment operation; the feedforward compensation can be directly issued without iterative solution, significantly shortening the disturbance response time.

[0061] Sub-step 3.1: Determine the sample mean and variance of the historical observation gap rate samples in real time.

[0062] Specifically, the historical observation gap rate sample is represented as . The number of sampling points is consistent with that in step 1.3; Indicates the first The group gap rate is the difference between the target absolute humidity of the inlet and outlet air vents and the actual absolute humidity of the inlet and outlet air vents.

[0063] Sub-step 3.2, Chebyshev distributed robust security conditions.

[0064] Specifically, based on historical observation gap rate samples, the Chebyshev inequality is generated, as shown in the following equation: In the formula, Represents probability. These are the sample mean and variance of the historical observed gap rate samples, respectively. Used to describe risk tolerance, in this example... Values ​​can be retrieved .

[0065] Substituting the sensitivity coefficient into the Chebyshev inequality, we obtain the simplified robustness deterministic constraint, as shown in the following equation: In the formula, All are sensitivity coefficients. The parameters to be adjusted represent the amount by which the rotor motor speed needs to be increased or decreased relative to the reference speed under the current operating conditions, and the amount by which the regeneration section inlet air temperature needs to be increased or decreased relative to the reference temperature.

[0066] Sub-step 3.3, sensitivity coefficient calibration.

[0067] Specifically, the trial operation dataset is first obtained, namely the temperature and humidity data collected in step 1. Combined with the controllable parameters output in sub-step 3.2, local one-dimensional linear regression is used again to process the data, and the real-time rotor motor speed under the current operating conditions is output after sensitivity coefficient calibration. Compared with reference temperature .

[0068] Step S140: Based on rotation speed distribution and temperature compensation, and combined with the temperature prediction perturbation of adsorption output by the mechanistic framework model, the MPC algorithm is used to solve the optimal rotation speed and temperature control sequence of a single rotor in real time.

[0069] In some embodiments, the model-predictive single-rotor dehumidification energy-saving optimization control method provided by the present invention includes the following steps in step S140: Step S141: Based on the current humidity error in the single-rotor airflow duct and the minimum values ​​of rotation speed distribution and temperature compensation, combined with the predicted disturbance and configuration parameters, a discrete model is constructed using the MPC algorithm.

[0070] Step S142: Obtain the configuration to get the prediction step size, energy consumption weight, balance factor and robust safety constraints, and optimize the discrete model based on the prediction step size, energy consumption weight, balance factor and robust safety constraints, combined with temperature threshold and airflow rate threshold, to obtain the optimization objective and constraints.

[0071] Step S143: Input the current temperature error, predicted disturbance, optimization objective and constraints, and configuration parameters of the discrete model into the QP solver. Calculate the optimal rotational speed and temperature control sequence in the rolling time domain within the QP solver.

[0072] In a specific embodiment, the single-rotor dehumidification energy-saving optimization control method based on model prediction provided by the present invention includes step 4, joint optimization scheduling based on model predictive control. In the rolling time domain, a discrete-time system model and optimization objective are constructed using distributed robust compensation (i.e., the output of step 3) and disturbance prediction. The optimal rotor speed and regeneration air temperature control sequence are solved in real time using the model predictive control (MPC) algorithm, taking into account both humidity stability and energy saving.

[0073] Feedforward + Feedback Dual-Drive: The robust compensation and initial value of the quadratic programming in step 3 are introduced into MPC, and combined with rolling optimization, to achieve proactive pre-adjustment and real-time correction of future disturbances.

[0074] Multi-objective balance: The MPC objective function simultaneously minimizes the sum of squared humidity deviation and energy consumption weighting, ensuring that comfort and energy saving are achieved in tandem.

[0075] Adaptation and learning: By correcting model parameters and weights through post-execution feedback, the control strategy can be continuously improved in long-term operation to adapt to material aging or changes in operating conditions.

[0076] Includes the following sub-steps: Sub-step 4.1, State-space model extension and introduction of feedforward initial values.

[0077] Specifically, based on the current humidity deviation, the minimum robust compensation amount, the disturbance prediction results, and the system configuration parameters, the system configuration parameters are loaded into the controller. The robust compensation amount output from sub-step 3.4 is used as an initial guess and input into the MPC algorithm in a warmstart manner to construct the final discrete model. This discrete model is used to predict the humidity deviation in the future. Feedforward ensures that the system has completed minimum robust compensation before MPC optimization; the initial value accelerates MPC convergence and reduces the online computational burden.

[0078] The expression for the discrete model is: In the formula, For humidity deviations over future time periods, , , These are all system configuration parameters. , This refers to the real-time rotational speed of the rotary motor and the reference temperature under the current operating conditions after the sensitivity coefficient has been calibrated.

[0079] Sub-step 4.2: Integrating MPC optimization objectives with robust constraints.

[0080] Specifically, the prediction step size, energy consumption weight, and balance factor are first determined, while the upper and lower limits of speed and temperature control (i.e., the speed adjustment and temperature control adjustment ranges are both between their respective set upper and lower limits) and robust safety constraints are controlled.

[0081] Among them, the energy consumption weight is divided into speed energy consumption weight and temperature energy consumption weight, which are used to measure the unit energy consumption increment brought about by each 1% increase in speed adjustment and the unit energy consumption increment brought about by each 1℃ increase in regenerated air temperature. The two are the coefficients of the energy consumption item in the secondary optimization objective, used to balance the trade-off between dehumidification effect and energy consumption.

[0082] The expression for robust safety constraints is: In the formula, , These are the minimum rotational speed and minimum temperature values ​​in the robust compensation parameters, respectively. Indicates the time step. To predict the step size, and when hour, ; .

[0083] Sub-step 4.3: Online MPC rolling solution.

[0084] Specifically, based on the current humidity deviation, disturbance prediction sequence, and robust safety constraints, combined with system configuration parameters, all of these are input into a QP solver (such as OSQP). The solver uses a rolling time-domain approach, executing only the control at the first moment each time, with the remaining sequence used for the next warm start, thus improving solution efficiency. This step, through real-time calculation of globally optimal multi-step control actions, achieves predictive compensation and energy consumption optimization for future disturbance predictions.

[0085] Step S150: Execute the optimal speed and temperature control sequence to optimize the control of single-rotor adsorption dehumidification and obtain the model correction parameters of the execution feedback.

[0086] The model-predictive single-rotor dehumidification energy-saving optimization control system provided by the present invention will be described below. The model-predictive single-rotor dehumidification energy-saving optimization control system described below can be referred to in correspondence with the model-predictive single-rotor dehumidification energy-saving optimization control method described above.

[0087] like Figure 2 As shown, in one embodiment, a model-predictive single-rotor dehumidification energy-saving optimization control system includes a data acquisition and processing module, an adsorption disturbance quantification module, a robust compensation module, an optimal control sequence solving module, and an optimization control execution module.

[0088] The data acquisition and processing module is used to collect temperature, humidity and flow data in the single-rotor airflow duct of the air handling system through integrated sensors, in order to establish raw time-series data with synchronized timestamps and temperature smoothing curves.

[0089] The adsorption perturbation quantification module is used to calculate the equilibrium adsorption curve based on the isotherm parameter set and temperature smoothing curve, and combined with kinetic constants, to construct a mechanistic framework model for predicting the effect of temperature on adsorption perturbation.

[0090] The robustness compensation module is used to perform secondary planning based on the mean and variance of the adsorption amount sample through a distributed robust mechanism to obtain the rotation speed distribution and temperature compensation of a single rotor.

[0091] The optimal control sequence solving module is used to solve the optimal speed and temperature control sequence of a single rotor in real time based on the predicted perturbation of adsorption amount by temperature output from the mechanistic framework model, based on speed allocation and temperature compensation.

[0092] The optimized control execution module is used to execute the optimal speed and temperature control sequence to optimize the control of single-rotor adsorption dehumidification and obtain the model correction parameters of the execution feedback.

[0093] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

Claims

1. A model-predictive-based energy-saving optimization control method for single-rotor dehumidification, characterized in that, The method includes: Temperature, humidity, and flow data in a single-rotor airflow duct of an air handling system are collected by integrating sensors to establish raw time-series data with synchronized timestamps and temperature smoothing curves. Based on the isotherm parameter set and the temperature smoothing curve, the equilibrium adsorption curve is calculated, and combined with the kinetic constant, a mechanistic framework model for predicting the effect of temperature on the adsorption amount is constructed. The distributed robust mechanism is used to perform secondary programming based on the mean and variance of the adsorption amount sample to obtain the rotation speed distribution and temperature compensation of a single rotor. Based on the aforementioned rotational speed distribution and temperature compensation, and combined with the temperature prediction perturbation of the adsorption amount output by the aforementioned mechanistic framework model, the MPC algorithm is used to solve the optimal rotational speed and temperature control sequence of a single rotor in real time. The optimal rotation speed and temperature control sequence is executed to optimize the control of single-rotor adsorption dehumidification and obtain the model correction parameters of the execution feedback.

2. The model-predictive-based energy-saving optimization control method for single-rotor dehumidification according to claim 1, characterized in that, The method of collecting temperature, humidity, and flow data in the single-rotor airflow duct of the air handling system using integrated sensors to establish original time-series data with synchronized timestamps and temperature smoothing curves includes: Multiple sets of temperature and humidity sensors and heat flow sensors are installed at equal intervals in the circumferential direction in the airflow duct at the inlet of the moisture absorption section to construct an integrated sensor for monitoring the temperature difference and flow rate of the airflow inlet and outlet, and each sensor in the integrated sensor is connected to the edge node. The temperature and humidity sensor and the heat flow sensor are polled according to the set sampling frequency to obtain temperature and humidity data and gas flow data. The temperature and humidity data and gas flow data are synchronized in time through the edge node to obtain the original time-series data with the synchronization timestamp. The temperature and humidity sensor is used to collect temperature and humidity data in the single-rotor airflow duct, the heat flow sensor is used to collect flow data, and the edge node is communicatively connected to the centralized controller.

3. The model-predictive-based energy-saving optimization control method for single-rotor dehumidification according to claim 2, characterized in that, The method of collecting temperature, humidity, and flow data in the single-rotor airflow duct of the air handling system using integrated sensors to establish original time-series data with synchronized timestamps and temperature smoothing curves also includes: Multiple sampling points are extracted from the original time-series data according to the set time window, and the mean temperature, average heat flow, and heat flow mutation value of all extracted sampling points are calculated based on the multiple sampling points to construct a feature vector; Based on the feature vector, an autoregressive plus exogenous input model is used to iteratively calculate the discrete predicted value within the first time period in the future using a first-order prediction formula, and the discrete predicted value is then processed by a three-point moving average to obtain the temperature smoothing curve.

4. The model-predictive-based energy-saving optimization control method for single-rotor dehumidification according to claim 1, characterized in that, The equilibrium adsorption curve is calculated based on the isotherm parameter set and the temperature smoothing curve, and combined with kinetic constants, a mechanistic framework model for predicting the effect of temperature on adsorption amount perturbation is constructed, including: Obtain the isotherm parameter set and kinetic constants, wherein the isotherm parameter set includes the maximum adsorption capacity, adsorption energy constant, and empirical index; Based on the temperature smoothing curve and isotherm parameter set, the Dubinin-Astakhov isotherm model is invoked to construct discrete curves, and the equilibrium adsorption curve is calculated based on the discrete curves.

5. The model-predictive-based energy-saving optimization control method for single-rotor dehumidification according to claim 4, characterized in that, The method for calculating the equilibrium adsorption curve based on the isotherm parameter set and the temperature smoothing curve, and constructing a mechanistic framework model for predicting temperature-induced adsorption perturbations by combining kinetic constants, further includes: Based on the equilibrium adsorption curve and kinetic constants, when the adsorption amount changes with time in accordance with first-order kinetics, the discrete time in the equilibrium adsorption curve is updated so as to calculate the dynamic adsorption curve based on the updated discrete time. A reference adsorption capacity is obtained, and the instantaneous efficiency decay ratio is calculated based on the dynamic adsorption curve to construct an adsorption efficiency curve. The instantaneous efficiency decay ratio is the ratio between the dynamic adsorption curve and the reference adsorption capacity.

6. The model-predictive-based energy-saving optimization control method for single-rotor dehumidification according to claim 1, characterized in that, The method of obtaining the rotational speed distribution and temperature compensation of a single impeller through secondary programming based on the sample mean and variance of adsorption amount using a distributed robust mechanism includes: The difference between the expected humidity and the actual humidity of multiple sets of air inlets and outlets of a single rotor is obtained, and the multiple sets of the difference are used as historical observation gap airflow rate samples to generate the Chebyshev inequality. The sensitivity coefficient is written into the Chebyshev inequality to obtain the robustness constraint. The robustness constraint is then used to calibrate the sensitivity coefficient based on the trial run dataset, and the speed distribution and temperature compensation of the single rotor are output as the reference operating point.

7. The model-predictive-based energy-saving optimization control method for single-rotor dehumidification according to claim 1, characterized in that, Based on the aforementioned rotational speed allocation and temperature compensation, and combined with the temperature-related perturbation of adsorption output from the mechanistic framework model, the optimal rotational speed and temperature control sequence for a single rotor are solved in real time using the MPC algorithm, including: Based on the current humidity error in the single-rotor airflow duct, as well as the minimum values ​​of rotation speed distribution and temperature compensation, and combined with the predicted disturbance and configuration parameters, a discrete model is constructed using the MPC algorithm. The configuration is obtained to obtain the prediction step size, energy consumption weight, balance factor and robust safety constraint. Based on the prediction step size, energy consumption weight, balance factor and robust safety constraint, the discrete model is optimized in combination with temperature threshold and airflow rate threshold to obtain the optimization objective and constraints.

8. The model-predictive-based energy-saving optimization control method for single-rotor dehumidification according to claim 7, characterized in that, Based on the aforementioned rotational speed distribution and temperature compensation, and combined with the temperature-related perturbation of adsorption output from the mechanistic framework model, the optimal rotational speed and temperature control sequence for a single rotor are solved in real time using the MPC algorithm. The method also includes: The current temperature error, predicted disturbance, optimization objective and constraints, and configuration parameters of the discrete model are input into the QP solver, in which the optimal rotational speed and temperature control sequence are calculated in the rolling time domain.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-8.

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