Double-rotating-wheel dehumidification energy-saving optimization control method oriented to multivariable cooperative regulation and control

Abstract

A double-rotating-wheel dehumidification energy-saving optimization control method oriented to multivariable cooperative regulation comprises the steps that state observation data and equipment limiting values of all measuring points are obtained, and a material regeneration demand estimation curve and a desorption boundary threshold set are generated; and according to the material regeneration demand estimation curve and the desorption boundary threshold set, calculating a regeneration heat trajectory and a residence time trajectory required by desorption, and generating a safe operation constraint set. And based on the regenerative heat trajectory, the residence time trajectory and the safe operation constraint set, determining a control setting sequence and a reference path at the current moment in combination with a decision amount corresponding to a set optimization target. And issuing the control setting sequence to an execution unit, controlling the execution unit to perform feedforward scheduling according to the reference path, and monitoring and outputting actual execution records and execution deviation data in real time. And based on the actual execution record and the execution deviation data, updating the estimated values of the adsorption capacity and the desorption temperature of the material, and correcting the regeneration heat track and the retention time track in the adjacent rolling time window.

Description

Technical Field

[0001] This application relates to the field of dehumidification and energy-saving optimization technology, and in particular to a dual-rotor dehumidification and energy-saving optimization control method for multi-variable collaborative regulation. Background Technology

[0002] The dual-rotor system consists of a pre-stage dehumidification rotor and a post-stage regeneration rotor, which maintain steady-state speed and phase matching through a coaxial drive mechanism. Air first passes through the dehumidification rotor located in the fresh air duct to absorb moisture and reduce load, then enters the post-stage regeneration rotor, formed by the coupling of the regeneration air duct and the regeneration heat source, for desorption and regeneration. The system is also equipped with external cold and heat source connection pipelines for regulating the air's temperature and humidity. To ensure that the hot and cold zones do not cross-contaminate, the rotors are equipped with sealing and isolation structures to prevent heat from the regeneration side from returning to the dehumidification side and causing efficiency loss. A multi-point temperature and humidity sensor array is arranged along the fresh and regeneration air ducts to achieve real-time monitoring of air conditions and the degree of material regeneration. Overall, the pre-stage system is responsible for adsorbing water vapor, while the post-stage system is responsible for driving desorption. The two form a closed-loop temperature and humidity series, achieving the recycling of energy and adsorption capacity through drive shaft linkage. It is a two-stage functional coupling system with continuous "dehumidification-regeneration" synergy as its core.

[0003] Currently, a key but long-neglected technical defect in dual-rotor dehumidification systems is that the regeneration process cannot provide energy on demand, leading to insufficient desorption or overheating. Traditional control only considers the outlet humidity, continuously increasing the regeneration temperature as soon as the humidity rises slightly, without truly understanding whether the adsorbent is nearing saturation or whether there are still regenerable sites. As a result, either "insufficient regeneration leads to dehumidification failure" or "over-regeneration causes waste of heat energy," easily leading to technical problems such as "lagging compensation" or "blindly increasing the temperature for compensation." Summary of the Invention

[0004] This application provides a dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation, which is used to solve the technical problems of lag-type compensation and blind temperature rise compensation in the prior art.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of this invention discloses a dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation, the method comprising: Acquire the state observation data and equipment limits of each measuring point, and generate a material regeneration demand estimation curve and a set of desorption boundary thresholds based on the state observation data and equipment limits; Based on the material regeneration demand estimation curve and the desorption boundary threshold set, calculate the regeneration heat trajectory and residence time trajectory required for desorption, and generate a set of safe operation constraints; Based on the regenerated heat trajectory, residence time trajectory, and safe operation constraint set, and combined with the decision quantities corresponding to the set optimization objectives, the control setting sequence and reference path at the current moment are determined through rolling optimization. The control setting sequence is sent to the execution unit to control the execution unit to perform feedforward scheduling according to the reference path, and to monitor and output the actual execution record and execution deviation data in real time; Based on the actual execution records and execution deviation data, the estimated values ​​of material adsorption capacity and desorption temperature are updated, and the regeneration heat trajectory and residence time trajectory within adjacent rolling time windows are corrected.

[0007] Furthermore, the step of acquiring state observation data and equipment limits at each measuring point, and generating a material regeneration demand estimation curve and a desorption boundary threshold set based on the state observation data and equipment limits, includes: Obtain the state observation data and equipment limits of each measuring point, and construct the state observation vector and equipment limit vector respectively using the state observation data and equipment limits as components; Based on the state observation vector and equipment limit vector, the instantaneous dehumidification capacity and regeneration heat estimation point are calculated. The instantaneous dehumidification capacity is the product of the difference between the absolute humidity at the inlet of the fresh air duct and the absolute humidity at the outlet of the dehumidification rotor and the mass flow rate of the fresh air duct. The regeneration heat estimation point is the engineering estimation point of the equivalent heat required for regeneration. The components of the state observation vector are the inlet dry-bulb temperature of the fresh air duct, the inlet absolute humidity of the fresh air duct, the temperature of the regenerated air duct, the absolute humidity of the regenerated air duct, the volumetric flow rate of the fresh air, the volumetric flow rate of the regenerated air, the dehumidifier rotor speed, the regenerator rotor speed, and the absolute humidity of the dehumidifier rotor outlet; the components of the equipment limit vector are the upper and lower limit rated values ​​of each equipment corresponding to the state observation data, as well as the upper limit of the allowable temperature of the regenerated air duct and the upper limit of the sealing leakage index.

[0008] Furthermore, the step of acquiring state observation data and equipment limits at each measuring point, and generating a material regeneration demand estimation curve and a desorption boundary threshold set based on the state observation data and equipment limits, further includes: Based on the state observation vector, instantaneous dehumidification capacity, and regeneration heat estimation point, the regeneration residence time is approximately calculated through the rotor geometry and coaxial drive relationship, and the effective adsorption capacity and lower limit of desorption initiation temperature of the material are estimated. Within the rolling time window, the dehumidification load is linearly extrapolated based on the load changes in the fresh air duct to estimate the dehumidification slope within the predicted time period, thereby obtaining the minimum regeneration temperature requirement and minimum residence time requirement to meet the set target desorption ratio. Based on the regeneration residence time, effective adsorption capacity of the material, lower limit of desorption initiation temperature, dehumidification slope, and minimum regeneration temperature and minimum residence time requirements to meet the set target desorption ratio, an estimated curve for the material regeneration requirement and a set of desorption boundary thresholds are generated.

[0009] Furthermore, the step of calculating the regeneration heat trajectory and residence time trajectory required for desorption based on the material regeneration demand estimation curve and the desorption boundary threshold set, and generating a safe operation constraint set, includes: Based on the set sampling period and rolling time window length, the material regeneration demand estimation curve is discretized according to the time domain network to output the minimum regeneration temperature requirement and minimum residence time requirement at the predicted time. Based on the minimum regeneration temperature requirement and minimum residence time requirement at the predicted time, and combined with the state observation data, the range of latent heat of water vapor condensation, the range of comprehensive efficiency of the regeneration section, and the instantaneous dehumidification capacity and extrapolation results at the corresponding time are determined. The minimum equivalent regeneration heat at each time is calculated, and the maximum regeneration rotor speed for the minimum residence time requirement is determined according to the geometric relationship between the coaxial drive and the regeneration zone.

[0010] Furthermore, the step of calculating the regeneration heat trajectory and residence time trajectory required for desorption based on the material regeneration demand estimation curve and the desorption boundary threshold set, and generating a safe operation constraint set, also includes: Based on the maximum regeneration rotor speed, the minimum equivalent regeneration heat is converted into average power demand and projected to the upper limit to obtain the projected power trajectory point. The dwell time is converted into an equivalent upper limit constraint of rotational speed, and projected in combination with the upper and lower limits of equipment rotational speed. The power trajectory point is then converted back into a heat trajectory point, so as to generate the regenerated heat trajectory and dwell time trajectory based on the heat trajectory point and the corresponding dwell time. Based on the state observation data and equipment limits, safe operation constraints are constructed, and these safe operation constraints are associated with the regenerated heat trajectory and residence time trajectory to obtain the set of safe operation constraints.

[0011] Furthermore, the step of determining the control setting sequence and reference path at the current moment through rolling optimization, based on the regenerated heat trajectory, residence time trajectory, and safe operation constraint set, combined with the decision quantity corresponding to the set optimization objective, includes: Based on the regenerated heat trajectory, residence time trajectory, and safe operation constraint set, a time-domain network and a demand reference sequence are defined according to the set sampling period and rolling time window length, so as to determine the decision quantity that satisfies the set optimization objective according to the time-domain network and the demand reference sequence; The temperature setting and regeneration air volume in the decision-making quantities are mapped to heat source power by calling the engineering equivalent model, and the heat source power is converted into delivered heat. Based on the delivered heat, regeneration residence time and safe operation constraint set, a hierarchical weighted objective function is constructed. Based on the hierarchical weighted objective function, Gauss-Newton linearization and sequential quadratic programming are used to solve the predictive control sequence, so as to generate a control setting sequence and a reference path according to the predictive control sequence; The decision parameters include the regenerated air duct heat source temperature setting, the fresh air duct volumetric flow rate setting, the regenerated air duct volumetric flow rate setting, the dehumidification impeller speed setting, the regeneration impeller speed setting, and the bypass ventilation damper opening setting.

[0012] Furthermore, the control setting sequence is sent to the execution unit to control the execution unit to perform feedforward scheduling according to the reference path, and to monitor and output actual execution records and execution deviation data in real time, including: Based on the control setting sequence and reference path, the amplitude and rate limits of each actuator in the execution unit are shaped, and the constraint monitoring signal is calculated and the redline proximity is evaluated. A weighted correction amount based on the redline proximity is constructed, and combined with the collaborative correction rules of different constraints in the constraint monitoring signal, the control setting sequence is softly corrected to obtain the corrected execution command sequence. The execution command sequence is sent to the execution unit, and in conjunction with the configured emergency trigger conditions, the actual execution records, constraint monitoring results, and execution deviation data are monitored and output in real time.

[0013] Furthermore, the step of updating the estimated values ​​of material adsorption capacity and desorption temperature based on the actual execution records and execution deviation data, and correcting the regeneration heat trajectory and residence time trajectory within adjacent rolling time windows, includes: Based on the actual execution records, constraint monitoring results, and execution deviation data, a residual sequence and mechanism parameter regression vector are constructed. The residual sequence consists of the residual between the delivered heat and the required heat, the residual between the actual residence time and the required residence time in the regeneration section, and the deviation of the outlet section of the fresh air duct after passing through the dehumidification rotor. Based on the residual sequence and the mechanism parameter regression vector, the equivalent capacity and time parameters are updated by recursive least squares method to obtain the updated mechanism parameter set. The minimum regeneration temperature and residence time of the prediction time window are updated and calculated based on the updated set of mechanism parameters to output the updated regeneration heat trajectory and residence time trajectory.

[0014] The second aspect of this invention discloses a dual-rotor dehumidification energy-saving optimization control device for multi-variable collaborative regulation, used to implement the steps of the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation as described in any one of the first aspects, the device comprising: The curve and threshold generation module is used to acquire the state observation data and equipment limits of each measuring point, and generate a material regeneration demand estimation curve and a set of desorption boundary thresholds based on the state observation data and equipment limits. The trajectory and operation constraint module is used to calculate the regeneration heat trajectory and residence time trajectory required for desorption based on the material regeneration demand estimation curve and the desorption boundary threshold set, and to generate a safe operation constraint set. The control path generation module is used to determine the control setting sequence and reference path at the current moment through rolling optimization, based on the regenerated heat trajectory, residence time trajectory and safe operation constraint set, combined with the decision quantity corresponding to the set optimization target; The execution deviation monitoring module is used to send the control setting sequence to the execution unit to control the execution unit to perform feedforward scheduling according to the reference path, and to monitor and output the actual execution record and execution deviation data in real time. The trajectory correction module is used to update the estimated values ​​of material adsorption capacity and desorption temperature based on the actual execution records and execution deviation data, and to correct the regeneration heat trajectory and residence time trajectory within adjacent rolling time windows.

[0015] A third aspect of the present invention discloses a terminal, including 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 described in the first aspect.

[0016] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method described in the first aspect.

[0017] Compared with the prior art, this application has the following advantages: (1) By combining the adsorption / desorption mass transfer-heat transfer coupling model with MPC, this invention enables the physical model to allow the system to see the "true desorption state inside the material" instead of just focusing on the air outlet parameters. MPC uses this mechanistic information to calculate the optimal regeneration heat path and residence time path in advance, effectively avoiding the technical problems of "lagging compensation" or "blindly increasing temperature compensation" in the prior art. The adsorbent can be effectively regenerated before the humidity rises, preventing the outlet humidity from "dropping first and then rebounding"; at the same time, the heating power is no longer increased without benefit, so that the regeneration process enters an energy-saving state of "just enough and not over-compensated", thereby upgrading the system from "passive correction dehumidification" to "feedforward optimal dehumidification".

[0018] (2) Within the rolling time window, this invention calculates the regeneration heat trajectory and residence time trajectory required to achieve full desorption at each future moment based on the material regeneration demand estimation curve and the desorption boundary threshold set, and forms a set of safe operation constraints. It refines "on-demand energy supply" into executable targets of "when, how much, and how long," and provides feedforward quantitative targets for "lag compensation" of technical defects. At the same time, predictive optimization puts the mechanism target into an executable setting of "multi-variable synergy," effectively eliminating the extensive strategy of "heating as soon as the outlet is seen," stably implementing the predicted optimal solution, preventing secondary waste or failure at the execution level, and further curbing the on-site risks of "overheating" and "insufficient regeneration." Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 This is a flowchart illustrating the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of the dual-rotor dehumidification energy-saving optimization control device for multi-variable collaborative regulation provided by the present invention.

[0022] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0024] like Figure 1 As shown, in one embodiment, a dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation includes the following steps: Step S110: Obtain the state observation data and equipment limits of each measuring point, and generate the material regeneration demand estimation curve and desorption boundary threshold set based on the state observation data and equipment limits.

[0025] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention includes the following steps in step S110: Step S111: Obtain the state observation data and equipment limits of each measuring point, and construct the state observation vector and equipment limit vector respectively using the state observation data and equipment limits as components.

[0026] Among them, the components in the state observation vector are the inlet dry bulb temperature of the fresh air duct, the inlet absolute humidity of the fresh air duct, the temperature of the regenerated air duct, the absolute humidity of the regenerated air duct, the volumetric flow rate of the fresh air, the volumetric flow rate of the regenerated air, the dehumidifier rotor speed, the regenerator rotor speed, and the absolute humidity of the dehumidifier rotor outlet; the components in the equipment limit vector are the upper and lower limit rated values ​​of each equipment corresponding to the state observation data, as well as the upper limit of the allowable temperature of the regenerated air duct and the upper limit of the sealing leakage index.

[0027] Step S112: Based on the state observation vector and the equipment limit vector, calculate the instantaneous dehumidification capacity and the estimated point of regeneration heat. The instantaneous dehumidification capacity is the product of the difference between the absolute humidity at the inlet of the fresh air duct and the absolute humidity at the outlet of the dehumidification rotor and the mass flow rate of the fresh air duct. The estimated point of regeneration heat is the engineering estimate of the equivalent heat required for regeneration.

[0028] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention further includes the following steps in step S110: Step S113: Based on the state observation vector, instantaneous dehumidification amount, and regeneration heat estimation point, the regeneration residence time is approximately calculated through the rotor geometry and coaxial drive relationship, and the effective adsorption capacity and desorption initiation temperature limit of the material are estimated.

[0029] Step S114: Within the rolling time window, the dehumidification load is linearly extrapolated based on the load change of the fresh air duct to estimate the dehumidification slope within the predicted time period, thereby obtaining the minimum regeneration temperature requirement and minimum residence time requirement to meet the set target desorption ratio.

[0030] Step S115: Based on the regeneration residence time, effective adsorption capacity of the material, lower limit of desorption initiation temperature, dehumidification slope, and minimum regeneration temperature and minimum residence time requirements to meet the set target desorption ratio, generate a material regeneration demand estimation curve and a set of desorption boundary thresholds.

[0031] In a specific embodiment, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention includes steps 1 to 5: Step 1: Mechanism modeling and online identification.

[0032] Based on an adsorption / desorption mass transfer-heat transfer coupling model, the system collects state observation data such as inlet air dry-bulb temperature, inlet air absolute humidity, regeneration channel temperature, regeneration channel absolute humidity, fresh air volumetric flow rate, regeneration air volumetric flow rate, two impeller speeds, and outlet air absolute humidity. This allows for online identification of the material's effective adsorption capacity, desorption initiation temperature range, and required residence time range, thereby generating an estimated curve of material regeneration demand and a set of desorption boundary thresholds for the next few minutes. This step transforms the judgment of "whether regeneration is needed and to what extent" from empirical judgment to a quantifiable benchmark, providing a mechanistic basis for addressing the "blindly increasing temperature" deficiency of existing technologies. The steps include the following sub-steps: Sub-step 1.1: Construction of data center and state observation vector.

[0033] Specifically, following the physical sequence of fresh air duct → pre-stage dehumidifier → regenerated air duct → post-stage dehumidifier, state observation data are collected at each measuring point, including the dry-bulb temperature at the inlet of the fresh air duct (obtained by the inlet sensor of the fresh air duct), the absolute humidity at the inlet of the fresh air duct (obtained by the inlet sensor of the fresh air duct), the temperature of the regenerated air duct (measured at the downstream measuring point of the heat source of the regenerated air duct), the absolute humidity of the regenerated air duct (obtained by the humidity sensor of the regenerated air duct), the volumetric flow rate of fresh air (read by the fresh air fan or air volume meter), the volumetric flow rate of regenerated air (read by the regenerated air fan or air volume meter), the rotational speed of the dehumidifier desiccant (read by the coaxial drive encoder), the rotational speed of the regenerated desiccant desiccant (read by the coaxial drive encoder), and the absolute humidity at the outlet of the dehumidifier desiccant (measured at the outlet section after the fresh air duct passes through the pre-stage dehumidifier desiccant). The above state observation data are then used as the components to construct the state observation vector corresponding to the structure of the regeneration system. Meanwhile, based on the upper limit rated values ​​of the equipment corresponding to the above-mentioned state observation data as each component, and combined with the upper limit of the allowable temperature of the regenerated air duct and the upper limit of the sealing leakage index (obtained by the inspection of the sealing and isolation structure or the comprehensive estimation of pressure difference and flow rate), an equipment limit vector is constructed.

[0034] Sub-step 1.2, estimation of heat and humidity balance and instantaneous dehumidification capacity.

[0035] Specifically, calculate the mass flow rate of the fresh air duct (air density is taken as an engineering constant range of 1.15-1.25 kg / m³). 3 The engineering estimates for instantaneous dehumidification capacity (the moisture flux converted from the absolute humidity difference between the outlet and inlet of the pre-stage dehumidifier rotor) and the equivalent heat required for regeneration are as follows: the mass flow rate of the fresh air duct is equal to the product of the air density and the volumetric flow rate of the fresh air; the instantaneous dehumidification capacity is equal to the product of the difference between the absolute humidity at the inlet of the fresh air duct and the absolute humidity at the outlet of the dehumidifier rotor and the mass flow rate of the fresh air duct; the equivalent heat required for regeneration is equal to the ratio of the product of the latent heat of condensation of water vapor and the instantaneous dehumidification capacity as the numerator and the overall efficiency of the regeneration section as the denominator.

[0036] Sub-step 1.3: Online identification of the material's effective adsorption capacity and effective temperature range.

[0037] Specifically, the residence time in the regeneration section is approximated using the rotor geometry and coaxial drive relationship (inversely proportional to the regeneration rotor speed). The residence time in the regeneration section is equal to the ratio of a geometric constant (empirical range 10-60 s·rpm) determined by the combined arc length of the regeneration air duct, the width of the partition, and the rotor diameter to the regeneration rotor speed. Then, a lower limit for the desorption initiation temperature zone is defined (significant desorption only occurs when the temperature exceeds the initiation value), and an engineering identification relationship is established.

[0038] In the formula, This refers to the instantaneous dehumidification capacity; This is the equivalent coefficient of the material's effective adsorption capacity; Temperature of the regenerated air duct; The lower limit of the desorption initiation temperature; The characteristic residence time constant of the material-flow field composite; This represents the length of the time window.

[0039] Finally, the residuals of the material's effective adsorption capacity, the lower limit of the desorption initiation temperature, and the characteristic residence time constant of the material-flow field complex are minimized using the recursive least squares method or other online identification methods, and then the required material effective adsorption capacity, the lower limit of the desorption initiation temperature, and the residence time are output.

[0040] Sub-step 1.4: Generation of the future window material recycling demand estimation curve and desorption boundary threshold.

[0041] Specifically, in the rolling time window Internally, the dehumidification load is linearly extrapolated (mainly based on load changes on the fresh air duct side), and the expression is:

[0042] In the formula, The dehumidification rate slope for the most recent steps is estimated (by combining the changes in absolute humidity at the fresh air inlet and the changes in fresh air volumetric flow rate). This is the extrapolated value of the dehumidification amount within the time window h.

[0043] The expression for estimating the dehumidification rate slope in the most recent steps is as follows:

[0044] Finally, the minimum regeneration temperature requirement to achieve the target desorption ratio is obtained. Minimum stay time requirement The expression is:

[0045]

[0046] In the formula, The target desorption ratio is set between 0.7 and 0.9. The available dwell time budget is derived from the coaxial drive constraint and the arc length of the regeneration air duct partition, and is inversely proportional to the regeneration impeller speed. Its range is constrained by the equipment limit vector.

[0047] Finally, based on the minimum regeneration temperature requirement to meet the target desorption ratio. Minimum stay time requirement This forms a material recycling demand estimation curve (minimum recycling temperature demand in a time series). Minimum stay time requirement The output includes the set of desorption boundary thresholds (including the minimum allowable regeneration temperature, the minimum allowable residence time, and the temperature deduction safety belt corresponding to the upper limit of the sealing leakage index), along with the current state observation vector.

[0048] Step S120: Calculate the regeneration heat trajectory and residence time trajectory required for desorption based on the material regeneration demand estimation curve and the desorption boundary threshold set, and generate a safe operation constraint set.

[0049] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention includes the following steps in step S120: Step S121: Based on the set sampling period and rolling time window length, the material regeneration demand estimation curve is discretized according to the time domain network to output the minimum regeneration temperature demand and minimum residence time demand at the predicted time.

[0050] Step S122: Based on the minimum regeneration temperature requirement and minimum residence time requirement at the predicted time, and combined with the state observation data, determine the range of latent heat of water vapor condensation, the range of comprehensive efficiency of the regeneration section, and the instantaneous dehumidification capacity and extrapolation results at the corresponding time. Calculate the minimum equivalent regeneration heat at each time and determine the maximum regeneration rotor speed for the minimum residence time requirement based on the geometric relationship between the coaxial drive and the regeneration zone.

[0051] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention further includes the following steps in step S120: Step S123: Based on the maximum regeneration rotor speed, the minimum equivalent regeneration heat is converted into average power demand and projected onto the upper limit to obtain the projected power trajectory point.

[0052] Step S124: Convert the residence time into an equivalent speed upper limit constraint, project it in conjunction with the upper and lower limits of the equipment speed, and convert the power trajectory points back into heat trajectory points. Generate the regenerated heat trajectory and residence time trajectory based on the heat trajectory points and the corresponding residence time.

[0053] Step S125: Construct safe operation constraints based on state observation data and equipment limits, and associate the safe operation constraints with the regenerated heat trajectory and residence time trajectory to obtain a set of safe operation constraints.

[0054] In a specific embodiment, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by this invention includes step 2, regeneration energy-time joint demand prediction. Within a rolling time window, based on the material regeneration demand estimation curve and the desorption boundary threshold set, the regeneration heat trajectory and residence time trajectory required to achieve full desorption at each future moment are calculated, forming a set of safe operation constraints (including the maximum allowable temperature of the regeneration channel, upper and lower limits of air volume, upper limit of leakage rate, upper limit of absolute humidity at the outlet, etc.). This step refines "on-demand energy supply" into executable objectives of "when, how much, and how long," and provides feedforward quantitative objectives for "lag compensation" of technical defects, including the following sub-steps: Sub-step 2.1: Discretize the forecast window and demand curve.

[0055] Specifically, the sampling period is set to a constant, ranging from 1 to 10 seconds; the rolling time window length is set to a positive integer, ranging from 12 to 60 seconds. The aforementioned material regeneration demand estimation curve is discretized using a time-domain network to obtain the minimum regeneration temperature demand and minimum dwell time demand at each future time.

[0056] Sub-step 2.2 derives the mapping between regeneration heat and rotation speed from the required temperature and residence time.

[0057] Specifically, based on the known material equivalent parameters and regeneration efficiency, the latent heat of water vapor condensation is assumed to be in the range of 2.3 × 10⁻⁶. 6 Up to 2.6×10 6 J / kg, assuming the overall efficiency of the regeneration section ranges from 0.4 to 0.8, and assuming the instantaneous dehumidification capacity at the current moment and its extrapolated value are given by the extrapolation formula in step 1, then the expression for the minimum equivalent regeneration heat requirement is:

[0058] In the formula, To meet the equivalent heat requirement for sufficient desorption at time k+h; The latent heat of condensation of water vapor is constant; This is an extrapolated value for dehumidification capacity; To improve overall regeneration efficiency.

[0059] Next, the upper limit of the rotational speed required to meet the minimum residence time requirement is determined by the combination of coaxial drive and regeneration partition (the lower the rotational speed, the longer the residence time; therefore, there exists a maximum permissible rotational speed to meet the minimum time requirement). Assuming the geometric constant range is 10⁻⁶⁰ s·rpm, then:

[0060] In the formula, To meet the minimum dwell time requirement at time k+h The maximum permissible regeneration rotor speed; It is a geometric constant determined by the arc length of the regenerated air duct, the width of the partition, and the diameter of the impeller.

[0061] Finally, the output consists of a sequence of regeneration heat points consisting of the minimum equivalent regeneration heat requirement and a sequence based on the minimum residence time requirement. The equivalent speed lower limit sequence of the maximum permissible regeneration rotor speed.

[0062] Sub-step 2.3 generates the predicted trajectory of regenerated heat and the predicted trajectory of residence time.

[0063] Specifically, the upper limit range of heat source power is taken from the equipment nameplate or on-site calibration value, and the equivalent heat demand point value is calculated. Converted to average power demand per beat And perform upper limit projection, the expression is:

[0064]

[0065] In the formula, Average power requirement per beat; The sampling period; This is the upper limit of the heat source power. These are the points on the projected regenerative power trajectory.

[0066] Then, the dwell time requirement is transformed into an upper bound constraint on the equivalent rotational speed. And combined with the upper and lower limits of the achievable speed of the equipment, the projection is expressed as:

[0067]

[0068] In the formula, This refers to the maximum regeneration rotor speed achievable by the equipment; These are the projected achievable dwell time trajectory points; The maximum regeneration rotor speed is at time k+h.

[0069] Finally, by converting the power trajectory points back to the heat trajectory points, we can obtain the final predicted trajectory of regenerated heat and its predicted residence time.

[0070] Sub-step 2.4 generates a set of safe operation constraints.

[0071] Specifically, the set of safe operation constraints includes upper limit constraints on regeneration channel temperature, air volume operating domain constraints (fresh air channel and regeneration air channel), sealing leakage index constraints (defined by the performance of sealing and isolation structures), and upper limit constraints on outlet absolute humidity (outlet cross section of the pre-stage dehumidifier rotor). By associating the above safe operation constraints with the predicted trajectory of regeneration heat and its residence time, a complete set of safe operation constraints can be generated.

[0072] Step S130: Based on the regenerated heat trajectory, residence time trajectory, and safe operation constraint set, and combined with the decision quantities corresponding to the set optimization objectives, the control setting sequence and reference path at the current moment are determined through rolling optimization.

[0073] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention includes the following steps in step S130: Step S131: Based on the regenerated heat trajectory, residence time trajectory, and safe operation constraint set, define the time domain network and demand reference sequence according to the set sampling period and rolling time window length, so as to determine the decision quantity that satisfies the set optimization objective according to the time domain network and demand reference sequence.

[0074] The decision parameters include the regenerated air duct heat source temperature setting, the fresh air duct volumetric flow rate setting, the regenerated air duct volumetric flow rate setting, the dehumidification impeller speed setting, the regeneration impeller speed setting, and the bypass ventilation damper opening setting.

[0075] Step S132: Call the engineering equivalent model to map the temperature setting and regeneration air volume in the decision quantity to heat source power, and convert the heat source power into delivered heat, so as to construct a hierarchical weighted objective function based on the delivered heat, regeneration residence time and safe operation constraint set.

[0076] Step S133: Based on the hierarchical weighted objective function, Gauss-Newton linearization and sequential quadratic programming are used to solve the predictive control sequence, so as to generate the control setting sequence and reference path according to the predictive control sequence.

[0077] In a specific embodiment, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by this invention includes step 3: model predictive control solution and multi-variable collaborative setting. An optimization problem is constructed with the objectives of "ensuring the outlet absolute humidity does not exceed the upper limit, prioritizing sufficient desorption, and comprehensively minimizing energy consumption." The decision variables include: regeneration channel temperature setting, fresh air volumetric flow rate, regeneration air volumetric flow rate, dehumidification rotor speed, regeneration rotor speed, and bypass ventilation damper opening. Rolling optimization is used to obtain the control setting sequence and reference path at the current moment, ensuring that sufficient desorption at the material level and minimum energy input at the system level are simultaneously achieved. This step uses predictive optimization to translate the mechanistic objective into an executable setting of "multi-variable collaboration," directly eliminating the crude strategy of "heating up the outlet as soon as possible." It includes the following sub-steps: Sub-step 3.1: Define the control time domain and decision quantity sequence.

[0078] Specifically, the sampling period is set to a constant, ranging from 1 to 10 seconds; the rolling optimization time window length is set to a positive integer, ranging from 10 to 60. The time-domain grid and the demand reference sequence and their decision vectors are defined (each setting corresponds to its system component), including the regeneration air duct heat source temperature setting, the fresh air duct volume flow rate setting, the regeneration air duct volume flow rate setting, the dehumidification impeller speed setting, the regeneration impeller speed setting, and the bypass ventilation damper opening setting (used for hot-side bypass or cold-side load reduction, with a value of 0 to 1).

[0079] Sub-step 3.2, accessibility mapping of energy transfer and dwell time.

[0080] Specifically, the temperature setting and regenerated air volume are mapped to heat source power using an engineering equivalent model, and then converted into deliverable heat within a given period. The expression is as follows:

[0081]

[0082] In the formula, Power of the heat source; The reference loss constant for the heat source is taken as 0 to 10% of the rated power; The equivalent heat transfer gain constant is determined by actual measurements of the heat exchanger and the regeneration air duct. This refers to the inlet temperature of the regenerated air duct heat exchanger. The overall efficiency of the regeneration section is taken as 0.4-0.8; The sampling period; The heat that can be delivered within the frame; Set the heat source temperature for the regenerated air duct; Set the volumetric flow rate for the regeneration air duct.

[0083] Subsequently, the energy constraint is satisfied to ensure that the delivered heat is greater than or equal to the required heat. At the same time, the dwell time of the regeneration section is satisfied by coaxial drive and partition geometry mapping (inversely proportional to the speed of the regeneration wheel) to satisfy the dwell time constraint that the achievable dwell time is greater than or equal to the required dwell time.

[0084] Sub-step 3.3: Assembly of the objective function and full constraints.

[0085] Specifically, based on the above-mentioned energy satisfaction constraints and dwell time satisfaction constraints, a hierarchical weighted objective function is constructed, which requires the objective function to have the minimum energy, follow the demand, and have smooth operation. The energy satisfaction constraints, dwell time satisfaction constraints, safe operation constraints, and actuator boundary and rate limits are combined.

[0086] Sub-step 3.4: Solve and generate the control setting sequence and reference path.

[0087] Specifically, the objective function is processed using linearization or quadratic approximation to obtain a convex optimization or sequential quadratic programming problem. Gauss-Newton linearization and sequential quadratic programming are then used to solve the next control sequence. The optimal solution from the first iteration is used as the current setting, and the entire prediction segment is retained as a reference path for the execution layer's feedforward tracking. During the setting distribution process, the regenerator air duct heat source temperature setting is distributed to the regenerator air duct heat source; the fresh air duct volumetric flow rate setting is distributed to the fresh air duct fan; the regenerator air duct volumetric flow rate setting is distributed to the regenerator air duct fan; the dehumidifier rotor speed setting and the regenerator rotor speed setting are distributed to the coaxial drive shaft or the two-rotor servo; and the bypass ventilation door opening setting is distributed to the bypass ventilation door actuator.

[0088] Step S140: The control setting sequence is sent to the execution unit to control the execution unit to perform feedforward scheduling according to the reference path, and to monitor and output the actual execution record and execution deviation data in real time.

[0089] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention includes the following steps in step S140: Step S141: Based on the control setting sequence and reference path, the amplitude and rate limits of each actuator in the execution unit are shaped, and the constraint monitoring signal is calculated and the redline proximity is evaluated.

[0090] Step S142: Construct a weighted correction amount based on the redline proximity, and combine it with the collaborative correction rules of different constraints in the constraint monitoring signal to perform soft correction on the control setting sequence, thereby obtaining the corrected execution command sequence.

[0091] Step S143: The execution command sequence is sent to the execution unit, and in combination with the configured emergency trigger conditions, the actual execution record, constraint monitoring results and execution deviation data are monitored and output in real time.

[0092] In a specific embodiment, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by this invention includes step 4: feedforward execution and constraint monitoring. The control setting sequence is sent to the heat source, fan, and servo execution unit, and feedforward scheduling is performed according to the reference path. Simultaneously, key constraints are monitored in real time, including outlet absolute humidity, material temperature rise rate, upper limit of regeneration channel temperature, fresh air to regeneration air ratio, and seal leakage indication. If deviation from or approaching the red line is detected, soft constraint correction and emergency rollback strategies are triggered (such as lowering the regeneration temperature setting, temporarily increasing the residence time, and fine-tuning the air ratio). This step stably implements the predicted optimal solution, preventing secondary waste or failure at the execution level, and further curbing the on-site risks of "overheating" and "insufficient regeneration," including the following sub-steps: Sub-step 4.1: Feedforward transmission and actuator limiting and shaping.

[0093] Specifically, each actuator is shaped according to the amplitude and rate limits in the equipment manual to prevent mechanical / thermal shock from heat sources, fans, and servos. The expression is:

[0094]

[0095] In the formula, This is the optimal setting for the current shot; The commands to be issued after the reshaping process include the regeneration channel temperature setting, fresh air and regeneration air volume flow rate, two impeller speeds, and bypass ventilation door opening. These are the lower and upper limits of the actuator; This is the actual execution value of the bid; This is the maximum adjustment amplitude per shot (an engineering constant, obtained according to equipment calibration).

[0096] Sub-step 4.2: Constraint monitoring signal calculation and redline proximity assessment.

[0097] Specifically, the constraint monitoring signals include dehumidification quality constraints (outlet cross-section of the pre-stage dehumidifier impeller, determined by the difference between the current dehumidification capacity and the maximum dehumidification capacity), material temperature rise rate (material sensor in the subsequent regeneration zone, determined by the rate of change of temperature difference between adjacent stages), upper limit of regeneration channel temperature (determined by the difference between the current stage temperature and the maximum temperature in the regeneration channel), airflow ratio (determined by the ratio of volumetric flow rate of the regeneration air channel to the fresh air channel), and seal leakage indication (approximately obtained based on the mass conservation approximation of airflow and differential pressure at the end of the channel). During the red line proximity assessment, the red line proximity is normalized, with a value of 0 indicating safety and a value of 1 indicating reaching the red line.

[0098] Sub-step 4.3: Fine-tune the feedforward using the soft constraint trimmer.

[0099] Specifically, a weighted correction quantity based on the proximity to the red line is constructed. Based on the equipment mapping back to the heat source, two fans, coaxial drive, and bypass ventilation damper, the expression is:

[0100] In the formula, The diagonal gain matrix is ​​used (the design range is calibrated according to the actuator sensitivity). This is a component saturation function used to... Each component in the equation is limited to the range of 0-1.

[0101] In this embodiment, for the collaborative correction rules for different constraints, when the regeneration temperature approaches the upper limit, the regeneration channel temperature setting is reduced, the residence time is extended (the regeneration rotor speed is reduced), and the regeneration air volume is slightly increased to maintain desorption; when the outlet absolute humidity approaches the upper limit, the regeneration temperature setting is slightly increased without exceeding the upper temperature limit, while the dehumidification rotor speed is reduced to increase the adsorption residence time, and the air ratio is slightly increased towards the upper half of the upper and lower limits; when leakage increases, the pressure difference between fresh air and regeneration air is reduced, the differential pressure is reduced by simultaneously fine-tuning the two air volumes, and the bypass opening is appropriately increased to slow down the heat accumulation on the heat side.

[0102] Sub-step 4.4: Triggering and executing the emergency rollback strategy.

[0103] Specifically, emergency trigger conditions are defined. When the temperature exceeds the upper limit, the regeneration temperature setting is pulled back to within the safety zone, the regeneration rotor speed is immediately reduced to compensate for the dwell time, the bypass ventilation door is opened to the emergency position, and the air ratio is maintained at the middle of the range. When the outlet humidity exceeds the upper limit, the regeneration air volume is slightly increased and the fresh air volume is reduced without exceeding the upper temperature limit, the dehumidification rotor speed is reduced, and the bypass is briefly increased if necessary to reduce heat load fluctuations. When the leakage exceeds the upper limit, the pressure difference between the two channels is reduced (the air volume of both channels is reduced simultaneously), the pressure difference indication of the sealing and isolation structure is checked, and the temperature rise rate is limited. Finally, actual execution records (including issued data and actual feedback data), constraint monitoring results (the closeness of each constraint indicator to the red line), and execution deviation data (the deviation between the set value and the measured value) are generated.

[0104] Step S150: Based on the actual execution records and execution deviation data, update the estimated values ​​of material adsorption capacity and desorption temperature, and correct the regeneration heat trajectory and residence time trajectory within adjacent rolling time windows.

[0105] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention includes the following steps in step S150: Step S151: Based on the actual execution records, constraint monitoring results and execution deviation data, construct the residual sequence and mechanism parameter regression vector. The residual sequence consists of the residual between the delivered heat and the required heat, the residual between the actual residence time and the required residence time in the regeneration section, and the deviation of the outlet section of the fresh air duct after passing through the dehumidification rotor.

[0106] Step S152: Based on the residual sequence and the regression vector of the mechanism parameters, the equivalent capacity and time parameters are updated by recursive least squares method to obtain the updated set of mechanism parameters.

[0107] Step S153: Based on the updated set of mechanism parameters, the minimum regeneration temperature and residence time of the predicted time window are updated and calculated to output the updated regeneration heat trajectory and residence time trajectory.

[0108] In a specific embodiment, the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation provided by the present invention includes step 5, feedback correction and closed-loop convergence. Based on actual execution records and deviation data, the online estimates of the effective adsorption capacity of the material and the desorption onset temperature range are updated, and the regeneration heat and residence time predictions in the next rolling window are corrected; the disturbance estimates and weights in the model predictive control are simultaneously corrected, so that the system continuously maintains the operating state of "completing effective desorption before the arrival of wet load and maintaining stable absolute humidity at the outlet under the condition of minimizing energy consumption", eliminating the root causes of "first decrease and then rebound" and "ineffective temperature rise". This step forms an adaptive closed loop of mechanism and optimization, ultimately solving the technical problem of "the regeneration process cannot supply energy on demand", including the following sub-steps: Sub-step 5.1: Residual construction and data alignment.

[0109] Specifically, the residuals of deliverable heat within the model and the model requirements are constructed, the residuals of actual residence time in the regeneration section and the requirements are constructed, the outlet quality deviation (the outlet cross section of the fresh air duct after passing through the pre-stage dehumidification impeller) is constructed, and the mechanism identification regression vector (determined by the actual execution value) is constructed.

[0110] Sub-step 5.2: Online calibration of mechanism parameters.

[0111] Specifically, the adsorption capacity and residence time are updated using the ground-progressive least squares method. The parameter vector and regression nominal are defined, and then updated in the form of a forgetting factor to obtain the updated set of mechanistic parameters.

[0112] Sub-step 5.3: Predicted trajectory and adaptive correction of disturbance.

[0113] Specifically, based on the updated set of mechanistic parameters, the minimum regeneration temperature and residence time for the next window are recalculated, and the disturbance term is estimated by the average deviation between the outlet humidity and the regeneration temperature, forming a new predicted trajectory for regeneration heat and residence time.

[0114] Sub-step 5.4, MPC weight adaptation and closed-loop convergence criterion.

[0115] Specifically, based on the updated regenerated heat prediction trajectory and residence time prediction trajectory, combined with the default rate of the aforementioned constraint indicators (regenerated temperature exceedance rate, outlet humidity exceedance rate, energy consumption redundancy index) and the energy consumption deviation adjustment weight coefficient (range 0.05-0.3), the closed-loop convergence criterion is determined based on the adjusted weight coefficient. If all criteria are met simultaneously, it is considered to have converged and enters the small-step update.

[0116] The following describes the dual-rotor dehumidification energy-saving optimization control device for multi-variable collaborative regulation provided by the present invention. The dual-rotor dehumidification energy-saving optimization control device for multi-variable collaborative regulation described below can be referred to in correspondence with the dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation described above.

[0117] like Figure 2 As shown, in one embodiment, a dual-rotor dehumidification energy-saving optimization control device for multi-variable collaborative regulation includes a curve and threshold generation module, a trajectory and operation constraint module, a control path generation module, an execution deviation monitoring module, and a trajectory correction module.

[0118] The curve and threshold generation module is used to obtain the state observation data and equipment limits of each measuring point, and generate the material regeneration demand estimation curve and desorption boundary threshold set based on the state observation data and equipment limits.

[0119] The trajectory and operational constraint module is used to calculate the regeneration heat trajectory and residence time trajectory required for desorption based on the material regeneration demand estimation curve and the desorption boundary threshold set, and to generate a set of safe operational constraints.

[0120] The control path generation module is used to determine the control setting sequence and reference path at the current moment through rolling optimization, based on the regenerated heat trajectory, residence time trajectory, and set of safe operation constraints, combined with the decision quantities corresponding to the set optimization objectives.

[0121] The execution deviation monitoring module is used to send the control setting sequence to the execution unit so that the execution unit can perform feedforward scheduling according to the reference path, and monitor and output the actual execution record and execution deviation data in real time.

[0122] The trajectory correction module is used to update the estimated values ​​of material adsorption capacity and desorption temperature based on actual execution records and execution deviation data, and to correct the regeneration heat trajectory and residence time trajectory within adjacent rolling time windows.

[0123] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0124] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0125] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0126] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0127] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0128] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the foregoing claims.

[0129] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A dual-rotor dehumidification energy-saving optimization control method for multivariate coordinated regulation, characterized in that, The method comprises: acquiring state observation data and equipment limit values of each measuring point, and generating material regeneration demand estimation curve and desorption boundary threshold set based on the state observation data and equipment limit values; calculating regeneration heat demand trajectory and residence time trajectory required for desorption according to the material regeneration demand estimation curve and desorption boundary threshold set, and generating a set of safe operation constraints; based on the regeneration heat trajectory, residence time trajectory and set of safe operation constraints, and in combination with the decision quantity corresponding to the set optimization target, determining the control setting sequence and reference path at the current time through rolling optimization; issuing the control setting sequence to an execution unit to control the execution unit to perform feedforward scheduling according to the reference path, and monitoring and outputting actual execution records and execution deviation data in real time; based on the actual execution records and execution deviation data, updating the estimated values of material adsorption capacity and desorption temperature, and correcting the regeneration heat trajectory and residence time trajectory in the adjacent rolling time window.

2. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 1, characterized in that, The acquisition of the state observation data and equipment limit values of each measuring point, and the generation of the material regeneration demand estimation curve and desorption boundary threshold set based on the state observation data and equipment limit values, comprises: acquiring state observation data and equipment limit values of each measuring point, and constructing state observation vectors and equipment limit value vectors by taking the state observation data and equipment limit values as components respectively; based on the state observation vectors and equipment limit value vectors, calculating instantaneous dehumidification amount and regeneration heat estimation point, the instantaneous dehumidification amount being the product of the difference between the absolute humidity at the inlet of the fresh air channel and the absolute humidity at the outlet of the dehumidification runner and the mass flow of the fresh air channel, and the regeneration heat estimation point being the engineering estimation point of the equivalent heat required for regeneration; wherein each component in the state observation vector is the dry bulb temperature at the inlet of the fresh air channel, the absolute humidity at the inlet of the fresh air channel, the temperature of the regeneration air channel, the absolute humidity of the regeneration air channel, the volume flow of the fresh air, the volume flow of the regeneration air, the rotation speed of the dehumidification runner, the rotation speed of the regeneration runner and the absolute humidity at the outlet of the dehumidification runner; and each component in the equipment limit value vector is the upper and lower limit rating value of each equipment corresponding to the state observation data, and the upper limit of the allowable temperature of the regeneration air channel and the upper limit of the sealing leakage index.

3. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 2, characterized in that, The acquisition of the state observation data and equipment limit values of each measuring point, and the generation of the material regeneration demand estimation curve and desorption boundary threshold set based on the state observation data and equipment limit values, further comprises: based on the state observation vectors, instantaneous dehumidification amount and regeneration heat estimation point, approximating the regeneration residence time through the geometric relationship of the runner and the coaxial driving relationship, and estimating the effective adsorption capacity of the material and the lower limit of the desorption effective temperature; within the rolling time window, linearly extrapolating the dehumidification load according to the load change of the fresh air channel to estimate the dehumidification amount slope in the prediction time period, so as to obtain the minimum regeneration temperature demand and the minimum residence time demand that meet the set target desorption proportion; based on the regeneration residence time, the effective adsorption capacity of the material, the lower limit of the desorption effective temperature, the dehumidification amount slope and the minimum regeneration temperature demand and the minimum residence time demand that meet the set target desorption proportion, generating the material regeneration demand estimation curve and the desorption boundary threshold set.

4. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 3, characterized in that, The regeneration heat trajectory and the residence time trajectory required for desorption are calculated according to the material regeneration demand estimation curve and the desorption boundary threshold set, and a safe operation constraint set is generated, including: Based on the set sampling period and the rolling time window length, the material regeneration demand estimation curve is discretized in the time domain network to output the minimum regeneration temperature demand and the minimum residence time demand at the predicted time; Based on the minimum regeneration temperature demand and the minimum residence time demand at the predicted time, the water vapor condensation latent heat range, the regeneration section comprehensive efficiency range, and the instantaneous dehumidification amount and extrapolation result at the corresponding time are determined combined with the state observation data, the minimum equivalent regeneration heat at each time is calculated, and the maximum regeneration runner speed of the minimum residence time demand is determined according to the geometric relationship of the coaxial driving and the regeneration partition.

5. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 4, characterized in that, The regeneration heat trajectory and the residence time trajectory required for desorption are calculated according to the material regeneration demand estimation curve and the desorption boundary threshold set, and a safe operation constraint set is generated, including: Based on the maximum regeneration runner speed, the minimum equivalent regeneration heat is converted into average power demand and projected to the upper limit to obtain the projected power trajectory point; The residence time is converted into an equivalent speed upper limit constraint, the power trajectory point is calculated back to a heat trajectory point combined with the upper and lower limits of the equipment speed, and the regeneration heat trajectory and the residence time trajectory are generated based on the heat trajectory point and the corresponding residence time; According to the state observation data and the equipment limit value, a safe operation constraint is constructed, and the safe operation constraint is associated with the regeneration heat trajectory and the residence time trajectory to obtain the safe operation constraint set.

6. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 4, characterized in that, Based on the regeneration heat trajectory, the residence time trajectory, and the safe operation constraint set, the control setting sequence and the reference path at the current time are determined through rolling optimization combined with the decision quantity corresponding to the set optimization target, including: Based on the regeneration heat trajectory, the residence time trajectory, and the safe operation constraint set, the time domain network and the demand reference sequence are defined according to the set sampling period and the rolling time window length to determine the decision quantity that meets the set optimization target according to the time domain network and the demand reference sequence; The temperature setting and the regeneration air volume in the decision quantity are mapped to the heat source power by calling the engineering equivalent model, and the heat source power is converted into delivered heat to construct a hierarchical weighted objective function according to the delivered heat, the regeneration residence time, and the safe operation constraint set; Based on the hierarchical weighted objective function, the prediction control sequence is solved by using Gauss-Newton linearization and sequential quadratic programming, and the control setting sequence and the reference path are generated according to the prediction control sequence; The decision quantity is the regeneration air channel heat source temperature setting, the fresh air channel volume flow setting, the regeneration air channel volume flow setting, the dehumidification runner speed setting, the regeneration runner speed setting, and the bypass damper opening degree setting.

7. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 6, characterized in that, The control setting sequence is issued to the execution unit to control the execution unit to perform feedforward scheduling according to the reference path, and actual execution records and execution deviation data are monitored and output in real time, including: shaping the amplitude and rate limits of each actuator in the execution unit based on the control setting sequence and the reference path, and calculating a constraint monitoring signal and an red line proximity; constructing a weighted correction amount based on the red line proximity, and combining a cooperative correction rule of different constraints in the constraint monitoring signal to perform soft correction on the control setting sequence to obtain a modified execution command sequence; issuing the execution command sequence to the execution unit, combining the configured emergency trigger condition, and monitoring and outputting actual execution records, constraint monitoring results, and execution deviation data in real time.

8. The dual rotary dehumidification energy-saving optimization control method for multivariate synergistic regulation, according to claim 7, characterized in that, updating the estimated values of the material adsorption capacity and desorption temperature based on the actual execution records and execution deviation data, and correcting the regeneration heat trajectory and residence time trajectory in the adjacent rolling time window, including: constructing a residual sequence and a mechanism parameter regression vector based on the actual execution records, constraint monitoring results, and execution deviation data, the residual sequence being composed of residuals of delivered heat and required heat, residuals of actual residence time and required residence time in the regeneration section, and outlet cross-section deviations after the fresh air channel passes through the dehumidification runner; updating the equivalent capacity and time parameters through recursive least squares based on the residual sequence and the mechanism parameter regression vector to obtain an updated mechanism parameter set; updating and calculating the minimum regeneration temperature and residence time of the prediction time window according to the updated mechanism parameter set to output the updated regeneration heat trajectory and residence time trajectory. 9.A terminal, comprising a processor and a storage medium; characterized in that: the storage medium is configured to store instructions; the processor is configured to operate according to the instructions to perform the steps of the multivariable cooperative control-oriented dual-rotor dehumidification energy-saving optimization control method according to any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the steps of the multivariable cooperative control-oriented dual-rotor dehumidification energy-saving optimization control method according to any one of claims 1-8.

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