Double-screw dehumidification energy-saving optimization control method for multivariable coordinated control

Abstract

A kind of double-runner dehumidification energy-saving optimization control method for multivariate collaborative regulation, comprising: obtaining the state observation data and equipment limit value of each measuring point, generating material regeneration demand estimation curve and desorption boundary threshold set.According to material regeneration demand estimation curve and desorption boundary threshold set, calculate the regeneration heat trajectory and residence time trajectory required for desorption, generate a set of safe operation constraints.Based on regeneration heat trajectory, residence time trajectory and a set of safe operation constraints, combined with the decision quantity corresponding to the set optimization target, determine the control setting sequence and reference path at the current time.The control setting sequence is issued to the execution unit, and the execution unit is scheduled according to the reference path, and the actual execution record and execution deviation data are monitored and output in real time.Based on actual execution record and execution deviation data, update the estimated value of material adsorption capacity and desorption temperature, and correct the regeneration heat trajectory and residence time trajectory in adjacent rolling time window.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dehumidification energy saving optimization, and in particular to a double-rotor dehumidification energy saving optimization control method for multivariable collaborative regulation. BACKGROUND

[0002] The double-rotor system is composed of a front-stage dehumidification rotor and a rear-stage regeneration rotor, which are matched in stable speed and phase through a coaxial driving mechanism: air first passes through the dehumidification rotor arranged in the fresh air channel to complete moisture absorption and load reduction, and then enters the regeneration rotor formed by the coupling of the regeneration air channel and the regeneration heat source to perform desorption regeneration; the system is simultaneously provided with a cold source and a heat source coupling pipeline on the periphery for adjusting the air in a heat and humidity condition; a sealing and partition structure is arranged around the rotor to prevent the heat on the regeneration side from being pumped back to the dehumidification side, thereby avoiding efficiency loss; a multi-point temperature and humidity sensor array is arranged along the fresh air and regeneration air channels to realize real-time monitoring of the air state and the material regeneration degree. Overall, the front stage is responsible for adsorbing water vapor, and the rear stage is responsible for driving desorption, and the two constitute a heat and humidity series closed loop, realize the recycling of energy and adsorption capacity through the driving shaft linkage, and are a double-stage function coupling system with continuous cooperation of "dehumidification-regeneration" as the core.

[0003] At present, in the double-rotor dehumidification system, a key but long-neglected technical defect is that "the regeneration process cannot be powered on demand, resulting in insufficient or excessive heating of desorption". The traditional control only looks at the outlet humidity, and once the humidity rises slightly, the regeneration temperature is continuously increased, but it does not really know whether the adsorbent inside has approached saturation or whether there are still regenerable sites, resulting in "insufficient regeneration leading to dehumidification collapse" or "excessive regeneration causing heat waste", and the technical problems of "lag compensation" or "blind temperature rise compensation" are more likely to occur. SUMMARY

[0004] The present application provides a double-rotor dehumidification energy saving optimization control method for multivariable collaborative regulation, which solves the technical problems of lag compensation and blind temperature rise compensation in the prior art.

[0005] The present application adopts the following technical solutions.

[0006] The present application discloses a double-rotor dehumidification energy saving optimization control method for multivariable collaborative regulation, which comprises:

[0007] Obtaining state observation data and equipment limit values of 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 limit values;

[0008] According to the material regeneration demand estimation curve and the desorption boundary threshold set, a regeneration heat trajectory and a residence time trajectory required for desorption are calculated, and a safe operation constraint set is generated;

[0009] Based on the regeneration heat trajectory, the residence time trajectory, and the safe operation constraint set, a control setting sequence and a reference path at the current time are determined through rolling optimization by combining a decision quantity corresponding to a set optimization target;

[0010] The control setting sequence is issued to an 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;

[0011] Based on the actual execution records and the execution deviation data, estimated values of material adsorption capacity and desorption temperature are updated, and the regeneration heat trajectory and the residence time trajectory in an adjacent rolling time window are corrected.

[0012] Further, the state observation data and the equipment limit value of each measuring point are obtained, and the material regeneration demand estimation curve and the desorption boundary threshold set are generated based on the state observation data and the equipment limit value, including:

[0013] The state observation data and the equipment limit value of each measuring point are obtained, and a state observation vector and an equipment limit value vector are constructed by taking the state observation data and the equipment limit value as components, respectively;

[0014] Based on the state observation vector and the equipment limit value vector, an instantaneous dehumidification amount and a regeneration heat estimation point are calculated, the instantaneous dehumidification amount is the product of the difference between the inlet absolute humidity of the fresh air channel and the outlet absolute humidity of the dehumidification runner and the mass flow of the fresh air channel, and the regeneration heat estimation point is an engineering estimation point of the equivalent heat required for regeneration;

[0015] Wherein, each component in the state observation vector is the inlet dry bulb temperature of the fresh air channel, the inlet absolute humidity of the fresh air channel, the regeneration air channel temperature, the regeneration air channel absolute humidity, the fresh air volume flow, the regeneration air volume flow, the dehumidification runner speed, the regeneration runner speed, and the dehumidification runner outlet absolute humidity; each component in the equipment limit value vector is the upper and lower limit rated 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.

[0016] Further, the state observation data and the equipment limit value of each measuring point are obtained, and the material regeneration demand estimation curve and the desorption boundary threshold set are generated based on the state observation data and the equipment limit value, further including:

[0017] Based on the state observation vector, instantaneous dehumidification amount and regeneration heat estimation point, the regeneration residence time is approximately calculated by the geometric relationship of the runner and the coaxial driving relationship, and the effective adsorption capacity of the material and the lower limit of the desorption effective temperature are estimated;

[0018] Within the rolling time window, the dehumidification load is linearly extrapolated according to the load change of the fresh air channel to estimate the dehumidification amount slope in the prediction period, so as to obtain the minimum regeneration temperature requirement and the minimum residence time requirement that meet the set target desorption proportion;

[0019] 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 requirement and the minimum residence time requirement that meet the set target desorption proportion, the material regeneration requirement estimation curve and the desorption boundary threshold set are generated.

[0020] Further, the regeneration heat trajectory and the residence time trajectory required for desorption are calculated according to the material regeneration requirement estimation curve and the desorption boundary threshold set, and a safe operation constraint set is generated, including:

[0021] Based on the set sampling period and the rolling time window length, the material regeneration requirement estimation curve is discretized according to the time domain network to output the minimum regeneration temperature requirement and the minimum residence time requirement at the prediction time;

[0022] Based on the minimum regeneration temperature requirement and the minimum residence time requirement at the prediction 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 lowest residence time requirement is determined according to the geometric relationship of the coaxial driving and the regeneration partition.

[0023] Further, the regeneration heat trajectory and the residence time trajectory required for desorption are calculated according to the material regeneration requirement estimation curve and the desorption boundary threshold set, and a safe operation constraint set is generated, including:

[0024] Based on the maximum regeneration runner speed, the minimum equivalent regeneration heat is converted into average power requirement and projected to the upper limit to obtain the projected power trajectory point;

[0025] The residence time is converted into an equivalent speed upper limit constraint, which is projected combined with the upper and lower limits of the equipment speed, and the power trajectory point is calculated back to a heat trajectory point, so as to generate the regeneration heat trajectory and the residence time trajectory based on the heat trajectory point and the corresponding residence time;

[0026] A safe operation constraint is constructed according to the state observation data and the equipment limit value, and the safe operation constraint is associated with the regeneration heat trajectory and the residence time trajectory to obtain the safe operation constraint set.

[0027] Further, based on the regeneration heat trajectory, the residence time trajectory and the safe operation constraint set, a decision quantity corresponding to a set optimization target is determined, and a control setting sequence and a reference path at a current time are determined through rolling optimization, including:

[0028] Based on the regeneration heat trajectory, the residence time trajectory and the safe operation constraint set, a time domain network and a demand reference sequence are defined according to a set sampling period and a rolling time window length, so as to determine a decision quantity meeting a set optimization target according to the time domain network and the demand reference sequence.

[0029] An engineering equivalent model is called to map the temperature setting and the regeneration air volume in the decision quantity into a heat source power, and the heat source power is converted into delivered heat, so as to construct a hierarchical weighted objective function according to the delivered heat, the regeneration residence time and the safe operation constraint set.

[0030] Based on the hierarchical weighted objective function, a Gauss-Newton linearization and a sequential quadratic programming are used to solve a predictive control sequence, so as to generate a control setting sequence and a reference path according to the predictive control sequence.

[0031] The decision quantity is a regeneration air passage heat source temperature setting, a fresh air passage volume flow setting, a regeneration air passage volume flow setting, a dehumidification runner speed setting, a regeneration runner speed setting and a bypass air door opening degree setting.

[0032] Further, the control setting sequence is sent to an 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:

[0033] The amplitude and rate limits of each actuator in the execution unit are shaped based on the control setting sequence and the reference path, and a constraint monitoring signal and an assessment of red line proximity are calculated.

[0034] A weighted correction quantity based on the red line proximity is constructed, and the control setting sequence is soft corrected to obtain a corrected execution command sequence in combination with a cooperative correction rule of different constraints in the constraint monitoring signal.

[0035] The execution command sequence is sent to the execution unit, and actual execution records, constraint monitoring results and execution deviation data are monitored and output in real time in combination with a configured emergency trigger condition.

[0036] Further, the actual execution record and execution deviation data are used to update the estimated value of the material adsorption capacity and desorption temperature, and correct the regeneration heat trajectory and residence time trajectory in the adjacent rolling time window, including:

[0037] Based on the actual execution record, constraint monitoring result and execution deviation data, a residual sequence and mechanism parameter regression vector are constructed, the residual sequence is composed of the residual of the delivered heat and the required heat, the residual of the actual residence time and the required residence time in the regeneration section, and the outlet section deviation after the fresh air channel passes through the dehumidification runner;

[0038] Based on the residual sequence and mechanism parameter regression vector, the equivalent capacity and time parameter are updated by recursive least squares to obtain an updated mechanism parameter set;

[0039] According to the updated mechanism parameter set, the minimum regeneration temperature and residence time of the prediction time window are updated and calculated to output the updated regeneration heat trajectory and residence time trajectory.

[0040] The second aspect of the application discloses a double-rotor dehumidification energy-saving optimization control device for multivariable collaborative regulation, which is used to realize the steps of the double-rotor dehumidification energy-saving optimization control method for multivariable collaborative regulation, and the device comprises:

[0041] A curve and threshold generation module is used to obtain state observation data of each measuring point and equipment limit value, and generate a material regeneration demand estimation curve and a desorption boundary threshold set based on the state observation data and the equipment limit value;

[0042] A trajectory and operation constraint module is used to calculate a regeneration heat trajectory and a residence time trajectory required for desorption according to the material regeneration demand estimation curve and the desorption boundary threshold set, and generate a safe operation constraint set;

[0043] A control path generation module is used to determine a control setting sequence and a reference path at the current time through rolling optimization based on the regeneration heat trajectory, the residence time trajectory and the safe operation constraint set, and combined with a decision quantity corresponding to a set optimization target;

[0044] An execution deviation monitoring module is used to issue the control setting sequence to an execution unit to control the execution unit to perform feedforward scheduling according to the reference path, and monitor and output actual execution record and execution deviation data in real time;

[0045] A trajectory correction module is used to update the estimated value of the material adsorption capacity and desorption temperature based on the actual execution record and execution deviation data, and correct the regeneration heat trajectory and residence time trajectory in the adjacent rolling time window.

[0046] The third aspect of the present application discloses a terminal, comprising a processor and a storage medium; characterized in that:

[0047] The storage medium is used for storing instructions.

[0048] The processor is used for operating according to the instructions to perform the steps of the method of the first aspect.

[0049] The fourth aspect of the present application 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 of the first aspect.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] (1) The present application combines the adsorption / desorption mass transfer-heat transfer coupling model with MPC, so that the physical model enables the system to see the "real desorption state inside the material", instead of only focusing on the air outlet parameters. MPC uses this mechanism to calculate the optimal regeneration heat path and residence time path in advance, effectively avoiding the technical problems of "lagging compensation" or "blind temperature compensation" in the prior art. The effective regeneration of the adsorbent can be completed before the humidity rises, and the phenomenon of "first drop and then rebound" of the outlet humidity is no longer allowed. At the same time, the heating power is no longer increased without benefit, so that the regeneration link enters the energy-saving state of "just sufficient and not excessive compensation", and the system is upgraded from "passive correction type dehumidification" to "feedforward optimal type dehumidification".

[0052] (2) In the rolling time window, the present application estimates the regeneration demand curve and the desorption boundary threshold set, calculates the regeneration heat trajectory and residence time trajectory required to achieve sufficient desorption at each future time, and forms a safe operation constraint set. The "on-demand energy supply" is refined into "when, how much, and how long" executable targets, providing a feedforward quantitative target for the technical defect of "lagging compensation". At the same time, the mechanism target is brought down to the executable setting of "multivariable coordination" through predictive optimization, effectively eliminating the extensive strategy of "warming up by looking at the outlet", stabilizing the predicted optimal scheme, preventing secondary waste or failure at the execution level, and further suppressing the on-site risks of "excessive heating" and "insufficient regeneration". BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0054] Figure 1 is a flowchart of the multi-variable coordinated control method for dual-rotor dehumidification energy-saving optimization control provided by the present application.

[0055] Figure 2 is a structural schematic diagram of a dual-rotor dehumidification energy-saving optimization control device for multivariable collaborative regulation provided by the present application.

[0056] The specific embodiments of the present application have been shown and described in the above drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to restrict the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0057] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The same numbers are used in different drawings to represent the same or similar elements. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims.

[0058] As shown in Figure 1 in one embodiment, a dual-rotor dehumidification energy-saving optimization control method for multivariable collaborative regulation includes the following steps:

[0059] Step S110, obtaining 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.

[0060] In some embodiments, the dual-rotor dehumidification energy-saving optimization control method for multivariable collaborative regulation provided by the present application specifically includes the following steps in step S110:

[0061] Step S111, obtaining state observation data and equipment limit values of each measuring point, and respectively constructing state observation vector and equipment limit value vector with the state observation data and equipment limit values as components.

[0062] Wherein, each component in the state observation vector is the inlet dry bulb temperature of the fresh air channel, the inlet absolute humidity of the fresh air channel, the temperature of the regeneration air channel, the absolute humidity of the regeneration air channel, the volume flow of fresh air, the volume flow of regeneration air, the speed of the dehumidification rotor, the speed of the regeneration rotor, and the absolute humidity at the outlet of the dehumidification rotor; each component in the equipment limit value vector is the upper and lower limit rated 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.

[0063] Step S112, based on the state observation vector and the device limit vector, the instantaneous dehumidification amount and the regeneration heat estimation point are calculated, the instantaneous dehumidification amount is 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 product of the mass flow rate of the fresh air channel, and the regeneration heat estimation point is the engineering estimation point of the equivalent heat required for regeneration.

[0064] In some embodiments, the present application provides a double-rotor dehumidification energy-saving optimization control method for multivariable collaborative regulation, and step S110 specifically further includes the following steps:

[0065] Step S113, based on the state observation vector, the instantaneous dehumidification amount, and the regeneration heat estimation point, the regeneration residence time is calculated by approximating the relationship between the runner geometry and the coaxial drive, and the effective adsorption capacity of the material and the lower limit of the desorption effective temperature are estimated.

[0066] Step S114, within the rolling time window, the dehumidification load is linearly extrapolated according to the load change of the fresh air channel to estimate the dehumidification amount slope in the prediction period, and the minimum regeneration temperature requirement and the minimum residence time requirement that meet the set target desorption ratio are obtained.

[0067] Step S115, 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 requirement and the minimum residence time requirement that meet the set target desorption ratio, the material regeneration requirement estimation curve and the desorption boundary threshold set are generated.

[0068] In specific embodiments, the present application provides a double-rotor dehumidification energy-saving optimization control method for multivariable collaborative regulation, including steps 1-5:

[0069] Step 1, mechanism modeling and online identification.

[0070] Based on the adsorption / desorption mass transfer-heat transfer coupling model, the inlet air dry bulb temperature, the inlet air absolute humidity, the regeneration channel temperature, the regeneration channel absolute humidity, the fresh air volume flow rate, the regeneration air volume flow rate, the two-rotor speed, and the outlet air absolute humidity are collected, and the effective adsorption capacity of the material, the desorption effective temperature interval, and the required residence time interval are identified online, and then the material regeneration requirement estimation curve and the desorption boundary threshold set for future minutes are generated. This step changes the "whether regeneration is needed and to what extent" from experience judgment to quantifiable criteria, and gives a mechanism basis for the "blind temperature rise" of the prior art defects, including the following sub-steps:

[0071] Sub-step 1.1, data center and state observation vector construction.

[0072] Specifically, the state observation data of each measuring point is collected in the physical order of fresh air channel → front-stage rotary wheel → regeneration air channel → rear-stage rotary wheel, including fresh air channel inlet dry bulb temperature (obtained by fresh air channel inlet sensor), fresh air channel inlet absolute humidity (obtained by fresh air channel inlet sensor), regeneration air channel temperature (downstream measuring point of regeneration air channel heat source), regeneration air channel absolute humidity (obtained by regeneration air channel humidity sensor), fresh air volume flow (read and obtained by fresh air fan or air flow meter), regeneration air volume flow (read and obtained by regeneration air fan or air flow meter), dehumidification rotary wheel speed (read by coaxial drive encoder), regeneration rotary wheel speed (read by coaxial drive encoder), and dehumidification rotary wheel outlet absolute humidity (measured at the outlet cross section of fresh air channel after passing through the front-stage dehumidification rotary wheel), and the above state observation data is used as the components to construct the state observation vector corresponding to the regeneration system structure. Meanwhile, the upper limit rated value of the equipment corresponding to each state observation data is used as the basis of each component, and the upper limit of the allowable temperature of the regeneration air channel and the upper limit of the sealing leakage index (obtained by sealing and partition structure inspection or differential pressure-flow comprehensive estimation) are combined to construct the equipment limit value vector.

[0073] Sub-step 1.2, thermal-hygroscopic balance and instantaneous dehumidification amount estimation.

[0074] Specifically, the fresh air channel mass flow (air density is taken as the engineering constant range 1.15-1.25 kg / m 3 ), instantaneous dehumidification amount (water flux converted from the absolute humidity difference between the outlet and inlet of the front-stage dehumidification rotary wheel), and equivalent heat required for regeneration are calculated. The fresh air channel mass flow is equal to the product of air density and fresh air volume flow; the instantaneous dehumidification amount is equal to the product of the difference between the fresh air channel inlet absolute humidity and the dehumidification rotary wheel outlet absolute humidity and the fresh air channel mass flow; and the equivalent heat required for regeneration is equal to the ratio of the product of the latent heat of water vapor condensation and the instantaneous dehumidification amount as the numerator and the regeneration section comprehensive efficiency as the denominator.

[0075] Sub-step 1.3, online identification of material effective adsorption capacity and effective temperature zone.

[0076] Specifically, the regeneration section residence time is approximately obtained by using the geometric relationship and coaxial drive relationship of the rotary wheel (inversely proportional to the regeneration rotary wheel speed), which is equal to the ratio of the geometric constant (empirical range 10-60 s·turn / min) determined by the arc length of the regeneration air channel, the partition width, and the wheel diameter to the regeneration rotary wheel speed. Then, the lower limit of the desorption effective temperature zone (significant desorption occurs when the temperature exceeds the effective value) is defined and the engineering identification relationship is established:

[0077]

[0078] In the formula, is the instantaneous dehumidification amount; effective adsorption capacity of the material; temperature of the regeneration air channel; lower limit of the desorption onset temperature; characteristic residence time constant of the material-flow field composite; length of the time window.

[0079] Finally, the recursive least squares method or other online identification method is used to minimize the residual of the effective adsorption capacity of the material, the lower limit of the desorption onset temperature, and the characteristic residence time constant of the material-flow field composite, and the required effective adsorption capacity of the material, the lower limit of the desorption onset temperature, and the residence time are output.

[0080] Sub-step 1.4, future window material regeneration demand estimation curve and desorption boundary threshold generation.

[0081] Specifically, in the rolling time window , the dehumidification load is linearly extrapolated (mainly based on the load change of the fresh air channel), and the expression is:

[0082]

[0083] In the formula, is the dehumidification amount slope estimate of the last several steps (calculated by combining the absolute humidity change at the inlet of the fresh air channel and the volume flow change of the fresh air); is the extrapolated value of the dehumidification amount in the time window h.

[0084] Wherein, the expression of the dehumidification amount slope estimate of the last several steps is:

[0085]

[0086] Finally, the minimum regeneration temperature requirement and the minimum residence time requirement that meet the target desorption ratio are obtained, and the expression is:

[0087]

[0088]

[0089] In the formula, is the target desorption ratio, taking a value of 0.7-0.9; is the available residence time budget, which is derived from the coaxial drive constraint and the partition arc length of the regeneration air channel, and is inversely proportional to the regeneration runner speed, and the range is constrained by the equipment limit vector.

[0090] Finally, based on the minimum regeneration temperature requirement and the minimum residence 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.

[0091] 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.

[0092] 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:

[0093] 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.

[0094] 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.

[0095] 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:

[0096] Step S123: 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.

[0097] 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.

[0098] 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.

[0099] In specific embodiments, the present application provides a dual-rotor dehumidification energy-saving optimization control method for multivariate coordinated regulation, step 2, regenerative energy-time joint demand prediction. Within the rolling time window, according to the material regeneration demand estimation curve and the desorption boundary threshold set, the regenerative heat trajectory and the residence time trajectory required to achieve sufficient desorption at each future time are calculated, and a safe operation constraint set (including the highest allowable temperature of the regeneration channel, the upper and lower limits of the air volume, the upper limit of the leakage rate, the upper limit of the outlet absolute humidity, etc.) is formed. This step refines the "on-demand energy supply" into an executable goal of "when, how much, and how long", provides a feedforward quantitative target for the "lag compensation" of technical defects, including the following sub-steps:

[0100] Sub-step 2.1, prediction window and demand curve discretization.

[0101] Specifically, let the sampling period be a constant, ranging from 1 to 10 s; let the rolling time window length be a positive integer, ranging from 12 to 60 s. Discretize the material regeneration demand estimation curve obtained in the foregoing according to the time domain network to obtain the minimum regeneration temperature demand and the minimum residence time demand at each future time.

[0102] Sub-step 2.2, derive the regeneration heat and rotation speed mapping from the demand temperature and residence time.

[0103] Specifically, from the known material equivalent parameters and regeneration efficiency, let the water vapor condensation latent heat range be 2.3×10 6 to 2.6×10 6 J / kg, let the regeneration section comprehensive efficiency range be 0.4-0.8, and let the instantaneous dehumidification amount and its extrapolated value at the current time be given by the extrapolation formula in step 1, then the expression of the minimum equivalent regeneration heat demand is:

[0104]

[0105] In the formula, is the equivalent heat demand to meet sufficient desorption at time k+h; is the water vapor condensation latent heat constant; is the dehumidification amount extrapolated value; is the regeneration comprehensive efficiency.

[0106] Then, from the combined relationship of coaxial driving and regeneration partition, the upper limit of the rotation speed that meets the minimum residence time demand is determined (the lower the rotation speed, the longer the residence time, so there is a maximum allowed rotation speed to meet the minimum time demand). Let the geometric constant range be 10-60 s·turn / min, then:

[0107]

[0108] In the formula, to meet the minimum residence time requirement at time k+h and the maximum allowable regenerative runner rotational speed; a geometric constant determined by the regenerative channel arc length, the zone width, and the runner diameter.

[0109] Finally, output the regenerative heat point sequence composed of the minimum equivalent regenerative heat requirement and the equivalent rotational speed lower limit sequence based on the maximum allowable regenerative runner rotational speed to meet the minimum residence time requirement at time k+h. and the maximum allowable regenerative runner rotational speed.

[0110] Sub-step 2.3, form the regenerative heat prediction trajectory and the residence time prediction trajectory.

[0111] Specifically, the upper limit range of the heat source power is taken as the equipment nameplate or field calibration value, and the equivalent heat requirement point value is converted into the average power requirement per beat and is projected upward, with the expression being:

[0112]

[0113]

[0114] In the formula, is the average power requirement per beat; is the sampling period; is the upper limit of the heat source power; is the projected regenerative power trajectory point.

[0115] Then, the residence time requirement is converted into the equivalent rotational speed upper limit constraint and is projected in combination with the upper and lower limits of the device achievable rotational speed, with the expression being:

[0116]

[0117]

[0118] In the formula, is the maximum regenerative runner rotational speed achievable by the device; is the projected achievable residence time trajectory point; is the maximum regenerative runner rotational speed at time k+h.

[0119] Finally, the power trajectory point is back-calculated into the heat trajectory point, i.e., the final regenerative heat prediction trajectory and its residence time prediction trajectory are obtained.

[0120] Sub-step 2.4, generate a set of safe operation constraints.

[0121] Specifically, the safety operation constraint set includes a temperature upper limit constraint of the regeneration channel, a wind volume working domain constraint (fresh air channel and regeneration air channel), a sealing leakage index constraint (defined by the sealing and partition structure performance), and an outlet absolute humidity control upper limit (outlet cross section of the pre-stage dehumidification runner), and the safety operation constraints are associated with the regeneration heat prediction trajectory and the stay time prediction trajectory, so that a complete safety operation constraint set is generated.

[0122] In step S130, based on the regeneration heat trajectory, the stay time trajectory, and the safety operation constraint set, the control setting sequence and the reference path at the current time are determined by rolling optimization combined with the decision quantity corresponding to the set optimization target.

[0123] In some embodiments, the double-runner dehumidification energy-saving optimization control method for multivariable collaborative regulation provided by the present application specifically includes the following steps in step S130:

[0124] In step S131, based on the regeneration heat trajectory, the stay time trajectory, and the safety operation constraint set, a time-domain network and a demand reference sequence are defined according to a set sampling period and a rolling time window length, so as to determine the decision quantity that meets the set optimization target according to the time-domain network and the demand reference sequence.

[0125] Among them, 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 air door opening degree setting.

[0126] In step S132, 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 to the delivered heat, so as to construct a hierarchical weighted objective function according to the delivered heat, the regeneration stay time, and the safety operation constraint set.

[0127] In step S133, based on the hierarchical weighted objective function, the Gauss-Newton linearization and the sequential quadratic programming are used to solve the prediction control sequence, so as to generate the control setting sequence and the reference path according to the prediction control sequence.

[0128] In specific embodiments, the application provides a dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation, step 3, model predictive control solution and multi-variable collaborative setting. An optimization problem is constructed with the goal of "ensuring that the outlet absolute humidity does not exceed the upper limit, prioritizing sufficient desorption, and minimizing energy consumption in an integrated manner", and the decision variables include: regeneration channel temperature setting, fresh air volume flow, regeneration air volume flow, dehumidification rotor speed, regeneration rotor speed, and bypass damper opening. The current time control setting sequence and reference path are obtained by using rolling optimization to ensure that sufficient desorption at the material level and minimum energy input at the system level are synchronized. This step uses predictive optimization to achieve the mechanism goal on the executable setting of "multi-variable collaboration", directly eliminating the extensive strategy of "adding heat when looking at the outlet", including the following sub-steps:

[0129] Sub-step 3.1, control time domain and decision variable sequence definition.

[0130] Specifically, the sampling period is constant, with a value of 1-10s; the rolling optimization time window length is a positive integer, with a value of 10-60, the time domain grid and demand reference sequence and its decision variable vector (each setting corresponds to its system component) are defined, including regeneration air channel heat source temperature setting, fresh air channel volume flow setting, regeneration air channel volume flow setting, dehumidification rotor speed setting, regeneration rotor speed setting, and bypass damper opening setting (for hot side bypass or cold side load reduction, value 0-1).

[0131] Sub-step 3.2, energy transfer and residence time accessibility mapping.

[0132] Specifically, the temperature setting and regeneration air volume are mapped to the heat source power by the engineering equivalent model, and converted to the deliverable heat in the beat, the expression is:

[0133]

[0134]

[0135] In the formula, is the heat source power; is the heat source reference loss constant, ranging from 0 to 10% of the rated power; is the equivalent heat transfer gain constant, identified by the heat exchanger and the regeneration air channel; is the regeneration air channel heat exchanger inlet temperature; is the regeneration section comprehensive efficiency, ranging from 0.4 to 0.8; is the sampling period; is the deliverable heat in the beat; is the regeneration air channel heat source temperature setting; is the regeneration air channel volume flow setting.

[0136] Afterwards, the energy satisfaction constraint is guaranteed to deliver heat greater than or equal to the required heat, while the same shaft driving and the partition geometry mapping regenerative section residence time (inversely proportional to the regenerative runner speed) satisfy the residence time satisfaction constraint, which is to achieve a residence time greater than or equal to the required residence time.

[0137] Sub-step 3.3, the objective function is assembled with the full constraint set.

[0138] Specifically, based on the above-mentioned energy satisfaction constraint and residence time satisfaction constraint, a hierarchical weighted objective function is constructed, which requires the objective function to be energy-minimized, to follow the demand and to be smooth in action, and to satisfy the energy satisfaction constraint and the residence time satisfaction constraint, the safety operation constraint set, and the actuator boundary and speed limit.

[0139] Sub-step 3.4, solving and generating the control setting sequence and the reference path.

[0140] Specifically, the objective function is processed according to the current linearization or quadratic approximation to obtain a convex optimization or sequential quadratic programming problem, and the next control sequence is solved by using Gauss-Newton linearization and sequential quadratic programming. The first beat optimal solution is used as the current setting, and the whole section prediction is reserved as the reference path for the execution layer to feedforward track. During the setting delivery process, the regenerative air channel heat source temperature setting is delivered to the regenerative air channel heat source; the new air channel volume flow setting is delivered to the new air channel fan; the regenerative air channel volume flow setting is delivered to the regenerative air channel fan; the dehumidification runner speed setting and the regenerative runner speed setting are delivered to the same shaft driving shaft or the two runner servo; and the bypass damper opening degree setting is delivered to the bypass damper actuator.

[0141] Step S140, the control setting sequence is delivered 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.

[0142] In some embodiments, the present application provides a double-runner dehumidification energy-saving optimization control method for multivariable collaborative regulation, and step S140 specifically includes the following steps:

[0143] Step S141, the amplitude and speed limit of each actuator in the execution unit are shaped based on the control setting sequence and the reference path, and the constraint monitoring signal and the red line proximity degree are calculated.

[0144] Step S142, a weighted correction amount based on the red line proximity degree is constructed, and the control setting sequence is soft corrected to obtain a modified execution command sequence in combination with the collaborative correction rules of different constraints in the constraint monitoring signal.

[0145] Step S143, the execution command sequence is issued to the execution unit, combined with the configured emergency trigger condition, real-time monitoring and output actual execution record, constraint monitoring results and execution deviation data.

[0146] In a specific embodiment, the present application provides a dual-rotor dehumidification energy-saving optimization control method for multi-variable collaborative regulation, step 4, feedforward execution and constraint monitoring. The control setting sequence is issued to the heat source, fan and servo execution unit, and the feedforward scheduling is performed according to the reference path; at the same time, real-time monitoring is performed on key constraints, including outlet absolute humidity, material temperature rise rate, regeneration channel temperature upper limit, fresh air and regeneration air flow ratio, sealing leakage indicator, etc. Once it is found that it deviates or approaches the red line, the soft constraint correction and emergency rollback strategy (such as reducing the regeneration temperature setting, temporarily increasing the residence time, and fine-tuning the air ratio) is triggered. This step stabilizes the predicted optimal solution and prevents secondary waste or failure at the execution level, further suppressing the on-site risks of "overheating" and "insufficient regeneration", including the following sub-steps:

[0147] Sub-step 4.1, feedforward issuance and actuator amplitude shaping.

[0148] Specifically, each actuator is shaped according to the amplitude and rate limit of the device manual to prevent mechanical / thermal shock of the heat source and fan, servo, and the expression is:

[0149]

[0150]

[0151] In the formula, is the optimal setting of the current beat; is the shaped issuance command (including regeneration channel temperature setting, fresh air and regeneration air volume flow, two-rotor speed, bypass damper opening); is the lower limit and upper limit of the actuator; is the actual execution value of the upper beat; is the maximum adjustment amplitude of a single beat (engineering constant, obtained according to device calibration).

[0152] Sub-step 4.2, constraint monitoring signal calculation and red line proximity evaluation.

[0153] Specifically, the constraint monitoring signal includes dehumidification quality constraint (the outlet cross-section of the front-stage dehumidification wheel, determined by the difference between the current tap and the maximum dehumidification amount), material temperature rise rate (the rear-stage regeneration area material sensor, determined by the temperature difference change rate of the adjacent tap), regeneration channel temperature upper limit (determined by the difference between the current tap temperature of the regeneration channel and the maximum temperature), air ratio (determined by the volume flow ratio of the regeneration air channel to the fresh air channel), and sealing leakage indication (obtained based on the channel end air volume and the approximate mass conservation differential pressure). In the red line proximity evaluation process, the red line proximity is normalized, with a value of 0 being safe and a value of 1 reaching the red line.

[0154] Sub-step 4.3, soft constraint modifier fine-tuning feedforward.

[0155] Specifically, the weighted correction quantity based on the red line proximity is constructed , which is mapped back to the heat source, two air fans, coaxial driving, and bypass air door according to the device, and the expression is:

[0156]

[0157] In the formula, is a diagonal gain matrix (the design range is calibrated according to the actuator sensitivity); is a component saturation function, which limits each component in to between 0 and 1.

[0158] In this embodiment, the cooperative correction rules for different constraints are as follows: when the regeneration temperature approaches the upper limit, the regeneration channel temperature setting is reduced, the residence time is prolonged (the regeneration wheel 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 temperature upper limit, the dehumidification wheel speed is reduced to increase the adsorption residence time, and the air ratio is slightly pushed to the upper half of the upper and lower limits; when the leakage increases, the pressure difference between the fresh air and the regeneration air is reduced, the differential pressure is lowered by simultaneously fine-tuning the air volume of the two, and the bypass opening degree is appropriately increased to release the heat side accumulation.

[0159] Sub-step 4.4, emergency rollback strategy triggering and execution.

[0160] Specifically, the emergency trigger condition is defined, the regeneration temperature is set back to the safety zone when the temperature exceeds the upper limit, the regeneration runner speed is immediately reduced to compensate for the residence time, the bypass damper is opened to the emergency position, and the air ratio is maintained at the middle of the interval; 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 temperature upper limit, the dehumidification runner speed is reduced, and the bypass is temporarily increased to reduce the heat load fluctuation if necessary; when the leakage exceeds the upper limit, the pressure difference of the two channels is reduced (the air volume of the two channels is simultaneously reduced), the pressure difference of the sealing and partition structure is checked, and the temperature rise rate is limited. Finally, the actual execution record (including the issued data and the actual feedback data), the constraint monitoring result (the proximity of each constraint index to the red line) and the execution deviation data (the deviation between the set value and the measured value) are formed.

[0161] Step S150, based on the actual execution record and the execution deviation data, updating the estimated values of the material adsorption capacity and the desorption temperature, and correcting the regeneration heat trajectory and the residence time trajectory in the adjacent rolling time window.

[0162] In some embodiments, the present application provides a double-rotor dehumidification energy-saving optimization control method for multivariable collaborative regulation, step S150 specifically includes the following steps:

[0163] Step S151, based on the actual execution record, the constraint monitoring result and the execution deviation data, constructing a residual sequence and a mechanism parameter regression vector, the residual sequence being composed of the residual of the delivered heat and the required heat, the residual of the actual residence time and the required residence time in the regeneration section, and the outlet cross-section deviation after the fresh air channel passes through the dehumidification rotor.

[0164] Step S152, based on the residual sequence and the mechanism parameter regression vector, updating the equivalent capacity and the time parameter by the recursive least squares method to obtain an updated mechanism parameter set.

[0165] Step S153, according to the updated mechanism parameter set, updating and calculating the minimum regeneration temperature and the residence time of the prediction time window to output the updated regeneration heat trajectory and the residence time trajectory.

[0166] In specific embodiments, the present application provides a dual-rotor dehumidification energy-saving optimization control method for multivariate coordinated regulation, step 5, feedback correction and closed-loop convergence. Based on actual execution records and deviation data, update the online estimation of material effective adsorption capacity and desorption effective temperature interval, and correct the regeneration heat and residence time prediction in the next rolling window; simultaneously correct the disturbance estimation and weight in model predictive control, so that the system continuously maintains the operating state of "completing effective desorption before wet load arrives and maintaining stable outlet absolute humidity under the condition of minimum energy consumption", eliminating the root causes of "first drop and then rebound" and "ineffective warming". This step forms an adaptive closed loop of mechanism and optimization, ultimately solving the technical problem of "regeneration process unable to supply energy on demand", including the following sub-steps:

[0167] Sub-step 5.1, residual construction and data alignment.

[0168] Specifically, construct the residual of deliverable heat and model demand, the residual of actual residence time and demand in the regeneration section, the outlet quality deviation (after the fresh air channel passes through the previous stage dehumidification rotor), and the mechanism identification regression vector (determined by actual execution value).

[0169] Sub-step 5.2, online correction of mechanism parameters.

[0170] Specifically, use the least squares method to update the adsorption capacity and residence time, define the parameter vector and regression nominal, and update it in the form of forgetting factor to obtain the updated mechanism parameter set.

[0171] Sub-step 5.3, prediction trajectory and disturbance adaptive correction.

[0172] Specifically, based on the updated mechanism parameter set, recalculate the minimum regeneration temperature and residence time in the next window, the disturbance term uses the average deviation estimation of outlet humidity and regeneration temperature to form a new regeneration heat and residence time prediction trajectory.

[0173] Sub-step 5.4, MPC weight adaptation and closed-loop convergence criterion.

[0174] Specifically, according to the updated regeneration heat prediction trajectory and residence time prediction trajectory, combined with the violation rate of the aforementioned constraint indicators (regeneration temperature out-of-range rate, outlet humidity out-of-range rate, energy consumption redundancy index) and energy consumption deviation adjustment weight coefficient (range 0.05-0.3), determine the closed-loop convergence criterion based on the adjusted weight coefficient. If all criteria are met, it is considered to have converged and enters the small step update.

[0175] 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.

[0176] 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.

[0177] The curve and threshold generation module is used to acquire 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] It should be understood that all the steps in the flowcharts are not necessarily performed in the order presented in the flowcharts. Unless explicitly stated in this document, the execution of the steps is not necessarily limited to the order presented in the flowcharts. Moreover, at least some of the steps in the flowcharts can include multiple sub-steps or stages, which are not necessarily performed at the same time, but can be performed at different times, and the execution of the sub-steps or stages is not necessarily sequential, but can be performed in a round-robin or alternating manner with other steps or sub-steps or stages of other steps.

[0184] It should be understood that the above-described apparatus embodiments are merely illustrative, and the apparatus of the present application can also be implemented in other manners. For example, the division of the units / modules in the above-described embodiments is merely a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units / modules or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0185] In addition, unless specifically stated otherwise, each functional unit / module in each embodiment of the present application can be integrated in one unit / module, or each unit / module can exist physically, or two or more units / modules can be integrated together. The above-mentioned integrated unit / module can be realized in the form of hardware or in the form of a software program module.

[0186] In the above-described embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments. Each technical feature of the above-described embodiments can be combined arbitrarily, and in order to make the description concise, not all possible combinations of the technical features in the above-described embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0187] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present application cover any and all variations of the application that come within the scope of the claims and their equivalents. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the application indicated by the following claims.

[0188] It should be understood that the present application is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be effected therein by those skilled in the art without departing from the scope of the application.

Claims

1. A dual-rotor dehumidification energy-saving optimization control method for multivariate coordinated regulation, characterized in that, The method comprises: Obtaining state observation data and equipment limits of each measuring point, and generating material regeneration demand estimation curve and desorption boundary threshold set based on the state observation data and equipment limits; According to the material regeneration demand estimation curve and the desorption boundary threshold set, the regeneration heat trajectory and the residence time trajectory required for desorption are calculated, and a safe operation constraint set is generated; 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 by combining the decision quantity corresponding to the set optimization target; 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 to monitor and output actual execution records and execution deviation data in real time; Based on the actual execution records and execution deviation data, the estimated values of the material adsorption capacity and the desorption temperature are updated, and the regeneration heat trajectory and the residence time trajectory in the adjacent rolling time window are corrected; The calculation of the regeneration heat trajectory and the residence time trajectory required for desorption based on the material regeneration demand estimation curve and the desorption boundary threshold set comprises: Based on the set sampling period and the rolling time window length, the material regeneration demand estimation curve is discretized in 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 based on 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 drive and the regeneration partition; The calculation of the regeneration heat trajectory and the residence time trajectory required for desorption based on the material regeneration demand estimation curve and the desorption boundary threshold set further comprises: Based on the maximum regeneration runner speed, the minimum equivalent regeneration heat is converted into average power demand and projected to obtain the projected power trajectory point; The residence time is converted into an equivalent speed upper limit constraint, which is projected in combination with the upper and lower limits of the equipment speed to calculate the heat trajectory point from the power trajectory point, so as to generate the regeneration heat trajectory and the residence time trajectory based on the heat trajectory point and the corresponding residence time; According to the state observation data and equipment limits, safe operation constraints are constructed, and the safe operation constraints are associated with the regeneration heat trajectory and the residence time trajectory to obtain the safe operation constraint set; The determination of the control setting sequence and the reference path at the current time based on the regeneration heat trajectory, the residence time trajectory and the safe operation constraint set, in combination with the decision quantity corresponding to the set optimization target through rolling optimization, comprises: Based on the regenerative heat trajectory, the residence time trajectory, and the set of safe operation constraints, a time-domain network and a demand reference sequence are defined according to a set sampling period and a rolling time window length, so as to determine a decision variable meeting a set optimization target according to the time-domain network and the demand reference sequence; An engineering equivalent model is called to map the temperature setting and the regenerative air volume in the decision variable into a heat source power, and to convert the heat source power into delivered heat, so as to construct a hierarchical weighted objective function according to the delivered heat, the regenerative residence time, and the set of safe operation constraints; Based on the hierarchical weighted objective function, a Gauss-Newton linearization and a sequential quadratic programming are adopted to solve a predictive control sequence, so as to generate a control setting sequence and a reference path according to the predictive control sequence. The decision variable includes a regenerative air passage heat source temperature setting, a fresh air passage volume flow rate setting, a regenerative air passage volume flow rate setting, a dehumidification rotary wheel rotating speed setting, a regenerative rotary wheel rotating speed setting, and a bypass air door opening degree setting.

2. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 1, characterized in that, The state observation data and the equipment limit value of each measuring point are obtained, and a material regeneration demand estimation curve and a desorption boundary threshold set are generated based on the state observation data and the equipment limit value, including: The state observation data and the equipment limit value of each measuring point are obtained, and a state observation vector and an equipment limit value vector are constructed by taking the state observation data and the equipment limit value as components, respectively; Based on the state observation vector and the equipment limit value vector, an instantaneous dehumidification amount and a regenerative heat estimation point are calculated, the instantaneous dehumidification amount being the product of the difference between the fresh air passage inlet absolute humidity and the dehumidification rotary wheel outlet absolute humidity and the fresh air passage mass flow rate, and the regenerative heat estimation point being an engineering estimation point of the equivalent heat required for regeneration; Each component in the state observation vector is the fresh air passage inlet dry bulb temperature, the fresh air passage inlet absolute humidity, the regenerative air passage temperature, the regenerative air passage absolute humidity, the fresh air volume flow rate, the regenerative air volume flow rate, the dehumidification rotary wheel rotating speed, the regenerative rotary wheel rotating speed, and the dehumidification rotary wheel outlet absolute humidity, respectively; and each component in the equipment limit value vector is the upper and lower limit rated value of each equipment corresponding to the state observation data, and the upper limit of the regenerative air passage allowable temperature 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 state observation data and the equipment limit value of each measuring point are obtained, and a material regeneration demand estimation curve and a desorption boundary threshold set are generated based on the state observation data and the equipment limit value, including: Based on the state observation vector, the instantaneous dehumidification amount, and the regenerative heat estimation point, the regenerative residence time is approximately calculated through the rotary wheel geometry and the coaxial driving relationship, and the effective adsorption capacity of the material and the lower limit of the desorption effective temperature are estimated; Within the rolling time window, the dehumidification load is linearly extrapolated according to the load change of the fresh air passage, so as to estimate the dehumidification amount slope in the prediction time period, and to obtain the minimum regenerative temperature demand and the minimum residence time demand meeting the set target desorption proportion; Based on the regenerative residence time, the effective adsorption capacity of the material, the lower limit of the desorption effective temperature, the dehumidification amount slope, and the minimum regenerative temperature demand and the minimum residence time demand meeting the set target desorption proportion, the material regeneration demand estimation curve and the desorption boundary threshold set are generated.

4. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 1, characterized in that, 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: The amplitude and rate limits of each actuator in the execution unit are shaped based on the control setting sequence and the reference path, and constraint monitoring signals and red line proximity degrees are calculated; A weighted correction amount based on the red line proximity degree is constructed, and the control setting sequence is soft corrected to obtain a corrected execution command sequence in combination with the cooperative correction rules of different constraints in the constraint monitoring signals; The execution command sequence is sent to the execution unit, and the actual execution records, constraint monitoring results and execution deviation data are monitored and output in real time in combination with the configured emergency trigger condition.

5. The dual rotary dehumidification energy-saving optimization control method for multivariable coordinated control according to claim 4, characterized in that, The estimated values of the material adsorption capacity and desorption temperature are updated based on the actual execution records and execution deviation data, and the regeneration heat trajectory and residence time trajectory in the adjacent rolling time window are corrected, including: Based on the actual execution records, constraint monitoring results and execution deviation data, a residual sequence and a mechanism parameter regression vector are constructed, 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; Based on the residual sequence and the mechanism parameter regression vector, the equivalent capacity and time parameters are updated by the recursive least squares method to obtain an updated mechanism parameter set; The minimum regeneration temperature and residence time of the prediction time window are updated and calculated according to the updated mechanism parameter set to output the updated regeneration heat trajectory and residence time trajectory.

6. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to perform the steps of the double-runner dehumidification energy-saving optimization control method facing multivariable cooperative regulation according to any one of claims 1-5.

7. 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 double-runner dehumidification energy-saving optimization control method facing multivariable cooperative regulation according to any one of claims 1-5.

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