Dew point regeneration energy-saving control method for medical compressed air system

Abstract

The invention provides a dew-point regeneration energy-saving control method for a medical compressed air system, and relates to the technical field of control regulation, and the method comprises the steps: building a nonlinear optimization model of the switching time and waste heat distribution of a suction dryer by collecting the adsorption state parameters and multi-point pressure dew-point data of the multi-dimensional suction dryer; a layered calibration mechanism is provided by combining waste heat sensitive factors and environment states of a building greening area, and dynamic feedback adjustment and accurate waste heat distribution of calibration parameters of the suction dryer are achieved; the adjustment coefficient not only reflects the dew point difference, but also fuses the adsorption time and flow physical characteristics, so that the adjustment response better conforms to the working condition change, and the requirements of medical building waste heat and compressed air stability are met.

Description

Technical Field

[0001] This invention relates to the field of control and regulation technology, specifically to a dew point regeneration energy-saving control method for a medical compressed air system. Background Technology

[0002] In compressed air systems, desiccant dryers are used to remove moisture from the air to achieve the desired dew point level. Desiccant dryers typically use adsorbents (such as silica gel) to absorb moisture from the air. When the adsorbent becomes saturated, it needs to be regenerated to restore its moisture absorption capacity. During regeneration, especially with heated regeneration methods, a significant amount of heat energy is consumed to raise the temperature of the adsorbent and promote moisture evaporation. This process not only consumes energy but also generates significant waste heat. If not recovered and utilized, this heat energy also becomes part of the system's energy loss. Current technologies fail to effectively combine the needs of medical building applications with green areas and the operating parameters of compressed air systems, failing to achieve precise allocation and dynamic optimization control of waste heat resources.

[0003] In the prior art, the announcement number CN111624911B, entitled "A Control System and Method for Multiple Absorbent Dryers Based on Main Pipe Pressure Dew Point," describes a method that involves first setting an upper limit for the pressure dew point value and the switching interval time of the absorbent dryer. Then, it detects the real-time pressure dew point value and the current adsorption time of the absorbent dryer. When the real-time pressure dew point value exceeds the upper limit, the switching is sorted according to the current adsorption time of the absorbent dryer, and then it is determined whether a switching is necessary.

[0004] In existing technologies, the control of desiccant dryers is typically based on a single parameter or static model, lacking a comprehensive consideration of the dynamic relationship between adsorption state parameters and dew point changes. This makes it difficult to accurately reflect real-time changes in desiccant dryer performance, leading to slow response or over-adjustment of the calibration mechanism. Furthermore, existing waste heat utilization schemes are mostly based on fixed recovery rates or empirical models, failing to incorporate the object's heating state indicators and environmental condition data at preset locations. This results in a disconnect between waste heat allocation and actual needs, affecting waste heat utilization efficiency and the optimization of waste heat utilization at preset locations. The question then arises: how can waste heat be effectively utilized by using it for heating objects at preset locations? Summary of the Invention

[0005] The purpose of this invention is to provide a dew point regeneration energy-saving control method for medical compressed air systems, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for dew point regeneration energy-saving control of a medical compressed air system, comprising the following steps: Step S1: Collect the adsorption status parameters of each desiccant in the compressed air system during the monitoring period, and at the same time detect the pressure dew point value of the main pipe and the pressure dew point value of the corresponding branch pipe of each desiccant. Step S2: Construct a waste heat correlation model for each preset location object. The waste heat correlation model is based on the combination of different main pipe pressure dew point values ​​and object heating status indicators to calculate the waste heat sensitivity factor of each preset location object at the current monitoring time. Step S3: Based on the collected main pipe pressure dew point value and adsorption state parameters, construct a nonlinear optimization model of the desiccant switching time and waste heat distribution to determine the initial switching time of each desiccant and the initial valve opening and closing strategy for heat medium recovery of waste heat. Step S4: Obtain the waste heat sensitivity factor of each preset location object, and introduce the environmental status data of the preset location object. Establish a waste heat demand prediction model based on the multivariate nonlinear regression model. The waste heat demand prediction model implements a first-level calibration mechanism for the initial switching time of each desiccant and the initial valve opening and closing strategy for the heat medium recovery of waste heat through a hierarchical control strategy. Step S5: Collect feedback data characterized by the adsorption state parameters of each desiccant after performing the first-level calibration mechanism, calculate the deviation ratio factor of the branch pipe pressure dew point value and the main pipe pressure dew point value of each desiccant, and perform correlation analysis between the deviation ratio factor of each desiccant and the corresponding adsorption state parameters to generate adjustment coefficients for adjusting the first-level calibration mechanism corresponding to each desiccant.

[0007] Compared with existing technologies, the beneficial effects of this invention are as follows: By collecting multi-dimensional adsorption state parameters of the desiccant and multi-point pressure dew point data, a nonlinear optimization model of the desiccant switching time and waste heat distribution is constructed. Combined with the waste heat sensitive factors and environmental conditions of the building's green area, a layered calibration mechanism is proposed, realizing dynamic feedback adjustment of the desiccant calibration parameters and precise waste heat distribution. The adjustment coefficient not only reflects the dew point difference but also integrates the physical characteristics of adsorption time and flow rate, making the adjustment response more in line with the changes in working conditions and meeting the stable requirements of waste heat and compressed air in medical buildings. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram of the technical route of the overall method of the present invention; Figure 3 This is a schematic diagram of the overall process architecture of the dew point regeneration energy-saving control method for medical compressed air systems. Detailed Implementation

[0009] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0010] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0011] Example 1: Please see Figures 1 to 3 The present invention provides a technical solution: A dew point regeneration energy-saving control method for a medical compressed air system, applicable to medical buildings with green areas, involves installing auxiliary heating pipes connected to the waste heat output of the compressed air system within the green area to utilize waste heat to provide thermal support for objects in predetermined locations. Specific steps include: Step S1: Collect the adsorption status parameters of each desiccant in the compressed air system during the monitoring period, and at the same time detect the pressure dew point value of the main pipe and the pressure dew point value of the corresponding branch pipe of each desiccant. Further explanation: The monitoring period is set to 10 consecutive minutes, with data sampled once per second, for a total of 600 sampling points; the adsorption state parameters of the i-th absorbent dryer during the monitoring period include adsorption time. Adsorption temperature and adsorption flow ; The adsorption state parameters are collected by the control module and sensors equipped in each desiccant dryer as follows: The adsorption time is denoted as... : Unit: seconds, representing the cumulative adsorption operation time of the i-th desiccant during the monitoring period. The adsorption temperature is denoted as... The unit is degrees Celsius (°C), representing the thermodynamic state of the adsorption process of the i-th desiccant during the monitoring period. The adsorption flow rate is denoted as... Unit: cubic meters per minute (m 3 / min), representing the volumetric airflow rate processed by the i-th desiccant during the monitoring period.

[0012] The pressure dew point values ​​of the main pipe and branch pipes are measured using pressure dew point sensors, specifically including: Record the air dew point in the main pipe as : Indicates the air dew point in the system main pipe at the k-th second during the monitoring period, in °C.

[0013] The air dew point inside the branch pipe is recorded as... : Indicates the air dew point in the branch pipe corresponding to the i-th desiccant at the k-th second during the monitoring period, in °C.

[0014] The average air dew point in the main pipe during the monitoring period will be used as the main pipe pressure dew point value. ; Where M1=600 is the total number of sampling points in this embodiment; The average dew point of the air in the branch pipe of the i-th desiccant during the monitoring period is taken as the branch pipe pressure dew point value. ; Anomaly detection was performed on adsorption time, temperature, and flow rate data. Data points exceeding ±2 standard deviations of the mean were removed, and the effective mean was recalculated.

[0015] Example 2: Step S2: Construct a waste heat correlation model for each preset location object. The waste heat correlation model is based on the combination of different main pipe pressure dew point values ​​and object heating status indicators to calculate the waste heat sensitivity factor of each preset location object at the current monitoring time. Further explanation: It should be noted that the preset location objects in this embodiment are the various vegetation areas within the building greening area; The heat output ends of the auxiliary heating pipes are respectively set in the environmental space where the object is located and in the root soil area at the preset location; The heat output end of the auxiliary heating pipeline is specifically configured as follows: Heat dissipation in the environmental space: A heat exchanger is installed in the canopy space of each pre-set vegetation location, that is, the vegetation leaves and the surrounding atmospheric space; the heat exchanger transfers the waste heat to the air around the leaves through the circulation of heat medium in the pipes, thereby increasing the temperature of the leaves; copper pipes are selected and covered with high thermal conductivity insulation material, the pipe diameter is 20mm, and the arrangement length is calculated based on the vegetation leaf area index to ensure that the kinetic energy of air circulation is not significantly hindered.

[0016] Heat source installation in the root soil area: A micro heat exchange pipeline network is laid at a depth of 15-30cm in the root soil layer of the vegetation. The pipeline is made of corrosion-resistant polyethylene (PER) material, with a diameter of 15mm and a laying density of 1.5 meters per square meter. This pipeline network is connected through auxiliary heating pipelines and is driven by a circulating pump to flow warm fluid, which directly applies waste heat to the root soil to increase the active temperature of the roots.

[0017] In this embodiment, the preferred heat transfer medium for the pipeline is a 45°C constant-temperature aqueous solution, with a flow rate set at 0.3m / s to 0.5m / s to ensure that the heat transfer efficiency matches the fluid resistance.

[0018] The object's thermal state index is used to describe the root activity and leaf activity of the object at the preset location; the waste heat correlation model includes: A correlation analysis was performed between the root activity and the main pipe pressure dew point value of each preset location object to obtain the root heat sensitivity factor; a correlation analysis was performed between the leaf activity and the main pipe pressure dew point value of each preset location object to obtain the leaf heat sensitivity factor; a comprehensive analysis was performed by combining the root heat sensitivity factor and the leaf heat sensitivity factor to generate the waste heat sensitivity factor, which is used to evaluate the waste heat response characteristics and sensitivity of each preset location object. It should be noted that the following parameters were collected using soil sensors buried in the root zone: Collect soil temperature of the j-th preset location object Soil moisture content and soil oxygen concentration The root activity of the object at the j-th preset location is obtained by analyzing these parameters using a weighted average method. ; Root activity in this embodiment The calculation formula is as follows: in, , and These are the weighting coefficients of the corresponding parameters; It reflects the sensitivity of soil temperature to root activity; Reflects the influence of soil moisture content; Reflects the influence of oxygen concentration; j represents the j-th preset location object; it should be noted that soil temperature Soil moisture content and soil oxygen concentration By using a uniform dimensionless processing method, the output range is limited to the interval (0,1); in this embodiment... , and The values ​​are 0.5, 0.3, and 0.2 respectively. Collect chlorophyll fluorescence parameters of the object at the j-th preset location. Leaf surface temperature The leaf activity of the j-th preset position object is obtained by analyzing these parameters using a weighted average method. ; Leaf activity The calculation formula is as follows: in, and These are the weighting coefficients of the corresponding parameters; These are the maximum fluorescence change value and the maximum fluorescence value, respectively; maximum fluorescence change value The fluorescence signal of chlorophyll, under dark adaptation conditions during photosynthesis, increases from its baseline value to its maximum value after irradiation with saturated light; the baseline value of the fluorescence signal... It is the lowest fluorescence emitted by chlorophyll molecules under dark-adapted conditions, with partially closed photosynthetic reaction centers, when stimulated only by weak probe light; the maximum fluorescence value. It is the maximum fluorescence released by chlorophyll molecules after absorbing the maximum saturation light intensity under conditions where all photosynthetic reaction centers are fully open; The maximum photochemical quantum efficiency of photosystem II (PSII) is an important indicator for measuring the photosynthetic capacity of plants. The formula is: Setting healthy plants The value is in the range of 0.75-0.85; A value below 0.75 indicates that the plant is under stress during photosynthesis, including drought, excessive temperature, and excessive soil salinity. This embodiment uses correlation analysis between leaf fluorescence detection values ​​and pressure dew point to indirectly assess the impact of residual heat on vegetation leaf growth and efficiency; and The values ​​are 0.7 and 0.3 respectively; Using a linear regression model to study root activity With the main pipe pressure dew point value By correlation, the root thermal sensitivity factor of the j-th preset location object is obtained. ; in, The root thermal sensitivity factor of the j-th preset location object; and The regression coefficients are fitted to the j-th preset position object; the leaf activity is analyzed using a linear regression model. With the main pipe pressure dew point value By correlation, the leaf thermal sensitivity factor of the j-th preset position object is obtained. ; in, The leaf thermal sensitivity factor of the j-th preset position object; and The regression coefficients obtained by fitting the j-th preset position object; Using weighted average quantitative synthesis and The residual heat sensitivity factor of the object at the j-th preset location is obtained. : in, and These are the weighting coefficients of the corresponding sensitivity factors; This embodiment and The sum of these values ​​is 1, and all values ​​are within the interval (0,1). and This embodiment is based on the experimental stage of vegetation thermal energy utilization; and The values ​​were taken as 0.6 and 0.4 respectively; the real-time main pipe pressure dew point value was used. Calculate the residual heat sensitivity factor of the j-th preset location object at the current monitoring time. This embodiment " The subscript index "" indicates the abbreviation of "current".

[0019] Example 3: Step S3: Based on the collected main pipe pressure dew point value and adsorption state parameters, construct a nonlinear optimization model of the desiccant switching time and waste heat distribution to determine the initial switching time of each desiccant and the initial valve opening and closing strategy for heat medium recovery of waste heat. Further explanation: Define the total number of desiccant dryers in the compressed air system as N; denoted as the total duration of the monitoring period. This embodiment seconds; obtain the adsorption state parameters, including adsorption time, of the i-th desiccant during the monitoring period. Adsorption temperature and adsorption flow The current adsorption state of the i-th desiccant is denoted as . ,in Indicates the adsorption state. Indicates the regeneration state; The initial opening degree of the waste heat recovery valve of the i-th desiccant dryer satisfy Set the initial switching time for the i-th desiccant. ,satisfy ; The nonlinear optimization model for the switching time of the desiccant dryer and the distribution of waste heat is defined by the following objective function: in, It is the i-th desiccant at the initial switching moment and initial opening The objective function value is as follows; Is the i-th desiccant at the initial opening degree? The corresponding waste heat recovery efficiency; It is the waste heat recovery factor of the i-th desiccant dryer; It is a penalty for pressure dew point stability, and It is a constant term; used to adjust the effect of the penalty term on the total value of the objective function. It is the standard deviation of pressure dew point fluctuation; Used for standard deviation of pressure dew point fluctuation If the value is too high, the system will face the problem of unstable pressure, which poses a risk to the operation of equipment and the long-term effectiveness of waste heat recovery. Therefore, this penalty item is set. The logic for maximizing is explained as follows: The waste heat generated during the operation of the desiccant dryer is a resource that can be recycled and utilized in energy-saving and environmentally friendly construction. By maximizing this part, the overall recycling capacity of the system can be improved, and low-energy-consumption building equipment can be matched.

[0020] While the stability of the system pressure dew point is important, it is not a direct objective. Its optimization aims to avoid additional risks arising from fluctuations caused by high efficiency. Therefore, the optimization method reflects a trade-off between efficiency and stability.

[0021] Choosing the maximization option among multiple options means focusing on improving the overall system resource utilization, thereby maximizing the overall system benefit by improving waste heat recovery efficiency and suppressing unnecessary fluctuations.

[0022] It should be further explained that: Represented as a linear function ; in, This represents the relationship between maximum waste heat recovery efficiency and fully open valve; the waste heat recovery factor of the i-th desiccant dryer. Defined as: The parameters are explained as follows: It is the energy conversion efficiency coefficient, in this embodiment The value is 0.8, which reflects the overall conversion efficiency; the higher the value, the higher the recovery potential. This is the remaining operating time before the current desiccant is switched. The later the switching time, the shorter the remaining operating time and the less space there is for waste heat recovery. This is a traffic reference value used for traffic normalization to ensure consistency of indicator dimensions. (This is from an example.) The value is 10; This is a temperature reference value used for temperature normalization, in this embodiment. The value is 40; This is a dew point reference value, used as a threshold for judging waste heat recovery efficiency. Above this value, waste heat recovery decreases. (This is from an example.) The value is 20; This is the adsorption temperature adjustment coefficient, used to determine the weight of the influence of adsorption temperature in waste heat recovery. (This is from an example.) The value is 0.05; This reflects the effective waste heat utilization value of the i-th desiccant dryer; Standard deviation of pressure dew point fluctuation The formula is as follows: in, The dew point value of the branch pipe pressure of the i-th desiccant dryer is the real-time value, reflecting the system pressure stability; =0.1 is used to adjust the weight between waste heat recovery and system stability; By adjusting the value of λ, the focus on pressure dew point fluctuations can be weakened or strengthened. If λ is large, the objective function focuses more on stability, which means that fluctuations in the pressure dew point will be strictly suppressed.

[0023] If λ is small, the objective function will focus more on the efficiency of waste heat recovery, while pressure fluctuations may be considered a secondary effect. Specifically: Prioritize pressure stability: If the system has high requirements for pressure dew point stability, a larger λ should be set, λ>1; in this case, the optimization algorithm will focus more on suppressing pressure dew point fluctuations, at the cost of sacrificing some waste heat recovery efficiency.

[0024] Prioritize maximizing waste heat recovery benefits: If the impact of pressure dew point fluctuations is small, a smaller λ should be selected, where λ < 0.5. This method will focus on increasing the total amount of waste heat recovered.

[0025] In actual operation, λ is set according to actual conditions, and a suitable value is determined through experiments; in this embodiment, for the absorbent dryer where stability is relatively important, the value of λ is set to be between 0.1 and 0.5. For energy-saving systems that prioritize waste heat recovery efficiency, the value of λ is set to be within the range of 0.01 to 0.1, including the endpoint value.

[0026] Further: Set the initial switching time for the i-th desiccant. Less than the maximum allowable adsorption time and control cycle of the desiccant: in, It is the lower limit of the control cycle, set according to equipment safety specifications and maintenance requirements; The initial switching times of adjacent desiccant dryers should be staggered by at least 30 seconds to avoid drastic impacts on system pressure and waste heat recovery caused by simultaneous switching. Indicates and Adjacent initial handover times; specific implementation steps are as follows: The optimization process of the objective function includes the following main steps: Generate 50 individual chromosomes that conform to the initial distribution, each chromosome containing and The initial values ​​are then calculated. The objective function value for each individual is then calculated. The optimization effect is evaluated using a roulette wheel selection method, where individuals with higher fitness are selected as parents based on their fitness values. Uniform crossover is performed on the selected parents with a 70% probability to generate new offspring. Gaussian mutation is performed on the gene loci of the offspring with a 10% probability to increase population diversity. All offspring individuals are ensured to meet the constraints of switching time and valve opening, and the switching time interval is adjusted. The two best individuals are directly retained for the next generation, and the remaining individuals are replaced by offspring. The maximum number of iterations or convergence conditions are checked; if met, optimization stops. The best individuals are selected as the final optimization result, and the optimal switching time and valve opening for each desiccant dryer are obtained.

[0027] The evolutionary process of each generation includes the following steps: Calculate the fitness value of all individuals in the current population. Parent selection: Select suitable individuals for reproduction based on fitness. Offspring generation: Generate new offspring individuals through crossover and mutation operations. Constraint correction: Correct the constraints on the newly generated offspring to ensure their legitimacy. Elite retention: Directly retain the two best individuals from the current generation to the next generation. Population update: Replace the remaining population members with offspring to form a new population. Check termination condition: If the termination condition is met, stop evolution; otherwise, continue the evolution of the next generation.

[0028] Each individual is represented as a chromosome in the genetic algorithm, containing the encoding of all decision variables. In this scheme, the chromosome consists of a 2N-dimensional real vector, where N is the total number of desiccant cells, with the specific structure as follows: This is the initial switching time of the i-th desiccant, in seconds, with a range of... .

[0029] satisfy .

[0030] It should be noted that: In this embodiment, the system has 3 air dryers, so the chromosome is a 6-dimensional vector, for example: The initial population is generated through uniform distribution to ensure full coverage of the search space: Switching time initialization: For N=3 and Second: Valve opening initialization: Therefore, the following are examples of individual chromosomes in the initial population: The population size was set to 50 to balance search capability with computational resource requirements.

[0031] The fitness function is used to evaluate the quality of each individual, and is related to the objective function. Maintain consistency: The goal is to maximize It should be noted that in genetic algorithms, the fitness function needs to be maximized; therefore, to ensure that the algorithm correctly evaluates the quality of individuals during the optimization process, the fitness value should be consistent with the sign of the objective function. This means: If the objective function is a maximization problem: the fitness function should directly use the value of the objective function, ensuring that a higher fitness value indicates a greater advantage for the individual in the optimization direction. Based on the fitness value of each individual... To determine the probability of it being selected, the specific steps are as follows: Calculate fitness values: Calculate the fitness value for each individual in the population. .

[0032] Calculate the probability of selecting the i-th desiccant. : Where H is the population size. Let be the fitness value of the i-th desiccant; Probability-based selection of parent individuals: A random sampling method is used, based on... Choose the parent individual to generate the next generation.

[0033] Uniform crossover ensures that each gene locus has the same crossover probability by randomly exchanging genes between parent individuals. 1. Selecting parent pairs: The parent individuals selected in the selection operation form crossover pairs.

[0034] 2. Gene exchange: For each gene locus, a preset crossover probability is used. Gene exchange occurs, resulting in two offspring individuals.

[0035] For mutation operations: Gaussian mutation increases population diversity by making small, random adjustments to the genes of some offspring individuals. The specific steps are as follows: For each gene locus, with a preset mutation probability Perform mutation assessment. For the selected gene loci, add a subset with a mean of 0 and a standard deviation of [value missing]. Normally distributed random numbers. Ensure that the mutated numbers are... and They remain within their respective legal scopes.

[0036] The elite preservation strategy ensures that the best individuals are retained in each generation to prevent the loss of the optimal solution: In each generation, the two individuals with the highest fitness values ​​are selected as elites. These elite individuals are directly copied into the next generation population without participating in crossover or mutation operations. The optimization process will terminate when any of the following conditions are met: The maximum number of iterations is set to be reached when the genetic iteration reaches a certain number. Generations. The convergence condition is that the improvement in the objective function is less than 0.01% over 10 consecutive generations. For each individual in the population, all... Sort the data to ensure that the switching times of any two air dryers differ by at least 30 seconds. If any two air dryers are found to have... If the interval is less than 30 seconds, adjust the latter. For the former +30 seconds. Ensure the adjustment is complete. Not exceeding their respective maximum allowed handover times and control cycle .

[0037] Furthermore, the waste heat recovery factor in this embodiment The impact of parameter changes is analyzed as follows: Initial switching time near At the end, Decrease, waste heat recovery factor A decrease in the number of waste heat recovery opportunities indicates that switching too late limits the chances of waste heat recovery; conversely, an earlier switch provides more time for waste heat recovery.

[0038] By limiting This is to prevent equipment risks caused by exceeding the maximum adsorption time.

[0039] Increase adsorption temperature The value will increase the factor. This indicates that the higher the temperature, the greater the potential for heat recovery; physically, higher adsorption heat can be effectively recovered.

[0040] Larger flow Increase waste heat flow and linearly amplify This reflects the differences in waste heat resources within the system.

[0041] The higher the factor The decrease indicates the impact of water vapor in humid air on waste heat generation and recovery.

[0042] Initial opening Increase waste heat recovery efficiency The opening increases linearly, but excessive opening will put a burden on the system pipeline pressure, so optimization and balance are required.

[0043] An increase indicates a decrease in system stability. The strategy is to adjust the penalty term in the objective function to ensure that the fluctuation of system pressure parameters does not exceed the safe range.

[0044] Example 4: Step S4: Obtain the waste heat sensitivity factor of each preset location object, and introduce the environmental status data of the preset location objects in the building green area. Establish a waste heat demand prediction model based on the multivariate nonlinear regression model. The waste heat demand prediction model implements a first-level calibration mechanism for the initial switching time of each desiccant and the initial valve opening and closing strategy for the heat medium to recover waste heat through a hierarchical control strategy. To further explain, the primary calibration mechanism specifically includes: Environmental condition data includes the area's average air temperature. Regional relative humidity Sunlight intensity and wind speed ; Based on environmental status data and residual heat sensitivity factors, predict the future time of building green areas. Total waste heat demand ; Represented as: Wherein, the input variable vector For current environmental status parameters: Define waste heat sensitivity factor vector M2 represents the total number of preset vegetation locations; The specific form of multivariate nonlinear regression adopts the following polynomial function containing cross terms and quadratic terms: in, It is a constant term. yes Characterization of environmental parameters; yes The linear regression coefficients, It is the residual heat sensitivity factor of the j-th preset location object; It is the linear regression coefficient of the waste heat sensitive factor; , , These are the quadratic regression coefficients; k1 and This is used to represent the combination of indices for environmental variables in environmental state data, each used to characterize... Any combination of two environmental parameters; This embodiment predicts latency. A 10-minute interval was selected to ensure the timeliness of waste heat demand forecasting and the accuracy of heating adjustments.

[0045] Using historically collected environmental condition data, waste heat sensitivity factors, and corresponding actual waste heat demand data, the parameters of the above regression model are fitted based on the least squares method.

[0046] Furthermore, the hierarchical control strategy is implemented as follows: The hierarchical control strategy includes upper-level control and lower-level control; For upper-level control: using a multivariate nonlinear regression model, based on the currently collected environmental state data and the residual heat sensitivity factor of the preset location object, the future time is predicted. Waste heat demand in building green areas ; The forecast results are used as a benchmark for heating demand, and reference values ​​are transmitted to lower-level control systems to guide dynamic scheduling. For lower-level control: Based on the demand benchmark provided by upper-level control, a first-level calibration mechanism is implemented through a nonlinear optimal scheduling algorithm to adjust the initial switching time of the desiccant and the initial valve opening and closing strategy for the recovery of waste heat of the heat medium. Total waste heat demand in this embodiment The impact of parameter changes in the calculation formula on the model and their physical significance are analyzed below: When the air temperature When it increases, the corresponding coefficient in the regression model The corresponding positive impact makes the forecast of waste heat demand... The increase reflects the rise in vegetation heat demand as air temperature increases.

[0047] Regional relative humidity Changes modulate the impact of water evaporation through their regression coefficients, thereby regulating demand.

[0048] Sunlight intensity At higher levels, interactive items The weighting of vegetation-sensitive factors is increased to reflect the comprehensive impact of photosynthesis on heat demand.

[0049] wind speed The change affects air cooling heat dissipation through its negative coefficient, thus reducing the adjustment demand.

[0050] Residual heat sensitive factor The higher the value, the stronger the response capability of the object at the preset location to residual heat. This is demonstrated in the model through... Its secondary interaction items are reflected in the direct adjustment of the size of the demand.

[0051] Further explanation: Regarding upper-level control: This relates to the waste heat supply value predicted based on the existing initial strategy. Compare and calculate the supply-demand gap. : like Exceeding the preset threshold Then, an adjustment command is generated and fed back to the lower-level control; in this embodiment ; The adjustment command format is set as demand adjustment rate. ,Right now and The percentage of deviation; The percentage deviation of the representation requirement from the initial strategy; Further explanation of the nonlinear optimal scheduling algorithm for lower-level control: Determining the input parameters for lower-level control includes: based on Let the set of initial switching times for the i-th desiccant be defined as follows: Let the initial opening set of the i-th desiccant be set as follows: Demand adjustment rate based on upper-level control transmission ; The primary calibration mechanism is designed as follows: Demand adjustment rate As multiplication factors, the initial switching time of the desiccant and the initial opening degree of the waste heat recovery valve are adjusted respectively, and the specific mathematical expression is as follows: in, It is the switching time of the i-th desiccant after calibration by the primary calibration mechanism; The range of values ​​is ; It is the opening degree of the waste heat recovery valve of the i-th desiccant after calibration by the first-level calibration mechanism; The value range is [0,1]. It is the switching time adjustment coefficient. This is used to control the adjustment range at the switching time to prevent excessive fluctuations. It is the adjustment coefficient for the opening degree of the waste heat recovery valve. This is used to control the adjustment range of the opening of the waste heat recovery valve to avoid exceeding the safe range. If the adjustment result exceeds the preset time boundary, it will be cropped to 0 or the end of the cycle; if the result exceeds the range of 0 to 1 after adjustment, it will be limited to that range. After the adjustment is completed, it will automatically determine whether the switching time of the adjacent desiccant meets the requirement of an interval of ≥30 seconds. If not, it will be postponed or brought forward to meet the condition.

[0052] Using the calibrated switching time and valve opening as initial values, a nonlinear optimal scheduling algorithm is used for further fine-tuning to minimize waste heat supply error while satisfying system safety and operational constraints.

[0053] The optimized model continues to execute, outputting the final switching time and valve opening execution command.

[0054] In this embodiment, the control system performs data acquisition, demand forecasting, error calculation, and tiered calibration in 10-minute cycles. Each cycle updates parameters based on the latest environmental and vegetation activity status to ensure the real-time accuracy of waste heat supply. Adjustment coefficient. , The selection of parameters has been verified through actual testing to ensure stable system response and normal equipment lifespan. This step, based on the demand signal output by the waste heat demand prediction model, achieves real-time connection between the initial operating strategy and dynamic environmental demand. The hierarchical control strategy clearly defines the responsibility allocation between upper-level demand prediction and lower-level optimal scheduling, realizing primary calibration of initial parameters and dynamic adjustment within safety limits, ensuring efficient and continuous waste heat utilization of the system.

[0055] It should be noted that: in this embodiment, the waste heat supply value predicted by the existing initial strategy refers to the future time predicted according to the initial operation strategy based on the set of switching times and valve openings determined in step S3. The total waste heat supply value; the specific determination steps are as follows: The set of switching times and valve openings are applied to the system operation model to simulate future moments. The operating status of each absorbent dryer at that time.

[0056] According to each desiccant The system monitors the switching status and valve opening at various times, and calculates the waste heat supply of each desiccant dryer. : in, For the i-th desiccant at the initial opening... Waste heat recovery efficiency; For the i-th desiccant at the initial switching time The waste heat recovery factor at the previous time. By summing the waste heat supply of all desiccant dryers, the system's future time value can be obtained. Total waste heat supply value : In this embodiment, the initial supply value is used as a benchmark to determine the deviation between the predicted demand and supply, and to guide the adjustment of the tiered control strategy.

[0057] Example 5: Step S5: Collect feedback data characterized by the adsorption state parameters of each desiccant after performing the first-level calibration mechanism, calculate the deviation ratio factor of the branch pipe pressure dew point value and the main pipe pressure dew point value of each desiccant, and perform correlation analysis between the deviation ratio factor of each desiccant and the corresponding adsorption state parameters to generate adjustment coefficients for adjusting the first-level calibration mechanism corresponding to each desiccant.

[0058] Further explanation: Define the deviation scaling factor for the i-th desiccant dryer. The relative ratio of the branch pipe pressure dew point value to the main pipe pressure dew point value; calculated using the formula within the monitoring period to quantify the difference between equipment performance and system status: in, It is the deviation ratio factor of the i-th desiccant dryer; it represents the degree of dew point deviation of the desiccant dryer relative to the main pipe. It is a small constant to prevent division by zero; this embodiment To avoid calculation errors due to a zero denominator, we take 0.01; when the branch pipe pressure dew point value of the i-th desiccant dryer... Increase the deviation ratio factor An increase indicates that the i-th desiccant dryer has problems such as decreased adsorption performance or insufficient drying; the total pressure dew point value When the humidity is increased, the deviation ratio factor decreases as a whole, assuming other conditions remain unchanged. This indicates that the overall humidity of the system increases and the proportion of the difference between the pressure and dew point of other desiccant dryers decreases.

[0059] For each desiccant dryer, the set of adsorption state parameters is defined as the set of feedback data characterized by the adsorption state parameters. Combined with deviation ratio factor Forming sample pairs ; A first correlation function between the deviation proportionality factor and the adsorption state parameters is established using a multiple linear regression model. ; Characterizing functional relationships; The specific characteristics are as follows: in, It is the intercept; u1, u2, and u3 are regression coefficients, representing the weights of each parameter's influence on the deviation factor; These are the residual values; using the monitoring dataset, least squares fitting is performed to obtain u0, u1, u2, and u3. Based on the first correlation function... The regression model output is used to calculate the adjustment coefficient for the primary calibration mechanism in the i-th desiccant dryer. ; in, This is an example of a parameter adjustment used to limit the adjustment range and ensure system stability. The value is 0.8; The larger the adjustment coefficient, the higher the adjustment coefficient. The smaller the value, the more likely it is to decrease the adjustment range of the corresponding first-level calibration of the desiccant dryer to prevent overcompensation; adsorption time and Changes in this factor indirectly affect the deviation factor, which is reflected in the regression coefficient as an indirect impact on the adjustment coefficient. The generated adjustment coefficient... The primary calibration mechanism affects the timing of the desiccant switch. and waste heat recovery valve opening Adjustments: in, It is the switching time of the i-th desiccant after adjustment of the adjustment coefficient; It is the opening degree of the waste heat recovery valve of the i-th desiccant after adjustment by the adjustment coefficient.

[0060] The above adjustments ensure that different desiccant dryers adaptively adjust the calibration intensity based on differences in their operational feedback.

[0061] Boundary checks are performed on the adjusted parameters to ensure that the switching time is within the specified range. The opening degree of the waste heat recovery valve is in [0,1].

[0062] Maintain the rule that the switching interval is ≥30 seconds, and complete the final scheduling parameter output.

[0063] The interaction and numerical change relationships of the parameters in the adjustment formula in this embodiment are explained as follows: Main pipe pressure dew point value An increase will lead to a deviation ratio factor The denominator increases, thereby reducing the overall deviation proportionality factor and reducing rough estimation and adjustment errors.

[0064] Branch pipe pressure dew point value Increase the deviation ratio factor An increase in the value indicates a decrease in the drying efficiency of the equipment, thus reducing the adjustment range of its first-level calibration to avoid overcompensation that could lead to system pressure instability.

[0065] Adsorption time If the regression model shows a negative correlation coefficient, it indicates that the longer the running time, the lower the drying performance may be, thereby increasing the deviation ratio and causing the adjustment coefficient to decrease accordingly.

[0066] Adsorption temperature deviation scaling factor The influence of the adjustment coefficient reflects the dynamic impact of the equipment's thermal state on drying performance.

[0067] Adsorption flow The increase will affect the deviation ratio factor due to the increase in drying load. By adjusting the coefficients to regulate the calibration intensity, multivariate coordinated control can be achieved.

[0068] Example 6: Application of the dew point regeneration energy-saving control method based on the correlation analysis of deviation proportional factor and adsorption state parameters in the compressed air system of medical buildings: This embodiment selects the compressed air system of a large medical building as the test object. The system is equipped with six desiccant dryers, numbered "Desiccant A", "Desiccant B", "Desiccant C", "Desiccant D", "Desiccant E", and "Desiccant F". The medical building has a green area, and auxiliary heating pipes are connected to the waste heat output end of the compressed air system to provide heat support for the green vegetation. The experiment focuses on the utilization of waste heat in the green area of ​​the medical building and the energy-saving control of dew point regeneration of the compressed air system, in order to improve the operating efficiency of the compressed air system and maximize the utilization of waste heat.

[0069] The test cycle was set at 600 seconds (10 minutes) per control cycle. All monitoring data in the system were collected once per second to ensure that the data fully reflected the actual fluctuations of the system's state. Details of the experimental implementation process are as follows: Dew point sensors installed in the main compressed air system pipe and the branch pipes of each desiccant dryer are used to acquire real-time pressure dew point data for both the main pipe and each branch pipe. Each desiccant dryer is equipped with a temperature sensor and a flow meter to collect data on the adsorption temperature (°C) and adsorption flow rate (m³) during the adsorption process. 3 The system measures the heat output per minute (%) and the cumulative adsorption time (seconds). Waste heat within the green area is transferred through auxiliary heating pipes. Real-time monitoring of pipe temperature and heat flow helps analyze the system's thermal state, ensuring a dynamic and rational distribution of waste heat.

[0070] Calculate the deviation ratio factor for each desiccant dryer, quantify the difference between the dew point of the desiccant dryer branch pipe and the dew point of the main pipe, and indirectly reflect the dehumidification performance and energy recovery efficiency of the desiccant dryer. Using the deviation proportionality factor as the dependent variable and adsorption time, adsorption temperature, and adsorption flow rate as independent variables, the influence of adsorption state on the deviation proportionality factor was analyzed using multiple linear regression, and the following correlation model was established: The model parameters were fitted using the least squares method, and the model accuracy was optimized by taking into account the characteristics of the medical building environment.

[0071] Based on the deviation proportionality factor, the adjustment coefficient for the primary calibration mechanism is defined as follows: in The adjustment range is controlled, and the adjustment coefficient is applied to the switching time of the desiccant and the dynamic adjustment of the valve opening. The waste heat is used to maximize support for the needs of the green area and save energy consumption in medical buildings.

[0072] The calculated adjustment coefficients are applied to the control system to form a closed loop. Real-time feedback data ensures that the adjustment parameters conform to changes in equipment operation and building load, promoting energy conservation and effective utilization of waste heat in the compressed air system. Experimental data and analysis are as follows: Table 1: Examples of Energy Saving and Effective Utilization of Waste Heat The following analysis is based on the data table of six desiccant dryers for a medical compressed air system provided in the embodiments, focusing on the deviation ratio factor. and corresponding adjustment coefficients A detailed analysis and sectioned discussion were conducted to quantify the beneficial effects of this invention in the generation of adjustment coefficients based on the correlation analysis of deviation ratio factor and adsorption state parameters. The deviation proportionality factor in the table ranges from 0.0303 to 0.1515, and the adjustment coefficient ranges from 0.8788 to 0.9758, showing a typical negative correlation. Based on the numerical distribution and the rationality of engineering adjustments, the adjustment ranges for the deviation proportionality factor are divided as follows: The deviation scaling factor is set to a range of 0.03≤ <0.06, 0.06≤ <0.12, ≥0.12; Set adjustment coefficient The intervals are 0.9520≤ <0.9800, 0.9040≤ <0.9520, <0.9040; The above-mentioned interval division takes into account both the continuity and safety of engineering adjustments, avoids excessive jumps, and guides the step-by-step optimization of adjustment coefficients.

[0073] For the first adjustment interval 0.03≤ The following explanation applies to values ​​<0.06; The deviation ratios of C and E in the desiccant dryer are relatively low, corresponding to the highest adjustment factor. The suction dryer A is slightly higher at 0.0606, corresponding to an adjustment coefficient of 0.9515.

[0074] The small deviation within this range indicates that the pressure dew point of the desiccant branch pipe is close to that of the main pipe, indicating excellent system drying performance. The adsorption state parameters suggest stable operation. The adjustment coefficient is close to 1, indicating that the adjustment range of the primary calibration mechanism is small, maintaining stable equipment operation and avoiding unnecessary energy waste.

[0075] Within this region, a slight increase in the deviation factor (e.g., from 0.0303 to 0.0606) results in a decrease in the adjustment coefficient of approximately 2.5% (from 0.9765 to 0.9515). This exhibits a sensitive yet mild adjustment relationship, ensuring that the system can make limited adjustments for small performance fluctuations, maintaining equipment lifespan and system stability.

[0076] For this range, the switching time of the desiccant and the valve opening should be maintained or finely adjusted, with the adjustment range limited to within 3%. Priority should be given to ensuring stable operation and efficient output of waste heat, and the frequency of maintenance and optimization should be appropriately reduced.

[0077] For the second adjustment interval 0.06≤ The following explanation applies to values ​​<0.12; The deviation ratio between the desiccant dryer D and B has increased, and the adjustment coefficient has decreased accordingly to the range of approximately 0.91 to 0.95.

[0078] An increase in the deviation factor indicates that the performance difference between the desiccant and the overall system begins to emerge in this range, due to a decrease in dehumidification efficiency caused by longer adsorption time or abnormal temperature. The system addresses this by reducing the adjustment coefficient by 10% (a reduction of 5% to 8% compared to the first range) and increasing the intensity of the primary calibration mechanism to achieve more proactive dynamic correction in response to performance fluctuations.

[0079] Within this range, as the deviation factor increases from 0.06 to 0.12, the adjustment coefficient decreases by approximately 4–6 percentage points, exhibiting a relatively linear negative correlation. Abnormal changes in adsorption time and temperature amplify the deviation factor, guiding the adjustment coefficient to decrease and triggering a larger adjustment of the equipment's operating parameters.

[0080] During this adjustment period, it is recommended to advance the switching time by 5% to 8% and adjust the valve opening accordingly to enhance the dryer's adaptability to the load. At the same time, monitor the adsorption status parameters in real time for secondary verification to avoid excessive operation that could cause equipment vibration.

[0081] For the third adjustment interval For values ​​≥0.12, the following explanation applies: The deviation ratio of the desiccant F reaches a maximum of 0.1515, while the adjustment coefficient drops to a minimum of 0.8788, reflecting the maximum adjustment force.

[0082] A high deviation factor reflects a significant deviation in system performance due to equipment aging, malfunctions, or extreme changes in operating conditions. The adjustment factor is reduced to below 88% to ensure that the primary calibration mechanism can significantly adjust switching timing and valve opening, achieving rapid response and energy efficiency restoration.

[0083] In the deviation increase range of 0.03, the adjustment coefficient decreased by nearly 5 percentage points, indicating that when equipment performance deteriorates, the nonlinearity of the system's adjustment intervention intensifies, significantly different from the low deviation range. This nonlinear adjustment reflects the adaptive capability of the invention, effectively preventing the overall system efficiency from decreasing due to the malfunction of a single device.

[0084] For the desiccant in this section, an advanced optimization control strategy should be activated in real time, dynamically adjusting the valve opening by more than 10%, and the switching time should be reasonably advanced. This should be coordinated with the equipment maintenance or replacement plan to ensure the long-term stable operation of the system, while also providing non-invasive protection for the supply of waste heat from building greening.

[0085] By utilizing the mathematical correlation between the deviation proportionality factor and multiple adsorption parameters, the difference adjustment coefficient is calculated in real time to achieve zoned and classified management. This method differs from the traditional single fixed calibration method and significantly improves the accuracy of fine-tuning and the flexibility of the system.

[0086] By quantitatively combining the actual operating status of the equipment, the adjustment coefficient not only reflects the dew point difference, but also integrates the physical characteristics of adsorption time and flow rate, making the adjustment response more in line with the changes in operating conditions and meeting the stable needs of waste heat and compressed air in medical buildings.

[0087] The adjustment coefficient calculation formula and interval division proposed in this invention have significant mathematical and engineering rationality, effectively ensuring that the adjustment intensity is adjusted proportionally with the deviation factor, and combining the influencing factors of adsorption state to achieve dynamic optimization of multidimensional adjustment parameters, effectively avoiding misadjustment and equipment fatigue.

[0088] The differentiated range adjustment strategy reasonably balances energy-saving demands and equipment performance requirements. Different adjustment ranges and strategies are formulated for different ranges, taking into account system stability and waste heat utilization. This invention demonstrates the significant technological advancement of the invention in the energy-saving retrofit of compressed air systems in medical buildings.

[0089] Based on the above-described interval adjustment scheme and the data in the table, the adjustment range and regulation strategy of the primary calibration mechanism corresponding to different intervals of the deviation ratio factor are clarified, ensuring that the matching degree between the adjustment action and the equipment performance reaches more than 95%. Specifically, the adjustment in the slight deviation interval does not exceed 5%, the adjustment intensity is moderately increased by 10% in the medium deviation interval, and an adjustment of more than 12% is applied in the high deviation interval to ensure effective control of abnormal states.

[0090] Figure 1 The interaction logic between the control method of this invention and the core hardware system is demonstrated. Figure 1The left / center section shows a medical compressed air execution system, which mainly includes an adsorption dryer and a heat recovery device. It is physically connected to a preset location (including the heating area of ​​the medical building) through a pipeline network, illustrating the physical basis of compressed air preparation and waste heat recovery. Figure 1 The flowchart shown in the middle illustrates the core control steps of this embodiment: Step 1 "Multidimensional data acquisition and dew point monitoring": This corresponds to the first flowchart in the figure, which means real-time acquisition of the adsorption state parameters of the desiccant and simultaneous detection of the pressure dew point values ​​of the main pipe and each branch pipe, corresponding to step S1. Step 2 "Waste Heat Correlation Modeling and Optimization Decision": This corresponds to the second flowchart in the figure. It means building a waste heat correlation model and a nonlinear optimization model based on the collected data, calculating the waste heat sensitivity factor and determining the initial switching time and valve strategy, corresponding to steps S2 and S3. Step 3 "Demand Forecasting and Hierarchical Calibration Control": This corresponds to the third flowchart in the diagram. It means establishing a waste heat demand forecasting model based on environmental status data and implementing a first-level calibration mechanism for the control strategy, which corresponds to step S4. Step 4, "Deviation Feedback Analysis and Dynamic Adjustment": This corresponds to the fourth flowchart in the diagram. It involves collecting feedback data after the first-level calibration, calculating the dew point deviation ratio factor, and generating adjustment coefficients to achieve dynamic closed-loop optimization of the system. This corresponds to step S5.

[0091] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, Min-Max Normalization and Z-Score standardization. The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0092] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A dew point regeneration energy-saving control method for a medical compressed air system, wherein the waste heat output end of the compressed air system is connected to an auxiliary heating pipe to utilize waste heat to provide thermal energy support for objects at preset locations, characterized in that... The specific steps for thermal support include: Step S1: Collect the adsorption status parameters of each desiccant in the compressed air system during the monitoring period, and at the same time detect the pressure dew point value of the main pipe and the pressure dew point value of the corresponding branch pipe of each desiccant. Step S2: Construct a waste heat correlation model for each preset location object. The waste heat correlation model is based on the combination of different main pipe pressure dew point values ​​and object heating status indicators to calculate the waste heat sensitivity factor of each preset location object at the current monitoring time. Step S3: Based on the collected main pipe pressure dew point value and adsorption state parameters, construct a nonlinear optimization model of the desiccant switching time and waste heat distribution to determine the initial switching time of each desiccant and the initial valve opening and closing strategy for heat medium recovery of waste heat. Step S4: Obtain the waste heat sensitivity factor of each preset location object, and introduce the environmental status data of the preset location object. Establish a waste heat demand prediction model based on the multivariate nonlinear regression model. The waste heat demand prediction model implements a first-level calibration mechanism for the initial switching time of each desiccant and the initial valve opening and closing strategy for the heat medium recovery of waste heat through a hierarchical control strategy. Step S5: Collect feedback data characterized by the adsorption state parameters of each desiccant after performing the first-level calibration mechanism, calculate the deviation ratio factor of the branch pipe pressure dew point value and the main pipe pressure dew point value of each desiccant, and perform correlation analysis between the deviation ratio factor of each desiccant and the corresponding adsorption state parameters to generate adjustment coefficients for adjusting the first-level calibration mechanism corresponding to each desiccant.

2. The dew point regeneration energy-saving control method for a medical compressed air system according to claim 1, characterized in that: Set the adsorption state parameters of the i-th desiccant during the monitoring period, including adsorption time. Adsorption temperature and adsorption flow ; The average air dew point in the main pipe during the monitoring period will be used as the main pipe pressure dew point value. ; The average dew point of the air in the branch pipe of the i-th desiccant during the monitoring period is taken as the branch pipe pressure dew point value. .

3. The dew point regeneration energy-saving control method for a medical compressed air system according to claim 2, characterized in that: The object's thermal state index is used to describe the object's thermal sensitivity to waste heat supply at the preset location; The waste heat correlation model generates waste heat sensitivity factors to evaluate the waste heat response characteristics and sensitivity of objects at each preset location. Let the residual heat sensitivity factor of the j-th preset location object be denoted as . .

4. The dew point regeneration energy-saving control method for a medical compressed air system according to claim 3, characterized in that: Define the total number N of desiccant dryers in the compressed air system; The total duration of the monitoring period is recorded as follows: The current adsorption state of the i-th desiccant is denoted as . ,in Indicates the adsorption state. Indicates the regeneration state; Set the initial opening degree of the waste heat recovery valve of the i-th desiccant dryer. satisfy ; Set the initial switching time of the i-th desiccant. ,satisfy ; The nonlinear optimization model for the switching time of the desiccant dryer and the distribution of waste heat is defined by the following objective function: in, It is the i-th desiccant at the initial switching moment and initial opening The objective function value is as follows; Is the i-th desiccant at the initial opening degree? The corresponding waste heat recovery efficiency; It is the waste heat recovery factor of the i-th desiccant dryer; It is a penalty for pressure dew point stability, and It is a constant term; It is the standard deviation of pressure dew point fluctuation.

5. The dew point regeneration energy-saving control method for a medical compressed air system according to claim 4, characterized in that: The primary calibration mechanism specifically includes: Environmental condition data includes the area's average air temperature. Regional relative humidity Sunlight intensity and wind speed ; Based on environmental status data and residual heat sensitivity factors, predict the future time of building green areas. Total waste heat demand ; The hierarchical control strategy includes upper-level control and lower-level control; For upper-level control: using a multivariate nonlinear regression model, based on the currently collected environmental state data and the residual heat sensitivity factor of the preset location object, the future time is predicted. Waste heat demand in building green areas ; The forecast results are used as a benchmark for heating demand, and reference values ​​are transmitted to lower-level control systems to guide dynamic scheduling. For lower-level control: Based on the demand benchmark provided by upper-level control, a first-level calibration mechanism is implemented through a nonlinear optimal scheduling algorithm to adjust the initial switching time of the desiccant and the initial valve opening and closing strategy for the recovery of waste heat of the heat medium.

6. The dew point regeneration energy-saving control method for a medical compressed air system according to claim 5, characterized in that: Further explanation regarding upper-level control: The waste heat supply value predicted based on the existing initial strategy. Compare and calculate the supply-demand gap. : like Exceeding the preset threshold Then, adjustment commands are generated and fed back to the lower-level control. The adjustment command format is set as demand adjustment rate. ,Right now and The percentage of deviation; Further explanation of the nonlinear optimal scheduling algorithm for lower-level control: Determining the input parameters for lower-level control includes: based on Let the set of initial switching times for the i-th desiccant be defined as follows: Let the initial opening set of the i-th desiccant be set as follows: ; Demand adjustment rate based on upper-level control transmission ; Demand adjustment rate As multiplication factors, the initial switching time of the desiccant and the initial opening of the waste heat recovery valve are adjusted respectively, specifically as follows: in, It is the switching time of the i-th desiccant after calibration by the primary calibration mechanism; The range of values ​​is ; It is the opening degree of the waste heat recovery valve of the i-th desiccant after calibration by the first-level calibration mechanism; The value range is [0,1]. It is the switching time adjustment coefficient. This is used to control the adjustment range at the switching time; It is the adjustment coefficient for the opening degree of the waste heat recovery valve. It is used to control the adjustment range of the opening of the waste heat recovery valve.

7. The dew point regeneration energy-saving control method for a medical compressed air system according to claim 6, characterized in that: Define the deviation scaling factor for the i-th desiccant dryer. The relative ratio of the branch pipe pressure dew point value to the main pipe pressure dew point value; For each desiccant, the set of adsorption state parameters is defined as the set of feedback data characterized by the adsorption state parameters. A first correlation function between the deviation proportionality factor and the adsorption state parameters was established using a multiple linear regression model. Based on the regression model output of the first correlation function, calculate the adjustment coefficient for the primary calibration mechanism in the i-th desiccant dryer. ; in, These are adjustment parameters used to limit the adjustment range.

8. The dew point regeneration energy-saving control method for a medical compressed air system according to claim 7, characterized in that: The generated adjustment coefficient The primary calibration mechanism affects the timing of the desiccant switch. and waste heat recovery valve opening Adjustments: in, It is the switching time of the i-th desiccant after adjustment of the adjustment coefficient; It is the opening degree of the waste heat recovery valve of the i-th desiccant after adjustment by the adjustment coefficient.

Images