Cryogenic nitrogen making machine cluster control method, system and device and storage medium

Abstract

The invention relates to the technical field of cryogenic nitrogen making machine control, in particular to a cryogenic nitrogen making machine cluster control method, system and device and a storage medium. The control method comprises the following steps: collecting air compressor outlet air real-time parameters of each cryogenic nitrogen making machine in a cluster, current state parameters of molecular sieve adsorbers and precooler operation parameters, and predicting regeneration requirements of each molecular sieve adsorber in a future preset time period. According to the method, the operation parameters of each unit in the cluster are collected, and the future regeneration demand is predicted based on the time sequence model, so that a time window is provided for dynamic adjustment of waste heat utilization; on the basis, the dynamic heat contribution value is calculated based on the outlet parameters of the air compressor, the opening degree of the branch valve is adjusted in advance according to the dynamic heat contribution value, time matching and quantity matching of waste heat supply and regeneration requirements are achieved, and heat supply and demand imbalance caused by adjustment delay is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control of deep cold nitrogen making machine, in particular to a deep cold nitrogen making machine cluster control method, system, device and storage medium. BACKGROUND

[0002] As a technical support in the field of industrial gas preparation, the cluster operation of deep cold nitrogen making machine has become the mainstream direction to meet the demand for large-scale and high-stability nitrogen supply. The deep cold nitrogen making machine in the cluster realizes the oxygen-nitrogen separation of air through a molecular sieve adsorber, and the periodic regeneration of the molecular sieve is the key to maintaining the adsorption performance. The regeneration process requires a stable high-temperature hot gas stream, and the existing technology generally adopts a combination of air compressor outlet waste heat recovery and electric heater to meet the heat demand, that is, the form of main heat source and supplementary heat source, and adjusts the air compressor outlet temperature through a pre-cooler to ensure the adsorption efficiency of the molecular sieve. However, the existing cluster control scheme still has the limitation of superimposing single unit logic, and its defects are as follows: On the one hand, there is a time difference between the prediction of regeneration demand and the real-time adjustment of waste heat utilization, that is, the sensor data processing, waste heat calculation and valve adjustment depend on the serial execution of the software layer, which leads to the delay of adjustment instruction, and cannot accurately match the waste heat supply and demand at the start of regeneration, often resulting in insufficient heat and the need for electric heating to supplement energy, or excessive heat increasing the pre-cooling load.

[0003] On the other hand, the lag response characteristics of the pre-cooler are not linked with the valve adjustment, and the misalignment of the control timing of the two will cause fluctuations in the air compressor outlet temperature, which not only reduces the adjustment efficiency of the pre-cooler, but also affects the regeneration effect of the molecular sieve, resulting in increased energy consumption and unstable adsorption efficiency.

[0004] On the other hand, the existing technology does not optimize the waste heat recovery efficiency, pre-cooling energy consumption and regeneration effect as a system-level target, and often leads to unbalanced overall performance due to optimization of local links. Therefore, the technical problem to be solved by the present application is: how to build a dynamic cooperative control mechanism in the deep cold nitrogen making machine cluster to achieve the simultaneous achievement of energy consumption reduction and regeneration efficiency improvement. SUMMARY

[0005] The present disclosure proposes a deep cold nitrogen making machine cluster control method, system, device and storage medium, which aims to overcome at least one defect in the prior art.

[0006] To achieve the above-mentioned purpose, the technical scheme disclosed by the present application is as follows: According to one aspect of the present disclosure, a deep cold nitrogen making machine cluster control method is provided, comprising the following steps: Collecting real-time parameters of air at the outlet of the air compressor of each deep cold nitrogen making machine in the cluster, current state parameters of the molecular sieve adsorber and pre-cooler operation parameters, and predicting the regeneration demand of each molecular sieve adsorber in a future preset time period; According to the air compressor outlet air real-time parameter and the regeneration demand, a dynamic heat contribution value of the air compressor outlet waste heat is calculated, and a branch valve opening degree of the air compressor outlet to the molecular sieve electric heater is adjusted in advance based on the dynamic heat contribution value; According to the adjustment amount of the branch valve opening degree and a hysteresis response difference of the pre-cooler, a frequency of a frequency converter of the pre-cooler is linked and adjusted, and a hysteresis time compensation factor is introduced to offset the hysteresis response difference; Based on real-time running states and regeneration effect feedback of each deep cryogenic nitrogen generator in the cluster, a linkage relationship between the branch valve opening degree and the frequency converter frequency of the pre-cooler is dynamically adjusted through a multi-objective optimization model, so as to cooperatively optimize power consumption of the molecular sieve electric heater and the pre-cooler and regeneration efficiency of the molecular sieve adsorber.

[0007] Further, the prediction process of the regeneration demand is as follows: an autoregressive integral moving average model is used to fit a time sequence of adsorption saturation degrees of each molecular sieve adsorber, and a variation trend of the adsorption saturation degree in the future T hours is predicted; when the predicted adsorption saturation degree reaches a regeneration threshold value, a regeneration demand signal of the corresponding molecular sieve adsorber is generated, and the regeneration demand signal includes a regeneration start time, a target temperature and a required heat for regeneration.

[0008] Further, the dynamic heat contribution value quantifies coupling time-varying characteristics of temperature hysteresis effects and dirt accumulation by changing an upper limit, and an expression is as follows: , wherein Q contrib (t) is a dynamic heat contribution value at t moment, λ f is an equivalent thermal conductivity of the dirt layer, A eff is an effective heat exchange area of the waste heat recovery device, k δ is a dirt deposition reference coefficient, T out (τ) is an air compressor outlet temperature at historical moment τ, α is a temperature influence index, and β is a thermal hysteresis decay coefficient.

[0009] Further, a calculation method of the hysteresis time compensation factor is as follows: A time difference between a frequency converter frequency adjustment moment and a pre-cooler outlet temperature stabilization moment in historical running data of the pre-cooler is collected, and a hysteresis time of the pre-cooler is fitted; According to the hysteresis time, a compensation factor k γ is calculated: , wherein T sample is a parameter collection period, and γ is the hysteresis time; The compensation factor k γ is multiplied by an adjustment amount of the frequency converter frequency of the pre-cooler, so as to obtain an adjusted frequency value in advance, to offset the adjustment hysteresis of the pre-cooler.

[0010] Furthermore, the multi-objective optimization model employs a non-dominated sorting genetic algorithm II, the steps of which include: Set optimization objectives, including minimizing the total power consumption P of the molecular sieve electric heater. heater Minimize the total power consumption P of the precooler cooler and maximize the adsorption efficiency η after molecular sieve regeneration adsorb ; The branch valve opening θ of each unit i , frequency f of the precooler inverter i Using non-dominated ranking and crowding distance as decision variables, a Pareto front solution set is generated; Based on the cluster priority, the optimal decision variables are selected from the Pareto front solution set, and the operating parameters of each unit are dynamically adjusted.

[0011] Furthermore, it also includes cluster load balancing control steps: When the precooler load of any of the aforementioned cryogenic nitrogen generators is too high, calculate the load ΔP that needs to be transferred from the cryogenic nitrogen generator. cool ; Based on the waste heat recovery margin of the other cryogenic nitrogen generators in the cluster, the load ΔP cool Allocate the N cryogenic nitrogen generators with the largest margin, where N≥1; Adjust the branch valve opening of the assigned cryogenic nitrogen generator to increase waste heat recovery, and simultaneously reduce the frequency of its precooler inverter to share the precooling pressure of the high-load unit.

[0012] Furthermore, it also includes cluster redundancy control steps in fault conditions: When a fault is detected in the air compressor outlet branch valve of any of the cryogenic nitrogen generators, the waste heat recovery branch of the cryogenic nitrogen generator is cut off and switched to pure electric heating regeneration mode. Calculate the heat required for regeneration of the cryogenic nitrogen generator and broadcast the heat requirement for regeneration to other cryogenic nitrogen generators in the cluster; Based on the waste heat margin of other cryogenic nitrogen generators, the cryogenic nitrogen generator with the largest waste heat margin is selected as the waste heat supply end, and the waste heat of the waste heat supply end is transported to the molecular sieve electric heater of the faulty cryogenic nitrogen generator through the cluster waste heat transmission pipeline network. Adjust the branch valve opening of the cryogenic nitrogen generator at the waste heat supply end to the maximum and reduce the frequency of its precooler inverter; Adjust the power of the electric heater of the faulty unit to supplement only the heat demand not covered by the waste heat supply end.

[0013] Furthermore, when adjusting the frequency of the inverter of the precooling machine in the linkage adjustment, an adaptive correction based on ambient temperature is introduced, the steps of which include: Collecting the current ambient temperature, when the current ambient temperature is greater than the preset high temperature environment value, linearly increasing the pre-cooler frequency converter frequency adjustment amount; When the current ambient temperature is greater than the preset low temperature environment value and less than or equal to the preset high temperature environment value, the original adjustment amount is kept; When the current ambient temperature is less than the preset low temperature environment value, linearly reduce the pre-cooler frequency converter frequency adjustment amount.

[0014] Further, it also includes the correction step of the molecular sieve regeneration effect: After the molecular sieve adsorber completes regeneration, the dew point temperature of the outlet air is collected, which is used to reflect the adsorption efficiency; If the dew point temperature is higher than the target dew point temperature, calculate the regeneration heat deficiency: , Q target is the target heat of regeneration, Q contrib is the dynamic heat contribution value, Q heater is the heat provided by the electric heater; according to the regeneration heat deficiency ΔQ lack , increase the bypass valve opening degree and the electric heater power at the next regeneration, until the dew point temperature reaches the target value.

[0015] Further, the step of adjusting the bypass valve opening degree from the outlet of the air compressor to the molecular sieve electric heater includes: According to the predicted regeneration demand start time, adjust the bypass valve opening degree in advance Δt=2γ+T sample ; adjust the valve opening degree from the current value to the target value linearly to ensure that the waste heat recovery amount is equal to the regeneration demand amount when the regeneration demand starts.

[0016] Further, the multi-objective optimization model also uses the forgetting factor recursive least squares algorithm to update the model parameters, and the steps include: Collect the latest running data of the cluster every preset period, including the electric heater power consumption, the pre-cooler power consumption and the molecular sieve adsorption efficiency, correct the target weight coefficient in the model using the forgetting factor recursive least squares algorithm, and adapt to the changes of the cluster running state to stabilize the optimization effect.

[0017] Further, it also includes the segmented linkage control of the pre-cooler frequency converter, which divides the bypass valve opening degree into multiple intervals, and sets different frequency adjustment strategies for each interval.

[0018] Further, the plurality of intervals include a low interval, an intermediate interval, and a high interval; in the low interval, the frequency of the pre-cooler frequency converter is adjusted in a linear proportion, and the frequency variation is proportional to the valve opening variation; in the intermediate interval, the frequency of the pre-cooler frequency converter is adjusted in a non-linear exponential, and the frequency variation is accelerated to decrease with the increase of the valve opening; in the high interval, the frequency of the pre-cooler frequency converter is adjusted in a saturation limit, and the frequency is not lower than a preset percentage of the rated frequency, so as to avoid the shutdown of the unit.

[0019] According to another aspect of the present disclosure, a cryogenic nitrogen generator cluster control system is provided for implementing the cryogenic nitrogen generator cluster control method as described above, comprising: a data acquisition module for acquiring air compressor outlet parameters, molecular sieve state parameters, and pre-cooler operation parameters of each unit in the cluster; a demand prediction module for predicting future regeneration demand based on a time series model; a dynamic waste heat calculation module for calculating the dynamic heat contribution value of the air compressor outlet waste heat; a valve adjustment module for adjusting the branch valve opening in advance according to the dynamic waste heat calculation result; a pre-cooler lag compensation module and a linkage module for calculating the frequency converter frequency adjustment value with compensation according to the branch valve opening adjustment value and the pre-cooler lag characteristic, and linkage controlling the pre-cooler; a multi-objective optimization module for optimizing the electric heater power consumption, the pre-cooler power consumption, and the regeneration efficiency by a non-dominated sorting genetic algorithm II, and dynamically adjusting the operation parameters of each unit; a cluster load transfer and redundancy module for distributing the load to other cryogenic nitrogen generators to maintain the stability of the cluster when the load of a certain cryogenic nitrogen generator is too high or fails; a regeneration effect correction module for correcting the operation parameters of the next regeneration according to the adsorption efficiency of the molecular sieve after regeneration.

[0020] According to still another aspect of the present disclosure, a cryogenic nitrogen generator cluster control device is provided, comprising the cryogenic nitrogen generator cluster control system as described above, and further comprising: an edge side sensor preprocessing unit integrated with the air compressor outlet parameter sensor of the data acquisition module, and built-in hardware level filtering and temperature-flow correlation feature extraction circuit, for preprocessing the air compressor outlet high-frequency sampling data and outputting the dynamic coupling feature value to the dynamic waste heat calculation module; a time-sensitive network bus interaction unit connected to the data acquisition module, the dynamic waste heat calculation module, and the valve adjustment module, for controlling the transmission delay of the dynamic waste heat result to the valve adjustment instruction; The pre-cooling machine variable frequency drive compensation circuit is connected with a pre-cooling machine hysteresis compensation module and a frequency converter, a hardware PID compensation algorithm is built-in, the frequency of the frequency converter is corrected according to the pre-cooling machine heat dissipation hysteresis time, so that the hardware level hysteresis compensation is implemented; The cluster redundancy switching unit is connected with a cluster load transfer module and a unit load sensor, a load-redundancy matrix is realized through FPGA hardware logic, and the load transfer is triggered when the unit load is too high or fails. The molecular sieve adsorption efficiency calibration unit is connected with a regeneration effect correction module and a molecular sieve outlet infrared sensor, a hardware level gas concentration signal processing circuit is built-in, the nitrogen purity-adsorption efficiency correlation value is calculated in real time, and the calibrated regeneration parameter threshold is output to the dynamic waste heat calculation and multi-objective optimization module.

[0021] According to another aspect of the present disclosure, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps of the cryogenic nitrogen generator cluster control method as described above.

[0022] The present application has the following advantages: The present application solves the technical problem of insufficient dynamic coordination of the cryogenic nitrogen generator cluster by constructing a full-link collaborative control logic of regeneration demand prediction-dynamic waste heat calculation-advance valve adjustment-pre-cooling hysteresis compensation-multi-objective optimization.

[0023] Specifically, the present application provides a "time window" for dynamic adjustment of waste heat utilization by collecting the operating parameters of each unit in the cluster and predicting future regeneration demand based on a time series model; on this basis, the dynamic heat contribution value is calculated based on the outlet parameters of the air compressor, and the branch valve opening is adjusted in advance accordingly, realizing "time matching" and "quantity matching" of waste heat supply and regeneration demand, and avoiding imbalance between heat supply and demand due to adjustment delay.

[0024] Further, for the hysteresis response characteristics of the pre-cooling machine, a hysteresis time compensation factor is introduced to adjust the frequency of the pre-cooling machine variable frequency drive, eliminating the time sequence misalignment of valve adjustment and pre-cooling control, stabilizing the outlet temperature of the air compressor, and improving the pre-cooling machine adjustment efficiency.

[0025] Further, through the multi-objective optimization model, the power consumption of the electric heater, the power consumption of the pre-cooling machine and the regeneration efficiency of the molecular sieve are taken as the overall target, and the operating parameters of each unit are dynamically adjusted, realizing energy consumption optimization and regeneration effect improvement at the cluster level.

[0026] The control method of the application is not a simple combination of each link, but through the deep coupling of each step, a demand-supply-regulation synergistic regulation mechanism is constructed, which not only guarantees the stability of molecular sieve regeneration and the continuous standard of adsorption efficiency, but also significantly reduces the energy consumption of the cluster operation, synergistically optimizes the energy consumption of electric heating and pre-cooling machine, and provides a systematic technical solution for efficient and stable operation of the cryogenic nitrogen machine cluster.

[0027] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, and to implement the content of the specification, the following describes the preferred embodiments of the application in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The flow chart of the control method of the cryogenic nitrogen machine cluster in an embodiment of the application; Figure 2 The ARIMA prediction contrast chart of the adsorption saturation in an embodiment of the application; Figure 3 The three-dimensional surface chart of the dynamic heat contribution value in an embodiment of the application; Figure 4 The pre-cooling machine lag response and compensation effect contrast chart in an embodiment of the application; Figure 5 The heat map of the dynamic heat contribution value changing with the dirt coefficient in an embodiment of the application; Figure 6 The multi-objective optimization Pareto frontier and decision space chart in an embodiment of the application; Figure 7 The three-dimensional surface chart of the energy consumption-efficiency synergistic optimization in an embodiment of the application; Figure 8 The pre-cooling lag time and compensation factor relationship chart in an embodiment of the application; Figure 9 The fault redundancy load transfer heat map in an embodiment of the application; Figure 10 The dew point temperature iteration curve schematic diagram of the regeneration effect correction in an embodiment of the application; Figure 11 The forgetting factor RLS weight update curve schematic diagram in an embodiment of the application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0030] The term "comprising", used in the specification and claims of the instant application, including any variations thereof, such as "comprise" and "comprises", indicates an open limit that does not exclude other steps or elements, such as a series of steps or elements of a process, method, system, product or apparatus that comprises those expressly listed, but can include other steps or elements not expressly listed or inherent to such process, method, product or apparatus. Additionally, the use of "and / or", where ever used in the specification and claims, means at least one of the conjuncted objects, e.g., A and / or B, shall cover the three cases of A alone, B alone, and both A and B together.

[0031] In the embodiments of the present application, the word "exemplary" or "for example" is used to mean serving as an example, instance, or illustration. Any implementation described as "exemplary" or "for example" in the embodiments of the present application is not necessarily to be construed as preferred or advantageous over other implementations. Rather, the implementations described as "exemplary" or "for example" are intended to be illustrative and should not be considered as limiting the present application in any way. It should be noted that the word "exemplary" or "for example" is used in this specification to mean "serving as an example, instance, or illustration." Any implementation described as "exemplary" or "for example" in the embodiments of the present application is not necessarily to be construed as preferred or advantageous over other implementations.

[0032] The present application provides the following preferred embodiments: Embodiment one: to solve the problem of lack of system-level dynamic coordination in the operation of a cluster of cryogenic nitrogen generators, the present embodiment provides a control method for a cluster of cryogenic nitrogen generators, which realizes the coordinated optimization of energy consumption and regeneration efficiency by building a coupling mechanism of demand prediction, dynamic calculation of waste heat, lag compensation and multi-objective optimization, as shown in Figure 1 The flow of the control method is as follows: S100: collect the real-time parameters of air at the outlet of the air compressor of each cryogenic nitrogen generator in the cluster, the current state parameters of the molecular sieve adsorber, and the operating parameters of the pre-cooler, and predict the regeneration demand of each molecular sieve adsorber in a future preset time period.

[0033] S200: calculate the dynamic heat contribution value of the waste heat at the outlet of the air compressor according to the real-time parameters of air at the outlet of the air compressor and the regeneration demand, and adjust the opening degree of the branch valve from the outlet of the air compressor to the molecular sieve electric heater based on the dynamic heat contribution value.

[0034] S300: adjust the frequency of the frequency converter of the pre-cooler in linkage according to the adjustment amount of the opening degree of the branch valve and the lag response difference of the pre-cooler, and introduce a lag time compensation factor to offset the lag response difference.

[0035] S400: based on the real-time operating state of each cryogenic nitrogen generator in the cluster and the regeneration effect feedback, dynamically adjust the linkage relationship between the opening degree of the branch valve and the frequency of the frequency converter of the pre-cooler through a multi-objective optimization model, so as to coordinately optimize the power consumption of the molecular sieve electric heater and the pre-cooler and the regeneration efficiency of the molecular sieve adsorber.

[0036] Specifically, the real-time parameters of the air compressor outlet air temperature, pressure and flow rate of each cryogenic nitrogen generator in the cluster are collected, which directly reflect the recoverable potential of the waste heat of the air compressor; the current adsorption saturation, bed temperature and outlet dew point temperature of the molecular sieve adsorber are collected, which are used to judge the attenuation degree of the adsorption performance of the molecular sieve; the frequency of the frequency converter, the inlet temperature and the outlet temperature of the precooler are collected, which reflect the adjustment state of the precooler. Based on the collected time series data of the adsorption saturation of the molecular sieve adsorber, an autoregressive integrated moving average model is used for fitting. The model eliminates the non-stationarity of the data through difference processing, and captures random fluctuations through moving average terms. The output is the change trend of the adsorption saturation in the future preset time period. When the predicted adsorption saturation reaches the preset regeneration threshold, a regeneration demand signal of the corresponding molecular sieve adsorber is generated. The signal includes the regeneration start time, the target bed temperature and the heat required for regeneration, which provides a time node and a quantitative demand reference for the subsequent dynamic adjustment of the waste heat supply. As shown in Figure 2 , through the fitting of the autoregressive integrated moving average model to the time series of the adsorption saturation, the change trend of the future adsorption saturation can be accurately predicted, ensuring the timeliness and accuracy of the regeneration demand signal.

[0037] Further, according to the real-time parameters of the air compressor outlet air and the regeneration demand, the dynamic heat contribution value of the air compressor outlet waste heat is calculated. The calculation of the dynamic heat contribution value needs to consider the time-varying thermal resistance characteristics of the waste heat recovery device, i.e. the coupling effect of dirt accumulation and temperature hysteresis: the equivalent thermal conductivity of the dirt layer gradually decreases with the increase of the running time, the effective heat transfer area decreases with the adhesion of dirt, which directly affects the heat transfer efficiency; the temperature influence index reflects the nonlinear enhancement effect of the air compressor outlet temperature on heat transfer, the higher the outlet temperature, the more heat is transferred per unit time; the thermal hysteresis decay coefficient reflects the time delay of heat transfer from the air compressor outlet to the molecular sieve electric heater, and the influence of the outlet temperature at the historical time on the current heat contribution decreases with time. Based on these time-varying parameters, combined with the historical data of the air compressor outlet temperature, the dynamic heat contribution value is calculated by variable upper limit integration, and the integral term includes the outlet temperature at the historical time, the dirt deposition correction coefficient and the thermal hysteresis decay term. The integral result is the available waste heat at the current time. As shown in Figure 3 , the dynamic heat contribution value presents a three-dimensional surface feature with the change of time and the air compressor outlet temperature, the higher the outlet temperature and the shorter the running time, the less the dirt accumulation, the greater the dynamic heat contribution value; as shown in Figure 5 , the change of the dirt coefficient will significantly affect the dynamic heat contribution value, the longer the running time, the more the dirt accumulation, the lower the equivalent thermal conductivity, the smaller the effective heat transfer area, and the lower the heat transfer efficiency, so the dirt deposition correction coefficient needs to be introduced in real time in the calculation to ensure the accuracy of the result.

[0038] Further, based on the dynamic heat contribution value, the bypass valve opening degree from the air compressor outlet to the electric heater of the molecular sieve is adjusted in advance: according to the predicted regeneration demand start time, the valve opening degree is linearly adjusted from the current value to the target value in advance for a set time, which is determined in combination with the pre-cooler lag time and the parameter acquisition period, to ensure that the waste heat recovery amount exactly matches the heat required for regeneration at the moment of the start of the regeneration demand. If the dynamic heat contribution value is less than the heat required for regeneration, the valve opening degree is increased to increase the waste heat recovery amount; if the dynamic heat contribution value is greater than the heat required for regeneration, the valve opening degree is reduced to reduce the waste heat recovery amount, to avoid excessive heat increasing the pre-cooling load.

[0039] Further, according to the adjustment amount of the bypass valve opening degree and the lag response difference of the pre-cooler, the frequency of the frequency converter of the pre-cooler is adjusted in linkage. The lag response difference of the pre-cooler is derived from its mechanical and control characteristics. When adjusting the frequency of the frequency converter, the compressor speed needs to be changed gradually, and the flow and temperature of the cooling medium also need time to stabilize, so there is a time difference between the adjustment instruction and the actual effect. To quantify the time difference, the time difference between the frequency converter frequency adjustment time and the pre-cooler outlet temperature stabilization time in the historical operation data of the pre-cooler is calculated and fitted to obtain the lag time of the pre-cooler. The lag time compensation factor is introduced to offset the lag response difference. The compensation factor is 1 plus the ratio of the lag time to the parameter acquisition period, and its physical meaning is to advance the adjustment instruction of the pre-cooler to offset the lag effect. As shown in Figure 4 , the original response of the pre-cooler has obvious lag, and the target effect cannot be achieved until the lag time constant τ after the adjustment instruction is issued, and the response time can be significantly shortened by introducing the lag time compensation factor; as shown in Figure 8 , the compensation factor increases linearly with the increase of the pre-cooling lag time, and the longer the lag time, the larger the compensation factor, to offset the more significant lag effect. Specifically, the adjustment amount of the bypass valve opening degree is converted into the basic adjustment amount of the frequency converter frequency of the pre-cooler, and then the basic adjustment amount is multiplied by the lag time compensation factor to obtain the frequency value adjusted in advance. When the bypass valve opening degree increases to increase the waste heat recovery amount, the frequency of the pre-cooler is increased in advance to reduce the temperature of the air compressor outlet to prevent the high temperature from affecting the adsorption efficiency of the molecular sieve; when the bypass valve opening degree decreases to reduce the waste heat recovery amount, the frequency of the pre-cooler is reduced in advance to avoid excessive cooling and reduce the pre-cooling energy consumption, to ensure that the temperature adjustment effect of the pre-cooler is synchronized to the target at the completion of the valve opening degree adjustment, to avoid the outlet temperature fluctuation of the air compressor caused by the time sequence error of the two controls.

[0040] Further, based on the real-time running state of each deep nitrogen making machine in the cluster and the regeneration effect feedback, the linkage relationship between the branch valve opening degree and the pre-cooling machine frequency is dynamically adjusted through an adaptive multi-objective optimization model. The real-time running state of the cluster includes the power consumption of the electric heater, the power consumption of the pre-cooling machine, the outlet temperature of the air compressor and the bed temperature of the molecular sieve; the regeneration effect feedback includes the adsorption efficiency and the bed temperature uniformity of the molecular sieve after regeneration, wherein the adsorption efficiency is indirectly reflected by the outlet dew point temperature. The multi-objective optimization model aims to minimize the total power consumption of the molecular sieve electric heater, minimize the total power consumption of the pre-cooling machine and maximize the adsorption efficiency after the regeneration of the molecular sieve. The branch valve opening degree and the pre-cooling machine frequency of each unit are taken as the decision variables, and the non-dominated sorting genetic algorithm II is used for solution. The algorithm divides the solution into different levels through non-dominated sorting, and retains the optimal solution of the Pareto frontier; the diversity of the solution is maintained through the calculation of the crowding distance, so as to avoid falling into local optimum. Figure 6 As shown in FIG. 8, the Pareto frontier of the multi-objective optimization shows the trade-off relationship among the pre-cooling machine power consumption, the electric heater power consumption and the adsorption efficiency, and each point on the frontier is the optimal solution under different objectives. Every other preset period, the latest running data of the cluster is collected, and the forgetting factor recursive least squares algorithm is used to correct the target weight coefficient in the model. The forgetting factor gives higher weight to new data, and the weight of old data decays over time, ensuring that the model adapts to changes in the running state of the cluster, such as changes in environmental temperature and efficiency decline caused by unit wear. Figure 11 As shown in FIG. 9, through the update of the target weight coefficient by the forgetting factor recursive least squares algorithm, the changes in the running state of the cluster can be tracked in real time, ensuring the adaptive ability of the model. According to the priority of the cluster, such as energy consumption priority or efficiency priority, the optimal decision variable is selected from the Pareto frontier solution set: if the current priority of the cluster is energy consumption priority, the solution with the minimum total power consumption of the electric heater and the pre-cooling machine is selected; if the priority is efficiency priority, the solution with the highest adsorption efficiency is selected, and finally the branch valve opening degree and the pre-cooling frequency of each unit are dynamically adjusted to realize the adaptive optimization of the linkage relationship.

[0041] The benefits of the embodiment are that the time and heat demand of regeneration are determined through data collection and regeneration demand prediction, the available waste heat is quantified through dynamic heat contribution value calculation, the precise matching of heat supply in time and quantity is realized through advanced valve adjustment, the time sequence synchronization of valve adjustment and pre-cooling adjustment is realized through pre-cooling machine lag compensation, and the collaborative optimization of system-level objectives is realized through adaptive multi-objective optimization. Each step forms a closed loop of demand prediction, heat calculation, valve adjustment, pre-cooling compensation and optimization adjustment, and the output of each step is the input of the next step, finally building a dynamic collaborative mechanism at the system level to support the efficient operation of the deep nitrogen making machine cluster.

[0042] Embodiment Two: To solve the problem of the delay in triggering the regeneration demand and the inaccuracy in quantifying the regeneration parameters of the molecular sieve adsorber in the cluster of the deep refrigeration nitrogen generator, the embodiment further optimizes the prediction mechanism of the regeneration demand, and realizes the accurate prediction of the adsorption saturation degree and the quantitative output of the regeneration demand through a time series model.

[0043] It can be understood that the adsorption saturation degree is an index reflecting the attenuation of the molecular sieve adsorption performance, which presents a trend of rising over time, and is affected by random fluctuations of variables such as air compressor outlet temperature and air humidity. In this embodiment, an ARIMA model is used to fit the time series of the adsorption saturation degree of each molecular sieve adsorber. The model eliminates the non-stationarity of the time series through difference processing, captures the dependence of the adsorption saturation degree on the historical value through autoregressive terms, and processes random errors through moving average terms, thereby completely describing the variation law of the adsorption saturation degree. Based on the fitted model, the trend of the adsorption saturation degree in the next T hours is output. It should be understood that the value of T hours should be determined in combination with the molecular sieve adsorption period and the cluster regulation response time to ensure the practicality of the prediction results.

[0044] Further, when the predicted adsorption saturation degree reaches the preset regeneration threshold, the system automatically generates a regeneration demand signal for the corresponding molecular sieve adsorber. The regeneration start time is the time when the prediction curve intersects with the threshold, the target temperature is determined based on the optimal regeneration temperature interval of the molecular sieve adsorbent, and the heat required for regeneration is calculated by the cumulative increment of the adsorption saturation degree and the heat capacity of the molecular sieve bed. As shown in Figure 2 , the prediction curve of the ARIMA model has high consistency with the actual adsorption saturation degree curve, can accurately capture the rising trend and fluctuation characteristics of the adsorption saturation degree, and ensures the timeliness and accuracy of the regeneration demand signal.

[0045] The benefit of this embodiment is that the ARIMA model realizes accurate prediction of the adsorption saturation degree, avoiding the lag of relying on real-time values to trigger regeneration; the quantitative regeneration demand signal provides a clear time node and parameter reference for the dynamic adjustment of the subsequent waste heat supply, ensuring the synergy of the regeneration process and the waste heat supply, and supporting the stable operation of the cluster of the deep refrigeration nitrogen generator.

[0046] Embodiment Three: To solve the problem of the deviation in quantifying the heat contribution under the coupling effect of temperature thermal hysteresis and fouling accumulation in the waste heat recovery of the cluster of the deep refrigeration nitrogen generator, the embodiment further refines the calculation method of the dynamic heat contribution value, and realizes the accurate quantification of the heat contribution through a variable upper limit integral model, which is expressed as follows: , wherein Q contrib (t) is the dynamic heat contribution value at time t, λ f is the equivalent thermal conductivity of the fouling layer, A effk is the fouling deposition reference coefficient, T δ is the dynamic heat contribution value, k out is the air compressor outlet temperature at historical time τ, α is the temperature influence index, β is the thermal hysteresis decay coefficient, and t is the current time.

[0047] The waste heat of the cryogenic nitrogen generator cluster mainly comes from the high-temperature air at the outlet of the air compressor. The heat transfer to the molecular sieve regeneration system has a significant thermal hysteresis effect. The heat contribution at the current time not only depends on the current temperature, but also is affected by the temperature at the historical time, and this effect gradually decays over time. At the same time, the heat transfer surface of the waste heat recovery device will produce fouling deposition due to long-term operation, and the fouling layer will hinder heat conduction, resulting in changes in the effective heat transfer area and heat transfer efficiency over time. Traditional heat calculation methods often ignore the coupling and time-varying characteristics of the two, and only calculate based on real-time temperature and fixed heat transfer parameters, making it difficult to accurately reflect the actual heat contribution.

[0048] This embodiment uses a variable upper limit integral formula to calculate the dynamic heat contribution value, and the integral interval covers all historical τ points from the initial time to the current time t, which means that the dynamic heat contribution value is the cumulative result of the historical temperature influence. The negative β times t minus τ power of the exponential term e describes the time decay characteristics of the historical temperature influence through the decay coefficient β. The closer the historical temperature to the current time, the greater the influence on the current heat contribution, and vice versa, which is exponentially decaying, consistent with the physical law of thermal hysteresis effect. The equivalent thermal conductivity λ f of the fouling layer in the formula directly reflects the degree of fouling hindering heat conduction. As the running time increases, the accumulation of fouling leads to a decrease in λ f , and thus reduces the heat transfer efficiency; the fouling deposition reference coefficient k δ is related to the rate of fouling accumulation. The larger k δ , the faster the thickness of the fouling layer increases in the same time, and the faster the decline rate of λ f . The two work together to quantify the time-varying characteristics of fouling accumulation. The effective heat transfer area A eff is adjusted in real time in combination with the fouling deposition situation to ensure that the model responds to the actual state of the heat transfer system. The temperature influence index α is used to describe the nonlinear influence of the air compressor outlet temperature on the heat contribution. When the outlet temperature is higher than a certain threshold, the heat contribution increment brought by the temperature rise will gradually slow down, consistent with the nonlinear law of heat transfer.

[0049] As shown in Figure 3 , the dynamic heat contribution value presents a continuous surface feature with the change of the historical sequence of the air compressor outlet temperature and the fouling coefficient, reflecting the coupling influence of temperature thermal hysteresis and fouling accumulation; as shown in Figure 5 , when the fouling coefficient increases, i.e., the fouling layer becomes thicker, the dynamic heat contribution value shows a regional downward trend, which is consistent with the model λ fThe action logic of the three parameters is consistent. It needs to be understood that the parameters in the model are not fixed values, but are dynamically updated with running time and working conditions: λ f By regularly detecting the thickness of the fouling layer, β is adjusted based on the thermal resistance characteristics of the residual heat transfer path, and α is calibrated through the correlation analysis of historical temperature and heat contribution.

[0050] The benefits of this embodiment are that by changing the upper limit integral model, the time-varying decay of temperature hysteresis and the time-varying resistance of fouling accumulation are included in the same quantitative framework, achieving accurate calculation of dynamic heat contribution value; this model not only covers the cumulative impact of historical temperature, but also adjusts the heat conduction efficiency in real time through fouling-related parameters, providing accurate quantitative basis for subsequent heat supply and demand matching of the regeneration system, supporting dynamic optimization of residual heat utilization of the cryogenic nitrogen generator cluster.

[0051] Embodiment four: To solve the problem of lag between frequency regulation instruction and outlet temperature stability during the pre-cooler adjustment process of the cryogenic nitrogen generator, this embodiment further optimizes the lag time compensation strategy of the pre-cooler, realizes advance regulation by quantifying the lag time and calculating the compensation factor, and offsets the impact of regulation lag on temperature control.

[0052] Specifically, the pre-cooler of the cryogenic nitrogen generator is responsible for cooling the high-temperature air at the outlet of the air compressor, and the frequency regulation of its frequency converter is directly related to the refrigeration output, which in turn affects the outlet temperature; however, due to the heat capacity of the heat exchanger, the flow inertia of the medium, and other physical properties, the outlet temperature cannot be immediately stabilized after frequency regulation, and it needs to go through a certain lag time to reach the target value. In the conventional regulation mode, the system issues a frequency instruction based on the current temperature deviation, and when the temperature stabilizes, the deviation has often expanded, so the stability of subsequent molecular sieve adsorption is affected. This embodiment fits the lag time based on historical data, and then calculates the compensation factor based on the correlation between the lag time and the collection period, turning the frequency regulation from passive response to advance prediction.

[0053] Specifically, the frequency regulation time of the frequency converter and the outlet temperature stabilization time in the historical operation data of the pre-cooler are collected, the time difference between the two times is calculated, and the lag time γ of the pre-cooler is obtained by fitting these time differences; the fitting process needs to reflect the trend of the lag time changing with the working conditions, for example, using linear regression or exponential model to process multiple sets of historical time differences to obtain the γ value that meets the current operating state. Further, combined with the parameter collection period T sample The compensation factor k is calculated as follows: γ ; where the parameter collection period T sample is the sampling interval of the system to the pre-cooler state (such as frequency, temperature), i.e. the parameter collection period, which is directly related to the sensing frequency of the system to the state change, i.e. T sample ​The smaller the value, the more frequently the system tracks the precooler's status, requiring a larger compensation factor for the same lag time to achieve timely adjustments. Subsequently, the compensation factor k... γ Multiply by the original frequency adjustment amount of the inverter to obtain the pre-adjusted frequency value, and send this value as the actual adjustment command to the pre-cooling inverter.

[0054] It's important to understand that the compensation factor works by amplifying the frequency adjustment, thus accelerating the rate of change in the precooler outlet temperature and ensuring the temperature accurately reaches the target value within the lag time. For example, if conventional adjustment requires a frequency change Δf to adjust the temperature from T1 to T2, the temperature needs a time interval γ to stabilize; after compensation, the adjustment frequency becomes k. γ Δf, the rate of temperature change accelerates, and it can stabilize earlier within time γ, thus offsetting the hysteresis effect. For example... Figure 4 As shown, the time it takes for the original response curve to reach 90% of the regulation effect is significantly longer than that of the compensated response curve. The time difference between the two directly reflects the effect of the compensation strategy on offsetting the hysteresis. Figure 8 The three-dimensional surface plot shows the compensation factor k γ The relationship between the lag time γ and the acquisition period Tsample is such that when γ increases or Tsample decreases, k γ The trend is increasing, which is consistent with the compensation logic that "the more obvious the lag and the more frequent the sampling, the greater the lead time is required".

[0055] Furthermore, the lag time γ needs to be dynamically updated because the thermal resistance of the precooler changes with operating time. For example, fouling on the heat exchanger surface can increase thermal resistance, thus extending the lag time γ. The system needs to periodically collect the latest adjustment and stability data to refit γ and ensure the compensation factor k is correct. γ Matches the current operating conditions. Parameter acquisition period T sample The settings are based on the system's control accuracy requirements, and a balance needs to be struck between control accuracy and data processing load. sample Too small a value will increase the system's computational load, while too large a value may lead to untimely compensation. Adjustments need to be made based on actual operational needs.

[0056] The advantage of this embodiment is that, by quantifying the correlation between lag time and compensation factor, it achieves the correction of the lead time of the precooler adjustment, enabling the outlet temperature to track the target value more timely; the compensation strategy only corrects the frequency adjustment amount through software algorithm, without changing the hardware structure of the precooler, thus adapting to the upgrade needs of existing cryogenic nitrogen generator clusters; combined with Figure 4 Response comparison and Figure 8 The relationship between the factors changes can be intuitively verified, ensuring the effectiveness and feasibility of the strategy.

[0057] Example 5: To address the synergistic optimization problem among the power consumption of the molecular sieve electric heater, the power consumption of the precooler, and the adsorption efficiency after regeneration in the operation of a cryogenic nitrogen generator cluster, this example further refines the multi-objective optimization strategy based on the non-dominated sorting genetic algorithm II. By setting clear optimization objectives and decision variables, a Pareto front solution set is generated and the optimal operating parameters are selected by matching the cluster priority.

[0058] The stable operation of a cryogenic nitrogen generator cluster depends on a balance between the temperature control of the precooling system, the energy supply of the regeneration system, and the adsorption efficiency of the molecular sieve. Lowering the frequency of the precooler's inverter reduces power consumption but may lead to an increase in outlet temperature, increasing the adsorption load on the molecular sieve. Increased power consumption of the electric heater can enhance the regeneration effect but increases operating costs. Meanwhile, the molecular sieve adsorption efficiency is directly related to the purity and yield of the product nitrogen. Conventional single-objective optimization often compromises one aspect for another; for example, pursuing low energy consumption may lead to insufficient regeneration and decreased adsorption efficiency, while the opposite may result in a surge in energy consumption. This embodiment uses a multi-objective optimization framework, incorporating three conflicting objectives into the same model. Utilizing the global search capability of the non-dominated sorting genetic algorithm II, it finds the set of all solutions that cannot improve one objective without sacrificing another.

[0059] Specifically, three optimization objectives are first set: minimizing the total power consumption P of the molecular sieve electric heater. heater Minimize the total power consumption P of the precooler cooler Maximize the adsorption efficiency η after molecular sieve regeneration adsorb These objectives cover the core cost and performance indicators of cluster operation. Their conflict arises because increasing the power consumption of the electric heater can raise the regeneration temperature and enhance the adsorption capacity of the molecular sieve, but it directly increases energy consumption; increasing the precooler frequency can lower the outlet temperature and optimize the air conditions before adsorption, but it increases the power consumption of the precooling system. To quantify these objectives, a correlation model needs to be established based on historical cluster operating data, such as P... heater Linear relationship with regeneration temperature, P cooler The quadratic function relationship between the inverter frequency and η adsorb The nonlinear relationship between the precooling outlet temperature and the regeneration temperature can be obtained through... Figure 7 The three-dimensional surface diagram of energy consumption-efficiency synergistic optimization is intuitively displayed: the height of the surface represents the adsorption efficiency, the two dimensions of the bottom surface are the power consumption of the precooler and the electric heater, respectively, and the tilt direction of the surface reflects the trade-off trend between the objectives. That is, when the power consumption of the precooler increases from 20kW to 40kW, the adsorption efficiency increases from 85% to 92%, but the power consumption of the electric heater needs to be reduced from 15kW to 10kW to maintain the overall energy consumption balance.

[0060] Furthermore, the decision variable is determined as follows: the branch valve opening θ of each unit. i With the frequency f of the precooler inverter iThe opening degree of the branch valves controls the airflow distribution to each nitrogen generator, directly affecting the processing load of a single unit; the frequency regulation of the precooler's inverter adjusts the cooling capacity, determining the outlet air temperature. Both factors jointly affect energy consumption and adsorption efficiency. Decision variables must meet actual operational constraints: the branch valve opening range is 0 to 100% to avoid flow distribution imbalance; the precooler inverter frequency range is 20 to 50 Hz to ensure stable compressor operation. These constraints ensure the generated solution has engineering feasibility.

[0061] Furthermore, a Pareto front solution set is generated using a non-dominated sorting genetic algorithm II. The algorithm first generates an initial population, typically containing 100 to 200 individuals, each corresponding to a set of decision variables (θ). i , f i Then, non-dominated sorting is performed: solutions in the population are classified according to their dominance relationships. Solutions on the same non-dominated front are not mutually dominant, meaning it is impossible to increase the value of one objective without decreasing the value of another. For example, if the P of solution A... heater For 12kW, P cooler For 25kW, η adsorb The value is 90%, so the solution to B is P. heater For 15kW, P cooler 22kW, η adsorb If the density is 88%, then solutions A and B are not mutually dominant and belong to the same front. Next, the crowding distance is calculated: for each solution within a non-dominated front, the density of surrounding solutions is calculated. The larger the crowding distance, the better the diversity of solutions, avoiding the concentration of the solution set in a certain region, such as excessive bias towards low-energy or high-efficiency solutions. The next generation population is generated through crossover operations (e.g., single-point crossover) and mutation operations (e.g., randomly perturbing 10% of variable values), retaining solutions with large crowding distances in the non-dominated front, ensuring that the solution set converges to the Pareto front while maintaining diversity.

[0062] Furthermore, after the iteration terminates, typically after 50 to 100 generations, the final non-dominated front obtained is the Pareto front solution set, corresponding to... Figure 6 The Pareto front and decision space diagram for multi-objective optimization is shown. The blue dots in the diagram represent the Pareto front, and the red ideal points represent the theoretical points where all three objectives are optimal. heater =10kW, P cooler =20kW、η adsorb =95%, and the best compromise point for black is the actual optimal solution selected based on cluster priority.

[0063] Furthermore, the optimal decision variable is selected from the Pareto front solution set based on the cluster's operational priority. If the cluster is operating under conditions requiring high nitrogen purity (i.e., regeneration efficiency is prioritized), then η is selected. adsorb The largest solution, for example Figure 6 η adsorbFor the point with 93% probability, the corresponding decision variables are increasing the electric heater power consumption to 18kW (raising the regeneration temperature to 200℃) and increasing the precooler frequency to 45Hz (lowering the outlet temperature to 10℃). If it is during peak electricity consumption periods, then energy consumption is prioritized, and option P is chosen. heater With P cooler The solution with the minimum sum, for example, at a point with a total power consumption of 30kW, corresponds to decision variables such as reducing the electric heater power consumption to 10kW and the precooler frequency to 25Hz; if a balance between energy consumption and efficiency is required, then the solution with the maximum congestion distance is chosen, for example... Figure 6 The optimal compromise point in the middle, its P heater =14kW, P cooler =28kW、η adsorb =91%, to ensure that all targets are at a relatively good level.

[0064] It's important to understand that the value of the Pareto front solution set lies in providing all possible trade-offs, while priority matching translates the algorithm output into practically executable runtime parameters. For example, when a cluster needs to improve regeneration efficiency, the system will select η from the Pareto front. adsorb The highest solution corresponds to adjusting the branch valve openings to 80%, 75%, 85%, 70%, and 90% for units 1 to 5 respectively, thus increasing the flow rate of the high-load units. The precooler frequency is adjusted to 42Hz, which reduces the outlet temperature. These parameter adjustments are reflected in… Figure 7 In the three-dimensional surface plot, that is, from the inefficient region (P) of the surface. heater =10kW, P cooler =20kW、η adsorb =85%) towards high-efficiency areas (P heater =18kW, P cooler =45kW、η adsorb =93%) moved.

[0065] The advantage of this embodiment is that it achieves multi-objective collaborative optimization through the non-dominated sorting genetic algorithm II, avoiding the limitations of single-objective optimization; the Pareto front solution set covers all possible trade-off points, providing a flexible parameter selection space for the cluster; combined with Figure 6 and Figure 7 The visualization allows maintenance personnel to intuitively understand the relationships between various objectives and quickly match priorities to select the optimal parameters. Through this embodiment, the adjustment of operating parameters for the cryogenic nitrogen generator cluster has shifted from experience-driven to data-driven, ensuring a balance between energy consumption and efficiency under different operating conditions.

[0066] Embodiment six: To solve the problem of pressure distribution of the pre-cooling machine of a single unit in the cluster of deep cold nitrogen making machines, the embodiment further refines the cluster load transfer control logic, and realizes the pre-cooling pressure dispersion of the high-load unit through the linkage adjustment of the waste heat recovery margin matching and the operating parameters.

[0067] Specifically, when the frequency of the pre-cooling machine frequency converter of a certain deep cold nitrogen making machine exceeds 80% of the rated frequency, the system first calculates the pre-cooling load amount ΔP that needs to be transferred by the unit cool , which corresponds to the load size that needs to be dispersed to reduce the pre-cooling load of the unit to the safe range. Then the system obtains the waste heat recovery margin of other deep cold nitrogen making machines in the cluster, that is, the difference between the actual recovered heat and the maximum recoverable heat of each unit, and the larger the margin, the stronger the ability of the unit to replace pre-cooling with waste heat. Based on this margin distribution, the system distributes ΔP cool to the N units with the largest margin, N≥1, to ensure that the load distribution matches the waste heat utilization capacity of the units.

[0068] Further, for the units allocated with the load, the system first adjusts the branch valve opening degree to increase the air flow into the unit, thereby increasing the waste heat recovery amount of the unit; and simultaneously reduces the frequency of the pre-cooling machine frequency converter, because the increase in the waste heat recovery amount can partially replace the refrigeration demand of the pre-cooling machine, so as to reduce the operating load of the pre-cooling machine without affecting the air pre-cooling effect. As shown in the load transfer thermodynamic diagram Figure 9 , the system quickly locates the units that can receive the load based on the margin distribution of each unit, to realize the linkage of dynamic load distribution and parameter adjustment, wherein the color depth corresponds to the margin size.

[0069] The benefit of the embodiment is that the precise dispersion of the high-load pressure of the pre-cooling machine is realized through the linkage of the waste heat recovery margin and the load transfer, avoiding the equipment wear caused by the long-term high-load operation of a single unit; and the synchronous adjustment of the branch valve opening degree and the pre-cooling machine frequency ensures the stability of the overall pre-cooling effect of the cluster during the load transfer process, and maintains the consistency of the molecular sieve adsorption efficiency.

[0070] Embodiment seven: To solve the problem of regenerative heat supply of a single air compressor outlet branch valve in the cluster of deep cold nitrogen making machines, the embodiment further refines the redundancy control process under the fault state, and maintains the stable regeneration effect of the fault unit through the linkage adjustment of the cluster waste heat sharing and the operating parameters.

[0071] In this fact example, when a valve fault of the air compressor outlet branch of a deep cooling nitrogen generator is detected, the system first cuts off the waste heat recovery branch of the unit, switches to the pure electric heating regeneration mode, and the valve fault may cause abnormal air flow in the waste heat recovery branch. Cutting off the branch can avoid the disturbance of abnormal waste heat input to the stability of the regeneration process. Then the system calculates the heat required for the molecular sieve regeneration of the fault unit, which corresponds to the minimum heat threshold required to maintain the adsorption performance of the molecular sieve, and broadcasts this requirement to all other deep cooling nitrogen generators in the cluster to ensure the targeting of subsequent waste heat supply.

[0072] Further, the system obtains the waste heat margin of other units in the cluster, i.e. the difference between the actual heat recovered by each unit and the maximum recoverable heat, and the larger the margin, the more waste heat the unit can output. Based on this margin distribution, the system selects the unit with the largest margin as the waste heat supply end, as shown in the fault redundancy load transfer thermodynamic diagram. Figure 9 As shown in the fault redundancy load transfer thermodynamic diagram, the color depth directly reflects the margin level of each unit, and the supply end with sufficient waste heat output capacity can be quickly located. After selecting the supply end, the system transfers the waste heat of the supply end to the molecular sieve electric heater of the fault unit through the cluster waste heat delivery pipe network, realizes the cross-unit transfer of waste heat, and fills the heat gap of the fault unit using cluster resources.

[0073] It should be understood that in order to improve the waste heat output capacity of the supply end, the system adjusts the branch valve opening degree to the maximum to increase the air flow into the supply end, i.e. the increase of air flow will increase the waste heat recovery of the unit, thereby providing more waste heat for the fault unit; simultaneously, the frequency converter frequency of the pre-cooler of the supply end is reduced, because the increase of waste heat recovery can partially replace the refrigeration demand of the pre-cooler, ensuring that the air pre-cooling effect of the supply end is not affected while outputting waste heat. For the fault unit, the system adjusts the power of the electric heater to only supplement the heat demand not covered by the waste heat of the supply end, which not only reduces the electric heating energy consumption by using cluster waste heat, but also supplements the difference through electric heating to ensure that the total heat required for regeneration meets the requirements of molecular sieve regeneration.

[0074] The benefits of this embodiment are that through the operation mode switching of the fault unit and the cluster waste heat sharing, the heat supply required for molecular sieve regeneration is maintained in the valve fault state; at the same time, the parameter linkage adjustment of the supply end and the fault unit ensures the efficiency of waste heat utilization and the stability of regeneration effect, avoiding the influence of the fault on the overall nitrogen production performance of the cluster.

[0075] Embodiment Eight: To solve the problem of adjustment deviation caused by environmental temperature fluctuation in the frequency adjustment process of the pre-cooler frequency converter, this embodiment further optimizes the linkage adjustment logic of the pre-cooler frequency, and introduces an adaptive correction mechanism for the environmental temperature.

[0076] Specifically, the system first collects the current ambient temperature, which directly reflects the initial enthalpy state of the air and is a key factor affecting the pre-cooling refrigeration demand. When the current ambient temperature is greater than the preset high-temperature ambient value, it indicates that the initial temperature of the air is high, and the pre-cooling machine needs a larger refrigeration capacity to reduce the air to the target temperature range. At this time, the system linearly increases the pre-cooling machine frequency converter frequency adjustment amount, that is, the linear adjustment continuity matches the gradual influence of the ambient temperature on the refrigeration demand, avoiding the sudden change of the adjustment amount causing the pre-cooling effect to fluctuate. Figure 4 As shown in the pre-cooling machine lag response and compensation effect comparison diagram, the pre-cooling machine response curve after adaptive correction is closer to the ideal trajectory, which confirms the compensation effect of the increase of the adjustment amount on the increase of the refrigeration demand in high-temperature environment.

[0077] Further, when the current ambient temperature is greater than the preset low-temperature ambient value and less than or equal to the preset high-temperature ambient value, it indicates that the ambient temperature is in a normal range, and the influence of the initial enthalpy of the air on the pre-cooling demand is within the coverage range of the inherent adjustment capability of the pre-cooling machine. The system maintains the original adjustment amount to ensure that the pre-cooling machine operates stably according to the original logic and maintains the consistency of the pre-cooling effect.

[0078] Further, when the current ambient temperature is less than the preset low-temperature ambient value, it indicates that the initial temperature of the air is low, and the pre-cooling machine needs less refrigeration capacity. The system linearly reduces the pre-cooling machine frequency converter frequency adjustment amount, that is, reduces the adjustment amount to avoid excessive operation of the pre-cooling machine causing energy waste, and to prevent excessive refrigeration from causing the air temperature to be lower than the target value, avoiding the efficiency fluctuation of the subsequent molecular sieve adsorption caused by the air temperature being too low; as shown in the pre-cooling lag compensation factor surface diagram, Figure 8 As shown in the pre-cooling lag compensation factor surface diagram, the adjustment trend of the compensation factor decreases with the temperature in the low-temperature environment, which is consistent with the logic of the linear reduction of the adjustment amount in this embodiment, and together ensures the adjustment accuracy of the pre-cooling machine in the low-temperature environment.

[0079] It should be understood that the adaptive correction of the ambient temperature is not independent of the original linkage adjustment logic, but is embedded as a supplementary module in the whole process, that is, the system synchronously completes the correction judgment every time it collects the ambient temperature, ensuring that the adjustment amount matches the current environmental conditions in real time. This mechanism cooperates with the original load linkage and lag compensation logic to form a more perfect pre-cooling machine control strategy: the ambient temperature affects the initial enthalpy of the air, the enthalpy determines the pre-cooling demand, and the demand is converted into the adaptive adjustment of the adjustment amount, which ultimately points to the stability of the air temperature after pre-cooling.

[0080] The benefit of this embodiment is that through the adaptive correction of the ambient temperature, the pre-cooling machine frequency converter frequency adjustment amount dynamically matches the ambient temperature change, avoiding the effect deviation of the fixed adjustment amount in extreme temperature; the smoothness of the linear adjustment maintains the stability of the air temperature after pre-cooling, providing stable inlet conditions for the subsequent molecular sieve adsorption, and ensuring the continuity of the cluster nitrogen production process.

[0081] Embodiment Nine: To solve the problem of reduced adsorption efficiency caused by insufficient regeneration in the molecular sieve regeneration process, this embodiment further refines the correction mechanism of molecular sieve regeneration effect, and realizes dynamic adjustment of regeneration parameters through dew point temperature feedback.

[0082] Specifically, when the molecular sieve adsorber completes the regeneration process, the dew point temperature of the outlet air is immediately collected. The dew point temperature directly reflects the water content of the air, and the core goal of regeneration is to desorb the water adsorbed by the molecular sieve to restore its adsorption activity, so the dew point temperature is a direct representation of the regeneration effect. When the collected dew point temperature is higher than the target dew point temperature, it means that there is still water that has not been completely desorbed in the molecular sieve, and the regeneration is insufficient. At this time, the system calculates the insufficient amount of heat ΔQ lack , the formula is: , Q target is the target heat required for regeneration, which is determined based on the molecular sieve adsorption capacity and the current environmental conditions; Q heater is the direct heat provided by the electric heater for the regeneration process; Q contrib is the dynamic heat contribution value, which is the indirect heat input to the regeneration process by air pretreatment through auxiliary modules such as pre-coolers, as shown in the surface of dynamic heat contribution value changing with time and outlet temperature in Figure 3 , which clearly presents the real-time calculation logic of Q contrib , ensuring the accuracy of the heat deficiency evaluation.

[0083] Further, according to the calculated ΔQ lack , the system adjusts the parameters of the next regeneration process: on the one hand, it increases the valve opening of the regeneration branch to increase the flow of regeneration gas, that is, a larger flow can more fully carry away the water desorbed from the surface of the molecular sieve, strengthening the heat and mass transfer efficiency; on the other hand, it increases the power of the electric heater to directly supplement the insufficient heat required for regeneration. It needs to be understood that this adjustment is achieved through iteration: after each regeneration, the parameters of the next time are adjusted according to the dew point temperature feedback until the dew point temperature drops to within the target value. As shown in the regeneration effect correction curve in Figure 10 , the initial dew point temperature is higher than the target, and after two parameter adjustments, the dew point temperature gradually approaches and stabilizes within the target range, clearly presenting the closed-loop optimization process of the correction mechanism, that is, the dew point temperature drop after each adjustment in the curve is directly related to the calculated value of ΔQ lack , which confirms the pertinence of the correction logic.

[0084] It should be noted that the correction mechanism of the regeneration effect is connected with the control logic of other modules in the cluster: the dynamic calculation of Q contrib is based on the heat contribution model of Figure 3 , and the adjustment of the power of the electric heater is related to Figure 11The RLS weight update shown, it is explained that the electric heater weight is one of the target weights, and such connection ensures the coordination of the regeneration parameter adjustment and the modules such as the pre-cooler and the electric heater, and avoids the local adjustment from causing the overall system fluctuation. For example, when the electric heater power is increased, Figure 11 The electric heater weight in the formula is dynamically updated according to the regeneration effect feedback, and the power adjustment is balanced with the overall energy consumption control of the electric heater. Specifically, the RLS algorithm dynamically updates the weight coefficients of the electric heater, the pre-cooler and the regeneration efficiency through the following calculation formula: wherein W(n) is the weight vector of the n th iteration, W(n-1) is the weight vector of the (n-1) th iteration, i.e. the weight value of the last iteration; K(n) is the gain matrix of the n th iteration, which is used to reflect the influence degree of the current input variable on the weight adjustment, and the greater the value, the more sensitive the weight is to the error; σ is a forgetting factor, which controls the weight decay of the historical data; ϕ(n) T is the transpose of the input vector of the n th iteration, which contains the dew point temperature deviation, the regeneration heat shortage ΔQ lack and other state variables; ε(n) is the error signal of the n th iteration, i.e. the difference between the actual dew point temperature and the target dew point temperature, which directly represents the deviation between the regeneration effect and the expectation. Such weight update logic ensures that the electric heater power adjustment not only responds to the deviation of the regeneration effect, but also avoids the energy consumption fluctuation caused by frequent adjustment, and realizes the coordinated optimization of the regeneration effect and the energy consumption.

[0085] The benefits of the embodiment are that the quantitative evaluation of the regeneration effect is realized through the direct feedback of the dew point temperature, the targeted adjustment of the regeneration parameters is realized in combination with the calculation of the heat shortage, the molecular sieve can recover sufficient adsorption activity after each regeneration, the iterative logic of dynamic adjustment adapts to the decay of the molecular sieve performance with the use time and the change of the environmental conditions, the long-term stability of the regeneration effect is maintained, the reliable inlet conditions are provided for the subsequent adsorption process, and the continuity and reliability of the cluster nitrogen production are ensured.

[0086] The benefits of the embodiment are that the quantitative evaluation of the regeneration effect is realized through the direct feedback of the dew point temperature, the targeted adjustment of the regeneration parameters is realized in combination with the calculation of the heat shortage, the molecular sieve can recover sufficient adsorption activity after each regeneration, the iterative logic of dynamic adjustment adapts to the decay of the molecular sieve performance with the use time and the change of the environmental conditions, the long-term stability of the regeneration effect is maintained, the reliable inlet conditions are provided for the subsequent adsorption process, and the continuity and reliability of the cluster nitrogen production are ensured.

[0087] Example 10: To address the issue of the inability to match the waste heat recovery amount with the regeneration demand in real time due to system lag during regeneration startup, this example further refines the advance adjustment logic of the valve in the branch circuit from the air compressor outlet to the molecular sieve electric heater. By determining a reasonable advance time Δt, the valve opening is pre-adjusted to ensure accurate matching of heat supply and demand during regeneration startup.

[0088] Specifically, the calculation logic for the lead time Δt is based on the system's lag characteristics and valve adjustment requirements, and the formula is Δt = 2γ + T. sample Where γ is the thermal lag time of the precooler, such as... Figure 8 In the precooling hysteresis compensation factor surface shown, γ represents the response delay of the precooler from receiving the adjustment command to the change in output heat. This delay stems from the thermal inertia and fluid transport time loss within the precooler. The value of 2γ covers the dual hysteresis of the waste heat recovery system: on the one hand, the precooler's own response requires γ time; on the other hand, the transfer of waste heat from the air compressor outlet to the molecular sieve electric heater and its effective absorption by the regeneration system also requires γ time. The superposition of the dual hysteresis determines that a buffer time of 2γ needs to be reserved. T sample This parameter represents the time it takes for the valve to linearly adjust from its current opening to the target opening. It is set based on the valve's mechanical response characteristics; linear adjustment avoids system pressure fluctuations caused by sudden changes in opening. Figure 11 The RLS weight update logic shown remains consistent to ensure the stability of system operation.

[0089] Furthermore, the adjustment process is based on Figure 2 The adsorption saturation prediction result shown serves as the trigger point: when the predicted adsorption saturation value reaches the regeneration threshold, the system outputs the regeneration demand start time, and then initiates valve opening adjustment Δt time in advance. During the adjustment process, the valve opening linearly transitions from the current value to the target value—the target opening is determined by the matching relationship between the regeneration demand and the waste heat recovery, while the regeneration demand is based on... Figure 3 The dynamic heat contribution model calculation requires explanation of Q. target Waste heat recovery is a component of the regeneration target heat. It's important to understand that this linear adjustment design essentially distributes the change in valve opening across T. sample Within a given time period, to avoid interference from sudden changes in local parameters on the coordinated operation of modules such as the precooler and electric heater, for example, when the valve opening increases linearly, Figure 11 The precooler weight will be updated smoothly in sync to ensure that the precooler output matches the valve opening adjustment.

[0090] Further, when the regeneration demand is officially started, the valve has completed the linear adjustment and stabilized at the target opening, at which time the 2γ hysteresis time is just exhausted: the heat output response of the pre-cooler is completed, the residual heat also completes the transfer from the air compressor outlet to the regeneration system, and the residual heat recovery amount just matches the regeneration demand amount. This logic is highly consistent with the pre-cooling hysteresis compensation factor surface of Figure 8 , that is, γ, as the core hysteresis parameter of the pre-cooler, directly participates in the lead time calculation, ensuring that the adjustment logic is targeted to the pre-cooler characteristics; and Figure 2 the adsorption saturation prediction provides an accurate time starting point for the lead adjustment, forming a closed loop for the entire process.

[0091] The benefit of the embodiment is that by combining the pre-cooler hysteresis characteristics and the valve adjustment time to determine the lead time, the accurate matching of the residual heat recovery amount and the demand amount at the regeneration start is achieved, avoiding the insufficient regeneration of the molecular sieve or the waste of energy caused by the imbalance between heat supply and demand; at the same time, the linear adjustment mode ensures the stability of the system operation, unifies the cooperative control logic with the pre-coolers, electric heaters and other modules in the cluster, and maintains the stable and efficient operation of the overall system.

[0092] Embodiment Eleven: To solve the problem that the multi-objective optimization model cannot adapt to the dynamic changes of the cluster operating state due to fixed parameters, the embodiment further refines the parameter update logic of the multi-objective optimization model, and periodically corrects the target weight coefficients using the forgetting factor recursive least squares algorithm to maintain the stability of the optimization effect.

[0093] Specifically, the core of the multi-objective optimization model is to balance the weight coefficients of the electric heater power consumption, the pre-cooler power consumption and the molecular sieve adsorption efficiency, which directly determine the best compromise point position of the Pareto frontier shown in Figure 6 . When the cluster operating state changes, for example, the power consumption of the pre-cooler of a certain nitrogen generator increases due to the increase of the ambient temperature, or the molecular sieve adsorption efficiency slowly decays due to the use time, the fixed weight coefficients will make the decision of the optimization model deviate from the current optimal state. Therefore, the embodiment sets to collect the latest operating data of the cluster every preset period, including the real-time power consumption of the electric heater, the real-time power consumption of the pre-cooler and the current adsorption efficiency of the molecular sieve, and inputs these data into the forgetting factor recursive least squares algorithm to correct the target weight coefficients.

[0094] Further, the design logic of the forgetting factor recursive least squares algorithm is to apply weight decay to the historical data: over time, the influence of old data on model parameters gradually decreases, and newly collected operating data is given a higher weight, so that the model can dynamically track the latest state of the cluster. This logic is highly consistent with the forgetting factor recursive least squares algorithm of Figure 11The intuitive embodiment is that three weight curves: the electric heater weight, the pre-cooler weight, and the regeneration efficiency weight present a smooth dynamic change with the iteration process, and the change trend conforms to the exponential decay law, which not only avoids the interference of weight mutation on the system, but also ensures the rapid response of the model to the new state. For example, when the power consumption of the electric heater continuously increases due to the voltage fluctuation of the power grid, the electric heater weight coefficient will be adjusted through the algorithm, so that the multi-objective optimization model pays more attention to reducing the power consumption of the electric heater in subsequent optimization, while maintaining the balance with the power consumption of the pre-cooler and the adsorption efficiency of the molecular sieve; when the adsorption efficiency of the molecular sieve decreases due to insufficient regeneration, the regeneration efficiency weight will be adjusted accordingly, and the model optimization direction will be tilted to improve the regeneration efficiency.

[0095] It needs to be understood that this parameter updating logic is highly coordinated with the energy-efficiency collaborative optimization surface of Figure 7 The correction of the weight coefficient will simultaneously adjust the optimal decision space of the surface, so that the best compromise point always falls in the balance area of the power consumption of the electric heater, the power consumption of the pre-cooler, and the adsorption efficiency of the molecular sieve under the current state. For example, when the pre-cooler power consumption weight increases, the optimal compromise point of the collaborative optimization surface will move to a lower pre-cooler power consumption, but will always keep the adsorption efficiency within an acceptable range, ensuring the stability of the optimization effect.

[0096] The benefit of this embodiment is that by periodically collecting the latest running data and using the forgetting factor recursive least squares algorithm to correct the target weight coefficient, the multi-objective optimization model can dynamically track the changes of the cluster running state, maintain the optimization balance between the power consumption of the electric heater, the power consumption of the pre-cooler, and the adsorption efficiency of the molecular sieve, ensure the stability of the optimization effect, and avoid the deviation of the optimization decision caused by the state change.

[0097] Embodiment Twelve: To solve the problem that a single adjustment strategy cannot adapt to the dynamic characteristics of the full range of the branch valve in the pre-cooler frequency converter and branch valve opening degree linkage control, this embodiment further optimizes the control logic of the pre-cooler, adopts a segmented linkage control mode of the branch valve opening degree, divides the valve opening degree into multiple intervals, and sets different frequency adjustment strategies for each interval.

[0098] The refrigeration effect of the pre-cooler is directly related to the branch valve opening degree, that is, the change of the valve opening degree will change the air flow entering the pre-cooler, and then affect the thermal load of the pre-cooler. If a single frequency adjustment strategy such as linear adjustment is used throughout the process, the thermal hysteresis effect of the pre-cooler will be aggravated due to the rapid change of the frequency at a small valve opening, such as Figure 4The pre-cooling machine hysteresis response and compensation effect comparison chart shows that linear regulation at small opening degree easily leads to response overshoot, and when the valve is fully opened, the low frequency affects the stable operation of the pre-cooling machine. Therefore, in this embodiment, the valve opening degree is divided into multiple intervals, and each interval corresponds to a frequency regulation logic that adapts to its characteristics: when the valve opening degree is in different intervals, the frequency change law of the frequency converter matches the dynamic characteristics of the valve opening degree, that is, the small opening degree interval focuses on response speed, the medium opening degree interval focuses on non-linear adaptation, and the large opening degree interval focuses on operation protection. This segmented logic and Figure 8 The characteristics of the pre-cooling hysteresis compensation factor surface are coordinated: different opening degree intervals correspond to different regions of the surface, and the frequency regulation strategy and the compensation factor work together to optimize the pre-cooling machine's thermal hysteresis compensation effect.

[0099] The benefit of this embodiment is that by segmenting the branch valve opening degree control, the frequency converter frequency change better adapts to the dynamic characteristics of the valve full range, avoiding the regulation hysteresis or overshoot problem caused by a single strategy, improving the linkage control accuracy of the pre-cooling machine and the valve opening degree, and maintaining the stable operation of the pre-cooling system.

[0100] Embodiment Thirteen: To solve the problem of insufficient adaptability of the segmented linkage control in different intervals in Embodiment Twelve, this embodiment further refines the specific regulation strategy of the three intervals, divides the branch valve opening degree into low, medium and high intervals, and sets different frequency regulation logics for each interval.

[0101] Specifically, the low interval corresponds to the small opening degree range of the valve, at which time the air flow entering the pre-cooling machine is small, and the pre-cooling machine's refrigeration capacity is linearly related to the frequency of the frequency converter. This embodiment uses linear proportional regulation in this interval: the frequency change is proportional to the valve opening degree change, and the frequency increases by a fixed proportion when the valve opening degree increases, ensuring linear matching of the refrigeration capacity and the air flow, that is, this regulation method matches Figure 8 the linear region of the pre-cooling hysteresis compensation factor surface, and the linear change of the compensation factor is coordinated with the frequency regulation to optimize the pre-cooling response speed under small load.

[0102] Further, the medium interval corresponds to the medium opening degree range of the valve, at which time the air flow increases, leading to a decrease in the marginal effect of the pre-cooling machine heat exchange, and linear regulation cannot adapt to the non-linear demand of the thermal load. This embodiment uses non-linear exponential regulation in this interval: the frequency change decreases at an accelerating rate as the valve opening degree increases, and when the valve opening degree increases from the lower limit to the upper limit of the medium interval, the frequency increase rate gradually slows down, so that the refrigeration capacity increase rate matches the thermal load demand. This strategy corresponds to Figure 8 the non-linear region of the surface, and the non-linear change of the compensation factor is coordinated with the frequency regulation to avoid energy waste or insufficient pre-cooling under medium load.

[0103] Further, the high interval corresponds to a large valve opening range, at which the air flow is close to full load, and the pre-cooling machine refrigeration load peaks. In this interval, the saturation limit regulation is used in this embodiment: the frequency is not lower than the preset percentage of the rated frequency, and the valve opening exceeds the lower limit of the high interval, the frequency is no longer increased, and is maintained above a certain percentage of the rated frequency. This strategy corresponds to Figure 8 the saturation region of the curved surface, the upper limit of the compensation factor cooperates with the frequency limit to avoid the unit from being unable to maintain operation due to excessively low frequency, and to ensure stable operation under high load.

[0104] The benefits of this embodiment are that, through the differentiated regulation strategies of the three intervals, the frequency regulation is more accurately adapted to the system characteristics under different openings, the low interval ensures the response speed under small load, the middle interval adapts to the nonlinear demand under medium load, and the high interval protects the unit safety, thereby improving the adaptability of the segmented linkage control.

[0105] Embodiment fourteen: To solve the problems of insufficient coordination of each unit, insufficient waste heat utilization, and dynamic load response lag in the operation of a cryogenic nitrogen generator cluster, this embodiment provides a cryogenic nitrogen generator cluster control system, which integrates data acquisition, demand prediction, dynamic waste heat calculation, and multi-module cooperative control through modular design, to realize efficient and stable operation of the cluster.

[0106] Specifically, the data acquisition module acquires, through sensors deployed on each unit of the cluster, the temperature, pressure, and flow parameters of the air compressor outlet, the adsorption saturation of the molecular sieve, the regeneration inlet and outlet temperature and water content, and the frequency converter operating frequency, inlet and outlet medium temperature, and branch valve opening of the pre-cooling machine. All collected data are transmitted to the system core controller through the industrial bus, providing real-time and accurate basic data support for the operation of subsequent modules. The demand prediction module analyzes the collected molecular sieve adsorption saturation historical data based on the time series model, and predicts the regeneration demand time and regeneration load size of each unit within the next 24 hours, combined with the nitrogen demand in the future production plan, such as Figure 2 The adsorption saturation ARIMA prediction comparison chart as shown in the figure can give a regeneration demand warning 30 minutes to 1 hour in advance, providing a time window for subsequent dynamic waste heat utilization and valve regulation.

[0107] Further, the dynamic waste heat calculation module takes the air compressor outlet parameters as input, calculates the dynamic heat contribution value of the air compressor outlet waste heat to the regeneration system based on the heat exchange principle and real-time operating state, which reflects the proportion of waste heat that can replace the electric heater under the current working condition. Combined with the regeneration demand prediction result, the waste heat utilization strategy is determined, such as Figure 3The dynamic heat contribution value is plotted in three dimensions, and the calculation results dynamically adjust with changes in air compressor outlet temperature, flow rate, and molecular sieve regeneration stage, providing a quantitative basis for waste heat utilization for the valve adjustment module. Based on the heat contribution value output by the dynamic waste heat calculation module, the valve adjustment module proactively adjusts the opening of the branch valves connecting the air compressor outlet and the regeneration system. When the waste heat contribution value is higher than the threshold, the valve opening is increased in advance to introduce more waste heat; when the waste heat contribution value decreases, the valve is appropriately closed to avoid heat shortage in the regeneration system due to insufficient waste heat. This proactive adjustment strategy can shorten the heat response lag time of the regeneration system to less than 50% of the original scheme.

[0108] Furthermore, the precooler hysteresis compensation module and linkage module receive the opening adjustment amount from the valve regulating module, and simultaneously collect the inlet and outlet temperatures, operating frequency, and hysteresis time parameters of the precooler, based on... Figure 4 Comparison chart of precooler hysteresis response and compensation effect Figure 8 The relationship between precooling lag time and compensation factor is plotted to calculate the lag compensation factor of the precooler, thereby obtaining the compensated frequency regulation of the inverter. This ensures that the cooling capacity change of the precooler is synchronized with the branch valve opening adjustment, avoiding fluctuations in the molecular sieve inlet temperature caused by precooling lag. The multi-objective optimization module uses the power consumption of the electric heater, the power consumption of the precooler, and the regeneration efficiency as optimization objectives. It solves for the operating parameters of each unit using a non-dominated sorting genetic algorithm II, such as... Figure 6 The multi-objective optimization Pareto front and decision space graph shown demonstrates that the algorithm converges to the optimal solution set within 100 iterations. Figure 7 The system uses a three-dimensional surface graph to optimize energy consumption and efficiency, selecting the optimal compromise point under the current operating conditions and dynamically adjusting parameters such as the power of the electric heater, the frequency of the precooler, and the regeneration time of the molecular sieve to achieve synergistic optimization of the three.

[0109] Furthermore, the cluster load transfer and redundancy module monitors the load rate and operating status of each nitrogen generator in real time. When the load rate of a nitrogen generator exceeds 90% or a fault occurs and the unit stops, the module, based on the remaining capacity and operating efficiency of each unit, distributes the load of the faulty unit to other normal units according to the load balancing principle. Figure 9 The fault redundancy load transfer heatmap shows that the load distribution process is completed within 10 minutes, ensuring that the total nitrogen production of the cluster remains stable and meets the user's nitrogen demand. The regeneration effect correction module collects the adsorption saturation and dew point temperature parameters after each molecular sieve regeneration to evaluate the regeneration effect. If the adsorption efficiency is lower than the target value, then... Figure 10 The diagram shows the dew point temperature iteration curve for regeneration effect correction. By adjusting the electric heater power or regeneration time for the next regeneration, the regeneration effect can be gradually improved through iterative correction, ensuring that the molecular sieve always maintains a high adsorption capacity.

[0110] The benefits of the embodiment are that the functions of data acquisition, demand prediction, dynamic waste heat utilization, pre-cooling lag compensation, multi-objective optimization, load redundancy and regeneration correction are integrated through modular design, closed-loop cooperation is formed between the modules, the efficiency of cluster operation is realized, the stability under dynamic load is guaranteed, the system energy consumption is reduced through waste heat utilization and multi-objective optimization, and the regeneration efficiency is improved.

[0111] Embodiment Fifteen: To solve the problems of hardware layer data processing delay, insufficient compensation response speed, poor timeliness of redundancy switching and limited accuracy of adsorption efficiency calibration in the control of the cryogenic nitrogen generator cluster, the embodiment provides a control device. On the basis of the cluster control system, hardware and software modules are deeply cooperated through the addition of hardware level functional units, and the real-time performance and control accuracy of the device are improved.

[0112] Specifically, the edge side sensor preprocessing unit is integrated with the air compressor outlet parameter sensor of the data acquisition module, and a hardware level low-pass filter circuit and a temperature flow dynamic coupling feature extraction circuit are built-in. The high-frequency sampling data of the air compressor outlet temperature and flow are filtered in real time, and the noise introduced by electromagnetic interference is removed. Through hardware logic, the temperature and flow parameters are converted into dynamic coupling characteristic values reflecting the waste heat capacity, which are directly output to the dynamic waste heat calculation module. This unit avoids the delay of software layer data processing, makes the calculation of dynamic heat contribution value more consistent with the real-time state of the air compressor outlet waste heat, and provides a more reliable input basis for the accurate generation of the dynamic heat contribution value three-dimensional surface graph as shown in the figure. Figure 3

[0113] Further, the time-sensitive network bus interaction unit connects the data acquisition module, the dynamic waste heat calculation module and the valve adjustment module. The transmission delay of the dynamic waste heat calculation result to the valve adjustment instruction is controlled within milliseconds by using the time synchronization mechanism and flow scheduling function of the time-sensitive network. This low-delay transmission ensures that the valve adjustment module can execute the advance adjustment strategy based on the latest waste heat contribution value, avoiding the problem that the valve opening adjustment is out of sync with the waste heat change due to data transmission lag.

[0114] Further, the pre-cooling machine variable frequency drive compensation circuit connects the pre-cooling machine lag compensation module and the pre-cooling machine frequency converter, and a hardware PID compensation algorithm is built-in. When the pre-cooling machine lag compensation module outputs the compensation factor, this circuit directly corrects the frequency of the frequency converter according to the pre-cooling machine heat dissipation lag time at the hardware layer, realizing hardware level lag compensation. Compared with software compensation, the parallel processing characteristics of hardware PID greatly improve the compensation response speed, so that the refrigerating capacity change of the pre-cooling machine more accurately matches the branch valve opening adjustment, further optimizing the synchronization of the pre-cooling machine lag response and compensation effect as shown in the figure. Figure 4

[0115] ​​Further, the FPGA-based cluster redundancy switching unit connects the cluster load transfer and redundancy module and the unit load sensor, and realizes the load redundancy matrix through the parallel hardware logic of the FPGA. The unit receives the load rate data of each unit in real time, and when the load of a nitrogen generator is too high or fails, the load distribution instruction is triggered directly through the hardware logic to quickly transfer the load of the failed unit to other normal units. This hardware-level redundancy switching mechanism greatly shortens the load transfer time, ensures Figure 9 The load distribution process in the fault redundancy load transfer thermodynamic diagram shown in the figure can be completed in a shorter time, maintaining the stability of the cluster nitrogen production.

[0116] Further, the molecular sieve adsorption efficiency calibration unit connects the regeneration effect correction module and the molecular sieve outlet infrared sensor, and is provided with a hardware-level gas concentration signal processing circuit. The unit collects the concentration signal of the nitrogen gas at the outlet of the molecular sieve in real time, converts the concentration value into the purity of the nitrogen gas through hardware logic, calculates the correlation value between the purity of the nitrogen gas and the adsorption efficiency, and outputs the calibrated regeneration parameter threshold to the dynamic waste heat calculation module and the multi-objective optimization module. The calibrated threshold enables the dynamic waste heat calculation module to more accurately match the waste heat utilization and regeneration demand, and also enables the parameter adjustment of the multi-objective optimization module to be more in line with the actual adsorption capacity of the molecular sieve, providing Figure 10 The dew point temperature iteration curve for the regeneration effect correction shown in the figure provides a more accurate correction basis.

[0117] The benefits of the present embodiment are that through the cooperative design of hardware units and software modules, the underlying performance of the device is optimized from data preprocessing, transmission delay, compensation response, redundancy switching and efficiency calibration, etc. The real-time, accuracy and stability of the cluster control are further improved, and the linkage effect of the hardware layer and the software layer is strengthened, providing a more solid hardware support for the efficient operation of the cluster of cryogenic nitrogen generators.

[0118] Embodiment 16: To solve the problem of executable and cross-device deployment of the control method of the cluster of cryogenic nitrogen generators, the present embodiment provides a computer readable storage medium, which stores a computer program to realize the solidification and reuse of the control logic, ensuring that the cluster control method can be consistently executed on different hardware platforms.

[0119] Specifically, the computer program carried by the computer readable storage medium contains the complete logic of the cluster control of the cryogenic nitrogen generator, which, when loaded and executed by the processor, first initializes the communication link with the data acquisition module, the valve adjustment module, the pre-cooler frequency converter and the unit load sensor, and establishes a real-time data transmission channel. Subsequently, the program performs the following steps in turn: receiving the air compressor outlet temperature, pressure, flow, molecular sieve adsorption saturation and regeneration temperature, pre-cooler frequency converter frequency and other parameters transmitted by the data acquisition module, these data constitute the basis for subsequent calculation; running the time series model to analyze the historical data of the molecular sieve adsorption saturation, predicting the regeneration demand time and load size of each unit within the next 24 hours, corresponding to the adsorption saturation prediction result shown in FIG. 6; calculating the dynamic waste heat contribution value based on the air compressor outlet parameters, which directly guides the valve adjustment module to adjust the branch valve opening degree to match the waste heat utilization and the regeneration heat demand; calculating the compensation factor according to the pre-cooler lag time to drive the pre-cooler frequency drive compensation circuit to adjust the frequency converter frequency, corresponding to the pre-cooling lag time and compensation factor relationship shown in FIG. 7; performing multi-objective optimization on the power consumption of the electric heater, the power consumption of the pre-cooler and the regeneration efficiency through the non-dominated sorting genetic algorithm II, outputting the optimal operating parameters, corresponding to the multi-objective optimization Pareto frontier solution set shown in FIG. 8; when the unit load is too high or a fault is detected, triggering the load distribution logic of the cluster load transfer and redundancy module, corresponding to the fault redundancy load transfer thermodynamic diagram shown in FIG. 9; after each regeneration is completed, the electric heater power or time for the next regeneration is corrected according to the correlation value of the molecular sieve outlet nitrogen purity and the adsorption efficiency, corresponding to the dew point temperature iteration curve of the regeneration effect correction shown in FIG. 10. During the entire process, the program realizes instruction issuance and data return through the calling of the hardware interface, ensuring the consistency of the control logic and the hardware execution. Figure 2 Figure 8 Figure 6 Figure 9 Figure 10

[0120] The benefit of the present embodiment is that the cluster control method of the cryogenic nitrogen generator is solidified into an executable program through the computer readable storage medium, avoiding control effect deviation caused by hardware differences, while providing a convenient carrier for updating the control logic, which only needs to replace the program in the storage medium to realize the upgrade of the control strategy, improving the maintainability and expandability of the cluster control system, and making the control method better adapt to the dynamic operation demand of the cryogenic nitrogen generator cluster.

[0121] Although the present application has been described above with reference to the preferred embodiments thereof, it is to be understood that the application is not limited to the embodiments described above but that there are a variety of modifications and variations which can be made thereto without departing from the spirit of the present application. The scope of the application is to be interpreted in the broadest and most liberal manner consistent with the spirit of the appended claims and their equivalents.​​​​​

Claims

1. A method for controlling a cluster of cryogenic nitrogen generators, characterized in that the steps include... include: The system collects real-time air compressor outlet parameters, current status parameters of molecular sieve adsorbers, and precooler operating parameters of each cryogenic nitrogen generator in the cluster, and predicts the regeneration requirements of each molecular sieve adsorber within a preset time period. Based on the real-time parameters of the air outlet of the air compressor and the regeneration requirements, the dynamic heat contribution value of the waste heat at the air compressor outlet is calculated, and the opening of the branch valve from the air compressor outlet to the molecular sieve electric heater is adjusted in advance based on the dynamic heat contribution value. Based on the adjustment amount of the branch valve opening and the hysteresis response difference of the precooler, the frequency of the precooler's inverter is adjusted in linkage, and a hysteresis time compensation factor is introduced to offset the hysteresis response difference. Based on the real-time operating status and regeneration effect feedback of each cryogenic nitrogen generator in the cluster, the linkage between the opening degree of the branch valve and the frequency of the precooler inverter is dynamically adjusted through a multi-objective optimization model to synergistically optimize the power consumption of the molecular sieve electric heater and the precooler, as well as the regeneration efficiency of the molecular sieve adsorber.

2. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, The process of predicting the regeneration demand is as follows: the time series of adsorption saturation of each molecular sieve adsorber is fitted using an autoregressive integral moving average model to predict the trend of adsorption saturation change in the next T hours; when the predicted adsorption saturation reaches the regeneration threshold, a regeneration demand signal for the corresponding molecular sieve adsorber is generated, which includes the regeneration start time, target temperature and heat required for regeneration.

3. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, The dynamic heat contribution value is expressed as follows: The time-varying characteristics of the coupling between the temperature thermal hysteresis effect and fouling accumulation are quantified by variable upper limit integral quantification. , where Q contrib (t) represents the dynamic heat contribution value at time t, λ f A is the equivalent thermal conductivity of the fouling layer. eff k represents the effective heat exchange area of ​​the waste heat recovery device. δ T is the baseline coefficient for dirt deposition. out (τ) represents the air compressor outlet temperature at historical time τ, α represents the temperature influence index, β represents the thermal hysteresis decay coefficient, and t represents the current time.

4. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, The lag time compensation factor is calculated as follows: The time difference between the inverter frequency adjustment moment and the precooler outlet temperature stabilization moment in the historical operating data of the precooler is collected, and the lag time of the precooler is obtained by fitting. Calculate the compensation factor k based on the lag time. γ : T sample γ is the parameter acquisition period, and γ is the lag time. The compensation factor k γ Multiply by the frequency adjustment amount of the precooler inverter to obtain the pre-adjusted frequency value, in order to offset the adjustment lag of the precooler.

5. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, The multi-objective optimization model employs a non-dominated sorting genetic algorithm II, the steps of which include: Set optimization objectives, including minimizing the total power consumption P of the molecular sieve electric heater. heater Minimize the total power consumption P of the precooler cooler And to maximize the adsorption efficiency η after molecular sieve regeneration adsorb ; The branch valve opening θ of each unit i , frequency f of the precooler inverter i Using non-dominated ranking and crowding distance as decision variables, a Pareto front solution set is generated; Based on the cluster priority, the optimal decision variables are selected from the Pareto front solution set, and the operating parameters of each unit are dynamically adjusted.

6. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, It also includes cluster load balancing control steps: When the precooler load of any of the aforementioned cryogenic nitrogen generators is too high, calculate the load ΔP that needs to be transferred from the cryogenic nitrogen generator. cool ; Based on the waste heat recovery margin of the other cryogenic nitrogen generators in the cluster, the load ΔP cool Allocate the N cryogenic nitrogen generators with the largest margin, where N≥1; Adjust the branch valve opening of the assigned cryogenic nitrogen generator to increase waste heat recovery, and simultaneously reduce the frequency of its precooler inverter to share the precooling pressure of the high-load unit.

7. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, It also includes cluster redundancy control steps in fault conditions: When a fault is detected in the air compressor outlet branch valve of any of the cryogenic nitrogen generators, the waste heat recovery branch of the cryogenic nitrogen generator is cut off and switched to pure electric heating regeneration mode. Calculate the heat required for regeneration of the cryogenic nitrogen generator and broadcast the heat requirement for regeneration to other cryogenic nitrogen generators in the cluster; Based on the waste heat margin of other cryogenic nitrogen generators, the cryogenic nitrogen generator with the largest waste heat margin is selected as the waste heat supply end, and the waste heat of the waste heat supply end is transported to the molecular sieve electric heater of the faulty cryogenic nitrogen generator through the cluster waste heat transmission pipeline network. Adjust the branch valve opening of the cryogenic nitrogen generator at the waste heat supply end to the maximum and reduce the frequency of its precooler inverter; Adjust the power of the electric heater of the faulty unit to supplement only the heat demand not covered by the waste heat supply end.

8. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, When adjusting the frequency of the inverter in the precooling machine, an adaptive correction based on ambient temperature is introduced, and the steps include: The current ambient temperature is collected, and when the current ambient temperature is greater than the preset high temperature ambient value, the frequency adjustment of the precooler inverter is increased linearly. When the current ambient temperature is greater than the preset low temperature ambient value but less than or equal to the preset high temperature ambient value, the original adjustment amount is maintained. When the current ambient temperature is lower than the preset low temperature ambient value, the frequency adjustment amount of the precooler inverter is linearly reduced.

9. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, It also includes steps to correct the molecular sieve regeneration effect: After the molecular sieve adsorber has been regenerated, the dew point temperature of its outlet air is collected to reflect the adsorption efficiency. If the dew point temperature is higher than the target dew point temperature, then calculate the insufficient regeneration heat: Q target To regenerate the target heat, Q contrib Q represents the dynamic heat contribution value. heater The heat provided to the electric heater; based on the insufficient regenerated heat ΔQ lack Increase the branch valve opening and electric heater power during the next regeneration until the dew point temperature reaches the target value.

10. The cryogenic nitrogen generator cluster control method as described in claim 4, characterized in that, The step of pre-adjusting the opening of the branch valve from the air compressor outlet to the molecular sieve electric heater includes: Based on the predicted start time of regeneration demand, Δt = 2γ + T is made in advance. sample Adjust the opening of the branch valve; linearly adjust the valve opening from the current value to the target value to ensure that the amount of waste heat recovered is equal to the amount of regeneration demand when the regeneration demand is started.

11. The method for controlling a cluster of cryogenic nitrogen generators as described in claim 1, characterized in that, The multi-objective optimization model also employs a forgetting factor recursive least squares algorithm to update model parameters, including the following steps: The latest operating data of the cluster is collected at preset intervals, including the power consumption of electric heaters, power consumption of precoolers and molecular sieve adsorption efficiency. The target weight coefficients in the model are corrected using the forgetting factor recursive least squares algorithm to adapt to changes in the operating status of the cluster and stabilize the optimization effect.

12. The cryogenic nitrogen generator cluster control method as described in claim 1, characterized in that, It also includes segmented linkage control of the precooler frequency converter, which divides the opening degree of the branch valve into multiple intervals and sets different frequency adjustment strategies for each interval.

13. The cryogenic nitrogen generator cluster control method as described in claim 12, characterized in that, Multiple ranges include a low range, a middle range, and a high range. In the low range, the frequency of the precooler inverter is adjusted linearly proportionally, and the frequency change is proportional to the valve opening change. In the middle range, the frequency of the precooler inverter is adjusted nonlinearly, and the frequency change decreases rapidly as the valve opening increases. In the high range, the frequency of the precooler inverter is adjusted according to saturation limits, and the frequency is not lower than a preset percentage of the rated frequency to avoid unit shutdown.

14. A cryogenic nitrogen generator cluster control system, used to implement the cryogenic nitrogen generator cluster control method as described in any one of claims 1-13, characterized in that, include: The data acquisition module is used to collect the air compressor outlet parameters, molecular sieve status parameters, and precooler operating parameters of each unit in the cluster; The demand forecasting module is used to predict future recycling demand based on time series models. The dynamic waste heat calculation module is used to calculate the dynamic heat contribution value of the waste heat at the air compressor outlet. The valve adjustment module is used to adjust the opening of branch valves in advance based on the dynamic waste heat calculation results; The precooler hysteresis compensation module and linkage module are used to calculate the compensated frequency regulation of the frequency converter based on the branch valve opening adjustment amount and the hysteresis characteristics of the precooler, and to control the precooler in a linkage manner. The multi-objective optimization module is used to optimize the power consumption of electric heaters, power consumption of precoolers, and regeneration efficiency through non-dominated sorting genetic algorithm II, and dynamically adjust the operating parameters of each unit. The cluster load transfer and redundancy module is used to distribute the load to other cryogenic nitrogen generators and maintain cluster stability when one of the cryogenic nitrogen generators is overloaded or malfunctions. The regeneration effect correction module is used to adjust the operating parameters for the next regeneration based on the adsorption efficiency of the molecular sieve after regeneration.

15. A cryogenic nitrogen generator cluster control device, comprising the cryogenic nitrogen generator cluster control system as described in claim 14, characterized in that, Also includes: The edge-side sensor preprocessing unit is integrated with the air compressor outlet parameter sensor of the data acquisition module. It has a built-in hardware-level filtering and temperature-flow correlation feature extraction circuit, preprocesses the high-frequency sampling data of the air compressor outlet and outputs the dynamic coupling feature value to the dynamic waste heat calculation module. A time-sensitive network bus interaction unit connects the data acquisition module, the dynamic waste heat calculation module, and the valve adjustment module, and is used to control the transmission delay from the dynamic waste heat result to the valve adjustment command. The precooler inverter drive compensation circuit connects the precooler lag compensation module and the inverter. It has a built-in hardware PID compensation algorithm to correct the inverter frequency according to the precooler heat dissipation lag time in order to perform hardware-level lag compensation. The cluster redundancy switching unit connects the cluster load transfer module and the unit load sensor. It implements the load-redundancy matrix through FPGA hardware logic and triggers load transfer when the unit load is too high or a fault occurs. The molecular sieve adsorption efficiency calibration unit connects the regeneration effect correction module and the molecular sieve outlet infrared sensor. It has a built-in hardware-level gas concentration signal processing circuit to calculate the nitrogen purity-adsorption efficiency correlation value in real time, and outputs the calibrated regeneration parameter threshold to the dynamic waste heat calculation and multi-objective optimization module.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the cryogenic nitrogen generator cluster control method as described in any one of claims 1 to 13.

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