Intelligent load scheduling method and system for high-energy-consumption distillation section

Abstract

The present application relates to the technical field of distillation control, and discloses an intelligent load scheduling method and system for a high-energy-consumption distillation section. The method comprises the following steps: identifying high-energy-consumption time periods and load demand trends based on fluctuation characteristics, comparing the identification results with historical energy efficiency characteristics to output a load prediction result; establishing an energy efficiency optimization target according to the load prediction result, dynamically coordinating a load distribution ratio based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and converting the coordinated load distribution scheme into an optimized scheduling instruction; analyzing the optimized scheduling instruction to obtain a preliminary operation set point, and using a feedback control mechanism to correct the deviation between the preliminary operation set point and real-time monitoring parameters. The present application can improve the accuracy and timeliness of load scheduling, can guarantee the product quality stability of the distillation separation process, and can maximize the reduction of energy loss in the high-energy-consumption link, thereby effectively enhancing the energy efficiency level and overall reliability of the distillation section operation.

Description

Technical Field

[0001] This invention relates to the field of distillation control technology, and in particular to a method and system for intelligent load scheduling of high-energy-consuming distillation sections. Background Technology

[0002] Distillation, as a core unit operation for separating mixtures in industries such as chemical and petrochemical, relies on reboilers to provide heat and reflux systems to maintain separation accuracy. The energy consumption of these two types of equipment accounts for 30%-50% of the total energy consumption of the entire production process, making it a typical high-energy-consuming link. Traditional fixed load scheduling methods based on manual experience are difficult to respond to changes in operating conditions in real time, and are prone to problems such as excessive reboiler heating power and mismatch between reflux ratio and actual load, resulting in energy waste and increased operating costs. At the same time, if load scheduling cannot accurately match the separation demand, it may also lead to an imbalance in the gas-liquid two-phase flow state in the column, affecting product separation efficiency and quality stability. Therefore, the industry urgently needs to use intelligent load scheduling methods to achieve dynamic optimization of energy consumption and operating performance of the distillation section, so as to adapt to complex operating conditions and meet the industry's demand for energy conservation and consumption reduction.

[0003] In practical applications, existing load scheduling technologies for high-energy-consuming distillation sections often suffer from insufficient and incomplete perception of the distillation section's operating status. For example, in the parameter acquisition phase, the focus is often on conventional macroscopic parameters such as temperature and pressure, failing to fully capture microscopic characteristic parameters such as the flow state of the trays within the column and the equilibrium of gas-liquid distribution. This results in the constructed distillation section status data failing to fully reflect the actual operating conditions, thus affecting the accuracy of subsequent fluctuation feature extraction. Consequently, there are discrepancies between the identification of high-energy-consuming periods and the prediction of load demand trends. Furthermore, when establishing energy efficiency optimization targets, existing scheduling schemes often fail to fully correlate the coupling relationship between the decline trend of mass transfer efficiency within the column and load allocation. This leads to a lack of dynamic adaptability in load allocation coordination, making it difficult to adjust the load weights of each column section in real time according to changes in operating conditions. In addition, during the execution of scheduling instructions, the deviation correction mechanism for the initial operation setpoint often relies on single parameter feedback, failing to effectively consider the synchronous response characteristics between multiple devices and the temporal impact of dynamic changes in key components. This easily leads to inconsistencies between the timing of operation signals and scheduling instructions, ultimately resulting in the load scheduling effect failing to achieve the expected energy efficiency optimization targets and even affecting the operational stability of the distillation section. Summary of the Invention

[0004] This invention provides a method and system for intelligent load scheduling of high-energy-consuming distillation sections to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for intelligent load scheduling of high-energy-consuming distillation sections, comprising:

[0006] S1, collecting real-time operation parameters of the distillation section, verifying integrity of the real-time operation parameters to obtain a data integrity state, integrating the real-time operation parameters and the data integrity state to obtain distillation section state data;

[0007] S2, extracting fluctuation characteristics in the distillation section state data, identifying high energy consumption periods and load demand trends based on the fluctuation characteristics, comparing the identification results with historical energy efficiency characteristics to output a load prediction result;

[0008] S3, establishing an energy efficiency optimization target according to the load prediction result, dynamically coordinating a load distribution ratio based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and converting the coordinated load distribution scheme into an optimized scheduling instruction;

[0009] S4, analyzing the optimized scheduling instruction to obtain a preliminary operation set point, using a feedback control mechanism to correct the deviation between the preliminary operation set point and the real-time monitoring parameters to obtain a corrected operation signal;

[0010] S5, verifying the timing consistency of the corrected operation signal and the optimized scheduling instruction to obtain a final adjustment signal.

[0011] Preferably, the real-time operation parameters of the distillation section include:

[0012] The broadband acoustic wave signals generated by the gas-liquid two-phase flow scouring the tower plate are collected by the acoustic emission sensor arranged on the tower wall.

[0013] The high-frequency energy attenuation coefficient associated with the tower plate aperture characteristics in the broadband acoustic wave signal is extracted.

[0014] The timing change data of the high-frequency energy attenuation coefficient is taken as an acoustic characteristic parameter representing the flow state of the tower plate and is included in the real-time operation parameters.

[0015] Preferably, the extraction of the fluctuation characteristics in the distillation section state data includes:

[0016] The temperature gradient change rate between the rectification section and the distillation section is monitored to identify the instability interval of the temperature field.

[0017] Based on the instability interval, the steady-state fluctuation characteristics representing normal working conditions and the transient transition characteristics representing abnormal transitions are separated.

[0018] The steady-state fluctuation characteristics and the transient transition characteristics are integrated to output a standardized fluctuation characteristic set for energy efficiency state evaluation.

[0019] Preferably, the identification of high energy consumption periods and load demand trends based on the fluctuation characteristics, and the comparison of the identification results with historical energy efficiency characteristics to output a load prediction result, includes:

[0020] We analyze the transient transition characteristics in the standardized fluctuation feature set and examine the coupling strength between their occurrence frequency and reboiler heating power fluctuations.

[0021] The relationship between the decay trend of the associated acoustic characteristic parameters and the evolution of the coupling strength indicates that when the frequency of the transient transition characteristics increases and is accompanied by an abnormal increase in heating power, the distillation section is determined to have entered a high-energy-consumption period due to restricted tray flow.

[0022] Based on the historical evolution pattern of steady-state fluctuation characteristics, the trend of load demand required to maintain the target separation purity is inferred.

[0023] The load forecast results are obtained by matching potential high-energy-consumption periods and load demand trends with a historical energy efficiency feature database.

[0024] Preferably, establishing energy efficiency optimization targets based on load forecasting results includes:

[0025] Analyze load forecast results to identify the distribution of high-energy-consumption periods and load intensity in future cycles;

[0026] Based on the distribution of high energy consumption periods and load intensity, the operating boundaries of reboiler unit energy consumption and reflux ratio are defined.

[0027] Based on the stability requirements of the operating boundary and acoustic characteristic parameters, quantitative targets for load smoothness and energy consumption reduction rate are set to establish energy efficiency optimization targets.

[0028] Preferably, the step of dynamically coordinating the load allocation ratio based on the energy efficiency optimization target to obtain a coordinated load allocation scheme, and converting the coordinated load allocation scheme into an optimized scheduling instruction, includes:

[0029] The system senses the changing trend of liquid holdup in different sections of the tower, identifies areas of uneven gas-liquid distribution, and predicts the decline trend of mass transfer efficiency in these areas.

[0030] Based on the mass transfer efficiency decay trend and energy efficiency optimization objectives, the load allocation weights among each tower section are coordinated.

[0031] The coordinated load allocation weights are converted into specific operating parameters for each tower section to form a coordinated load allocation scheme.

[0032] The coordinated load allocation scheme is encoded into standardized command messages that can be executed by the distributed control system, and execution timing tags are attached to the command messages to form optimized scheduling instructions.

[0033] Preferably, the parsing and optimization of the scheduling instructions to obtain the preliminary operation setpoint includes:

[0034] The load distribution strategy in the deconstruction optimization scheduling instruction is decomposed, and the target steam load and the target reflux ratio of each distillation column section are extracted;

[0035] The target steam load and the target reflux ratio are mapped to the initial opening of the reboiler regulating valve and the initial frequency of the reflux pump to form the preliminary operation set point.

[0036] Preferably, the deviation between the preliminary operation set point and the real-time monitoring parameter is corrected by using a feedback control mechanism to obtain a corrected operation signal, which includes:

[0037] The synchronous response characteristics of the column top condenser and the column bottom reboiler are obtained;

[0038] According to the synchronous response characteristics, a pressure-temperature composite compensation mechanism is established;

[0039] Through the pressure-temperature composite compensation mechanism, the timing deviation of the preliminary operation set point is corrected to obtain the corrected operation signal.

[0040] Preferably, the timing consistency of the corrected operation signal and the optimization scheduling instruction is verified to obtain a final adjustment signal, which includes:

[0041] The concentration distribution change trajectory of the key components in the column is monitored to obtain the component dynamic response characteristics;

[0042] The timing matching degree of the component dynamic response characteristics and the optimization scheduling instruction is compared;

[0043] When the timing deviation is detected, a feedforward compensation signal is injected to eliminate the phase lag;

[0044] The stability of the tray efficiency in the load adjustment process is evaluated, and the safety boundary correction is performed on the compensated operation signal to output the final adjustment signal.

[0045] In order to solve the above problems, the present application also provides an intelligent load scheduling system for high-energy-consumption distillation section, which comprises:

[0046] A data acquisition module is used to collect real-time operation parameters of the distillation section, verify the integrity of the real-time operation parameters to obtain a data integrity state, integrate the real-time operation parameters and the data integrity state to obtain distillation section state data;

[0047] A result prediction module is used to extract fluctuation characteristics from the distillation section state data, identify high-energy-consumption periods and load demand trends based on the fluctuation characteristics, compare the identification results with historical energy efficiency characteristics to output a load prediction result;

[0048] The instruction generation module is configured to establish an energy efficiency optimization target according to the load prediction result, dynamically coordinate the load distribution proportion based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and convert the coordinated load distribution scheme into an optimized scheduling instruction.

[0049] The instruction correction module is configured to analyze the optimized scheduling instruction to obtain a preliminary operation set point, correct the deviation between the preliminary operation set point and the real-time monitoring parameter by using a feedback control mechanism, and obtain a corrected operation signal.

[0050] The instruction verification module is configured to verify the timing consistency between the corrected operation signal and the optimized scheduling instruction, and obtain a final adjustment signal.

[0051] Compared with the prior art, the present application has the following beneficial effects:

[0052] 1. By constructing a complete intelligent scheduling closed loop from distillation section state data integration, load trend prediction, to load distribution optimization, scheduling instruction correction and timing verification, the dynamic adaptation of load scheduling and real-time working condition of the distillation section can be realized, the accuracy and timeliness of load scheduling are significantly improved, the product quality stability of the distillation separation process can be guaranteed, the energy loss of the high energy consumption link can be maximized, and the energy efficiency level and overall reliability of the distillation section operation are effectively enhanced.

[0053] 2. By capturing the micro operation characteristics of the tower to improve the comprehensiveness of state recognition, by using precise fluctuation feature extraction to enhance the accuracy of load trend prediction, and by relying on a scientific load coordination mechanism and a multi-parameter compensation correction method to improve the accuracy of scheduling instruction execution, the deviation and phase lag problems in the load adjustment process are effectively avoided, and the energy saving effect and stability of the distillation section operation are further enhanced, thereby providing a more solid technical guarantee for the efficient and safe operation of the high energy consumption distillation section. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The flowchart of the intelligent load scheduling method of the high energy consumption distillation section provided by an embodiment of the present application is shown.

[0055] Figure 2 The functional module diagram of the intelligent load scheduling system of the high energy consumption distillation section provided by an embodiment of the present application is shown.

[0056] The implementation of the object of the present application, the functional characteristics and the advantages will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0057] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0058] The embodiment of the present application provides a high-energy-consumption distillation section intelligent load scheduling method. The execution subject of the high-energy-consumption distillation section intelligent load scheduling method includes but is not limited to at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the high-energy-consumption distillation section intelligent load scheduling method can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be a stand-alone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0059] Embodiment 1, refer to Figure 1 As shown in the figure, it is a flowchart of the high-energy-consumption distillation section intelligent load scheduling method provided by an embodiment of the present application. In this embodiment, the high-energy-consumption distillation section intelligent load scheduling method includes:

[0060] S1, collecting real-time operation parameters of the distillation section, verifying the integrity of the real-time operation parameters to obtain a data integrity state, integrating the real-time operation parameters and the data integrity state to obtain distillation section state data;

[0061] S2, extracting fluctuation characteristics in the distillation section state data, identifying high-energy-consumption periods and load demand trends based on the fluctuation characteristics, comparing the identification results with historical energy efficiency characteristics to output a load prediction result;

[0062] S3, establishing an energy efficiency optimization target according to the load prediction result, dynamically coordinating a load distribution ratio based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and converting the coordinated load distribution scheme into an optimized scheduling instruction;

[0063] S4, analyzing the optimized scheduling instruction to obtain a preliminary operation set point, using a feedback control mechanism to correct the deviation between the preliminary operation set point and the real-time monitoring parameter to obtain a corrected operation signal;

[0064] S5, verifying the timing consistency of the corrected operation signal and the optimized scheduling instruction to obtain a final adjustment signal.

[0065] In a preferred embodiment, collecting real-time operation parameters of the distillation section includes:

[0066] By arranging acoustic emission sensors on the distillation column wall, broadband acoustic wave signals generated by the gas-liquid two-phase flow scouring the column tray are collected;

[0067] extracting a high-frequency energy attenuation coefficient associated with the column plate aperture characteristics from the wide-band acoustic wave signals;

[0068] The time series variation data of the high-frequency energy attenuation coefficient is taken as an acoustic characteristic parameter representing the flow state of the column plate and is included in the real-time operation parameters.

[0069] Specifically, acoustic emission sensors are selected and uniformly arranged along the tower wall at different heights of the distillation column. The sensor probe is attached to the tower wall and fixed with a sealing pad. The wide-band acoustic wave signals generated when the gas-liquid two-phase flow in the tower washes the column plate are captured in real time. After the signals are converted into electrical signals by the built-in conditioning circuit of the sensor, they are transmitted to the data acquisition module for storage.

[0070] The collected wide-band acoustic wave signals are segmented and intercepted, and the signal segments corresponding to the column plate regions are retained. The high-frequency signals are separated by a hardware filtering circuit. The energy variation law of the high-frequency signals corresponding to different aperture column plates is compared. The energy loss during the signal propagation is compared point by point to calculate the high-frequency energy attenuation coefficient associated with the column plate aperture characteristics.

[0071] The high-frequency energy attenuation coefficient values are extracted at a fixed time interval of 1 second to form continuous time series variation data. The time series variation data is taken as an acoustic characteristic parameter representing the flow state of the column plate and is recorded in the real-time operation parameter database together with the distillation section temperature, pressure and other parameters.

[0072] In summary, the wide-band acoustic wave signals generated when the gas-liquid two-phase flow in the tower washes the column plate are collected by acoustic emission sensors. The time series data of the high-frequency energy attenuation coefficient associated with the column plate aperture is extracted. The micro-characteristic parameters such as the flow state of the column plate are supplemented, breaking the traditional limitation of focusing only on macro-parameters such as temperature and pressure. The integrity of the real-time operation parameters is verified and the data integrity status is recorded. The problem of misjudgment of working conditions caused by data loss or abnormality can be avoided. After the rich real-time operation parameters and the data integrity status are integrated, the distillation section state data formed can comprehensively and truly reflect the running conditions of the distillation section, providing accurate and reliable data support for the subsequent extraction of fluctuation characteristics, identification of high-energy consumption period, prediction of load demand trend and establishment of energy efficiency optimization target. The problem of incomplete state perception and insufficient data reliability in the prior art is effectively solved.

[0073] In a preferred embodiment, the fluctuation characteristics in the distillation section state data are extracted, including:

[0074] Monitoring the temperature gradient change rate between the rectification section and the stripping section to identify the instability interval of the temperature field;

[0075] Based on the instability interval, the steady-state fluctuation characteristics representing normal working conditions and the transient transition characteristics representing abnormal transition are separated;

[0076] Integrate the steady fluctuation characteristics and transient transition characteristics, and output a standardized fluctuation characteristic set for energy efficiency state evaluation.

[0077] Specifically, platinum resistance temperature sensors are arranged at each key tray of the rectifying section and the stripping section, temperature values at each point are synchronously collected at fixed time intervals, a temperature gradient change rate is obtained by calculating the ratio of the temperature difference value of the corresponding positions at adjacent time points to the time interval, a rate threshold value during stable operation is preset, and a temperature change interval exceeding the threshold value is marked as an unstable interval of the temperature field.

[0078] With the identified unstable interval as a limit, temperature change data outside the unstable interval is extracted as a steady fluctuation characteristic representing a normal working condition, and the change amplitude and occurrence frequency of the part of data are recorded; temperature change data in the unstable interval is extracted as a transient transition characteristic representing an abnormal transition, and the mutation start time, end time and change amount of the part of data are recorded.

[0079] The change amplitude and occurrence frequency data in the obtained steady fluctuation characteristic are classified and arranged, the mutation time and change amount data in the transient transition characteristic are classified and arranged, a unified working condition description format is arranged, and a complete standardized fluctuation characteristic set for energy efficiency state evaluation is formed.

[0080] In the embodiment, the high energy consumption period and the load demand trend are identified based on the fluctuation characteristics, the identification result is compared with historical energy efficiency characteristics to output a load prediction result, including:

[0081] The transient transition characteristics in the standardized fluctuation characteristic set are analyzed, and the coupling strength between the occurrence frequency and the reboiler heating power fluctuation is analyzed;

[0082] The evolution relationship between the attenuation trend of the acoustic characteristic parameter and the coupling strength is associated, when the frequency of the transient transition characteristic increases and is accompanied by abnormal lifting of the heating power, it is determined that the distillation section enters a high energy consumption period caused by limited flow through the tray;

[0083] According to the historical evolution mode of the steady fluctuation characteristics, the load demand trend required to maintain the target separation purity is inferred;

[0084] The potential high energy consumption period, the load demand trend and the historical energy efficiency characteristic library are matched, and the load prediction result is integrated.

[0085] Specifically, the transient transition characteristic data in the standardized fluctuation characteristic set is extracted, the real-time fluctuation data of the reboiler heating power is synchronously recorded, the changes of the two in each period are corresponded, the synchronous changes of the occurrence frequency of the transient transition characteristic and the fluctuation amplitude of the heating power in the same period are counted, and the coupling strength between the two is analyzed.

[0086] By retrieving the temporal variation data of acoustic characteristic parameters and analyzing the attenuation trend of its high-frequency energy attenuation coefficient, this trend is compared with the evolution of coupling strength at each moment. When the frequency of transient transition characteristics continues to increase and is accompanied by an increase in reboiler heating power beyond the normal range, it is determined that the distillation section has entered a high-energy consumption period due to restricted tray flow.

[0087] Historical data on steady-state fluctuation characteristics are extracted, and their evolution patterns in different periods are analyzed to form historical evolution patterns. The fit between the current steady-state fluctuation characteristics and the historical patterns is compared, and combined with the target separation purity requirements, the load demand trend required to maintain this purity is inferred.

[0088] Records of high energy consumption periods, load demand trends, and energy efficiency performance under different operating conditions are retrieved from the historical energy efficiency feature database. The potential high energy consumption periods and inferred load demand trends identified in this study are compared and matched with the data in the database one by one. The historical records with the highest matching degree are selected and integrated with the actual situation of this operating condition to obtain the load forecast results.

[0089] Specifically, as a preferred embodiment, the coupling strength is calculated using the following formula:

[0090]

[0091] The coupling strength coefficient is a dimensionless number between -1 and 1. Indicates within the time window The frequency of occurrence of transient transition characteristics within the body. Indicates within the time window The fluctuation range of the reboiler heating power. Indicates the analysis time period Within, the average frequency of transient transition characteristics, Indicates the analysis time period The average value of the reboiler heating power fluctuation within the reboiler. This indicates the total number of time periods within the selected analysis period.

[0092] It should also be noted that the frequency of transient transition features is obtained from transient transition feature data in the standardized fluctuation feature set, and the number of occurrences in each window is counted after dividing the data into set time windows; the fluctuation amplitude of reboiler heating power is obtained by collecting reboiler heating power data in real time and calculating the difference between the maximum and minimum power values ​​in each time window; the average values ​​of the two are the sum of the corresponding values ​​of all time periods within the analysis period divided by the total number of time periods, which is the total number of windows divided within the analysis period selected according to actual monitoring needs.

[0093] The calculation obtains the coupling strength coefficient by comparing the frequency of the transient jump feature in each time window, the deviation of the reboiler heating power fluctuation amplitude from the average value, and the synchronization degree of the changes of the two by numerical correlation operation. The coupling strength coefficient intuitively reflects the correlation closeness of the two, is dimensionless, and has a value between -1 and 1.

[0094] Generally speaking, when the frequency of the transient jump feature and the reboiler heating power fluctuation amplitude are both higher or lower than the average value, the coupling strength coefficient tends to 1, indicating that the two are positively correlated and the correlation degree is enhanced. When one is higher than the average value and the other is lower than the average value, the coefficient tends to -1, indicating a negative correlation. When there is no obvious synchronization rule in the changes of the two, the coefficient tends to 0, indicating a weak correlation.

[0095] Generally speaking, by monitoring the temperature gradient change rate between the rectifying section and the stripping section to identify the instability interval of the temperature field, separating the steady-state fluctuation feature representing normal working conditions and the transient jump feature representing abnormal transition based on the instability interval, and integrating to form a standardized fluctuation feature set for energy efficiency state evaluation, the state change details of the distillation section under different working conditions can be accurately captured, avoiding misjudgment of the working conditions caused by incomplete or ambiguous feature extraction. By analyzing the transient jump feature in the standardized fluctuation feature set and the coupling strength of the reboiler heating power fluctuation, and correlating the attenuation trend of the acoustic characteristic parameter, the high energy consumption period of the distillation section due to the limitation of the column plate flow can be accurately determined, solving the problem of identification deviation of the high energy consumption period in the prior art. According to the historical evolution mode of the steady-state fluctuation feature, the load demand trend required to maintain the target separation purity is inferred, the potential high energy consumption period, the load demand trend, and the historical energy efficiency feature library are matched, so that the output load prediction result is more in line with the actual operation demand, providing accurate and reliable basis for subsequent establishment of energy efficiency optimization target, and avoiding disconnection between load scheduling and actual separation demand.

[0096] In a preferred embodiment, the energy efficiency optimization target is established according to the load prediction result, which includes:

[0097] Analyzing the load prediction result to identify the distribution of high energy consumption periods and load intensity in the future period;

[0098] Based on the distribution of high energy consumption periods and load intensity, the operating boundaries of reboiler unit energy consumption and reflux ratio are determined;

[0099] Based on the operating boundaries and the stability requirements of the acoustic characteristic parameters, the quantitative targets of load smoothing degree and energy consumption reduction rate are set, and the energy efficiency optimization target is established.

[0100] Specifically, the time axis of the load prediction result is split into fixed-length segments, and whether each segment is marked as a potential high-energy-consumption period is checked. The start and end times of all high-energy-consumption periods are integrated to form a distribution rule, and the average value and peak value of the load in each period are counted to completely identify the distribution of high-energy-consumption periods and the load intensity in the future period.

[0101] The operation records of the reboiler under the same load intensity in the history are called, the unit energy consumption and reflux ratio data of the corresponding period are extracted, the extreme values that may lead to substandard separation purity or equipment damage are eliminated in combination with the load fluctuation characteristics of the high-energy-consumption period, and finally the operation boundaries of the unit energy consumption and reflux ratio of the reboiler are determined.

[0102] The operation boundaries are compared to determine that the load adjustment cannot exceed the range, and the load fluctuation interval in which the high-frequency energy attenuation coefficient of the acoustic characteristic parameter remains stable is monitored to determine the allowed change range of the load smoothness. In combination with the historical energy consumption data of the high-energy-consumption period, the energy consumption reduction rate is set, and the two quantitative targets are integrated to form the energy efficiency optimization target.

[0103] Specifically, the purpose of quantifying the load smoothness is to suppress the violent fluctuation of the load to reduce the energy loss and equipment stress caused by frequent adjustment. As a specific quantification method, the load smoothness index defined by the following formula can be used as one of the targets:

[0104]

[0105] wherein, the load smoothness index, the closer the value is to 1, the more stable the load sequence is; is the total load value of the distillation section in the th scheduling period; is the total number of periods included in a complete scheduling period, is the absolute value of the load change between adjacent two scheduling periods.

[0106] The quantification of the energy consumption reduction rate aims to directly reduce the energy consumption per unit product. The target can be set to reduce the energy consumption per unit product by a specific percentage on the current benchmark in the next scheduling period.

[0107] By simultaneously pursuing the maximization of the load smoothness index and the minimization of the energy consumption per unit product, the preventive energy efficiency optimization target is formed.

[0108] It should be noted that the total load value is the total load data of the distillation section in each dispatching period, which is monitored in real time or determined according to the load distribution scheme; the total number of periods is the total number of periods obtained by dividing a complete dispatching cycle at a fixed time interval; and the absolute value of the load change between adjacent dispatching periods is obtained by subtracting the total load value of the previous period from the total load value of the next period, and then taking the absolute value of the difference.

[0109] In the calculation, the absolute value sum of the load change of all adjacent periods is first accumulated, then the total load value of all periods in a dispatching cycle is accumulated and multiplied by 2, and the load smoothing degree index is obtained by subtracting 1 from the ratio of the former sum to the latter sum. The load smoothing degree index quantifies the stability of the load sequence, and the core is to suppress the sharp fluctuation of the load and reduce the energy loss and device stress caused by frequent adjustment.

[0110] In general, the smaller the load change between adjacent periods, the smaller the absolute value sum of the accumulated load change, the closer the load smoothing degree index to 1, and the more stable the load sequence; the greater the load fluctuation between adjacent periods, the greater the sum, and the closer the index to 0, the worse the stability of the load sequence.

[0111] In this embodiment, the load distribution proportion is dynamically coordinated based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, which includes:

[0112] The liquid holdup change trend of different sections in the tower is perceived, the gas-liquid distribution imbalance area is identified, and the mass transfer efficiency attenuation trend is predicted in the gas-liquid distribution imbalance area;

[0113] Based on the mass transfer efficiency attenuation trend and the energy efficiency optimization target, the load distribution weight between each tower section is coordinated;

[0114] The coordinated load distribution weight is converted into specific operation parameters of each tower section to form a coordinated load distribution scheme;

[0115] The coordinated load distribution scheme is encoded into a standardized instruction message executable by the distributed control system, and an execution timing label is attached to the instruction message to form an optimized scheduling instruction.

[0116] Specifically, a differential pressure sensor is installed below each tray corresponding to each tower section of the distillation tower, differential pressure data of each sensor is collected at a fixed time interval, the liquid holdup change trend is reflected through the differential pressure change, the liquid holdup change amplitude of each tower section is compared, the area with a change amplitude exceeding the normal range is marked as a gas-liquid distribution imbalance area, and the mass transfer efficiency attenuation trend is inferred in combination with the correlation data of the historical mass transfer efficiency and liquid holdup of the area.

[0117] By analyzing the rate of decline in mass transfer efficiency and combining it with the requirements for load smoothness and energy consumption reduction rate in the energy efficiency optimization objectives, the load allocation weight of tower sections with rapid mass transfer efficiency decline is reduced, while the load allocation weight of tower sections with stable mass transfer efficiency is reasonably increased, thereby coordinating the load allocation weight among tower sections.

[0118] Based on the coordinated load allocation weights for each tower section, and referring to the operating boundaries of reboiler unit energy consumption and reflux ratio, the weight values ​​are converted into specific operating parameters such as reboiler heating capacity and reflux flow rate for each tower section, and integrated to form a coordinated load allocation scheme.

[0119] According to the communication protocol format of the distributed control system, the contents of the coordinated load allocation scheme are encoded to generate standardized instruction messages. The specific execution time nodes of each operation parameter are added to the message as execution timing tags, and finally, optimized scheduling instructions are formed.

[0120] In summary, load forecast results are analyzed to identify the distribution and intensity of high-energy-consumption periods in the future cycle. Based on this, the operating boundaries of the reboiler's unit energy consumption and reflux ratio are defined. Combined with the stability requirements of acoustic characteristic parameters, quantitative targets for load smoothness and energy consumption reduction rate are set. This ensures that the established energy efficiency optimization targets not only meet actual operating conditions but also take into account equipment operational stability, avoiding the optimization direction deviation caused by traditional optimization targets being out of touch with operating conditions or ignoring key parameters. The liquid holdup change trend in different sections of the tower is sensed to identify areas of uneven gas-liquid distribution and predict the mass transfer efficiency decay trend in these areas. Then, combined with the energy efficiency optimization targets, the load allocation weights between each tower section are coordinated, allowing the load allocation ratio to be dynamically adjusted according to operating conditions. This solves the problem that traditional fixed allocation methods are difficult to adapt to changes in mass transfer efficiency. The coordinated load allocation weights are transformed into specific operating parameters to form an allocation scheme, which is further encoded into standardized command messages executable by the distributed control system and attached with execution timing tags. This ensures that the optimized scheduling commands can be directly executed with clear timing, providing a clear basis for subsequent precise operations.

[0121] In a preferred embodiment, parsing and optimizing scheduling instructions to obtain a preliminary operation setpoint includes:

[0122] Deconstruct and optimize the load allocation strategy in the scheduling instructions, and extract the target steam load and target reflux ratio for each distillation column section;

[0123] The target steam load and target reflux ratio are mapped to the initial opening of the reboiler control valve and the initial frequency of the reflux pump, thus forming the initial operating setpoint.

[0124] Specifically, the standardization instruction message in the split optimization scheduling instruction is split, the execution time sequence label is stripped, the content related to the load distribution strategy is focused, the corresponding information is extracted one by one according to the division identification of each distillation column section, the target steam load value and the target reflux ratio value required to be reached by each column section are determined, and the target steam load and the target reflux ratio of each distillation column section are completely obtained.

[0125] The historical matching record of the reboiler regulating valve opening degree and the steam load is called, the target steam load of each column section is extracted, the matching regulating valve opening degree in the corresponding historical record is found as the initial opening degree of the reboiler regulating valve, the historical matching record of the reflux pump frequency and the reflux ratio is called, the matching pump frequency in the corresponding historical record is found as the initial frequency of the reflux pump according to the target reflux ratio of each column section, and the initial opening degree and the initial frequency are integrated to form the preliminary operation set point.

[0126] In the embodiment, the deviation between the preliminary operation set point and the real-time monitoring parameter is corrected by using a feedback control mechanism to obtain a corrected operation signal, which includes:

[0127] The synchronous response characteristics of the overhead condenser and the column still reboiler are obtained.

[0128] According to the synchronous response characteristics, a pressure-temperature composite compensation mechanism is established.

[0129] Through the pressure-temperature composite compensation mechanism, the time sequence deviation of the preliminary operation set point is corrected to obtain the corrected operation signal.

[0130] Specifically, a pressure sensor is installed at the outlet of the overhead condenser, a temperature sensor is installed at the inlet of the column still reboiler, the running data of the two are synchronously collected at the same time interval, the time difference and the change amplitude of the condenser pressure change and the reboiler temperature change after the preliminary operation set point is issued are recorded, and the synchronous response characteristics of the overhead condenser and the column still reboiler are formed.

[0131] According to the obtained synchronous response characteristics, the correlation law of the condenser pressure fluctuation and the reboiler temperature fluctuation under different working conditions is sorted out, the corresponding temperature compensation range when the pressure deviates is determined, the corresponding pressure compensation range when the temperature deviates is determined, and the pressure-temperature composite compensation mechanism is established.

[0132] The monitoring parameters of the overhead pressure and the column still temperature are collected in real time, the time sequence difference of the pressure and the temperature standard values corresponding to the preliminary operation set point is compared, the time sequence of the temperature setting is adjusted according to the pressure deviation through the pressure-temperature composite compensation mechanism, the time sequence of the pressure setting is adjusted according to the temperature deviation, the time sequence deviation of the preliminary operation set point is corrected, and finally the corrected operation signal is obtained.

[0133] Overall, the load distribution strategy in the deconstruction optimization scheduling instruction is decomposed, the target steam load and the target reflux ratio of each distillation column section are extracted, and they are mapped to the initial opening of the reboiler regulating valve and the initial frequency of the reflux pump, so that the preliminary operation set point can accurately dock the scheduling requirements, avoid the problem that the scheduling instruction is difficult to land due to abstraction, ensure that the operation set is directly related to the load distribution target, and then obtain the synchronous response characteristics of the column top condenser and the column bottom reboiler. According to the characteristics, a pressure-temperature compound compensation mechanism is established, which breaks through the limitations of traditional single parameter feedback correction, can simultaneously consider the coordinated operation state of multiple devices, effectively corrects the time sequence deviation between the preliminary operation set point and the real-time monitoring parameters, and makes the corrected operation signal not only meet the core requirements of the optimization scheduling instruction, but also adapt to the real-time working condition changes of the distillation section, avoiding problems such as reboiler power excess and reflux ratio imbalance caused by the deviation between the set point and the actual parameter, and ensuring the energy consumption control precision and separation performance stability of the distillation process.

[0134] In a preferred embodiment, the time sequence consistency of the corrected operation signal and the optimization scheduling instruction is verified to obtain the final adjustment signal, which includes:

[0135] Monitoring the concentration distribution change trajectory of the key components in the column, and obtaining the dynamic response characteristics of the components;

[0136] Comparing the time sequence matching degree of the dynamic response characteristics of the components and the optimization scheduling instruction;

[0137] When the time sequence deviation is detected, a feedforward compensation signal is injected to eliminate the phase lag;

[0138] Evaluating the stability of the tray efficiency during the load adjustment process, and performing safety boundary correction on the compensated operation signal to output the final adjustment signal.

[0139] Specifically, online component concentration analyzers are installed at each key separation column section of the distillation column, and key component concentration data at each point are synchronously collected at fixed time intervals. The concentration change with time is continuously recorded, the complete change path of the concentration from the initial value to the target value is combed, the concentration distribution change trajectory of the key components in the column is formed, and then the dynamic response characteristics of the components are obtained.

[0140] Extract the execution time sequence label in the optimization scheduling instruction, and clearly define the load adjustment requirements at each time. According to the same time axis, the change time of the dynamic response characteristics of the components is point-by-point corresponding to the load adjustment time required by the instruction, and the distribution of the time difference between the two is counted. In this way, the time sequence matching degree of the dynamic response characteristics of the components and the optimization scheduling instruction is compared.

[0141] Set the qualified range of timing matching degree, when the statistical time difference exceeds the range, it is determined that there is a timing deviation, a feedforward compensation signal of corresponding amplitude is generated according to the direction and size of the deviation, the signal is injected into the corrected operation signal, the execution time of the operation signal is adjusted in advance, and the phase lag problem is eliminated.

[0142] The high-frequency energy attenuation coefficient collected by the acoustic emission sensor is used to monitor the flow state of the tray, so as to evaluate the stability of the tray efficiency in the load adjustment process, and the preset safe operation boundary of the tray efficiency is called, if the compensated operation signal may cause the tray efficiency to exceed the boundary, the signal amplitude is adjusted to the safe range, and the final adjustment signal is output after the safety boundary correction is completed.

[0143] In summary, the concentration distribution change trajectory of the key components in the tower is monitored to obtain the dynamic response characteristics of the components, which can be directly related to the separation effect of the distillation core, and the timing deviation caused by relying on indirect parameters such as temperature and pressure is avoided. By comparing the timing matching degree of the dynamic response characteristics of the components and the optimized scheduling instruction, the time difference between the operation signal and the instruction requirement can be accurately positioned, and the influence of timing misplacement on load scheduling effect is prevented. When the timing deviation is detected, a feedforward compensation signal is injected to eliminate the phase lag, the problem of response lag of traditional feedback correction is solved, the operation adjustment is synchronized with the instruction requirement, the stability of the tray efficiency in the load adjustment process is evaluated, the safety boundary correction is performed on the compensated operation signal, the operation risk caused by the load adjustment exceeding the tray tolerance range is avoided, and finally the adjustment signal not only meets the timing consistency requirement, but also guarantees the safe and stable operation of the distillation section, effectively improving the reliability and safety of load scheduling.

[0144] Embodiment 2, as shown in Figure 2 Figure 1 is a functional module diagram of a high-energy distillation section intelligent load scheduling system according to an embodiment of the present application.

[0145] The high-energy distillation section intelligent load scheduling system 100 can be installed in an electronic device. According to the functions implemented, the high-energy distillation section intelligent load scheduling system 100 can include a data acquisition module 101, a result prediction module 102, an instruction generation module 103, an instruction correction module 104, and an instruction verification module 105. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, and are stored in the memory of the electronic device.

[0146] In this embodiment, the functions of each module / unit are as follows:

[0147] The data acquisition module 101 is used to collect real-time operation parameters of the distillation section, verify the integrity of the real-time operation parameters to obtain a data integrity state, integrate the real-time operation parameters and the data integrity state to obtain distillation section state data;

[0148] The result prediction module 102 is configured to extract fluctuation characteristics in the distillation section state data, identify high energy consumption periods and load demand trends based on the fluctuation characteristics, and compare the identification results with historical energy efficiency characteristics to output a load prediction result.

[0149] The instruction generation module 103 is configured to establish an energy efficiency optimization target according to the load prediction result, dynamically coordinate a load distribution ratio based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and convert the coordinated load distribution scheme into an optimized scheduling instruction.

[0150] The instruction correction module 104 is configured to analyze the optimized scheduling instruction to obtain a preliminary operation set point, correct a deviation between the preliminary operation set point and real-time monitoring parameters by using a feedback control mechanism, and obtain a corrected operation signal.

[0151] The instruction verification module 105 is configured to verify time sequence consistency of the corrected operation signal and the optimized scheduling instruction, and obtain a final adjustment signal.

[0152] In several embodiments provided in the present application, it should be understood that the disclosed method and system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and another division mode can be used in actual implementation.

[0153] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0154] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software function modules.

[0155] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0156] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Artificial intelligence is a theory, method, technology and application system for simulating, extending and expanding human intelligence by using a digital computer or a machine controlled by a digital computer, perceiving an environment, acquiring knowledge and using the knowledge to obtain optimal results.

[0157] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An intelligent load scheduling method for high energy consumption distillation sections, characterized in that, The method comprises: S1, collecting real-time operation parameters of the distillation section, verifying the integrity of the real-time operation parameters to obtain data integrity state, integrating the real-time operation parameters and the data integrity state to obtain distillation section state data, wherein the collecting real-time operation parameters of the distillation section comprises: Collecting broadband acoustic wave signals generated by the gas-liquid two-phase flow scouring the tower tray through the acoustic emission sensor arranged on the wall of the distillation column; Extracting the high-frequency energy attenuation coefficient associated with the tower tray aperture characteristics in the broadband acoustic wave signals; The time sequence change data of the high-frequency energy attenuation coefficient is taken as an acoustic characteristic parameter representing the flow state of the tower tray and is included in the real-time operation parameters; S2, extracting the fluctuation characteristics in the distillation section state data, identifying the high energy consumption period and the load demand trend based on the fluctuation characteristics, and comparing the identification results with the historical energy efficiency characteristics to output the load prediction result, wherein the extraction of the fluctuation characteristics in the distillation section state data comprises: Monitoring the temperature gradient change rate between the rectification section and the distillation section, and identifying the instability interval of the temperature field; Based on the instability interval, separate the steady-state fluctuation characteristics representing normal working conditions and the transient transition characteristics representing abnormal transitions; Integrating the steady-state fluctuation characteristics and the transient transition characteristics to output a standardized fluctuation characteristic set for energy efficiency state evaluation; The identification of the high energy consumption period and the load demand trend based on the fluctuation characteristics, and the comparison of the identification results with the historical energy efficiency characteristics to output the load prediction result, comprises: Analyzing the transient transition characteristics in the standardized fluctuation characteristic set to analyze the coupling strength between its frequency and the reboiler heating power fluctuation; Correlate the attenuation trend of the acoustic characteristic parameter with the evolution relationship of the coupling strength. When the frequency of the transient transition characteristics increases and is accompanied by abnormal lifting of the heating power, it is determined that the distillation section enters the high energy consumption period caused by the limitation of the tower tray flow; According to the historical evolution mode of the steady-state fluctuation characteristics, the load demand trend required to maintain the target separation purity is inferred; Match the potential high energy consumption period, the load demand trend and the historical energy efficiency characteristic library, and integrate to obtain the load prediction result; S3, establishing an energy efficiency optimization target according to the load prediction result, dynamically coordinating the load distribution ratio based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and converting the coordinated load distribution scheme into an optimized scheduling instruction, wherein the establishment of the energy efficiency optimization target according to the load prediction result comprises: Analyzing the load prediction result to identify the distribution of high energy consumption periods and load intensity in the future period; Based on the high energy consumption period distribution and the load intensity, the operating boundaries of the reboiler unit energy consumption and the reflux ratio are determined; Based on the operating boundary and the stability requirement of the acoustic characteristic parameter, set the quantization target of the load smoothing degree and the energy consumption reduction rate to establish the energy efficiency optimization target; The dynamic coordination of the load distribution ratio based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and the conversion of the coordinated load distribution scheme into an optimized scheduling instruction, comprises: Sensing the liquid holdup change trend of different sections in the tower, identifying the gas-liquid distribution imbalance area, and predicting the mass transfer efficiency attenuation trend in the gas-liquid distribution imbalance area; Based on the mass transfer efficiency attenuation trend and the energy efficiency optimization target, the load distribution weight among the tower sections is coordinated; The coordinated load distribution weight is converted into specific operation parameters of each tower section to form a coordinated load distribution scheme; The coordinated load distribution scheme is coded into a standardized instruction message executable by the distributed control system, and a timing label is added to the instruction message to form an optimized scheduling instruction; S4, the preliminary operation set point is obtained by analyzing the optimized scheduling instruction, and the deviation between the preliminary operation set point and the real-time monitoring parameter is corrected by using a feedback control mechanism to obtain a corrected operation signal; S5, verify the timing consistency of the corrected operation signal and the optimized scheduling instruction to obtain a final adjustment signal.

2. The intelligent load scheduling method for high energy consumption distillation section as claimed in claim 1 wherein, The preliminary operation set point is obtained by analyzing the optimized scheduling instruction, comprising: deconstructing the load distribution strategy in the optimized scheduling instruction to extract the target steam load and the target reflux ratio of each distillation tower section; mapping the target steam load and the target reflux ratio into the initial opening of the reboiler regulating valve and the initial frequency of the reflux pump to form the preliminary operation set point.

3. The intelligent load scheduling method for high energy consumption distillation section as claimed in claim 1 wherein, The deviation between the preliminary operation set point and the real-time monitoring parameter is corrected by using a feedback control mechanism to obtain a corrected operation signal, comprising: obtaining the synchronous response characteristics of the overhead condenser and the reboiler; establishing a pressure-temperature compound compensation mechanism according to the synchronous response characteristics; correcting the timing deviation of the preliminary operation set point through the pressure-temperature compound compensation mechanism to obtain the corrected operation signal.

4. The intelligent load scheduling method for high energy consumption distillation section as claimed in claim 1 wherein, The timing consistency of the corrected operation signal and the optimized scheduling instruction is verified to obtain a final adjustment signal, comprising: monitoring the concentration distribution change trajectory of the key components in the tower to obtain the component dynamic response characteristics; comparing the component dynamic response characteristics with the timing matching degree of the optimized scheduling instruction; when the timing deviation is detected, a feedforward compensation signal is injected to eliminate the phase lag; evaluate the stability of the tray efficiency during the load adjustment process, and make safety boundary correction to the compensated operation signal to output the final adjustment signal.

5. An intelligent load scheduling system for high energy consumption distillation section, characterized in that it is applied to realize the intelligent load scheduling method for high energy consumption distillation section in claim 1, and comprises: a data acquisition module for collecting real-time operation parameters of the distillation section, verifying the integrity of the real-time operation parameters to obtain a data integrity state, integrating the real-time operation parameters and the data integrity state to obtain distillation section state data, wherein the real-time operation parameters of the distillation section are collected by: collecting wideband acoustic signals generated by the gas-liquid two-phase flow scouring the tray through acoustic emission sensors arranged on the distillation tower wall; extracting the high-frequency energy attenuation coefficient associated with the tray aperture characteristics in the wideband acoustic signals; the timing change data of the high-frequency energy attenuation coefficient is taken as an acoustic characteristic parameter representing the flow state of the tray and is included in the real-time operation parameters; a result prediction module for extracting fluctuation characteristics from the distillation section state data, identifying high energy consumption periods and load demand trends based on the fluctuation characteristics, and comparing the identification results with historical energy efficiency characteristics to output load prediction results, wherein the fluctuation characteristics in the distillation section state data are extracted by: monitoring the temperature gradient change rate between the rectification section and the stripping section to identify the instability interval of the temperature field; Based on the instability interval, the steady-state fluctuation characteristics representing normal working conditions and the transient transition characteristics representing abnormal transitions are separated; The steady-state fluctuation characteristics and the transient transition characteristics are integrated to output a standardized fluctuation characteristic set for energy efficiency state evaluation; The fluctuation characteristics are used to identify high energy consumption periods and load demand trends, and the identification results are compared with historical energy efficiency characteristics to output load prediction results, including: The transient transition characteristics in the standardized fluctuation characteristic set are analyzed to analyze the coupling strength between their occurrence frequency and the reboiler heating power fluctuation; The evolution relationship between the attenuation trend of the acoustic characteristic parameter and the coupling strength is associated. When the frequency of the transient transition characteristics increases and is accompanied by abnormal lifting of the heating power, it is determined that the distillation section enters a high energy consumption period caused by limited column plate flow; According to the historical evolution mode of the steady-state fluctuation characteristics, the load demand trend required to maintain the target separation purity is inferred; The potential high energy consumption period, the load demand trend, and the historical energy efficiency characteristic library are matched to obtain the load prediction result; The instruction generation module is used to establish an energy efficiency optimization target according to the load prediction result, dynamically coordinate the load distribution ratio based on the energy efficiency optimization target to obtain a coordinated load distribution scheme, and convert the coordinated load distribution scheme into an optimized scheduling instruction, wherein the energy efficiency optimization target is established according to the load prediction result, including: Analyzing the load prediction result to identify the distribution of high energy consumption periods and load intensity in the future period; Based on the distribution of high energy consumption periods and load intensity, the operating boundaries of reboiler unit energy consumption and reflux ratio are determined; Based on the operating boundaries and the stability requirements of the acoustic characteristic parameters, the quantification targets of load smoothing degree and energy consumption reduction rate are set, and the energy efficiency optimization target is established; The coordinated load distribution scheme is converted into an optimized scheduling instruction, including: Sensing the liquid holdup change trend in different sections of the column, identifying the gas-liquid distribution imbalance area, and predicting the mass transfer efficiency attenuation trend in the gas-liquid distribution imbalance area; Based on the mass transfer efficiency attenuation trend and the energy efficiency optimization target, the load distribution weights among the tower sections are coordinated; The coordinated load distribution weights are converted into specific operating parameters of each tower section to form a coordinated load distribution scheme; The coordinated load distribution scheme is encoded into a standardized instruction message executable by a distributed control system, and an execution timing label is attached to the instruction message to form an optimized scheduling instruction; The instruction correction module is used to analyze the optimized scheduling instruction to obtain a preliminary operation set point, and to correct the deviation between the preliminary operation set point and the real-time monitoring parameter by using a feedback control mechanism to obtain a corrected operation signal; The instruction verification module is used to verify the timing consistency of the corrected operation signal and the optimized scheduling instruction to obtain a final adjustment signal.

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