Energy consumption intelligent management method and device for chemical industrial park and storage medium

Abstract

This invention relates to the field of energy management technology, and particularly to a method, device, and storage medium for intelligent energy management in chemical industrial parks. The method includes: extracting stable operating characteristic data based on regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate, and steam drum liquid level to determine a stable operating state; determining the waste heat boiler heat recovery state coefficient based on exhaust gas temperature and flue gas oxygen content; extracting operating characteristic data based on regenerator dense phase bed temperature and pipeline pressure to determine operating characteristics; making operating mode decisions based on the stable operating state and waste heat boiler heat recovery state coefficient, and generating external heat exchanger control commands; and adjusting the generation process of external heat exchanger control commands based on the operating characteristics. This invention achieves dynamic coordination between the heat load of the catalytic cracking regeneration system and the heat recovery demand of the waste heat boiler, significantly improving waste heat utilization efficiency while ensuring safe operation of the unit.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to an intelligent energy management method, device and storage medium for chemical industrial parks. Background Technology

[0002] The heavy oil catalytic cracking unit is the core secondary processing unit in the integrated refining and chemical industrial park. Its reverse regeneration system and the supporting waste heat boiler for regenerated flue gas form a strongly coupled thermodynamic system. The reverse regeneration system removes excess heat from the regenerator through an external heat exchanger to control the bed temperature. The heat load changes of the external heat exchanger directly affect the temperature and flow rate of the regenerated flue gas entering the waste heat boiler, thus determining the boiler's self-produced steam and exhaust heat loss. Currently, the operation and control of the two systems are relatively independent: the reverse regeneration system prioritizes regenerator temperature stability, and the external heat exchanger is frequently adjusted with large amplitudes, resulting in drastic fluctuations in the flue gas parameters at the waste heat boiler inlet, and the exhaust temperature deviating from the design value for a long time; the waste heat boiler only maintains combustion through a closed-loop system of oxygen content at the tail end and cannot actively intervene in the upstream heat extraction conditions. This segmented operation mode of "emphasizing stability at the source and energy efficiency at the tail end" results in a large amount of high-grade regenerated flue gas waste heat not being effectively recovered, and the overall energy efficiency of the unit remains low for a long time.

[0003] Existing technologies lack a synergistic control method that balances the operational stability of the regeneration system with the heat recovery efficiency of the waste heat boiler, making it difficult to achieve both safety and energy efficiency optimization under complex and variable raw material properties and product schemes. Summary of the Invention

[0004] The purpose of this invention is to provide a method, device, and storage medium for intelligent energy management in chemical industrial parks, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for intelligent energy consumption management in chemical industrial parks, comprising:

[0007] Based on the regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate and steam drum liquid level, the stable operating characteristic data are extracted, and then the stable operating state is determined.

[0008] The state coefficient of waste heat boiler heat recovery is determined based on the flue gas temperature and oxygen content.

[0009] Operating characteristic data are extracted based on the regenerator dense phase bed temperature and pipeline pressure, and then the operating characteristics are determined based on the operating characteristic data;

[0010] Based on the stable operating state and the state coefficient of the waste heat boiler heat recovery, the operation mode decision is made and the external heat exchanger control command is generated.

[0011] The process of generating control commands for external heat exchangers is adjusted based on operational characteristics.

[0012] Furthermore, the coefficient of variation (CVT) of the regenerated flue gas temperature sequence, the coefficient of variation (CVQ) of the regenerated flue gas volumetric flow rate sequence, and the coefficient of variation (CVN) of the steam production flow rate sequence are calculated respectively within the current analysis period.

[0013] The sum of the absolute values ​​of the differences between adjacent sampling points in the steam drum liquid level sequence within the analysis period is calculated and recorded as the absolute change in steam drum liquid level. The absolute change in steam drum liquid level is divided by the number of sampling points in the analysis period minus 1, and then divided by the time interval between adjacent sampling points to obtain the average change rate of steam drum liquid level YWB.

[0014] Furthermore, the operational stability index FG is determined based on CVT, CVQ, CVN, and YWB. The operational stability index FG is compared with the first preset stability threshold CVt to determine the operational stability state. When FG is less than or equal to CVt, the operational stability state of the current analysis period is determined to be a fluctuating state; otherwise, the operational stability state of the current analysis period is determined to be a stable state.

[0015] Further, calculate the average value of the flue gas temperature within the current analysis period, and record it as the average flue gas temperature. At the same time, calculate the average value of the flue gas oxygen content, and record it as the average flue gas oxygen content.

[0016] The flue gas temperature rise Ps is determined based on the average flue gas temperature, where Ps = average flue gas temperature - design flue gas temperature of waste heat boiler.

[0017] The oxygen content deviation Yp is determined based on the average oxygen content of flue gas, where Yp = average oxygen content of flue gas - oxygen content control target value;

[0018] The state coefficient KE of the waste heat boiler heat recovery is determined based on the flue gas temperature rise Ps and the oxygen content deviation Yp, KE=1-[r1×min(1, max(0,Ps / preset temperature rise threshold))+r2×min(1,max(0,Yp / preset deviation threshold))];

[0019] Where r1 is the temperature rise weight and r2 is the oxygen content weight, and r1+r2=1.

[0020] Furthermore, the maximum value of the dense phase bed temperature of the regenerator during the analysis period is obtained and recorded as the peak temperature of the regenerator bed; the standard deviation σnet of the main steam network pressure during the analysis period is calculated.

[0021] The thermal safety margin TS of the regenerator is calculated based on the peak temperature of the regenerator bed, TS=max(0,(high interlock value of regenerator temperature-peak temperature of regenerator bed)) / preset temperature difference threshold;

[0022] The standard deviation of the main steam pipeline pressure σnet determines the pipeline pressure buffer coefficient KP, KP=1-min(1,σnet / preset pressure fluctuation threshold).

[0023] Furthermore, the operating characteristics SI are determined based on the regenerator thermal safety margin TS and the pipeline pressure buffer coefficient KP, where SI = c1 × TS + c2 × KP.

[0024] Where c1 is the thermal weight, c2 is the pipeline pressure weight, and c1+c2=1.

[0025] Furthermore, when the operating stable state is stable and KE is greater than or equal to the preset state threshold k0, the operating mode of the next analysis cycle is determined to be stable operation with guaranteed efficiency, and the external heat exchanger control command △v for the next analysis cycle is set to -β×LI×(1-FG).

[0026] When the stable operating state is fluctuating, the operating mode of the next analysis cycle is determined to be anti-disturbance stable operation, and the external heat exchanger control command Δv for the next analysis cycle is set to γ×(1-FG);

[0027] When the operating state is stable and KE is less than the preset state threshold k0, the operating mode of the next analysis cycle is determined to be energy efficiency priority, and the external heat exchanger control command △v for the next analysis cycle is set to -η×(k0-KE)×min(1,FG / CVg).

[0028] Wherein, CVg is the second preset stability threshold, LI is the average value of the fluidizing air valve position of the heat exchanger outside the current analysis cycle, β is the fine-tuning gain coefficient, γ is the anti-disturbance gain coefficient, and η is the energy efficiency gain coefficient.

[0029] Furthermore, when SI is less than s0 and the operating mode is disturbance-resistant stable operation, the disturbance-resistant gain coefficient is adjusted to γ1, γ1=γ×{1+ln[4×(s0-SI)+1] / ln5}, to adjust the generation process of the external heat exchanger control command; otherwise, the generation process of the external heat exchanger control command is not adjusted.

[0030] According to another aspect of this application, an intelligent energy management device for chemical industrial parks is provided, comprising:

[0031] The stability analysis unit is used to extract operational stability characteristic data based on regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate and steam drum liquid level, and then determine the operational stability state.

[0032] The state analysis unit is used to determine the state coefficient of the waste heat boiler heat recovery based on the flue gas temperature and oxygen content.

[0033] The operating characteristic determination unit is used to extract operating characteristic data based on the regenerator dense phase bed temperature and pipeline pressure, and then determine the operating characteristics based on the operating characteristic data;

[0034] The decision-making unit is used to make operational mode decisions based on the stable operating state and the state coefficient of the waste heat boiler heat recovery and to generate external heat exchanger control commands.

[0035] The optimization unit is used to adjust the generation process of control commands for the external heat exchanger based on the operating characteristics.

[0036] According to another aspect of this application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device on which the computer-readable storage medium is located to perform the energy consumption intelligent management method for chemical industrial parks as claimed in the claims during runtime.

[0037] The beneficial effects of this invention are as follows: This invention constructs a state perception and decision-making control system with "operational stability, heat recovery efficiency, and safety margin" as its core. By extracting stable characteristics such as the coefficient of variation of flue gas parameters and the rate of change of steam drum liquid level, it achieves quantitative judgment of operational stability; by integrating flue gas temperature rise and oxygen content deviation, it establishes a real-time evaluation index for the heat recovery status of the waste heat boiler; and by constructing a multi-dimensional safety margin system through regenerator bed temperature peak and pipeline pressure fluctuation, it provides a dynamic safety boundary for control commands. Based on this, it intelligently decides the operation mode according to the combined characteristics of stable state and energy efficiency state, generating three types of differentiated control commands: efficiency-preserving fine-tuning, disturbance rejection enhancement, and energy efficiency optimization, and introducing a safety margin to adaptively correct the control gain. This invention achieves dynamic coordination between the heat load of the reverse regeneration system and the heat recovery demand of the waste heat boiler, significantly improving waste heat utilization efficiency while ensuring the safe operation of the device. Furthermore, it is entirely based on existing instrumentation and control systems, possessing strong engineering practicality and promotional value. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating the intelligent energy management method for chemical industrial parks in this embodiment.

[0040] Figure 2 This is a flowchart illustrating the method for determining the stable operating state in this embodiment.

[0041] Figure 3 This is a flowchart illustrating the method for determining the running characteristics in this embodiment.

[0042] Figure 4 This is a schematic diagram of the intelligent energy management device used in a chemical industrial park according to this embodiment. Detailed Implementation

[0043] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0044] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0045] Specifically, this embodiment is applied to a heavy oil catalytic cracking unit in a large-scale integrated refining and chemical industrial park in northern China, which is equipped with a regenerated flue gas waste heat boiler.

[0046] Please see Figure 1 As shown, this is a flowchart illustrating the intelligent energy management method for chemical industrial parks in this embodiment. Before the method is executed, the system synchronously collects data through monitoring terminals deployed in the regenerative braking system and waste heat boiler, including:

[0047] Regenerated flue gas data: The temperature of the regenerated flue gas is collected by a K-type armored thermocouple installed on the outlet flue at the top of the regenerator; the volumetric flow rate of the regenerated flue gas is collected by a calibrated dual Venturi flow meter on the same flue.

[0048] Waste heat boiler emission data: The oxygen content of the flue gas is collected by a direct-insertion zirconia analyzer installed in the tail flue of the waste heat boiler; the exhaust temperature is collected by an S-type thermocouple on the flue.

[0049] Steam-side data: Steam production pressure is collected through a pressure transmitter on the waste heat boiler drum; steam production flow rate is collected through a standard orifice plate flow meter on the main steam outlet pipe of the drum; and the drum level is collected through a differential pressure level transmitter on the drum.

[0050] Regeneration system data: Real-time acquisition of regenerator dense phase bed temperature, main air flow rate, and external heat exchanger fluidizing air valve position via the OPC interface of the device's distributed control system;

[0051] System configuration data: The waste heat boiler design flue gas temperature and regenerator temperature high interlock value are read from the real-time database or preset configuration file of the unit's energy management system. The waste heat boiler design flue gas temperature refers to the flue gas temperature designed after heat recovery under rated operating conditions, which is used as a benchmark for evaluating the actual flue gas condition. The regenerator temperature high interlock value refers to the safe process upper limit of the regenerator dense phase bed temperature. When the temperature exceeds this value, the unit's emergency shutdown interlock will be triggered. The steam drum liquid level high interlock value and low interlock value refer to the safe operating upper and lower limits of the waste heat boiler steam drum liquid level.

[0052] The above parameters are inherent known parameters of the catalytic cracking unit and the waste heat boiler. When implementing this invention, they need to be read from the real-time database or preset configuration file of the unit's energy management system.

[0053] This embodiment does not impose specific limitations on the data collection method described above; those skilled in the art can freely set it according to their needs.

[0054] The method includes:

[0055] Step S1: Extract stable operation characteristic data based on regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate and steam drum liquid level, and then determine the stable operation state.

[0056] Specifically, based on multi-source time-series data such as regenerated flue gas, steam production, and steam drum liquid level, multi-dimensional stability features characterizing the overall operational stability of the unit are extracted, providing a reliable operating condition discrimination benchmark for subsequent operation mode decisions.

[0057] Please see Figure 2 As shown, the method for determining the stable operating state includes:

[0058] Step S11: Extract stable operation characteristic data based on regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate, and steam drum liquid level.

[0059] Specifically, the coefficient of variation (CVT) of the regenerated flue gas temperature series, the coefficient of variation (CVQ) of the regenerated flue gas volumetric flow rate series, and the coefficient of variation (CVN) of the steam production flow rate series are calculated for the current analysis period, where:

[0060] CVT = σT / μT;

[0061] CVQ = σQ / μQ;

[0062] CVN = σN / μN;

[0063] The sum of the absolute values ​​of the differences between adjacent sampling points in the steam drum liquid level sequence within the analysis period is calculated and recorded as the absolute change in steam drum liquid level. The absolute change in steam drum liquid level is divided by the number of sampling points in the analysis period minus 1, and then divided by the time interval between adjacent sampling points to obtain the average change rate of steam drum liquid level YWB.

[0064] Specifically, the coefficient of variation is used to quantitatively characterize the relative fluctuations of flue gas temperature, flue gas flow rate, and steam production flow rate, overcoming the limitation that it is difficult to compare across operating conditions by simply using absolute deviation; the average change rate of steam drum liquid level is used to effectively identify frequent fluctuations or periodic disturbances in the steam drum, thus achieving a refined characterization of the dynamic characteristics of the thermodynamic system.

[0065] Please continue reading. Figure 2 As shown, the method for determining the stable operating state further includes:

[0066] Step S12: Determine the operational stability index based on the operational stability characteristic data, and then determine the operational stability state.

[0067] Specifically, the operational stability index FG is determined based on CVT, CVQ, CVN and YWB, where FG = 1 - [w1×min(1,CVT / first preset fluctuation threshold) + w2×min(1,CVQ / second preset fluctuation threshold) + w3×min(1,CVN / third preset fluctuation threshold) + w4×min(1,YWB / preset rate of change).

[0068] The stability index FG is compared with the first preset stability threshold CVt to determine the stable state of operation. When FG is less than or equal to CVt, the stable state of operation in the current analysis period is determined to be a fluctuating state; otherwise, the stable state of operation in the current analysis period is determined to be a stable state.

[0069] Where σT is the standard deviation of the regenerated flue gas temperature series, μT is the average value of the regenerated flue gas temperature series, σQ is the standard deviation of the regenerated flue gas volumetric flow rate series, μQ is the average value of the regenerated flue gas volumetric flow rate series, σN is the standard deviation of the steam production flow rate series, μN is the average value of the steam production flow rate series, w1 is the temperature weight, w2 is the flow rate weight, w3 is the steam production weight, w4 is the liquid level weight, and w1+w2+w3+w4=1.

[0070] Specifically, in this embodiment, the first preset fluctuation threshold is 0.1, the second preset fluctuation threshold is 0.12, the optimal value of the third preset fluctuation threshold is 0.08, the preset change rate is 0.05 / min, the temperature weight is 0.3, the flow rate weight is 0.2, the steam production weight is 0.3, the liquid level weight is 0.2, and the first preset stability threshold is 0.6.

[0071] Specifically, multidimensional stability features are integrated into a normalized operational stability index, transforming the abstract concept of volatility into a clear quantitative criterion. By comparing with a preset stability threshold, stable and volatile states are objectively distinguished, eliminating the subjectivity and instability of relying on human experience for judgment.

[0072] Please continue reading. Figure 1 As shown, the intelligent energy management method for chemical industrial parks also includes:

[0073] Step S2: Determine the state coefficient of waste heat boiler heat recovery based on the flue gas temperature and oxygen content.

[0074] Specifically, calculate the average value of the flue gas temperature within the current analysis period and record it as the average flue gas temperature. At the same time, calculate the average value of the flue gas oxygen content and record it as the average flue gas oxygen content.

[0075] The flue gas temperature rise Ps is determined based on the average flue gas temperature, where Ps = average flue gas temperature - design flue gas temperature of waste heat boiler.

[0076] The oxygen content deviation Yp is determined based on the average oxygen content of flue gas, where Yp = average oxygen content of flue gas - oxygen content control target value;

[0077] The state coefficient KE of the waste heat boiler heat recovery is determined based on the flue gas temperature rise Ps and the oxygen content deviation Yp, KE=1-[r1×min(1, max(0,Ps / preset temperature rise threshold))+r2×min(1,max(0,Yp / preset deviation threshold))];

[0078] Where r1 is the temperature rise weight and r2 is the oxygen content weight, and r1+r2=1.

[0079] Specifically, in this implementation, the oxygen content control target value is 3%, the preset temperature rise threshold is 30 ℃, the preset deviation threshold is 1.0.05, the temperature rise weight is 0.6, and the oxygen content weight is 0.4.

[0080] Specifically, by using two key energy efficiency indicators, namely flue gas temperature and flue gas oxygen content, a quantitative model of the heat recovery state coefficient is constructed to achieve online diagnosis and dynamic assessment of the degree of thermal efficiency degradation of waste heat boilers.

[0081] Please continue reading. Figure 1 As shown, the intelligent energy management method for chemical industrial parks also includes:

[0082] Step S3: Extract operating characteristic data based on the regenerator dense phase bed temperature, steam drum liquid level, and pipeline pressure, and then determine the operating characteristics based on the operating characteristic data.

[0083] Please see Figure 2 As shown, the method for determining the operational characteristics includes:

[0084] Step S31: Extract operating characteristic data based on the regenerator dense phase bed temperature and pipeline pressure. The operating characteristic data includes the regenerator thermal safety margin and the pipeline pressure buffer coefficient.

[0085] Specifically, the maximum value of the dense phase bed temperature in the regenerator is obtained within the analysis period and recorded as the peak temperature of the regenerator bed; the standard deviation σnet of the main steam network pressure is calculated within the analysis period.

[0086] The thermal safety margin TS of the regenerator is calculated based on the peak temperature of the regenerator bed, TS=max(0,(high interlock value of regenerator temperature-peak temperature of regenerator bed)) / preset temperature difference threshold;

[0087] The standard deviation of the main steam pipeline pressure σnet determines the pipeline pressure buffer coefficient KP, KP=1-min(1,σnet / preset pressure fluctuation threshold).

[0088] Specifically, in this embodiment, the preset temperature difference threshold is 50°C and the preset pressure fluctuation threshold is 0.3 MPa.

[0089] Specifically, the remaining safety space from the high-temperature interlock protection boundary is measured by the regenerator thermal safety margin quantification device; the reaction strength of turbine-side disturbances on the heat extraction system is characterized by the pipeline pressure buffer coefficient.

[0090] Please continue reading. Figure 2 As shown, the method for determining the operational characteristics further includes:

[0091] Step S32: Determine the operating characteristics based on the operating characteristic data.

[0092] Specifically, the operating characteristics SI are determined based on the regenerator thermal safety margin TS and the pipeline pressure buffer coefficient KP, where SI = c1 × TS + c2 × KP.

[0093] Where c1 is the thermal weight, c2 is the pipeline pressure weight, and c1+c2=1.

[0094] Specifically, in this embodiment, the thermal weight is 0.7 and the pipeline pressure weight is 0.3.

[0095] Specifically, by weighting and integrating the margin coefficients of the two safety dimensions into a comprehensive operational characteristic, a one-dimensional representation of the overall safety status of the device is achieved, providing a simple, continuous, and calculable input variable for the correction of safety-oriented control commands.

[0096] Please continue reading. Figure 1 As shown, the intelligent energy management method for chemical industrial parks also includes:

[0097] Step S4: Based on the stable operating state and the state coefficient of the waste heat boiler heat recovery, make an operation mode decision and generate external heat exchanger control instructions.

[0098] Specifically, when the operating stable state is stable and KE is greater than or equal to the preset state threshold k0, the operating mode of the next analysis cycle is determined to be stable operation with guaranteed efficiency, and the external heat exchanger control command △v for the next analysis cycle is set to -β×LI×(1-FG).

[0099] When the stable operating state is fluctuating, the operating mode of the next analysis cycle is determined to be anti-disturbance stable operation, and the external heat exchanger control command Δv for the next analysis cycle is set to γ×(1-FG);

[0100] When the operating state is stable and KE is less than the preset state threshold k0, the operating mode of the next analysis cycle is determined to be energy efficiency priority, and the external heat exchanger control command △v for the next analysis cycle is set to -η×(k0-KE)×min(1,FG / CVg).

[0101] Wherein, CVg is the second preset stability threshold, LI is the average value of the fluidizing air valve position of the heat exchanger outside the current analysis cycle, β is the fine-tuning gain coefficient, γ is the anti-disturbance gain coefficient, and η is the energy efficiency gain coefficient.

[0102] Specifically, the external heat exchanger control command △v generated based on the aforementioned steps is used to adjust the set value of the fluidizing air valve position of the external heat exchanger at the beginning of the next analysis cycle. The specific adjustment method is as follows: the fluidizing air valve position L0 of the external heat exchanger at the end of the current analysis cycle is algebraically added to the control command △v to obtain the preliminary target valve position set value Vset = L0 + △v for the next analysis cycle; subsequently, the preliminary target valve position set value is limited to ensure that it is within a safe and executable range: if Vset is lower than the preset valve position safety lower limit Vmin, the final valve position command Vset value is Vmin; if Vset is higher than the preset valve position safety upper limit Vmax, the final valve position command Vset value is Vmax; if it does not exceed the limit, then Vset = Vset, where the lower safety limit of valve position Vmin and the upper safety limit of valve position Vmax need to be set according to the design specifications and safe operating procedures of the specific device. For example, to prevent the interruption of fluidizing air from causing the catalyst bed in the heat exchanger to die, Vmin is usually set to 0.1; to avoid the pipeline being flushed or losing the adjustment margin due to the valve being fully open, Vmax is usually set to 0.9.

[0103] Specifically, in this embodiment, the fine-tuning gain coefficient is 0.05, the anti-interference gain coefficient is 0.15, the energy efficiency gain coefficient is 0.1, the preset state threshold is 0.8, and the second preset stability threshold is 0.85.

[0104] Specifically, based on the dual-dimensional discrimination of stable operation and heat recovery status, the operating conditions of the device are intelligently classified into three typical scenarios: "stable operation with guaranteed efficiency", "stable operation with resistance to disturbances", and "energy efficiency priority". Differentiated external heat exchanger adjustment strategies are designed for different scenarios, realizing adaptive control of "ensuring efficiency when stable, resisting disturbances when fluctuating, and pursuing optimization when inefficient".

[0105] Please continue reading. Figure 1 As shown, the intelligent energy management method for chemical industrial parks also includes:

[0106] Step S5: Adjust the generation process of the external heat exchanger control command based on the operating characteristics.

[0107] Specifically, when SI is less than s0 and the operating mode is disturbance-resistant stable operation, the disturbance-resistant gain coefficient is adjusted to γ1, γ1=γ×{1+ln[4×(s0-SI)+1] / ln5}, to adjust the generation process of the external heat exchanger control command. Otherwise, the generation process of the external heat exchanger control command is not adjusted, and s0 is the preset operating characteristic.

[0108] Specifically, in this embodiment, the preset operating characteristic is 0.6.

[0109] Specifically, in this embodiment, the analysis period is 10 minutes and the sampling interval is 1 second.

[0110] Specifically, a dynamic correlation mechanism between operating characteristics and control gain is established. When the safety margin of the device decreases, the control intensity is actively amplified to quickly return to the safe zone. When the device is at a high-risk boundary, a smooth and abrupt enhancement adjustment is achieved through a logarithmic gain amplification function, which not only ensures safety but also avoids secondary impacts on the system caused by command jumps.

[0111] Please see Figure 4 As shown, the intelligent energy management device for chemical industrial parks includes:

[0112] The stability analysis unit is used to extract operational stability characteristic data based on regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate and steam drum liquid level, and then determine the operational stability state.

[0113] The state analysis unit is used to determine the state coefficient of the waste heat boiler heat recovery based on the flue gas temperature and oxygen content.

[0114] The operating characteristic determination unit is used to extract operating characteristic data based on the regenerator dense phase bed temperature and pipeline pressure, and then determine the operating characteristics based on the operating characteristic data;

[0115] The decision-making unit is used to make operational mode decisions based on the stable operating state and the state coefficient of the waste heat boiler heat recovery and to generate external heat exchanger control commands.

[0116] The optimization unit is used to adjust the generation process of control commands for the external heat exchanger based on the operating characteristics.

[0117] The intelligent energy management device for chemical industrial parks provided in this application can execute the intelligent energy management method for chemical industrial parks provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0118] This application also provides a computer-readable storage medium, which is a tangible physical storage medium that can store the aforementioned computer program and various types of data used in the program; the physical storage medium includes, but is not limited to, existing physical storage media or combinations thereof, such as random access memory, read-only memory, optical disk, and hard disk.

[0119] Those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable programs, data structures, program modules, or other data). Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable programs, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and can include any information delivery medium.

[0120] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for intelligent energy consumption management in chemical industrial parks, characterized in that, include: Based on the regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate and steam drum liquid level, the stable operating characteristic data are extracted, and then the stable operating state is determined. The state coefficient of waste heat boiler heat recovery is determined based on the flue gas temperature and oxygen content. Operating characteristic data are extracted based on the regenerator dense phase bed temperature and pipeline pressure, and then the operating characteristics are determined based on the operating characteristic data; Based on the stable operating state and the state coefficient of the waste heat boiler heat recovery, the operation mode decision is made and the external heat exchanger control command is generated. The process of generating control commands for external heat exchangers is adjusted based on operational characteristics.

2. The intelligent energy consumption management method for chemical industrial parks according to claim 1, characterized in that, Calculate the coefficient of variation (CVT) of the regenerated flue gas temperature sequence, the coefficient of variation (CVQ) of the regenerated flue gas volumetric flow rate sequence, and the coefficient of variation (CVN) of the steam production flow rate sequence within the current analysis period. The sum of the absolute values ​​of the differences between adjacent sampling points in the steam drum liquid level sequence within the analysis period is calculated and recorded as the absolute change in steam drum liquid level. The absolute change in steam drum liquid level is divided by the number of sampling points in the analysis period minus 1, and then divided by the time interval between adjacent sampling points to obtain the average change rate of steam drum liquid level YWB.

3. The intelligent energy consumption management method for chemical industrial parks according to claim 2, characterized in that, The operational stability index FG is determined based on CVT, CVQ, CVN, and YWB. The operational stability index FG is compared with the first preset stability threshold CVt to determine the operational stability state. When FG is less than or equal to CVt, the operational stability state of the current analysis period is determined to be a fluctuating state; otherwise, the operational stability state of the current analysis period is determined to be a stable state.

4. The intelligent energy consumption management method for chemical industrial parks according to claim 3, characterized in that, Calculate the average value of the flue gas temperature within the current analysis period, and record it as the average flue gas temperature. At the same time, calculate the average value of the flue gas oxygen content, and record it as the average flue gas oxygen content. The flue gas temperature rise Ps is determined based on the average flue gas temperature, where Ps = average flue gas temperature - design flue gas temperature of waste heat boiler. The oxygen content deviation Yp is determined based on the average oxygen content of flue gas, where Yp = average oxygen content of flue gas - oxygen content control target value; The state coefficient KE of the waste heat boiler heat recovery is determined based on the flue gas temperature rise Ps and the oxygen content deviation Yp, KE=1-[r1×min(1, max(0,Ps / preset temperature rise threshold))+r2×min(1,max(0,Yp / preset deviation threshold))]; Where r1 is the temperature rise weight and r2 is the oxygen content weight, and r1+r2=1.

5. The intelligent energy consumption management method for chemical industrial parks according to claim 4, characterized in that, Obtain the maximum value of the dense phase bed temperature in the regenerator during the analysis period, and record it as the peak temperature of the regenerator bed; calculate the standard deviation σnet of the main steam network pressure during the analysis period; The thermal safety margin TS of the regenerator is calculated based on the peak temperature of the regenerator bed. TS = max(0, (high interlock value of regenerator temperature - peak temperature of regenerator bed)) / preset temperature difference threshold. The standard deviation of the main steam pipeline pressure σnet determines the pipeline pressure buffer coefficient KP, KP=1-min(1,σnet / preset pressure fluctuation threshold).

6. The intelligent energy consumption management method for chemical industrial parks according to claim 5, characterized in that, The operating characteristics SI are determined based on the regenerator thermal safety margin TS and the pipeline pressure buffer coefficient KP, where SI = c1 × TS + c2 × KP. Where c1 is the thermal weight, c2 is the pipeline pressure weight, and c1+c2=1.

7. The intelligent energy consumption management method for chemical industrial parks according to claim 6, characterized in that, When the operating state is stable and KE is greater than or equal to the preset state threshold k0, the operating mode of the next analysis cycle is determined to be stable operation with guaranteed efficiency, and the external heat exchanger control command △v for the next analysis cycle is set to -β×LI×(1-FG). When the stable operating state is fluctuating, the operating mode of the next analysis cycle is determined to be anti-disturbance stable operation, and the external heat exchanger control command Δv for the next analysis cycle is set to γ×(1-FG); When the operating state is stable and KE is less than the preset state threshold k0, the operating mode of the next analysis cycle is determined to be energy efficiency priority, and the external heat exchanger control command △v for the next analysis cycle is set to -η×(k0-KE)×min(1,FG / CVg). Wherein, CVg is the second preset stability threshold, LI is the average value of the fluidizing air valve position of the heat exchanger outside the current analysis cycle, β is the fine-tuning gain coefficient, γ is the anti-disturbance gain coefficient, and η is the energy efficiency gain coefficient.

8. The intelligent energy consumption management method for chemical industrial parks according to claim 7, characterized in that, When SI is less than s0 and the operating mode is disturbance rejection and stable operation, the disturbance rejection gain coefficient is adjusted to γ1, γ1=γ×{1+ln[4×(s0-SI)+1] / ln5}, to adjust the generation process of the external heat exchanger control command. Otherwise, the generation process of the external heat exchanger control command is not adjusted.

9. An intelligent energy consumption management device for chemical industrial parks, applied to the intelligent energy consumption management method for chemical industrial parks as described in any one of claims 1-8, characterized in that, include: The stability analysis unit is used to extract operational stability characteristic data based on regenerated flue gas temperature, regenerated flue gas volumetric flow rate, steam production flow rate and steam drum liquid level, and then determine the operational stability state. The state analysis unit is used to determine the state coefficient of the waste heat boiler heat recovery based on the flue gas temperature and oxygen content. The operating characteristic determination unit is used to extract operating characteristic data based on the regenerator dense phase bed temperature and pipeline pressure, and then determine the operating characteristics based on the operating characteristic data; The decision-making unit is used to make operational mode decisions based on the stable operating state and the state coefficient of the waste heat boiler heat recovery and to generate external heat exchanger control commands. The optimization unit is used to adjust the generation process of control commands for the external heat exchanger based on the operating characteristics.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device on which the computer-readable storage medium is located to execute the intelligent energy management method for chemical industrial parks as described in any one of claims 1-8 during runtime.

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