Low-energy-consumption intelligent optimization method and system for capturing co2 based on solid adsorption

Abstract

The present application relates to the technical field of carbon neutralization, and particularly relates to a low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture, comprising the following steps: obtaining historical operation parameters of all units in a CO2 capture system, historical desulfurization outlet CO2 capture rates and historical equipment load parameters; calculating a historical optimal energy consumption index according to the historical desulfurization outlet CO2 capture rates and the historical equipment load parameters, and marking historical operation parameters of a corresponding time period of the historical optimal energy consumption index as historical optimal operation parameters; calculating a regulation trend value of the historical operation parameters according to the historical operation parameters and the historical equipment load parameters of the corresponding time period, and calculating a regulation trend amplitude value according to the regulation trend value; the present application can continuously perform low-energy-consumption optimization, and finally obtain maximized CO2 capture efficiency and minimized equipment load parameters.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of carbon neutralization, in particular to a low-energy-consumption intelligent optimization method and system for capturing CO2 based on solid adsorption. BACKGROUND

[0002] In the field of CCUS application, the CO2 capture technology after combustion can be widely applied to flue gas CO2 capture of thermal power, cement and steel, the CO2 capture technology of solid adsorption material has the characteristics of strong adaptability, no corrosion and no secondary pollution, is suitable for large-scale CO2 capture and has great commercial application potential. The CO2 capture technology is the basis and premise of the development of the CCUS technology, and accounts for about 60%-70% of the total energy consumption of the CCUS.

[0003] At present, the difficulty of CCUS popularization lies in that the energy consumption is too high. The energy consumption of the capture system is an important index for evaluating the performance of the flue gas CO2 capture system. However, the solid adsorption CO2 capture system energy consumption optimization technology of the thermal power generation industry is lagging behind, and there is a lack of a low-energy-consumption intelligent optimization method for solid adsorption CO2 capture. SUMMARY

[0004] The main purpose of the application is to provide a low-energy-consumption intelligent optimization method for capturing CO2 based on solid adsorption.

[0005] The application provides a low-energy-consumption intelligent optimization method for capturing CO2 based on solid adsorption, which comprises the following steps:

[0006] The historical operation parameters of all units in the CO2 capture system, the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters are obtained;

[0007] The historical optimal energy consumption index is calculated according to the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, and the historical operation parameters of the corresponding time period of the historical optimal energy consumption index are marked as the historical optimal operation parameters;

[0008] The regulation trend value of the historical operation parameters is calculated according to the historical operation parameters and the historical equipment load parameters of the corresponding time period, and the regulation trend amplitude value is calculated according to the regulation trend value;

[0009] The real-time operation parameters of all units in the CO2 capture system, the real-time desulfurization outlet CO2 capture rate and the real-time equipment load are obtained, and the real-time operation parameters at least include the adsorption temperature, the desorption temperature, the adsorption pressure, the desorption pressure and the gas flow rate;

[0010] The real-time energy consumption index is calculated according to the real-time equipment load and the real-time desulfurization outlet CO2 capture rate;

[0011] judging the size of the real-time energy consumption index of the single unit and the historical optimal energy consumption index;

[0012] If greater, the real-time operation parameter is regulated to the historical optimal operation parameter according to the regulation trend value and the regulation trend amplitude value until the historical optimal operation parameter is reached;

[0013] If less, the real-time energy consumption index is updated to the historical optimal energy consumption index, and the real-time operation parameter corresponding to the real-time energy consumption index is marked as the historical optimal operation parameter;

[0014] The optimal equipment load parameter is obtained according to the historical optimal operation parameter.

[0015] As preferred, the step of obtaining the historical desulfurization outlet CO2 capture rate is:

[0016] The inlet CO2 concentration and the outlet CO2 concentration per unit time of each unit are obtained;

[0017] The inlet flue gas flow and the outlet flue gas flow per unit time of each unit are obtained;

[0018] The inlet flue gas density and the outlet flue gas density per unit time of each unit are obtained;

[0019] The desulfurization outlet CO2 capture rate per unit time is calculated according to the inlet CO2 concentration, the outlet CO2 concentration, the inlet flue gas flow, the outlet flue gas flow, the inlet flue gas density and the outlet flue gas density, wherein the calculation formula is:

[0020]

[0021] Wherein, C b represents the desulfurization outlet CO2 capture rate per unit time, Q O represents the outlet flue gas flow, P O represents the outlet flue gas density, C O represents the inlet CO2 concentration, Q i represents the inlet flue gas flow, P i represents the inlet flue gas density, C I represents the inlet CO2 concentration;

[0022] The desulfurization outlet CO2 capture rate per unit time of all units is counted to obtain the historical desulfurization outlet CO2 capture rate.

[0023] As preferred, the step of calculating the historical optimal energy consumption index according to the historical desulfurization outlet CO2 capture rate and the historical equipment load parameter is as follows:

[0024] The equipment load parameter per unit time of all units is obtained through the historical equipment load parameter;

[0025] The unit time energy consumption index of the single unit is calculated according to the unit time equipment load parameter of the single unit and the unit time desulfurization outlet CO2 capture rate of the single unit, wherein the calculation formula is:

[0026] F = ω1 * E(z) + ω2 * C b ;

[0027] F represents the unit time energy consumption index of the single unit, E(z) represents the unit time equipment load parameter of the single unit, ω1 represents the load weight, C b represents the unit time desulfurization outlet CO2 capture rate of the single unit, and ω2 represents the CO2 capture rate weight;

[0028] The unit time energy consumption indexes of all units are counted, and the unit time energy consumption index with the lowest value is the historical optimal energy consumption index.

[0029] As preferred, the calculation formula for calculating the regulation trend value of the historical operation parameter according to the historical operation parameter and the historical equipment load parameter of the corresponding time period is:

[0030]

[0031] Wherein, y k and h k represent the values of the historical operation parameter and the historical equipment load parameter of the corresponding time period at the kth regulation, y k+i and h k+i represent the values of the historical operation parameter and the historical equipment load parameter of the corresponding time period at the k+i th regulation, and v represents the regulation trend value.

[0032] As preferred, the formula for calculating the regulation trend amplitude value according to the regulation trend value is:

[0033]

[0034] Wherein, w3 represents the regulation trend amplitude value, h k+i and h k represent the values of the historical equipment load parameter at the k+i th regulation and at the kth regulation respectively, and Δv represents the difference between the regulation trend value at h k+i and the regulation trend value at h k .

[0035] As preferred, the step of regulating the real-time operation parameter to the historical optimal operation parameter according to the regulation trend value and the regulation trend amplitude value until the historical optimal operation parameter is reached, comprising:

[0036] Obtaining the single unit environmental impact value and the total unit environmental impact value;

[0037] Obtain the optimal operating parameter value and historical optimal operating parameter value of a single unit;

[0038] Obtain historical optimal operating parameters of a single unit;

[0039] Calculate the control trend value of the real-time operating parameters of a single unit at the k+1th control of the corresponding real-time equipment load parameters based on the environmental impact value of the single unit, the environmental impact values ​​of all units, the importance value of the optimal operating parameters of the single unit, the importance value of the historical optimal operating parameters, the historical optimal operating parameters of the single unit, and the historical optimal operating parameters;

[0040]

[0041] Among them, v i,d (k+1) represents the control trend value of the real-time operating parameters of a single unit at the k+1th control of the corresponding real-time equipment load parameters, c1 represents the weighted value of the optimal operating parameters of a single unit, c2 represents the weighted value of the historical optimal operating parameters, r1 represents the environmental impact value of a single unit, r2 represents the environmental impact value of all units, w3 represents the control trend value, i represents the real-time operating parameter, d represents the real-time equipment load parameter, It represents the parameters of the real-time operating parameters at the time of the kth adjustment of the corresponding real-time equipment load. represents the control trend value of the real-time operating parameter when the corresponding real-time equipment load parameter is regulated for the kth time, g d represents the historical optimal operating parameters, P i,d Indicates the historical optimal operating parameters of a single unit;

[0042] The parameters of the real-time operating parameters at the k+1th regulation of the corresponding real-time equipment load are calculated based on the control trend value of the real-time operating parameters of the single unit at the k+1th regulation of the corresponding real-time equipment load and the value of the real-time operating parameters of the single unit at the kth regulation, wherein the calculation formula is:

[0043]

[0044] Among them, x i,d (k+1) represents the real-time operating parameters of a single unit at the k+1th adjustment of the corresponding real-time equipment load, v i,d (k+1) The control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are adjusted for the k+1th time, Indicates the value of the real-time operating parameters of a single unit at the kth regulation;

[0045] Judge x i,d (k+1)the size of the real-time energy consumption index and the historical optimal energy consumption index, when less than or equal to, stop regulating, and x i,d (k+1) is updated to the historical optimal operation parameter.

[0046] The application also discloses a low-energy-consumption intelligent optimization system based on solid adsorption CO2 capture, which is used for a low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture and comprises the following steps of:

[0047] A real-time monitoring module is configured to monitor the operation states of all units in the CO2 capture system in real time.

[0048] A historical data storage module is configured to store the historical operation parameters, the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters of all units in the CO2 capture system.

[0049] An intelligent regulation module is configured to calculate and update the historical optimal operation parameters according to the data in the historical data storage module and real-time data.

[0050] An environmental impact assessment module is configured to assess the influence of the operation environment of each unit in the CO2 capture system on the real-time operation parameters.

[0051] Preferably, the intelligent regulation module comprises:

[0052] A fault detection and diagnosis unit is configured to determine whether all units are in a normal operation state or an abnormal operation state through real-time monitoring and data analysis, and to alarm when an abnormal operation state is detected, so as to reduce the risk of unplanned shutdown and improve the reliability and availability of the system.

[0053] The application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture when executing the computer program.

[0054] The application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture when executed by a processor.

[0055] The application has the beneficial effect that the low-energy-consumption optimization can be continuously performed, the historical optimal energy consumption index and the historical optimal operation parameter are calculated by obtaining the historical operation parameters, the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, an initial optimization target can be formed for the real-time operation parameters, each single unit in the CO2 capture system is independently regulated on this basis, the real-time operation parameters continuously approach the historical optimal operation parameter, the historical optimal operation parameter is updated in real time for optimization, and finally the maximum CO2 capture efficiency and the minimum equipment load parameter are obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of a method flow according to an embodiment of the present invention.

[0057] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0058] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] like Figure 1 As shown, the present application provides a low-energy intelligent optimization method for solid adsorption CO2 capture, comprising the following steps:

[0060] S1. Obtain historical operating parameters, historical desulfurization outlet CO2 capture rates, and historical equipment load parameters of all units in the CO2 capture system;

[0061] S2. Calculate the historical optimal energy consumption index based on the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, and mark the historical operating parameters of the time period corresponding to the historical optimal energy consumption index as the historical optimal operating parameters;

[0062] S3. Calculate the control trend value of the historical operating parameters based on the historical operating parameters and the historical equipment load parameters of the corresponding time period, and calculate the control trend amplitude value based on the control trend value;

[0063] S4. Obtaining real-time operating parameters of all units in the CO2 capture system, real-time desulfurization outlet CO2 capture rate, and real-time equipment load. The real-time operating parameters include at least adsorption temperature, desorption temperature, adsorption pressure, desorption pressure, and gas flow rate.

[0064] S5. Calculate the real-time energy consumption index based on the real-time equipment load and the real-time desulfurization outlet CO2 capture rate;

[0065] S6. Determine the difference between the real-time energy consumption index of a single unit and the historical optimal energy consumption index;

[0066] If it is greater, the real-time operating parameters are adjusted toward the historical optimal operating parameters according to the control trend value and the control trend amplitude value until the historical optimal operating parameters are reached;

[0067] If it is less than, the real-time energy consumption index is updated to the historical optimal energy consumption index, and the real-time operating parameters corresponding to the real-time energy consumption index are marked as the historical optimal operating parameters;

[0068] S7. Obtain optimal equipment load parameters based on historical optimal operating parameters.

[0069] The historical optimal energy consumption index and the historical optimal operation parameter are calculated by obtaining the historical operation parameter, the historical desulfurization outlet CO2 capture rate and the historical equipment load parameter, which can form an initial optimization target for the real-time operation parameter. On this basis, each single unit in the CO2 capture system is independently regulated and controlled, so that the real-time operation parameter continuously approaches the historical optimal operation parameter, and the historical optimal operation parameter is updated in time for optimization, and finally the maximum CO2 capture efficiency and the minimum equipment load parameter are obtained.

[0070] The obtaining step of the historical desulfurization outlet CO2 capture rate:

[0071] Obtain the inlet CO2 concentration and the outlet CO2 concentration of each unit per unit time;

[0072] Obtain the inlet flue gas flow and the outlet flue gas flow of each unit per unit time;

[0073] Obtain the inlet flue gas density and the outlet flue gas density of each unit per unit time;

[0074] Calculate the desulfurization outlet CO2 capture rate per unit time according to the inlet CO2 concentration, the outlet CO2 concentration, the inlet flue gas flow, the outlet flue gas flow, the inlet flue gas density and the outlet flue gas density, wherein the calculation formula is:

[0075]

[0076] Wherein, C b represents the desulfurization outlet CO2 capture rate per unit time, Q O represents the outlet flue gas flow, P O represents the outlet flue gas density, C O represents the inlet CO2 concentration, Q i represents the inlet flue gas flow, P i represents the inlet flue gas density, C I represents the inlet CO2 concentration;

[0077] Statistically obtain the historical desulfurization outlet CO2 capture rate of all units per unit time.

[0078] For example, we have two units A and B, and the following are their measurement data:

[0079] Unit A:

[0080] Inlet CO2 concentration = 1500 ppm;

[0081] Outlet CO2 concentration = 450 ppm;

[0082] Inlet flue gas flow = 10000 m3 / h;

[0083] Outlet flue gas flow = 9800 m3 / h

[0084] Inlet flue gas density = 1.293 kg / m3

[0085] Outlet flue gas density = 1.270 kg / m3

[0086] Unit B:

[0087] Inlet CO2 concentration = 1400 ppm

[0088] Outlet CO2 concentration = 420 ppm

[0089] Inlet flue gas flow = 11000 m3 / h

[0090] Outlet flue gas flow = 10500 m3 / h

[0091] Inlet flue gas density = 1.293 kg / m3

[0092] Outlet flue gas density = 1.280 kg / m3

[0093] Desulfurization outlet CO2 capture rate of Unit A:

[0094]

[0095] Desulfurization outlet CO2 capture rate of Unit B:

[0096]

[0097] For example, this is the data in a unit of time, record these data for statistics, form a historical data set, and thus obtain the historical desulfurization outlet CO2 capture rate.

[0098] The steps of calculating the historical optimal energy consumption index according to the historical desulfurization outlet CO2 capture rate and the historical equipment load parameter are as follows:

[0099] Obtain the unit time equipment load parameter of all units through the historical equipment load parameter;

[0100] Calculate the unit time energy consumption index of a single unit according to the unit time equipment load parameter of a single unit and the unit time desulfurization outlet CO2 capture rate of a single unit, wherein the calculation formula is:

[0101] F = ω1*E(z) + ω2*C b ;

[0102] F represents the unit time energy consumption index of a single unit, E(z) represents the unit time equipment load parameter of a single unit, ω1 represents the load weight, C bω2 represents the CO2 capture rate weight; the size of ω2 and ω1 means the importance of CO2 capture rate and load, higher ω2 represents pursuing higher CO2 capture rate, lower ω1 represents pursuing lower load, in order to realize the maximization of CO2 capture rate and minimization of load, for example, ω2 and ω1 are set to 10 and 0.1 respectively, which means that the operation parameters of the whole system will be adjusted to the direction of larger CO2 capture rate and smaller load, and the two data can be adjusted according to the production index;

[0103] The unit time energy consumption index of all units is counted, and the unit time energy consumption index with the lowest value is the historical optimal energy consumption index.

[0104] For example, there are two units A and B, and the following is their measurement data:

[0105] Unit A:

[0106] Unit time equipment load parameter = 1000 kW

[0107] Unit time desulfurization outlet CO2 capture rate = 69.1%

[0108] Unit B:

[0109] Unit time equipment load parameter = 1200 kW

[0110] Unit time desulfurization outlet CO2 capture rate = 68.8%

[0111] Assume that the load weight = 0.1, and the CO2 capture rate weight = 10.

[0112] Unit time energy consumption index of unit A:

[0113] 0.1*1000+10*0.691=106.91

[0114] Unit time energy consumption index of unit B:

[0115] 0.1*1200+10*0.688=126.88

[0116] In the two units, the unit time energy consumption index of unit B is 126.88, and the unit time energy consumption index of unit A is 106.91. Therefore, the unit time energy consumption index with the smaller value is 106.91, which is the historical optimal energy consumption index.

[0117] The characteristic is that the calculation formula of the adjustment trend value of the historical operation parameter is calculated according to the historical operation parameter and the historical equipment load parameter of the corresponding time period:

[0118]

[0119] wherein y k and h k represent the values of the historical operating parameter and the historical equipment load parameter of the corresponding time period at the kth control, respectively, yk+ i and h k+i represent the values of the historical operating parameter and the historical equipment load parameter of the corresponding time period at the k+i th control, respectively, and v represents the control trend value.

[0120] For example, there are the following historical data:

[0121] The 1st control (k = 1):

[0122] Historical operating parameter = 350℃ (adsorption temperature)

[0123] Historical equipment load parameter = 1000 kW

[0124] The 5th control (k+i = 5):

[0125] Historical operating parameter = 360℃

[0126] Historical equipment load parameter = 1020 kW

[0127] Calculate the control trend value:

[0128]

[0129] This means that whenever the equipment load parameter increases by 1 kW, the adsorption temperature increases by about 0.5℃. This indicates that the adsorption temperature presents a positive correlation trend with the change of the equipment load parameter.

[0130] The formula for calculating the control trend amplitude value according to the control trend value is:

[0131]

[0132] wherein w3 represents the control trend amplitude value, h k+i and h k represent the values of the historical equipment load parameter at the k+i th control and at the kth control, respectively, and Δv represents the difference between the control trend value at h k+i and the control trend value at h k .

[0133] Considering that as the equipment load parameter is adjusted, the operating parameter cannot present a linear proportional relationship with it after the equipment load parameter reaches a certain value, for example, when the pressure in the system is low, increasing the equipment load parameter can quickly increase the pressure, and when the pressure is high, providing the equipment load parameter can only increase the pressure by a small amount, therefore, the difference between the control trend values is set to more accurately control.

[0134] According to the regulation trend value and the regulation trend amplitude value, the real-time operation parameter is regulated to the historical optimal operation parameter until the step of reaching the historical optimal operation parameter, comprising:

[0135] Obtaining the single-unit environmental impact value and the overall-unit environmental impact value; considering the difference between the single-unit operation environment and the overall-unit operation environment, such as different flue gas compositions and different concentrations, different single-unit environmental impact values and overall-unit environmental impact values can be set, and when the single-unit environment and the average environment of the overall unit are greatly different, a larger single-unit environmental impact value is set, for example, the single-unit environmental impact value is set to 2.5 and the overall-unit environmental impact value is set to 1.5, which can make the real-time operation parameter more inclined to regulate to the historical optimal operation parameter of the single unit.

[0136] Obtaining the single-unit optimal operation parameter emphasis value and the historical optimal operation parameter emphasis value; the historical optimal operation parameter emphasis value represents the optimal operation parameter of the overall unit, considering the difference between the single unit and the overall unit except for the environmental impact, the single-unit optimal operation parameter emphasis value can affect the amplitude of the regulation of the real-time operation parameter to the single-unit optimal operation parameter, and the historical optimal operation parameter emphasis value can affect the amplitude of the regulation of the single-unit optimal operation parameter to the historical optimal operation parameter, a larger single-unit optimal operation parameter emphasis value will make the real-time operation parameter more inclined to regulate to the single-unit optimal operation parameter, and a larger historical optimal operation parameter emphasis value will make the real-time operation parameter more inclined to regulate to the historical optimal operation parameter, for example, the single-unit optimal operation parameter emphasis value and the historical optimal operation parameter emphasis value are set to 0.6 and 0.4 respectively, which can make the real-time operation parameter more inclined to regulate to the single-unit optimal operation parameter

[0137] Obtaining the single-unit historical optimal operation parameter; the operation parameter corresponding to the optimal energy consumption index per unit of time of the single unit is the single-unit historical optimal operation parameter;

[0138] According to the single-unit environmental impact value, the overall-unit environmental impact value, the single-unit optimal operation parameter emphasis value, the historical optimal operation parameter emphasis value, the single-unit historical optimal operation parameter, and the historical optimal operation parameter, the regulation trend value of the real-time operation parameter of the single unit at the k+1th regulation of the corresponding real-time device load parameter is calculated;

[0139]

[0140] Wherein, v i,d (k+1)The real-time operation parameter of the single unit represents the regulation trend value of the corresponding real-time device load parameter in the k+1th regulation, c1 represents the optimal operation parameter value of the single unit, c2 represents the historical optimal operation parameter value, r1 represents the environmental impact value of the single unit, r2 represents the environmental impact value of all units, w3 represents the regulation trend value, i represents the real-time operation parameter, and d represents the real-time device load parameter. The real-time operation parameter represents the parameter of the corresponding real-time device load in the kth regulation, The real-time operation parameter represents the regulation trend value of the corresponding real-time device load parameter in the kth regulation, g d The historical optimal operation parameter represents P i,d The historical optimal operation parameter of the single unit represents P

[0141] The real-time operation parameter of the single unit represents the regulation trend value of the corresponding real-time device load parameter in the k+1th regulation, and the real-time operation parameter of the single unit represents the value in the kth regulation. The parameter of the real-time operation parameter in the k+1th regulation of the corresponding real-time device load is calculated, and the calculation formula is:

[0142]

[0143] Wherein, x i,d (k+1) The real-time operation parameter of the single unit represents the parameter of the corresponding real-time device load in the k+1th regulation, v i,d (k+1) The real-time operation parameter of the single unit represents the regulation trend value of the corresponding real-time device load parameter in the k+1th regulation, The real-time operation parameter of the single unit represents the value in the kth regulation.

[0144] The real-time operation parameter of the single unit represents the value in the kth regulation. i,d (k+1) The real-time energy consumption index under x i,d (k+1) The real-time energy consumption index under x

[0145] By combining the real-time operating parameters of each unit with the regulation trend value, the regulation trend amplitude value, the single unit environmental impact value, the total unit environmental impact value, the single unit optimal operating parameter weight value and the historical optimal operating parameter weight value, the regulation trend value under the corresponding real-time device load parameter can be continuously updated and optimized, and the real-time operating parameters can be further updated and regulated according to the updated regulation trend value, so as to realize continuous optimization, and finally achieve the single unit historical optimal operating parameter, evaluate all single unit historical optimal operating parameters, the minimum real-time energy consumption index under all single unit historical optimal operating parameters, the new historical optimal operating parameter, and realize low-energy consumption optimization.

[0146] The application also discloses a low-energy-consumption intelligent optimization system based on solid adsorption CO2 capture, which is used for a low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture and comprises the following modules.

[0147] A real-time monitoring module is used for monitoring the operating states of all units in the CO2 capture system in real time, including but not limited to adsorption temperature, desorption temperature, adsorption pressure, desorption pressure and gas flow rate, and transmitting the data to the central processing unit for processing in real time.

[0148] A historical data storage module is used for storing the historical operating parameters, the historical desulfurization outlet CO2 capture rate and the historical device load parameter of all units in the CO2 capture system, so that the central processing unit can access the data for analysis and optimization calculation.

[0149] An intelligent regulation module is used for calculating and updating the historical optimal operating parameter according to the data in the historical data storage module and the real-time data, and can automatically adjust the operating parameters of all units in the CO2 capture system to realize minimization of energy consumption and maximization of CO2 capture efficiency.

[0150] An environmental impact evaluation module is used for evaluating the influence of the operating environment of all units in the CO2 capture system on the real-time operating parameters, and adjusting the regulation trend value according to the single unit environmental impact value and the total unit environmental impact value, so as to ensure that the real-time operating parameters are more in line with the actual operating environment.

[0151] The intelligent regulation module comprises the following modules.

[0152] A fault detection and diagnosis unit is used for judging whether all units are in a normal operating state or an abnormal operating state through real-time monitoring and data analysis, and alarming when an abnormal operating state is detected, so as to reduce the risk of unplanned shutdown and improve the reliability and availability of the system.

[0153] The application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of the solid adsorption CO2 capture low-energy consumption intelligent optimization method when executing the computer program.

[0154] The application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the solid adsorption CO2 capture low-energy consumption intelligent optimization method when executed by a processor.

[0155] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium provided by the present application and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM) and the like.

[0156] It should be noted that in this document, the terms "comprising", "containing" or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article or method. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of another identical element in the process, device, article or method including the element.

[0157] The above merely describes preferred embodiments of the present application, and is not intended to limit the patent scope of the present application, and any equivalent structure or equivalent process conversion made by using the content of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A low-energy intelligent optimization method for solid adsorption CO2 capture, characterized in that: The following steps are involved: Obtain historical operating parameters, historical desulfurization outlet CO2 capture rates, and historical equipment load parameters for all units in the CO2 capture system; The historical optimal energy consumption index is calculated based on the historical desulfurization outlet CO2 capture rate and the historical equipment load parameters, and the historical operating parameters of the time period corresponding to the historical optimal energy consumption index are marked as the historical optimal operating parameters; Calculate the control trend value of the historical operating parameters based on the historical operating parameters and the historical equipment load parameters of the corresponding time period, and calculate the control trend amplitude value based on the control trend value; Obtaining real-time operating parameters of all units in the CO2 capture system, real-time desulfurization outlet CO2 capture rate, and real-time equipment load, wherein the real-time operating parameters include at least adsorption temperature, desorption temperature, adsorption pressure, desorption pressure, and gas flow rate; Calculate the real-time energy consumption index based on the real-time equipment load and the real-time desulfurization outlet CO2 capture rate; Determine the magnitude of the real-time energy consumption index of a single unit and the historical optimal energy consumption index; If it is greater than, the real-time operating parameter is regulated toward the historical optimal operating parameter according to the regulation trend value and the regulation trend amplitude value until the historical optimal operating parameter is reached; if it is less than, the real-time energy consumption index is updated to the historical optimal energy consumption index, and the real-time operating parameter corresponding to the real-time energy consumption index is marked as the historical optimal operating parameter; Obtain optimal equipment load parameters based on historical optimal operating parameters.

2. The low-energy intelligent optimization method for solid adsorption CO2 capture according to claim 1 is characterized in that: The steps for obtaining the historical desulfurization outlet CO2 capture rate are as follows: Obtain the inlet CO2 concentration and outlet CO2 concentration of each unit per unit time; Obtain the inlet and outlet flue gas flow rates per unit time for each unit; Obtain the inlet and outlet flue gas density per unit time for each unit; The desulfurization outlet CO2 capture rate per unit time is calculated based on the inlet CO2 concentration, outlet CO2 concentration, inlet flue gas flow rate, outlet flue gas flow rate, inlet flue gas density and outlet flue gas density. The calculation formula is: Among them, C b Indicates the CO2 capture rate at the desulfurization outlet per unit time, Q O Indicates the outlet flue gas flow rate, P O Indicates the outlet smoke density, C O Indicates the inlet CO2 concentration, Q i Indicates the inlet flue gas flow rate, P i Indicates the inlet flue gas density, C I Indicates the inlet CO2 concentration; The CO2 capture rate per unit time at the desulfurization outlet of each unit is statistically analyzed to obtain the historical desulfurization outlet CO2 capture rate.

3. The low-energy intelligent optimization method for solid adsorption CO2 capture according to claim 2 is characterized in that: The steps for calculating the historical optimal energy consumption index based on the historical desulfurization outlet CO2 capture rate and historical equipment load parameters are as follows: Obtain the unit time equipment load parameters of all units through historical equipment load parameters; The energy consumption per unit time of a single unit is calculated based on the equipment load parameter per unit time of a single unit and the CO2 capture rate per unit time of the desulfurization outlet of a single unit. The calculation formula is: F=ω1*E(z)+ω2*C b ; F represents the energy consumption index per unit time of a single unit, E(z) represents the equipment load parameter per unit time of a single unit, ω1 represents the load weight, C b represents the CO2 capture rate per unit time at the desulfurization outlet of a single unit, and ω2 represents the CO2 capture rate weight; The energy consumption per unit time of all units is counted, and the energy consumption per unit time with the lowest value is the historical optimal energy consumption index.

4. The low-energy intelligent optimization method for solid adsorption CO2 capture according to claim 3 is characterized in that: The calculation formula for calculating the control trend value of the historical operating parameters based on the historical operating parameters and the historical equipment load parameters of the corresponding time period is: Among them, y k and h k yk+i and h respectively represent the historical operating parameters and the historical equipment load parameters of the corresponding time period at the kth regulation value. k+i They represent the historical operating parameters and the historical equipment load parameters of the corresponding time period at the time of the k+i-th regulation, and v represents the regulation trend value.

5. The low-energy intelligent optimization method for solid adsorption CO2 capture according to claim 4 is characterized in that: The formula for calculating the control trend amplitude value according to the control trend value is: Among them, w3 represents the amplitude of the control trend, h k+i and h k Indicates the historical equipment load parameter values ​​at the k+ith regulation and the kth regulation, Δv indicates the value of the equipment load parameter at h k+i The control trend value and h k The difference in the control trend value at .

6. The low-energy intelligent optimization method for solid adsorption CO2 capture according to claim 5 is characterized in that: The step of regulating the real-time operating parameter toward the historical optimal operating parameter according to the regulation trend value and the regulation trend amplitude value until the historical optimal operating parameter is reached includes: Obtain the environmental impact value of a single unit and the environmental impact value of all units; Obtain the optimal operating parameter value and historical optimal operating parameter value of a single unit; Obtain the historical optimal operating parameters of a single unit; Calculate the control trend value of the real-time operating parameters of a single unit at the k+1th control of the corresponding real-time equipment load parameters based on the environmental impact value of the single unit, the environmental impact values ​​of all units, the importance value of the optimal operating parameters of the single unit, the importance value of the historical optimal operating parameters, the historical optimal operating parameters of the single unit, and the historical optimal operating parameters; Among them, v i,d (k+1) represents the control trend value of the real-time operating parameters of a single unit at the k+1th control of the corresponding real-time equipment load parameters, c1 represents the weighted value of the optimal operating parameters of a single unit, c2 represents the weighted value of the historical optimal operating parameters, r1 represents the environmental impact value of a single unit, r2 represents the environmental impact value of all units, w3 represents the control trend value, i represents the real-time operating parameter, d represents the real-time equipment load parameter, It represents the parameters of the real-time operating parameters at the time of the kth adjustment of the corresponding real-time equipment load. represents the control trend value of the real-time operating parameter when the corresponding real-time equipment load parameter is regulated for the kth time, g d represents the historical optimal operating parameters, P i,d Indicates the historical optimal operating parameters of a single unit; The parameters of the real-time operating parameters at the k+1th regulation of the corresponding real-time equipment load are calculated based on the control trend value of the real-time operating parameters of the single unit at the k+1th regulation of the corresponding real-time equipment load and the value of the real-time operating parameters of the single unit at the kth regulation, wherein the calculation formula is: Among them, x i,d (k+1) It represents the real-time operating parameters of a single unit at the time of the k+1th adjustment of the corresponding real-time equipment load. The control trend value of the real-time operating parameters of a single unit when the corresponding real-time equipment load parameters are adjusted for the k+1th time, Indicates the value of the real-time operating parameters of a single unit at the kth regulation; Judge x i,d (k+1) When the real-time energy consumption index under the optimal energy consumption index is less than or equal to the historical optimal energy consumption index, the regulation is stopped and x i,d (k+1) Updated to historically optimal operating parameters.

7. A low-energy-consumption intelligent optimization system based on solid adsorption CO2 capture, used in the low-energy-consumption intelligent optimization method based on solid adsorption CO2 capture according to claim 6, characterized in that: include: Real-time monitoring module, used to monitor the operating status of all units in the CO2 capture system in real time; A historical data storage module is used to store historical operating parameters of all units in the CO2 capture system, historical desulfurization outlet CO2 capture rates, and historical equipment load parameters; Intelligent control module, used to calculate and update historical optimal operating parameters based on the data in the historical data storage module and real-time data; Environmental impact assessment module: used to evaluate the impact of the operating environment of each unit in the CO2 capture system on real-time operating parameters.

8. The low-energy intelligent optimization system for solid adsorption CO2 capture according to claim 7 is characterized in that: The intelligent control module includes: The fault detection and diagnosis unit is used to determine whether all units are in normal operation or abnormal operation through real-time monitoring and data analysis, and to alarm when an abnormal operation state is detected.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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