A BOG pressure control method and system

Abstract

The present application provides a BOG pressure control method and system, the method comprising determining a first dynamic matrix model matching the liquid level height according to the liquid level height in a liquefied gas storage tank and a selection strategy; determining the optimal total load of all compressors through an integer optimizer of the BOG system based on the first dynamic matrix model, a storage tank pressure setting value, and a BOG flow rate generated by unloading; allocating the optimal total load to each compressor based on a load distribution strategy; and operating the compressor at the distributed workload to adjust the pressure of the storage tank.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to a BOG pressure control method and system. Background Art

[0002] The liquefied gas receiving station is composed of multiple devices. Relying on the mutual cooperation between the devices, the liquefied gas is stored in the tank from the transport ship and transported out for users. Liquefied gas can be liquefied natural gas, liquid hydrogen, liquefied carbon dioxide, etc. For example, the stability of the BOG system composed of the storage tank and the BOG (Boil Off Gas) compressor is the core of the entire receiving station control system. The pressure fluctuation of the BOG system of the receiving station will affect the stability of operations such as unloading and external transmission. Effective control of the BOG system pressure is the key to ensuring the safe and stable operation of the receiving station. At present, the research on the pressure control method of the BOG system of the receiving station mainly focuses on the design optimization of the process and the improvement of the operation method, while there is less research on the control strategy and control algorithm, and there is no research on the scheme based on the multivariable predictive control model. At present, the receiving station still generally adopts the traditional PID (proportional-integral-derivative) control in the pressure regulation of the BOG system. Most of the on-site operations need to rely on manual operation, and intelligent control cannot be achieved. In addition, the stability of the tank pressure is poor, resulting in excessive energy consumption of the compressor operation, and even BOG overpressure venting, which leads to safety, environmental protection and economic problems.

[0003] How to achieve intelligent control of the BOG system pressure of a liquefied gas receiving station is a problem to be solved by this application. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a BOG pressure control method and system.

[0005] The embodiment of the present application adopts the following technical solution: a BOG pressure control method, comprising:

[0006] Determining a first dynamic matrix model matching the liquid level height in the liquefied gas storage tank according to the liquid level height in the liquefied gas storage tank and the selection strategy;

[0007] Based on the first dynamic matrix model, the tank pressure setting value and the BOG flow rate generated by the unloading, the optimal total load of all compressors is determined by the integer optimizer of the BOG system;

[0008] Allocating the optimal total load to each of the compressors based on a load distribution strategy;

[0009] The compressor operates at the allocated workload to adjust the pressure of the storage tank.

[0010] In some embodiments, determining the liquid level height in a liquefied gas storage tank includes:

[0011] Detecting the liquid level of the liquefied gas in the storage tank by means of a liquid level sensor in the storage tank, and generating a liquid level signal;

[0012] The liquid level signal is sent to a selector of the BOG system.

[0013] In some embodiments, determining a first dynamic matrix model matching the liquid level height according to a selection strategy includes:

[0014] The selector sends a selection signal to the dynamic matrix model memory of the BOG system according to the liquid level interval where the liquid level represented by the liquid level signal is located according to the built-in selection strategy;

[0015] The dynamic matrix model memory determines a first dynamic matrix model adapted to the liquid level height based on the selection signal;

[0016] The dynamic matrix model memory includes a plurality of dynamic matrix models, and the number of the liquid level intervals is the same as the number of the dynamic matrix models.

[0017] In some embodiments, determining a first dynamic matrix model matching the liquid level height further includes:

[0018] When the liquid level height signal changes to a level that exceeds or is lower than a set ratio of the current liquid level interval, the first dynamic matrix model is switched to the second dynamic matrix model;

[0019] Wherein, the second dynamic matrix model matches the changed liquid level height.

[0020] In some embodiments, a compressor load input table is provided in the shaping optimizer, which is used to convert the compressor volume compression capacity and the corresponding compression and total load.

[0021] In some embodiments, the determining the optimal total load of all compressors by the integer optimizer of the BOG system based on the first dynamic matrix model, the tank pressure setting value and the BOG flow rate generated by the unloading includes:

[0022] Determine the optimal BOG flow rate discharged by all compressors based on the first dynamic matrix model, the tank pressure setting value and the BOG flow rate generated by unloading;

[0023] Send the optimal BOG flow rate to the compressor load input table;

[0024] Determine the optimum total load of the compressor by conversion based on the compressor load input table.

[0025] In some embodiments, the optimal total load of all compressors is determined by a truncated optimizer of the BOG system, including:

[0026] The optimal total load of all compressors is determined by the prediction model, the rolling optimization model and the feedback correction model of the integer optimizer.

[0027] In some embodiments, the dynamic matrix model includes a first matrix and a second matrix, wherein:

[0028] The first matrix is ​​a dynamic response matrix representing the total BOG flow rate discharged from the compressor to the pressure in the storage tank;

[0029] The second matrix is ​​a dynamic response matrix of BOG flow rate to the pressure in the storage tank caused by the unloading effect.

[0030] The embodiment of the present application also provides a system that uses any of the methods described in the above embodiments to automatically control the system pressure.

[0031] The beneficial effects of the embodiments of the present application are:

[0032] Different dynamic matrix models are determined according to the different liquid level heights of the tank, and the linear model of the selected operating point is used to reflect the operation process of the system in the vicinity, which can effectively overcome the impact of large-scale changes in the tank liquid level conditions on the tank pressure regulation.

[0033] By using an integer optimizer, the start and stop of each compressor and the combination of operation are automatically controlled through the compressor load distribution strategy, realizing intelligent control of the BOG system pressure, which helps to improve the automation and intelligence level of the system and reduce manual intervention and manual operation errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 This is a schematic diagram of a flow chart of a BOG pressure control method in an embodiment of the present application;

[0036] Figure 2 This is a schematic diagram of the switching mode of the dynamic matrix model when the liquid level rises in the embodiment of the present application;

[0037] Figure 3 Schematic diagram of the switching mode of the dynamic matrix model when the liquid level drops in the embodiment of the present application. DETAILED DESCRIPTION

[0038] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0039] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of the embodiments. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0040] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0041] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0042] It should also be understood that although the present application has been described with reference to some specific examples, those skilled in the art will be able to readily implement many other equivalent forms of the present application.

[0043] The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

[0044] Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments applied for are merely examples of the present application, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that obscure the present application. Therefore, the specific structural and functional details applied for herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present application in a variety of ways with substantially any suitable detailed structure.

[0045] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present application.

[0046] The present application provides a BOG pressure control method, combined with Figure 1 , the method comprises the following steps:

[0047] S10, determining a first dynamic matrix model matching the liquid level in the liquefied gas storage tank according to the liquid level and the selection strategy.

[0048] In some embodiments, determining the liquid level height in a liquefied gas storage tank includes:

[0049] S101, detecting the liquid level of the liquefied gas in the storage tank by means of a liquid level sensor in the storage tank, and generating a liquid level signal.

[0050] A liquid level sensor may be provided in the storage tank, and the liquid level sensor may be used to detect the height of the liquefied gas in the storage tank in real time. Here, the liquefied gas may be, but is not limited to, liquefied natural gas (LNG). The liquid level sensor may generate a liquid level height signal based on the liquid level height of the liquefied gas in the storage tank.

[0051] S102, sending the liquid level height signal to the selector of the BOG system.

[0052] The liquid level signal can be sent to the selector of the BOG system by wired or wireless means. The input of the selector is the liquid level signal of the liquefied gas in the storage tank. After receiving the liquid level signal, the selector can make corresponding operations based on the liquid level signal.

[0053] In some embodiments, determining a first dynamic matrix model matching the liquid level height according to a selection strategy includes:

[0054] S103, the selector sends a selection signal to a dynamic matrix model memory of the BOG system according to a built-in selection strategy based on the liquid level interval in which the liquid level represented by the liquid level signal is located.

[0055] Exemplarily, for example, the initially set liquid level intervals include three, namely, a low liquid level interval, a middle liquid level interval and a high liquid level interval. Among them, the low liquid level interval indicates that the liquid level height in the storage tank is between 4.8% and 35% (that is, the height of the liquefied gas accounts for 4.8% to 35% of the total height of the inner cavity of the storage tank), the middle liquid level interval indicates that the liquid level height in the storage tank is between 35% and 65%, and the high liquid level interval is between 65% and 98.5%. Of course, the liquid level intervals can also be divided into two, or more than four. The range size of each interval can also be set according to actual needs. This is only used as an example and does not constitute a limitation on the scope of protection of the claims.

[0056] The selector sends a selection signal to the dynamic matrix model memory of the BOG system according to the interval where the liquefied gas level in the tank is detected and the built-in selection strategy.

[0057] The built-in selection strategy can be as follows Figure 2The selection strategy shown is as follows: First, according to the initial range of the liquid level, the corresponding pressure model is used. If the initial value of the liquid level is between 4.8% and 30%, the dynamic matrix model corresponding to the low liquid level is used; when it is between 30% and 60%, the dynamic matrix model corresponding to the middle liquid level is used; when it is between 60% and 98.5%, the dynamic matrix model corresponding to the high liquid level is used. Then, when the liquid level changes, the model switching strategy can be as follows Figure 3 The switching strategy shown is: when the prediction model originally adopts the dynamic matrix model corresponding to the low liquid level, when the liquid level reaches 35%, it switches to the dynamic matrix model corresponding to the medium liquid level; when the prediction model originally adopts the dynamic matrix model corresponding to the medium liquid level, when the liquid level reaches 25%, it switches to the dynamic matrix model corresponding to the low liquid level, and when the liquid level reaches 65%, it switches to the dynamic matrix model corresponding to the high liquid level; when the prediction model adopts the dynamic matrix model corresponding to the high liquid level, when the liquid level reaches 55%, it switches to the dynamic matrix model corresponding to the medium liquid level.

[0058] S104: The dynamic matrix model memory determines a first dynamic matrix model adapted to the liquid level based on the selection signal.

[0059] The dynamic matrix model memory includes a plurality of dynamic matrix models, and the number of the liquid level intervals is the same as the number of the dynamic matrix models.

[0060] The dynamic matrix model storage may include a dynamic matrix model corresponding to a low liquid level, a dynamic matrix model corresponding to a medium liquid level, and a dynamic matrix model corresponding to a high liquid level.

[0061] After the dynamic matrix memory receives the selection signal, it determines the first dynamic matrix model according to the selection signal. For example, when the liquid level changes, when the prediction model originally uses the dynamic matrix model corresponding to the middle liquid level, when the liquid level reaches 25%, it switches to the dynamic matrix model corresponding to the low liquid level. At this time, the first dynamic matrix model is the dynamic matrix model corresponding to the low liquid level.

[0062] Here, taking three liquid level height intervals as an example, the number of dynamic matrices is also three, that is, each of the three liquid level height intervals corresponds to a dynamic matrix model.

[0063] In some embodiments, determining a first dynamic matrix model matching the liquid level height further includes:

[0064] S105, when the liquid level height signal changes to a level that exceeds or is lower than a set ratio of the current liquid level interval, the first dynamic matrix model is switched to a second dynamic matrix model.

[0065] Wherein, the second dynamic matrix model matches the changed liquid level height.

[0066] Continuing with the above embodiment, for example, when the liquid level changes, when the prediction model originally uses the first dynamic matrix model (dynamic matrix model corresponding to the middle liquid level), when the liquid level reaches 25%, it switches to the second dynamic matrix model (dynamic matrix model corresponding to the low liquid level). By switching different system models according to different liquid levels of the storage tank, the linear model of the selected operating point is used to reflect the operation process of the system in the vicinity, which can well overcome the influence of the large-scale changes in the liquid level operating conditions of the LNG storage tank on the pressure regulation of the storage tank.

[0067] S20, determining the optimal total load of all compressors through the integer optimizer of the BOG system based on the first dynamic matrix model, the tank pressure setting value, and the BOG flow rate generated by unloading.

[0068] In some embodiments, the integral optimizer is provided with a compressor load input table, which is used to convert the compressor volume compression capacity and the corresponding compression and total load. The compressor load input table is:

[0069]

[0070]

[0071] The optimal total load of all compressors is determined by the integer optimizer of the BOG system based on the first dynamic matrix model, the tank pressure setting value and the BOG flow rate generated by the unloading, including:

[0072] S201, determining the optimal BOG flow rate discharged by all compressors based on the first dynamic matrix model, the tank pressure setting value, and the BOG flow rate generated by unloading.

[0073] S202, sending the optimal BOG flow rate to the compressor load input table.

[0074] S203, determining the optimal total load of the compressor by conversion according to the compressor load input table.

[0075] The optimal total load of all compressors is determined by the prediction model, rolling optimization model and feedback correction model of the integer optimizer. Specifically:

[0076]

[0077] st

[0078]

[0079] u(·)∈{am 3 / h,bm 3 / h,…}

[0080] y min ≤y≤y max

[0081] in, is the prediction model part, Δu M (k) is the increment of the total BOG flow rate compressed and discharged by the compressor. The first matrix A is the dynamic response matrix of the total BOG flow rate discharged by the compressor to the tank pressure. Δd(k) is the increment of the BOG flow rate caused by the discharge effect. The second matrix D is the dynamic response matrix of the BOG flow rate caused by the discharge effect to the tank pressure. represents the initial value of prediction, Indicated in Δu M Output prediction value under the action of (k) and Δd(k); It is the scroll optimization part. is the set value of the input tank pressure, Q and R are the error weight matrix and control weight matrix respectively, and both are diagonal matrices. By solving Δu M (k), and take the first step of the solution sequence as the control quantity to derive the control quantity Δu at time k+1 M (k+1), to achieve repeated rolling optimization control; It is the feedback correction part. Indicates the 0 input response at the current moment, is the measurement value correction, h=[I,I…I] T is the feedback correction coefficient, is the displacement matrix; u(·)∈{am 3 / h,bm 3 / h,…} indicates that the input is the BOG flow rate that the compressor can discharge, which is a discrete value; y min ≤y≤y max Constrain tank pressure within a safe range.

[0082] The effect of the prediction model is that first u(·)∈{am 3 / h,bm 3 / h,…} indicates that the input is a discrete integer variable; secondly, A 1 ,D 1 ,…,A n ,D n Dynamic matrix model corresponding to different liquid level operating points, x e Represents the liquid level height. According to the liquid level in the tank, the dynamic matrix model is selected and substituted into the optimization problem for solution.

[0083] The integer optimizer uses traversal method, tree search method and hybrid integer quadratic programming to solve the optimization problem, and converts the BOG flow discharged by the optimal compressor into the total load of the compressor according to the compressor load input table.

[0084] The output of the integer optimizer is the optimal compressor total load solution sequence, and the first step of the solution sequence is sent to the distributor.

[0085] S30: Allocate the optimal total load to each of the compressors based on the load distribution strategy.

[0086] The input of the distributor is the optimal total load of the compressors calculated by the integer optimizer. According to the built-in load distribution logic, instructions are issued to each compressor to distribute the optimal total load to each of the compressors.

[0087] S40: The compressor operates at the allocated workload to adjust the pressure of the storage tank.

[0088] The compressor adjusts its workload after receiving instructions from the distributor, thereby stabilizing the pressure in the tank.

[0089] The control process is as follows: select the corresponding dynamic matrix model according to the liquid level height in the storage tank, perform calculations with the input and disturbance quantities, send the calculation results to the integer optimizer, obtain the optimal solution for the compressor load by solving the optimization problem, and issue instructions to each compressor through the distributor.

[0090] Different dynamic matrix models are determined according to the different liquid level heights of the tank, and the linear model of the selected operating point is used to reflect the operation process of the system in the vicinity, which can effectively overcome the impact of large-scale changes in the tank liquid level conditions on the tank pressure regulation.

[0091] By using an integer optimizer, the start and stop of each compressor and the combination of operation are automatically controlled through the compressor load distribution strategy, realizing intelligent control of the BOG system pressure, which helps to improve the automation and intelligence level of the system and reduce manual intervention and manual operation errors.

[0092] By using integer optimization and controlling the start and stop of each compressor through the compressor load distribution strategy, this controller can be applied to different receiving stations with good adaptability.

[0093] The embodiment of the present application also provides a system that uses any of the methods described in the above embodiments to automatically control the system pressure.

[0094] Multiple embodiments of the present application are described in detail above, but the present application is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications to the embodiments based on the concept of the present application, and these variations and modifications should all fall within the scope of protection required by the present application.

Claims

1. A BOG pressure control method, characterized in that: The method is applied to a BOG system, wherein the BOG system includes a storage tank and a compressor, including: According to the liquid level height in the liquefied gas storage tank and the selection strategy, a first dynamic matrix model matching the liquid level height is determined; wherein the determination of the liquid level height in the liquefied gas storage tank includes: detecting the liquid level height of the liquefied gas in the storage tank by a liquid level sensor in the storage tank to generate a liquid level height signal; and sending the liquid level height signal to the selector of the BOG system; According to the selection strategy, determining a first dynamic matrix model matching the liquid level height includes: The selector sends a selection signal to the dynamic matrix model memory of the BOG system according to the liquid level interval where the liquid level represented by the liquid level signal is located according to the built-in selection strategy; The dynamic matrix model memory determines a first dynamic matrix model adapted to the liquid level height based on the selection signal; The dynamic matrix model memory includes a plurality of dynamic matrix models, and the number of the liquid level intervals is the same as the number of the dynamic matrix models; Determining a first dynamic matrix model that matches the liquid level height also includes: When the liquid level height signal changes to a level that exceeds or is lower than a set ratio of the current liquid level interval, the first dynamic matrix model is switched to the second dynamic matrix model; Wherein, the second dynamic matrix model matches the changed liquid level height; Based on the first dynamic matrix model, the tank pressure setting value and the BOG flow rate generated by the unloading, the optimal total load of all compressors is determined by the integer optimizer of the BOG system; Allocating the optimal total load to each of the compressors based on a load distribution strategy; The compressor operates at the allocated workload to adjust the pressure of the storage tank.

2. The method according to claim 1, characterized in that The integer optimizer is provided with a compressor load input table, which is used to convert the compressor volume compression capacity and the corresponding compression and total load.

3. The method according to claim 2, characterized in that The optimal total load of all compressors is determined by the integer optimizer of the BOG system based on the first dynamic matrix model, the tank pressure setting value and the BOG flow rate generated by the unloading, including: Determine the optimal BOG flow rate discharged by all compressors based on the first dynamic matrix model, the tank pressure setting value and the BOG flow rate generated by unloading; Send the optimal BOG flow rate to the compressor load input table; Determine the optimum total load of the compressor by conversion based on the compressor load input table.

4. The method according to claim 1, characterized in that: The optimal total load of all compressors is determined by the integral optimizer of the BOG system, including: The optimal total load of all compressors is determined by the prediction model, the rolling optimization model and the feedback correction model of the integer optimizer.

5. The method according to claim 1, characterized in that The dynamic matrix model includes a first matrix and a second matrix, wherein: The first matrix is ​​a dynamic response matrix representing the total BOG flow rate discharged from the compressor to the pressure in the storage tank; The second matrix is ​​a dynamic response matrix of BOG flow rate to the pressure in the storage tank caused by the unloading effect.

6. A BOG pressure control system, characterized in that: The system pressure is automatically controlled by the method described in any one of claims 1 to 5.

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