Method and device for controlling gasification device in raw gas production process

Abstract

The invention provides a control method and device for a gasification device in a raw gas production process. The method comprises the following steps: acquiring operation data of the gasification device; constructing two regression models to respectively represent the relationship between the liquid level, the washing water quantity and the raw gas components and the relationship between the coal-oxygen ratio and the washing water quantity and the raw gas components; solving by using an effective gas content maximization target and performing a least square method to obtain control parameters of the model; and the control parameters are fused, target control parameters are determined, and optimized operation of the gasification device is achieved. Data analysis is performed on the basis of the measured value of the raw gas to determine the correlation among the parameters, and then double-closed-loop control is performed on the basis of maximization of effective gas components to ensure that the yield of the gasification device in the gasification process is in an optimal state, so that the problem that component optimization and control of the raw gas are manually completed in the prior art is solved. The efficiency is low; and the operation is tedious.

Description

Technical Field

[0001] The present invention relates to the field of coal gas production, and in particular to a control method for a gasification device in a raw coal gas production process, a control device for a gasification device in a raw coal gas production process, a computer-readable storage medium, and a gasification device. Background Art

[0002] In the current coal chemical production process, the coal gas chemical end mainly produces a large amount of raw coal gas. In terms of the quality of raw coal gas, it is necessary to reasonably measure the effective gas components (CO+H2) inside the raw coal gas, which requires both good control of the working conditions and accurate measuring instruments to achieve reasonable component analysis results, so that its internal components can be obtained more clearly, which is convenient for control and can provide optimization treatment measures for related working conditions to make the effective gas component content account for a larger proportion, ensure the full utilization of raw coal, and increase the utilization value of raw coal gas.

[0003] During the production process, the composition of the raw gas before entering the gasifier will fluctuate due to the influence of upstream working conditions, resulting in instability in the gasification process. In the existing technology, there is no mature solution and controller, so the optimization and control of the composition of the raw gas is achieved through manual calculation, which is inefficient and the operation process is cumbersome, resulting in the composition of the raw gas fluctuating within a large range over a period of time. Summary of the invention

[0004] The main purpose of the present application is to provide a control method for a gasification device in a raw gas production process, a control device for a gasification device in a raw gas production process, a computer-readable storage medium and a gasification device, so as to at least solve the problem that the composition optimization and control of the raw gas in the prior art are completed manually, which is inefficient and cumbersome to operate.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a control method for a gasification device in a raw gas production process is provided, comprising: acquiring historical operation data of the gasification device at preset time intervals to obtain a plurality of first target data, wherein the historical operation data include raw gas composition, quench chamber liquid level, branch washing water volume of a synthesis gas washing tower, and coal-oxygen ratio input to the gasification device; constructing a regression model for characterizing the relationship between the quench chamber liquid level and the branch washing water volume and the raw gas composition based on the first target data to obtain a first target model; determining the historical operation data with the highest content of effective components in the raw gas composition among the first target data as the second target data, and based on the second target data The target data is used to construct a regression model for characterizing the relationship between the branch washing water volume and the coal-oxygen ratio and the raw coal gas composition, and the second target model is obtained; based on the least squares method, the first target model and the second target model are solved respectively with the goal of obtaining the maximum value of the content of effective gas in the raw coal gas composition, and the first control parameter and the second control parameter are obtained, the first control parameter at least includes the quenching chamber liquid level and the branch washing water volume, and the second control parameter at least includes the coal-oxygen ratio and the branch washing water volume; based on the fusion of the first control parameter and the second control parameter, the target control parameter is determined, and the operation of the gasification device is controlled according to the target control parameter.

[0006] Optionally, a regression model for characterizing the relationship between the quench chamber liquid level and the branch washing water volume and the raw gas composition is constructed based on the first target data to obtain a first target model, including: determining the quench chamber liquid level and the branch washing water volume as independent variables X = [x1, x2] T , where x1 is the liquid level of the quenching chamber, and x2 is the amount of washing water in the branch; the content of effective gas in the raw coal gas component is determined as the dependent variable, and is determined as the dependent variable Y; based on the independent variable and the dependent variable, fitting is performed to obtain the first target model Y=ωX+b, where ω is the slope coefficient and ω=[ω1, ω2], and b is the intercept coefficient.

[0007] Optionally, based on the second target data, a regression model is constructed to characterize the relationship between the branch washing water volume and the coal-oxygen ratio and the crude coal gas composition to obtain the second target model, including: linear fitting of the branch washing water volume, the coal-oxygen ratio and the crude coal gas composition in the second target data to obtain the second target model cost=p1n1+p2n2, wherein cost is the crude coal gas composition in the second target data, p1 and p2 are fitting coefficients, n1 is the branch washing water volume in the second target data, and n2 is the coal-oxygen ratio in the second target data.

[0008] Optionally, based on the least squares method, the first target model and the second target model are solved respectively with the goal of maximizing the content of effective gas in the raw gas components, including: constructing a first target constraint based on the operating parameter restrictions of the synthesis gas washing tower in the gasification device to prevent the synthesis gas washing tower from overloading; constructing a second target constraint based on the sum of the raw gas components; and solving the first target model and the second target model respectively under the restrictions of the first target constraint and the second target constraint.

[0009] Optionally, a first target constraint is constructed based on the operating parameter restrictions of the synthesis gas washing tower in the gasification device, including: constructing a conversion relationship between the branch washing water volume of the synthesis gas washing tower and the corresponding washing water pump and compressor operating loads to obtain a target conversion relationship; determining that the operating loads corresponding to the washing water pump and the compressor are both less than the rated maximum load as a third target constraint; and converting the third target constraint through the target conversion relationship to obtain the first target constraint.

[0010] Optionally, the first control parameter is fused with the second control parameter to determine the target control parameter, including: performing weighted average calculation based on the branch washing water volume in the first control parameter and the branch washing water volume in the second control parameter to obtain the target washing water volume; updating the quenching chamber liquid level in the first control parameter based on the target washing water volume and the first target model to obtain the target quenching chamber liquid level; updating the coal-oxygen ratio in the second control parameter based on the target washing water volume and the second target model to obtain the target coal-oxygen ratio; determining the target washing water volume, the target quenching chamber liquid level and the target coal-oxygen ratio as the target control parameters.

[0011] Optionally, the raw coal gas composition includes effective gas content, dust content and water content, and the effective gas includes at least carbon monoxide, hydrogen and methane.

[0012] According to another aspect of the present application, a control device for a gasification device in a raw gas production process is provided, the device comprising: a first acquisition unit, for acquiring historical operation data of the gasification device at intervals of a preset time length to obtain a plurality of first target data, the historical operation data comprising raw gas composition, quench chamber liquid level, branch washing water volume of a synthesis gas washing tower, and coal-oxygen ratio input to the gasification device; a first construction unit, for constructing a regression model for characterizing the relationship between the quench chamber liquid level and the branch washing water volume, and the raw gas composition based on the first target data to obtain a first target model; a second construction unit, for determining the historical operation data with the highest content of effective components in the raw gas composition among the first target data as the second target data, based on the The second target data constructs a regression model for characterizing the relationship between the branch washing water volume and the coal-oxygen ratio and the raw coal gas composition to obtain the second target model; the first calculation unit is used to solve the first target model and the second target model based on the least squares method with the goal of obtaining the maximum value of the content of effective gas in the raw coal gas composition to obtain the first control parameter and the second control parameter, the first control parameter at least includes the quenching chamber liquid level and the branch washing water volume, and the second control parameter at least includes the coal-oxygen ratio and the branch washing water volume; the second calculation unit is used to fuse the first control parameter with the second control parameter to determine the target control parameter, and control the operation of the gasification device according to the target control parameter.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0014] According to another aspect of the present application, a gasification device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the described methods.

[0015] By applying the technical solution of the present application, firstly, the historical operation data of the gasification device is acquired at each preset time interval to obtain a plurality of first target data, wherein the historical operation data includes the raw gas composition, the liquid level in the quenching chamber, the amount of branch washing water in the synthesis gas washing tower, and the coal-oxygen ratio input to the gasification device; then, based on the first target data, a regression model for characterizing the relationship between the liquid level in the quenching chamber and the amount of branch washing water and the raw gas composition is constructed to obtain a first target model; then, the historical operation data with the highest content of effective components in the raw gas composition in each of the first target data is determined as the second target data, and based on the second target data, a regression model for characterizing the relationship between the liquid level in the quenching chamber and the amount of branch washing water is constructed to obtain a first target model. The second target model is obtained by using a regression model of the relationship between the amount of water and the coal-oxygen ratio and the composition of the raw coal gas; then, based on the least squares method, the first target model and the second target model are solved respectively with the goal of obtaining the maximum value of the content of effective gas in the raw coal gas composition, to obtain the first control parameter and the second control parameter, the first control parameter at least includes the liquid level of the quenching chamber and the amount of branch washing water, and the second control parameter at least includes the coal-oxygen ratio and the amount of branch washing water; finally, the first control parameter and the second control parameter are merged to determine the target control parameter, and the operation of the gasification device is controlled according to the target control parameter. The present application performs data analysis based on the measured value of raw coal gas to determine the correlation between the various parameter quantities, and then performs double closed-loop control based on the maximization of the effective gas component to ensure that the yield of the gasification device during the gasification process is in the optimal state, which solves the problem that the composition optimization and control of raw coal gas in the prior art are manually completed, which is inefficient and cumbersome to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for controlling a gasification device in a raw coal gas production process provided in an embodiment of the present application is shown;

[0017] Figure 2 A schematic flow chart of a method for controlling a gasification device in a raw coal gas production process provided in accordance with an embodiment of the present application is shown;

[0018] Figure 3 A structural block diagram of a control device for a gasification device in a raw coal gas production process provided in accordance with an embodiment of the present application is shown.

[0019] The above drawings include the following reference numerals:

[0020] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] As introduced in the background technology, there is no mature solution and controller in the prior art, so that the optimization and control of the composition of raw coal gas are achieved through manual calculation. In order to solve the problem that the optimization and control of the composition of raw coal gas in the prior art are completed manually, which is inefficient and cumbersome to operate, the embodiments of the present application provide a control method for a gasification device in a raw gas production process, a control device for a gasification device in a raw gas production process, a computer-readable storage medium and a gasification device.

[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a method for controlling a gasification device in a raw coal gas production process according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] In this embodiment, a control method for a gasification device in a raw gas production process running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 2 FIG. 1 is a flow chart of a method for controlling a gasification device in a raw coal gas production process according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0030] Step S201, acquiring historical operation data of the gasification device at preset intervals to obtain a plurality of first target data, wherein the historical operation data includes raw coal gas composition, liquid level in the quench chamber, branch washing water volume of the synthesis gas washing tower, and coal-oxygen ratio input to the gasification device;

[0031] Specifically, the raw gas composition refers to the concentration of various gas components, the quench chamber liquid level refers to the liquid level in the quench chamber, and the branch washing water volume of the synthesis gas washing tower refers to the washing water volume of each branch in the washing tower.

[0032] Step S202, constructing a regression model for characterizing the relationship between the liquid level in the quenching chamber and the amount of branch washing water, and the raw gas composition based on the first target data, to obtain a first target model;

[0033] Specifically, collect the first target data, including the historical operation data of the quench chamber liquid level, branch washing water volume, and raw gas composition, and ensure that the data are aligned in time so that the relationship between them can be effectively analyzed. Clean the original data, process missing values ​​and outliers to ensure the accuracy and completeness of the data. Perform data standardization or normalization to improve the prediction effect of the model.

[0034] Step S203, determining the historical operation data with the highest effective component content in the raw gas components in each first target data as the second target data, and constructing a regression model for characterizing the relationship between the branch washing water volume and the coal-oxygen ratio and the raw gas components based on the second target data to obtain a second target model;

[0035] Specifically, the effective components in the raw coal gas (e.g., carbon monoxide (CO), hydrogen (H2), methane (CH4), etc.) are confirmed and their ideal concentration ranges are determined. The total content of the effective components in each historical operation data is calculated, and the record with the highest content is identified. The historical operation data containing the highest content of the effective components is selected from the first target data as the second target data.

[0036] Step S204, based on the least square method, taking the maximum value of the effective gas content in the raw coal gas as the goal, respectively solving the first target model and the second target model to obtain the first control parameter and the second control parameter, the first control parameter at least includes the quench chamber liquid level and the branch washing water volume, and the second control parameter at least includes the coal-oxygen ratio and the branch washing water volume;

[0037] Specifically, the least square method is a mathematical optimization technique that finds the best function match for the data by minimizing the sum of squares of the errors. The least square method is used to fit the first objective model, and to find the combination of the quench chamber liquid level and the branch washing water volume that maximizes the effective gas content, and to find the combination of the coal-oxygen ratio and the branch washing water volume that maximizes the effective gas content.

[0038] Step S205: The first control parameter and the second control parameter are integrated to determine the target control parameter, and the operation of the gasification device is controlled according to the target control parameter.

[0039] Through the above steps, the system can determine the optimal target control parameters through the fusion algorithm based on the first control parameter and the second control parameter. These parameters can optimize the operation of the gasification device to the greatest extent, increase the content of effective gas in the raw coal gas, and achieve high efficiency and stability in the gasification process. This closed-loop control method based on data fusion and optimization has greatly improved the automation level and operation efficiency of the gasification device.

[0040] The embodiment of the present application provides a control method for a gasification device in a raw gas production process. First, historical operation data of the gasification device is acquired at preset intervals to obtain a plurality of first target data, wherein the historical operation data includes raw gas composition, quench chamber liquid level, branch washing water volume of a synthesis gas washing tower, and coal-oxygen ratio input to the gasification device; then, a regression model for characterizing the relationship between the quench chamber liquid level and branch washing water volume and raw gas composition is constructed based on the first target data to obtain a first target model; then, the historical operation data with the highest content of effective components in the raw gas composition among the first target data is determined as the second target data, and based on the second target data, a regression model for characterizing the relationship between the quench chamber liquid level and the branch washing water volume and the raw gas composition is constructed to obtain a first target model; The target data is used to construct a regression model to characterize the relationship between the branch washing water volume and the coal-oxygen ratio and the raw coal gas composition, and the second target model is obtained; then based on the least squares method, the first target model and the second target model are solved respectively with the goal of obtaining the maximum value of the content of effective gas in the raw coal gas composition, and the first control parameter and the second control parameter are obtained. The first control parameter at least includes the quenching chamber liquid level and the branch washing water volume, and the second control parameter at least includes the coal-oxygen ratio and the branch washing water volume; finally, the first control parameter and the second control parameter are merged to determine the target control parameter, and the operation of the gasification device is controlled according to the target control parameter. The present application performs data analysis based on the measured value of the raw coal gas to determine the correlation between the various parameter quantities, and then performs double closed-loop control based on the maximization of the effective gas component to ensure that the yield of the gasification device during the gasification process is in the optimal state, which solves the problem that the raw coal gas composition optimization and control in the prior art are completed manually, which is inefficient and cumbersome to operate.

[0041] As a possible implementation method, a regression model for characterizing the relationship between the liquid level in the quench chamber and the amount of branch washing water and the raw gas composition is constructed based on the first target data to obtain the first target model, including the following steps:

[0042] Step S301, determine the quench chamber liquid level and branch washing water volume as independent variables X = [x1, x2] T , where x1 is the liquid level in the quench chamber, and x2 is the amount of branch washing water;

[0043] Step S302, determining the content of effective gas in the raw coal gas as a dependent variable, and determining it as the dependent variable Y;

[0044] Step S303 , performing fitting based on the independent variable and the dependent variable to obtain a first target model Y=ωX+b, wherein ω is the slope coefficient and ω=[ω1, ω2], and b is the intercept coefficient.

[0045] Specifically, the above steps use the least squares method to establish a mathematical model. In actual modeling, the model with the highest accuracy is selected as the modeling solution, which is not limited to the least squares method. Take random forest as an example:

[0046] In regression models, random forests are a set of decision trees {h(x,θ t ),t=1,2,…,T}. Among them, θ t is a random variable that obeys independent and identical distribution, x is the independent variable, and T is the number of decision subtrees.

[0047] The regression model prediction results are:

[0048]

[0049] in, is the regression prediction result, h(x,θ t ) represents the output based on x and θ.

[0050] The offline model relationship between the liquid level X1 of the quenching chamber, the amount of washing water X2 of each branch of the syngas washing tower, and the current raw gas composition Y is established through random forest, and the model is first trained. In the process of model establishment and training, the calculation is constrained according to the constraint condition R, which is: 1) the optimal delivery power of the pump under the current total amount of washing water; 2) the maximum or minimum compression ratio of the compressor for oxygen delivery; 3) the total amount of raw gas components>=0%,<=100%. In this way, the relationship between the raw gas composition and the liquid level of the quenching chamber and the amount of washing water of each branch of the syngas washing tower is established.

[0051] As a possible implementation method, a regression model is constructed based on the second target data to characterize the relationship between the branch washing water volume and the coal-oxygen ratio and the raw gas composition, and the second target model is obtained, including:

[0052] Linear fitting is performed on the branch washing water volume, coal-oxygen ratio and raw coal gas composition in the second target data to obtain the second target model cost=p1n1+p2n2, where cost is the raw coal gas composition in the second target data, p1 and p2 are fitting coefficients, n1 is the branch washing water volume in the second target data, and n2 is the coal-oxygen ratio in the second target data.

[0053] Specifically, in practical applications, we must first mark the batch with the historical optimal raw gas composition in each production cycle as the target. The model relationship is obtained by the liquid level in the quenching chamber, the amount of washing water in each branch of the synthesis gas washing tower, and the oxygen-coal ratio to obtain the optimal raw gas composition for this batch.

[0054] As a possible implementation method, based on the least square method, the first target model and the second target model are solved respectively with the goal of maximizing the content of effective gas in the raw coal gas components, including the following steps:

[0055] Step S401, constructing a first target constraint based on operating parameter restrictions of a syngas scrubber in a gasification device to prevent the syngas scrubber from being overloaded;

[0056] Specifically, in production, the washing water volume and oxygen-coal ratio of each branch of the syngas washing tower are subject to upstream and downstream production constraints. The washing water volume of each branch of the syngas washing tower is affected by the pump capacity, and the oxygen-coal ratio is affected by the production load of the compressor. Assume that the constraints are: X < ub, ub = [ub1, ub2] T Among them, ub is the theoretical maximum load of the washing water pump and compressor, and X is the actual load of the washing water pump and compressor.

[0057] Step S402, constructing a second objective constraint based on the sum of raw gas components;

[0058] Specifically, the total amount of raw gas components cannot exceed 100%, and the minimum value cannot simply be greater than 0. The minimum amount of washing water and oxygen-coal ratio of each branch of the synthesis gas washing tower in the historical production data can be used as the respective minimum values:

[0059] lb1=min(x1)

[0060] lb2=min(x2)

[0061] lb=[lb1,lb2]

[0062] Among them, X is the minimum amount of washing water and oxygen-coal ratio of each branch, so it is necessary to check the historical data within the preset time period to find the minimum value min() as the constraint boundary.

[0063] Step S403: solving the first target model and the second target model respectively under the constraints of the first target constraint and the second target constraint.

[0064] Specifically, the raw gas composition of the batch is finally solved, and the washing water volume, oxygen-coal ratio and quench chamber liquid level of each branch of the synthesis gas washing tower of a certain stage thickener under the current operating conditions are calculated.

[0065] As a possible implementation method, a first objective constraint is constructed based on the operating parameter restrictions of the synthesis gas scrubber in the gasification device, including the following steps:

[0066] Step S501, constructing a conversion relationship between the branch washing water volume of the synthesis gas washing tower and the corresponding washing water pump and compressor operating load to obtain a target conversion relationship;

[0067] Step S502, determining that the operating loads corresponding to the washing water pump and the compressor are both less than the rated maximum load as the third objective constraint;

[0068] Specifically, ensure that the operating load of the wash water pump and the compressor does not exceed their rated maximum loads.

[0069] Step S503: convert the third objective constraint through the objective conversion relationship to obtain the first objective constraint.

[0070] Specifically, the washing water pump and compressor have maximum and minimum load working range requirements, and this constraint condition needs to be set according to the equipment attributes. When implementing, it is necessary to check the parameters of the travel range of each equipment and input them into the model constraint conditions of the modeling.

[0071] Through the above steps, we constructed the first objective constraint based on the operating parameter constraints of the syngas scrubber. This constraint ensures that when the branch scrubber water volume changes, the operating load of the scrubber pump and compressor is always within a safe and efficient range, thereby ensuring the stable operation of the gasification unit.

[0072] As a possible implementation manner, the target control parameter is determined based on the fusion of the first control parameter and the second control parameter, including the following steps:

[0073] Step S601, performing weighted average calculation based on the branch washing water volume in the first control parameter and the branch washing water volume in the second control parameter to obtain a target washing water volume;

[0074] Specifically, the weighted average calculation is performed using the branch washing water volume W1 in the first control parameter and the branch washing water volume W2 in the second control parameter. Assume that the weights are α and (1-α), respectively, where 0≤α≤1. The target washing water volume Wtarget is calculated as: Wtarget=α*W1+(1-α)*W2. The weight selection can be adjusted according to the actual situation to balance the importance of the two control parameters.

[0075] Step S602, updating the quenching chamber liquid level in the first control parameter based on the target washing water volume and the first target model to obtain the target quenching chamber liquid level;

[0076] Specifically, the first target model Y=ωX+b is used, where X=[x1, x2] includes the quenching chamber liquid level x1 and the branch washing water volume x2. The target washing water volume Wtarget has been determined in step S601, that is, x2=Wtarget. The target quenching chamber liquid level x1 is reversely solved by the first target model. Y and x2 are already known, and the target quenching chamber liquid level is assumed to be x1. Reverse solution model: n2=(cost-p1*Wtarget) / p2. Through this formula, the updated target quenching chamber liquid level x1 is obtained.

[0077] Step S603, updating the coal-oxygen ratio in the second control parameter based on the target washing water volume and the second target model to obtain the target coal-oxygen ratio;

[0078] Specifically, the second target model cost=p1*n1+p2*n2 is used, where n1 is the branch washing water volume, and n2 is the coal-oxygen ratio. The target washing water volume Wtarget has been determined, that is, n1=Wtarget.

[0079] The target coal-oxygen ratio n2 is reversely solved through the second target model. Given cost and n1, the target coal-oxygen ratio is set to n2. Reverse solution model: n2 = (cost-p1*Wtarget) / p2. Through this formula, the updated target coal-oxygen ratio n2 is obtained.

[0080] Step S604, determining the target washing water volume, the target quench chamber liquid level and the target coal-oxygen ratio as target control parameters.

[0081] Specifically, the target washing water volume Wtarget, the target quench chamber liquid level x1 and the target coal-oxygen ratio n2 are determined as the final target control parameters. These parameters will be used to guide the actual operation of the gasification device to ensure that each parameter is within the optimization and safety range.

[0082] Through the above steps, we have carried out parameter fusion based on the first control parameter and the second control parameter and determined the target control parameter. This method makes full use of the complementarity of various control parameters to ensure the optimal operation of the gasification unit in terms of washing water volume, quench chamber liquid level and coal-oxygen ratio. Through the selection of weights and the reverse solution of the model, the dynamic adjustment of various parameters is achieved.

[0083] As a possible implementation, the raw coal gas composition includes effective gas content, dust content and water content, and the effective gas includes at least carbon monoxide, hydrogen and methane.

[0084] Specifically, in the gasification device, the monitoring and adjustment of the raw coal gas composition is usually carried out in real time through online analytical instruments, and the operating parameters in the gasification process are adjusted based on real-time data to ensure the quality and energy efficiency of the gasification products. In addition, the real-time monitoring of the effective gas composition, dust content and water content of the raw coal gas provides an important basis for the subsequent gas purification, utilization and emission control, helping to achieve the goals of high efficiency, economy and environmental protection of the entire system.

[0085] The embodiment of the present application also provides a control device for a gasification device in a raw gas production process. It should be noted that the control device for a gasification device in a raw gas production process in the embodiment of the present application can be used to execute the control method for a gasification device in a raw gas production process provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0086] The following is an introduction to the control device of the gasification device in the raw coal gas production process provided in the embodiment of the present application.

[0087] Figure 3 1 is a structural block diagram of a control device for a gasification device in a raw coal gas production process according to an embodiment of the present application. Figure 3 As shown, the device includes: a first acquisition unit 10, a first construction unit 20, a second construction unit 30, a first calculation unit 40 and a second calculation unit 50.

[0088] A first acquisition unit 10 is used to acquire historical operation data of the gasification device at preset intervals to obtain a plurality of first target data, wherein the historical operation data includes raw coal gas composition, liquid level in the quench chamber, branch washing water volume of the synthesis gas washing tower, and coal-oxygen ratio input to the gasification device;

[0089] Specifically, the raw gas composition refers to the concentration of various gas components, the quench chamber liquid level refers to the liquid level in the quench chamber, and the branch washing water volume of the synthesis gas washing tower refers to the washing water volume of each branch in the washing tower.

[0090] A first construction unit 20 is used to construct a regression model for characterizing the relationship between the liquid level in the quench chamber and the amount of branch washing water, and the raw gas composition based on the first target data to obtain a first target model;

[0091] Specifically, collect the first target data, including the historical operation data of the quench chamber liquid level, branch washing water volume, and raw gas composition, and ensure that the data are aligned in time so that the relationship between them can be effectively analyzed. Clean the original data, process missing values ​​and outliers to ensure the accuracy and completeness of the data. Perform data standardization or normalization to improve the prediction effect of the model.

[0092] The second construction unit 30 is used to determine the historical operation data with the highest effective component content in the raw gas components among the first target data as the second target data, and to construct a regression model for characterizing the relationship between the branch washing water volume and the coal-oxygen ratio and the raw gas components based on the second target data to obtain a second target model;

[0093] Specifically, the effective components in the raw coal gas (e.g., carbon monoxide (CO), hydrogen (H2), methane (CH4), etc.) are confirmed and their ideal concentration ranges are determined. The total content of the effective components in each historical operation data is calculated, and the record with the highest content is identified. The historical operation data containing the highest content of the effective components is selected from the first target data as the second target data.

[0094] The first calculation unit 40 is used to solve the first target model and the second target model respectively based on the least square method with the goal of obtaining the maximum value of the content of effective gas in the raw coal gas components, and obtain the first control parameter and the second control parameter, the first control parameter at least includes the quench chamber liquid level and the branch washing water volume, and the second control parameter at least includes the coal-oxygen ratio and the branch washing water volume;

[0095] Specifically, the least square method is a mathematical optimization technique that finds the best function match for the data by minimizing the sum of squares of the errors. The least square method is used to fit the first objective model, and to find the combination of the quench chamber liquid level and the branch washing water volume that maximizes the effective gas content, and to find the combination of the coal-oxygen ratio and the branch washing water volume that maximizes the effective gas content.

[0096] The second calculation unit 50 is used to determine the target control parameter based on the fusion of the first control parameter and the second control parameter, and control the operation of the gasification device according to the target control parameter.

[0097] Through the above steps, the system can determine the optimal target control parameters through the fusion algorithm based on the first control parameter and the second control parameter. These parameters can optimize the operation of the gasification device to the greatest extent, increase the content of effective gas in the raw coal gas, and achieve high efficiency and stability in the gasification process. This closed-loop control method based on data fusion and optimization has greatly improved the automation level and operation efficiency of the gasification device.

[0098] The embodiment of the present application provides a control device for a gasification device in a raw coal gas production process, the device comprising: a first acquisition unit, a first construction unit, a second construction unit, a first calculation unit, and a second calculation unit. The device performs data analysis based on the measured value of the raw coal gas to determine the correlation between various parameter quantities, and then performs double closed-loop control based on the maximization of the effective gas component to ensure that the yield of the gasification device in the gasification process is in an optimal state, solving the problem that the raw coal gas composition optimization and control in the prior art is manually completed, which is inefficient and cumbersome to operate.

[0099] As a possible implementation manner, the first construction unit includes: an independent variable module, a dependent variable module and a first target model module.

[0100] The independent variable module is used to determine the liquid level of the quench chamber and the amount of branch washing water as independent variables X = [x1, x2] T , where x1 is the liquid level in the quench chamber, and x2 is the amount of branch washing water;

[0101] A dependent variable module is used to determine the content of effective gas in the raw coal gas composition as a dependent variable and determine it as the dependent variable Y;

[0102] The first target model module is used to perform fitting based on the independent variable and the dependent variable to obtain the first target model Y=ωX+b, wherein ω is the slope coefficient and ω=[ω1, ω2], and b is the intercept coefficient.

[0103] Specifically, the above steps use the least squares method to establish a mathematical model. In actual modeling, the model with the highest accuracy is selected as the modeling solution, which is not limited to the least squares method. Take random forest as an example:

[0104] In regression models, random forests are a set of decision trees {h(x,θ t ),t=1,2,…,T}. Among them, θ t is a random variable that obeys independent and identical distribution, x is the independent variable, and T is the number of decision subtrees.

[0105] The regression model prediction results are:

[0106]

[0107] in, is the regression prediction result, h(x,θ t ) represents the output based on x and θ.

[0108] The offline model relationship between the liquid level X1 of the quenching chamber, the amount of washing water X2 of each branch of the syngas washing tower, and the current raw gas composition Y is established through random forest, and the model is first trained. In the process of model establishment and training, the calculation is constrained according to the constraint condition R, which is: 1) the optimal delivery power of the pump under the current total amount of washing water; 2) the maximum or minimum compression ratio of the compressor for oxygen delivery; 3) the total amount of raw gas components>=0%,<=100%. In this way, the relationship between the raw gas composition and the liquid level of the quenching chamber and the amount of washing water of each branch of the syngas washing tower is established.

[0109] As a possible implementation manner, the second construction unit includes: a second target model module.

[0110] The second target model module is used to perform linear fitting on the branch washing water volume, coal-oxygen ratio and raw coal gas composition in the second target data to obtain the second target model cost=p1n1+p2n2, wherein cost is the raw coal gas composition in the second target data, p1 and p2 are fitting coefficients, n1 is the branch washing water volume in the second target data, and n2 is the coal-oxygen ratio in the second target data.

[0111] Specifically, in practical applications, we must first mark the batch with the historical optimal raw gas composition in each production cycle as the target. The model relationship is obtained by the liquid level in the quenching chamber, the amount of washing water in each branch of the synthesis gas washing tower, and the oxygen-coal ratio to obtain the optimal raw gas composition for this batch.

[0112] As a possible implementation manner, the first calculation unit includes: a first constraint module, a second constraint module and a solution module.

[0113] A first constraint module, configured to construct a first target constraint based on an operating parameter restriction of a syngas scrubber in a gasification device to prevent the syngas scrubber from being overloaded;

[0114] Specifically, in production, the washing water volume and oxygen-coal ratio of each branch of the syngas washing tower are subject to upstream and downstream production constraints. The washing water volume of each branch of the syngas washing tower is affected by the pump capacity, and the oxygen-coal ratio is affected by the production load of the compressor. Assume that the constraints are: X < ub, ub = [ub1, ub2] T Among them, ub is the theoretical maximum load of the washing water pump and compressor, and X is the actual load of the washing water pump and compressor.

[0115] A second constraint module, used for constructing a second target constraint based on the sum of raw gas components;

[0116] Specifically, the total amount of raw gas components cannot exceed 100%, and the minimum value cannot simply be greater than 0. The minimum amount of washing water and oxygen-coal ratio of each branch of the synthesis gas washing tower in the historical production data can be used as the respective minimum values:

[0117] lb1=min(x1)

[0118] lb2=min(x2)

[0119] lb=[lb1,lb2]

[0120] Among them, X is the minimum amount of washing water and oxygen-coal ratio of each branch, so it is necessary to check the historical data within the preset time period to find the minimum value min() as the constraint boundary.

[0121] The solution module is used to solve the first target model and the second target model respectively under the constraints of the first target constraint and the second target constraint.

[0122] Specifically, the raw gas composition of the batch is finally solved, and the washing water volume, oxygen-coal ratio and quench chamber liquid level of each branch of the synthesis gas washing tower of a certain stage thickener under the current operating conditions are calculated.

[0123] As a possible implementation manner, the first constraint module includes: a conversion submodule, a third constraint submodule and a conversion submodule.

[0124] A conversion submodule is used to construct a conversion relationship between the branch washing water volume of the synthesis gas washing tower and the corresponding washing water pump and compressor operating load to obtain a target conversion relationship;

[0125] A third constraint submodule, used for determining that the operating loads corresponding to the washing water pump and the compressor are both less than the rated maximum load as a third target constraint;

[0126] Specifically, ensure that the operating load of the wash water pump and the compressor does not exceed their rated maximum loads.

[0127] The conversion submodule is used to convert the third objective constraint through the objective conversion relationship to obtain the first objective constraint.

[0128] Specifically, the washing water pump and compressor have maximum and minimum load working range requirements, and this constraint condition needs to be set according to the equipment attributes. When implementing, it is necessary to check the parameters of the travel range of each equipment and input them into the model constraint conditions of the modeling.

[0129] Through the above steps, we constructed the first objective constraint based on the operating parameter constraints of the syngas scrubber. This constraint ensures that when the branch scrubber water volume changes, the operating load of the scrubber pump and compressor is always within a safe and efficient range, thereby ensuring the stable operation of the gasification unit.

[0130] As a possible implementation manner, the second calculation unit includes: a weighted calculation module, a first updating module, a second updating module and a determination module.

[0131] A weighted calculation module, used for performing weighted average calculation based on the branch washing water volume of the first control parameter and the branch washing water volume of the second control parameter to obtain a target washing water volume;

[0132] Specifically, the weighted average calculation is performed using the branch washing water volume W1 in the first control parameter and the branch washing water volume W2 in the second control parameter. Assume that the weights are α and (1-α), respectively, where 0≤α≤1. The target washing water volume Wtarget is calculated as: Wtarget=α*W1+(1-α)*W2. The weight selection can be adjusted according to the actual situation to balance the importance of the two control parameters.

[0133] A first updating module, configured to update the quenching chamber liquid level in the first control parameter based on the target washing water volume and the first target model to obtain a target quenching chamber liquid level;

[0134] Specifically, the first target model Y=ωX+b is used, where X=[x1, x2] includes the quenching chamber liquid level x1 and the branch washing water volume x2. The target washing water volume Wtarget has been determined in step S601, that is, x2=Wtarget. The target quenching chamber liquid level x1 is reversely solved by the first target model. Y and x2 are already known, and the target quenching chamber liquid level is assumed to be x1. Reverse solution model: n2=(cost-p1*Wtarget) / p2. Through this formula, the updated target quenching chamber liquid level x1 is obtained.

[0135] A second updating module, configured to update the coal-oxygen ratio in the second control parameter based on the target washing water volume and the second target model to obtain a target coal-oxygen ratio;

[0136] Specifically, the second target model cost=p1*n1+p2*n2 is used, where n1 is the branch washing water volume, and n2 is the coal-oxygen ratio. The target washing water volume Wtarget has been determined, that is, n1=Wtarget.

[0137] The target coal-oxygen ratio n2 is reversely solved through the second target model. Given cost and n1, the target coal-oxygen ratio is set to n2. Reverse solution model: n2 = (cost-p1*Wtarget) / p2. Through this formula, the updated target coal-oxygen ratio n2 is obtained.

[0138] The determination module is used to determine the target washing water volume, the target quench chamber liquid level and the target coal-oxygen ratio as target control parameters.

[0139] Specifically, the target washing water volume Wtarget, the target quench chamber liquid level x1 and the target coal-oxygen ratio n2 are determined as the final target control parameters. These parameters will be used to guide the actual operation of the gasification device to ensure that each parameter is within the optimization and safety range.

[0140] Through the above steps, we have carried out parameter fusion based on the first control parameter and the second control parameter and determined the target control parameter. This method makes full use of the complementarity of various control parameters to ensure the optimal operation of the gasification unit in terms of washing water volume, quench chamber liquid level and coal-oxygen ratio. Through the selection of weights and the reverse solution of the model, the dynamic adjustment of various parameters is achieved.

[0141] The control device of the gasification device in the raw coal gas production process includes a processor and a memory, and the first acquisition unit, the first construction unit, the second construction unit, the first calculation unit, the second calculation unit, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize the corresponding functions. The above modules are all located in the same processor; or, the above modules are located in different processors in any combination.

[0142] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the communication efficiency can be improved by adjusting the kernel parameters.

[0143] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0144] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the control method of the gasification device in the raw gas production process.

[0145] An embodiment of the present invention provides a gasification device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a control method for executing the gasification device in the above-mentioned raw coal gas production process.

[0146] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0152] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0153] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0154] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0155] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0156] 1) The control method of the gasification device in the raw gas production process of the present application first obtains the historical operation data of the gasification device at each preset time interval to obtain multiple first target data, the historical operation data including the raw gas composition, the liquid level of the quenching chamber, the branch washing water volume of the synthesis gas washing tower, and the coal-oxygen ratio input to the gasification device; then, based on the first target data, a regression model for characterizing the relationship between the liquid level of the quenching chamber and the branch washing water volume and the raw gas composition is constructed to obtain the first target model; then, the historical operation data with the highest content of effective components in the raw gas composition among the first target data is determined as the second target data, and based on the second target data, a regression model for characterizing the relationship between the liquid level of the quenching chamber and the branch washing water volume and the raw gas composition is constructed to obtain the first target model; The data is used to construct a regression model to characterize the relationship between the branch washing water volume and the coal-oxygen ratio and the raw coal gas composition, and the second target model is obtained; then based on the least squares method, the first target model and the second target model are solved respectively with the goal of obtaining the maximum value of the content of effective gas in the raw coal gas composition, and the first control parameter and the second control parameter are obtained. The first control parameter at least includes the quenching chamber liquid level and the branch washing water volume, and the second control parameter at least includes the coal-oxygen ratio and the branch washing water volume; finally, the first control parameter and the second control parameter are merged to determine the target control parameter, and the operation of the gasification device is controlled according to the target control parameter. The present application performs data analysis based on the measured value of the raw coal gas to determine the correlation between the various parameter quantities, and then performs double closed-loop control based on the maximization of the effective gas component to ensure that the yield of the gasification device during the gasification process is in the optimal state, which solves the problem that the raw coal gas composition optimization and control in the prior art are completed manually, which is inefficient and cumbersome to operate.

[0157] 2) The control device of the gasification device in the raw coal gas production process of the present application comprises: a first acquisition unit, a first construction unit, a second construction unit, a first calculation unit and a second calculation unit. The device performs data analysis based on the measured value of the raw coal gas to determine the correlation between the various parameter quantities, and then performs double closed-loop control based on the maximization of the effective gas composition to ensure that the yield of the gasification device in the gasification process is in the optimal state, which solves the problem that the raw coal gas composition optimization and control in the prior art are completed manually, which is inefficient and cumbersome to operate.

[0158] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A control method for a gasification device in a raw coal gas production process, characterized in that: include: Acquire historical operation data of the gasification device at preset intervals to obtain a plurality of first target data, wherein the historical operation data includes raw coal gas composition, liquid level in the quench chamber, branch washing water volume of the synthesis gas washing tower, and coal-oxygen ratio input to the gasification device; Based on the first target data, a regression model is constructed to characterize the relationship between the liquid level of the quench chamber and the amount of washing water in the branch circuit and the composition of the raw coal gas, so as to obtain a first target model; Determine the historical operation data with the highest effective component content in the raw coal gas components among the first target data as the second target data, and construct a regression model for characterizing the relationship between the branch washing water volume and the coal-oxygen ratio and the raw coal gas components based on the second target data to obtain a second target model; Based on the least square method, the first target model and the second target model are solved respectively with the goal of maximizing the content of effective gas in the raw coal gas component to obtain a first control parameter and a second control parameter, wherein the first control parameter at least includes the liquid level of the quenching chamber and the amount of washing water in the branch, and the second control parameter at least includes the coal-oxygen ratio and the amount of washing water in the branch; The first control parameter and the second control parameter are integrated to determine a target control parameter, and the operation of the gasification device is controlled according to the target control parameter.

2. The method according to claim 1, characterized in that A regression model is constructed based on the first target data to characterize the relationship between the liquid level in the quench chamber and the amount of washing water in the branch circuit and the raw gas composition, and a first target model is obtained, including: The quench chamber liquid level and the branch washing water volume are determined as independent variables X = [x1, x2] T , where x1 is the liquid level in the quenching chamber, and x2 is the amount of washing water in the branch circuit; Determine the content of effective gas in the raw coal gas composition as a dependent variable, and determine it as the dependent variable Y; Based on the independent variable and the dependent variable, fitting is performed to obtain the first target model Y=ωX+b, wherein ω is the slope coefficient and ω=[ω1, ω2], and b is the intercept coefficient.

3. The method according to claim 1, characterized in that: A regression model is constructed based on the second target data to characterize the relationship between the branch washing water volume and the coal-oxygen ratio and the raw coal gas composition, and the second target model is obtained, including: Linear fitting is performed on the branch washing water volume, the coal-oxygen ratio and the raw coal gas composition in the second target data to obtain the second target model cost=p1n1+p2n2, wherein cost is the raw coal gas composition in the second target data, p1 and p2 are fitting coefficients, n1 is the branch washing water volume in the second target data, and n2 is the coal-oxygen ratio in the second target data.

4. The method according to claim 1, characterized in that Based on the least square method, the first target model and the second target model are solved respectively with the goal of maximizing the content of effective gas in the raw coal gas component, including: Establishing a first objective constraint based on operating parameter limits of a syngas scrubber in the gasification device to prevent overloading of the syngas scrubber; constructing a second objective constraint based on the sum of the raw gas components; Under the constraints of the first target constraint and the second target constraint, the first target model and the second target model are solved respectively.

5. The method according to claim 1, characterized in that The first objective constraint is constructed based on the operating parameter restriction of the synthesis gas washing tower in the gasification device, including: Constructing a conversion relationship between the branch washing water volume of the synthesis gas washing tower and the corresponding washing water pump and compressor operating load to obtain a target conversion relationship; The operating loads corresponding to the washing water pump and the compressor are both less than the rated maximum load as a third objective constraint; The third objective constraint is converted using the objective conversion relationship to obtain the first objective constraint.

6. The method according to claim 1, characterized in that The method of fusing the first control parameter with the second control parameter to determine a target control parameter includes: A target washing water volume is obtained by performing a weighted average calculation based on the branch washing water volume in the first control parameter and the branch washing water volume in the second control parameter; Based on the target washing water volume and the first target model, the quenching chamber liquid level in the first control parameter is updated to obtain a target quenching chamber liquid level; updating the coal-oxygen ratio in the second control parameter based on the target washing water volume and the second target model to obtain a target coal-oxygen ratio; The target washing water volume, the target quench chamber liquid level and the target coal-oxygen ratio are determined as the target control parameters.

7. The method according to any one of claims 1 to 6, characterized in that The raw coal gas composition includes effective gas content, dust content and water content, and the effective gas includes at least carbon monoxide, hydrogen and methane.

8. A control device for a gasification device in a raw coal gas production process, characterized in that: The device comprises: A first acquisition unit is used to acquire historical operation data of the gasification device at intervals of a preset time length to obtain a plurality of first target data, wherein the historical operation data includes raw coal gas composition, liquid level in the quench chamber, branch washing water volume of the synthesis gas washing tower, and coal-oxygen ratio input to the gasification device; A first construction unit is used to construct a regression model for characterizing the relationship between the liquid level of the quench chamber and the amount of washing water in the branch circuit and the raw gas composition based on the first target data to obtain a first target model; A second construction unit is used to determine the historical operation data with the highest effective component content in the raw coal gas components in each of the first target data as the second target data, and to construct a regression model for characterizing the relationship between the branch washing water volume and the coal-oxygen ratio and the raw coal gas components based on the second target data to obtain the second target model; a first calculation unit, for solving the first target model and the second target model respectively based on the least square method with the goal of maximizing the content of effective gas in the raw coal gas component, to obtain a first control parameter and a second control parameter, wherein the first control parameter at least includes the liquid level of the quenching chamber and the amount of branch washing water, and the second control parameter at least includes the coal-oxygen ratio and the amount of branch washing water; The second calculation unit is used to determine a target control parameter based on the fusion of the first control parameter and the second control parameter, and control the operation of the gasification device according to the target control parameter.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A gasification device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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