Coal blending optimization method and device and electronic equipment

Abstract

The invention discloses a coal blending optimization method and device and electronic equipment. The method comprises the following steps: acquiring parameter information of various raw coals; analyzing and processing the parameter information by adopting a coal blending optimization model to obtain a coal blending optimization result, the target function of the coal blending optimization model comprises at least one of the following functions: a first target function for representing the lowest unit effective gas yield cost of the raw coal, and a second target function for representing the maximization of the effective gas yield of the raw coal; the third objective function is used for representing the highest combustion heat value of the raw coal; the fourth objective function is used for representing the lowest average cost of the combustion heat value of the raw coal; mixed coal is generated according to the coal blending optimization result, and the mixed coal comprises gasified coal and combustion coal meeting the industrial requirements. According to the method and the device, the technical problem that the coal blending benefit loss is relatively large due to the fact that a coal blending optimization method in related technologies is easily influenced by subjective factors and equipment loss and is lack of theoretical optimization calculation is solved.

Description

Technical Field

[0001] The present application relates to the field of coal chemical technology, and in particular to a coal blending optimization method, device and electronic equipment. Background Art

[0002] There are many varieties of coal in the market. Coal chemical enterprises can purchase coal of different qualities according to their own needs, process raw coal, and mix them into mixed coal that meets the production needs and standards of the enterprise. Among them, the coal blending process is the starting point of the product production process of coal chemical enterprises. Cost control and production of products that meet the requirements are crucial to the formulation of a reasonable and optimized coal blending plan. This process involves production capacity, equipment depreciation, environmental protection, energy consumption, procurement cost price and other issues. It is necessary to coordinate and optimize coal quality, procurement price, other costs, production capacity, and product properties. Therefore, the optimization of the entire coal blending process is a key link in the production control of coal chemical enterprises. The precise control of the coal blending process directly affects the economic benefits and costs of the enterprise.

[0003] However, in the actual coal blending process of coal chemical enterprises, the coal blending ratio of enterprises is mainly based on the historical personal experience of coal blending personnel, using a fixed coal blending ratio for production, which is subjective and lacks theoretical optimization calculations. The final coal blending plan will have the problem of insufficient coordination between cost and product properties; at the same time, the demand changes of subsequent devices cannot guarantee the smooth production of the enterprise in time, and may even threaten the bottom line of safe production of the enterprise. Secondly, due to the depreciation, loss, abnormal working conditions and other situations of coal blending equipment, it is easy for enterprises to have a disconnect between coal quality control and subsequent actual production conditions when formulating coal blending plans, inaccurate coal quality calculations, and difficulty in blending the best mixed coal according to the existing coal sources, resulting in a large loss of coal blending benefits.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a coal blending optimization method, device and electronic equipment to at least solve the technical problem that the coal blending optimization method in the related art is easily affected by subjective factors and equipment loss, and lacks theoretical optimization calculations, resulting in a large loss of coal blending benefits.

[0006] According to one aspect of an embodiment of the present application, a coal blending optimization method is provided, comprising: obtaining parameter information of a plurality of raw coals, wherein the parameter information is used to represent characteristic information of the raw coals in multiple dimensions; using a coal blending optimization model to analyze and process the parameter information to obtain a coal blending optimization result, wherein the objective function of the coal blending optimization model comprises at least one of the following: a first objective function for representing the lowest cost per unit effective gas production of the raw coal, a second objective function for representing the maximization of the effective gas production of the raw coal, a third objective function for representing the highest combustion calorific value of the raw coal, and a fourth objective function for representing the lowest average cost of the combustion calorific value of the raw coal; generating mixed coal according to the coal blending optimization result, wherein the mixed coal comprises gasification coal and combustion coal that meet industrial requirements.

[0007] Optionally, the coal blending optimization model is trained in the following manner: obtaining historical parameter information of raw coal; constructing an initial optimization model, and determining the parameters and number of iterations of the initial optimization model; training the initial optimization model based on the historical parameter information, and updating the parameters of the initial optimization model until the number of iterations is reached and then stopping the training to obtain the coal blending optimization model.

[0008] Optionally, the method also includes: determining the constraints of the coal blending optimization model, wherein the constraints include at least one of the following: raw coal inventory constraints, yield constraints, device processing capacity constraints, property constraints, product output constraints, product property constraints, combustion calorific value constraints, and boiler feed quantity constraints; constraining the objective function based on the constraints.

[0009] Optionally, the first objective function is determined in the following manner: determining the total amount of raw coal and the price of raw coal; determining the usage of target coal and the weight parameter of target coal in the coal blending optimization model, wherein the target coal is any one of the raw coals; determining the first objective function based on the total amount of raw coal, the price of raw coal, the usage of target coal and the weight parameter of target coal.

[0010] Optionally, the second objective function is determined in the following manner: the second objective function is determined based on the usage of the target coal and a weight parameter of the target coal.

[0011] Optionally, the third objective function is determined by: determining the unit calorific value of the target coal; and determining the third objective function based on the unit calorific value of the target coal and the usage of the target coal.

[0012] Optionally, the fourth objective function is determined in the following manner: the fourth objective function is determined based on the total amount of raw coal, the usage amount of target coal and the unit calorific value of the target coal.

[0013] According to another aspect of an embodiment of the present application, a coal blending optimization device is also provided, including: an acquisition module, used to acquire parameter information of multiple raw coals, wherein the parameter information is used to represent characteristic information of the raw coals in multiple dimensions; an analysis module, used to analyze and process the parameter information using a coal blending optimization model to obtain a coal blending optimization result, wherein the objective function of the coal blending optimization model includes at least one of the following: a first objective function for representing the lowest unit effective gas production cost of the raw coal, a second objective function for representing the maximization of the effective gas production of the raw coal, a third objective function for representing the highest combustion calorific value of the raw coal, and a fourth objective function for representing the lowest average cost of the combustion calorific value of the raw coal; a generation module, used to generate mixed coal based on the coal blending optimization result, wherein the mixed coal includes gasification coal and combustion coal that meet industrial requirements.

[0014] According to another aspect of the embodiment of the present application, there is also provided an electronic device, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute and implement the above-mentioned coal blending optimization method.

[0015] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, the non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned coal blending optimization method by running the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is also provided, including computer instructions, which implement the above-mentioned coal blending optimization method when executed by a processor.

[0017] In an embodiment of the present application, parameter information of a variety of raw coals is obtained, wherein the parameter information is used to represent characteristic information of the raw coal in multiple dimensions; a coal blending optimization model is used to analyze and process the parameter information to obtain a coal blending optimization result, wherein the objective function of the coal blending optimization model includes at least one of the following: a first objective function for representing the lowest cost of unit effective gas production of raw coal, a second objective function for representing the maximization of effective gas production of raw coal, a third objective function for representing the highest combustion calorific value of raw coal, and a fourth objective function for representing the lowest average cost of combustion calorific value of raw coal; mixed coal is generated according to the coal blending optimization result, wherein the mixed coal includes gasification coal and combustion coal that meet industrial requirements, thereby achieving the purpose of improving the coal blending efficiency of chemical enterprises based on the coal blending optimization model, thereby achieving the technical effect of optimizing the utilization rate of raw coal resources and reducing the production cost of coal blending, and further solving the technical problem that the coal blending optimization method in the related technology is easily affected by subjective factors and equipment loss, and lacks theoretical optimization calculation, resulting in a large loss of coal blending benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a hardware structure diagram of a computer terminal for implementing a coal blending optimization method according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a coal blending optimization method according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a coal blending optimization system architecture according to an embodiment of the present application;

[0022] Figure 4 It is a structural diagram of a coal blending optimization device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] 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.

[0024] 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 sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. 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.

[0025] In order to solve the problem of poor coal blending optimization efficiency in the related art, the present application embodiment provides a coal blending optimization method, which can be run on Figure 1 In the computer terminal shown, the computer terminal is described below.

[0026] The coal blending optimization method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a coal blending optimization method. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0027] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the coal blending optimization method in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned coal blending optimization method. 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, and these remote memories may be connected to the computer terminal 10 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 combinations thereof.

[0029] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, 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 module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0031] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0032] Under the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a coal blending optimization method. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the 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.

[0033] Figure 2 is a flow chart of a coal blending optimization method according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0034] Step S202, obtaining parameter information of multiple types of raw coal, wherein the parameter information is used to represent characteristic information of the raw coal in multiple dimensions.

[0035] In the above step S202, the parameter information of raw coal is the basis for the optimization of coal blending of chemical enterprises, and directly affects the economic benefits and product quality of the coal blending scheme. Among them, the parameter information can be obtained from multiple characteristic dimensions of raw coal, such as physical properties, chemical composition, combustion calorific value, price and cost, availability and other dimensions. The specific parameter information may include procurement restrictions of raw coal, property parameters of raw coal, market price parameters of raw coal, inventory upper limit and safety inventory of various coals in raw coal, processing capacity of equipment, product property requirements, product output requirements, inspection and maintenance information, market price data, equipment input and output data and other parameters.

[0036] Step S204, using the coal blending optimization model to analyze and process the parameter information to obtain the coal blending optimization result, wherein the objective function of the coal blending optimization model includes at least one of the following: a first objective function for representing the lowest unit effective gas production cost of the raw coal, a second objective function for representing the maximization of the effective gas production of the raw coal, a third objective function for representing the highest combustion calorific value of the raw coal, and a fourth objective function for representing the lowest average cost of the combustion calorific value of the raw coal.

[0037] In the above step S204, the parameter information of the above-mentioned multiple raw coals is analyzed and processed by using the pre-trained coal blending optimization model, and the coal blending optimization results of multiple raw coals can be obtained. Among them, the first objective function in the coal blending optimization model is used to calculate the cost of minimizing the unit effective gas output of raw coal, taking into account the price and usage of raw coal; the second objective function is used to calculate the maximum effective gas output of raw coal, taking into account the calorific value and gasification efficiency of raw coal; the third objective function is used to calculate the combustion calorific value of raw coal, taking into account the mixing ratio of various coals to achieve the highest combustion calorific value; the fourth objective function is used to calculate the average cost of the combustion calorific value of raw coal, and minimize the cost per unit calorific value while considering the maximum combustion calorific value. At the same time, the coal blending optimization model will also consider corresponding constraints during the analysis and processing process, such as inventory constraints, yield constraints, device processing capacity constraints, property constraints, product output constraints, product property constraints, combustion calorific value constraints, and boiler feed constraints, etc., to ensure the feasibility of the coal blending optimization results.

[0038] Step S206, generating mixed coal according to the coal blending optimization result, wherein the mixed coal includes gasification coal and combustion coal that meet industrial requirements.

[0039] In the above step S206, the mixed coal (gasification coal and combustion coal) generated according to the coal blending optimization result will be used in a gasifier or boiler to produce synthesis gas, electricity or other chemical products. Among them, the gasification coal is the mixed coal designed for the gasification process, which meets the coal quality requirements of the gasifier, such as reactivity, ash melting point, etc.; the combustion coal is the mixed coal designed for the combustion process, such as boiler coal, which meets the combustion efficiency and environmental protection requirements of chemical companies.

[0040] Through the above steps S202 to S206, the purpose of improving the coal blending efficiency of chemical enterprises based on the coal blending optimization model is achieved, thereby achieving the technical effect of optimizing the utilization rate of raw coal resources and reducing the production cost of coal blending, thereby solving the technical problem that the coal blending optimization method in the related technology is easily affected by subjective factors and equipment loss, and lacks theoretical optimization calculation, resulting in a large loss of coal blending benefits. The following is a detailed description.

[0041] In the above-mentioned coal blending optimization method, the coal blending optimization model is trained in the following way: obtaining the historical parameter information of the raw coal; constructing an initial optimization model, and determining the parameters and number of iterations of the initial optimization model; training the initial optimization model based on the historical parameter information, and updating the parameters of the initial optimization model until the number of iterations is reached and the training is stopped to obtain the coal blending optimization model.

[0042] In the embodiment of the present application, the training process of the coal blending optimization model is a typical machine learning process, involving steps such as data collection, model construction, parameter initialization, training and iterative optimization. The specific process can be as follows:

[0043] First, obtain historical parameter information of various raw coals, such as historical procurement restrictions of raw coal, historical property parameters of raw coal, historical market price parameters of raw coal, historical inventory limits and safety stocks of various types of raw coal, historical processing capacity of equipment, historical product property requirements, historical product output requirements, historical inspection and maintenance information, historical market price data, historical equipment input and output data and other historical parameters. The quality and quantity of historical parameter information directly affect the effect of model training, so the historical parameter information needs to be as comprehensive and accurate as possible to ensure that the model can learn effective coal blending rules.

[0044] Secondly, construct the initial optimization model, and select the appropriate mathematical model or algorithm according to the characteristics of the problem, such as linear programming, nonlinear programming, neural network, etc. And determine the parameters and number of iterations of the initial optimization model, where the model parameters include, for example, the weight of the objective function, the parameters of the constraint conditions, etc., and the model parameters are based on experience or preset values ​​in the initial stage; the number of iterations determines the training depth of the initial optimization model. Too many iterations may lead to overfitting, and too few iterations may cause the model to fail to fully learn.

[0045] Furthermore, the collected historical parameter information is used as training data and input into the initial optimization model for training. During the training process, the initial optimization model will try to adjust the parameters to minimize the prediction error or maximize a certain performance indicator. The training usually involves optimization algorithms, such as gradient descent, genetic algorithms, etc., to help the initial optimization model find the optimal solution in the parameter space. In each iteration, the parameters of the initial optimization model are updated according to the training results. Each iteration aims to improve the performance of the initial optimization model, and the strategy and speed (learning rate) of parameter update have an important impact on the training effect of the initial optimization model.

[0046] Once the preset number of iterations is reached, the training process of the initial optimization model ends. At this point, the initial optimization model has converged to a relatively stable optimal solution. At the same time, the model can be evaluated to ensure that its performance on historical data is satisfactory. If the model performs poorly, you can go back to the previous steps to make adjustments. Finally, after training and parameter updates, the resulting model is the coal blending optimization model, which is used to predict and optimize future coal blending plans.

[0047] It should be noted that the entire training process is an iterative and optimized process that requires continuous adjustment and testing to ensure the accuracy and practicality of the model. In addition, the training and optimization of the model also need to take into account the constraints and goals in actual production to ensure that the model's solution is feasible.

[0048] In the above coal blending optimization model, the first objective function is determined by: determining the total amount of raw coal and the price of raw coal; determining the usage of target coal and the weight parameter of target coal in the coal blending optimization model, where the target coal is any one of the raw coals; determining the first objective function based on the total amount of raw coal, the price of raw coal, the usage of target coal and the weight parameter of target coal. The specific expression is as follows:

[0049] c=(sumofRawCoal*CoalPrice) / f(∑(w(i)*CoalAmount(i))+b)

[0050] In the formula, c represents the cost of unit effective gas volume of various raw coals, which is one of the main optimization objectives and aims to minimize the production cost of each unit effective gas; sumofRawCoal represents the total amount of various raw coals, that is, the total amount of all raw coals consumed in the production process; CoalPrice represents the price of various raw coals, which may change with market fluctuations; f() represents a neural network function, such as a multi-layer perceptron, a convolutional neural network, etc., which is used to process and convert input data to predict effective gas production; w(i) represents the weight parameter of the target coal, that is, the neural network weight parameter of the i-th blending coal in the coal blending process, which is used to adjust the contribution of each coal to the total effective gas production; CoalAmount(i) represents the usage of the target coal, that is, the amount of the i-th coal used in the coal blending process; b represents a neural network parameter, usually called a bias term, which is used to adjust the output of the neural network.

[0051] In the above coal blending optimization model, the second objective function is determined in the following way: the second objective function is determined according to the usage of the target coal and the weight parameter of the target coal. The specific expression is as follows:

[0052] AmountofEffectiveGas=f(∑(w(i)*CoalAmount(i))+b)

[0053] Where Amount of Effective Gas represents the total effective gas volume of various raw coals, which is one of the main objectives of optimization and aims to maximize the effective gas production.

[0054] In the above coal blending optimization model, the third objective function is determined by: determining the unit calorific value of the target coal; determining the third objective function based on the unit calorific value of the target coal and the usage of the target coal. The specific expression is as follows:

[0055] k=∑(CoalAmount(i)*Coalkal(i))

[0056] Where k represents the combustion calorific value of various raw coals, which is one of the main optimization objectives and aims to maximize the total combustion calorific value of various raw coals; Coalkal(i) represents the unit calorific value of the target coal, that is, the unit combustion calorific value of the i-th blended coal in the coal blending process.

[0057] In the above coal blending optimization model, the fourth objective function is determined in the following way: the fourth objective function is determined according to the total amount of raw coal, the usage of target coal and the unit calorific value of target coal. The specific expression is as follows:

[0058] kc=∑(CoalAmount(i)*Coalkal(i)) / sumofRawCoal

[0059] Where kc represents the average cost of the calorific value of combustion of various raw coals, which is one of the main objectives of optimization, aiming to minimize the average cost of the calorific value of combustion of various raw coals.

[0060] Furthermore, in an embodiment of the present application, optimizing the objective function in the coal blending optimization model based on constraint conditions includes: determining the constraint conditions of the coal blending optimization model, wherein the constraint conditions include at least one of the following: raw coal inventory constraints, yield constraints, device processing capacity constraints, property constraints, product output constraints, product property constraints, combustion calorific value constraints, and boiler feed quantity constraints; constraining the objective function based on the constraint conditions.

[0061] The specific constraints are as follows:

[0062] 1) Inventory constraints: used to ensure that the inventory of coal is within a safe range, avoiding capital occupation caused by excessive inventory and preventing insufficient inventory from affecting production.

[0063] Inventory(i)≤Inventoryup(i)

[0064] Inventory(i)≥Inventorylow(i)

[0065] In the formula, Inventory(i) is the inventory of the i-th coal, Inventoryup(i) is the upper limit of the inventory of the i-th coal; Inventorylow(i) is the safety inventory of the i-th coal;

[0066] 2) Yield constraint: used to ensure that the conversion rate of raw materials during processing reaches a certain standard to optimize resource utilization.

[0067]

[0068] Where unitOut(i,j) is the flow rate of the jth outlet stream of processing unit i; unitIn(i,j) is the flow rate of the jth feed stream of processing unit i; unitYield(i,j) is the expected yield of the jth stream of processing unit i; deltal is the allowable error of the device yield.

[0069] 3) Device processing capacity constraints: used to ensure that the device's processing capacity is within the range of safety and efficiency.

[0070]

[0071] Where unitFeedUp(i) is the upper limit of the processing capacity of device i; unitFeedLow(i) is the lower limit of the processing capacity of secondary device i.

[0072] 4) Constraints on properties of mixed coal / raw coal: used to ensure that the specific properties of mixed coal or raw coal (such as sulfur content, ash content, etc.) are within the specified range.

[0073] CoalAbility(i,j)≤CoalAbilityup(i,j)

[0074] CoalAbility(i,j)≥CoalAbilitylow(i,j)

[0075] Where CoalAbility(i,j) is the j-th content ratio of the ith coal; CoalAbilityup(i,j) is the upper limit of the j-th content ratio of the ith coal; and CoalAbilitylow(i,j) is the lower limit of the j-th content ratio of the ith coal.

[0076] 5) Product output constraints: used to ensure that product output meets market demand while not exceeding production capacity.

[0077] productMatrix*m≤productLimitUp

[0078] -productMatrix*m≤-productLimitLow

[0079] In the formula, productMatrix is ​​the product output; productLimitUp is the upper limit of product output; productLimitUp is the lower limit of product output.

[0080] 6) Product property constraints: used to ensure that the effective gas ratio of the product is within a certain range to meet quality standards.

[0081] ProductRatio≥ProductRatiolow

[0082] ProductRatio≤ProductRatioup

[0083] In the formula, ProductRatio is the effective gas ratio of the product, ProductRatiolow is the lower limit of the effective gas ratio of the product, and ProductRatioup is the upper limit of the effective gas ratio of the product.

[0084] 7) Combustion calorific value constraint: used to ensure that the combustion calorific value of the mixed coal is within the range of safety and efficiency.

[0085] umofCoal*sumofCoal≤kalup

[0086] sumofCoal*sumofCoalkal≥kallow

[0087] In the formula, sumofCoal represents the total mass of the mixed coal, sumofCoalKal represents the calorific value of the mixed coal, kalup represents the upper limit of the heat of combustion, and kallow represents the minimum calorific value requirement of the heat of combustion.

[0088] 8) Boiler feed quantity constraint: used to ensure that the boiler feed quantity is within the range of safety and efficiency.

[0089] coallimit≤coallimitup

[0090] coallimit ≥ coallimitlow

[0091] In the formula, coallimit represents the feeding amount of mixed coal, coallimitup represents the upper limit of the feeding amount of mixed coal, and coallimitlow represents the lower limit of the feeding amount of mixed coal.

[0092] The above constraints together constitute the framework of the coal blending optimization model, ensuring the practicality and safety of the optimization process. Through these constraints, the coal blending optimization model can find the lowest cost and most efficient coal blending solution while meeting all operational and safety requirements.

[0093] It should be noted that different enterprise users often have different concerns about planning and scheduling. Therefore, users can select corresponding constraints according to actual business needs, such as production goals, cost control, quality standards, safety requirements, etc., generate personalized coal blending optimization models, and modify and adjust corresponding model constraint parameters.

[0094] In the embodiment of the present application, the coal blending optimization results and coal blending optimization plan are stored in the coal blending optimization system. When the enterprise management personnel need to adjust the coal blending plan (such as the proportion of various raw coals, expected costs, output and quality indicators, etc.) according to the actual production situation (such as actual production demand, market changes, equipment conditions, raw coal supply and other factors), the plan can be modified directly in the coal blending optimization system. When the modification is completed, it is sent to various departments and workshops for execution through the sending function. During the execution process, the on-site operators can feedback information according to the actual situation. If they encounter problems such as fluctuations in raw coal quality or equipment failures, the system can record this information and provide it to the management personnel. Based on the on-site feedback and production data, the system can perform optimization calculations again to cope with uncertainties and changes in the production process.

[0095] In addition, the coal blending optimization system also has an execution tracking function to monitor the execution of the coal blending plan and ensure that each link is operated according to the optimized plan. For example, in actual production, there are uncertain factors that will cause deviations between the plan and the actual situation. Users can use the coal blending optimization system to collect production information in a timely manner and track and compare it with the formulated coal blending plan. When there are differences, timely reminders are given to ensure the implementation of the plan.

[0096] Figure 3 is a schematic diagram of a coal blending optimization system architecture according to an embodiment of the present application, such as Figure 3 As shown in the figure, the coal blending optimization system realizes the optimization of the coal blending process by integrating multiple data sources, mathematical models and B / S application layer functions.

[0097] The details are as follows:

[0098] 1) Data source (interface layer): The data input part of the system, which is connected to the enterprise's existing information system through a variety of data interfaces to obtain necessary data. These interfaces may include: Laboratory Information Management System LIMS, which is used to provide quality inspection data of raw coal and products; Enterprise Resource Planning System ERP, which provides financial and procurement related data; Manufacturing Execution System MES, which provides production process and equipment operation data; Process Information Management System PI, which provides real-time production process data; Enterprise-specific Information System ISYS, which provides relevant business data; Supplier Relationship Management System SRM, which provides supplier and procurement management data.

[0099] 2) Mathematical model layer (building different models): involving coal blending optimization calculation, coal blending simulation scheduling, planning and actual tracking, quality prediction model, benefit prediction model and formula case library management. Among them, enterprises can select appropriate optimization goals (such as minimum cost, maximum output, highest calorific value, etc.) according to their own production needs and goals, and build personalized coal blending optimization models that meet the specific needs of the enterprise based on general models. These models may include: Personalized coal blending optimization model for load enterprise production requirements: adjust the coal blending plan according to different production loads and needs. Constraints and event configuration: According to the constraints in actual production (such as inventory restrictions, cost restrictions, quality standards, etc.) and emergencies (such as equipment failures, market changes, etc.), the model is adjusted in real time.

[0100] 3) B / S application layer (calculation, management): involves comprehensive display of coal blending optimization, coal blending plan management library, gas coal quality prediction, coal blending optimization, raw coal database, coal blending report output and system configuration. Once the model is established, the application layer will perform the following functions: perform detailed coal blending plan calculations based on the model provided by the mathematical model layer, and make adjustments based on actual conditions; manage and maintain the database that stores raw coal information and coal blending formulas to ensure data accuracy and timeliness; monitor the implementation of the coal blending plan, manage the production process, and ensure production efficiency and product quality.

[0101] The design of the entire coal blending optimization system aims to optimize the coal blending process of coal chemical enterprises, improve production efficiency, reduce costs, and ensure product quality through integrated data sources, flexible mathematical models, and efficient application layer functions.

[0102] In the embodiments of the present application, by establishing a comprehensive data architecture and an intelligent coal blending optimization model, the operating efficiency of gasifiers and boilers in the coal chemical industry is significantly improved. Specifically, the use of a neural network algorithm to optimize the calculation of raw coal can more accurately identify the complex relationship between coal quality and gas production and synthesis gas quality, and achieve the dual goals of cost control and product quality improvement. At the same time, the real-time adjustment and monitoring mechanism ensures the execution effect of the coal blending plan, and the continuous improvement of the raw coal database further enhances the predictive ability and stability of the model. Overall, this solution can effectively reduce production costs, increase effective gas production and combustion calorific value, while ensuring the lowest average cost of combustion calorific value, providing coal chemical enterprises with an efficient and intelligent coal blending optimization solution with significant economic benefits and industry promotion value.

[0103] According to an embodiment of the present application, a coal blending optimization device is provided. It should be noted that the coal blending optimization device of the embodiment of the present application can be used to execute the coal blending optimization method provided in the embodiment of the present application. The coal blending optimization device provided in the embodiment of the present application is introduced below.

[0104] Figure 4 is a structural diagram of a coal blending optimization device provided according to an embodiment of the present application. Figure 4 As shown, the device comprises:

[0105] An acquisition module 40 is used to acquire parameter information of various raw coals, wherein the parameter information is used to represent characteristic information of the raw coals in multiple dimensions;

[0106] The analysis module 42 is used to analyze and process the parameter information using the coal blending optimization model to obtain the coal blending optimization result, wherein the objective function of the coal blending optimization model includes at least one of the following: a first objective function for representing the lowest cost of unit effective gas production of raw coal, a second objective function for representing the maximization of effective gas production of raw coal, a third objective function for representing the highest combustion calorific value of raw coal, and a fourth objective function for representing the lowest average cost of combustion calorific value of raw coal;

[0107] The generation module 44 is used to generate mixed coal according to the coal blending optimization result, wherein the mixed coal includes gasification coal and combustion coal that meet industrial requirements.

[0108] Through the acquisition module 40, analysis module 42 and generation module 44 in the above-mentioned coal blending optimization device, the purpose of improving the coal blending efficiency of chemical enterprises based on the coal blending optimization model is achieved, thereby realizing the technical effect of optimizing the utilization rate of raw coal resources and reducing the production cost of coal blending, and further solving the technical problem that the coal blending optimization method in the related technology is easily affected by subjective factors and equipment loss, and lacks theoretical optimization calculation, resulting in a large loss of coal blending benefits.

[0109] The coal blending optimization device provided in the embodiment of the present application also includes a determination module 46, which is also used to obtain historical parameter information of the raw coal; construct an initial optimization model, and determine the parameters and number of iterations of the initial optimization model; train the initial optimization model based on the historical parameter information, and update the parameters of the initial optimization model until the training is stopped after the number of iterations is reached, so as to obtain the coal blending optimization model.

[0110] In the coal blending optimization device provided in the embodiment of the present application, the determination module is also used to determine the constraints of the coal blending optimization model, wherein the constraints include at least one of the following: raw coal inventory constraints, yield constraints, device processing capacity constraints, property constraints, product output constraints, product property constraints, combustion calorific value constraints and boiler feed quantity constraints; the objective function is constrained according to the constraints.

[0111] In the coal blending optimization device provided in the embodiment of the present application, the determination module is also used to determine the total amount of raw coal and the price of raw coal; determine the usage of target coal and the weight parameter of target coal in the coal blending optimization model, wherein the target coal is any one of the raw coals; determine the first objective function based on the total amount of raw coal, the price of raw coal, the usage of target coal and the weight parameter of target coal.

[0112] In the coal blending optimization device provided in the embodiment of the present application, the determination module is also used to determine the second objective function according to the usage of the target coal and the weight parameter of the target coal.

[0113] In the coal blending optimization device provided in the embodiment of the present application, the determination module is also used to determine the unit calorific value of the target coal; and determine the third objective function based on the unit calorific value of the target coal and the usage of the target coal.

[0114] In the coal blending optimization device provided in the embodiment of the present application, the determination module is also used to determine the fourth objective function based on the total amount of raw coal, the usage of the target coal and the unit calorific value of the target coal.

[0115] An embodiment of the present application further provides an electronic device, comprising: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute and implement the above-mentioned coal blending optimization method.

[0116] It should be noted that the above electronic equipment is used to execute Figure 2 The coal blending optimization method shown in the figure, therefore the relevant explanations and instructions in the above coal blending optimization method are also applicable to the electronic device and will not be repeated here.

[0117] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored computer program, wherein a device where the non-volatile storage medium is located executes the above-mentioned coal blending optimization method by running the computer program.

[0118] It should be noted that the above non-volatile storage medium is used to execute Figure 2 The coal blending optimization method shown, therefore the relevant explanations and instructions in the above coal blending optimization method are also applicable to the non-volatile storage medium, and will not be repeated here.

[0119] The embodiment of the present application also provides a computer program product, including computer instructions, which implement the above-mentioned coal blending optimization method when executed by a processor.

[0120] It should be noted that the above-mentioned computer program product is used to execute Figure 2 The coal blending optimization method shown in the figure, therefore the relevant explanations and instructions in the above coal blending optimization method are also applicable to the computer program product, which will not be repeated here.

[0121] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0122] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0125] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0127] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing coal blending, characterized in that: include: Acquire parameter information of multiple types of raw coal, wherein the parameter information is used to represent characteristic information of the raw coal in multiple dimensions; The parameter information is analyzed and processed by using a coal blending optimization model to obtain a coal blending optimization result, wherein the objective function of the coal blending optimization model includes at least one of the following: a first objective function for representing the lowest cost per unit effective gas output of the raw coal, a second objective function for representing the maximization of the effective gas output of the raw coal, a third objective function for representing the highest combustion calorific value of the raw coal, and a fourth objective function for representing the lowest average cost of the combustion calorific value of the raw coal; Mixed coal is generated according to the coal blending optimization result, wherein the mixed coal includes gasification coal and combustion coal that meet industrial requirements.

2. The method according to claim 1, characterized in that The coal blending optimization model is trained in the following way: Obtaining historical parameter information of the raw coal; Constructing an initial optimization model, and determining parameters and the number of iterations of the initial optimization model; The initial optimization model is trained according to the historical parameter information, and the parameters of the initial optimization model are updated until the training is stopped after the number of iterations is reached, thereby obtaining the coal blending optimization model.

3. The method according to claim 1, characterized in that The method further comprises: Determine the constraint conditions of the coal blending optimization model, wherein the constraint conditions include at least one of the following: inventory constraint, yield constraint, device processing capacity constraint, property constraint, product output constraint, product property constraint, combustion calorific value constraint, and boiler feed quantity constraint of the raw coal; The objective function is constrained according to the constraint condition.

4. The method according to claim 1, characterized in that: The first objective function is determined by: Determining the total amount of the raw coal and the price of the raw coal; Determine the usage of target coal and the weight parameter of the target coal in the coal blending optimization model, wherein the target coal is any one of the raw coals; The first objective function is determined according to the total amount of the raw coal, the price of the raw coal, the usage of the target coal and the weight parameter of the target coal.

5. The method according to claim 4, characterized in that The second objective function is determined by: The second objective function is determined according to the usage of the target coal and the weight parameter of the target coal.

6. The method according to claim 4, characterized in that The third objective function is determined in the following way: Determining the unit calorific value of the target coal; The third objective function is determined according to the unit calorific value of the target coal and the usage of the target coal.

7. The method according to claim 4, characterized in that The fourth objective function is determined by: The fourth objective function is determined according to the total amount of the raw coal, the usage of the target coal and the unit calorific value of the target coal.

8. A coal blending optimization device, characterized in that: include: An acquisition module, used for acquiring parameter information of multiple types of raw coal, wherein the parameter information is used for representing characteristic information of the raw coal in multiple dimensions; an analysis module, used for analyzing and processing the parameter information by using a coal blending optimization model to obtain a coal blending optimization result, wherein the objective function of the coal blending optimization model includes at least one of the following: a first objective function for representing the lowest cost per unit effective gas output of the raw coal, a second objective function for representing the maximization of the effective gas output of the raw coal, a third objective function for representing the highest combustion calorific value of the raw coal, and a fourth objective function for representing the lowest average cost of the combustion calorific value of the raw coal; A generation module is used to generate mixed coal according to the coal blending optimization result, wherein the mixed coal includes gasification coal and combustion coal that meet industrial requirements.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory, and is used to execute the coal blending optimization method described in any one of claims 1 to 7.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the coal blending optimization method according to any one of claims 1 to 7 by running the computer program.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the coal blending optimization method described in any one of claims 1 to 7 is implemented.

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