A system for determining the optimal parameters of raw materials during gasification and a method for creating an optimal scheme for mixing raw materials during gasification

Abstract

Provided in the embodiments of the present invention are a system and method for intelligent gasification blending, belonging to the technical field of the coal chemical industry. The system comprises a gasification blending subsystem. The gasification blending subsystem comprises: a raw material property rapid analysis module for obtaining raw material property parameters of raw materials according to the characteristic spectral line intensity of the in-furnace raw materials; a blended material property prediction module for establishing a prediction model and predicting blended material property parameters by means of the prediction model and according to the raw material property parameters and the proportion of raw materials; a blending scheme optimization module for establishing an optimization model and obtaining an optimized blending scheme by means of the optimization model according to the blended material property parameters; and a blending scheme economy evaluation module for outputting a blending scheme with the optimum technical economy. The present invention establishes a system for intelligent gasification blending with a complete life cycle process, and realizes the intelligent and accurate control of gasification blending.

Description

Intelligent gasification batching system and method Technical Field

[0001] This invention relates to the field of coal chemical technology, specifically to an intelligent gasification batching system and an intelligent gasification batching method. Background Technology

[0002] Stable operation of gasifiers is one of the key economic goals pursued by coal chemical enterprises. Gasifiers have specific operating conditions and are subject to strict limitations on the characteristics of the raw materials fed into the furnace. In actual production at coal chemical enterprises, the following problems may arise: First, the coal quality near the project site is not suitable for gasifiers and cannot be used locally; second, coal quality fluctuates greatly, making it impossible for gasifiers to operate stably for long periods; third, there are problems with the supply of the existing coal type, or significant price fluctuations, leading to unstable gasifier operation, corrosion, slag blockage, or even shutdown. Coal blending technology can effectively solve these problems, enabling the local use of coal near the project site, reducing costs; ensuring long-term stable operation of gasifiers, increasing efficiency; and improving the flexibility of coal use for projects, reducing risks. Traditional coal blending mainly relies on researchers in scientific research institutions to conduct numerous tedious conditional experiments to obtain solutions, or on the long-term accumulated operational experience of relevant factory staff. This approach suffers from low efficiency and poor accuracy, and cannot effectively obtain the optimal coal blending ratio for gasifiers. Meanwhile, gasification technology has been developed, promoted and applied on a large scale in my country. Coal is no longer the only gasification feedstock. Using various carbon-containing compounds as gasification feedstock has become an important development trend in coal gasification technology. Therefore, it is necessary to develop an intelligent gasification batching system that can construct the entire life cycle of the batching scheme.

[0003] Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent gasification batching system and method to solve the problems of low efficiency, poor accuracy, and inability to construct a full life cycle process for coal blending schemes in traditional coal blending technology.

[0005] To achieve the above objectives, in a first aspect of the present invention, an intelligent gasification batching system is provided, comprising:

[0006] The gasification batching subsystem includes a raw material property rapid analysis module, a batching property prediction module, a batching scheme optimization module, and a batching scheme economic evaluation module.

[0007] The raw material property rapid analysis module is used to receive the characteristic spectral line intensity of the raw material entering the furnace, and obtain the raw material property parameters based on the characteristic spectral line intensity;

[0008] The ingredient property prediction module is used to establish a prediction model, and predicts the property parameters of the ingredients based on the raw material property parameters and the preset ratio.

[0009] The ingredient scheme optimization module is used to establish an optimization model and, based on the property parameters of the ingredients, obtain the optimized ingredient scheme through the objective function in the optimization model.

[0010] The ingredient scheme economic evaluation module is used to perform technical and economic analysis on the optimized ingredient scheme and output the ingredient scheme with the best technical and economic efficiency.

[0011] Optionally, the system further includes a raw material property standard subsystem, which includes a raw material standard management module for furnace feed. The raw material standard management module for furnace feed is used to establish and store the mapping relationship between different types of gasifiers and their corresponding raw materials, so as to determine the raw materials for furnace feed after the type of gasifier is determined.

[0012] Optionally, the property parameters of the ingredients include basic properties, ash melting characteristics, slurry forming characteristics, and gasification reaction characteristics.

[0013] Optionally, the basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters.

[0014] Optionally, the system further includes an information management subsystem, which includes a raw material information management module, a gasifier information management module, an additive information management module, and a gasification ash and slag information management module.

[0015] The raw material information management module is used to store raw material property parameters obtained by the raw material property rapid analysis module;

[0016] The gasifier information management module is used to establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters;

[0017] The additive information management module is used to establish and store the mapping relationship between the predicted ash melting characteristics and slurry forming characteristics and their corresponding additives;

[0018] The gasification ash and slag information management module is used to establish and store a one-to-one mapping relationship between the type of gasifier, raw materials, gasifier operating parameters, and ash and slag properties.

[0019] Optionally, the gasification batching subsystem further includes a gasifier early warning module. The gasifier early warning module is used to establish a functional relationship between batching, gasifier operating parameters and ash properties, and predict the corresponding ash properties based on the batching and gasifier operating parameters of the batching scheme with the best technical and economic efficiency, and determine whether the obtained ash properties are abnormal.

[0020] Optionally, the gasifier early warning module is also used to compare the obtained ash properties with the pre-stored ash properties when it is determined that the ash properties are abnormal, and obtain the relevant parameters that cause the abnormal ash properties based on the mapping relationship between the pre-stored ash properties and the type of gasifier, raw materials and gasifier operating parameters.

[0021] In a second aspect of the present invention, an intelligent gasification batching method is provided, comprising a gasification batching sub-step, wherein the gasification batching sub-step includes:

[0022] The characteristic spectral line intensities of the raw materials fed into the furnace are received, and the raw material property parameters are obtained based on the characteristic spectral line intensities.

[0023] A prediction model is established to predict the property parameters of the ingredients based on the raw material property parameters and the raw material ratio.

[0024] An optimization model is established, and an optimized ingredient scheme is obtained through the optimization model based on the property parameters of the ingredients.

[0025] A techno-economic analysis is performed on the optimized ingredient scheme, and the optimal ingredient scheme with the best techno-economic performance is output.

[0026] Optionally, the method further includes a raw material property standardization sub-step, which includes:

[0027] Establish and store the mapping relationship between different types of gasifiers and their corresponding raw materials, so as to determine the raw materials to be fed into the gasifier after the type of gasifier is determined.

[0028] Optionally, the property parameters of the ingredients include basic properties, ash melting characteristics, slurry forming characteristics, and gasification reaction characteristics.

[0029] Optionally, the basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters.

[0030] Optionally, the method further includes an information management sub-step, which includes:

[0031] Store the raw material property parameters obtained by the raw material property rapid analysis module;

[0032] Establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters;

[0033] Establish and store the mapping relationship between the predicted ash melting characteristics and slurry forming characteristics and their corresponding additives;

[0034] Establish and store a one-to-one mapping relationship between the types of gasifiers, raw materials, gasifier operating parameters, and ash properties.

[0035] Optionally, the gasification feed preparation sub-step further includes:

[0036] Establish the functional relationship between batching, gasifier operating parameters and ash properties, and predict the corresponding ash properties based on the batching and gasifier operating parameters of the optimal technical and economic batching scheme, and determine whether the obtained ash properties are abnormal.

[0037] Optionally, the gasification batching sub-step further includes: when it is determined that the properties of the obtained ash are abnormal, comparing the properties of the obtained ash with the properties of the pre-stored ash, and obtaining the relevant parameters that cause the abnormal properties of the ash based on the mapping relationship between the properties of the pre-stored ash and the type of gasifier, raw materials and operating parameters of the gasifier.

[0038] The above-mentioned technical solution of the present invention obtains raw material property parameters through rapid analysis of raw materials, predicts the batching property parameters based on the obtained raw material property parameters through an established prediction model, optimizes the batching scheme through an established objective function based on the obtained batching property parameters, and conducts a techno-economic analysis on the optimized batching scheme to obtain the optimal batching scheme. This constructs an intelligent gasification batching system with a complete life cycle process, effectively improving the efficiency and accuracy of coal blending, realizing intelligent and precise control of gasification batching, and effectively reducing the adverse effects of raw material property problems on enterprises.

[0039] Specifically, the present invention also provides the following technical solutions:

[0040] Technical Solution 1: A gasification batching system, characterized in that it comprises:

[0041] The gasification batching subsystem includes a raw material property rapid analysis module, a batching property prediction module, a batching scheme optimization module, and a batching scheme economic evaluation module.

[0042] The raw material property rapid analysis module is used to receive the characteristic spectral line intensity of the raw material entering the furnace, and obtain the raw material property parameters based on the characteristic spectral line intensity;

[0043] The ingredient property prediction module is used to establish a prediction model, which includes: predicting the property parameters of the ingredients based on the raw material property parameters and the raw material ratio;

[0044] The ingredient scheme optimization module is used to establish an optimization model and, based on the property parameters of the ingredients, obtain an optimized ingredient scheme through the optimization model.

[0045] The ingredient scheme economic evaluation module is used to perform technical and economic analysis on the optimized ingredient scheme and output the ingredient scheme with the best technical and economic efficiency.

[0046] Technical Solution 2: The gasification batching system according to Technical Solution 1 is characterized in that the system further includes a raw material property standard subsystem, which includes a raw material standard management module for furnace feed. The raw material standard management module for furnace feed is used to establish and store the mapping relationship between different types of gasifiers and their corresponding raw materials, so as to determine the raw materials for furnace feed after the type of gasifier is determined.

[0047] Technical Solution 3: The gasification batching system according to any one of Technical Solutions 1-2, characterized in that the property parameters of the batching include basic properties, ash melting characteristics, slurry formation characteristics, and gasification reaction characteristics.

[0048] Preferably, the basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters;

[0049] More preferably, the property parameters of the ingredients predicted by the prediction model are industrial analysis parameters, elemental analysis parameters, calorific value parameters, and optionally, ash fusion characteristic parameters.

[0050] Technical Solution 4: The gasification batching system according to any one of Technical Solutions 1-3, characterized in that the system further includes an information management subsystem, which includes a raw material information management module, a gasifier information management module, an additive information management module, and a gasification ash and slag information management module.

[0051] The raw material information management module is used to store raw material property parameters obtained by the raw material property rapid analysis module;

[0052] The gasifier information management module is used to establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters;

[0053] The additive information management module is used to establish and store the mapping relationship between the predicted ash melting characteristics and slurry forming characteristics and their corresponding additives;

[0054] The gasification ash and slag information management module is used to establish and store a one-to-one mapping relationship between the type of gasifier, raw materials, gasifier operating parameters, and ash and slag properties.

[0055] Technical Solution 5: The gasification batching system according to any one of Technical Solutions 1-4, characterized in that the gasification batching subsystem further includes a gasifier early warning module, which is used to establish a functional relationship between batching, gasifier operating parameters and ash properties, and predict the corresponding ash properties based on the batching and gasifier operating parameters of the batching scheme with the best technical and economic efficiency, and determine whether the obtained ash properties are abnormal.

[0056] Technical Solution 6: The gasification batching system according to any one of Technical Solutions 1-5, characterized in that the gasifier early warning module is further used to compare the obtained ash and slag properties with the pre-stored ash and slag properties when it is determined that the obtained ash and slag properties are abnormal, and obtain the relevant parameters that cause the abnormal ash and slag properties based on the mapping relationship between the pre-stored ash and slag properties and the type of gasifier, raw materials and gasifier operating parameters.

[0057] Technical Solution 7: The gasification batching system according to any one of Technical Solutions 1-6, characterized in that the batching property prediction module is used to establish a prediction model, and further includes: based on the raw material property parameters, raw material ratio, and internal water content M... ad Grindability index (HGI) and specific surface area (S) BET The predicted property parameters of the ingredients, including, optionally, additive information, are predicted by the prediction model. The predicted property parameters of the ingredients include the slurry-forming properties and gasification reactivity of the ingredients, as well as, optionally, ash fusion characteristics.

[0058] Technical Solution 8: A gasification batching method, characterized in that it includes a gasification batching sub-step, wherein the gasification batching sub-step includes:

[0059] The characteristic spectral line intensities of the raw materials fed into the furnace are received, and the raw material property parameters are obtained based on the characteristic spectral line intensities.

[0060] Establishing a prediction model includes: predicting the property parameters of the ingredients based on the property parameters of the raw materials and the proportion of the raw materials using the prediction model;

[0061] An optimization model is established, and an optimized ingredient scheme is obtained through the optimization model based on the property parameters of the ingredients.

[0062] A techno-economic analysis is performed on the optimized ingredient scheme, and the optimal ingredient scheme with the best techno-economic performance is output.

[0063] Technical Solution 9: The gasification batching method according to Technical Solution 8, characterized in that the method further includes a raw material property standardization sub-step, the raw material property standardization sub-step including:

[0064] Establish and store the mapping relationship between different types of gasifiers and their corresponding raw materials, so as to determine the raw materials to be fed into the gasifier after the type of gasifier is determined.

[0065] Technical Solution 10: The gasification batching method according to any one of Technical Solutions 8-9, characterized in that the property parameters of the batching include basic properties, ash melting characteristics, slurry forming characteristics, and gasification reaction characteristics.

[0066] Preferably, the basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters;

[0067] More preferably, the property parameters of the ingredients predicted by the prediction model are industrial analysis parameters, elemental analysis parameters, calorific value parameters, and optionally, ash fusion characteristic parameters.

[0068] Technical Solution 11. The gasification batching method according to any one of Technical Solutions 8-10, characterized in that the method further includes an information management sub-step, the information management sub-step including:

[0069] Store the raw material property parameters obtained by the raw material property rapid analysis module;

[0070] Establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters;

[0071] Establish and store the mapping relationship between the predicted ash melting characteristics and slurry forming characteristics and their corresponding additives;

[0072] Establish and store a one-to-one mapping relationship between the types of gasifiers, raw materials, gasifier operating parameters, and ash properties.

[0073] Technical Solution 12: The gasification batching method according to any one of technical solutions 8-11, characterized in that the gasification batching sub-step further includes:

[0074] Establish the functional relationship between batching, gasifier operating parameters and ash properties, and predict the corresponding ash properties based on the batching and gasifier operating parameters of the optimal technical and economic batching scheme, and determine whether the obtained ash properties are abnormal.

[0075] Technical Solution 13: The gasification batching method according to any one of Technical Solutions 8-12, characterized in that the gasification batching sub-step further includes:

[0076] When the obtained ash and slag properties are determined to be abnormal, the obtained ash and slag properties are compared with the pre-stored ash and slag properties. Based on the mapping relationship between the pre-stored ash and slag properties and the type of gasifier, raw materials and gasifier operating parameters, the relevant parameters that caused the abnormal ash and slag properties are obtained.

[0077] Technical Solution 14: The gasification batching method according to any one of Technical Solutions 8-13, characterized in that,

[0078] The prediction model also includes: based on the raw material property parameters, raw material ratio, and internal water content M. ad Grindability index (HGI) and specific surface area (S) BETThe predicted property parameters of the ingredients, including, optionally, additive information, are predicted by the prediction model. The predicted property parameters of the ingredients include the slurry-forming properties and gasification reactivity of the ingredients, as well as, optionally, ash fusion characteristics.

[0079] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0080] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0081] Figure 1 is a schematic diagram of the system structure of an intelligent gasification batching system provided in one embodiment of the present invention;

[0082] Figure 2 is a system operation flowchart of an intelligent gasification batching system provided in one embodiment of the present invention;

[0083] Figure 3 is a flowchart of an intelligent gasification batching method provided by one embodiment of the present invention. Detailed Implementation

[0084] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0085] In embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0086] As shown in Figures 1 and 2, in a first aspect of the present invention, an intelligent gasification batching system is provided, comprising:

[0087] The gasification batching subsystem includes a rapid raw material property analysis module, a batching property prediction module, a batching scheme optimization module, and a batching scheme economic evaluation module.

[0088] The raw material property rapid analysis module is used to receive the characteristic spectral line intensities of the raw materials entering the furnace and obtain the raw material property parameters based on the characteristic spectral line intensities;

[0089] The ingredient property prediction module is used to establish a prediction model, which includes: predicting the property parameters of the ingredients based on the raw material property parameters and the raw material ratio;

[0090] The ingredient scheme optimization module is used to build an optimization model and obtain an optimized ingredient scheme based on the property parameters of the ingredients.

[0091] The ingredient scheme economic evaluation module is used to perform technical and economic analysis on the optimized ingredient scheme and output the ingredient scheme with the best technical and economic efficiency.

[0092] Thus, the above-described technical solution of this embodiment obtains raw material property parameters through rapid analysis of raw materials. Based on the obtained raw material property parameters and the preset raw material ratio, a prediction model is established to predict the batching property parameters. Based on the obtained batching property parameters, an optimization model is established to optimize the batching scheme. A techno-economic analysis is then performed on the optimized batching scheme to obtain the optimal batching scheme. This constructs an intelligent gasification batching system with a complete life cycle process, effectively improving the efficiency and accuracy of coal blending. By establishing a coal quality prediction model through computer technology, intelligent and precise control of gasification coal blending is achieved, which can effectively reduce the adverse effects of raw material property problems on enterprises.

[0093] Specifically, the stable operation of the gasifier is closely related to the properties of the feedstock. To maintain stable operation of the gasifier, the feedstock needs to be strictly limited. Different individual feedstocks have different characteristics. To meet the gasification requirements, the feedstock is often composed of a mixture of multiple individual feedstocks. Therefore, the feedstock formulation is particularly important. In this embodiment, the individual feedstocks can be carbonaceous materials such as coal, semi-coke, petroleum coke, biomass, sludge, oil sludge, and waste charcoal. Currently, offline analysis, which is commonly used for coal composition analysis, suffers from drawbacks such as slow analysis speed and cumbersome procedures. It cannot provide operators with real-time online reference data and is difficult to meet the needs of industrial production. This implementation method uses laser-induced plasma spectroscopy (LIBS) to obtain the characteristic spectral line intensities of the sample to be tested. LIBS refers to the process where, when a strong pulsed laser is focused onto a sample, the sample is instantly vaporized into high-temperature, high-density plasma. The excited plasma releases different rays, and the wavelength and intensity of the plasma emission spectral lines reflect the constituent elements and their concentrations in the measured object. LIBS has the advantages of fast detection speed, high sensitivity, low cost, and the ability to analyze multiple elements simultaneously. The raw material property rapid analysis module receives the characteristic spectral line intensities of the raw material to be tested from the LIBS detector. It then uses existing dedicated algorithms, such as spectral normalization algorithms and self-absorption correction algorithms, to calculate and analyze the characteristic spectral line intensities to obtain the raw material property parameters of the raw material to be tested. Individual raw materials are tested separately to obtain the property parameters of each individual raw material. The raw material properties parameters include the content of carbon, hydrogen, sulfur and ash-forming elements, ash content, volatile matter and calorific value.

[0094] The ingredient property prediction module establishes a prediction model that takes the property parameters and proportions of individual raw materials as input and the property parameters of the batch as output. The property parameters of the batch include basic properties, ash fusion characteristics, slurry formation characteristics, and gasification reaction characteristics; basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters. In this embodiment, the ingredient property prediction module predicts the basic properties, ash fusion characteristics, slurry formation characteristics, and gasification reaction characteristics of the batch by establishing basic property prediction models, ash fusion characteristic prediction models, slurry formation characteristic prediction models, and gasification reaction characteristic prediction models, respectively. The basic property prediction model is based on a linear weighted sum algorithm, while the ash fusion characteristic prediction model, slurry formation characteristic prediction model, and gasification reaction characteristic prediction model are based on a backpropagation neural network. The prediction models are not limited to the above algorithms and can also be based on other algorithms, such as convolutional neural networks.

[0095] The batching scheme optimization module establishes an objective function based on the requirements for the batching scheme. There can be one or more objective functions, resulting in multiple feasible batching schemes. For example, the objective function can be established based on requirements such as lowest batching price, highest calorific value, or best environmental performance. The constraints that the gasification batching must meet are determined. These constraints refer to the limitations imposed when finding the extreme value of the objective function. In this implementation, the constraints are the gasifier's requirements for the batching input, i.e., the gasifier's requirements for the property parameters of the batching and the range of variation of these property parameters. The batching property prediction module obtains the property parameters of individual raw materials and their proportion function relationship, acquiring the upper and lower limits of the gasifier's range of variation for the raw material property parameters, thus obtaining the constraints of the objective function. Based on the established objective function and considering the obtained constraints, an optimization model is established.

[0096] The economic evaluation module for the batching scheme has two main aspects. First, it can utilize process simulation software to perform techno-economic analysis on all optimized feasible batching schemes. Based on preset targets, it selects the scheme with suitable product structure and good economics as the optimal batching scheme, thereby achieving real-time online adjustment of the batching ratio. Second, it can adjust the gasifier's operating parameters to achieve the best product structure and economic efficiency while ensuring operational stability. Specifically, the techno-economic analysis of feasible batching schemes can be conducted based on actual needs and gasification process conditions, including feed rate, oxygen flow rate, additive dosage, gasification temperature, gasification pressure, and carbon conversion rate, using the composition of syngas and raw material costs as the judgment criteria.

[0097] In this embodiment, the system also includes a raw material property standard subsystem, which includes a raw material standard management module. This module establishes and stores the mapping relationship between different types of gasifiers and their corresponding raw materials, so that the raw materials can be determined after the type of gasifier is determined. Since different types of gasifiers have different requirements for their raw materials—for example, gasifiers have different requirements for the composition, proportion, and calorific value of the raw materials—the raw materials must meet these requirements for stable operation. By establishing and storing the mapping relationship between different types of gasifiers and their corresponding raw material requirements in advance through the raw material standard management module, it is beneficial to quickly retrieve the corresponding raw material requirements of the selected gasifier when predicting the raw material batching scheme for subsequent steps. It also allows for convenient switching between different gasifiers, effectively improving work efficiency.

[0098] The system in this embodiment also includes an information management subsystem, which includes a raw material information management module, a gasifier information management module, an additive information management module, and a gasification ash and slag information management module.

[0099] The raw material information management module is used to store the raw material property parameters obtained by the raw material property rapid analysis module;

[0100] The gasifier information management module is used to establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters;

[0101] The additive information management module is used to establish and store the mapping relationship between the predicted ash melting characteristics and slurry forming characteristics and their corresponding additives;

[0102] The gasification ash and slag information management module is used to establish and store a one-to-one mapping relationship between the type of gasifier, raw materials, gasifier operating parameters, and ash and slag properties.

[0103] Specifically, after the raw material property rapid analysis module predicts the raw material property parameters each time, it stores these parameters in the raw material information management module for easy querying and retrieval. Since different types of gasifiers have different operating parameters, to ensure stable operation, the gasifiers need to be strictly adjusted according to the operating parameters of each type. The gasifier information management module pre-establishes and stores the mapping relationship between various types of gasifiers and their corresponding operating parameters. This allows the gasifier operating parameters to be retrieved and adjusted after the optimal batching scheme is obtained in the batching scheme economic evaluation module, ensuring stable operation of the gasifier.

[0104] Since appropriate additives need to be added to the input raw materials during the prediction of ash melting characteristics using the ash melting characteristic prediction model and the prediction of slurry characteristics using the slurry characteristics prediction model, the mapping relationship between the ash melting characteristics and slurry characteristics prediction and their corresponding additives can be established and stored in the additive information management module in advance, so that the additives can be accurately and quickly called when performing ash melting prediction and slurry characteristics prediction.

[0105] In coal gasification chemical projects, coal gasification ash constitutes a significant proportion of solid waste. This ash consists of coarse and fine ash, and its composition is related to the ash content and composition of the coal used for gasification, as well as the gasification process. It mainly comprises SiO2, Al2O3, CaO, and residual carbon. By analyzing the composition of the gasification ash, it's possible to determine whether the feedstock and operating parameters of the gasifier meet requirements. By pre-establishing and storing the ash properties of different feedstocks under different gasifier operating parameters in the gasification ash information management module, a one-to-one mapping relationship is established between gasifier type, feedstock, gasifier operating parameters, and ash properties. This allows for the determination of whether the obtained gasification ash is abnormal by calling the pre-stored ash properties, and the diagnosis of the cause of the abnormality based on the mapping relationship.

[0106] To ensure the stable operation of the gasifier, the gasification batching subsystem of this embodiment also includes a gasifier early warning module. The gasifier early warning module is used to establish the functional relationship between batching, gasifier operating parameters and ash properties, and predict the corresponding ash properties based on the batching and gasifier operating parameters of the batching scheme with the best technical and economic efficiency, and determine whether the obtained ash properties are abnormal. The gasifier early warning module establishes an ash and slag property prediction model based on, but not limited to, a BP neural network. It receives the batching scheme from the batching scheme optimization module, and predicts the corresponding ash and slag properties based on the batching scheme and the corresponding gasifier operating parameters using the ash and slag property prediction model. It then matches and compares the ash and slag properties using the mapping relationship in the gasification ash and slag information management module, and determines whether the ash and slag properties are abnormal based on the comparison results. When an abnormality is detected, an alarm is triggered. The gasifier early warning module compares the obtained ash and slag properties with pre-stored ash and slag properties. Based on the pre-stored mapping relationship between ash and slag properties and the type of gasifier, raw materials, and gasifier operating parameters, it obtains the relevant parameters causing the abnormal ash and slag properties, thereby diagnosing the gasification process and identifying the source of the problem. If the ash and slag properties are abnormal, the proportion of individual raw materials is adjusted according to the suboptimal solution obtained by the batching scheme optimization module, and the proportion is updated by the batching property prediction module. The process of predicting the property parameters of the batching, obtaining the optimized batching scheme, conducting technical and economic evaluation and outputting the best batching scheme, as well as the ash and slag property prediction and diagnosis, is repeated until the batching scheme with the optimal ash and slag properties is obtained.

[0107] The following examples using specific data illustrate this implementation method:

[0108] Sample preparation

[0109] Coal samples with a particle size of less than 0.2 mm were prepared according to GB-T 474-2008 "Methods for preparing coal samples", and ash samples were prepared according to GB-T 1574-2007 "Analysis of coal ash composition".

[0110] Basic properties

[0111] In this invention, the TGA701 industrial analyzer can be used for industrial analysis of samples, the VARIO Macro elemental analyzer can be used for elemental analysis of samples, the IKA C6000 oxygen bomb calorimeter can be used to determine the calorific value, and the X-ray fluorescence spectrometer can be used to determine the ash chemical composition of samples. Industrial analysis of coal, also known as technical analysis or practical analysis, includes the determination of moisture, ash, and volatile matter in coal, as well as the calculation of fixed carbon. Elemental analysis of coal determines the content of the five elements: carbon, hydrogen, oxygen, nitrogen, and sulfur. In this invention, laser-induced plasma atomic emission spectrometry can also be used to determine the raw material property parameters, including the content of carbon, hydrogen, sulfur, and ash-forming elements, ash content, volatile matter, and calorific value.

[0112] Ash melting characteristics

[0113] According to GB / T 219-2008 "Determination of Ash Fusibility", the characteristic ash fusion temperatures (deformation temperature DT, softening temperature ST, hemispherical temperature HT, and flow temperature FT) of the samples were determined using a CAF-5 ash fusion point tester from Carbolite, UK. First, the ash sample was made into a triangular pyramid of a specific shape and size, placed in a high-temperature furnace under a weakly reducing atmosphere, and heated at a controlled rate. A camera recorded the changes in the ash cone during the heating process, and the characteristic ash fusion temperatures were obtained through software analysis or visual observation.

[0114] The determination process of the viscosity-temperature characteristics of coal ash was carried out in accordance with the power industry standard DL / T 660-2007 "Test Method for High-Temperature Viscosity Characteristics of Coal Ash". The viscosity-temperature characteristics of the sample ash were determined using an RV DV-Ⅲ high-temperature rotational viscometer from Theta Corporation, USA. The test procedure was as follows: ① Prepare coal ash; ② Determine the test temperature based on the flow temperature or complete liquidus temperature of the coal ash; ③ Pre-melt the coal ash in a high-temperature furnace, and after cooling, form slag for testing; ④ Place the slag in a test crucible, evacuate the vacuum tube, and then introduce a specified gas; ⑤ Heat to the test temperature, and start the test after the temperature stabilizes, with a cooling rate of 1℃ / min; ⑥ Stop the test when the viscosity value exceeds 300 Pa·s or higher, thus obtaining the relationship between the sample ash viscosity and temperature.

[0115] pulping characteristics

[0116] The apparent viscosity of coal-water slurry was determined using an NXS-4C coal-water slurry viscometer jointly developed by the National Engineering Research Center for Coal-Water Slurry and Chengdu Instrument Factory. The actual concentration of the coal-water slurry was determined according to GB / T 18856.2-2008 "Coal-water slurry test methods - Part 2: Determination of concentration". The flowability of the coal-water slurry was determined visually and classified into four levels: A for continuous flow, B for semi-continuous flow, C for intermittent flow, and D for non-flow. The actual concentration of the coal-water slurry was determined according to GB / T 18856.5-2008 "Coal-water slurry test methods - Part 5: Determination of stability".

[0117] Gasification reaction characteristics

[0118] The gasification reaction characteristics of the samples were determined using a NETZSCH STA 449F5 thermal analyzer (Germany). The test conditions were as follows: heating rate of 15℃ / min, heating range of 25–1400℃, high-purity N2 (99.999%) as protective gas at a flow rate of 20 mL / min, and a furnace atmosphere of a mixture of CO2 (99.999%) and N2 (99.999%) at a flow rate of 50 mL / min. Gasification reaction characteristic curves were obtained from the tests, from which the reactivity index was derived.

[0119] The gasifier was determined to be a GE coal-water slurry gasifier. The feedstock standard management module of the GE coal-water slurry gasifier specified the following requirements for feedstock: calorific value > 25.12 MJ / kg, internal moisture ≤ 8%, coal slurry concentration ≥ 60%, apparent viscosity ≤ 1500 mPa·s, ash content ≤ 13%, and ash fusion point ≤ 1300℃. Based on the feedstock requirements of the GE coal-water slurry gasifier and the on-site conditions, the feedstock was determined to be a blend of one type of coal and one type of semi-coking coal. The raw material property analysis module was used to analyze the properties of the two individual raw materials, obtaining the property parameters of each raw material. These parameters included the content of carbon, hydrogen, sulfur, and ash-forming elements, ash content, volatile matter, and calorific value. On-site experiments were conducted to obtain the ash fusion characteristics, slurry formation characteristics, and gasification reaction characteristics of the raw materials. Table 1 shows the basic properties and ash fusion characteristics of the coal and semi-coking coal; Table 2 shows the slurry formation characteristics of the coal and semi-coking coal; and Table 3 shows the gasification reaction characteristics of the coal and semi-coking coal.

[0120] Table 1: Basic Properties and Ash Fusion Characteristics of Coal and Semi-coke Fines

[0121]

[0122] Table 2: Slurry-forming characteristics of coal and semi-coke fines

[0123]

[0124] Table 3: Gasification reaction characteristics of coal and semi-coke powder

[0125]

[0126] The ingredient property prediction module establishes a basic property prediction model based on a linear weighted sum algorithm, and establishes a ash melting characteristic prediction model, a slurry formation characteristic prediction model, and a gasification reaction characteristic prediction model based on a BP neural network. The BP neural network is trained by generating a dataset by calling the raw material property parameters stored in the raw material information management module.

[0127] Taking the establishment of an ash fusion characteristic prediction model based on a BP neural network as an example, the establishment of the prediction model is illustrated: A total of 80 raw material samples were collected (see Table A below), and the basic properties and ash fusion characteristics of the coal samples were measured. The obtained experimental data were used to establish a coal blending prediction model, of which 40 samples were used to build the model and 40 samples were used to verify the model's prediction effect. The BP neural network has a three-layer network structure. The input layer contains individual raw material property parameters and proportions, and the output layer contains blending property parameters. The backpropagation algorithm is used to optimize the weights and thresholds of the BP neural network. The neurons in the hidden layers mostly use sigmoid transfer functions (e.g., tansig in Matlab), and the neurons in the output layer mostly use linear transfer functions (e.g., purelin in Matlab). The results show that the model prediction values ​​agree well with the measured values.

[0128] Table A

[0129]

[0130]

[0131] Using the property parameters of the raw materials as input, the property parameters of the ingredients are predicted by the basic property prediction model, the ash melting characteristic prediction model, the slurry formation characteristic prediction model, and the gasification reaction characteristic prediction model, respectively.

[0132] In this example, with the goal of minimizing ingredient costs, an objective function is established using the ingredient scheme economic evaluation module:

[0133] minP = 500X1 + 200X2,

[0134] The prices of coal and semi-coke fines are 500 yuan / ton and 200 yuan / ton, respectively. The constraints for determining the objective function are:

[0135] The blending ratio of semi-coke powder is 0 ≤ X2 ≤ 5%.

[0136] Caloric value of ingredients

[0137] Heat generation constraint Q net,ad,X ≥25.12,

[0138] Ash fusion properties of ingredients:

[0139] FT = f FT (SiO2,Al2O3,CaO,Fe2O3,MgO,Na2O,TiO2,SO3),

[0140] Ash fusion constraint FT≤1300,

[0141] Ingredient ash content

[0142] Ash Constraint A ad,X ≤13,

[0143] Ingredient moisture

[0144] Moisture constraint M ad,X ≤8,

[0145] The slurry-forming property of the ingredients, D = f D (M ad ,HGI,O ad ),

[0146] The gasification reactivity of the ingredients, R = f R (C d A d CaO, Fe2O3, MgO, Na2O, V daf ,S BET ),

[0147] Among them, M ad The water content is denoted by O, HGI by O, and O is the grindability index. ad For oxygen content, C d For carbon content, A d The ash content is represented by CaO, Fe2O3, MgO, and Na2O, which respectively represent the content of the listed oxides. daf S indicates the volatile matter content. BET M represents the specific surface area, where M ad The method for measuring internal water content can refer to GB2565-2014, and the method for measuring HGI grindability index can refer to GB / T 212-2008. BET The method for measuring specific surface area can be found in GB / T 19587-2017, while the others can be obtained from the raw material properties.

[0148] Using the objective function and constraints as input and the blending ratio as output, the optimal coal blending ratio is determined by an optimization model based on a genetic algorithm: 97% coal, 3% semi-coke powder, with a price of 491 yuan / ton. Genetic algorithms typically transform constrained problems into unconstrained ones using penalty functions. In this implementation, the raw material properties are used as individuals in the population, which is then divided into several subpopulations. Based on the fitness values ​​of the individuals, the optimal solution is obtained through a cyclical process of selection, crossover, and mutation. Since genetic algorithms are existing technology, their specific processes will not be elaborated upon here.

[0149] The gasification simulation and techno-economic evaluation of the obtained batching scheme were performed by calling the process simulation software through the economic evaluation module of the batching scheme. The gasification simulation is shown in Table 4, and the techno-economic evaluation is shown in Table 5.

[0150] Table 4: Gasification Simulation Results

[0151]

[0152] Table 5: Technological and Economic Evaluation Results

[0153]

[0154] Determine whether the current batching scheme meets the economic evaluation requirements. If it does, the current batching scheme is the optimal batching scheme. Otherwise, conduct an economic evaluation of the alternative feasible batching schemes until the optimal batching scheme is obtained. The alternative feasible batching schemes are the second-best schemes output by the objective function. The gasifier early warning module predicts the properties of the obtained ash residue. By calling the mapping relationship in the gasification ash residue information management module, the properties of the obtained ash residue are matched and compared. The gasifier early warning module issues an alarm message that the residual carbon content of coarse and fine ash residue has reached the upper limit. The operator compares the batching scheme by calling the raw material property parameters in the raw material information management module and the pre-stored ash residue property mapping relationship in the gasification ash residue information management module, thereby diagnosing the batching. For example, in this embodiment, if the comparison finds that the gasification reactivity of the coal used in the batching is too low, it is diagnosed as a possible cause of the high carbon content of the gasification residue. The batching property prediction module updates the proportion of individual raw materials, reduces the proportion of coal in the batching, and repeats the above process until the problem of high carbon content of gasification residue is finally solved and the optimal batching scheme is obtained.

[0155] As shown in Figure 3, in a second aspect of the present invention, an intelligent gasification batching method is provided, comprising a gasification batching sub-step, the gasification batching sub-step including:

[0156] The characteristic spectral line intensities of the raw materials fed into the furnace are received, and the raw material property parameters are obtained based on the characteristic spectral line intensities.

[0157] Establish a prediction model, which includes predicting the property parameters of the ingredients based on the property parameters and proportions of the raw materials;

[0158] An optimization model is established, and based on the property parameters of the ingredients, an optimized ingredient scheme is obtained through the optimization model;

[0159] A techno-economic analysis is performed on the optimized ingredient scheme, and the optimal ingredient scheme with the best techno-economic performance is output.

[0160] Optionally, the method further includes a raw material property standardization sub-step, which includes:

[0161] Establish and store the mapping relationship between different types of gasifiers and their corresponding raw materials, so as to determine the raw materials to be fed into the gasifier after the type of gasifier is determined.

[0162] Optionally, the property parameters of the ingredients include basic properties, ash melting characteristics, slurry forming characteristics, and gasification reaction characteristics.

[0163] Optionally, the basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters.

[0164] Optionally, the method further includes an information management sub-step, which includes:

[0165] Store the raw material property parameters obtained from the raw material property rapid analysis module;

[0166] Establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters;

[0167] Establish and store the mapping relationship between the predicted ash melting characteristics and slurry forming characteristics and their corresponding additives;

[0168] Establish and store a one-to-one mapping relationship between the types of gasifiers, raw materials, gasifier operating parameters, and ash properties.

[0169] Optionally, the gasification feed preparation sub-step further includes:

[0170] Establish the functional relationship between batching, gasifier operating parameters and ash properties, and predict the corresponding ash properties based on the batching and gasifier operating parameters of the optimal technical and economic batching scheme, and determine whether the obtained ash properties are abnormal.

[0171] Optionally, the gasification batching sub-step further includes: when it is determined that the properties of the obtained ash are abnormal, comparing the properties of the obtained ash with the properties of the pre-stored ash, and obtaining the relevant parameters that cause the abnormal properties of the ash based on the mapping relationship between the properties of the pre-stored ash and the type of gasifier, raw materials and gasifier operating parameters.

[0172] The above-mentioned technical solution of the present invention obtains raw material property parameters through rapid analysis of raw materials, predicts the batching property parameters based on the obtained raw material property parameters through an established prediction model, optimizes the batching scheme through an established objective function based on the obtained batching property parameters, and conducts a techno-economic analysis on the optimized batching scheme to obtain the optimal batching scheme. This constructs an intelligent gasification batching system with a complete life cycle process, effectively improving the efficiency and accuracy of coal blending, realizing intelligent and precise control of gasification batching, and effectively reducing the adverse effects of raw material property problems on enterprises.

[0173] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0175] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0176] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0177] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0178] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

Claims

1. A gasification batching system, characterized in that, Comprising: A gasification batching subsystem, which includes a raw material property rapid analysis module, a batching property prediction module, a batching scheme optimization module, and a batching scheme economic evaluation module; The raw material property rapid analysis module is used to receive the characteristic spectral line intensity of the raw material entering the furnace, and obtain the raw material property parameters of the raw material based on the characteristic spectral line intensity; The batching property prediction module is used to establish a prediction model, which includes: predicting the property parameters of the batching according to the raw material property parameters and the raw material ratio through the prediction model; The batching scheme optimization module is used to establish an optimization model and obtain an optimized batching scheme through the optimization model according to the property parameters of the batching; The batching scheme economic evaluation module is used to conduct a technical and economic analysis on the optimized batching scheme and output the batching scheme with the best technical and economic performance.

2. The gasification batching system according to claim 1, characterized in that, The system further includes a raw material property standard subsystem, and the raw material property standard subsystem includes a raw material standard management module for the raw material entering the furnace. The raw material standard management module for the raw material entering the furnace is used to establish and store the mapping relationship between different types of gasifiers and their corresponding raw materials, so as to determine the raw material entering the furnace after the type of gasifier is determined.

3. The gasification batching system according to claim 1, characterized in that, The property parameters of the batching include basic properties, ash fusion characteristics, slurring characteristics, and gasification reaction characteristics, Preferably, the basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters; More preferably, the property parameters of the batching predicted by the prediction model are industrial analysis parameters, elemental analysis parameters, calorific value parameters, and optionally ash fusion characteristic parameters.

4. The gasification batching system according to claim 3, characterized in that, The system further includes an information management subsystem, and the information management subsystem includes a raw material information management module, a gasifier information management module, an additive information management module, and a gasification ash and slag information management module; The raw material information management module is used to store the raw material property parameters obtained by the raw material property rapid analysis module; The gasifier information management module is used to establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters; The additive information management module is used to establish and store the mapping relationship between the prediction of ash fusion characteristics and slurring characteristics and their corresponding additives; The gasification ash and slag information management module is used to establish and store the one-to-one mapping relationship between the type of gasifier, raw material, gasifier operating parameters, and ash and slag properties.

5. The gasification batching system according to claim 4, characterized in that, The gasification batching subsystem further includes a gasifier warning module, which is used to establish a functional relationship between the batching, gasifier operating parameters, and ash and slag properties, and predict the corresponding ash and slag properties according to the batching and gasifier operating parameters of the batching scheme with the best technical and economic performance obtained, and determine whether the obtained ash and slag properties are abnormal.

6. The gasification batching system according to claim 5, characterized in that, The gasifier warning module is further used to, when it is determined that the ash and slag properties are abnormal, compare the obtained ash and slag properties with the pre-stored ash and slag properties, and obtain the relevant parameters causing the abnormal ash and slag properties according to the mapping relationship between the pre-stored ash and slag properties and the type of gasifier, raw material, and gasifier operating parameters.

7. The gasification batching system according to claim 1, characterized in that, The ingredient property prediction module is used to establish a prediction model, and it further includes: according to the raw material property parameters, raw material ratio, internal water content M ad , grindability index HGI, and specific surface area S BET , and optionally additive information, predicting the property parameters of the ingredient through the prediction model, and the predicted property parameters of the ingredient include the pulpability of the ingredient and the gasification reactivity of the ingredient and optionally the ash fusion characteristics.

8. A gasification batching method, characterized in that, Including a gasification batching sub-step, the gasification batching sub-step includes: Receive the intensity of the characteristic spectral lines of the feedstock charged into the furnace, and obtain the property parameters of the feedstock based on the intensity of the characteristic spectral lines; Establish a prediction model, which includes: predicting the property parameters of the burden based on the property parameters of the feedstock and the feedstock ratio through the prediction model; Establish an optimization model, and obtain an optimized burden plan through the optimization model based on the property parameters of the burden; Conduct a technical and economic analysis on the optimized burden plan, and output the burden plan with the best technical and economic performance.

9. The gasification batching method according to claim 8, characterized in that, The method further includes a sub-step for standardizing the feedstock properties, and the sub-step for standardizing the feedstock properties includes: Establish and store the mapping relationship between different types of gasifiers and their corresponding feedstocks, so as to determine the feedstock charged into the furnace after the type of gasifier is determined.

10. The gasification batching method according to claim 8, characterized in that, The property parameters of the burden include basic properties, ash fusion characteristics, slurry-forming characteristics, and gasification reaction characteristics. Preferably, the basic properties include industrial analysis parameters, elemental analysis parameters, grindability parameters, and calorific value parameters; More preferably, the property parameters of the burden predicted by the prediction model are industrial analysis parameters, elemental analysis parameters, calorific value parameters, and optionally ash fusion characteristic parameters.

11. The gasification batching method according to claim 10, characterized in that,The method further includes an information management sub-step, and the information management sub-step includes: Store the property parameters of the feedstock obtained by the rapid feedstock property analysis module; Establish and store the mapping relationship between different types of gasifiers and their corresponding operating parameters; Establish and store the mapping relationship between the prediction of ash fusion characteristics and slurry-forming characteristics and their corresponding additives; Establish and store the one-to-one mapping relationship between the type of gasifier, feedstock, gasifier operating parameters, and ash slag properties.

12. The gasification batching method according to claim 11, wherein, The gasification burden sub-step further includes: Establish the functional relationship between the burden, gasifier operating parameters, and ash slag properties, and predict the corresponding ash slag properties based on the burden and gasifier operating parameters of the burden plan with the best technical and economic performance obtained, and determine whether the obtained ash slag properties are abnormal.

13. The gasification batching method according to claim 12, wherein, The gasification burden sub-step further includes: When it is determined that the obtained ash slag properties are abnormal, compare the obtained ash slag properties with the pre-stored ash slag properties, and obtain the relevant parameters causing the abnormal ash slag properties based on the mapping relationship between the pre-stored ash slag properties and the type of gasifier, feedstock, and gasifier operating parameters.

14. The gasification batching method according to claim 8, wherein, Establish a prediction model, which further includes: based on the raw material property parameters, raw material ratio, internal water content M ad , grindability index HGI, and specific surface area S BET , and optionally additive information, predicting the property parameters of the batching through the prediction model, and the predicted property parameters of the batching include the pulpability and gasification reactivity of the batching and optionally the ash fusion characteristics.

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