Biomass pellet fuel calorific value increasing treatment system

Abstract

The invention discloses a biomass particle fuel calorific value increasing processing system, and relates to the technical field of biomass particle fuels, and the biomass particle fuel calorific value increasing processing system comprises a data acquisition module, a data analysis processing module, a resource scheduling module, a data storage module, a man-machine interaction module, a communication module and a background monitoring module; according to the invention, data is acquired through the data acquisition module, and the prediction model is trained through the data analysis processing module; outputting predicted parameter matching information by using the trained prediction model, controlling each processing device to produce the biomass particle fuel by using a resource scheduling module, and predicting the components of the biomass particle fuel prepared by mixing a plurality of different types of raw materials; therefore, on the basis of limited raw materials, the heat value of the product is improved on the premise of meeting user requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of biomass pellet fuels, and in particular to a system for improving the calorific value of biomass pellet fuels. Background Art

[0002] Biomass pellet fuel is a fuel made from green renewable biomass, with advantages such as high calorific value, high density, easy storage and transportation. Biomass pellet fuels can be classified into the following categories according to different raw material components: Bio-fruit shell type: pressed from rice husks, wheat husks, cottonseed husks, camellia oleifera husks, peach husks, peanut husks, etc. Bio-core type: mainly pressed from corn cobs. Bio-straw type: pressed from corn stalks, sorghum stalks, reed stalks, rice straw, wheat straw, and straw of legumes. Miscellaneous wood type: made from trees, branches, bark, wood chips, etc. Industrial by-product type: pressed from industrial by-products such as distiller's grains residue, furfural residue, and sugar furfural residue. As a renewable energy source, biomass pellet fuel has significant environmental protection advantages. The amount of carbon dioxide generated during its combustion is equivalent to the amount of carbon dioxide consumed during its growth process, so it is regarded as a "zero-emission" energy source. Using biomass pellet fuel can reduce the emissions of pollutants such as carbon dioxide, hydrocarbons, and nitrogen oxides, help reduce environmental pollution pressure, improve the energy structure, and improve energy utilization efficiency.

[0003] The performance and physical and chemical properties of biomass pellet fuels with different raw material components vary greatly. For example, common straw pellets: mainly made from straw, corn stalks and other crops, with a calorific value between 3000 and 3800 kcal, fast burning speed but high ash content, suitable for agricultural waste treatment and rapid heating; Miscellaneous wood pellets: with a relatively high calorific value, which can reach more than 4000 kcal, long burning duration and less ash content, suitable for household heating, industrial combustion, etc.; Solid wood pellets: high calorific value and less ash content, which is the most environmentally friendly choice among the three types of pellets.

[0004] The uses of biomass pellet fuels are also very extensive, commonly including civil heating and domestic energy use, industrial boiler fuels, power generation fuels, etc.; different types of biomass pellet fuels are provided for different user needs; even for biomass pellet fuels made from the same raw material, their calorific values are affected by various factors; for example: Moisture content: The moisture content in biomass pellet fuel will directly affect its combustion performance. Excessive moisture content will cause the fuel to consume more heat during combustion to evaporate the moisture, thereby reducing the calorific value and combustion efficiency of the fuel. Ash content: Ash is the non-combustible inorganic matter in biomass pellet fuel. The higher its content, the lower the calorific value and combustion temperature of the fuel. Ash will also form ash slag during combustion, affecting the normal operation of combustion equipment. Volatile content: Volatile matter is the gaseous combustible substance that can be released during the heating process of biomass pellet fuel. The higher the volatile content, the better the flammability of the fuel and the faster the combustion speed.

[0005] Density and size: The greater the density of biomass pellet fuel, the tighter its internal structure, and the more persistent the heat released during combustion. An appropriate pellet size is more conducive to complete combustion and improves the burn resistance.

[0006] Compression ratio: During the production process of biomass pellets, appropriately increasing the compression ratio can make the pellets more dense, increase the heat transfer efficiency inside the pellets, and enable them to burn more fully during combustion.

[0007] Additive use: To improve the combustion performance of biomass pellet fuel, some additives are usually added, such as binders, antioxidants, etc. These additives can improve the physical and chemical properties of the pellets and enhance their combustion efficiency and burn resistance.

[0008] Based on the above variable factors, it is difficult to meet the actual needs by using only one type of raw material to make pellet fuel. Therefore, a mixed raw material of multiple fuels has a wider application range. However, to maintain the calorific value of biomass pellet fuel to meet the calorific value performance index, it is necessary to reasonably control various links such as the content of each raw material, additive, and processing steps. In the existing technology, most rely on manual experience for proportion addition and control, which undoubtedly increases the control difficulty of each device during the production process. Therefore, a biomass pellet fuel calorific value improvement processing system is proposed, aiming to optimize the raw material ratio using existing raw materials and processing equipment, and improve the calorific value while meeting the requirements. Summary of the Invention

[0009] In view of the problems existing in the above background, the present invention is proposed.

[0010] Therefore, the problem to be solved by the present invention is how to calculate the ratio of each required raw material according to the specified requirements and optimize the processing steps, so as to improve the calorific value while meeting the requirements.

[0011] To solve the above technical problems, the present invention provides the following technical solutions:

[0012] The first aspect of the present invention provides a biomass pellet fuel calorific value improvement processing system, including a data acquisition module, a data analysis and processing module, a resource scheduling module, a data storage module, a human-computer interaction module, a communication module, and a background monitoring module;

[0013] The data acquisition module is used to collect the types and multivariate information of physical and chemical parameters of the raw materials of biomass pellet fuel;

[0014] The data analysis and processing module is used to preprocess the collected multivariate information and establish a prediction model; use the pre-trained prediction model for calculation and processing to generate predicted parameter ratio information;

[0015] A resource scheduling module, which is used to call processing equipment to process raw materials according to the predicted parameter ratio information generated by a prediction model to generate biomass pellet fuel that meets the requirements.

[0016] The data storage module and the background monitoring module are integrated in the backend server. Among them, the data storage module is used to save the prediction results generated by the prediction model each time; the background monitoring module is used to monitor the running status of the processing equipment in real time.

[0017] A communication module is used for communication between the front-end device, the backend server, and the cloud server.

[0018] A human-computer interaction module is used for inputting control parameters, outputting results, and manually revising control parameters.

[0019] Data is collected through a data collection module, and a prediction model is trained through a data analysis and processing module; the trained prediction model is used to output predicted parameter ratio information, and the resource scheduling module is used to control each processing equipment to produce biomass pellet fuel to increase the calorific value.

[0020] Preferably, the types of raw materials for biomass pellet fuel include biological fruit shells, biological fruit cores, biological straws, and miscellaneous woods.

[0021] The physical and chemical parameters include water content, ash content, volatile matter, particle size, density, and calorific value.

[0022] Preferably, the data analysis and processing module establishes a prediction model based on the neural network CNN; by analyzing the physical and chemical parameters of different types of raw materials, an identification vector is generated for each type of raw material; different raw materials are proportioned with different components, and the result is quickly obtained by direct vector calculation to generate predicted parameter ratio information.

[0023] Preferably, the established prediction model needs to use a training set and a validation set for deep learning until the predicted parameter ratio output meets the preset requirements.

[0024] Preferably, when the resource scheduling module schedules the processing equipment, it executes corresponding processing steps according to the predicted parameter ratio output by the prediction model.

[0025] The processing equipment includes a crusher, a pulverizer, a dryer, an extrusion granulator, and a supporting cooling system.

[0026] Preferably, the data storage module includes a local storage unit and a cloud storage unit; the local storage unit is used to store and record the processing data of local devices.

[0027] The cloud storage unit summarizes the data stored in the local storage unit and uses the cloud server to continuously iterate and optimize the prediction model. The updated prediction model can iterate and upgrade the local prediction model through the communication module.

[0028] Preferably, the background monitoring module includes a display unit and a monitoring unit; the monitoring unit collects the operation status information of each device in real time and displays it on the display unit in real time; the operation status information of the device includes the working status, load degree, operating temperature, pressure status, and fault information.

[0029] Preferably, the human-computer interaction module is integrated on the front-end controller; it includes an input unit, an output unit, and a controllable revision unit;

[0030] The input unit is used to input expected demand information into the front-end controller; the demand information includes the particle shape, calorific value, combustion speed, and usage information of the biomass pellet fuel;

[0031] The output unit is used to output the predicted parameter ratio information to the resource scheduling module, and control the processing equipment to process through the resource scheduling module;

[0032] The controllable revision unit is used to manually revise the input data and manually adjust the output parameter ratio information;

[0033] The human-computer interaction module further includes a data annotation unit, which is used to perform feature annotation on the preprocessed initial data collected by the data collection module.

[0034] The second aspect of the present invention provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned biomass pellet fuel calorific value improvement processing system are realized.

[0035] The third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned biomass pellet fuel calorific value improvement processing system are realized.

[0036] The beneficial effects of the present invention are as follows: First, the physical and chemical data of various types of raw materials are collected, the demand target is set, and then the boundary conditions are set. A small amount of data is used, and the particle swarm optimization algorithm is used to solve multiple parameters. Then, according to the calculated component parameter information, the production equipment is controlled for processing, and the performance of the product is tested. The influence of the mixing of various component raw materials on various performance parameters is found through a small number of experiments. Then, using the constructed neural network model, with the specific demand target as the input and the parameter ratios of various raw material components as the output, a prediction model is established. A small number of experimental samples are used to predict the physical and chemical parameters of the raw materials of multiple components, and combined with the relationship function between various parameters when multiple materials are mixed, the components of the biomass pellet fuel prepared by mixing multiple different types of raw materials are predicted. Thus, ultimately, on the basis of limited raw materials, the calorific value of the product is increased while meeting the user's needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a structural diagram of a biomass pellet fuel calorific value improvement processing system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0040] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0041] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0042] Embodiment 1

[0043] The first aspect of the present invention provides a biomass pellet fuel calorific value improvement processing system, including a data acquisition module, a data analysis and processing module, a resource scheduling module, a data storage module, a human-computer interaction module, a communication module, and a background monitoring module;

[0044] The data acquisition module is used to collect the types and multivariate physicochemical parameter information of the raw materials of biomass pellet fuel;

[0045] The data analysis and processing module is used to preprocess the collected multivariate information and establish a prediction model; use the pre-trained prediction model for calculation and processing to generate predicted parameter ratio information;

[0046] The resource scheduling module is used to call processing equipment to process raw materials according to the predicted parameter ratio information generated by the prediction model to generate biomass pellet fuel that meets the requirements;

[0047] The data storage module and the background monitoring module are integrated in the backend server; wherein, the data storage module is used to save the prediction results generated by the prediction model each time; the background monitoring module is used to monitor the running status of the processing equipment in real time;

[0048] The communication module is used for communication between the front-end device, the backend server, and the cloud server;

[0049] The human-computer interaction module is used to input control parameters, output results, and manually revise control parameters.

[0050] Collect data through the data acquisition module, and train the prediction model through the data analysis and processing module; use the trained prediction model to output predicted parameter ratio information, and use the resource scheduling module to control each processing equipment to produce biomass pellet fuel to improve the calorific value.

[0051] In this embodiment, the types of raw materials of biomass pellet fuel include biological fruit shells, biological fruit cores, biological straws, and miscellaneous woods; the physicochemical parameters include moisture content, ash content, volatile matter, particle size, density, and calorific value.

[0052] Among them, biological fruit shells include almond shells, pistachio shells, almond kernels, chestnut shells, melon seed shells, and peanut shells;

[0053] Biological fruit cores include corn cobs, peach pits, and almond cores;

[0054] Biological straws include corn straws, wheat straws, rice straws, cotton straws, soybean straws, and sorghum straws;

[0055] In this embodiment, it is necessary to first crush and pulverize each raw material so that it has the same particle size, and then dry and store it to reduce the moisture content of the raw materials.

[0056] The data analysis and processing module establishes a prediction model based on the neural network CNN; by analyzing the physical and chemical parameters of different types of raw materials, an identification vector is generated for each type of raw material; different raw materials are proportioned with different components, and the vector direct calculation is used to quickly obtain the result, so as to generate the predicted parameter proportion information. The established prediction model needs to use the training set and the validation set for deep learning until the predicted parameter proportion output meets the preset requirements.

[0057] In this embodiment, when the resource scheduling module schedules the processing equipment, it executes the corresponding processing steps according to the predicted parameter proportion output by the prediction model;

[0058] The processing equipment includes a crusher, a pulverizer, a dryer, an extrusion granulator and a supporting cooling system.

[0059] In this embodiment, the data storage module includes a local storage unit and a cloud storage unit; the local storage unit is used to store and record the processing data of local devices;

[0060] The cloud storage unit summarizes the data stored in the local storage unit and uses the cloud server to continuously iterate and optimize the prediction model. The updated prediction model can iterate and upgrade the local prediction model through the communication module.

[0061] In this embodiment, the background monitoring module includes a display unit and a monitoring unit; the monitoring unit collects the operation status information of each device in real time and displays it on the display unit in real time; the operation status information of the device includes the working status, the load degree, the running temperature, the pressure status, and the fault information.

[0062] In this embodiment, the human-computer interaction module is integrated on the front-end controller; it includes an input unit, an output unit and a controllable revision unit;

[0063] The input unit is used to input the expected demand information into the front-end controller; the demand information includes the particle shape, calorific value, combustion speed and usage information of the biomass pellet fuel;

[0064] The output unit is used to output the predicted parameter proportion information to the resource scheduling module, and control the processing equipment to process through the resource scheduling module;

[0065] The controllable revision unit is used to manually revise the input data and manually adjust the output parameter proportion information;

[0066] The human-computer interaction module also includes a data annotation unit, which is used to perform feature annotation on the initial data collected by the data collection module and preprocessed.

[0067] The following uses a specific case to introduce this solution in detail:

[0068] Table 1 Calorific Values of Common Straw-like Raw Materials

[0069] Type Higher calorific value (MJ / kg) Lower calorific value (MJ / kg) Corn straw 16.90 15.54 Wheat straw 16.67 15.18 Sorghum straw 16.37 15.07 Cotton straw 17.37 15.99 Soybean straw 17.59 16.15 Rice straw 15.24 13.97 Weed straw 16.26 14.94

[0070] As can be seen from the data in Table 1 above, even though they are all straw raw materials, there are significant differences in the calorific values of different plants. Therefore, the data acquisition module needs to collect as comprehensively as possible the types of different raw materials and their physical and chemical parameters, so as to facilitate the systematic analysis and processing of the raw materials in subsequent steps. The raw materials can be collected by referring to existing public databases or through manual quantitative experiments. The physical and chemical parameters of each raw material are represented in the form of vectors:

[0071] An(a1, a2, a3, a4, a5, a6)

[0072] Among them, An is different raw material types; a1, a2, a3, a4, a5, a6 respectively represent the water content, ash content, volatile matter, particle size, density, and calorific value data of raw material A.

[0073] After collecting the data, it needs to be processed by the data analysis and processing module and a prediction model is constructed. During the process of processing the data, it is necessary to first perform preprocessing steps such as data cleaning and denoising, and then set boundary conditions for the prediction model through manual annotation.

[0074] Specifically, taking the two-raw-material mixed granular fuel as an example;

[0075] A is straw-like A{A1, A2, …… An}

[0076] B is miscellaneous wood-like B{B1, B2, …… Bm}

[0077] n is the number of straw-like raw materials, and m is the number of miscellaneous wood-like raw materials;

[0078] Calculate the specific values of different physical and chemical parameters when the two raw materials are mixed, and directly perform operations through vectors to synthesize vector F:

[0079]

[0080] k1 + k2 < 0.98;

[0081]

[0082] In the formula, k1 is the addition ratio of straw-like raw materials, Qn is the addition ratio of different types of raw materials in straw-like raw materials; k2 is the addition ratio of miscellaneous tree-like raw materials, and Pm is the addition ratio of different types of raw materials in miscellaneous tree-like raw materials.

[0083] The calculated synthetic vector F can be expressed as:

[0084] F(f1, f2, f3, f4, f5, f6)

[0085] f1 is the calculated moisture content data, f2 is the calculated ash content data, f3 is the calculated volatile matter content data, f4 is the calculated particle size data, f5 is the calculated density data, and f6 is the calculated calorific value data;

[0086] Tf6 > T 预

[0087] where T is a correction factor, taking values from 0.7 to 0.9; T 预 is the expected calorific value required by the customer. Priority is given to meeting the calorific value requirement for Q n and P m to solve. Other boundary conditions can be set according to the actual needs of the user. Preferably, the particle swarm optimization algorithm is used for multi-parameter solution. The resource scheduling module controls each processing device to produce products based on the calculated parameters and conducts tests; feature annotation is performed on the obtained results to distinguish products that meet the requirements from those that do not; the products that do not meet the requirements are subjected to physical and chemical analysis to determine which specific indicator does not meet the requirements, and boundary conditions are set based on this requirement to perform another solution using the particle swarm optimization algorithm. The relationship function between various parameters when multiple materials are mixed is obtained.

[0088] Retain no less than 100 sets of component formulas of biomass pellet fuels that meet the requirements, construct a training set and a validation set based on this, establish a prediction model based on the neural network CNN, and predict the parameter ratio information that meets the requirements by inputting demand information. The resource scheduling module calls the processing equipment to process the raw materials according to the predicted parameter ratio information generated by the prediction model to produce biomass pellet fuels that meet the requirements, ultimately improving the calorific value of the products.

[0089] In this embodiment, demand templates can be preset in the prediction model; for example, templates such as civil heating, industrial boilers, power generation, etc.;

[0090] When the demand is for civil heating, it is necessary to increase the density and compression ratio of the biomass pellets to reduce the space occupied by the products and increase the fuel combustion time; therefore, when designing the raw material parameter ratio information, the parameters of the compression ratio can be adjusted to meet the requirements; in addition, raw materials such as miscellaneous wood and solid wood can be mainly used, combined with straw-based raw materials, to achieve the function of rapid ignition.

[0091] When the demand is for industrial boilers, industrial boilers require products with high calorific value and low ash content. Therefore, when selecting the raw material ratio, calculations and screening are carried out with the goal of reducing ash content.

[0092] When the demand is for power generation, due to the large amount of combustion used, it is necessary to consider the influence of volatile matter, reduce the impact on the environment, and reduce emissions; therefore, when selecting the raw material ratio, calculations and screenings are carried out with the goal of reducing ash content.

[0093] The above preset template only facilitates users to quickly determine the main requirements. The specific parameters can be input with the expected requirement information through the front-end controller; or the input data can be manually revised through the controllable revision unit, and the output parameter ratio information can be manually adjusted.

[0094] This embodiment only takes the conventional uniform mixing structure as an example for introduction; it is also of research significance for other forms of biomass pellet fuel processing; for example, the biomass pellet fuel can be set as a concentric circle wrapping structure or a wedge-shaped embedded structure; the concentric circle wrapping structure means that the inner part is the raw material with high calorific value and the outer part is the raw material with low calorific value that is easy to ignite. This structure can ensure the function of rapid ignition and has a long combustion time. The wedge-shaped embedded structure is to make the raw material with low calorific value into a wedge shape and insert it into the raw material with high calorific value. This structure can ensure the function of rapid ignition. At the same time, the ignition surface of the raw material with high calorific value is small, which is beneficial to extending the combustion time and is more suitable for some scenarios with low thermal requirements; such as indoor heating (the temperature does not need to be too high, mainly based on a long heat preservation time).

[0095] The present invention collects the physical and chemical data of various types of raw materials, first sets the demand target, and then sets the boundary conditions; uses the particle swarm optimization algorithm to solve multiple parameters, and then controls the production equipment for processing according to the calculated component parameter information, and conducts performance tests on the products; to determine the influence of the mixing of various component raw materials on various performance parameters; then uses the constructed neural network model, with the specific demand target as the input and the parameter ratio of various raw material components as the output, to establish a prediction model; combines the relationship function between various parameters when multiple materials are mixed to predict the components of biomass pellet fuel prepared by mixing multiple different types of raw materials; so as to ultimately improve the calorific value of the product on the basis of limited raw materials and meet the user's needs.

[0096] Embodiment 2

[0097] This embodiment also provides a computer device applicable to the biomass pellet fuel calorific value improvement processing system, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the biomass pellet fuel calorific value improvement processing system proposed in Embodiment 1.

[0098] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0099] Embodiment 4

[0100] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the biomass pellet fuel calorific value improvement processing system described in Embodiment 1.

[0101] The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A biomass pellet fuel calorific value enhancement processing system, characterized by: It includes data acquisition module, data analysis and processing module, resource scheduling module, data storage module, human-computer interaction module, communication module and background monitoring module; Data collection module, used to collect the types of raw materials of biomass pellet fuel and multivariate information of physical and chemical parameters; The data analysis and processing module is used to pre-process the collected multivariate information and establish a prediction model; the pre-trained prediction model is used for calculation and processing to generate predicted parameter ratio information; A resource scheduling module is used to call processing equipment to process raw materials according to the prediction parameter ratio information generated by the prediction model to generate biomass pellet fuel that meets the demand; The data storage module and the background monitoring module are integrated in the back-end server; the data storage module is used to save the prediction results generated by the prediction model each time; the background monitoring module is used to monitor the operating status of the processing equipment in real time; The communication module is used for communication between the front-end device and the back-end server and the cloud server; The human-computer interaction module is used to input control parameters, output results, and manually revise control parameters. The data is collected through the data acquisition module, and the prediction model is trained through the data analysis and processing module; the trained prediction model is used to output the predicted parameter ratio information, and the resource scheduling module is used to control each processing equipment to produce biomass pellet fuel to improve the calorific value.

2. A biomass pellet fuel calorific value enhancement processing system according to claim 1, characterized in that: The types of raw materials for biomass pellet fuel include biological fruit shells, biological fruit cores, biological straw, and miscellaneous wood; The physical and chemical parameters include moisture content, ash content, volatile matter, particle size, density, and calorific value.

3. A biomass pellet fuel calorific value enhancement processing system according to claim 1, characterized in that: The data analysis and processing module establishes a prediction model based on the neural network CNN; by analyzing the physical and chemical parameters of different types of raw materials, an identification vector is generated for each raw material; different raw materials are proportioned with different components, and vector direct calculation is used to quickly obtain results to generate predicted parameter proportion information.

4. A biomass pellet fuel calorific value enhancement processing system according to claim 3, characterized in that: The established prediction model needs to use training sets and validation sets for deep learning until the output prediction parameter ratio meets the preset requirements.

5. A biomass pellet fuel calorific value enhancement processing system according to claim 1, characterized in that: When the resource scheduling module schedules the processing equipment, the corresponding processing steps are executed according to the predicted parameter ratio output by the prediction model; The processing equipment includes crushers, pulverizers, dryers, extrusion granulators and supporting cooling systems.

6. A biomass pellet fuel calorific value enhancement processing system according to claim 1, characterized in that: The data storage module includes a local storage unit and a cloud storage unit; the local storage unit is used to store and record the processing data of the local device; The cloud storage unit aggregates the data stored in the local storage unit and uses the cloud server to continuously iterate and optimize the prediction model. The updated prediction model can iteratively upgrade the local prediction model through the communication module.

7. A biomass pellet fuel calorific value enhancement processing system according to claim 1, characterized in that: The background monitoring module includes a display unit and a monitoring unit; the monitoring unit collects the operating status information of each device in real time and displays it in real time on the display unit; the operating status information of the equipment includes working status, load degree, operating temperature, pressure status, and fault information.

8. A biomass pellet fuel calorific value enhancement processing system according to any one of claims 1 to 7, characterized in that: The human-computer interaction module is integrated on the front-end controller; it includes an input unit, an output unit and a controllable revision unit; The input unit is used to input expected demand information to the front-end controller; the demand information includes the particle shape, calorific value, combustion speed and usage information of the biomass pellet fuel; The output unit is used to output the predicted parameter ratio information to the resource scheduling module, and the processing equipment is controlled by the resource scheduling module to perform processing; The controllable revision unit is used to manually revise the input data and manually adjust the output parameter ratio information; The human-computer interaction module also includes a data labeling unit, which is used to perform feature labeling on the pre-processed raw data collected by the data collection module.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the biomass pellet fuel calorific value improvement processing system according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the biomass pellet fuel calorific value enhancement processing system according to any one of claims 1 to 8 are implemented.

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