Coal and cultivation solid waste biomass blending combustion control system and method

Abstract

The invention discloses a coal and cultivation solid waste biomass blending combustion control system and method which are applied to a boiler system. The system comprises a fuel detection and analysis module, a fuel analysis result is obtained through a fuel component analysis unit, a calorific value is calculated through a calorific value prediction unit, and the blending proportion of coal and biomass is determined through a proportion adjustment unit; the fuel flow control module is used for adjusting the mass flow of coal, biomass and blended fuel according to the blending proportion and the boiler load by utilizing a coal feeding, biomass feeding and blended fuel flow control device and then feeding the coal, the biomass and the blended fuel into the boiler; and the fuel air supply module adjusts air distribution parameters in real time according to the total mass flow of the blended fuel by means of a primary air flow control device and a secondary air flow control device. The invention aims to realize collaborative precise control of the mixing ratio and the air supply parameter based on fuel characteristic analysis, ensure stable and efficient combustion, solve the problem that the prior art cannot accurately and dynamically feed forward and match the fuel and the air supply volume, and especially establish a heat value prediction and air-material linkage mechanism for breeding solid waste biomass.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of coal and biomass blending combustion, and particularly relates to a coal and breeding solid waste biomass blending combustion control system and method. BACKGROUND

[0002] Biomass energy, especially the utilization of breeding solid waste and other biomass resources, has advantages in alleviating energy crisis and controlling environmental pollution. Blending combustion of biomass and coal is one of the important technical approaches to realize large-scale and efficient utilization. Coal and biomass have great differences in physical properties and chemical composition, which leads to problems such as poor fuel adaptability, unstable combustion conditions, low efficiency, and complex pollutant emission control in the blending combustion process.

[0003] To address these problems, in Chinese patent CN119267927A, a coal and biomass blending combustion anti-ash and slagging control system and method are disclosed. The system adjusts the blending ratio of coal and biomass through a fuel analysis system, and introduces a flue gas monitoring system to detect the flue gas after combustion, and then based on the fuel data and flue gas data, the operating parameters of the acoustic sootblower are controlled to solve the problem of aggravated ash and slagging in the blending combustion process. This scheme effectively ensures the heat transfer efficiency and operation stability of the boiler through post-cleaning means.

[0004] However, the existing technical solution still has certain limitations. Its control logic relies more on the feedback of the combustion results (flue gas), and there is no effective solution for optimizing the combustion process itself, especially in terms of how to achieve precise and dynamic feedforward matching between fuel and air supply based on the real-time changes in fuel characteristics. Specifically, traditional control systems often have difficulty in quickly and accurately obtaining key characteristics of the fuel (such as real-time calorific value), which leads to the inability to adjust the air supply and ratio in a timely manner based on changes in fuel composition, resulting in fluctuations in the heat release in the furnace, affecting the stability and efficiency of combustion. Especially for breeding solid waste biomass with complex composition and large fluctuations, there is a lack of a control mechanism that can quickly predict its calorific value and achieve precise linkage of air and fuel. SUMMARY

[0005] In view of the problems in the prior art, the present application provides a coal and breeding solid waste biomass blending combustion control system and method, which aims to delve deeper into the core of the combustion process, break through the limitations of relying only on feedback of combustion results, and based on analysis of fuel characteristics, achieve coordinated and precise control of blending ratio and air supply parameters, ensuring stable and efficient combustion from the source, solving the problem that the prior art cannot achieve precise and dynamic feedforward matching between fuel and air supply based on real-time changes in fuel characteristics, especially for breeding solid waste biomass with complex composition and large fluctuations, establishing a control mechanism that can quickly predict its calorific value and achieve linkage of air and fuel.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: According to a first aspect of the present invention, a coal and livestock solid waste biomass co-firing control system is provided, applied to a boiler system, the boiler system including a coal feeding bin, a biomass feeding bin, and a boiler, the co-firing control system comprising: The fuel detection and analysis module includes a fuel composition analysis unit, a calorific value prediction unit, and a proportioning unit. The fuel composition analysis unit is used to perform composition analysis on coal and biomass to obtain fuel analysis results. The calorific value prediction unit is used to calculate the calorific value of coal and biomass based on the fuel analysis results using a preset prediction model. The proportioning unit is used to calculate the blending ratio of coal and biomass based on boiler load demand, the fuel analysis results, and the predicted calorific value. The fuel flow control module includes a coal feed flow control device, a biomass feed flow control device, and a blended fuel flow control device. The coal feed flow control device and the biomass feed flow control device are respectively used to adjust the mass flow rates of coal and biomass entering the coal / biomass blending bin according to the blending ratio. The blended fuel flow control device is used to control the total mass flow rate of the blended fuel and send it into the boiler according to the boiler load demand, the blending ratio, and calorific value data. The fuel air supply module includes a primary air flow control device and a secondary air flow control device, which are used to adjust the air distribution parameters of the primary and secondary air in real time according to the total mass flow rate of the blended fuel.

[0007] In one possible implementation of the first aspect, the fuel composition analysis unit includes a near-infrared spectroscopy analysis device for analyzing the functional groups of coal and biomass by near-infrared spectroscopy to determine the content of C, H, O, N, and S elements in coal and biomass, thereby obtaining fuel analysis results.

[0008] In one possible implementation of the first aspect, the preset prediction model is a neural network model, which is a multilayer feedforward neural network, with input layer nodes corresponding to the content of C, H, O, N, and S elements, and output layer nodes corresponding to the calorific value.

[0009] In one possible implementation of the first aspect, the outlet of the coal / biomass mixing bin is connected to the blended fuel flow control device, the inlet of the coal / biomass mixing bin is connected to the coal feed flow control device and the biomass feed flow control device respectively, the input end of the coal feed flow control device is connected to the coal feed bin, and the input end of the biomass feed flow control device is connected to the biomass feed bin.

[0010] In one possible implementation of the first aspect, the fuel air delivery module further includes an air preheater and an air flow control unit, wherein the primary air flow control device and the secondary air flow control device are connected to the air preheater through the air flow control unit, and the air flow control unit is used to determine the air volume ratio of the primary air and the secondary air based on the total mass flow rate of the blended fuel.

[0011] In one possible implementation of the first aspect, a pretreatment module is provided before the biomass feeding silo. The pretreatment module includes a graded drying module and a harmful substance pre-adsorption module. The graded drying module uses heat pump drying technology to dry the biomass in two stages and is equipped with a humidity sensor to provide real-time feedback on the moisture content. The harmful substance pre-adsorption module uses hydrochloric acid-activated montmorillonite adsorbent to pre-adsorb heavy metal ions.

[0012] According to a second aspect of the present invention, a method for co-firing control of a coal and livestock solid waste biomass co-firing control system is provided, comprising: The fuel testing and analysis steps involve: analyzing the composition of coal and biomass using a fuel composition analysis unit to obtain fuel analysis results; calculating the calorific value of coal and biomass using a preset prediction model based on the fuel analysis results using a calorific value prediction unit; and calculating the blending ratio of coal and biomass using a proportioning unit based on boiler load demand, the fuel analysis results, and the predicted calorific value. The fuel flow control step involves adjusting the mass flow rates of coal and biomass entering the coal / biomass blending bin according to the blending ratio using a coal flow control device and a biomass flow control device, respectively; and controlling the total mass flow rate of the blended fuel according to the boiler load demand, the blending ratio, and calorific value data using a blended fuel flow control device before feeding it into the boiler. The fuel supply air control step involves adjusting the primary and secondary air distribution parameters in real time based on the total mass flow rate of the blended fuel using a primary air flow control device and a secondary air flow control device.

[0013] In one possible implementation of the second aspect, the step of performing compositional analysis on coal and biomass using a fuel composition analysis unit includes: Near-infrared spectroscopy analysis of coal and biomass is performed using a near-infrared spectroscopy analyzer to determine the functional groups of coal and biomass, thereby obtaining the content of C, H, O, N, and S elements in coal and biomass, which are used as the fuel analysis results.

[0014] In one possible implementation of the second aspect, the preset prediction model is a neural network model, and the neural network model is a multilayer feedforward neural network; The step of calculating the calorific value through the calorific value prediction unit includes: taking the content of C, H, O, N and S elements as the input of the input layer node, and calculating the calorific value output by the output layer node through the multi-layer feedforward neural network.

[0015] In one possible implementation of the second aspect, a preprocessing step is further included before the fuel detection and analysis step: The biomass is dried in stages using a graded drying module. The biomass is dried in two stages using heat pump drying technology, and the moisture content is fed back in real time using a humidity sensor. The biomass is pre-adsorbed of harmful substances through a harmful substance pre-adsorption module, in which montmorillonite adsorbent activated by hydrochloric acid is used to pre-adsorb heavy metal ions.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: This invention achieves precise feedforward control of the combustion process from the source by real-time detection and analysis of fuel characteristics and dynamic adjustment of blending ratios and air supply parameters based on predicted calorific value, effectively overcoming the limitations of existing technologies that rely on combustion result feedback. Specifically, this invention can quickly respond to changes in fuel characteristics, especially for biomass from livestock solid waste with complex and fluctuating compositions. It acquires key data in real time through a fuel composition analysis unit and a calorific value prediction unit, and controls fuel flow and air supply parameters accordingly, thereby ensuring stable heat release during combustion in the furnace and improving combustion efficiency and operational stability. Simultaneously, this feedforward control mechanism reduces the risk of ash accumulation and slagging due to poor fuel compatibility, lowering the complexity of pollutant emission control.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a control system for co-firing coal and livestock solid waste biomass, according to an embodiment of the present invention.

[0020] In the diagram: 1. Coal feed bin; 2. Biomass feed bin; 3. Fuel detection and analysis module; 4. Coal feed flow control device; 5. Biomass feed flow control device; 6. Coal / biomass blending bin; 7. Primary air flow control device; 8. Blended fuel flow control device; 9. Secondary air flow control device; 10. Boiler; 11. Air flow control unit; 12. Air preheater. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figure 1 As shown, this invention provides a coal and livestock solid waste biomass co-firing control system applied to a boiler system. The boiler system includes a coal feed silo 1, a biomass feed silo 2, and a boiler 10. The core of this system lies in achieving coordinated and precise control of the blending ratio and air supply parameters through real-time analysis of fuel characteristics, thereby ensuring a stable and efficient combustion process from the source. The system mainly includes a fuel detection and analysis module 3, a fuel flow control module, and a fuel air supply module. These modules are linked through data to form a control mechanism based on fuel characteristic feedforward, which is particularly suitable for the complex and fluctuating composition of livestock solid waste biomass.

[0023] The fuel detection and analysis module 3, as the core analysis unit of the system, includes a fuel composition analysis unit, a calorific value prediction unit, and a proportion adjustment unit.

[0024] The fuel composition analysis unit performs real-time composition analysis on the coal in coal feed 1 and the aquaculture solid waste biomass from biomass feed 2. The analysis process can be carried out online or near-line.

[0025] The calorific value prediction unit receives the fuel analysis results output by the fuel composition analysis unit and calculates the real-time calorific value of coal and biomass using a preset prediction model. For example, this prediction model can be a regression model or a neural network model based on a large amount of historical data, capable of quickly and accurately predicting the current calorific value of the fuel based on its composition, and is particularly suitable for aquaculture solid waste biomass with large calorific value fluctuations.

[0026] The proportional control unit calculates the optimal blending ratio of coal and biomass based on boiler load demand, fuel analysis results, and predicted calorific value data. It should be understood that the boiler load demand is given by the boiler main control system. Specifically, during the calculation, the proportional control unit ensures the stability of the total boiler input heat load while considering the impact of the low calorific value of biomass on combustion stability, thereby outputting a blending ratio command that satisfies both load requirements and optimizes combustion conditions.

[0027] The fuel flow control module is responsible for controlling the fuel flow rate entering the boiler according to the instructions of the fuel detection and analysis module 3. The fuel flow control module includes a coal feed flow control device 4, a biomass feed flow control device 5, and a blended fuel flow control device 8.

[0028] The coal feed flow control device 4 and the biomass feed flow control device 5 each receive blending ratio commands from the proportional adjustment unit. Based on the blending ratio commands, the coal feed flow control device 4 adjusts the mass flow rate of coal output from the coal feed hopper 1, and the biomass feed flow control device 5 adjusts the mass flow rate of biomass output from the biomass feed hopper 2. The fuels output by both in proportion are mixed in the coal / biomass blending hopper 6.

[0029] The blended fuel flow control device 8 is installed after the coal / biomass blending bin 6 and is used to control the total mass flow rate of the blended fuel fed into the boiler 10. The blended fuel flow control device 8 controls the total fuel flow rate based on the boiler load demand, blending ratio, and calorific value data provided by the calorific value prediction unit to ensure that the total heat input entering the boiler matches the current load requirements.

[0030] The fuel air supply module adjusts the air distribution parameters in real time based on the total mass flow rate of the blended fuel, achieving air-fuel linkage. The fuel air supply module includes a primary air flow control device 7 and a secondary air flow control device 9.

[0031] The primary air flow control device 7 is mainly used to deliver dry blended fuel into the boiler furnace and provide the oxygen required for the initial combustion of the fuel. Based on the total mass flow signal given by the blended fuel flow control device 8, the primary air flow control device 7 adjusts the primary air volume in real time to ensure that the ratio of fuel to primary air is maintained within the optimal range, avoiding incomplete combustion due to insufficient air volume or fuel dispersion due to excessive air volume.

[0032] The secondary air flow control device 9 is used for staged air supply to control the combustion intensity and flame shape in the furnace. The secondary air flow control device 9 is also adjusted based on the total mass flow rate of the blended fuel, and can also fine-tune the air volume in conjunction with furnace temperature or pressure signals to ensure stable combustion heat release in the furnace, promote complete fuel combustion, and reduce pollutant generation.

[0033] In actual operation, the above modules work together to form a closed-loop feedforward control. The fuel detection and analysis module 3 analyzes fuel characteristics and predicts calorific value in real time, and then calculates the optimized blending ratio; the fuel flow control module controls the feed rate of coal and biomass and the total fuel flow rate according to the blending ratio and total heat load requirements; the fuel air supply module quickly adjusts the air distribution parameters of primary and secondary air according to the total fuel flow rate. The entire process realizes the linkage from fuel characteristic analysis to air supply parameter adjustment, effectively overcoming the lag of traditional technology that relies on flue gas feedback, ensuring stable combustion of the boiler, and is particularly suitable for the co-firing of livestock solid waste biomass with variable composition.

[0034] In one possible implementation, the fuel composition analysis unit includes a near-infrared spectroscopy analysis device for analyzing the functional groups of coal and biomass using near-infrared spectroscopy to determine the content of C, H, O, N, and S elements in the coal and biomass, thereby obtaining fuel analysis results.

[0035] Specifically, the near-infrared spectroscopy analyzer enables rapid and non-destructive online detection of coal and biomass from livestock solid waste using near-infrared spectroscopy technology. The analyzer uses near-infrared light (typically in the wavelength range of 780-2526 nm) to irradiate flowing fuel samples, detecting the overtone and combination frequency absorption spectra of chemical bonds such as CH, OH, and NH in the molecules, thereby obtaining information on the functional groups of organic components in the fuel. By comparing and analyzing the collected near-infrared spectral data with a pre-established quantitative calibration model, the device can determine the content of key elements such as carbon (C), hydrogen (H), oxygen (O), nitrogen (N), and sulfur (S) in coal and biomass.

[0036] For example, a near-infrared spectroscopy analyzer is integrated into the coal and biomass feeding lines to enable real-time monitoring of raw fuels and transmit the obtained elemental content data to the calorific value prediction unit.

[0037] In one possible implementation, the preset prediction model is a neural network model, which is a multilayer feedforward neural network, where the input layer nodes correspond to the content of C, H, O, N, and S elements, and the output layer nodes correspond to the calorific value.

[0038] Specifically, the input layer of the multilayer feedforward neural network has five nodes, each corresponding to the content data of five key elements (C, H, O, N, and S) in the fuel provided by a near-infrared spectroscopy analyzer. These elemental contents serve as the basic input features of the model. The output layer of the network has one node, whose output value corresponds to the fuel calorific value prediction result calculated by the model. This calorific value prediction result directly reflects the theoretical heat release that can be achieved by the current fuel elemental composition.

[0039] It should be understood that the neural network model needs to be trained before practical application. The training process uses a large amount of historical data as a sample set, covering coal and livestock solid waste biomass from different sources and with different composition ratios, containing known elemental contents and their corresponding measured calorific values. Through learning algorithms such as backpropagation, the connection weights and biases within the network are continuously adjusted, ultimately enabling the model to establish a nonlinear mapping relationship from elemental content to calorific value. The trained model is integrated into the calorific value prediction unit. During system operation, this model can receive real-time elemental content data from the fuel composition analysis unit and calculate the corresponding calorific value. Based on the neural network's feedforward prediction mechanism, the large lag in traditional calorific value measurement is overcome, providing calorific value parameters for proportional regulation and airflow control.

[0040] For example, based on artificial neural network algorithms, the calorific value of fuel is predicted using elemental analysis of coal and biomass as follows: Elemental analysis data of a large number of coal and livestock solid waste biomass samples were collected, including the contents of major elements such as C, H, O, N, and S, as well as the corresponding calorific value data (determined experimentally), to form training and test sets.

[0041] Data preprocessing includes data cleaning to remove outliers and erroneous data, and normalization to map the element content and calorific value data to the [0, 1] interval, thereby accelerating the training speed and improving the convergence of the neural network. For example, for element content data, the formula is used... Normalization is performed, where and These are the minimum and maximum values ​​of the element's content in the dataset, respectively.

[0042] The structure of the neural network was determined, employing a multilayer feedforward neural network (MLP). The number of nodes in the input layer equals the number of features in the elemental analysis. For example, assuming the content of elements C, H, O, N, and S is 5 features, then the number of nodes in the input layer is 5. The number of nodes in the output layer is 1, representing the predicted heat value. The number of hidden layers and nodes was determined through experimentation and optimization, and was set to 2 hidden layers, with the first layer having 20 nodes and the second layer having 10 nodes. The activation function for the hidden layers is the ReLU function, and the output layer uses either a linear function or a sigmoid function.

[0043] The preprocessed training data is divided into input data. X (Element content matrix, each row represents the element content of a sample) and target data Y (Heat vector). Initialize the weights and biases of the neural network, using random initialization or an initialization method based on a specific distribution (such as the normal distribution). Choose the mean squared error (MSE) function. ,in m It is the sample size. It is the actual calorific value. It predicts the calorific value.

[0044] The neural network is trained using the backpropagation algorithm, and the loss function is minimized by continuously adjusting the weights and biases. The specific steps are as follows: Input data X The predicted output is calculated through forward propagation of each layer of the neural network. ; Based on the predicted output and target data Calculate the value of the loss function. L ; Backpropagation calculates the gradient of the loss function with respect to the weights and biases of each layer. Starting from the output layer, it propagates backward step by step to the input layer, updating the weights and biases according to the gradient descent method. The weight update formula is as follows: ,in It is the first i layer to the first j Layer weights It is the learning rate; Repeat the above steps for multiple iterations until the loss function converges to a preset value or reaches a preset number of iterations.

[0045] The trained neural network model is evaluated using test set data. The elemental content data of the test set is input into the model to obtain the predicted calorific value. The error index between the predicted value and the actual calorific value of the test set is calculated, such as the root mean square error (RMSE). The model is then evaluated. If the model performance is unsatisfactory, the following optimization methods are employed: Adjust the neural network structure by increasing or decreasing the number of hidden layers and nodes, then retrain and evaluate. Adjust the learning rate; if the learning rate is too high, the model may not converge; if the learning rate is too low, training will be very slow. A learning rate decay strategy can be used, gradually decreasing the learning rate as training progresses. Increase the amount of training data by collecting more coal and biomass sample data to improve the model's generalization ability.

[0046] Use regularization techniques, such as L 1 or L 2. Regularization: To prevent overfitting, a regularization term is added to the loss function. ( L 1. Regularization) or ( L 2. Regularization).

[0047] In practical applications, when new elemental analysis data of coal and biomass from livestock solid waste are obtained, the data is first preprocessed using the same steps as the training data. The preprocessed elemental content data is then input into a trained neural network model, which outputs the predicted calorific value as an estimate of the fuel's calorific value.

[0048] In one possible implementation, the outlet of the coal / biomass mixing bin 6 is connected to the blended fuel flow control device 8, the inlet of the coal / biomass mixing bin 6 is connected to the coal feed flow control device 4 and the biomass feed flow control device 5 respectively, the input end of the coal feed flow control device 4 is connected to the coal feed bin 1, and the input end of the biomass feed flow control device 5 is connected to the biomass feed bin 2.

[0049] Specifically, the coal / biomass blending bin 6 has two feed inlets at its inlet end, which are respectively connected to the outlet end of the coal feed flow control device 4 and the outlet end of the biomass feed flow control device 5. The input end of the coal feed flow control device 4 is connected to the outlet of the coal feed bin 1 through a coal conveying pipeline; correspondingly, the input end of the biomass feed flow control device 5 is connected to the outlet of the biomass feed bin 2 through a biomass conveying pipeline.

[0050] During operation, the coal flow from coal feed silo 1 is first metered and regulated by coal feed flow control device 4, and then transported to the corresponding inlet of coal / biomass blending silo 6. Simultaneously, the livestock solid waste biomass flow from biomass feed silo 2 is metered and regulated by biomass feed flow control device 5, and then transported to another inlet of coal / biomass blending silo 6. The two fuels are mixed in a set ratio within blending silo 6. The outlet of coal / biomass blending silo 6 is connected to the inlet of blended fuel flow control device 8 via a conveying pipeline. After preliminary mixing, the blended fuel is discharged from blending silo 6 and enters blended fuel flow control device 8. Blended fuel flow control device 8 performs final control of the total mass flow rate of the blended fuel, ensuring that the total fuel quantity fed into boiler 10 matches the boiler load demand.

[0051] In one possible implementation, the fuel air delivery module further includes an air preheater 12 and an air flow control unit 11. The primary air flow control device 7 and the secondary air flow control device 9 are connected to the air preheater 12 through the air flow control unit 11. The air flow control unit 11 is used to determine the air volume ratio of the primary air and the secondary air based on the total mass flow rate of the blended fuel.

[0052] In detail, the air preheater 12 utilizes the waste heat of the flue gas at the boiler tail to heat the air entering the boiler 10, thereby improving the boiler's thermal efficiency and aiding combustion. The primary air flow control device 7 and the secondary air flow control device 9 do not directly control the cold air; instead, they are connected to the outlet duct of the air preheater 12 via the air flow control unit 11. Specifically, after the ambient air is heated by the air preheater 12, it forms hot primary air and hot secondary air, which are then delivered to the inlets of the primary air flow control device 7 and the secondary air flow control device 9, respectively. The primary air flow control device 7 and the secondary air flow control device 9, acting as actuators, are responsible for throttling and regulating the hot air flow within their respective ducts.

[0053] The air flow control unit 11 receives the total mass flow signal of the blended fuel and, in conjunction with the air distribution principle of boiler combustion and the preset air-fuel ratio curve, performs real-time calculations to determine the required total primary air volume and secondary air volume under the current operating conditions, and calculates the optimal air volume ratio between the two. The calculated primary and secondary air volume setting commands are sent to the primary air flow control device 7 and the secondary air flow control device 9, respectively. The primary air flow control device 7 and the secondary air flow control device 9 act according to the commands, adjusting their respective damper openings to deliver the corresponding proportion and total amount of hot air into the boiler furnace.

[0054] Based on the feedforward signal of the total amount of fuel about to enter the boiler, the system pre-allocates the required air volume through the air flow control unit 11, realizing dynamic matching of air volume determined by fuel, and ensuring stable combustion conditions in the furnace.

[0055] In a preferred embodiment, a pretreatment module is provided before the biomass feeding silo 2. The pretreatment module includes a graded drying module and a harmful substance pre-adsorption module. The graded drying module uses heat pump drying technology to dry the biomass in two stages and is equipped with a humidity sensor to provide real-time feedback on the moisture content. The harmful substance pre-adsorption module uses hydrochloric acid-activated montmorillonite adsorbent to pre-adsorb heavy metal ions.

[0056] In other words, a pretreatment module is installed in the system before the biomass solid waste enters the biomass feeding silo 2. The pretreatment module pre-treats the raw biomass fuel to improve fuel quality and reduce pollution during subsequent combustion. The pretreatment module mainly includes a staged drying module and a harmful substance pre-adsorption module. The staged drying module adopts heat pump drying technology, which is carried out in two stages. The first stage is a low-temperature (80-90℃) rapid dehydration stage, which uses the dehumidification characteristics of the heat pump system to remove a large amount of free water from the surface of the biomass at a relatively low temperature. The second stage is a medium-temperature (100-120℃) deep drying stage, which further removes the bound water inside the biomass at a higher temperature, so that the final moisture content drops to 12%-15% and drying automatically stops. This moisture content range can ensure fuel ignition and avoid energy waste caused by over-drying. The pretreatment module is equipped with a humidity sensor, which detects the moisture content of the biomass in real time during the drying process and feeds the detection signal back to the controller of the drying system to regulate the degree of drying.

[0057] The hazardous substance pre-adsorption module is installed after the drying process. This module uses montmorillonite activated with 5% hydrochloric acid as the adsorbent. After hydrochloric acid activation, the layered structure of montmorillonite is expanded, its specific surface area increases, and its surface active sites can pre-adsorb a large number of heavy metal ions, enhancing its adsorption capacity and activity. During biomass fuel transportation, ensuring sufficient contact between the biomass fuel and the adsorbent allows for the pre-adsorption and fixation of volatile heavy metal ions (As, Pb) in the biomass, effectively reducing the problem of heavy metals volatilizing and being emitted with flue gas during subsequent high-temperature boiler combustion.

[0058] Through the synergistic effect of the pretreatment module, the moisture and heavy metal content of the original aquaculture solid waste biomass is optimized, which makes the feeding of biomass feed hopper 2 smooth and the combustion of blended fuel stable, reducing environmental pollution.

[0059] This invention also provides a method for controlling the co-firing of coal and livestock solid waste biomass in the system described in any of the above embodiments, achieving feedforward control of the co-firing process. The co-firing control method mainly includes a fuel detection and analysis step, a fuel flow control step, and a fuel air supply control step, as detailed below: In the fuel detection and analysis step, firstly, the coal from coal feed silo 1 and the aquaculture solid waste biomass from biomass feed silo 2 are analyzed in real time using a fuel composition analysis unit to obtain fuel analysis results. In a preferred embodiment, this process is performed using a near-infrared spectroscopy analyzer to quickly obtain the C, H, O, N, and S element content in the fuel.

[0060] Subsequently, the calorific value prediction unit calculates the real-time calorific value of coal and biomass based on the fuel analysis results using a preset prediction model. Preferably, the prediction model is a trained multi-layer feedforward neural network model, using the C, H, O, N, and S element contents as input layer nodes, and the output layer nodes corresponding to calculate the calorific value of the fuel.

[0061] Finally, the proportional control unit calculates and outputs the optimal blending ratio of coal and biomass based on the boiler load demand, the fuel analysis results, and the predicted calorific value data.

[0062] In the fuel flow control step, the coal feed flow control device 4 and the biomass feed flow control device 5 respectively receive blending ratio instructions. The coal feed flow control device 4 adjusts the mass flow rate of coal taken from and conveyed from the coal feed silo 1 accordingly; simultaneously, the biomass feed flow control device 5 adjusts the mass flow rate of biomass taken from and conveyed from the biomass feed silo 2. The two fuels enter the coal / biomass blending silo 6 in this ratio for mixing. Next, the blended fuel flow control device 8 calculates the total mass flow rate of blended fuel required to meet the current heat load based on the boiler load demand, the blending ratio, and the predicted calorific value data, and controls the blended fuel to be fed into the boiler 10.

[0063] In the fuel supply air control step, the primary air flow control device 7 and the secondary air flow control device 9 adjust their respective air distribution parameters in real time according to the total mass flow rate of the blended fuel. In one specific embodiment, the air flow control unit 11 first determines the total air volume required for the primary and secondary air and the air volume ratio between them based on the total mass flow rate of the blended fuel. Subsequently, the primary air flow control device 7 and the secondary air flow control device 9 adjust the flow rates of the hot primary air and hot secondary air heated by the air preheater 12, respectively, according to the air volume ratio, thereby achieving dynamic matching between the air supply parameters and the amount of fuel entering the furnace, ensuring a stable and efficient combustion process.

[0064] Optionally, before performing the fuel detection and analysis step, the method may further include a pretreatment step: performing two-stage heat pump drying on the raw biomass through a graded drying module, and controlling its final moisture content based on real-time feedback from a humidity sensor; subsequently, using a harmful substance pre-adsorption module, the dried biomass is pretreated with hydrochloric acid-activated montmorillonite adsorbent to pre-adsorb the heavy metal ions it contains.

[0065] Figure 1In this diagram, S1 represents the coal component detection and analysis signal; S2 represents the biomass detection and analysis signal from livestock solid waste; S3 represents the blended combustion analysis and processing signal; A1 represents the coal flow control signal; A2 represents the biomass flow control signal; and A3 represents the total flow control signal for blended fuels. L1 represents the primary air duct; L2 represents the conveying duct between the coal / biomass blending bin and the boiler; L3 represents the connecting duct between the heating air and the air flow control unit; and L4 represents the secondary air connecting duct between the air flow control unit and the boiler.

[0066] In the description of this invention, it should be understood that the terms "upper", "lower", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0067] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0068] In this invention, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0069] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0070] In this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0071] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention.

Claims

1. A control system for co-firing coal and livestock solid waste biomass, applied to a boiler system, the boiler system comprising a coal feeding bin (1), a biomass feeding bin (2), and a boiler (10), characterized in that, The co-firing control system includes: The fuel detection and analysis module (3) includes a fuel composition analysis unit, a calorific value prediction unit, and a proportioning unit. The fuel composition analysis unit is used to perform composition analysis on coal and biomass to obtain fuel analysis results. The calorific value prediction unit is used to calculate the calorific value of coal and biomass based on the fuel analysis results using a preset prediction model. The proportioning unit is used to calculate the blending ratio of coal and biomass based on boiler load demand, the fuel analysis results, and the predicted calorific value. The fuel flow control module includes a coal feed flow control device (4), a biomass feed flow control device (5), and a blended fuel flow control device (8). The coal feed flow control device (4) and the biomass feed flow control device (5) are respectively used to adjust the mass flow rate of coal and biomass entering the coal / biomass blending bin (6) according to the blending ratio. The blended fuel flow control device (8) is used to control the total mass flow rate of the blended fuel and send it into the boiler (10) according to the boiler load demand, the blending ratio, and calorific value data. The fuel air supply module includes a primary air flow control device (7) and a secondary air flow control device (9), which are used to adjust the air distribution parameters of the primary and secondary air in real time according to the total mass flow rate of the blended fuel.

2. The control system for co-firing coal and livestock solid waste biomass according to claim 1, characterized in that, The fuel composition analysis unit includes a near-infrared spectroscopy analysis device, which is used to analyze the functional groups of coal and biomass through near-infrared spectroscopy to determine the content of C, H, O, N and S elements in coal and biomass, and obtain fuel analysis results.

3. The control system for co-firing coal and livestock solid waste biomass according to claim 2, characterized in that, The preset prediction model is a neural network model, which is a multi-layer feedforward neural network. The input layer nodes correspond to the content of C, H, O, N and S elements, and the output layer nodes correspond to the calorific value.

4. The control system for co-firing coal and livestock solid waste biomass according to claim 1, characterized in that, The outlet of the coal / biomass mixing bin (6) is connected to the blended fuel flow control device (8), and the inlet of the coal / biomass mixing bin (6) is connected to the coal feed flow control device (4) and the biomass feed flow control device (5), respectively. The input end of the coal feed flow control device (4) is connected to the coal feed bin (1), and the input end of the biomass feed flow control device (5) is connected to the biomass feed bin (2).

5. The control system for co-firing coal and livestock solid waste biomass according to claim 1, characterized in that, The fuel air supply module also includes an air preheater (12) and an air flow control unit (11). The primary air flow control device (7) and the secondary air flow control device (9) are connected to the air preheater (12) through the air flow control unit (11). The air flow control unit (11) is used to determine the air volume ratio of the primary air and the secondary air according to the total mass flow rate of the blended fuel.

6. The control system for co-firing coal and livestock solid waste biomass according to claim 1, characterized in that, A pretreatment module is provided before the biomass feeding silo (2). The pretreatment module includes a graded drying module and a harmful substance pre-adsorption module. The graded drying module uses heat pump drying technology to dry the biomass in two stages and is equipped with a humidity sensor to provide real-time feedback on the moisture content. The harmful substance pre-adsorption module uses hydrochloric acid-activated montmorillonite adsorbent to pre-adsorb heavy metal ions.

7. A co-firing control method based on the coal and livestock solid waste biomass co-firing control system according to any one of claims 1 to 6, characterized in that, include: The fuel testing and analysis steps involve analyzing the composition of coal and biomass using a fuel composition analysis unit to obtain fuel analysis results. The calorific value prediction unit calculates the calorific value of coal and biomass based on the fuel analysis results using a preset prediction model. The proportioning unit calculates the blending ratio of coal and biomass based on boiler load demand, fuel analysis results, and predicted calorific value. In the fuel flow control step, the mass flow rates of coal and biomass entering the coal / biomass blending bin (6) are adjusted by the coal flow control device (4) and the biomass flow control device (5) according to the blending ratio; the total mass flow rate of the blended fuel is controlled by the blended fuel flow control device (8) according to the boiler load demand, the blending ratio and calorific value data and then sent to the boiler (10). In the fuel supply air control step, the air distribution parameters of the primary and secondary air are adjusted in real time according to the total mass flow rate of the blended fuel by the primary air flow control device (7) and the secondary air flow control device (9).

8. The method for controlling co-firing according to claim 7, characterized in that, The step of performing component analysis on coal and biomass using a fuel composition analysis unit includes: Near-infrared spectroscopy analysis of coal and biomass is performed using a near-infrared spectroscopy analyzer to determine the functional groups of coal and biomass, thereby obtaining the content of C, H, O, N, and S elements in coal and biomass, which are used as the fuel analysis results.

9. The method for controlling co-firing according to claim 8, characterized in that, The preset prediction model is a neural network model, and the neural network model is a multilayer feedforward neural network; The step of calculating the calorific value through the calorific value prediction unit includes: taking the content of C, H, O, N and S elements as the input of the input layer node, and calculating the calorific value output by the output layer node through the multi-layer feedforward neural network.

10. The method for controlling co-firing according to claim 7, characterized in that, Prior to the fuel detection and analysis step, a pretreatment step is also included: The biomass is dried in stages using a graded drying module. The biomass is dried in two stages using heat pump drying technology, and the moisture content is fed back in real time using a humidity sensor. The biomass is pre-adsorbed of harmful substances through a harmful substance pre-adsorption module, in which montmorillonite adsorbent activated by hydrochloric acid is used to pre-adsorb heavy metal ions.

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