Boiler control method and device, electronic equipment and storage medium

Abstract

The invention discloses a boiler control method and device, electronic equipment and a storage medium, the method and device are applied to a waste incineration boiler, and specifically, multiple pieces of original data are obtained; performing prediction processing based on the multiple pieces of original data to obtain a predicted heat value and a predicted density of the to-be-incinerated raw material; and controlling the waste incineration boiler based on the predicted heat value and the predicted density. The garbage calorific value three-dimensional spatio-temporal evolution model is constructed by dynamically capturing material pushing pressure gradient change and a temperature field in a hopper through a multi-mode sensing system. Multi-source heterogeneous data correction is performed by fusing dynamic characteristics of main steam flow in real time, so that operation of control equipment of the pusher is driven, sub-minute advanced regulation and control of the spreading rate are realized, and finally, the set value of the steam flow can be kept stable under various working conditions; therefore, the fluctuation of the evaporation capacity and the load is controlled within an allowable range.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental protection equipment, and more particularly to a boiler control method and device, an electronic device and a storage medium. BACKGROUND

[0002] The garbage incineration boiler is a clean boiler equipment for generating power and heating by using urban and rural household garbage, and belongs to the technical field of garbage incineration power generation and boiler control. The boiler can not only generate power and heat by incinerating garbage, but also save a large amount of mineral fuels such as coal and natural gas, reduce carbon emissions, and effectively alleviate the contradiction between the increasing demand for industrial heat and civil heating and environmental protection.

[0003] The heat value of garbage changes dramatically with the change of its density and type, and the ACC (Automatic Combustion Control) system of the garbage incineration boiler currently only relies on feedback regulation, which needs to go through a long control process of “detecting fluctuation→adjusting the speed of the grate→re-paving the garbage”. Due to the physical delay of garbage in the process of hopper-pushing-grate, as well as the thermal inertia of combustion itself, the pure feedback system essentially has an unovercome response delay when facing the main disturbance of the dramatic fluctuation of the heat value of the garbage into the furnace, which will lead to the continuous oscillation of the evaporation capacity and load. SUMMARY

[0004] Therefore, the present application provides a boiler control method and device, an electronic device and a storage medium, which are used for dynamically predicting the heat value of garbage based on multi-source perception, and implementing feedforward control on the garbage incineration boiler based on the prediction result, so as to control the fluctuation of the evaporation capacity and load within the allowable range.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following scheme:

[0006] A boiler control method applied to a garbage incineration boiler, the boiler control method comprising the steps of:

[0007] obtaining a plurality of original data;

[0008] performing prediction processing based on the plurality of original data to obtain a predicted heat value and a predicted density of the to-be-incinerated raw material;

[0009] controlling the garbage incineration boiler based on the predicted heat value and the predicted density.

[0010] Optionally, the plurality of original data includes the forward pressure value and the backward pressure value of the garbage incineration boiler, and the level, the highest surface temperature and the lowest surface temperature of the to-be-incinerated raw material.

[0011] Optionally, the prediction processing based on the plurality of original data obtains a predicted calorific value and a predicted density of the raw material to be incinerated, comprising the steps of:

[0012] The density prediction is performed based on the forward pressure value and the backward pressure value of the pusher of the waste incineration boiler to obtain a raw material density of the raw material to be incinerated;

[0013] The calorific value prediction is performed based on the raw material density to obtain the predicted calorific value of the raw material to be incinerated.

[0014] Optionally, the density prediction based on the pushing force of the pusher of the waste incineration boiler obtains a raw material density of the raw material to be incinerated, comprising the steps of:

[0015] The raw material resistance of the raw material to be incinerated is calculated based on the forward pressure value and the backward pressure value of the pusher;

[0016] The density prediction is performed based on the raw material resistance to obtain the raw material density.

[0017] Optionally, the calorific value prediction based on the raw material density obtains the predicted calorific value of the raw material to be incinerated, comprising the steps of:

[0018] The temperature variation is obtained based on the scanning of the temperature field of the surface of the raw material to be incinerated;

[0019] The predicted calorific value is calculated based on the temperature variation, the raw material density, the basic calorific value of the raw material to be incinerated, and a typical density reference value.

[0020] Optionally, the control of the waste incineration boiler based on the predicted calorific value and the predicted density comprises the steps of:

[0021] The feedforward control parameter is obtained based on the predicted calorific value and the predicted density;

[0022] The feedback control parameter is obtained based on the main steam flow of the waste incineration boiler;

[0023] The waste incineration boiler is controlled based on the feedforward control parameter and the feedback control parameter.

[0024] Optionally, the control of the waste incineration boiler based on the feedforward control parameter and the feedback control parameter comprises the steps of:

[0025] The feedforward control parameter and the feedback control parameter are output to the control device of the pusher, so that the control device controls the pusher based on the feedforward control parameter and the feedback control parameter.

[0026] A boiler control device is applied to a waste incineration boiler, the boiler control device comprising:

[0027] The data acquisition module is configured to acquire multiple raw data sets.

[0028] The parameter prediction module is configured to perform prediction processing based on the multiple raw data to obtain the predicted calorific value and predicted density of the raw material to be incinerated.

[0029] The control execution module is configured to control the waste incineration boiler based on the predicted calorific value and the predicted density.

[0030] An electronic device includes at least one processor and a memory connected to the processor, wherein:

[0031] The memory is used to store computer programs or instructions;

[0032] The processor is used to execute the computer program or instructions to enable the electronic device to implement the boiler control method as described above.

[0033] A computer-readable storage medium is applied to an electronic device, the storage medium carrying one or more computer programs that can be executed by the electronic device, thereby enabling the electronic device to implement the boiler control method as described above.

[0034] As can be seen from the above technical solution, this application discloses a boiler control method, device, electronic equipment, and storage medium. This method and device are applied to a waste incineration boiler, specifically involving acquiring multiple raw data sets; performing predictive processing based on the raw data sets to obtain the predicted calorific value and density of the raw material to be incinerated; and controlling the waste incineration boiler based on the predicted calorific value and density. This application relies on a multimodal sensing system to dynamically capture changes in the feeding pressure gradient and the temperature field within the hopper to construct a three-dimensional spatiotemporal evolution model of the waste calorific value. By real-time fusion of the dynamic characteristics of the main steam flow rate for multi-source heterogeneous data correction, the operation of the feeder's control equipment is driven, thereby achieving sub-minute-level advanced control of the feeding rate, ultimately achieving a steady-state of the target steam flow rate under all operating conditions, avoiding continuous fluctuations in evaporation and load. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a boiler control method according to an embodiment of this application;

[0037] Figure 2 This is a block diagram of a boiler control device according to an embodiment of this application;

[0038] Figure 3 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0040] In existing waste incineration boilers, waste is first placed into a waste bin, then grabbed by a grab bucket and placed into a hopper, where multiple parameters of the waste are measured. Finally, it is pushed into the grate by a pusher for combustion. This application, however, processes multiple parameters of the waste in the hopper to control the subsequent feed rate into the furnace. The pusher is also a device that pushes the waste before it enters the furnace.

[0041] This application implements control based on the following hardware facilities, specifically including a hydraulic pressure sensor, a radar level gauge, and an infrared thermal imager. The hydraulic pressure sensor is installed on the pusher's hydraulic cylinder, with a range of 0-250 bar and a linear error ≤ ±0.25%. The radar level gauge is installed on the top of the hopper, with a range of 0-10 m and an accuracy of ±5 mm. The infrared thermal imager is installed 3 m above the hopper, with a resolution of 640×480 or higher and a temperature range of -20℃ to 150℃. To address the problem of lag in the control response of existing waste incineration boilers, this application provides the following technical solution: by implementing feedforward control based on the prediction of density and calorific value, combined with traditional feedback control, the waste incineration boiler achieves stable control. The specific solution is as follows.

[0042] Figure 1 This is a flowchart of a boiler control method according to an embodiment of this application.

[0043] like Figure 1 As shown, the boiler control method provided in this embodiment is applied to a waste incineration boiler, specifically to the automatic combustion control system of the waste incineration boiler. This system can be understood as a computer, server, cloud platform, or embedded device with data computing and information processing capabilities. The boiler control method specifically includes the following steps:

[0044] S1. Obtain multiple raw data.

[0045] The raw data here includes various parameters of the raw materials to be incinerated entering the boiler. The raw materials can be garbage or straw with similar properties to garbage. The multiple raw data collected in this application include, but are not limited to, the forward pressure and backward pressure values ​​of the feeder of the waste incineration boiler, as well as the material level, maximum surface temperature, and minimum surface temperature of the raw materials to be incinerated.

[0046] The forward and backward pressure values ​​of the pusher are obtained based on hydraulic sensors or strain sensors mounted on the hydraulic equipment of the pusher. Material level data can be acquired using ultrasonic level gauges or laser scanners mounted within the hopper. The highest and lowest surface temperatures can be determined by scanning the surface temperature field of the feedstock to be incinerated using an infrared temperature sensor, thus identifying the highest and lowest values, i.e., the highest and lowest surface temperatures.

[0047] S2. Based on multiple raw data, predict the predicted calorific value and predicted density of the raw material to be incinerated.

[0048] The specific steps are as follows:

[0049] First, the density prediction value is obtained based on the forward and backward pressure values ​​of the pusher.

[0050] Specifically, hydraulic sensors are used to measure the forward pressure P1 and backward pressure P2 of the pusher in real time; simultaneously, a radar level gauge measures the average material level H_avg in the hopper, in meters. Based on this, the net thrust F_resistance is calculated according to the principle of mechanical equilibrium, with the following formula:

[0051] F_resistance = (P1×S1 - P2×S2)_forward - (P2×S2 - P1×S1)_backward.

[0052] Where S1 / S2 is the cross-sectional area of ​​the hydraulic cylinder of the pusher. P1 / P2 is the forward / reverse driving pressure of the hydraulic cylinder, in MPa.

[0053] When the material level height H_avg is constant, the waste density is proportional to the resistance F_. Therefore, the raw material density ρ' can be calculated based on the real-time density prediction model, and the specific formula is as follows:

[0054] ρ' = k × (F_resistance / H_avg),

[0055] Where k is the geometric coefficient of the hopper, obtained through calibration tests, with a typical value of 0.72. H_avg is the material level height directly measured by the radar level gauge, in meters. F_resistance is the waste resistance, measured directly by a hydraulic sensor. The calibration method for k is as follows: measure the basic resistance F0 of the pusher when the hopper is empty, and measure the total resistance F1 when standard material of known density ρ0 is loaded. Then: k = (ρ0 × H(avg)) / (F1 − F0). Then, the calorific value is predicted based on the raw material density to obtain the predicted calorific value.

[0056] Specifically, the temperature change is calculated by using an infrared thermal imager to scan the surface of the waste, obtaining the highest surface temperature T_max and the lowest surface temperature T_min.

[0057] Temperature change ΔT = T_max - T_min [unit: °C].

[0058] Based on the temperature change, a prediction process is performed, and the predicted calorific value H' is obtained through a dynamic correction model. The specific formula is as follows:

[0059] H'_calorific value = H_base + 0.15 × ΔT + 0.8 × (Δρ / ρ_ref),

[0060] Wherein, H_base represents the base calorific value of the waste, taken from the average calorific value of the previous batch. The update mechanism is: H(base, new) = 0.7 × H(base, old) + 0.3 × H(actual), where H(base, new) is the updated base calorific value, H(base, old) is the average calorific value of the previous batch, and H(actual) is obtained through analysis of flue gas composition and steam flow rate after incineration. The coefficients 0.15 and 0.8 were obtained by least squares fitting through combustion tests of 30 groups of waste with different compositions. Their physical meaning is that for every 1°C increase in temperature, the calorific value increases by 0.15%, and for every 1% deviation in density, the calorific value changes by 0.8%.

[0061] Δρ is the difference between the current density and the reference density, and the specific calculation formula is as follows:

[0062] Δρ = |ρ' - ρ_ref|,

[0063] Among them, ρ_ref is a typical density reference value for municipal solid waste, which is obtained by statistical analysis of waste density over multiple months. The normal fluctuation range is 0.4~0.5 t / m³, and this application selects 0.45 t / m³.

[0064] The prediction logic for each control parameter is shown in the table below:

[0065]

[0066] In addition, this application implements data correction by introducing an adaptive Kalman filter algorithm on the basis of the dynamic prediction model.

[0067] The system state vector is defined as X = [ρ', H'_calorific value]^T, which is the target that needs to be optimally estimated.

[0068] The observation vector is defined as Z = [F_resistance / H_avg, T_avg, Q_actual]^T, which represents the direct or indirect measurement value from the sensor. Here, Q_actual is the real-time reading of the main steam flow meter.

[0069] Working principle: The Kalman filter continuously weights and fuses model-based predictions with actual sensor observations through the system's state equations (based on the aforementioned mechanistic model) and observation equations. When there is a significant deviation between the actual observed value of the main steam flow rate Q_ and the steam output predicted based on the current H'_ calorific value, the filter automatically adjusts the trust weights and reverses the state estimates of H'_ calorific value and ρ', thereby effectively filtering out sensor noise and model errors and achieving multi-source heterogeneous data correction.

[0070] S3. Implement control of waste incineration boilers based on predicted calorific value and predicted density.

[0071] The specific control process is as follows:

[0072] First, based on the predicted calorific value and predicted density, feedforward control parameters are obtained. The specific details are as follows:

[0073] Input: Predicted calorific value H′ (kJ / kg), predicted density ρ′ (t / m³);

[0074] Output: Feedforward control parameter V_feedforward (m / h), which is the base speed for material laying.

[0075] Calculation logic and formulas:

[0076] The core objective of feedforward control is to calculate in advance the required feed rate to maintain the target steam flow rate based on the predicted energy characteristics of the waste. The core principle is to deliver waste with a specific total calorific value to the boiler per unit time to generate the target steam flow rate.

[0077] Standardized units and step-by-step calculation:

[0078] Step 1: Calculate the calorific value per unit volume of waste.

[0079] The predicted calorific value H′ is expressed in kJ / kg, representing the calorific value per kilogram of waste. The predicted density ρ′ is expressed in t / m³. Since 1 ton = 1000 kilograms, unit conversion is necessary. The formula for calculating the calorific value per unit volume Qvol (kJ / m³) is:

[0080] Qvol = H′ calorific value × (ρ′ × 1000),

[0081] ρ′×1000 converts the density unit from t / m³ to kg / m³.

[0082] Physical meaning: The total heat contained in 1 cubic meter of garbage.

[0083] Step 2: Calculate the total heat power required to meet the target steam flow rate:

[0084] The target main steam flow rate Q_target is in t / h.

[0085] In a boiler system, the amount of heat required to generate one ton of steam is a key parameter, known as the enthalpy of steam, denoted as hsteam (kJ / kg). This value is determined by the boiler's feedwater temperature, steam pressure, and temperature, and is a known value that can be accurately calculated using boiler design parameters. For typical waste incineration boilers, this value is usually in the range of 2600 to 3200 kJ / kg.

[0086] The formula for calculating the required total heat power Preq (kJ / h) is as follows:

[0087] Preq = Q_target × 1000 × hsteam

[0088] Q_target×1000 converts the steam flow rate from t / h to kg / h.

[0089] The physical meaning is the total heat that the boiler needs to obtain from waste incineration per hour in order to produce steam with the target Q_.

[0090] Step 3: Calculate the required waste volume flow rate and spreading speed:

[0091] The required waste volumetric flow rate Vvol (m³ / h) is:

[0092] Vvol=Preq / Qvol

[0093] = (Q_target × 1000 × hsteam) / (H′calorific value × ρ′ × 1000)

[0094] = (Q_target × hsteam) / (H′calorific value × ρ′)

[0095] With a constant feeder cross-sectional area A (m²), the volumetric flow rate directly corresponds to the feed reference velocity V_feedforward (m / h):

[0096] V_feedforward = Vvol / A = (Q_target × hsteam) / (H′calorific value × ρ′ × A),

[0097] The revised final formula:

[0098] V_feedforward = (Q_target × hsteam) / (H'calorific value × ρ′ × A),

[0099] Where hsteam is the unit steam enthalpy (kJ / kg), a known constant determined by boiler operating parameters. A is the cross-sectional area (m²) of the pusher, an inherent parameter of the equipment structure.

[0100] Revised Implementation Example:

[0101] Q_target = 50 t / h, H' calorific value = 7500 kJ / kgH, ρ′ = 0.45 t / m³, hsteam = 2800 kJ / kg (typical value), A = 9 m² (example value). Therefore: V_feedforward = (50 × 2800) / (7500 × 0.45 × 9) ≈ 4.61 m / h.

[0102] Then, based on the main steam flow rate of the waste incineration boiler, the feedback control parameters are calculated.

[0103] The inputs are the real-time value of the main steam flow rate Q_actual (t / h) and the target value Q_target (t / h), and the output is the feedback control parameter V_compensation (m / h), which is the speed compensation amount.

[0104] Calculation logic and formulas:

[0105] Feedback control is used to eliminate residual errors from feedforward predictions and unmodeled disturbances. It employs the industry-standard PID (Proportional-Integral-Derivative) control algorithm, with the specific formula as follows:

[0106]

[0107] Where e(t) = Q_target − Q_actual, representing the instantaneous deviation of the main steam flow rate, and Kp, Ki, and Kd are the proportional, integral, and derivative gain coefficients of the PID controller, respectively. These coefficients can be tuned in the field according to classical control theory (such as the Ziegler-Nichols method). Typical values ​​are Kp: 0.1 ~ 0.3, Ki: 0.01 ~ 0.05, Kd: 0.005 ~ 0.02.

[0108] In practical implementation, if e(t) = +2 t / h (actual flow rate is lower than the target) and Kp = 0.2, this proportional term contributes a speed compensation of +0.4 m / h, aiming to increase steam production by slightly accelerating the feeding process. Finally, the waste incineration boiler is controlled based on feedforward control parameters and feedback control parameters. Specifically, the feedforward control parameters and feedback control parameters are output to the feeder's control device, enabling the control device to control the feeder based on these parameters. The inputs are the feedforward control parameter V_feedforward and the feedback control parameter V_compensation; the output is the final execution command V_final (m / h).

[0109] Calculation logic and formulas:

[0110] By combining the "predictability" of feedforward with the "precision" of feedback, the final control command is generated.

[0111] V_final = V_feedforward + V_compensation.

[0112] During actual execution, the controller sends the calculated final V_ command to the hydraulic servo system and other actuators of the pusher. This precisely controls the pushing speed, stabilizing it at the final V_.

[0113] This application addresses large-amplitude and slow-dynamic disturbances caused by sudden changes in the calorific value and density of waste, and performs feedback fine-tuning to eliminate model errors. It also addresses small-amplitude and fast-dynamic disturbances (such as brief waste jamming or minor fluctuations in airflow). Ultimately, it achieves rapid, stable, and accurate tracking of the actual main steam flow rate Q_Q against the target Q.

[0114] This application achieves sub-minute-level advance control through the following feedforward-feedback composite control loop, with its total response time controlled within 60 seconds:

[0115] The feedforward control path achieves "advance" control:

[0116] Input: Corrected H'_calorific value and ρ'.

[0117] Calculation: The feedforward controller calculates the base velocity for material spreading in real time based on the target steam flow rate Q_target: V_feedforward = (Q_target × hsteam) / (H' calorific value × ρ′ × A).

[0118] This instruction is given to the feeder before the waste enters the furnace for combustion, directly counteracting the main disturbances caused by changes in the calorific value and density of the waste, reducing the traditional minute-level feedback delay to the second level.

[0119] Feedback control path achieves steady state:

[0120] Input: Main steam flow rate setpoint Q_target and actual value Q_actual deviation e(t).

[0121] Calculation: Using a standard PID controller, the speed compensation V is calculated based on the deviation e(t).

[0122] ,

[0123] Function: To eliminate small and rapid disturbances that cannot be covered by the feedforward model.

[0124] Integration and Execution:

[0125] The final control command is V_final = V_feedforward + V_compensation.

[0126] The command is sent to the hydraulic servo valve of the pusher via the analog output module.

[0127] From the acquisition of new sensor data to the final output of V_ command, the typical value of the entire control cycle is 20-30 seconds, thus achieving "sub-minute" level regulation.

[0128] As can be seen from the above technical solution, this embodiment provides a boiler control method applied to a waste incineration boiler. Specifically, it involves acquiring multiple raw data sets; performing predictive processing based on these raw data sets to obtain the predicted calorific value and density of the raw material to be incinerated; and controlling the waste incineration boiler based on the predicted calorific value and density. This application relies on a multimodal sensing system to dynamically capture changes in the feeding pressure gradient and the temperature field within the hopper to construct a three-dimensional spatiotemporal evolution model of the waste calorific value. By real-time fusion of the dynamic characteristics of the main steam flow rate for multi-source heterogeneous data correction, the operation of the feeder's control equipment is driven, thereby achieving sub-minute-level advanced control of the feeding rate. Ultimately, this ensures the stability of the steam flow rate setpoint under various operating conditions, thus controlling fluctuations in evaporation and load within allowable ranges.

[0129] In addition, this application can also achieve the following effects:

[0130] A leap in response speed: Thanks to the adoption of pre-furnace sensing and feedforward control, this application reduces the control response time from over 10 minutes in traditional feedback to the sub-minute level. This is an order-of-magnitude improvement, and neither of the two patents disclosed nor could achieve such a rapid response.

[0131] Significant improvement in control accuracy: According to actual project measurement data, after applying the technical solution of this application, the steam flow fluctuation amplitude was reduced by more than 30% (from ±10% to ±4.5%). This proves that the technical solution has excellent effect in solving the core problem of "load oscillation".

[0132] Convenience and reliability of engineering implementation: Compared with the complex AI models of existing technologies, the mechanism model of this application does not require a large amount of training data, has low computing resource requirements, strong interpretability, is easier to deploy and maintain in industrial sites, and has higher reliability.

[0133] Furthermore, it is emphasized again here that all parameters are obtained through three standardized methods: calibration experiments, direct measurement, and historical statistics. The calculation logic of this application is based on well-known physical principles such as mechanical equilibrium, heat conduction, and mass conservation. All actual measured parameters are measured within the hopper before entering the furnace, and there are no temporal inconsistencies. The calorific value prediction model of this application is not a baseless "black box" model, but is constructed based on clear physical mechanisms and engineering-feasible calibration methods. Its core idea is that there is a quantifiable correlation between the macroscopic physical properties of waste (pushing resistance, surface temperature) and its microscopic components (combustible material content, moisture content) and chemical energy (calorific value).

[0134] The overall framework of the computational logic is as follows:

[0135] Density can be derived from mechanical properties: In a hopper with a fixed cross-section, the pushing resistance per unit height of material directly reflects the compaction of the waste, i.e., its density.

[0136] The calorific value is corrected by a combination of thermal properties and density:

[0137] Temperature (α × ΔT): Primarily reflects the biological activity and chemical composition of the waste. Waste that undergoes vigorous fermentation and generates heat (usually with high organic matter content) or contains high-calorific-value materials such as plastics will have higher surface temperatures and greater temperature differences.

[0138] Density (β × (Δρ / ρ_ref)): Primarily reflects the basic composition of waste. At constant temperature, a significant deviation of density from the baseline value usually indicates a change in the proportion of combustible materials (such as plastics and paper) or non-combustible materials (such as ash and moisture).

[0139] Therefore, this computational logic transforms the complex and difficult-to-measure waste composition problem into a correlation analysis of directly measurable physical quantities (pressure, level, temperature), which is reasonable and efficient in engineering. In summary, this application is not simply a concept of "control based on calorific value prediction," but rather provides a unique and complete technical solution organically combined with three parts: "multimodal physical sensing," "mechanism prediction model," and "feedforward-feedback composite control."

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0141] Although the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous.

[0142] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0143] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer.

[0144] Figure 2 This is a flowchart of a boiler control device according to an embodiment of this application.

[0145] like Figure 2As shown, the boiler control device provided in this embodiment is applied to a waste incineration boiler, specifically to the automatic combustion control system of the waste incineration boiler. This system can be understood as a computer, server, cloud platform, or embedded device with data computing and information processing capabilities. The boiler control device specifically includes a data acquisition module 10, a parameter prediction module 20, and a control execution module 30.

[0146] The data acquisition module is used to acquire multiple raw data sets.

[0147] The raw data here includes various parameters of the raw materials to be incinerated entering the boiler. The raw materials can be garbage or straw with similar properties to garbage. The multiple raw data collected in this application include, but are not limited to, the forward pressure and backward pressure values ​​of the feeder of the waste incineration boiler, as well as the material level, maximum surface temperature, and minimum surface temperature of the raw materials to be incinerated.

[0148] The forward and backward pressure values ​​of the pusher are obtained based on hydraulic sensors or strain sensors mounted on the hydraulic equipment of the pusher. Material level data can be acquired using ultrasonic level gauges or laser scanners mounted within the hopper. The highest and lowest surface temperatures can be determined by scanning the surface temperature field of the feedstock to be incinerated using an infrared temperature sensor, thus identifying the highest and lowest values, i.e., the highest and lowest surface temperatures.

[0149] The parameter prediction module is used to predict the calorific value and density of the feedstock to be incinerated based on multiple raw data.

[0150] The specific steps are as follows:

[0151] First, the density prediction value is obtained based on the forward and backward pressure values ​​of the pusher.

[0152] Specifically, hydraulic sensors are used to measure the forward pressure P1 and backward pressure P2 of the pusher in real time; simultaneously, a radar level gauge measures the average material level H_avg in the hopper, in meters. Based on this, the net thrust F_resistance is calculated according to the principle of mechanical equilibrium, with the following formula:

[0153] F_resistance = (P1×S1 - P2×S2)_forward - (P2×S2 - P1×S1)_backward.

[0154] Where S1 / S2 is the cross-sectional area of ​​the hydraulic cylinder of the pusher. P1 / P2 is the forward / reverse driving pressure of the hydraulic cylinder, in MPa.

[0155] When the material level height H_avg is constant, the waste density is proportional to the resistance F_, therefore the raw material density ρ' can be calculated based on the following formula:

[0156] ρ' = k × (F_resistance / H_avg),

[0157] Where k is the hopper geometric coefficient, obtained through calibration tests, and its typical value can be assumed to be 0.72.

[0158] Then, the calorific value is predicted based on the density of the raw material.

[0159] Specifically, the temperature change is calculated by using an infrared thermal imager to scan the surface of the waste, obtaining the highest surface temperature T_max and the lowest surface temperature T_min.

[0160] Temperature change ΔT = T_max - T_min [unit: °C].

[0161] Based on the temperature change, a prediction process is performed, and the predicted calorific value H' is obtained using the following formula:

[0162] H'_calorific value = H_base + 0.15 × ΔT + 0.8 × (Δρ / ρ_ref),

[0163] Wherein, - the basic calorific value H_base is the basic calorific value, taken from the average value of the previous batch; Δρ is the difference between the current density and the reference density, and the specific calculation formula is as follows:

[0164] Δρ = |ρ' - ρ_ref|,

[0165] Wherein, ρ_ref is a typical density reference value for municipal solid waste, which is selected as 0.45 t / m³ in this application.

[0166] The prediction logic for each control parameter is shown in the table below:

[0167]

[0168] The control execution module is used to control the waste incineration boiler based on the predicted calorific value and predicted density.

[0169] The specific control process is as follows:

[0170] First, based on the predicted calorific value and predicted density, feedforward control parameters are obtained.

[0171] Then, based on the main steam flow rate of the waste incineration boiler, the feedback control parameters are calculated.

[0172] Finally, the waste incineration boiler is controlled based on feedforward control parameters and feedback control parameters. Specifically, the feedforward control parameters and feedback control parameters are output to the control device of the feeder, so that the control device controls the feeder based on the feedforward control parameters and feedback control parameters.

[0173] As can be seen from the above technical solution, this embodiment provides a boiler control device applied to a waste incineration boiler. Specifically, it acquires multiple raw data points; performs predictive processing based on these raw data points to obtain the predicted calorific value and density of the raw material to be incinerated; and controls the waste incineration boiler based on the predicted calorific value and density. This application relies on a multimodal sensing system to dynamically capture changes in the feeding pressure gradient and the temperature field within the hopper to construct a three-dimensional spatiotemporal evolution model of the waste calorific value. By real-time fusion of the dynamic characteristics of the main steam flow rate for multi-source heterogeneous data correction, the operation of the feeder's control equipment is driven, thereby achieving sub-minute-level advanced control of the feeding rate. Ultimately, it can maintain the setpoint of the steam flow rate stable under various operating conditions, thus controlling the fluctuations in evaporation and load within allowable ranges.

[0174] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0175] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0176] Figure 3 This is a block diagram of an electronic device according to an embodiment of this application.

[0177] The following is for reference. Figure 3 This document illustrates a structural diagram suitable for implementing the electronic device in the embodiments of this disclosure. The terminal device in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this disclosure.

[0178] The electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from an input device 306 into a random access memory (RAM) 303. The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0179] Typically, the following devices can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although electronic devices with various devices are shown in the figures, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0180] This application also provides an embodiment of a computer-readable storage medium.

[0181] The aforementioned computer-readable storage medium is used in electronic devices and carries one or more computer programs. When these programs are executed by the electronic device, the device acquires multiple raw data sets; performs predictive processing based on the raw data sets to obtain the predicted calorific value and density of the raw material to be incinerated; and controls the waste incineration boiler based on the predicted calorific value and density. This application utilizes a multimodal sensing system to dynamically capture changes in the feeding pressure gradient and the temperature field within the hopper to construct a three-dimensional spatiotemporal evolution model of the waste calorific value. By real-time fusion of the dynamic characteristics of the main steam flow rate for multi-source heterogeneous data correction, the operation of the feeder's control equipment is driven, thereby achieving sub-minute-level advanced control of the feeding rate. Ultimately, this maintains a stable steam flow rate setpoint under various operating conditions, thus controlling fluctuations in evaporation and load within allowable ranges.

[0182] It should be noted that the computer-readable medium disclosed in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0183] In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0184] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0185] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0186] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0187] The technical solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A boiler control method applied to a waste incineration boiler, characterized in that, The boiler control method includes the following steps: Obtain multiple raw data sets; Based on the aforementioned raw data, predictive processing is performed to obtain the predicted calorific value and predicted density of the raw material to be incinerated. The waste incineration boiler is controlled based on the predicted calorific value and the predicted density.

2. The boiler control method as described in claim 1, characterized in that, The multiple raw data include the forward pressure value and backward pressure value of the feeder of the waste incineration boiler, as well as the material level, highest surface temperature and lowest surface temperature of the raw material to be incinerated.

3. The boiler control method as described in claim 2, characterized in that, The step of performing prediction processing based on the multiple raw data to obtain the predicted calorific value and predicted density of the raw material to be incinerated includes the following steps: Density prediction is performed based on the forward and backward pressure values ​​of the feeder of the waste incineration boiler to obtain the raw material density of the raw material to be incinerated. Based on the density of the raw material, a calorific value prediction is performed to obtain the predicted calorific value of the raw material to be incinerated.

4. The boiler control method as described in claim 3, characterized in that, The method of performing density prediction based on the thrust of the feeder of the waste incineration boiler to obtain the density of the raw material to be incinerated includes the following steps: The raw material resistance to be incinerated is calculated based on the forward and backward pressure values ​​of the pusher. The density of the raw material is obtained by performing density prediction based on the raw material resistance.

5. The boiler control method as described in claim 3, characterized in that, The step of performing calorific value prediction based on the raw material density to obtain the predicted calorific value of the raw material to be incinerated includes the following steps: The temperature change is obtained by scanning the temperature field on the surface of the raw material to be incinerated; The predicted calorific value is obtained by calculating based on the temperature change, the density of the raw material, the basic calorific value of the raw material to be incinerated, and the typical density reference value.

6. The boiler control method as described in claim 1, characterized in that, The control of the waste incineration boiler based on the predicted calorific value and the predicted density includes the following steps: Based on the predicted calorific value and the predicted density, feedforward control parameters are obtained; Feedback control parameters are obtained by calculating based on the main steam flow rate of the waste incineration boiler. The waste incineration boiler is controlled based on the feedforward control parameters and the feedback control parameters.

7. The boiler control method as described in claim 6, characterized in that, The control of the waste incineration boiler based on the feedforward control parameters and the feedback control parameters includes the following steps: The feedforward control parameters and the feedback control parameters are output to the control device of the feeder of the waste incineration boiler, so that the control device controls the feeder based on the feedforward control parameters and the feedback control parameters.

8. A boiler control device, applied to a waste incineration boiler, characterized in that, The boiler control device includes: The data acquisition module is configured to acquire multiple raw data sets. The parameter prediction module is configured to perform prediction processing based on the multiple raw data to obtain the predicted calorific value and predicted density of the raw material to be incinerated. The control execution module is configured to control the waste incineration boiler based on the predicted calorific value and the predicted density.

9. An electronic device, characterized in that, The electronic device includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instructions to enable the electronic device to implement the boiler control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium for use in electronic devices, characterized in that, The storage medium carries one or more computer programs that can be executed by the electronic device, thereby enabling the electronic device to implement the boiler control method as described in any one of claims 1 to 7.

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