Solid waste reconstruction experiment furnace and intelligent temperature control system thereof
By using a bulk moisture sensor and a surface element sensor combined with a heterogeneous-spatiotemporal fusion correction module in the experimental furnace, the problems of feedback control lag and surface detection misjudgment were solved, enabling rapid and accurate temperature control of heterogeneous solid waste and ensuring the accuracy and repeatability of experimental results.
Patent Information
- Application Number
- CN202511723092.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-01-20
AI Technical Summary
When processing heterogeneous solid waste, existing experimental furnaces suffer from large temperature fluctuations due to feedback control lag and misjudgment by single-surface detection, making it difficult to achieve precise and stable control.
By combining a bulk moisture sensor and a surface element sensor with a heterogeneous spatiotemporal fusion correction module, material properties are diagnosed in real time, and precise control commands are generated through a control method that combines feedforward prediction and feedback correction.
It enables rapid and accurate temperature control of heterogeneous solid waste, reduces furnace temperature fluctuations, and ensures the accuracy and repeatability of experimental results.
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Figure CN121363870A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of thermal equipment control, in particular to a solid waste reconstruction experimental furnace and an intelligent temperature control system thereof. BACKGROUND
[0002] Thermo-chemical conversion of solid waste is an important way to realize its resource utilization. In related experimental research, the experimental furnace is a key equipment, and the accurate and stable control of the furnace temperature is the core prerequisite to ensure the accuracy and repeatability of experimental results.
[0003] However, the characteristics of solid waste bring great challenges to temperature control. The existing experimental furnace temperature control mostly relies on feedback control systems based on thermocouples and other temperature measuring elements. The essence of this control method is "after correction": when the material enters the furnace and has caused the temperature to deviate from the set value, the control system begins to adjust the heating power.
[0004] The inherent lag of this feedback control is acceptable when dealing with materials with stable composition, but it exposes serious defects when dealing with solid waste. Solid waste has high heterogeneity, and its key attributes such as moisture content, ash content, and heat value may change instantaneously and dramatically during the feeding process. When a large amount of high-moisture or low-heat-value material suddenly enters the furnace, it will form a huge thermal load disturbance. Relying solely on feedback control will inevitably cause significant fluctuations in the furnace temperature, making it difficult to meet the constant working conditions required by experiments.
[0005] In order to overcome the feedback lag, some technologies attempt to introduce feedforward control, that is, to detect the material before it enters the furnace. However, accurate online detection of heterogeneous solid waste itself is a big problem. For example, sensors that rely solely on surface detection are easily deceived by the surface state of the material. A piece of high-moisture material inside may have dry carbon powder attached to its surface, causing the sensor to misjudge it as a high-heat-value material, thus giving an incorrect feedforward compensation, which actually exacerbates the temperature fluctuations. Therefore, the existing technology lacks an effective temperature control method that can accurately diagnose the real thermal mass load of heterogeneous solid waste and make rapid and proactive compensation accordingly. SUMMARY
[0006] In view of the deficiencies of the prior art, the present application provides a solid waste reconstruction experimental furnace and an intelligent temperature control system thereof, which solves the problem of large furnace temperature fluctuations caused by the inherent lag of feedback control and the inability to cope with the severe thermal load disturbance caused by the heterogeneity of solid waste; and solves the problem of inaccurate material real thermal mass load and ineffective or even exacerbated disturbance caused by the misjudgment of single or surface detection method when introducing feedforward control.
[0007] To achieve the above purpose, the present application realizes the following technical scheme: a solid waste reconstruction experimental furnace, comprising: furnace body; feeding device; heating device; temperature measuring probe; an in-situ thermal mass load diagnosis module installed at a discharging outlet of the feeding device, the in-situ thermal mass load diagnosis module comprising: a bulk moisture sensor and a surface element sensor; a control cabinet electrically connected with the in-situ thermal mass load diagnosis module, the temperature measuring probe, and the heating device respectively.
[0008] Preferably, the bulk moisture sensor is a microwave resonance moisture sensor.
[0009] Preferably, the surface element sensor is a laser-induced breakdown spectroscopy probe.
[0010] Preferably, the control cabinet is a programmable logic controller or an industrial control computer.
[0011] Preferably, the feeding device is a screw feeder.
[0012] An intelligent temperature control system of a solid waste reconstruction experimental furnace, used for controlling a solid waste reconstruction experimental furnace, comprising: an in-situ thermal mass load diagnosis module, comprising: a bulk moisture sensor for acquiring a bulk moisture signal of material about to enter the furnace; and a surface element sensor for acquiring a surface element signal of the material about to enter the furnace; a heterogeneous-time-space fusion correction module connected with the in-situ thermal mass load diagnosis module, for receiving the bulk moisture signal and the surface element signal, analyzing consistency or conflict of instantaneous change trends of the bulk moisture signal and the surface element signal, judging signal cooperation or conflict working conditions, generating a dynamic confidence weight in the conflict working condition, correcting the signals by using the dynamic confidence weight, and outputting fused material attribute parameters; a thermal mass load feedforward prediction module connected with the heterogeneous-time-space fusion correction module, for calculating an instantaneous heat demand according to the fused material attribute parameters, and generating a feedforward compensation instruction based on the instantaneous heat demand; a feedback correction control module for acquiring an actual furnace temperature, and generating a feedback correction instruction according to a deviation of the actual furnace temperature from a set temperature; a control instruction superposition unit connected with the thermal mass load feedforward prediction module and the feedback correction control module respectively, for superimposing the feedforward compensation instruction and the feedback correction instruction to generate a total control instruction.
[0013] Preferably, the fused material attribute parameters output by the isomer-time-space fusion correction module include a fused moisture parameter, a fused carbon content parameter and a fused ash parameter.
[0014] Preferably, the model used by the thermal mass load feedforward prediction module to calculate the instantaneous heat demand is a mechanism model based on thermal chemical equilibrium.
[0015] Preferably, the specific manner in which the isomer-time-space fusion correction module determines the conflict condition is that when the bulk phase moisture signal is detected to exceed a first preset moisture threshold and the surface element signal exceeds a second preset carbon content threshold, it is determined that the surface element sensing unit is misreading the surface, which is the conflict condition.
[0016] Preferably, the specific manner in which the isomer-time-space fusion correction module generates the dynamic confidence weight under the conflict condition is to reduce the confidence weight of the surface element signal and increase the confidence weight of the bulk phase moisture signal to preferentially use the bulk phase moisture signal.
[0017] The present application provides a solid waste reconstruction experimental furnace and an intelligent temperature control system thereof. The present application has the following beneficial effects: 1. The present application sets a bulk phase moisture sensor and a surface element sensor, and uses an isomer-time-space fusion correction module to analyze the conflict of the two isomer signals and dynamically correct the weight, thereby solving the problem of surface misreading of a single sensor when measuring heterogeneous solid waste, and significantly improving the real-time diagnostic accuracy and reliability of the material attribute parameters of the furnace.
[0018] 2. The present application uses a thermal mass load feedforward prediction module, which can calculate the instantaneous heat demand caused by the material entering the furnace in advance according to the above-mentioned accurately diagnosed material parameters, and generate a feedforward compensation instruction. This feedforward control method can actively and quickly compensate for the thermal load disturbance caused by the dramatic fluctuation of material properties, overcome the hysteresis of traditional feedback control, and effectively prevent the large fluctuation of the furnace temperature.
[0019] 3. The present application combines the feedforward compensation instruction with the feedback correction instruction through a control instruction superposition unit to form a feedforward-feedback composite control structure. This structure fully combines the fast response capability of feedforward control and the accuracy of feedback control in eliminating steady-state deviation, so that the experimental furnace can still maintain high control accuracy and strong robustness when facing solid waste with complex and variable composition, and realize accurate and stable control of the furnace temperature. BRIEF DESCRIPTION OF DRAWINGS
[0020] Figure 1 FIG. 1 is a structural schematic diagram of the solid waste reconstruction experimental furnace of the present application; Figure 2 FIG. 2 is a system module diagram of the present application. DETAILED DESCRIPTION
[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.
[0022] Please refer to the drawings in the specification of the present application Figure 1 - the drawings in the specification of the present application Figure 2 The embodiments of the present application provide a solid waste reconstruction experimental furnace and an intelligent temperature control system thereof.
[0023] The present application provides a solid waste reconstruction experimental furnace, comprising: a furnace body; a feeding device; a heating device; a temperature measuring probe; The furnace body is the main place for solid waste thermochemical reaction.
[0024] The feeding device is used for conveying materials into the furnace body. In a specific embodiment, the feeding device can be a screw feeder, which has a determined discharging outlet and can realize stable control and metering of the mass flow rate of the materials by adjusting the rotating speed and the like.
[0025] The solid waste reconstruction experimental furnace, wherein the feeding device is a screw feeder.
[0026] The heating device is used for providing heat for the furnace body, for example, can be an electric heating element such as an electric resistance wire, a silicon-carbon rod or a silicon-molybdenum rod arranged around the furnace body.
[0027] The temperature measuring probe, for example, a K-type or S-type thermocouple, has a measuring end installed in a temperature measuring hole of the furnace body, and is used for monitoring the actual temperature in the furnace, i.e., the actual temperature of the furnace.
[0028] The in-situ thermal-mass load diagnosis module is installed at the discharging outlet of the feeding device, and ensures that the properties of the materials are diagnosed online at the moment when the materials leave the feeding device and enter the furnace body. The in-situ thermal-mass load diagnosis module comprises a bulk water content sensor and a surface element sensor.
[0029] The in-situ thermal-mass load diagnosis module is installed at the discharging outlet of the feeding device, and the in-situ thermal-mass load diagnosis module comprises a bulk water content sensor and a surface element sensor. Specifically, the bulk moisture sensor is used to obtain the bulk moisture signal of the material to be fed into the furnace. In a preferred embodiment, the bulk moisture sensor is a microwave resonance moisture sensor. The sensor utilizes the strong penetration of microwave signals to the material, enabling measurement of the dielectric constant change of the entire material, thereby accurately reflecting the bulk moisture content inside the material and avoiding measurement deviation caused by dry or excessively wet material surface.
[0030] The solid waste reconstruction experimental furnace, wherein the bulk moisture sensor is a microwave resonance moisture sensor.
[0031] The surface element sensor is used to obtain the surface element signal of the material to be fed into the furnace. In a preferred embodiment, the surface element sensor is a laser-induced breakdown spectroscopy probe (LIBS). The probe burns the surface of the material through high-energy laser pulses, causing it to vaporize and generate plasma. By analyzing the spectrum released when the plasma cools down, the relative content of elements such as carbon, hydrogen, oxygen, and elements such as silicon, aluminum, calcium that make up ash on the surface of the material can be obtained in real time.
[0032] The solid waste reconstruction experimental furnace, wherein the surface element sensor is a laser-induced breakdown spectroscopy probe.
[0033] The control cabinet is the control core of the experimental furnace of the present application. In an embodiment, the control cabinet can be a programmable logic controller (PLC) or an industrial control computer (IPC), which has data acquisition, logic operation and control instruction output capabilities.
[0034] The solid waste reconstruction experimental furnace, wherein the control cabinet is a programmable logic controller or an industrial control computer.
[0035] The control cabinet is respectively electrically or signal connected with the in-situ thermal mass load diagnosis module (specifically its bulk moisture sensor and surface element sensor), temperature measuring probe and heating device. For example, the control cabinet receives the signals of the sensors through analog or digital input interfaces, and controls the power regulator of the heating device through control signal output interfaces (such as PWM signals or analog voltage / current signals).
[0036] The control cabinet is respectively electrically connected with the in-situ thermal mass load diagnosis module, the temperature measuring probe, and the heating device; The control cabinet is configured to execute the functions of an intelligent temperature control system, which is realized through a series of functional modules. These functional modules specifically include: heterogeneous-spatiotemporal fusion correction module, thermal mass load feedforward prediction module, feedback correction control module and control instruction superposition unit. These modules can be logic programs fixed in the PLC, or software packages running on the IPC.
[0037] Furthermore, the control cabinet is configured to execute the functions of the following modules: The isomerism-time-space fusion correction module is connected with the in-situ thermal mass load diagnosis module, is used for receiving the bulk water content signal acquired by the bulk water content sensor and the surface element signal acquired by the surface element sensor, analyzing the consistency or conflict of the instantaneous change trend of the bulk water content signal and the surface element signal, judging the signal cooperation or conflict working condition, and generating a dynamic confidence weight in the conflict working condition, correcting the signal by using the dynamic confidence weight, and outputting the fused material attribute parameter; The thermal mass load feedforward prediction module is connected with the isomerism-time-space fusion correction module, is used for calculating the instantaneous heat demand according to the fused material attribute parameter, and generating a feedforward compensation instruction based on the instantaneous heat demand; The feedback correction control module is connected with the temperature measurement probe, is used for generating a feedback correction instruction according to the deviation between the actual temperature of the hearth acquired by the temperature measurement probe and the set temperature; The control instruction superposition unit is connected with the thermal mass load feedforward prediction module and the feedback correction control module respectively, is used for superimposing the feedforward compensation instruction and the feedback correction instruction to generate a total control instruction, and outputting the total control instruction to the heating device.
[0038] The signal input end of the isomerism-time-space fusion correction module is connected with the in-situ thermal mass load diagnosis module. The module is used for receiving the bulk water content signal (for example, a microwave phase shift or amplitude signal reflecting the bulk water content) acquired by the bulk water content sensor and the surface element signal (for example, a carbon or ash characteristic peak intensity signal obtained by LIBS spectral analysis) acquired by the surface element sensor in real time.
[0039] The core function of the isomerism-time-space fusion correction module is to analyze and process the two kinds of sensor signals which are different in source and principle (isomerism). The module is configured to analyze the consistency or conflict of the instantaneous change trend of the bulk water content signal and the surface element signal. In the normal working condition, when the material attribute changes, the two kinds of signals should present a certain cooperative change rule.
[0040] However, in some specific working conditions, for example, when dry carbon powder adheres to the surface of high-moisture material (such as sludge), the bulk water content sensor will report high moisture, while the surface element sensor will report high carbon content and low moisture, and the two will conflict.
[0041] Therefore, the isomerism-time-space fusion correction module is used for judging the signal cooperation or conflict working condition. In a specific implementation, the specific way of judging the conflict working condition is that when the bulk water content signal is detected to be greater than a first preset moisture threshold (for example, indicating that the overall moisture content of the material is greater than 40%), and the surface element signal (for example, the carbon content obtained by LIBS analysis) is greater than a second preset carbon content threshold (for example, indicating that the surface of the material is rich in carbon), the system determines that the surface element sensing unit has surface misreading, which is the conflict working condition.
[0042] The solid waste reconstruction experimental furnace, wherein the specific manner in which the heterogeneous-time-space fusion correction module determines the conflict working condition is that when it is detected that the bulk water content signal exceeds a first preset water content threshold and the surface element signal exceeds a second preset carbon content threshold, it is determined that the surface element sensing unit has surface misreading, which is the conflict working condition.
[0043] After determining the conflict working condition, the heterogeneous-time-space fusion correction module dynamically adjusts the trust degree of the two signals, that is, generates a dynamic confidence weight, and corrects the signals using the dynamic confidence weight. Specifically, in the conflict working condition, the weight generation manner of the module is to actively reduce the confidence weight of the surface element signal and simultaneously increase the confidence weight of the bulk water content signal, so as to preferentially adopt the bulk water content signal that can reflect the overall heat value and water evaporation load of the material.
[0044] The solid waste reconstruction experimental furnace, wherein the specific manner in which the heterogeneous-time-space fusion correction module generates a dynamic confidence weight in the conflict working condition is to reduce the confidence weight of the surface element signal and increase the confidence weight of the bulk water content signal, so as to preferentially adopt the bulk water content signal.
[0045] The heterogeneous-time-space fusion correction module finally outputs a set of fused material attribute parameters. In one embodiment, the fused material attribute parameters include a fused water content parameter, a fused carbon content parameter, and a fused ash content parameter. These parameters are input data that can more accurately reflect the true heat load of the material after the above conflict determination and dynamic weight correction.
[0046] The solid waste reconstruction experimental furnace, wherein the fused material attribute parameters output by the heterogeneous-time-space fusion correction module include a fused water content parameter, a fused carbon content parameter, and a fused ash content parameter.
[0047] The input end of the heat load feedforward prediction module is connected to the output end of the heterogeneous-time-space fusion correction module. The module is used to calculate the heat disturbance, that is, the instantaneous heat demand, generated after the material enters the furnace according to the fused material attribute parameters and the feed rate of the material, and generate a feedforward compensation instruction based on the instantaneous heat demand.
[0048] In one embodiment, the model used by the heat load feedforward prediction module to calculate the instantaneous heat demand is a mechanism model based on thermal chemical equilibrium.
[0049] The solid waste reconstruction experimental furnace, wherein the model used by the heat load feedforward prediction module to calculate the instantaneous heat demand is a mechanism model based on thermal chemical equilibrium.
[0050] The mechanism model can be specifically represented as:
[0051] wherein: Q demand is the instantaneous heat demand (unit: kW), representing the net heat power needed to compensate for the part of the material to reach the set temperature and complete the reaction; m is the real-time material mass flow rate of the feeding device (unit: kg / s), which can be calibrated in advance from the operating parameters of the feeding device (such as screw speed) ; W fusion is the fused moisture parameter (mass fraction) ; c fusion is the fused carbon content parameter (mass fraction) ; ( Note: the model can also include a fusion , i.e. the fused ash parameter, which is a simplified example) ; C p,dry is the specific heat capacity of the dry basis of the material; C p,water is the specific heat capacity of water; T set is the set temperature of the furnace; T0 is the initial temperature of the material before entering the furnace (which can be set to room temperature) ; L v is the latent heat of vaporization of water; ΔH comb is the combustion heat value of combustible substances (calculated in terms of carbon) in the material (exothermic is negative, so the minus sign in the formula).
[0052] Through this model, the fused moisture parameter, the fused carbon content parameter (and the fused ash parameter), and the material mass flow rate can be used to accurately calculate the heating heat demand of the material (the first two terms of the formula), the vaporization heat absorption (the third term), and the combustion heat release (the fourth term), thereby determining the total instantaneous heat demand Q demand .
[0053] The solid waste reconstruction experimental furnace according to claim 7, wherein the mechanism model calculates the vaporization heat absorption, the combustion heat release, and the heating heat demand of the material by using the fused moisture parameter, the fused carbon content parameter, the fused ash parameter, and the material mass flow rate of the feeding device, to determine the instantaneous heat demand.
[0054] The feedforward compensation instruction is generated based on this Q demand , and its role is to compensate for the heating power in advance before the material enters the furnace and the actual fluctuation of the furnace temperature has not occurred.
[0055] The input end of the feedback correction control module is connected with the temperature measuring probe. The module is used to compare the actual temperature of the furnace obtained by the temperature measuring probe with the preset set temperature, generate a feedback correction instruction according to the deviation between the two, for example, by using a PID (proportion-integral-derivative) control algorithm. The feedback correction instruction is mainly used to correct the long-term drift of the mechanism model and compensate for slow heat disturbances (such as changes in furnace wall heat dissipation) that are not captured by the feedforward model.
[0056] The input end of the control instruction superposition unit is connected with the thermal mass load feedforward prediction module and the feedback correction control module. The unit is used to superimpose the feedforward compensation instruction (for responding to fast and large material disturbances) and the feedback correction instruction (for eliminating steady-state deviation and slow disturbances), for example, by arithmetic addition or weighted summation, to generate a total control instruction.
[0057] The output end of the control instruction superposition unit is connected with the heating device. The total control instruction is finally sent to the power controller (such as a silicon-controlled power regulator) of the heating device to accurately control the heating power thereof, so as to realize stable control of the furnace temperature.
[0058] The embodiment of the present application solves the problem of multi-sensor data conflict by using the in-situ thermal mass load diagnosis module to obtain multi-dimensional data of the material before entering the furnace, and through the heterogeneous-time-space fusion correction module, accurate material attribute parameters are obtained. Based on this, by combining feedforward prediction and feedback correction, accurate, fast and robust control of the temperature of the solid waste reconstruction experimental furnace is realized.
[0059] Although the embodiments of the present application have been shown and described, it can be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.
Claims
1. A solid waste reconstitution experimental furnace, characterized by, The application relates to a furnace body, a feeding device, a heating device, a temperature measuring probe, an in-situ thermal mass load diagnosis module, and a control cabinet. The body phase moisture sensor is a microwave resonance moisture sensor. The surface element sensor is a laser-induced breakdown spectroscopy probe. The control cabinet is a programmable logic controller or an industrial control computer. The feeding device is a screw feeder. The application relates to an in-situ thermal mass load diagnosis module, which comprises a body phase moisture sensor for acquiring a body phase moisture signal of material to be fed into a furnace and a surface element sensor for acquiring a surface element signal of the material to be fed into the furnace. An isomer-time-space fusion correction module is connected to the in-situ thermal mass load diagnosis module and is used for receiving the body phase moisture signal and the surface element signal, analyzing consistency or conflict of instantaneous change trends of the body phase moisture signal and the surface element signal, judging a signal cooperation or conflict condition, generating a dynamic confidence weight under the conflict condition, correcting the signal by using the dynamic confidence weight, and outputting a fused material attribute parameter.
2. The solid waste reconstruction experimental furnace according to claim 1, characterized in that, A thermal mass load feedforward prediction module is connected to the isomer-time-space fusion correction module and is used for calculating an instantaneous heat demand according to the fused material attribute parameter and generating a feedforward compensation instruction based on the instantaneous heat demand.
3. The solid waste reconstruction experimental furnace according to claim 1, characterized in that, A feedback correction control module is used for acquiring an actual furnace temperature and generating a feedback correction instruction according to a deviation between the actual furnace temperature and a set temperature.
4. The solid waste reconstruction experimental furnace according to claim 1, characterized in that, A control instruction superposition unit is connected to the thermal mass load feedforward prediction module and the feedback correction control module and is used for superimposing the feedforward compensation instruction and the feedback correction instruction to generate a total control instruction.
5. The solid waste reconstruction experimental furnace according to claim 1, characterized in that, The fused material attribute parameter output by the isomer-time-space fusion correction module comprises a fused moisture parameter, a fused carbon content parameter and a fused ash content parameter.
6. An intelligent temperature control system for a solid waste reconstitution experimental furnace, for controlling the solid waste reconstitution experimental furnace according to any one of claims 1-5, characterized in that, The model used by the thermal mass load feedforward prediction module for calculating the instantaneous heat demand is a mechanism model based on thermal chemical equilibrium. The specific manner in which the isomer-time-space fusion correction module judges the conflict condition is that when the body phase moisture signal exceeds a first preset moisture threshold value and the surface element signal exceeds a second preset carbon content threshold value, it is determined that surface misreading occurs in the surface element sensor, which is the conflict condition. The specific manner in which the isomer-time-space fusion correction module generates the dynamic confidence weight under the conflict condition is that the confidence weight of the surface element signal is reduced and the confidence weight of the body phase moisture signal is increased, so that the body phase moisture signal is preferentially acquired. 7. The intelligent temperature control system of a solid waste reconstitution experimental furnace according to claim 6, characterized in that, 8. The intelligent temperature control system of a solid waste reconstitution experimental furnace according to claim 6, characterized in that, 9. The intelligent temperature control system of a solid waste reconstitution experimental furnace according to claim 6, wherein, 10. The intelligent temperature control system of a solid waste reconstitution experimental furnace according to claim 9, wherein,