Multistage intermittent infrared heating device and control method thereof

Abstract

The invention discloses a multistage intermittent infrared heating device and a control method thereof in the technical field of road maintenance, and the multistage intermittent infrared heating device comprises an infrared heating module, the infrared heating module is arranged in a thermal insulation protection structure, the thermal insulation protection structure is installed at the foremost end of a heating vehicle, and the infrared heating module is provided with a temperature measuring module in a matched manner; the temperature measuring module and the infrared heating module are both connected with the central control unit, and the central control unit is arranged in a cab of the heating vehicle. Road surface temperature distribution before and after heating is obtained through the temperature measuring module, heating power is dynamically adjusted through the central control unit, a three-vehicle coordinated operation mode is combined, multi-stage intermittent heating is achieved, and precise and uniform heating of the surface layer and the inner layer of the road surface in a staged mode is achieved through modular design and an intelligent temperature control algorithm.

Description

Technical Field

[0001] The invention relates to a multi-stage intermittent infrared heating device and a control method thereof in the technical field of road maintenance. Background Art

[0002] The pavement in-situ heat regeneration heating unit is the core machinery for modern road maintenance, and the heating device therein is the key component of the unit.

[0003] However, the heating device of the traditional on-site heat regeneration equipment for pavement usually adopts the method of continuous heating for the pavement, which leads to the problems of large temperature difference between the surface and the inside of the heated pavement, scorching of the pavement, etc. It can be seen that the heating device of the traditional on-site heat regeneration equipment for pavement generally has the following defects: large temperature difference between the surface and the inside of the heated pavement; continuous heating leads to carbonization of the surface asphalt, and the performance retention rate of the recycled material is less than 70%; low energy utilization rate (<45%), serious heat loss; lack of adaptive control ability, and unable to adapt to changes in ambient temperature.

[0004] Therefore, in view of the requirements of the pavement in-situ heat regeneration heating unit for the heating device's uniform heating of the pavement surface and inside, energy utilization rate, and adaptive control ability, it is urgent to develop a heating device and a control method for the pavement in-situ heat regeneration heating unit, which has the characteristics of uniform heating of the pavement surface and inside, high energy utilization rate, and strong adaptive control ability. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-stage intermittent infrared heating device and a control method thereof. In view of the defects of existing heating devices such as poor heating uniformity of the surface and inner layers of the road surface, high energy consumption, and low adaptive control ability, the present invention realizes accurate and uniform heating of the surface and inner layers of the road surface in stages through modular design and intelligent temperature control algorithm.

[0006] To achieve the above-mentioned objectives, the present invention provides a multi-stage intermittent infrared heating device, including an infrared heating module, which is arranged in a thermal insulation protection structure, and the thermal insulation protection structure is installed at the front end of a heating vehicle. The infrared heating module is equipped with a temperature measuring module, and the temperature measuring module and the infrared heating module are both connected to a central control unit, and the central control unit is arranged in the cab of the heating vehicle.

[0007] Compared with the prior art, the beneficial effect of the present invention lies in that the temperature distribution of the road surface before and after heating is obtained through the temperature measurement module, the heating power is dynamically adjusted by the central control unit, and multi-stage intermittent heating is achieved in combination with the coordinated operation of three vehicles. Through modular design and intelligent temperature control algorithm, the surface and inner layers of the road surface can be accurately and evenly heated in stages.

[0008] As a further improvement of the present invention, the infrared heating module includes an infrared heating plate, which is arranged in the thermal insulation protection structure. The infrared heating plate is embedded with a heating element. The infrared heating plate is also equipped with a reflective plate, which is arranged directly above the infrared heating plate and is in the thermal insulation protection structure.

[0009] In this way, the heating element can be used for rapid and long-term temperature increase, and the infrared heating plate can be used to quickly transfer the heat emitted by the heating element downward. At the same time, the reflective plate will reflect the heat dissipated upwards back downward, thereby improving the utilization rate of heat, enhancing the heating effect, and avoiding energy waste.

[0010] As a further improvement of the present invention, the thermal insulation protection structure includes an insulation lining made of an aerogel composite ceramic fiber material, the outer periphery of the insulation lining is covered with a stainless steel shell, the infrared heating module is arranged in the insulation lining, and an insulation cavity is formed between the insulation lining and the infrared heating module.

[0011] In this way, by using the thermal insulation lining made of aerogel composite ceramic fiber material, the temperature can be concentrated in the thermal insulation cavity to prevent heat from dissipating to the surroundings, further improving the utilization rate of heat and enhancing the heating effect.

[0012] As a further improvement of the present invention, the central control unit includes a fuzzy PID controller and an adaptive learning controller, which is connected to the infrared heating module via a CAN bus communication protocol and receives temperature data from the temperature measuring module.

[0013] In this way, the temperature can be dynamically adjusted through the fuzzy PID controller and the adaptive learning controller to improve the heating effect.

[0014] As a further improvement of the present invention, the infrared heating plate is an aluminum alloy heating plate made of aluminum alloy 6061; the heating element is a carbon fiber bellows made of high-purity polyacrylonitrile-based carbon fiber, and the wall thickness of the bellows is 0.8-1.2mm; the reflector plate adopts an infrared thermal radiation nano reflector.

[0015] In this way, due to the low resistivity of carbon fiber, it heats up instantly after power is turned on, avoiding the lag effect of traditional metal wire heaters and achieving rapid response: the corrugated tube structure makes the heat flow diffuse in a wave shape, and the lateral temperature deviation is less than 1.0℃, achieving uniform radiation; carbon fiber has no oxidation problem in humid and salt fog environments, is suitable for field operations, and improves corrosion resistance. The infrared radiation reflector is an infrared thermal radiation nano reflector with high infrared reflectivity, ultra-smoothness, good high temperature resistance and corrosion resistance. It is used in the on-site heat regeneration heating module of the road surface, which significantly improves the infrared thermal radiation reflectivity of the unit.

[0016] As a further improvement of the present invention, the temperature measurement module includes a front infrared thermal imager and a rear infrared thermal imager, the front infrared thermal imager is installed 10 cm below the front edge of the stainless steel shell, and the rear infrared thermal imager is installed 10 cm below the rear edge of the stainless steel shell.

[0017] In this way, the front thermal imager is used to collect the initial temperature distribution; the rear thermal imager monitors the temperature field of the road surface after heating, thereby providing real-time temperature data for dynamically adjusting the heating power.

[0018] In order to achieve the above object, the present invention also provides a control method for a multi-stage intermittent infrared heating device, comprising the following steps:

[0019] Step 1, obtaining the road surface temperature distribution before and after heating by using an infrared thermal imager;

[0020] Step 2: Dynamically adjust the heating power based on the fuzzy PID algorithm, and combine the coordinated operation of the three vehicles to achieve multi-level intermittent heating;

[0021] Step 3: According to the real-time temperature feedback, the heating power is dynamically adjusted through the fuzzy PID controller and the adaptive learning controller.

[0022] Compared with the prior art, the beneficial effect of the present invention lies in that the stamping servo control subsystem and the mold adjustment servo control subsystem in the servo press control system are effectively connected through the slider displacement sensor, and the bottom dead point displacement of the press can be monitored and automatically adjusted in real time at any time and under any working conditions, thereby ensuring the stamping accuracy of the press and the quality of the stamped parts and improving the stamping processing efficiency.

[0023] As a further improvement of the present invention, the multi-stage intermittent heating procedure in step 1 is divided into the following three stages:

[0024] (1) Preheating stage: The target road surface temperature is determined to be 110±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 70%-80% of the rated power. Combined with the average road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of the subsequent road sections.

[0025] (2) Transition heating stage: The target road surface temperature is determined to be 150±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 60%-70% of the rated power. Combined with the average road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of the subsequent road sections.

[0026] (3) Main heating stage: The target road surface temperature is determined to be 180±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 40%-50% of the rated power. Combined with the maximum road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of subsequent sections.

[0027] In this way, through three stages of heating, the road surface temperature is gradually increased, thereby ensuring that the temperature of the asphalt in the road surface is gradually increased from the surface layer to the inner layer, avoiding the surface layer temperature from rising too high all at once and being damaged.

[0028] A control method for a multi-stage intermittent infrared heating device, characterized in that the fuzzy PID algorithm control equation of the fuzzy PID controller is: Among them, K p , K i , K d Online optimization by fuzzy rule base;

[0029] e(t): real-time temperature error, i.e. the difference between the target temperature and the measured temperature, in °C;

[0030] t: instantaneous time of measuring temperature;

[0031] Kp: Proportional coefficient, dynamically adjusts the response speed of heating power according to the error size;

[0032] Ki: Integral coefficient, used to eliminate steady-state errors and adjust power through historical error accumulation;

[0033] Kd: differential coefficient, suppresses temperature overshoot and predicts future trends through error change rate;

[0034] Fuzzy rule base optimization: based on error e(t) and error change rate Kp, Ki, and Kd are dynamically adjusted through the fuzzy rule base.

[0035] In this way, the heating power is adjusted through the fuzzy PID algorithm.

[0036] A control method for a multi-stage intermittent infrared heating device, characterized in that: an adaptive learning algorithm of an adaptive learning controller, based on an online regression model SGDRegressor, optimizes heating power,

[0037] The adaptive learning algorithm quantifies the difference between the model prediction value and the actual value through the mean square error loss function J(θ), the specific formula is:

[0038]

[0039] Where, θ: heating power, hθ (x): predicted temperature model, y: measured temperature,

[0040] i: represents the index of the i-th sample in the training data set of temperature, m: the number of training samples of temperature;

[0041] By adjusting θ j , minimizing the loss function J(θ), so that the model can accurately predict the temperature and optimize the control parameters;

[0042] Update the parameters via stochastic gradient descent (SGD):

[0043]

[0044] Where, j represents the index of the jth sample in the training dataset of heating power, θ j represents the jth heating power, and the learning rate α = 0.01, which is an iterative step set by oneself and belongs to empirical assignment;

[0045] By updating the model every 10 minutes based on 100 sets of newly collected temperature data, energy consumption is gradually reduced.

[0046] Such an adaptive learning module is based on an online regression model (SGDRegressor) to optimize the heating parameters. Through the road surface temperature data fed back by the infrared thermal imager behind each heating device, it optimizes the power of the heating device and the heating interval time, so that the energy consumption is reduced by 5%-8% with the cumulative use time. The smaller J(θ) is, the closer the temperature predicted by the model is to the actual temperature, the more optimized the heating power of the control system is, and the lower the energy consumption and road surface temperature difference are. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a structural schematic diagram of the infrared heating device of the present invention.

[0048] Figure 2 It is a schematic diagram of the heat insulation structure of the present invention.

[0049] Figure 3 It is a schematic diagram of the working principle of the present invention.

[0050] Figure 4 This is the dynamic regulation rule table of the heating module power.

[0051] Figure 5 This is a performance comparison table of three heating devices.

[0052] Among them, 1 is a thermal insulation protection structure, 2 is a heating element, 3 is a rear infrared thermal imager, 4 is a front infrared thermal imager, 5 is a thermal insulation cavity, 6 is an infrared heating plate, and 7 is a reflective plate. DETAILED DESCRIPTION

[0053] The present invention is further described below in conjunction with the accompanying drawings:

[0054] like Figure 1-2 A multi-stage intermittent infrared heating device is shown, including an infrared heating module, which is arranged in a thermal insulation protection structure 1, and the thermal insulation protection structure 1 is installed at the front end of the heating vehicle. The infrared heating module is equipped with a temperature measuring module, and the temperature measuring module and the infrared heating module are both connected to a central control unit, and the central control unit is arranged in the cab of the heating vehicle.

[0055] The infrared heating module includes an infrared heating plate 6, which is arranged in the thermal insulation protection structure 1. The infrared heating plate 6 is embedded with a heating element 2. The infrared heating plate 6 is also equipped with a reflecting plate 7, which is arranged directly above the infrared heating plate 6 and is in the thermal insulation protection structure 1.

[0056] The thermal insulation protection structure 1 includes a thermal insulation lining made of aerogel composite ceramic fiber material, the outer periphery of the thermal insulation lining is covered with a stainless steel shell, an infrared heating module is arranged in the thermal insulation lining, and a thermal insulation cavity 5 is formed between the thermal insulation lining and the infrared heating module.

[0057] The central control unit includes a fuzzy PID controller and an adaptive learning controller, which is connected to the infrared heating module through a CAN bus communication protocol and receives temperature data from the temperature measuring module.

[0058] The infrared heating plate 6 is an aluminum alloy heating plate made of aluminum alloy 6061; the heating element 2 is a carbon fiber bellows made of high-purity polyacrylonitrile-based carbon fiber, and the wall thickness of the bellows is 0.8-1.2 mm; the reflector 7 is an infrared heat radiation nano reflector 7.

[0059] The temperature measurement module includes a front infrared thermal imager 4 and a rear infrared thermal imager 3. The front infrared thermal imager 4 is installed 10 cm below the front edge of the stainless steel shell, and the rear infrared thermal imager 3 is installed 10 cm below the rear edge of the stainless steel shell.

[0060] like Figure 3 A control method for a multi-stage intermittent infrared heating device is shown, comprising the following steps:

[0061] Step 1, obtaining the road surface temperature distribution before and after heating by using an infrared thermal imager;

[0062] (1) Preheating stage: The target road surface temperature is determined to be 110±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 70%-80% of the rated power. Combined with the average road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of the subsequent road sections.

[0063] (2) Transition heating stage: The target road surface temperature is determined to be 150±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 60%-70% of the rated power. Combined with the average road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of the subsequent road sections.

[0064] (3) Main heating stage: The target road surface temperature is determined to be 180±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 40%-50% of the rated power. Combined with the maximum road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of subsequent sections.

[0065] Step 2: Dynamically adjust the heating power based on the fuzzy PID algorithm, and combine the coordinated operation of the three vehicles to achieve multi-level intermittent heating;

[0066] The fuzzy PID algorithm control equation of the fuzzy PID controller is: Among them, K p , K i , K d Online optimization by fuzzy rule base;

[0067] e(t): real-time temperature error, i.e. the difference between the target temperature and the measured temperature, in °C;

[0068] t: instantaneous time of measuring temperature;

[0069] Kp: Proportional coefficient, dynamically adjusts the response speed of heating power according to the error size;

[0070] Ki: Integral coefficient, used to eliminate steady-state errors and adjust power through historical error accumulation;

[0071] Kd: differential coefficient, suppresses temperature overshoot and predicts future trends through error change rate;

[0072] Fuzzy rule base optimization: based on error e(t) and error change rate Kp, Ki, and Kd are dynamically adjusted through the fuzzy rule base.

[0073] For example: If e(t)>5℃ and Then increase Kp to quickly increase the temperature; if e(t)<-5℃ and Then reduce Ki to prevent integral windup.

[0074] Step 3: According to the real-time temperature feedback, the heating power is dynamically adjusted through the fuzzy PID controller and the adaptive learning controller.

[0075] The adaptive learning algorithm of the adaptive learning controller is based on the online regression model SGDRegressor to optimize the heating power.

[0076] The adaptive learning algorithm quantifies the difference between the model prediction value and the actual value through the mean square error loss function J(θ), the specific formula is:

[0077]

[0078] Where, θ: heating power, h θ (x): predicted temperature model, y: measured temperature,

[0079] i: represents the index of the i-th sample in the training data set of temperature, m: the number of training samples of temperature;

[0080] By adjusting θ j , minimizing the loss function J(θ), so that the model can accurately predict the temperature and optimize the control parameters;

[0081] Update the parameters via stochastic gradient descent (SGD):

[0082]

[0083] Where, j represents the index of the jth sample in the training dataset of heating power, θ j represents the jth heating power, and the learning rate α = 0.01, which is an iterative step set by oneself and belongs to empirical assignment;

[0084] By updating the model every 10 minutes based on 100 sets of newly collected temperature data, energy consumption is gradually reduced.

[0085] In the present invention, the infrared heating module has a rated power of 3500kW and includes an infrared heating plate 6, a heating element 2 and an infrared radiation reflecting plate 7. The infrared heating plate 6 is an aluminum alloy heating plate, and aluminum alloy 6061 is selected, which has excellent high temperature oxidation resistance, high mechanical strength and creep resistance, and is suitable for long-term high-power operation.

[0086] The heating element 2 is a carbon fiber bellows type heating element 2, which uses high-purity polyacrylonitrile-based carbon fiber (PAN-CF), with a thermal conductivity of ≥1200W / (m K), an electrothermal conversion efficiency of >98%, and a radiation wavelength concentrated in the 2-15μm band, which is highly coincident with the asphalt heat absorption peak (3-5μm). The wall thickness of the bellows is designed to be 0.8-1.2mm. It has been verified through finite element thermodynamic simulation that this thickness can balance mechanical strength (compression resistance ≥50MPa) and thermal response speed (it only takes 30 seconds to heat up to rated power).

[0087] This heating element 2 has the following advantages:

[0088] Fast response: Carbon fiber has low resistivity and generates heat instantly after power is applied, avoiding the lag effect of traditional wire heaters.

[0089] Uniform radiation: The bellows structure allows the heat flow to diffuse in a wave-like manner, with a lateral temperature deviation of <1.0°C.

[0090] Corrosion resistance: Carbon fiber has no oxidation problem in humid and salt spray environments and is suitable for field operations.

[0091] The infrared radiation reflector 7 is an independently developed infrared thermal radiation nano reflector 7, application number: 2025100740767. The infrared thermal radiation nano reflector 7 includes a substrate and a nano coating. The reflector 7 has the characteristics of high infrared reflectivity, ultra-smoothness, good high temperature resistance and corrosion resistance, and is used in the road surface in-situ heat regeneration heating module to significantly improve the infrared thermal radiation reflectivity of the unit.

[0092] The temperature measurement module includes a front infrared thermal imager 4 and a rear infrared thermal imager 3, with a temperature measurement range of -20℃~600℃, which meets the requirements of all stages of asphalt heating. The resolution is 640×480 pixels, the spatial resolution is 0.68mrad, and it can identify small temperature gradients on the road surface (such as 0.1℃ / cm). The accuracy is ±2℃, and it supports the NUC (non-uniformity correction) function to eliminate the lens thermal drift error. The frame rate is 30Hz, and a thermal cloud map of the road surface temperature is generated to obtain the highest temperature, the lowest temperature and the average temperature.

[0093] The front thermal imager is installed 10 cm below the front edge of the heating device to collect the initial temperature distribution; the rear thermal imager is installed 10 cm above the rear edge of the heating device to monitor the temperature field of the road surface after heating.

[0094] The central control unit includes a fuzzy PID controller and an adaptive learning controller. The central control unit uses the road surface temperature data collected in real time by the temperature measurement module to dynamically adjust the heating power of the heating module through the fuzzy PID controller and the adaptive learning controller to achieve a multi-level intermittent heating mode.

[0095] The thermal insulation protective cover is located above the infrared heating plate 6. The thermal insulation protective cover is made of aerogel composite ceramic fiber and is covered with a stainless steel shell.

[0096] The specific working principle is as follows:

[0097] The working process is generally divided into three stages: preheating stage, transition heating stage, and main heating stage. The three-car coordination method is adopted, and the heating cars are arranged in the front, middle, and back. Figure 3 shown.

[0098] The central control unit uses the road surface temperature data collected in real time by the temperature measurement module to dynamically adjust the heating power of the heating module through the fuzzy PID controller and adaptive learning controller through post-feedback to achieve a multi-level intermittent heating mode. The dynamic adjustment rule of the heating module power is as follows: Figure 4 shown.

[0099] Embodiment 1

[0100] AC-13 Asphalt Pavement Regeneration (Three-vehicle Collaborative Operation)

[0101] First car group (warm-up stage)

[0102] Target temperature: T 目标 =110℃

[0103] Average temperature of front thermal imager: T 初始 =38℃

[0104] Rated power: P 额定 =3500kW

[0105] Initial power calculation: Based on the initial road surface temperature, the heating power is initially set to 71% of the rated power (2485kW). Vehicle speed 3m / min.

[0106] P 实际 =0.71×3500=2485kW

[0107] Average temperature of rear thermal imager: T 反馈 =98℃

[0108] Error calculation: e(t) = 110-98 = 12°C

[0109] Power adjustment: According to the fuzzy PID algorithm, Kp increases to quickly compensate for the error, and the power is increased to:

[0110] P 调整 =0.79×3500=2765kW

[0111] Effect after adjustment: The heating power was adjusted to 79% of the rated power (2765kW), the vehicle speed was 3m / min, and the average temperature of the rear thermal imager in the subsequent section was 108℃.

[0112] The second car group (transition heating stage)

[0113] Target temperature: T 目标 =150±5℃

[0114] Average temperature of front thermal imager: T 初始 =98℃

[0115] Rated power: P 额定=3500kW

[0116] Initial power calculation: Based on the initial road surface temperature, the heating power is initially set to 69% of the rated power (2415kW). Vehicle speed 3m / min.

[0117] P 实际 =0.69×3500=2415kW

[0118] Average temperature of rear thermal imager: T 反馈 =161°C.

[0119] Power adjustment: According to the fuzzy PID algorithm, K i To prevent integral saturation, the power is reduced to:

[0120] P 调整 =0.60×3500=2100kW

[0121] Effect after adjustment: The heating power was adjusted to 60% of the rated power (2100kW), the vehicle speed was 3m / min, and the average temperature of the rear thermal imager in the subsequent section was 152℃.

[0122] The third car group (main heating stage)

[0123] Target temperature: T 目标 =180±5℃

[0124] Maximum temperature of front thermal imager: T 初始 =162℃

[0125] Rated power: P 额定 =3500kW

[0126] Initial power calculation: Based on the initial road surface temperature, the heating power is initially set to 41% of the rated power (1435kW). Vehicle speed 3m / min.

[0127] P 实际 =0.41×3500=1435kW

[0128] Maximum temperature of rear thermal imager: T 反馈 =176℃.

[0129] Power adjustment: According to the fuzzy PID algorithm, Kp increases to quickly compensate for the error, and the power is increased to:

[0130] P 调整 =0.44×3500=1540kW

[0131] Effect after adjustment: The heating power was adjusted to 44% of the rated power (1540kW), the vehicle speed was 3m / min, and the maximum temperature of the rear thermal imager in the subsequent section was 182℃.

[0132] Final effect: The temperature difference between the surface and inside of the road is 7°C, the energy consumption ratio is 0.27, and the performance retention rate of recycled materials is >96%.

[0133] Comparative Example 1: Traditional continuous heating equipment

[0134] (1) Continuous heating with gas flame, constant power of 3000kW;

[0135] (2) The temperature fluctuates by ±1.5°C. The temperature difference between the surface and the inside of the road surface reaches 60°C. The surface temperature exceeds 200°C, resulting in carbonization, while the deep temperature is only 140°C.

[0136] (3) The energy consumption ratio is 1, and the performance retention rate of the recycled material is only 68%.

[0137] Comparative Example 2: Fixed intermittent heating device

[0138] (1) Using a fixed heating / interval time ratio (2:1);

[0139] (2) The temperature fluctuates by ±1.5°C. The temperature difference between the surface and the inside of the road surface reaches 30°C. The surface temperature exceeds 180°C, resulting in carbonization, while the deep temperature is only 150°C.

[0140] (3) The energy consumption ratio is 0.37, and the performance retention rate of recycled materials is only 82%;

[0141] (4) Poor adaptability, unable to adjust parameters according to material properties.

[0142] Note: Energy consumption ratio = actual energy consumption / energy consumption of traditional equipment, with the energy consumption of traditional continuous heating equipment as the calculation basis.

[0143] The performance comparison table between the three is as follows Figure 5 shown.

[0144] Embodiment 2:

[0145] SMA-13 ​​Asphalt Pavement Regeneration (Three-vehicle Collaborative Operation)

[0146] First car group (warm-up stage)

[0147] Target temperature: T 目标 =110±5℃

[0148] Average temperature of front thermal imager: T 初始 =25℃

[0149] Rated power: P 额定 =3500kW

[0150] Initial power calculation: Based on the initial road surface temperature, the heating power is initially set to 74% of the rated power (2590kW). Vehicle speed is 3m / min.

[0151] P 实际 =0.74×3500=2590kW

[0152] Average temperature of rear thermal imager: T 反馈 =102℃.

[0153] Power adjustment: According to the fuzzy PID algorithm, Kp increases to quickly compensate for the error, and the power is increased to:

[0154] P 调整 =0.78×3500=2730kW

[0155] Effect after adjustment: The heating power was adjusted to 78% of the rated power (2730kW), the vehicle speed was 3m / min, and the average temperature of the rear thermal imager in the subsequent section was 114℃.

[0156] The second car group (transition heating stage)

[0157] Target temperature: T 目标 =150±5℃

[0158] Average temperature of front thermal imager: T 初始 =102℃

[0159] Rated power: P 额定 =3500kW

[0160] Initial power calculation: Based on the initial road surface temperature, the heating power is initially set to 68% of the rated power (2380kW). Vehicle speed 3m / min.

[0161] P 实际 =0.68×3500=2380kW

[0162] Average temperature of rear thermal imager: T 反馈 =158℃.

[0163] Power adjustment: According to the fuzzy PID algorithm, K i To prevent integral saturation, the power is reduced to:

[0164] P 调整 =0.64×3500=2240kW

[0165] Effect after adjustment: The heating power was adjusted to 64% of the rated power (2240kW), the vehicle speed was 3m / min, and the average temperature of the rear thermal imager in the subsequent section was 151℃.

[0166] The third car group (main heating stage)

[0167] Target temperature: T 目标 =180±5℃

[0168] Maximum temperature of front thermal imager: T 初始 =160℃

[0169] Rated power: P 额定 =3500kW

[0170] Initial power calculation: Based on the initial road surface temperature, the heating power is initially set to 40% of the rated power (1400kW). Vehicle speed 3m / min.

[0171] P 实际 =0.40×3500=1400kW

[0172] Maximum temperature of rear thermal imager: 183℃.

[0173] Power adjustment: The maximum road surface temperature is fed back in real time by the rear thermal imager and compared with the target temperature according to the fuzzy PID algorithm. No adjustment is required.

[0174] Effect: The temperature difference between the surface and inside of the road is 6°C, the energy consumption ratio is 0.25, and based on the online regression model (SGDRegressor), the adaptive learning module cumulatively optimizes the energy consumption to reduce by 8%, and there is no surface carbonization phenomenon.

[0175] The present invention collects the road surface temperature field data before and after heating in real time through an infrared thermal imager, optimizes the heating parameters based on an online regression model (SGDRegressor), dynamically adjusts the heating power and the intermittent time, and the target temperature of the preheating stage is 110±5℃, the target temperature of the transition heating stage is 150±5℃, and the final temperature of the main heating stage is 180±5℃, which effectively avoids the road surface heating temperature from being too high or too low, and reduces the temperature difference between the surface and the inside of the road surface. The heating device of the present invention can prevent the road surface from being scorched, reduce the temperature difference between the surface and the inside of the road surface to 6-10℃, and reduce energy consumption by 38%, effectively solving the problems of road surface carbonization, excessive temperature difference between the surface and the inside of the road surface, and energy waste caused by traditional continuous heating.

[0176] The present invention is not limited to the above-mentioned embodiments. On the basis of the technical solution disclosed herein, technicians in this field can make some substitutions and deformations to some technical features therein according to the disclosed technical content without creative labor, and these substitutions and deformations are all within the protection scope of the present invention.

Claims

1. A multi-stage intermittent infrared heating device, characterized in that: It includes an infrared heating module, which is arranged in a thermal insulation protection structure. The thermal insulation protection structure is installed in the front end of the heating vehicle. The infrared heating module is equipped with a temperature measuring module. Both the temperature measuring module and the infrared heating module are connected to a central control unit, and the central control unit is arranged in the cab of the heating vehicle.

2. A multi-stage intermittent infrared heating device according to claim 1, characterized in that: The infrared heating module comprises an infrared heating plate, which is arranged in the thermal insulation protection structure. A heating element is embedded in the infrared heating plate. The infrared heating plate is also equipped with a reflecting plate, which is arranged directly above the infrared heating plate and in the thermal insulation protection structure.

3. A multi-stage intermittent infrared heating device according to claim 2, characterized in that: The thermal insulation protection structure includes a thermal insulation lining made of aerogel composite ceramic fiber material, the outer periphery of the thermal insulation lining is covered with a stainless steel shell, an infrared heating module is arranged in the thermal insulation lining, and a thermal insulation cavity is formed between the thermal insulation lining and the infrared heating module.

4. A multi-stage intermittent infrared heating device according to claim 3, characterized in that: The central control unit includes a fuzzy PID controller and an adaptive learning controller, which is connected to the infrared heating module through a CAN bus communication protocol and receives temperature data from the temperature measuring module.

5. A multi-stage intermittent infrared heating device according to claim 4, characterized in that: The infrared heating plate is an aluminum alloy heating plate made of aluminum alloy 6061; the heating element is a carbon fiber bellows made of high-purity polyacrylonitrile-based carbon fiber, and the wall thickness of the bellows is 0.8-1.2mm; The reflective plate adopts an infrared heat radiation nano reflective plate.

6. A multi-stage intermittent infrared heating device according to claim 5, characterized in that: The temperature measurement module includes a front infrared thermal imager and a rear infrared thermal imager. The front infrared thermal imager is installed 10 cm below the front edge of the stainless steel shell, and the rear infrared thermal imager is installed 10 cm below the rear edge of the stainless steel shell.

7. A control method for a multi-stage intermittent infrared heating device, characterized in that: The following steps are included: Step 1, obtaining the road surface temperature distribution before and after heating by using an infrared thermal imager; Step 2: Dynamically adjust the heating power based on the fuzzy PID algorithm, and combine the coordinated operation of the three vehicles to achieve multi-level intermittent heating; Step 3: According to the real-time temperature feedback, the heating power is dynamically adjusted through the fuzzy PID controller and the adaptive learning controller.

8. The control method of a multi-stage intermittent infrared heating device according to claim 7, characterized in that: The multi-stage intermittent heating in step 1 is divided into the following three stages: (1) Preheating stage: The target road surface temperature is determined to be 110±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 70%-80% of the rated power. Combined with the average road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of the subsequent road sections. (2) Transition heating stage: The target road surface temperature is determined to be 150±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 60%-70% of the rated power. Combined with the average road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of the subsequent road sections. (3) Main heating stage: The target road surface temperature is determined to be 180±5°C. Combined with the average road surface temperature measured by the front infrared thermal imager, the heating power is set to 40%-50% of the rated power. Combined with the maximum road surface temperature after heating measured by the rear infrared thermal imager, the target temperature is compared to dynamically adjust the heating power of subsequent sections.

9. A control method for a multi-stage intermittent infrared heating device according to claim 8, characterized in that: The fuzzy PID algorithm control equation of the fuzzy PID controller is: Among them, K p , K i , K d Online optimization by fuzzy rule base; e(t): real-time temperature error, i.e. the difference between the target temperature and the measured temperature, in °C; t: instantaneous time of measuring temperature; Kp: Proportional coefficient, dynamically adjusts the response speed of heating power according to the error size; Ki: Integral coefficient, used to eliminate steady-state errors and adjust power through historical error accumulation; Kd: differential coefficient, suppresses temperature overshoot and predicts future trends through error change rate; Fuzzy rule base optimization: based on error e(t) and error change rate Kp, Ki, and Kd are dynamically adjusted through the fuzzy rule base.

10. The control method of a multi-stage intermittent infrared heating device according to claim 9, characterized in that: The adaptive learning algorithm of the adaptive learning controller is based on the online regression model SGDRegressor to optimize the heating power. The adaptive learning algorithm quantifies the difference between the model prediction value and the actual value through the mean square error loss function J(θ), the specific formula is: Where, θ: heating power, h θ (x): predicted temperature model, y: measured temperature, i: represents the index of the i-th sample in the training data set of temperature, m: the number of training samples of temperature; By adjusting θ j , minimizing the loss function J(θ), so that the model can accurately predict the temperature and optimize the control parameters; Update the parameters via stochastic gradient descent (SGD): Where, j represents the index of the jth sample in the training dataset of heating power, θ j represents the jth heating power, and the learning rate α = 0.01, which is an iterative step length set by oneself and belongs to empirical assignment; By updating the model every 10 minutes based on 100 sets of newly collected temperature data, energy consumption is gradually reduced.

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