Waste dump waste heat recovery power generation system

Abstract

The invention relates to the technical field of thermoelectric power generation, and discloses a gangue dump waste heat recovery power generation system, which comprises an acquisition module, a power generation module and a control module, the positioning module is used for confirming the high-temperature area of the gangue dump, and the positioning module comprises an acquisition unit, a processing unit, a point fixing unit and a judgment unit; the oil supply module comprises a heat exchange part, and the heat exchange part is arranged in a high-temperature area of the gangue dump; the cooling module comprises a cooling pipeline and a water pump, the water pump is connected with the cooling pipeline, and the cooling pipeline is arranged on one side of the gangue dump; the power generation module is connected with the oil supply module and the cooling module and comprises a thermoelectric generator; and the energy storage module is electrically connected with the power generation module. According to the method, the temperature difference generators are arranged at the high-temperature points of the gangue dump, the spontaneous combustion condition is fundamentally destroyed through continuous export of heat energy, and the re-combustion risk caused by a traditional method is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of waste heat recovery, and in particular to a waste heat recovery power generation system for a waste rock pile. Background Art

[0002] In the coal mining industry, the treatment of coal gangue has become an environmental and safety hazard that needs to be solved urgently. Coal gangue is a solid waste generated during coal mining and washing. It has a huge accumulation volume and is difficult to handle. During the stacking process, coal gangue is prone to spontaneous combustion due to the combustible substances contained in it. In the existing treatment of coal mine gangue heaps, two technical means are currently mainly used: grouting and covering. The grouting method is to inject slurry into the inside of the gangue heap to fill the gaps between the gangue, thereby isolating oxygen and inhibiting the occurrence of spontaneous combustion. The covering method is to cover a certain thickness of soil or other materials on the surface of the gangue heap to prevent oxygen from entering the inside of the gangue through physical isolation. These two methods can temporarily control the spontaneous combustion problem of the gangue heap to a certain extent, but they have obvious disadvantages and limitations in practical applications. First, the implementation effect of the grouting method is limited by the penetration range and uniformity of the slurry. Due to the complex internal structure of the gangue heap, it is difficult for the slurry to completely cover all areas, resulting in the risk of oxygen infiltration in some areas. In addition, the slurry may solidify or become ineffective in the long-term natural environment and cannot continue to play the role of isolating oxygen. The soil covering method is easily damaged by natural factors, such as rain erosion, wind erosion and temperature changes, which cause the covering layer to gradually become thinner or cracks to appear, allowing oxygen to re-enter the waste rock pile and cause re-ignition.

[0003] Therefore, it is necessary to design a waste heat recovery power generation system for waste rock heap to solve the problems existing in current technology. Summary of the invention

[0004] In view of this, the present invention proposes a waste heat recovery power generation system for waste rock dump, aiming to solve the current problem of re-ignition of waste rock dump.

[0005] The present invention proposes a waste heat recovery power generation system for a waste rock pile, comprising:

[0006] Acquisition module, including temperature sensor;

[0007] A positioning module is used to confirm the high-temperature area of ​​the waste rock pile, and the positioning module includes a collection unit, a processing unit, a fixed-point unit and a judgment unit;

[0008] The oil supply module comprises a heat exchange part, wherein the heat exchange part is arranged in a high temperature area of ​​the waste rock pile;

[0009] A cooling module includes a cooling pipe and a water pump, wherein the water pump is connected to the cooling pipe, and the cooling pipe is arranged on one side of the waste rock pile;

[0010] A power generation module connected to the oil supply module and the cooling module, wherein the power generation module includes a thermoelectric generator;

[0011] The energy storage module is electrically connected to the power generation module.

[0012] Furthermore, the heat exchange part includes a heat exchange pipe, an oil pump and an oil storage tank, the oil pump is connected to the oil storage tank, the heat exchange pipe is located in the high temperature area of ​​the waste rock pile, and the heat exchange pipe passes through the high temperature area of ​​the waste rock pile and is connected to the temperature difference generator.

[0013] Furthermore, the thermoelectric generator includes a hot end interface, a hot end outlet, a cold end interface, a cold end outlet and a thermoelectric power generation unit. The heat exchange pipe enters through the hot end interface and flows out from the hot end outlet, the cooling pipe enters through the cold end interface and flows out from the cold end outlet, and the thermoelectric power generation unit generates an electromotive force based on the temperature difference between the hot end and the cold end.

[0014] Furthermore, the acquisition unit is configured to acquire the temperatures of a plurality of waste rock piles based on the temperature sensor, and the acquisition unit is further configured to perform data cleaning on the acquired temperatures of the plurality of waste rock piles.

[0015] Further, the processing unit is configured to mark high temperature abnormal points based on the temperatures of the plurality of waste rock piles after data cleaning, and generate a thermal abnormal data graph based on the data after the high temperature abnormal points are marked;

[0016] Expanding the thermal anomaly data graph into a three-dimensional model, and meshing the three-dimensional model;

[0017] Based on the structure of the waste rock pile, a three-dimensional geometric model is constructed, and combined with the thermal anomaly data map, high heat areas are highlighted.

[0018] Furthermore, the positioning unit is configured to determine a flow path of the heat exchange pipe according to the high heat area.

[0019] Furthermore, when determining the flow path of the heat exchange pipe according to the high heat area, it includes:

[0020] Extracting the three-dimensional coordinates of the high-calorie area;

[0021] Based on the three-dimensional coordinates and the temperature value, obtain the highest temperature points of several high-calorie areas as the center point;

[0022] Based on the center point data of the high heat area, a flow path of the heat exchange pipe is determined.

[0023] Furthermore, when determining the flow path of the heat exchange pipe according to the high heat area, it also includes:

[0024] According to the determined flow path of the heat exchange pipe, the smoothness of the flow path of the heat exchange pipe is optimized, and smoothed path data is generated;

[0025] Based on the smoothed path data and temperature distribution data, optimizing the temperature gradient distribution of the path through which the heat exchange pipe flows, and generating path data after temperature gradient optimization;

[0026] The optimal flow path of the heat exchange pipeline is determined based on the path data after the temperature gradient is optimized.

[0027] Further, the judgment unit is configured to collect temperature data at the hot end outlet;

[0028] Inputting the temperature data at the hot end outlet into a pre-trained neural network model to determine a risk indicator, wherein the risk indicator includes a high temperature indicator, a low temperature indicator, and a normal indicator;

[0029] When the risk indicator is a high temperature indicator, the judging unit controls the oil pump to reduce the oil delivery speed;

[0030] When the risk indicator is a low temperature indicator, the judging unit controls the oil pump to increase the oil delivery speed.

[0031] Furthermore, the temperature data at the hot end outlet is input into a pre-trained neural network model to determine the risk identification, including: collecting historical temperature data, the historical temperature data including the temperature of the heat exchange pipe, the temperature difference between the cold end and the hot end, and the oil delivery speed of the oil pump;

[0032] Sampling the historical temperature data according to a preset ratio to obtain a training set and a validation set;

[0033] Obtaining a pre-selected neural network model, and iteratively training the neural network model according to the training set, and determining a loss value of the iteratively trained neural network model through the validation set;

[0034] If the loss value of the current validation set tends to be stable and the loss value of the neural network model after iterative training continues to decrease, the iterative training is stopped to obtain the pre-trained neural network model;

[0035] The temperature data at the hot end outlet is input into the pre-trained neural network model to obtain the risk identification.

[0036] Compared with the prior art, the beneficial effect of the present invention is that: through the linkage of the temperature sensor and the positioning module, the temperature distribution of the waste rock mountain can be monitored and a three-dimensional temperature model can be constructed. With the help of data collection and data marking of the acquisition unit, data integration and model construction of the processing unit, and spatial positioning of the fixed-point unit, the position, depth and core temperature parameters of the high-temperature point can be accurately identified, which solves the limitations of low efficiency and narrow coverage of traditional manual inspections and improves the early warning capability of the potential hazards of spontaneous combustion in the waste rock mountain. The heat exchange part of the oil supply module and the cooling module cooperate to combine heat damage control with energy conversion. After the heat exchange part is embedded in the high-temperature area, it absorbs the heat energy accumulated in the waste rock mountain through heat exchange to reduce the temperature of the high-temperature area, inhibit the chain development of the oxidation reaction, and block the risk of spontaneous combustion from the source. At the same time, the heat energy is transferred to the oil supply system to increase the oil circuit temperature, and a stable temperature difference environment is formed with the cooling module, so that the thermoelectric generator continues to generate electromotive force. This heat-to-electricity conversion mechanism not only realizes the resource utilization of waste heat energy, but also forms a closed-loop control system through the coupling of active cooling and passive power generation to improve the power generation efficiency. The power generation module adopts temperature difference power generation technology, which has the advantages of strong environmental adaptability, noiseless operation, and low maintenance cost. With the energy storage function of the energy storage module, it can achieve self-sustaining operation in remote mining areas without external power supply, which is particularly suitable for the scattered operation scenes of waste rock heaps. The coordinated operation of each module forms a complete technical chain of "monitoring-positioning-temperature control-power generation-energy storage", which not only solves the problem of spontaneous combustion prevention and control of waste rock heaps, but also forms a new type of clean energy supply mode, which reduces greenhouse gas emissions, prevents the spread of toxic gases and soil pollution, maintains the thermal balance of waste rock heaps through continuous heat transfer, and has a more lasting prevention and control effect through active intervention mode compared with passive fire extinguishing. The judgment unit adjusts the transmission rate of the heat exchange part in real time according to the temperature change of the heat exchange part to prevent the temperature difference from being too high or too low, thereby improving the stability of power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0038] Figure 1 A structural block diagram of a waste heat recovery power generation system for a waste rock pile provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0040] In some embodiments of the present application, see Figure 1 As shown, a waste heat recovery power generation system for a waste rock pile includes:

[0041] Acquisition module, including temperature sensor,

[0042] The positioning module is used to confirm the high-temperature area of ​​the waste rock pile. The positioning module includes a collection unit, a processing unit, a fixed-point unit and a judgment unit.

[0043] The oil supply module includes a heat exchange part, which is arranged in the high temperature area of ​​the waste rock pile.

[0044] The cooling module includes a cooling pipe and a water pump. The water pump is connected to the cooling pipe. The cooling pipe is arranged on one side of the waste rock pile.

[0045] The power generation module is connected to the oil supply module and the cooling module. The power generation module includes a thermoelectric generator.

[0046] The energy storage module is electrically connected to the power generation module.

[0047] Specifically, the acquisition module includes a temperature sensor, which is used to detect the temperature of the waste rock pile and provide data support for the subsequent positioning module to locate the high-temperature points of the waste rock pile. The positioning module includes an acquisition unit, a processing unit, a judgment unit and a fixed-point unit. The acquisition unit collects the temperature data of the temperature sensor and marks the high-temperature abnormal points of the waste rock pile. Then the processing unit cleans the temperature data and further constructs a three-dimensional waste rock pile temperature model. Finally, the positioning unit locates the high-temperature area and obtains the positioning data. The positioning data includes but is not limited to the position, depth, and core high-temperature points. The high-temperature area of ​​the waste rock pile is prone to cause The high-risk and high-probability area of ​​ignition of the waste rock pile causes the oil supply module of the thermoelectric generator to flow through the high-temperature area for heat exchange. The temperature of the high-temperature area is reduced by heat exchange, and the temperature of the oil supply module is increased. At this time, the temperature of the oil supply module forms a temperature difference with the cooling module of the thermoelectric generator, generating an electromotive force, so that the thermoelectric generator generates electricity, and then the electric energy is stored in the energy storage module. The function of the judgment unit is to detect the temperature at the hot end outlet of the thermoelectric generator through the temperature sensor. When the temperature is too low or too high, it will affect the failure of the thermoelectric generator. Therefore, the judgment unit is used to judge whether the temperature of the heat exchange pipe is inconsistent and control the heat exchange part in time. It can be understood that the temperature sensor detects the temperature of the waste rock pile in real time, providing an accurate data basis for the positioning module. Through a series of processing, the positioning module can find the high-temperature area of ​​the waste rock pile, including key information such as location, depth, and core high-temperature points. These high-temperature areas are high-risk areas that cause spontaneous combustion of the waste rock pile. It is crucial to discover and take measures in time. By allowing the oil supply module to flow through the high-temperature area for heat exchange, the temperature of the high-temperature area can be reduced, the hidden danger of spontaneous combustion can be nipped in the bud, and the safety of the surrounding environment and personnel can be guaranteed. In this way, the effective recovery and reuse of waste energy is achieved. The waste rock mountain contains a large amount of thermal energy, which has been often ignored and wasted in the past. This embodiment utilizes the thermal energy in the high-temperature area of ​​the waste rock mountain to allow the oil supply module to absorb heat and increase its own temperature. At the same time, the cooling pipe and water pump of the cooling module maintain a low temperature, so that a temperature difference is formed between the oil supply module and the cooling module. The thermoelectric generator uses this temperature difference to generate electromotive force for power generation, converting the originally wasted thermal energy into electrical energy. The recycling of energy not only reduces dependence on traditional energy, but also improves the efficiency of energy utilization. Spontaneous combustion of waste rock mountains will release a large amount of harmful gases and dust, causing serious pollution to the atmospheric environment. By reducing the temperature of the high-temperature area of ​​the waste rock mountain, the possibility of spontaneous combustion is reduced, which also reduces the emission of harmful pollutants and improves the surrounding air quality.

[0048] In some embodiments of the present application, the heat exchange part includes a heat exchange pipe, an oil pump and an oil storage tank. The oil pump is connected to the oil storage tank. The heat exchange pipe is located in the high-temperature area of ​​the waste rock pile, and the heat exchange pipe runs through the high-temperature area of ​​the waste rock pile and is connected to the temperature difference generator.

[0049] Specifically, the oil pump is responsible for continuously delivering low-temperature oil to the heat exchange pipe outside the gangue mountain. The heat exchange pipe is laid according to several high-temperature areas determined by the high-temperature data provided by the positioning module to ensure that the heat exchange pipe flows through the high-temperature area for heat exchange. The oil storage tank is connected to the oil pump. The oil pump draws the low-temperature oil in the oil storage tank into the heat exchange pipe. After the heat exchange pipe flows through the remaining high-temperature areas, it flows into the temperature difference generator. The heat exchange pipe uses a seamless steel pipe.

[0050] It is understandable that the oil pump continuously delivers low-temperature oil to the heat exchange pipe outside the waste rock pile, providing a stable cold source for heat exchange. The heat exchange pipe is laid in several high-temperature areas in a targeted manner based on the precise high-temperature data provided by the positioning module. It can maximize the contact between the low-temperature oil and the high-temperature area, and fully absorb the heat energy released by the waste rock pile. In this way, the heat energy of different areas of the waste rock pile is fully utilized, the heat that was originally wasted is collected to the maximum extent, the energy utilization rate is improved, and the effective conversion of waste energy is realized.

[0051] In some embodiments of the present application, the thermoelectric generator includes a hot end interface, a hot end outlet, a cold end interface, a cold end outlet and a thermoelectric power generation unit. The heat exchange pipe enters through the hot end interface and flows out from the hot end outlet, the cooling pipe enters through the cold end interface and flows out from the cold end outlet, and the thermoelectric power generation unit generates an electromotive force based on the temperature difference between the hot end and the cold end.

[0052] Specifically, after the heat exchange pipe flows out of the oil storage tank, it enters the hot end interface and flows out from the hot end outlet. The water pump of the cooling module continuously outputs low-temperature fluid to the cooling pipe, and the low-temperature fluid of the cooling pipe flows in through the cold end interface and then flows out from the cold end outlet. The cooling pipe also uses seamless steel pipes, and the thermoelectric generator generates electricity based on the thermoelectric effect, which is also called the Seebeck effect. The Seebeck effect refers to when two different conductors or semiconductor materials are connected into a closed loop and there is a temperature difference at the two connection points, an electromotive force will be generated in the loop, thereby forming an electric current.

[0053] In this embodiment, the hot end interface and hot end outlet, the cold end interface and cold end outlet of the thermoelectric generator are respectively connected to medium channels of different temperatures. The heat exchange pipeline enters through the hot end interface and flows out from the hot end outlet.

[0054] The oil with higher temperature forms the hot end of the thermoelectric generator. The cooling pipeline enters through the cold end interface and flows out from the cold end outlet. What flows in it is low-temperature coolant, forming the cold end of the thermoelectric generator.

[0055] The component used to contact the cold end and the hot end to generate electromotive force is usually composed of multiple thermocouple pairs, which are made by connecting two different thermoelectric materials. In this thermoelectric generator, one side of the thermoelectric power generation unit is in contact with the high-temperature oil at the hot end, and the other side is in contact with the coolant at the cold end, thus forming a temperature difference at both ends of the thermocouple. According to the Seebeck effect, this temperature difference will cause an electromotive force to be generated inside the thermocouple. Multiple thermocouple pairs are connected in series or in parallel to form a thermoelectric power generation unit, which can superimpose the weak electromotive force generated by a single thermocouple, thereby outputting a sufficiently large amount of electrical energy and realizing the process of converting thermal energy into electrical energy.

[0056] In some embodiments of the present application, the acquisition unit is configured to collect temperatures of several waste rock piles based on a temperature sensor, and the acquisition unit is further configured to perform data cleaning on the acquired temperatures of the several waste rock piles.

[0057] In some embodiments of the present application, the processing unit is configured to mark high temperature abnormal points based on the temperatures of several waste rock piles after data cleaning, and generate a thermal abnormal data map based on the data after marking the high temperature abnormal points.

[0058] The thermal anomaly data map is expanded into a three-dimensional model and the three-dimensional model is meshed.

[0059] Based on the structure of the waste rock pile, a three-dimensional geometric model is constructed, and combined with the thermal anomaly data map to highlight the high heat areas.

[0060] Specifically, spontaneous combustion of waste rock piles is a serious safety hazard that may cause dangerous situations such as fires and explosions, posing a huge threat to surrounding personnel, equipment and the environment. Traditional temperature detection methods can often only obtain surface or local temperature information, making it difficult to fully and accurately grasp the thermal state inside the waste rock pile. By collecting temperatures at different depths and locations, we can gain an in-depth understanding of the temperature distribution inside the waste rock pile. The acquisition unit identifies these temperature data and removes outliers and noise, so that potential areas of spontaneous combustion risk can be discovered in advance. By collecting temperatures at different depths and locations, the positioning unit can accurately locate high-temperature areas inside the waste rock pile. The heat energy in these high-temperature areas can be used by subsequent power generation modules, etc., to achieve energy recovery and reuse. Marking high-temperature anomalies helps to collect and convert heat energy in a more targeted manner, improve energy utilization efficiency,

[0061] For the temperature data of several waste rock piles after data cleaning, high temperature anomaly points are marked and a comprehensive thermal anomaly data map is generated through a spatial interpolation algorithm. For example, the cleaned data can be imported into a geographic information system (GIS) to extract key features in the thermal anomaly data map, including but not limited to high probability areas, temperature gradients, and spatial distribution. The two-dimensional thermal anomaly probability map is then expanded into a three-dimensional model, and high heat areas are highlighted to observe which areas in the three-dimensional model are high heat areas. The three-dimensional model is then gridded, so that the three-dimensional model of the waste rock pile is divided into grid areas, for example, using gridding tools such as Gmsh and TetGen.

[0062] It is understandable that the traditional temperature detection method has great limitations. It can only obtain surface or local temperature information, and it is difficult to fully grasp the internal thermal state of the waste rock mountain. In this embodiment, the acquisition unit collects the temperature of several waste rock mountains at different depths and positions based on the temperature sensor, and can deeply understand the internal temperature distribution. At the same time, the acquisition unit cleans the acquired temperature data, identifies and removes abnormal values ​​and noise, which makes the data more accurate and reliable. In this way, potential spontaneous combustion risk areas can be discovered in advance, and timely measures can be taken to eliminate safety hazards, avoid fires, explosions and other dangerous situations caused by spontaneous combustion of waste rock mountains, and effectively protect the safety of surrounding personnel, equipment and environment. The positioning unit can accurately locate the high-temperature areas inside the waste rock mountain with the help of the collected temperature data at different depths and positions. The heat energy contained in these high-temperature areas can be used by subsequent power generation modules, etc. to achieve energy recovery and reuse. The waste rock mountain was originally a potential security threat. Now it is converted into electrical energy through heat energy, which improves the utilization efficiency of resources and reduces dependence on traditional energy. Marking high temperature anomaly points helps to collect and convert heat energy more targetedly, making the energy collection and conversion process more efficient and further improving energy utilization efficiency. The processing unit marks high temperature anomaly points on the waste rock pile temperature after data cleaning, and generates a thermal anomaly data map based on the marked data. The spatial interpolation algorithm is used to generate a comprehensive thermal anomaly data map, which can also be imported into the geographic information system (GIS) to extract key features in the thermal anomaly data map, such as high probability areas, temperature gradients, spatial distribution, etc. This helps staff to understand the thermal state of the waste rock pile more intuitively and analyze the distribution law and trend of thermal anomalies. Expanding the two-dimensional thermal anomaly probability map into a three-dimensional model and highlighting the high heat area allows staff to observe the model from a three-dimensional perspective, clearly see which areas are high heat areas, and more comprehensively grasp the thermal distribution of the waste rock pile. The three-dimensional model is gridded, and the three-dimensional model of the waste rock pile is divided into grid areas using tools such as Gmsh and TetGen, which is convenient for subsequent more accurate numerical simulation and analysis, and provides a more scientific and accurate basis for the decision-making of the waste rock pile pipeline layout.

[0063] In some embodiments of the present application, the positioning unit is configured to determine a flow path of the heat exchange pipe according to a high heat area.

[0064] In some embodiments of the present application, when determining the flow path of the heat exchange pipe according to the high heat area, it includes:

[0065] Extract the 3D coordinates of high heat areas,

[0066] Based on the three-dimensional coordinates and temperature values, the highest temperature point of several high-heat areas is obtained as the center point.

[0067] Based on the center point data of the high heat area, determine the flow path of the heat exchange pipe.

[0068] It is understandable that in the process of determining the path of the heat exchange pipeline to improve the thermal efficiency of the pipe, it is key to grasp the core information of the high heat area. After completing the comprehensive collection, cleaning, marking of the temperature data inside the waste rock pile, and the construction of the thermal anomaly data map and three-dimensional model, the planning stage of the heat exchange pipeline path is entered. First, the three-dimensional coordinates of all high heat areas should be extracted. These coordinates are the basis for in-depth understanding of the spatial location of the high heat area. Each three-dimensional coordinate corresponds to the temperature of a specific location inside the waste rock pile, and they together outline the outline of the high heat area. Subsequently, based on these three-dimensional coordinates and the corresponding temperature value data, a detailed analysis is carried out for each high heat area. Since the high heat area is not an isolated point, but an area with a certain range, it is defined by multiple three-dimensional coordinates. Within this area, the temperature of each point is different. By comparing the temperature values ​​corresponding to these three-dimensional coordinates one by one, the point with the highest temperature is selected. The three-dimensional coordinates of this highest temperature point are determined as the center point of the high heat area. This center point represents the location where the heat is most concentrated in the area, ensuring that the heat exchange pipeline can pass through the center point. After determining the center points of all high-heat areas, it is necessary to comprehensively consider the positional relationship of these center points to plan the flow path of the heat exchange pipe. During the planning process, it is necessary to ensure that the heat exchange pipe can pass through the center point of each high-heat area. When the heat exchange pipe contacts the center point of the high-heat area, it can obtain heat to the greatest extent and reduce the loss of heat during the transfer process, thereby improving the heat exchange efficiency of the heat exchange pipe. At the same time, when planning the path, it is necessary to avoid overly complex bends and overly long paths in the pipeline to reduce the difficulty and cost of pipeline laying and maintenance. Through path planning, the heat exchange pipe can operate efficiently in the complex environment of the waste rock mountain, make full use of the thermal energy of the high-heat area, and maximize the recovery and utilization of energy.

[0069] In some embodiments of the present application, when determining the flow path of the heat exchange pipe based on the high heat area, it also includes:

[0070] According to the determined heat exchange pipe flow path, the smoothness of the heat exchange pipe flow path is optimized and the smoothed path data is generated.

[0071] Based on the smoothed path data and temperature distribution data, the temperature gradient distribution of the heat exchange pipe flow path is optimized, and the path data after temperature gradient optimization is generated.

[0072] The optimal flow path of the heat exchange pipe is determined based on the path data after temperature gradient optimization.

[0073] It can be understood that the path of the heat exchange pipe is optimized to ensure that the path of the heat exchange pipe satisfies the requirement that it first passes through the area with lower temperature and then is laid to the area with higher temperature. The path is as smooth as possible, and the temperature gradient and heat distribution on the path are average. First, the known flow path is composed of a series of three-dimensional coordinate points. The square of the modulus of the third-order derivative of each point on the path is added to the square of the curvature of the point, and then all points on the path are integrated. Through high-order derivative constraints and curvature integration: Among them, P represents the path data after smoothing, r(t) is the parameterized representation of the path, k(t) is the curvature of the path at point t, and when P is the smallest, the path data after smoothing is generated to reduce the tortuosity of the heat exchange pipeline layout, and then the square of the modulus of the temperature gradient of each point on the path is multiplied by a weight function, and then all points on the path are integrated. Among them, ▽T(r(t)) is the temperature gradient, ω(t) is the weight function used to adjust the importance of temperature gradients in different areas, and G(P) is the path data after temperature gradient optimization. When G(P) is the smallest, the path data after temperature gradient optimization is generated to ensure that the temperature gradient of the path changes reasonably and avoid local temperatures that are too high or too low. Finally, the path data is obtained to determine the final heat exchange pipeline layout path.

[0074] In some embodiments of the present application, the judgment unit is configured to collect temperature data at the hot end outlet.

[0075] The temperature data at the hot end outlet is input into the pre-trained neural network model to determine the risk identification, which includes the high temperature identification, low temperature identification and normal identification.

[0076] When the risk indicator is a high temperature indicator, the judgment unit controls the oil pump to reduce the oil delivery speed.

[0077] When the risk mark is a low temperature mark, the judgment unit controls the oil pump to increase the oil delivery speed.

[0078] In some embodiments of the present application, the temperature data at the hot end outlet is input into a pre-trained neural network model, and when determining the risk identification, it includes: collecting historical temperature data, the historical temperature data includes the temperature of the heat exchange pipe, the temperature difference between the cold end and the hot end, and the oil pump delivery speed,

[0079] The historical temperature data is sampled according to the preset ratio to obtain the training set and the validation set.

[0080] Obtain a pre-selected neural network model, iteratively train the neural network model based on the training set, and determine the loss value of the iteratively trained neural network model through the validation set.

[0081] If the loss value of the current validation set tends to be stable and the loss value of the neural network model after iterative training continues to decrease, the iterative training is stopped to obtain the pre-trained neural network model.

[0082] The temperature data at the hot end outlet is input into the pre-trained neural network model to obtain the risk identification.

[0083] Specifically, first collect historical temperature data, which includes the temperature of the heat exchange pipe, the temperature difference between the cold end and the hot end, and the oil pump delivery speed. The temperature of the heat exchange pipe directly reflects the thermal state of the fluid in the pipe. The temperature difference between the cold end and the hot end is a key factor affecting the power generation efficiency of the thermoelectric generator, while the oil pump delivery speed is closely related to the rate of heat transfer. Set the corresponding high temperature mark or low temperature mark for the historical temperature data according to the corresponding temperature. According to the optimal temperature range for stable power generation of the thermoelectric generator, when the temperature is higher than the upper limit of this range, it is set as a high temperature mark, and when it is lower than the lower limit of the range, it is set as a low temperature mark. If it is within the range, it can be regarded as a normal state and does not participate in the training of high and low temperature judgment.

[0084] The data is divided into a training set and a validation set according to a certain ratio. The training set is used for model parameter learning, so that the model can learn the mapping relationship between temperature data and high and low temperature labels from the data. The validation set is used to adjust the model's hyperparameters (such as learning rate, number of hidden layer neurons, etc.) during the training process to prevent the model from overfitting. The collected historical temperature data (including the temperature of the heat exchange pipe, the temperature difference between the cold end and the hot end, and the oil pump delivery speed) is input into the neural network model. According to the current weight and bias parameters, the model calculates the weighted summation and activation function between neurons, and finally passes from the input layer to the hidden layer to the output layer to obtain the prediction results of the high and low temperature state. For example, if a multi-layer perceptron (MLP) is used, the input layer receives the temperature data, the hidden layer performs nonlinear transformation on the features, and the output layer outputs a numerical value representing the probability of high or low temperature.

[0085] Based on the prediction results and the pre-set high and low temperature labels, the selected loss function is used to calculate the loss value. For the binary classification problem (high temperature or low temperature), the cross entropy loss function formula is:

[0086] Where N is the number of samples, y i is the true label of the i-th sample (high temperature is 1, low temperature is 0), is the model's predicted probability for the i-th sample. The loss value reflects the degree of difference between the model's prediction result and the true label. The larger the loss value, the less accurate the model's prediction. The data set is divided into a training set and a validation set. During the training process, in addition to observing the loss value of the training set, the performance of the model needs to be regularly evaluated on the validation set. Calculate indicators such as the loss value and accuracy on the validation set. If the loss value on the validation set also decreases with the increase in the number of training rounds, and begins to stabilize or slightly increase after a certain round of training, while the loss value of the training set continues to decrease, it may mean that the model is overfitting. At this point, it is necessary to stop training and select the model with the smallest loss value on the validation set as the final model, because this model has good generalization ability on unseen data and the loss function is relatively minimal.

[0087] At the beginning of training, the model begins to learn effective features related to the target task from the training set. Since the data of the training set and the validation set have certain similarities, these features learned by the model can not only make its predictions on the training set more accurate (as shown by the decrease in the loss value of the training set), but also be transferred to the validation set, so that the loss value on the validation set also decreases. For example, in the task of judging high and low temperature states based on temperature data, the model may have learned the initial association between temperature and high and low temperature labels. This association is applicable to both the training set and the validation set, so the loss values ​​of both sets are reduced. As the training progresses, the model continuously adjusts its own parameters to better fit the data. At this stage, the generalization ability of the model gradually improves, and it can make more accurate predictions for different data samples. Therefore, the loss value of the validation set continues to decrease, which indicates that the model gradually grasps the general laws of the data during the learning process, rather than just remembering the individual features of the training set. After a certain number of rounds of training, the model may have over-learned on the training set. It begins to remember some noise and the characteristics of specific samples in the training set, rather than the general pattern of the data. Although these specific features can make the model's predictions on the training set more and more accurate, resulting in a continuous decrease in the loss value of the training set, these over-learned features are not applicable to new data in the validation set. Instead, they interfere with the model's judgment, causing the validation set loss value to no longer decrease or even begin to increase. For example, the model may over-focus on the temperature fluctuation characteristics of certain special cases in the training set, which do not exist in the validation set, resulting in poor prediction results on the validation set. If the complexity of the model (such as the number of layers and neurons in the neural network) is too high, the model has sufficient ability to fit all the details in the training set relative to the size and complexity of the data set. As training progresses, the model will continue to explore subtle features in the training set, resulting in overfitting of the training set. However, due to different data, the validation set cannot benefit from these overly complex fits, and the loss value will tend to stabilize or increase. In contrast, the training set loss value will continue to decrease as the model continues to adapt to its data.

[0088] In the above embodiment, the linkage between the temperature sensor and the positioning module can monitor the temperature distribution of the waste rock mountain and build a three-dimensional temperature model. With the help of data collection and data marking of the acquisition unit, data integration and model construction of the processing unit, and spatial positioning of the fixed-point unit, the location, depth and core temperature parameters of the high-temperature point can be accurately identified, which solves the limitations of low efficiency and narrow coverage of traditional manual inspections and improves the early warning capability of the potential hazards of spontaneous combustion in the waste rock mountain. The cooperation between the heat exchange part of the oil supply module and the cooling module combines heat damage control with energy conversion. After the heat exchange part is embedded in the high-temperature area, it absorbs the heat energy accumulated in the waste rock mountain through heat exchange to reduce the temperature of the high-temperature area, inhibit the chain development of the oxidation reaction, and block the risk of spontaneous combustion from the source. At the same time, the heat energy is transferred to the oil supply system to increase the oil circuit temperature, and a stable temperature difference environment is formed with the cooling module, so that the thermoelectric generator continues to generate electromotive force. This heat-to-electricity conversion mechanism not only realizes the resource utilization of waste heat energy, but also forms a closed-loop control system through the coupling of active cooling and passive power generation to improve power generation efficiency. The power generation module adopts temperature difference power generation technology, which has the advantages of strong environmental adaptability, noiseless operation, and low maintenance cost. With the energy storage function of the energy storage module, it can achieve self-sustaining operation in remote mining areas without external power supply, which is particularly suitable for the scattered operation scenes of waste rock heaps. The coordinated operation of each module forms a complete technical chain of "monitoring-positioning-temperature control-power generation-energy storage", which not only solves the problem of spontaneous combustion prevention and control of waste rock heaps, but also forms a new type of clean energy supply mode, which reduces greenhouse gas emissions, prevents the spread of toxic gases and soil pollution, maintains the thermal balance of waste rock heaps through continuous heat transfer, and has a more lasting prevention and control effect through active intervention mode compared with passive fire extinguishing. The judgment unit adjusts the transmission rate of the heat exchange part in real time according to the temperature change of the heat exchange part to prevent the temperature difference from being too high or too low, thereby improving the stability of power generation.

[0089] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0090] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A waste heat recovery power generation system for waste rock heap, characterized in that: include: Acquisition module, including temperature sensor; A positioning module is used to confirm the high-temperature area of ​​the waste rock pile, and the positioning module includes a collection unit, a processing unit, a fixed-point unit and a judgment unit; The oil supply module comprises a heat exchange part, wherein the heat exchange part is arranged in a high temperature area of ​​the waste rock pile; A cooling module includes a cooling pipe and a water pump, wherein the water pump is connected to the cooling pipe, and the cooling pipe is arranged on one side of the waste rock pile; A power generation module connected to the oil supply module and the cooling module, wherein the power generation module includes a thermoelectric generator; The energy storage module is electrically connected to the power generation module.

2. The waste heat recovery power generation system for waste rock dump according to claim 1 is characterized in that: The heat exchange part includes a heat exchange pipe, an oil pump and an oil storage tank. The oil pump is connected to the oil storage tank. The heat exchange pipe is located in the high-temperature area of ​​the waste rock pile, and the heat exchange pipe runs through the high-temperature area of ​​the waste rock pile and is connected to the temperature difference generator.

3. The waste heat recovery power generation system for waste rock dump according to claim 2 is characterized in that: The thermoelectric generator includes a hot end interface, a hot end outlet, a cold end interface, a cold end outlet and a thermoelectric power generation unit. The heat exchange pipe enters through the hot end interface and flows out from the hot end outlet, the cooling pipe enters through the cold end interface and flows out from the cold end outlet, and the thermoelectric power generation unit generates an electromotive force based on the temperature difference between the hot end and the cold end.

4. The waste heat recovery power generation system for waste rock dump according to claim 3 is characterized in that: The acquisition unit is configured to acquire temperatures of a plurality of waste rock piles based on the temperature sensor, and the acquisition unit is further configured to perform data cleaning on the acquired temperatures of the plurality of waste rock piles.

5. The waste heat recovery power generation system for waste rock dump according to claim 4 is characterized in that: The processing unit is configured to mark high temperature abnormal points based on the temperatures of the plurality of waste rock piles after data cleaning, and generate a thermal abnormal data graph based on the data after the high temperature abnormal points are marked; Expanding the thermal anomaly data graph into a three-dimensional model, and meshing the three-dimensional model; Based on the structure of the waste rock pile, a three-dimensional geometric model is constructed, and combined with the thermal anomaly data map, high heat areas are highlighted.

6. The waste heat recovery power generation system for waste rock dump according to claim 5 is characterized in that: The positioning unit is configured to determine a flow path of the heat exchange pipe according to the high heat area.

7. The waste heat recovery power generation system for waste rock dump according to claim 6 is characterized in that: When determining the flow path of the heat exchange pipe according to the high heat area, it includes: Extracting the three-dimensional coordinates of the high-calorie area; Based on the three-dimensional coordinates and the temperature value, obtain the highest temperature points of several high-calorie areas as the center point; Based on the center point data of the high heat area, a flow path of the heat exchange pipe is determined.

8. The waste heat recovery power generation system for waste rock dump according to claim 7 is characterized in that: When determining the flow path of the heat exchange pipe according to the high heat area, it also includes: According to the determined flow path of the heat exchange pipe, the smoothness of the flow path of the heat exchange pipe is optimized, and smoothed path data is generated; Based on the smoothed path data and temperature distribution data, optimizing the temperature gradient distribution of the path through which the heat exchange pipe flows, and generating path data after temperature gradient optimization; The optimal flow path of the heat exchange pipeline is determined based on the path data after the temperature gradient is optimized.

9. The waste heat recovery power generation system for waste rock dump according to claim 8, characterized in that: The judgment unit is configured to collect temperature data at the hot end outlet; Inputting the temperature data at the hot end outlet into a pre-trained neural network model to determine a risk indicator, wherein the risk indicator includes a high temperature indicator, a low temperature indicator, and a normal indicator; When the risk indicator is a high temperature indicator, the judging unit controls the oil pump to reduce the oil delivery speed; When the risk indicator is a low temperature indicator, the judging unit controls the oil pump to increase the oil delivery speed.

10. The waste heat recovery power generation system for waste rock dump according to claim 9, characterized in that: Inputting the temperature data at the hot end outlet into a pre-trained neural network model to determine the risk identification includes: collecting historical temperature data, the historical temperature data including the temperature of the heat exchange pipe, the temperature difference between the cold end and the hot end, and the oil delivery speed of the oil pump; Sampling the historical temperature data according to a preset ratio to obtain a training set and a validation set; Obtaining a pre-selected neural network model, and iteratively training the neural network model according to the training set, and determining a loss value of the iteratively trained neural network model through the validation set; If the loss value of the current validation set tends to be stable and the loss value of the neural network model after iterative training continues to decrease, the iterative training is stopped to obtain the pre-trained neural network model; The temperature data at the hot end outlet is input into the pre-trained neural network model to obtain the risk identification.

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