Design method and system for a flue gas waste heat recovery device

Abstract

The present invention provides a design method and system for a flue gas waste heat recovery device, including: collecting on-site layout data of a power plant, constructing a heating equipment model of the power plant, using a deep neural network to perform feature extraction on the heating equipment model, obtaining several waste heat generation locations of the power plant and marking them in the heating equipment model, obtaining a waste heat distribution model, adding a preset virtual plate heat exchanger and a preset virtual absorption heat pump to the waste heat distribution model for performing a heat collection experimental simulation, obtaining an optimal heat collection scheme for the power plant, adding corresponding plate heat exchangers and absorption heat pumps to the power plant according to the preferred heat collection scheme, and adding corresponding heat collection supervision equipment to the power plant at the same time, respectively debugging the data transmission channels between each of the plate heat exchangers and the absorption heat pump and the preset heat collection supervision equipment, and generating a flue gas waste heat recovery device for the power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of waste heat recovery, and in particular to a design method and system for a flue gas waste heat recovery device. Background Art

[0002] A power plant, also known as an electric power plant, is a facility that converts different forms of energy (such as fossil fuels, nuclear energy, hydropower, wind power, solar power, etc.) into electricity. Power plants are a vital part of modern society's infrastructure, providing essential energy for daily life and industrial activities.

[0003] With technological advancements and increasing environmental protection requirements, power plants are moving towards greater efficiency, cleanliness, and sustainability. However, due to the unique nature of power plants, they tend to generate significant amounts of heat during electricity production. Current engineering applications typically use direct-contact spray towers for cooling to improve heat exchange efficiency. However, this method consumes significant amounts of water, is not only environmentally friendly, but can also cause damage to power plant equipment. Furthermore, different power plants have varying internal facilities, making a single waste heat recovery device unsuitable for every plant.

[0004] Therefore, the present invention provides a design method and system for a flue gas waste heat recovery device. Summary of the Invention

[0005] The present invention provides a design method and system for a flue gas waste heat recovery device, which constructs a waste heat recovery device based on actual data of a power plant, increases the utilization rate of flue gas waste heat, thereby enhancing heating capacity and reducing operating energy consumption.

[0006] The present invention provides a design method for a flue gas waste heat recovery device, comprising:

[0007] Step 1: Collecting on-site layout data of the power plant and constructing a heating equipment model of the power plant;

[0008] Step 2: Using a deep neural network to extract features from the heating equipment model, several waste heat generation locations of the power plant are obtained and marked in the heating equipment model to obtain a waste heat distribution model;

[0009] Step 3: Adding a preset virtual plate heat exchanger and a preset virtual absorption heat pump to the waste heat distribution model to perform a heat collection experiment simulation to obtain an optimal heat collection scheme for the power plant;

[0010] Step 4: adding corresponding plate heat exchangers and absorption heat pumps to the power plant according to the preferred heat collection scheme, and adding corresponding heat collection monitoring equipment to the power plant;

[0011] Step 5: Debugging the data transmission channels between each of the plate heat exchanger and the absorption heat pump and the preset heat collection monitoring device respectively to generate the flue gas waste heat recovery device of the power plant.

[0012] In one practicable manner,

[0013] The step 1 comprises:

[0014] Step 11: Acquire an on-site image of the power plant, perform image recognition on the on-site image, and determine a number of on-site devices included in the power plant;

[0015] Step 12: Searching for device attributes corresponding to each of the field devices in the big data, and selecting heating devices according to the device attributes;

[0016] Step 13: Perform on-site data collection on each of the heating devices to obtain device data corresponding to each of the heating devices, input the device data into the corresponding device position in the on-site image, and generate a heating device model of the power plant.

[0017] In one practicable manner,

[0018] The step 2 comprises:

[0019] Step 21: Obtaining model parameters contained in the heating equipment model, establishing a three-dimensional equipment image of the power plant, performing convolution processing on the three-dimensional equipment image using the deep neural network, obtaining equipment features corresponding to each heating equipment based on the convolution processing results, combining the equipment features based on the model position of each heating equipment in the heating model, and generating a plurality of multi-dimensional heating features of the power plant;

[0020] Step 22: using the deep network to perform convolution processing on each of the multidimensional heating features, obtaining a plurality of local features corresponding to each of the multidimensional heating features, and superimposing the local features onto the corresponding multidimensional heating features to obtain a plurality of heating function information of the power plant;

[0021] Step 23: constructing a heat supply dynamic diagram of the power plant based on the heat supply function information, capturing heat flow characteristics of the power plant in the heat supply dynamic diagram, determining a plurality of waste heat generation locations of the power plant, and marking each of the waste heat generation locations in the heat supply equipment model;

[0022] Step 24: Input the equipment characteristics corresponding to each of the heating equipment into the heating equipment model to perform heating simulation, obtain the waste heat accumulation trend corresponding to each of the waste heat generation locations, and generate a waste heat distribution model of the power plant.

[0023] In one practicable manner,

[0024] The step 3 comprises:

[0025] Step 31: running the waste heat distribution model to obtain the waste heat accumulation amount corresponding to each waste heat generation location per unit time, determining the waste heat recovery threshold corresponding to each waste heat generation location, and adjusting the first device parameter of the preset virtual plate heat exchanger and the second device parameter of the preset virtual absorption heat pump based on the waste heat recovery threshold to obtain a device to be selected corresponding to each waste heat generation location;

[0026] Step 32: establishing a plurality of device matching schemes based on the selected devices corresponding to each of the waste heat generation locations, adding corresponding devices to the waste heat distribution model based on each of the device matching schemes, and performing a heat collection experiment simulation to obtain the total heat collection corresponding to each of the device matching schemes;

[0027] Step 33: Filter the target device matching solution with the highest total heat collection, obtain the heat collection efficiency and heat production efficiency corresponding to each waste heat generation position in the waste heat distribution model, adjust the parameters of the target device whose heat collection efficiency is lower than the heat production efficiency, and generate the optimal heat collection solution for the power plant.

[0028] In one practicable manner,

[0029] Also includes:

[0030] When performing the heat collection experiment simulation, heat is collected at each of the waste heat generation locations to obtain the heat collection efficiency and heat production efficiency corresponding to each of the waste heat generation locations, and an efficiency list corresponding to each of the device matching solutions is generated and displayed.

[0031] In one practicable manner,

[0032] The step 4 comprises:

[0033] Step 41: determining the heat collection device corresponding to each waste heat generation location according to the preferred heat collection scheme, generating and displaying a device installation guidance scheme;

[0034] Step 42: After the heat collecting devices are installed, each heat collecting device is connected to a preset heat collecting monitoring device.

[0035] In one practicable manner,

[0036] The step 5 comprises:

[0037] Step 51: setting a corresponding first data transmission channel for each of the plate heat exchangers according to the corresponding first data transmission mode between each of the plate heat exchangers and the preset heat collection monitoring device;

[0038] Step 52: Setting a corresponding second data transmission channel for each absorption heat pump according to the corresponding second data transmission mode between each absorption heat pump and the preset heat collection monitoring device;

[0039] Step 53: Control the preset heat collection monitoring device to collect the first data corresponding to each of the plate heat exchangers and the second data corresponding to each of the absorption heat pumps, so as to construct the flue gas waste heat recovery device of the power plant.

[0040] In one practicable manner,

[0041] Also includes:

[0042] The real-time recovery data of the flue gas waste heat recovery device is obtained and transmitted to a designated terminal for display.

[0043] The present invention provides a design system for a flue gas waste heat recovery device, comprising:

[0044] A model building module, used to collect on-site layout data of the power plant and build a heating equipment model of the power plant;

[0045] a feature processing module for extracting features from the heating equipment model using a deep neural network, obtaining a plurality of waste heat generation locations of the power plant and marking them in the heating equipment model to obtain a waste heat distribution model;

[0046] an experimental simulation module, configured to add a preset virtual plate heat exchanger and a preset virtual absorption heat pump to the waste heat distribution model to perform a heat collection experimental simulation, thereby obtaining an optimal heat collection scheme for the power plant;

[0047] a heat collection setting module, configured to add corresponding plate heat exchangers and absorption heat pumps to the power plant according to the preferred heat collection scheme, and to add corresponding heat collection monitoring equipment to the power plant;

[0048] The device composition module is used to debug the data transmission channel between each of the plate heat exchangers and the absorption heat pump and the preset heat collection supervision equipment respectively, to generate the flue gas waste heat recovery device of the power plant.

[0049] In one practicable manner,

[0050] The feature processing module includes:

[0051] a deep convolution unit, configured to obtain model parameters contained in the heating equipment model, establish a three-dimensional equipment image of the power plant, perform convolution processing on the three-dimensional equipment image using the deep neural network, obtain equipment features corresponding to each heating equipment based on the convolution processing results, combine the equipment features based on the model position of each heating equipment in the heating model, and generate a plurality of multi-dimensional heating features of the power plant;

[0052] a repeated convolution unit, configured to perform convolution processing on each of the multidimensional heating features using the deep network to obtain a plurality of local features corresponding to each of the multidimensional heating features, and superimpose the local features onto the corresponding multidimensional heating features to obtain a plurality of heating function information of the power plant;

[0053] a waste heat locating unit, configured to construct a heat supply dynamic map of the power plant based on the heat supply function information, capture heat flow characteristics of the power plant in the heat supply dynamic map, determine a plurality of waste heat generation locations of the power plant, and mark each of the waste heat generation locations in the heat supply equipment model;

[0054] The heat supply simulation unit is used to input the equipment characteristics corresponding to each of the heat supply equipment into the heat supply equipment model to perform heat supply simulation, obtain the waste heat accumulation trend corresponding to each of the waste heat generation positions, and generate a waste heat distribution model of the power plant.

[0055] The achievable beneficial effects of the above technical solution are: in order to improve the heat collection efficiency of the power plant, the power plant's heating equipment model is first constructed based on the power plant's on-site layout data, and then the deep neural network is used to extract features from the heating equipment model, and several waste heat generation locations of the power plant are determined to construct a waste heat distribution model of the power plant. Then, the virtual device is input into the waste heat distribution model for heat collection experimental simulation, and the optimal heat collection scheme of the power plant is obtained, so that the power plant is arranged on-site and a complete set of flue gas waste heat recovery equipment is constructed. In this way, not only can the waste heat generated by the power plant equipment be quickly collected, but the working conditions of each device can also be monitored to reduce damage to the equipment.

[0056] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0059] Figure 1 Schematic diagram of the workflow of a design method for a flue gas waste heat recovery device according to an embodiment of the present invention;

[0060] Figure 2 Schematic diagram of the composition of a design system for a flue gas waste heat recovery device in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0062] Example 1

[0063] This embodiment provides a design method for a flue gas waste heat recovery device, such as Figure 1 Shown, including:

[0064] Step 1: Collecting on-site layout data of the power plant and constructing a heating equipment model of the power plant;

[0065] Step 2: Using a deep neural network to extract features from the heating equipment model, several waste heat generation locations of the power plant are obtained and marked in the heating equipment model to obtain a waste heat distribution model;

[0066] Step 3: Adding a preset virtual plate heat exchanger and a preset virtual absorption heat pump to the waste heat distribution model to perform a heat collection experiment simulation to obtain an optimal heat collection scheme for the power plant;

[0067] Step 4: adding corresponding plate heat exchangers and absorption heat pumps to the power plant according to the preferred heat collection scheme, and adding corresponding heat collection monitoring equipment to the power plant;

[0068] Step 5: Debugging the data transmission channels between each of the plate heat exchanger and the absorption heat pump and the preset heat collection monitoring device respectively to generate the flue gas waste heat recovery device of the power plant.

[0069] In this example, the field layout data represents the layout of various equipment within the power plant;

[0070] In this example, the heating equipment model represents the result of using modeling technology to present the structure of the heating equipment in the power plant;

[0071] In this example, the waste heat distribution model represents the result of using modeling technology to present the waste heat of heating equipment in the power plant;

[0072] In this example, the virtual plate heat exchanger represents a virtual structure established in the model space to represent the function of the plate heat exchanger, and the preset virtual absorption heat pump represents a virtual structure established in the model space to represent the function of the absorption heat pump;

[0073] In this example, the heat collection experiment simulation refers to the process of simulating the heat collection effect of the plate heat exchanger and absorption heat pump using the waste heat distribution model;

[0074] In this example, the heat collection monitoring device refers to a device used to monitor the operation process of the plate heat exchanger and the absorption heat pump.

[0075] The working principle and beneficial effects of the above technical solution are as follows: In order to improve the heat collection efficiency of the power plant, the heating equipment model of the power plant is first constructed based on the on-site layout data of the power plant, and then the features of the heating equipment model are extracted using a deep neural network. Several waste heat generation locations of the power plant are determined to construct a waste heat distribution model of the power plant. Then, the virtual device is input into the waste heat distribution model for heat collection experimental simulation, and the optimal heat collection scheme of the power plant is obtained. The power plant is then arranged on-site and a complete flue gas waste heat recovery device is constructed. In this way, not only can the waste heat generated by the power plant equipment be quickly collected, but the working status of each device can also be monitored to reduce damage to the equipment.

[0076] Example 2

[0077] Based on Example 1, the design method of a flue gas waste heat recovery device, step 1, includes:

[0078] Step 11: Acquire an on-site image of the power plant, perform image recognition on the on-site image, and determine a number of on-site devices included in the power plant;

[0079] Step 12: Searching for device attributes corresponding to each of the field devices in the big data, and selecting heating devices according to the device attributes;

[0080] Step 13: Perform on-site data collection on each of the heating devices to obtain device data corresponding to each of the heating devices, input the device data into the corresponding device position in the on-site image, and generate a heating device model of the power plant.

[0081] In this example, device attributes represent the properties of a task that a field device can perform.

[0082] The working principle and beneficial effects of the above technical solution are as follows: by identifying the on-site images of the power plant to determine the on-site equipment contained in the power plant, and then analyzing the equipment properties of each on-site equipment to determine the heating equipment in the power plant, and finally collecting the equipment data of the heating equipment on-site to construct a heating equipment model of the power plant. In this way, the influence of non-heating equipment in the power plant can be eliminated, and a complete heating equipment model can be constructed to ensure the validity of this design result.

[0083] Example 3

[0084] Based on Example 1, the design method of a flue gas waste heat recovery device, step 2, includes:

[0085] Step 21: Obtaining model parameters contained in the heating equipment model, establishing a three-dimensional equipment image of the power plant, performing convolution processing on the three-dimensional equipment image using the deep neural network, obtaining equipment features corresponding to each heating equipment based on the convolution processing results, combining the equipment features based on the model position of each heating equipment in the heating model, and generating a plurality of multi-dimensional heating features of the power plant;

[0086] Step 22: using the deep network to perform convolution processing on each of the multidimensional heating features, obtaining a plurality of local features corresponding to each of the multidimensional heating features, and superimposing the local features onto the corresponding multidimensional heating features to obtain a plurality of heating function information of the power plant;

[0087] Step 23: constructing a heat supply dynamic diagram of the power plant based on the heat supply function information, capturing heat flow characteristics of the power plant in the heat supply dynamic diagram, determining a plurality of waste heat generation locations of the power plant, and marking each of the waste heat generation locations in the heat supply equipment model;

[0088] Step 24: Input the equipment characteristics corresponding to each of the heating equipment into the heating equipment model to perform heating simulation, obtain the waste heat accumulation trend corresponding to each of the waste heat generation locations, and generate a waste heat distribution model of the power plant.

[0089] In this example, the equipment characteristics represent the characteristics exhibited by a heating equipment when it is in operation;

[0090] In this example, the multi-dimensional heating feature represents the combination of the device features of multiple heating devices to obtain the features presented when the multiple heating devices are working simultaneously.

[0091] In this example, local features represent the result of dividing the multidimensional heating feature into several local parts and performing detailed processing on each local part;

[0092] In this example, the heating function information represents several functions that the power plant exhibits during the heating process;

[0093] In this example, the heat flow characteristics represent the characteristics of the heat generated by the power plant when it flows on site;

[0094] In this example, the heating dynamic diagram represents the heating process of the power plant in an animated manner;

[0095] In this example, the residual heat accumulation trend refers to a tendency of heat accumulation at a residual heat generation location without external interference.

[0096] The working principle and beneficial effects of the above technical solution are as follows: first, a three-dimensional equipment image of the power plant is established according to the model parameters of the heating equipment model, and then convolution processing is performed on it using a neural network to obtain the equipment characteristics of each heating equipment, and then the equipment characteristics are further combined to obtain several multidimensional heating characteristics of the power plant, and then the local characteristics of each multidimensional heating characteristic are analyzed by convolution processing, and the heating function information of the power plant is generated by feature superposition, and a heating dynamic diagram of the power plant is constructed. The heat flow characteristics of the power plant are captured in the dynamic diagram, and the waste heat generation position that generates heat accumulation is determined according to the direction and speed of heat flow. Finally, the heating equipment model is used to simulate the heating, and the waste heat accumulation trend corresponding to each waste heat generation position is determined, and a waste heat distribution model of the power plant is generated. In this way, each heating equipment in the power plant can be analyzed, and the functions of multiple heating equipment can be analyzed at the same time to generate an effective waste heat distribution model, thereby improving the accuracy of subsequent device settings.

[0097] Example 4

[0098] Based on Example 1, the design method of a flue gas waste heat recovery device, step 3, includes:

[0099] Step 31: running the waste heat distribution model to obtain the waste heat accumulation amount corresponding to each waste heat generation location per unit time, determining the waste heat recovery threshold corresponding to each waste heat generation location, and adjusting the first device parameter of the preset virtual plate heat exchanger and the second device parameter of the preset virtual absorption heat pump based on the waste heat recovery threshold to obtain a device to be selected corresponding to each waste heat generation location;

[0100] Step 32: establishing a plurality of device matching schemes based on the selected devices corresponding to each of the waste heat generation locations, adding corresponding devices to the waste heat distribution model based on each of the device matching schemes, and performing a heat collection experiment simulation to obtain the total heat collection corresponding to each of the device matching schemes;

[0101] Step 33: Filter the target device matching solution with the highest total heat collection, obtain the heat collection efficiency and heat production efficiency corresponding to each waste heat generation position in the waste heat distribution model, adjust the parameters of the target device whose heat collection efficiency is lower than the heat production efficiency, and generate the optimal heat collection solution for the power plant.

[0102] In this example, the unit time is 45 minutes;

[0103] In this example, the waste heat recovery threshold value indicates the minimum recovery speed when the device recovers heat;

[0104] In this example, the devices to be selected are: a plate heat exchanger that completes parameter adjustment and an absorption heat pump that completes parameter adjustment;

[0105] In this example, when the heat collection efficiency is not less than the heat generation efficiency, it indicates that the heat in the power plant is in an accumulation state.

[0106] The working principle and beneficial effects of the above technical solution are as follows: by running the waste heat distribution model, the amount of waste heat accumulation at each waste heat generation location within 45 minutes is determined, thereby generating a waste heat recovery threshold to adjust the parameters of the preset virtual plate heat exchanger and the preset virtual absorption heat pump, and further constructing a device matching scheme through combination. After adding corresponding devices to the waste heat distribution model, a heat collection experiment simulation is performed. The simulation method can improve the efficiency of device selection, reduce the investment of manpower and material resources, and reduce the expenditure of the power plant. Then, according to the total heat collection amount of each device matching scheme, the target device matching scheme is selected, and then the target device matching scheme is fine-tuned according to the actual situation of each waste heat generation location to generate the optimal heat collection scheme for the power plant. Relevant personnel can arrange heat collection in the power plant under the guidance of this scheme, which improves the work efficiency of relevant personnel and reduces the probability of power plant shutdown.

[0107] Example 5

[0108] Based on Example 4, the design method of a flue gas waste heat recovery device further includes:

[0109] When performing the heat collection experiment simulation, heat is collected at each of the waste heat generation locations to obtain the heat collection efficiency and heat production efficiency corresponding to each of the waste heat generation locations, and an efficiency list corresponding to each of the device matching solutions is generated and displayed.

[0110] The working principle and beneficial effects of the above technical solution: Displaying the heat collection efficiency and heat production efficiency corresponding to each waste heat generation location during the experiment to relevant personnel can help relevant personnel preliminarily judge the completeness of the power plant equipment functions and improve the work quality of the power plant.

[0111] Example 6

[0112] Based on Example 1, the design method of a flue gas waste heat recovery device, step 4, includes:

[0113] Step 41: determining the heat collection device corresponding to each waste heat generation location according to the preferred heat collection scheme, generating and displaying a device installation guidance scheme;

[0114] Step 42: After the heat collecting devices are installed, each heat collecting device is connected to a preset heat collecting monitoring device.

[0115] In this example, the heat collection devices include: a plate heat exchanger and an absorption heat pump.

[0116] The working principle and beneficial effects of the above technical solution: Under the guidance of the optimal heat collection scheme, corresponding heat collection devices are installed for each waste heat generation location, and they are all connected to the heat collection supervision equipment for operation supervision, thus constructing a complete flue gas waste heat recovery device.

[0117] Example 7

[0118] Based on Example 1, the design method of a flue gas waste heat recovery device, step 5, includes:

[0119] Step 51: setting a corresponding first data transmission channel for each of the plate heat exchangers according to the corresponding first data transmission mode between each of the plate heat exchangers and the preset heat collection monitoring device;

[0120] Step 52: Setting a corresponding second data transmission channel for each absorption heat pump according to the corresponding second data transmission mode between each absorption heat pump and the preset heat collection monitoring device;

[0121] Step 53: Control the preset heat collection monitoring device to collect the first data corresponding to each of the plate heat exchangers and the second data corresponding to each of the absorption heat pumps, so as to construct the flue gas waste heat recovery device of the power plant.

[0122] The working principle and beneficial effects of the above technical solution are as follows: the plate heat exchanger and absorption heat pump are connected to the preset heat collection supervision equipment and debugged to ensure the functional integrity of each component, and a flue gas waste heat recovery device suitable for this power plant is constructed.

[0123] Example 8

[0124] Based on Example 1, the design method of a flue gas waste heat recovery device further includes:

[0125] The real-time recovery data of the flue gas waste heat recovery device is obtained and transmitted to a designated terminal for display.

[0126] The working principle and beneficial effects of the above technical solution: relevant personnel can understand the working status of the flue gas waste heat recovery device at any time through the designated terminal.

[0127] Example 9

[0128] This example provides a design system for a flue gas waste heat recovery device. Figure 2 Shown, including:

[0129] A model building module, used to collect on-site layout data of the power plant and build a heating equipment model of the power plant;

[0130] a feature processing module for extracting features from the heating equipment model using a deep neural network, obtaining a plurality of waste heat generation locations of the power plant and marking them in the heating equipment model to obtain a waste heat distribution model;

[0131] an experimental simulation module, configured to add a preset virtual plate heat exchanger and a preset virtual absorption heat pump to the waste heat distribution model to perform a heat collection experimental simulation, thereby obtaining an optimal heat collection scheme for the power plant;

[0132] a heat collection setting module, configured to add corresponding plate heat exchangers and absorption heat pumps to the power plant according to the preferred heat collection scheme, and to add corresponding heat collection monitoring equipment to the power plant;

[0133] The device composition module is used to debug the data transmission channel between each of the plate heat exchangers and the absorption heat pump and the preset heat collection supervision equipment respectively, to generate the flue gas waste heat recovery device of the power plant.

[0134] In this example, the field layout data represents the layout of various equipment within the power plant;

[0135] In this example, the heating equipment model represents the result of using modeling technology to present the structure of the heating equipment in the power plant;

[0136] In this example, the waste heat distribution model represents the result of using modeling technology to present the waste heat of heating equipment in the power plant;

[0137] In this example, the virtual plate heat exchanger represents a virtual structure established in the model space to represent the function of the plate heat exchanger, and the preset virtual absorption heat pump represents a virtual structure established in the model space to represent the function of the absorption heat pump;

[0138] In this example, the heat collection experiment simulation refers to the process of simulating the heat collection effect of the plate heat exchanger and absorption heat pump using the waste heat distribution model;

[0139] In this example, the heat collection monitoring device refers to a device used to monitor the operation process of the plate heat exchanger and the absorption heat pump.

[0140] The working principle and beneficial effects of the above technical solution are as follows: In order to improve the heat collection efficiency of the power plant, the heating equipment model of the power plant is first constructed based on the on-site layout data of the power plant, and then the features of the heating equipment model are extracted using a deep neural network. Several waste heat generation locations of the power plant are determined to construct a waste heat distribution model of the power plant. Then, the virtual device is input into the waste heat distribution model for heat collection experimental simulation, and the optimal heat collection scheme of the power plant is obtained. The power plant is then arranged on-site and a complete flue gas waste heat recovery device is constructed. In this way, not only can the waste heat generated by the power plant equipment be quickly collected, but the working status of each device can also be monitored to reduce damage to the equipment.

[0141] Example 10

[0142] Based on Example 9, the design system for a flue gas waste heat recovery device, the feature processing module, includes:

[0143] a deep convolution unit, configured to obtain model parameters contained in the heating equipment model, establish a three-dimensional equipment image of the power plant, perform convolution processing on the three-dimensional equipment image using the deep neural network, obtain equipment features corresponding to each heating equipment based on the convolution processing results, combine the equipment features based on the model position of each heating equipment in the heating model, and generate a plurality of multi-dimensional heating features of the power plant;

[0144] a repeated convolution unit, configured to perform convolution processing on each of the multidimensional heating features using the deep network to obtain a plurality of local features corresponding to each of the multidimensional heating features, and superimpose the local features onto the corresponding multidimensional heating features to obtain a plurality of heating function information of the power plant;

[0145] a waste heat locating unit, configured to construct a heat supply dynamic map of the power plant based on the heat supply function information, capture heat flow characteristics of the power plant in the heat supply dynamic map, determine a plurality of waste heat generation locations of the power plant, and mark each of the waste heat generation locations in the heat supply equipment model;

[0146] The heat supply simulation unit is used to input the equipment characteristics corresponding to each of the heat supply equipment into the heat supply equipment model to perform heat supply simulation, obtain the waste heat accumulation trend corresponding to each of the waste heat generation positions, and generate a waste heat distribution model of the power plant.

[0147] In this example, the equipment characteristics represent the characteristics exhibited by a heating equipment when it is in operation;

[0148] In this example, the multi-dimensional heating feature represents the combination of the device features of multiple heating devices to obtain the features presented when the multiple heating devices are working simultaneously.

[0149] In this example, local features represent the result of dividing the multidimensional heating feature into several local parts and performing detailed processing on each local part;

[0150] In this example, the heating function information represents several functions that the power plant exhibits during the heating process;

[0151] In this example, the heat flow characteristics represent the characteristics of the heat generated by the power plant when it flows on site;

[0152] In this example, the heating dynamic diagram represents the heating process of the power plant in an animated manner;

[0153] In this example, the residual heat accumulation trend refers to a tendency of heat accumulation at a residual heat generation location without external interference.

[0154] The working principle and beneficial effects of the above technical solution are as follows: first, a three-dimensional equipment image of the power plant is established according to the model parameters of the heating equipment model, and then convolution processing is performed on it using a neural network to obtain the equipment characteristics of each heating equipment, and then the equipment characteristics are further combined to obtain several multidimensional heating characteristics of the power plant, and then the local characteristics of each multidimensional heating characteristic are analyzed by convolution processing, and the heating function information of the power plant is generated by feature superposition, and a heating dynamic diagram of the power plant is constructed. The heat flow characteristics of the power plant are captured in the dynamic diagram, and the waste heat generation position that generates heat accumulation is determined according to the direction and speed of heat flow. Finally, the heating equipment model is used to simulate the heating, and the waste heat accumulation trend corresponding to each waste heat generation position is determined, and a waste heat distribution model of the power plant is generated. In this way, each heating equipment in the power plant can be analyzed, and the functions of multiple heating equipment can be analyzed at the same time to generate an effective waste heat distribution model, thereby improving the accuracy of subsequent device settings.

[0155] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A design method for a flue gas waste heat recovery device, characterized in that: include: Step 1: Collecting on-site layout data of the power plant and constructing a heating equipment model of the power plant; Step 2: Using a deep neural network to extract features from the heating equipment model, several waste heat generation locations of the power plant are obtained and marked in the heating equipment model to obtain a waste heat distribution model; Step 3: Adding a preset virtual plate heat exchanger and a preset virtual absorption heat pump to the waste heat distribution model to perform a heat collection experiment simulation to obtain an optimal heat collection scheme for the power plant; Step 4: adding corresponding plate heat exchangers and absorption heat pumps to the power plant according to the preferred heat collection scheme, and adding corresponding heat collection monitoring equipment to the power plant; Step 5: Debugging the data transmission channels between each of the plate heat exchanger and the absorption heat pump and the preset heat collection monitoring device to generate the flue gas waste heat recovery device of the power plant; The step 2 comprises: Step 21: Obtaining model parameters contained in the heating equipment model, establishing a three-dimensional equipment image of the power plant, performing convolution processing on the three-dimensional equipment image using the deep neural network, obtaining equipment features corresponding to each heating equipment based on the convolution processing results, combining the equipment features based on the model position of each heating equipment in the heating equipment model, and generating a plurality of multi-dimensional heating features of the power plant; Step 22: Using the deep neural network to perform convolution processing on each of the multidimensional heating features, obtain a number of local features corresponding to each of the multidimensional heating features, and superimpose the local features onto the corresponding multidimensional heating features to obtain a number of heating function information of the power plant; Step 23: constructing a heat supply dynamic diagram of the power plant based on the heat supply function information, capturing heat flow characteristics of the power plant in the heat supply dynamic diagram, determining a plurality of waste heat generation locations of the power plant, and marking each of the waste heat generation locations in the heat supply equipment model; Step 24: Inputting the equipment characteristics corresponding to each of the heating equipment into the heating equipment model to perform a heating simulation, obtaining a waste heat accumulation trend corresponding to each waste heat generation location, and generating a waste heat distribution model of the power plant; The step 3 comprises: Step 31: running the waste heat distribution model to obtain the waste heat accumulation amount corresponding to each waste heat generation location per unit time, determining the waste heat recovery threshold corresponding to each waste heat generation location, and adjusting the first device parameter of the preset virtual plate heat exchanger and the second device parameter of the preset virtual absorption heat pump based on the waste heat recovery threshold to obtain a device to be selected corresponding to each waste heat generation location; Step 32: establishing a plurality of device matching schemes based on the selected devices corresponding to each of the waste heat generation locations, adding corresponding devices to the waste heat distribution model based on each of the device matching schemes, and performing a heat collection experiment simulation to obtain the total heat collection corresponding to each of the device matching schemes; Step 33: Screening a target device matching solution with the highest total heat collection, obtaining the heat collection efficiency and heat production efficiency corresponding to each waste heat generation location in the waste heat distribution model, adjusting parameters of the target device whose heat collection efficiency is lower than the heat production efficiency, and generating an optimal heat collection solution for the power plant; The step 5 comprises: Step 51: setting a corresponding first data transmission channel for each of the plate heat exchangers according to the corresponding first data transmission mode between each of the plate heat exchangers and the preset heat collection monitoring device; Step 52: Setting a corresponding second data transmission channel for each absorption heat pump according to the corresponding second data transmission mode between each absorption heat pump and the preset heat collection monitoring device; Step 53: Control the preset heat collection monitoring device to collect the first data corresponding to each of the plate heat exchangers and the second data corresponding to each of the absorption heat pumps, so as to construct the flue gas waste heat recovery device of the power plant.

2. The design method of a flue gas waste heat recovery device according to claim 1, characterized in that: The step 1 comprises: Step 11: Acquire an on-site image of the power plant, perform image recognition on the on-site image, and determine a number of on-site devices included in the power plant; Step 12: Searching for device attributes corresponding to each of the field devices in the big data, and selecting heating devices according to the device attributes; Step 13: Perform on-site data collection on each of the heating devices to obtain device data corresponding to each of the heating devices, input the device data into the corresponding device position in the on-site image, and generate a heating device model of the power plant.

3. The design method of a flue gas waste heat recovery device according to claim 1, characterized in that: Also includes: When performing the heat collection experiment simulation, heat is collected at each of the waste heat generation locations to obtain the heat collection efficiency and heat production efficiency corresponding to each of the waste heat generation locations, and an efficiency list corresponding to each of the device matching solutions is generated and displayed.

4. The design method of a flue gas waste heat recovery device according to claim 1, characterized in that: The step 4 comprises: Step 41: determining the heat collection device corresponding to each waste heat generation location according to the preferred heat collection scheme, generating and displaying a device installation guidance scheme; Step 42: After the heat collecting devices are installed, each heat collecting device is connected to a preset heat collecting monitoring device.

5. The design method of a flue gas waste heat recovery device according to claim 1, characterized in that: Also includes: The real-time recovery data of the flue gas waste heat recovery device is obtained and transmitted to a designated terminal for display.

6. A design system for a flue gas waste heat recovery device, used to implement the design method for a flue gas waste heat recovery device according to claim 1, characterized in that: include: A model building module, used to collect on-site layout data of the power plant and build a heating equipment model of the power plant; a feature processing module for extracting features from the heating equipment model using a deep neural network, obtaining a plurality of waste heat generation locations of the power plant and marking them in the heating equipment model to obtain a waste heat distribution model; an experimental simulation module, configured to add a preset virtual plate heat exchanger and a preset virtual absorption heat pump to the waste heat distribution model to perform a heat collection experimental simulation, thereby obtaining an optimal heat collection scheme for the power plant; a heat collection setting module, configured to add corresponding plate heat exchangers and absorption heat pumps to the power plant according to the preferred heat collection scheme, and to add corresponding heat collection monitoring equipment to the power plant; The device composition module is used to debug the data transmission channel between each of the plate heat exchangers and the absorption heat pump and the preset heat collection supervision equipment, thereby generating the flue gas waste heat recovery device of the power plant.

7. The design system of a flue gas waste heat recovery device according to claim 6, characterized in that: The feature processing module includes: a deep convolution unit, configured to obtain model parameters contained in the heating equipment model, establish a three-dimensional equipment image of the power plant, perform convolution processing on the three-dimensional equipment image using the deep neural network, obtain equipment features corresponding to each heating equipment based on the convolution processing results, combine the equipment features based on the model position of each heating equipment in the heating equipment model, and generate a plurality of multi-dimensional heating features of the power plant; a repeated convolution unit, configured to perform convolution processing on each of the multidimensional heating characteristics using the deep neural network to obtain a plurality of local features corresponding to each of the multidimensional heating characteristics, and superimpose the local features onto the corresponding multidimensional heating characteristics to obtain a plurality of heating function information of the power plant; a waste heat locating unit, configured to construct a heat supply dynamic map of the power plant based on the heat supply function information, capture heat flow characteristics of the power plant in the heat supply dynamic map, determine a plurality of waste heat generation locations of the power plant, and mark each of the waste heat generation locations in the heat supply equipment model; The heat supply simulation unit is used to input the equipment characteristics corresponding to each of the heat supply equipment into the heat supply equipment model to perform heat supply simulation, obtain the waste heat accumulation trend corresponding to each of the waste heat generation positions, and generate a waste heat distribution model of the power plant.

Images