Heating system energy efficiency evaluation method and system based on thorium-based molten salt boiler

Abstract

The invention discloses a heating system energy efficiency evaluation method and system based on a thorium-based molten salt boiler, and relates to the technical field of heating system energy efficiency evaluation, and the method comprises the steps: S1, determining a use scene and characteristics; s2, collecting scene related data; s3, determining evaluation indexes and weights; s4, performing energy efficiency evaluation calculation; s5, analyzing an evaluation result; s6, optimizing the system; and S7, effect tracking and continuous improvement. According to the heating system energy efficiency evaluation method and system based on the thorium-based molten salt boiler, a set of special energy efficiency evaluation indexes and standards is formulated according to the characteristics of the heating system so as to comprehensively measure the energy efficiency performance of the system, and the appropriate energy efficiency evaluation indexes are selected and reasonable weights are set according to the scene characteristics of different use scenes of the heating system so as to improve the energy efficiency evaluation efficiency of the heating system. Therefore, the energy efficiency score of the heating system in different scenes is obtained, the energy efficiency level is evaluated, the energy efficiency evaluation result of the heating system is quantified, and accurate evaluation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy efficiency evaluation of heating systems, and in particular to an energy efficiency evaluation method and system for heating systems based on thorium-based molten salt boilers. Background Art

[0002] Traditional fossil fuels such as oil, natural gas and coal have limited reserves, and the use of these fuels will produce a large amount of pollutants, causing serious impacts on the environment. With the continuous growth of global energy demand and the increasing awareness of environmental protection, the development of new energy has become an urgent and important task for mankind. Nuclear energy, as a clean and efficient form of energy, has always been a hot topic of research. However, traditional nuclear energy technology has problems such as nuclear waste disposal and nuclear radiation safety, which limit its widespread application. In order to overcome these problems, researchers have been exploring safer and cleaner nuclear fuels and reactor technologies.

[0003] In this context, thorium-based molten salt reactor technology came into being. As an abundant nuclear fuel, thorium has the advantages of huge reserves and low radioactivity. Molten salt reactors use liquid fluoride salts as coolants and reactor materials, which have higher safety and efficiency. Therefore, the heating system based on thorium-based molten salt boilers has been developed and has become a new heating technology with broad application prospects.

[0004] The heating system based on thorium-based molten salt boiler is a new type of heating technology, which combines multiple components such as reactor, chemical processing equipment, intelligent control system, condensation plug, emergency reserve system, heat exchange unit and user heating system. It has the characteristics of small modularization, high safety and strong controllability, and can effectively solve the problems of serious air pollution and poor environmental performance in existing technologies at a low cost.

[0005] For example, the patent document with application number 201820257398.0 discloses a heating system based on a thorium-based molten salt boiler. Through three-level energy transfer, it can control the heat within a temperature range suitable for user heating, avoiding the problem of uncontrollable heat energy caused by excessive heat energy. It is highly safe and controllable, and can effectively solve the technical problems of severe air pollution and poor environmental performance in the prior art at a low cost.

[0006] However, the existing heating systems based on thorium-based molten salt boilers similar to the above-mentioned applications still have some shortcomings and gaps in energy efficiency evaluation. Although thorium-based molten salt boilers have great potential as an innovative heating technology, due to their novelty and complexity, specialized energy efficiency evaluation methods and systems have not been fully developed and applied, which is not conducive to an intuitive understanding of the operating efficiency, energy consumption and potential improvement space of the heating system.

[0007] Therefore, it is urgent to improve this shortcoming. The present invention studies and improves the existing technical deficiencies and provides a heating system energy efficiency evaluation method and system based on a thorium-based molten salt boiler. Summary of the invention

[0008] The object of the present invention is to provide a method and system for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler, so as to solve the problems raised in the above-mentioned background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] In a first aspect, the present invention provides a method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler, comprising the following specific steps:

[0011] S1. Identify usage scenarios and characteristics: Determine the specific usage scenarios of the heating system, such as residential, commercial buildings, industrial plants, etc., and understand the unique needs of each scenario, such as heating area, heat load changes, operating hours, etc.;

[0012] S2. Collect scenario-related data: Determine the scope of data collection based on the usage scenario of the heating system, and select different ways to collect data based on the type of scenario-related data;

[0013] S3. Determine the evaluation indicators and weights: According to the characteristics of the scenario, select appropriate energy efficiency evaluation indicators, such as thermal efficiency, energy consumption indicators, emission indicators, etc., and set reasonable weights for each indicator according to its importance and impact on system energy efficiency;

[0014] S4. Energy efficiency evaluation and calculation: Sort, clean and standardize the collected data to ensure the accuracy and comparability of the data, and calculate the energy efficiency score of the heating system in different scenarios based on the evaluation indicators and weights, and assess the energy efficiency level of the heating system;

[0015] S5. Analysis of evaluation results: Compare the calculated energy efficiency evaluation results with industry standards, similar systems or historical data, and analyze the energy efficiency performance of the system in different scenarios;

[0016] S6. System optimization: By analyzing the energy efficiency evaluation results, identify the energy efficiency bottlenecks or shortcomings of the heating system in specific scenarios, and formulate specific energy efficiency improvement plans for the identified energy efficiency bottlenecks, including technical improvements, operation strategy adjustments, etc., to optimize the heating system or adjust the operation mode;

[0017] S7. Effect tracking and continuous improvement: After the optimization plan is implemented, the energy efficiency performance of the heating system will be continuously tracked, the optimization effect will be evaluated, and based on the tracking results, the heating system will be continuously improved and optimized to continuously improve the energy efficiency level.

[0018] Furthermore, in step S1, the usage scenarios and requirements of the heating system are as follows:

[0019] Large commercial buildings: Large commercial buildings such as shopping malls, hotels, office buildings, etc. require large-area heating services to meet the high heat load requirements of the building and ensure stable and comfortable indoor temperature;

[0020] Industrial areas: Industrial areas such as factories, warehouses, and industrial parks require continuous and stable heat sources;

[0021] Public facilities and venues: Public facilities and venues such as hospitals, schools, and stadiums require reliable and comfortable heating services to ensure a constant indoor temperature in any weather conditions;

[0022] Residential areas: such as multi-storey apartment buildings, villa areas and other residential areas, which require stable and energy-saving heating effects while satisfying residents' pursuit of a comfortable living environment;

[0023] Remote areas and areas without central heating: In remote areas or areas without central heating facilities, independent heating solutions are needed to provide reliable heating services to local residents.

[0024] Furthermore, in step S2, the types of scene-related data and the ways of collecting them are as follows:

[0025] Environmental data: collects environmental parameters such as climate conditions, outdoor temperature, humidity, etc. at the scene location;

[0026] Load data: record the changes in heat load in different time periods, including peak load and average load;

[0027] Operation data: collect real-time operation data of the heating system, such as fuel consumption, molten salt temperature, boiler operation status, etc.

[0028] The environmental data are collected through:

[0029] Meteorological stations: Use real-time or historical data from local meteorological stations to obtain meteorological parameters such as temperature, humidity, wind speed, etc., or cooperate with meteorological departments to obtain data through API interfaces or data sharing;

[0030] On-site sensors: By installing environmental sensors such as temperature and humidity around the heating system, on-site environmental parameters can be monitored and recorded in real time. The sensors are connected to the data acquisition system to realize automatic data collection and storage.

[0031] Satellite remote sensing: For large-scale environmental data collection, satellite remote sensing technology is used to obtain information such as surface temperature and vegetation cover, or it can be obtained through professional remote sensing data processing institutions;

[0032] The load data collection methods include:

[0033] Heat load calculation software: Use professional heat load calculation software to calculate the heat load demand of the heating system based on the building's structure, materials, thermal insulation performance and other parameters. The software is based on building thermodynamics principles and experimental data and can provide relatively accurate load data;

[0034] User survey and statistics: Through user survey or statistics, we can understand the user's heating needs and habits, such as indoor temperature setting, switching time, etc., to reflect the actual load of the heating system, which helps to more accurately evaluate the performance of the system;

[0035] Real-time load monitoring: Install flow, pressure and other sensors in the heating system to monitor and record the system load in real time to reflect the load changes of the heating system in different time periods, providing an important basis for energy efficiency evaluation;

[0036] The collection methods of the operation data include:

[0037] Boiler monitoring system: Install a boiler monitoring system to monitor and record key parameters such as boiler operating status, fuel consumption, molten salt temperature, etc. in real time, and collect them through the monitoring system's interface or data export function;

[0038] Data collection and transmission: Use data collectors or PLC devices to collect data from various sensors and instruments in the heating system, and transmit the data to the central control system or remote server via wired or wireless means to achieve centralized storage and management of data;

[0039] Manual recording and inspection: For some data that cannot be collected automatically or items that require regular inspection, arrange staff to perform manual recording and inspection. For example, the operating status and maintenance status of the molten salt boiler can be recorded regularly.

[0040] Furthermore, in step S4, the calculation formula of the heating system energy efficiency is as follows:

[0041]

[0042] Among them, S represents the energy efficiency score, k i represents the weight of the i-th energy efficiency evaluation index, Es i Represents the i-th energy efficiency evaluation index, i represents the number of energy efficiency evaluation indicators, and n∈ is a positive integer.

[0043] Furthermore, the energy efficiency evaluation index is converted to a unified evaluation scale through standardization before calculation. The standardization adopts linear transformation or maximum and minimum value method, and the processed energy efficiency evaluation index is between 0 and 1, and Es is obtained.i ∈[0,1], S∈[0,1].

[0044] Furthermore, the corresponding relationship between the energy efficiency score and the energy efficiency level is as follows:

[0045]

[0046] Among them, S represents the energy efficiency score, S1 represents the first level, i.e. the optimal level, and its score range is [0.85, 1]; S2 represents the second level, and its score range is [0.7, 0.85); S3 represents the third level, and its score range is [0.55, 0.7); S4 represents the fourth level, and its score range is [0.4, 0.55); S5 represents the fifth level, i.e. the worst level, and its score range is [0, 0.4).

[0047] Furthermore, the energy efficiency evaluation of the usage scenario is as follows:

[0048] First, in the use scenario of the large commercial building, the energy efficiency evaluation indicators and their corresponding weights are as follows:

[0049] System thermal efficiency: reflects the thermal energy utilization efficiency of the heating system, with a weight of 40%;

[0050] Energy consumption per unit area: reflects the energy consumption level per unit area of ​​the building, with a weight of 30%;

[0051] Temperature control stability: evaluates the system's ability to stably control indoor temperature, with a weight of 30%;

[0052] Combined with the calculation formula of energy efficiency score, we can get:

[0053] S 1 =40%*S_te+30%*Ec+30%*TCS

[0054] Among them, S 1 It represents the energy efficiency evaluation value of the heating system in the use scenario of large commercial buildings, S_te represents the system thermal efficiency, Ec represents the energy consumption per unit area, and TCS represents the temperature control stability;

[0055] Second, in the usage scenario of the industrial area, the energy efficiency evaluation indicators and their corresponding weights are as follows:

[0056] Thermal efficiency and capacity ratio: Combined with the evaluation of system thermal efficiency and industrial production capacity, the weight is 45%;

[0057] Energy consumption cost ratio: Evaluate the proportion of heating system energy consumption in the overall operating cost, with a weight of 30%;

[0058] Equipment reliability: reflects the stability and reliability of the heating system in an industrial environment, with a weight of 25%;

[0059] Combined with the calculation formula of energy efficiency score, we can get:

[0060] S 2 =45%*TEPR+30%*ECCP+25%*ER

[0061] Among them, S 2 It represents the energy efficiency evaluation value of the heating system in the use scenario of industrial area, TEPR represents the thermal efficiency to capacity ratio, ECCP represents the energy consumption cost ratio, and ER represents the equipment reliability;

[0062] The third type, the energy efficiency evaluation indicators and their corresponding weights for public facilities and venues are as follows:

[0063] System thermal efficiency: Same as large commercial buildings, reflecting the efficiency of thermal energy utilization, with a weight of 40%;

[0064] Comfort index: evaluates the contribution of the heating system to the indoor environmental comfort, with a weight of 35%;

[0065] Response time: reflects the system's response speed to temperature changes, with a weight of 25%;

[0066] Combined with the calculation formula of energy efficiency score, we can get:

[0067] S 3 =40%*S_te+35%*CI+25%*RT

[0068] Among them, S 3 It represents the energy efficiency evaluation value of the heating system in the use scenario of large commercial buildings, S_te represents the system thermal efficiency, CI represents the comfort index, and RT represents the response time;

[0069] Fourth, for residential areas, the energy efficiency evaluation indicators and their corresponding weights are as follows:

[0070] Residents’ satisfaction: The residents’ satisfaction with the heating effect is investigated, with a weight of 35%;

[0071] Energy consumption per household: reflects the energy consumption level of each household in the residential area, with a weight of 30%;

[0072] Energy efficiency ratio: evaluates the energy efficiency performance of the heating system under different environmental conditions, with a weight of 35%;

[0073] Combined with the calculation formula of energy efficiency score, we can get:

[0074] S 4 =35%*RS+30%*UH_ec+35%*COP

[0075] Among them, S 4 represents the energy efficiency evaluation value of the heating system in the residential area, RS represents the residents' satisfaction, UH_ec represents the unit household energy consumption, and COP represents the energy efficiency ratio;

[0076] The fifth type, the energy efficiency evaluation indicators and their corresponding weights for remote areas and areas without central heating are as follows:

[0077] Self-sufficiency rate: assesses the independence of the heating system in the region’s energy supply, with a weight of 30%;

[0078] Energy efficiency ratio: Considering the limited energy supply in remote areas, energy efficiency ratio is particularly important, with a weight of 40%;

[0079] Equipment durability and maintenance cost: reflects the stability and maintenance cost of the system in harsh environments, with a weight of 30%;

[0080] Combined with the calculation formula of energy efficiency score, we can get:

[0081] S 5 =30%*S_sr+40%*COP+30%*MC

[0082] Among them, S 5 It represents the energy efficiency evaluation value of the heating system in remote areas and areas without central heating. S_sr represents the system thermal efficiency, COP represents the energy efficiency ratio, and MC represents the equipment durability and maintenance cost.

[0083] In a second aspect, the present invention provides a heating system energy efficiency evaluation system based on a thorium-based molten salt boiler, which is applied to the above-mentioned heating system energy efficiency evaluation method based on a thorium-based molten salt boiler, and the system comprises:

[0084] Scenario analysis module: responsible for determining the specific usage scenarios of the heating system and understanding the unique needs of each scenario;

[0085] Data collection module: responsible for collecting relevant data according to the usage scenarios and needs of the heating system;

[0086] The data processing module is responsible for processing the collected raw data and standardizing it;

[0087] Indicator determination module: responsible for selecting appropriate energy efficiency evaluation indicators according to the characteristics of the scenario, and setting reasonable weights for each indicator based on its importance and impact on system energy efficiency;

[0088] Evaluation and calculation module: responsible for processing the collected data, and calculating the energy efficiency score of the heating system in different scenarios according to the evaluation indicators and weights, and assessing the energy efficiency level of the heating system;

[0089] Result analysis module: responsible for analyzing the energy efficiency performance of the system in different scenarios;

[0090] System optimization module: responsible for identifying the energy efficiency bottlenecks or shortcomings of the heating system in specific scenarios, and formulating specific energy efficiency improvement plans based on the identified energy efficiency bottlenecks, optimizing the heating system or adjusting the operation mode;

[0091] Effect tracking module: responsible for continuously tracking the energy efficiency performance of the heating system after the optimization plan is implemented, evaluating the optimization effect, and continuously improving and optimizing the heating system.

[0092] Furthermore, the data processing module specifically includes:

[0093] Data sorting unit: used to preliminarily sort the collected raw data, classify, sort and archive the data according to preset rules and formats, and conduct preliminary checks on the data to identify and process possible outliers or duplicate data;

[0094] Data cleaning unit: used to identify and correct errors, anomalies and missing values ​​in the data, including deleting invalid data, filling missing values, and correcting incorrect data types or formats;

[0095] Standardized unit: used to convert data into a unified standard format or standard range, including data normalization, standardization or discretization operations to eliminate dimensional differences and order of magnitude differences between different data items, making the data more comparable and interpretable.

[0096] Furthermore, the system also includes:

[0097] A processor, configured to execute computer instructions to enable the system to execute: a scenario analysis module, a data collection module, a data processing module, an indicator determination module, an evaluation calculation module, a result analysis module, a system optimization module, and an effect tracking module;

[0098] A memory is used to store computer instructions. A computer program is stored in the memory. When the computer program is executed by the processor, the above-mentioned method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler is implemented.

[0099] The present invention provides a method and system for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler, which has the following beneficial effects:

[0100] The present invention formulates a set of special energy efficiency evaluation indicators and standards in view of the characteristics of the heating system, so as to comprehensively measure the energy efficiency performance of the system, and selects suitable energy efficiency evaluation indicators and sets reasonable weights in view of the scenario characteristics of different usage scenarios of the heating system, so as to perform targeted calculations, thereby obtaining the energy efficiency score of the heating system in different scenarios, assessing the energy efficiency level of the heating system, quantifying the energy efficiency evaluation results of the heating system, and achieving accurate evaluation. In addition, by analyzing the energy efficiency evaluation results, the energy efficiency bottlenecks or shortcomings of the heating system in specific scenarios are identified, so as to optimize the heating system, improve the energy efficiency level of the heating system, and continuously track and optimize the optimized system. The evaluation method and evaluation system provided by the present invention can help understand the operating efficiency, energy consumption and potential improvement space of the heating system, and can provide more solid support for its application and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is a schematic diagram of the operation flow of the method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler according to the present invention;

[0102] Figure 2 It is a schematic diagram of the operation flow of the energy efficiency evaluation system of the heating system based on the thorium-based molten salt boiler of the present invention;

[0103] Figure 3 This is a usage scenario-data type category diagram of the energy efficiency evaluation system for a heating system based on a thorium-based molten salt boiler of the present invention;

[0104] Figure 4 It is a schematic diagram of a data processing module of the energy efficiency evaluation system of a heating system based on a thorium-based molten salt boiler of the present invention. DETAILED DESCRIPTION

[0105] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0106] like Figure 1 As shown in the figure, the energy efficiency evaluation method of the heating system based on the thorium-based molten salt boiler includes the following specific steps:

[0107] S1. Identify usage scenarios and characteristics: Determine the specific usage scenarios of the heating system, such as residential, commercial buildings, industrial plants, etc., and understand the unique needs of each scenario, such as heating area, heat load changes, operating hours, etc. The usage scenarios and requirements of the heating system are as follows:

[0108] Large commercial buildings: Large commercial buildings such as shopping malls, hotels, office buildings, etc. require large-area heating services to meet the high heat load requirements of the building and ensure stable and comfortable indoor temperature;

[0109] Industrial areas: Industrial areas such as factories, warehouses, and industrial parks require continuous and stable heat sources;

[0110] Public facilities and venues: Public facilities and venues such as hospitals, schools, and stadiums require reliable and comfortable heating services to ensure a constant indoor temperature in any weather conditions;

[0111] Residential areas: such as multi-storey apartment buildings, villa areas and other residential areas, which require stable and energy-saving heating effects while satisfying residents' pursuit of a comfortable living environment;

[0112] Remote areas and areas without central heating: In remote areas or areas without central heating facilities, independent heating solutions are needed to provide reliable heating services for local residents;

[0113] S2. Collect scenario-related data: Determine the data collection scope according to the usage scenario of the heating system, and select different ways to collect the data based on the type of scenario-related data; the types of scenario-related data and their collection methods are as follows:

[0114] Environmental data: collects environmental parameters such as climate conditions, outdoor temperature, humidity, etc. at the scene location;

[0115] Load data: record the changes in heat load in different time periods, including peak load and average load;

[0116] Operation data: collect real-time operation data of the heating system, such as fuel consumption, molten salt temperature, boiler operation status, etc.

[0117] The environmental data are collected through:

[0118] Meteorological stations: Use real-time or historical data from local meteorological stations to obtain meteorological parameters such as temperature, humidity, wind speed, etc., or cooperate with meteorological departments to obtain data through API interfaces or data sharing;

[0119] On-site sensors: By installing environmental sensors such as temperature and humidity around the heating system, on-site environmental parameters can be monitored and recorded in real time. The sensors are connected to the data acquisition system to realize automatic data collection and storage.

[0120] Satellite remote sensing: For large-scale environmental data collection, satellite remote sensing technology is used to obtain information such as surface temperature and vegetation cover, or it can be obtained through professional remote sensing data processing institutions;

[0121] The load data collection methods include:

[0122] Heat load calculation software: Use professional heat load calculation software to calculate the heat load demand of the heating system based on the building's structure, materials, thermal insulation performance and other parameters. The software is based on building thermodynamics principles and experimental data and can provide relatively accurate load data;

[0123] User survey and statistics: Through user survey or statistics, we can understand the user's heating needs and habits, such as indoor temperature setting, switching time, etc., to reflect the actual load of the heating system, which helps to more accurately evaluate the performance of the system;

[0124] Real-time load monitoring: Install flow, pressure and other sensors in the heating system to monitor and record the system load in real time to reflect the load changes of the heating system in different time periods, providing an important basis for energy efficiency evaluation;

[0125] The collection methods of the operation data include:

[0126] Boiler monitoring system: Install a boiler monitoring system to monitor and record key parameters such as boiler operating status, fuel consumption, molten salt temperature, etc. in real time, and collect them through the monitoring system's interface or data export function;

[0127] Data collection and transmission: Use data collectors or PLC devices to collect data from various sensors and instruments in the heating system, and transmit the data to the central control system or remote server via wired or wireless means to achieve centralized storage and management of data;

[0128] Manual recording and inspection: For some data that cannot be collected automatically or items that need to be checked regularly, arrange staff to perform manual recording and inspection. For example, the operating status and maintenance status of the molten salt boiler can be recorded regularly.

[0129] S3. Determine the evaluation indicators and weights: According to the characteristics of the scenario, select appropriate energy efficiency evaluation indicators, such as thermal efficiency, energy consumption indicators, emission indicators, etc., and set reasonable weights for each indicator according to its importance and impact on system energy efficiency;

[0130] S4. Energy efficiency evaluation and calculation: Sort, clean and standardize the collected data to ensure the accuracy and comparability of the data, and calculate the energy efficiency score of the heating system in different scenarios based on the evaluation indicators and weights, and assess the energy efficiency level of the heating system;

[0131] The calculation formula for the energy efficiency of the heating system is as follows:

[0132]

[0133] Among them, S represents the energy efficiency score, k irepresents the weight of the i-th energy efficiency evaluation index, Es i represents the i-th energy efficiency evaluation index, i represents the number of energy efficiency evaluation indicators, and n∈ is a positive integer;

[0134] Before calculation, the energy efficiency evaluation index is converted to a unified evaluation scale through standardization. The standardization process uses linear transformation or maximum and minimum value method, and the processed energy efficiency evaluation index is between 0 and 1. i ∈[0,1], S∈[0,1];

[0135] The corresponding relationship between energy efficiency score and energy efficiency level is as follows:

[0136]

[0137] Among them, S represents the energy efficiency score, S1 represents the first level, that is, the best level, and its score range is [0.85, 1]; S2 represents the second level, and its score range is [0.7, 0.85); S3 represents the third level, and its score range is [0.55, 0.7); S4 represents the fourth level, and its score range is [0.4, 0.55); S5 represents the fifth level, that is, the worst level, and its score range is [0, 0.4);

[0138] The energy efficiency evaluation of the usage scenarios is as follows:

[0139] First, in the use scenario of the large commercial building, the energy efficiency evaluation indicators and their corresponding weights are as follows:

[0140] System thermal efficiency: reflects the thermal energy utilization efficiency of the heating system, with a weight of 40%;

[0141] Energy consumption per unit area: reflects the energy consumption level per unit area of ​​the building, with a weight of 30%;

[0142] Temperature control stability: evaluates the system's ability to stably control indoor temperature, with a weight of 30%;

[0143] Combined with the calculation formula of energy efficiency score, we can get:

[0144] S 1 =40%*S_te+30%*Ec+30%*TCS

[0145] Among them, S 1 It represents the energy efficiency evaluation value of the heating system in the use scenario of large commercial buildings, S_te represents the system thermal efficiency, Ec represents the energy consumption per unit area, and TCS represents the temperature control stability;

[0146] Second, in the usage scenario of the industrial area, the energy efficiency evaluation indicators and their corresponding weights are as follows:

[0147] Thermal efficiency and capacity ratio: Combined with the evaluation of system thermal efficiency and industrial production capacity, the weight is 45%;

[0148] Energy consumption cost ratio: Evaluate the proportion of heating system energy consumption in the overall operating cost, with a weight of 30%;

[0149] Equipment reliability: reflects the stability and reliability of the heating system in an industrial environment, with a weight of 25%;

[0150] Combined with the calculation formula of energy efficiency score, we can get:

[0151] S 2 =45%*TEPR+30%*ECCP+25%*ER

[0152] Among them, S 2 It represents the energy efficiency evaluation value of the heating system in the use scenario of industrial area, TEPR represents the thermal efficiency to capacity ratio, ECCP represents the energy consumption cost ratio, and ER represents the equipment reliability;

[0153] The third type, the energy efficiency evaluation indicators and their corresponding weights for public facilities and venues are as follows:

[0154] System thermal efficiency: Same as large commercial buildings, reflecting the efficiency of thermal energy utilization, with a weight of 40%;

[0155] Comfort index: evaluates the contribution of the heating system to the indoor environmental comfort, with a weight of 35%;

[0156] Response time: reflects the system's response speed to temperature changes, with a weight of 25%;

[0157] Combined with the calculation formula of energy efficiency score, we can get:

[0158] S 3 =40%*S_te+35%*CI+25%*RT

[0159] Among them, S 3 It represents the energy efficiency evaluation value of the heating system in the use scenario of large commercial buildings, S_te represents the system thermal efficiency, CI represents the comfort index, and RT represents the response time;

[0160] Fourth, for residential areas, the energy efficiency evaluation indicators and their corresponding weights are as follows:

[0161] Residents’ satisfaction: The residents’ satisfaction with the heating effect is investigated, with a weight of 35%;

[0162] Energy consumption per household: reflects the energy consumption level of each household in the residential area, with a weight of 30%;

[0163] Energy efficiency ratio: evaluates the energy efficiency performance of the heating system under different environmental conditions, with a weight of 35%;

[0164] Combined with the calculation formula of energy efficiency score, we can get:

[0165] S 4 =35%*RS+30%*UH_ec+35%*COP

[0166] Among them, S 4 represents the energy efficiency evaluation value of the heating system in the residential area, RS represents the residents' satisfaction, UH_ec represents the unit household energy consumption, and COP represents the energy efficiency ratio;

[0167] The fifth type, the energy efficiency evaluation indicators and their corresponding weights for remote areas and areas without central heating are as follows:

[0168] Self-sufficiency rate: assesses the independence of the heating system in the region’s energy supply, with a weight of 30%;

[0169] Energy efficiency ratio: Considering the limited energy supply in remote areas, energy efficiency ratio is particularly important, with a weight of 40%;

[0170] Equipment durability and maintenance cost: reflects the stability and maintenance cost of the system in harsh environments, with a weight of 30%;

[0171] Combined with the calculation formula of energy efficiency score, we can get:

[0172] S 5 =30%*S_sr+40%*COP+30%*MC

[0173] Among them, S 5 It represents the energy efficiency evaluation value of the heating system in remote areas and areas without central heating. S_sr represents the system thermal efficiency, COP represents the energy efficiency ratio, and MC represents the equipment durability and maintenance cost.

[0174] S5. Analysis of evaluation results: Compare the calculated energy efficiency evaluation results with industry standards, similar systems or historical data, and analyze the energy efficiency performance of the system in different scenarios;

[0175] S6. System optimization: By analyzing the energy efficiency evaluation results, identify the energy efficiency bottlenecks or shortcomings of the heating system in specific scenarios, and formulate specific energy efficiency improvement plans for the identified energy efficiency bottlenecks, including technical improvements, operation strategy adjustments, etc., to optimize the heating system or adjust the operation mode;

[0176] S7. Effect tracking and continuous improvement: After the optimization plan is implemented, the energy efficiency performance of the heating system will be continuously tracked, the optimization effect will be evaluated, and based on the tracking results, the heating system will be continuously improved and optimized to continuously improve the energy efficiency level.

[0177] like Figure 2-Figure 4 As shown, the energy efficiency evaluation system of the heating system based on the thorium-based molten salt boiler includes:

[0178] Scenario analysis module: responsible for determining the specific usage scenarios of the heating system and understanding the unique needs of each scenario;

[0179] Data collection module: responsible for collecting relevant data according to the usage scenarios and needs of the heating system;

[0180] The data processing module is responsible for processing the collected raw data and standardizing it;

[0181] Indicator determination module: responsible for selecting appropriate energy efficiency evaluation indicators according to the characteristics of the scenario, and setting reasonable weights for each indicator based on its importance and impact on system energy efficiency;

[0182] Evaluation and calculation module: responsible for processing the collected data, and calculating the energy efficiency score of the heating system in different scenarios according to the evaluation indicators and weights, and assessing the energy efficiency level of the heating system;

[0183] Result analysis module: responsible for analyzing the energy efficiency performance of the system in different scenarios;

[0184] System optimization module: responsible for identifying the energy efficiency bottlenecks or shortcomings of the heating system in specific scenarios, and formulating specific energy efficiency improvement plans based on the identified energy efficiency bottlenecks, optimizing the heating system or adjusting the operation mode;

[0185] Effect tracking module: responsible for continuously tracking the energy efficiency performance of the heating system after the optimization plan is implemented, evaluating the optimization effect, and continuously improving and optimizing the heating system;

[0186] The data processing module specifically includes:

[0187] Data sorting unit: used to preliminarily sort the collected raw data, classify, sort and archive the data according to preset rules and formats, and conduct preliminary checks on the data to identify and process possible outliers or duplicate data;

[0188] Data cleaning unit: used to identify and correct errors, anomalies and missing values ​​in the data, including deleting invalid data, filling missing values, and correcting incorrect data types or formats;

[0189] Standardized unit: used to convert data into a unified standard format or standard range, including data normalization, standardization or discretization operations to eliminate dimensional differences and order of magnitude differences between different data items, making the data more comparable and interpretable;

[0190] The system further comprises:

[0191] A processor, configured to execute computer instructions to enable the system to execute: a scenario analysis module, a data collection module, a data processing module, an indicator determination module, an evaluation calculation module, a result analysis module, a system optimization module, and an effect tracking module;

[0192] A memory is used to store computer instructions. A computer program is stored in the memory. When the computer program is executed by the processor, the above-mentioned method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler is implemented.

[0193] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. Energy efficiency evaluation method of heating system based on thorium-based molten salt boiler, It is characterized in that The specific steps include: S1. Identify usage scenarios and characteristics: Determine the specific usage scenarios of the heating system and understand the unique needs of each scenario; S2. Collect scenario-related data: Determine the scope of data collection based on the usage scenario of the heating system, and select different ways to collect data based on the type of scenario-related data; S3. Determine the evaluation indicators and weights: Select appropriate energy efficiency evaluation indicators based on the characteristics of the scenario, and set reasonable weights for each indicator based on its importance and impact on system energy efficiency; S4. Energy efficiency evaluation and calculation: organize, clean and standardize the collected data, and calculate the energy efficiency score of the heating system in different scenarios according to the evaluation indicators and weights, and evaluate the energy efficiency level of the heating system; S5. Analysis of evaluation results: Compare the calculated energy efficiency evaluation results with industry standards, similar systems or historical data, and analyze the energy efficiency performance of the system in different scenarios; S6. System optimization: By analyzing the energy efficiency evaluation results, identify the energy efficiency bottlenecks or shortcomings of the heating system in specific scenarios, and formulate specific energy efficiency improvement plans for the identified energy efficiency bottlenecks, optimize the heating system or adjust the operation mode; S7. Effect tracking and continuous improvement: After the optimization plan is implemented, the energy efficiency performance of the heating system will be continuously tracked, the optimization effect will be evaluated, and based on the tracking results, the heating system will be continuously improved and optimized to continuously improve the energy efficiency level.

2. The method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler according to claim 1, It is characterized in that In step S1, the usage scenarios and requirements of the heating system are as follows: Large commercial buildings: require large-area heating services to meet the high heat load requirements of the building and ensure stable and comfortable indoor temperature; Industrial areas: require a continuous and stable heat source; Public facilities and venues: requiring reliable and comfortable heating services to ensure a constant indoor temperature in all weather conditions; Residential area: requires stable and energy-saving heating effect, while satisfying residents' pursuit of a comfortable living environment; Remote areas and areas without central heating: independent heating solutions are needed to provide reliable heating services to local residents.

3. The method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler according to claim 1, It is characterized in that In step S2, the types of scene-related data and the ways of collecting them are as follows: Environmental data: collect environmental parameters of the scene location; Load data: record the changes in heat load in different time periods, including peak load and average load; Operation data: collect real-time operation data of the heating system; The environmental data are collected through: Meteorological stations: Use real-time or historical data from local meteorological stations to obtain meteorological parameters, or cooperate with meteorological departments to obtain data through API interfaces or data sharing; On-site sensors: By installing environmental sensors around the heating system, on-site environmental parameters are monitored and recorded in real time. The sensors are connected to the data acquisition system to realize automatic data collection and storage. Satellite remote sensing: For large-scale environmental data collection, information is obtained using satellite remote sensing technology or through professional remote sensing data processing agencies; The load data collection methods include: Heat load calculation software: Use professional heat load calculation software to calculate the heat load demand of the heating system according to the parameters of the building. The software is based on the principles of building thermodynamics and experimental data and can provide relatively accurate load data; User survey and statistics: Through user survey or statistics, understand the user's demand and habits for heating to reflect the actual load of the heating system; Real-time load monitoring: Install flow, pressure and other sensors in the heating system to monitor and record the system load in real time to reflect the load changes of the heating system in different time periods; The collection methods of the operation data include: Boiler monitoring system: Install a boiler monitoring system to monitor and record the key parameters of the boiler in real time, and collect them through the monitoring system's interface or data export function; Data collection and transmission: Use data collectors or PLC devices to collect data from various sensors and instruments in the heating system, and transmit the data to the central control system or remote server via wired or wireless means to achieve centralized storage and management of data; Manual recording and inspection: For certain data that cannot be collected automatically or items that require regular inspection, arrange staff to perform manual recording and inspection.

4. The method for evaluating energy efficiency of a heating system based on a thorium-based molten salt boiler according to claim 2, It is characterized in that In step S4, the calculation formula of the heating system energy efficiency is as follows: Among them, S represents the energy efficiency score, k i represents the weight of the i-th energy efficiency evaluation index, Es i Represents the i-th energy efficiency evaluation index, i represents the number of energy efficiency evaluation indicators, and n∈ is a positive integer.

5. The method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler according to claim 4, It is characterized in that The energy efficiency evaluation index is converted to a unified evaluation scale through standardization before calculation. The standardization adopts linear transformation or maximum and minimum value method, and the energy efficiency evaluation index after processing is between 0 and 1. i ∈[0,1], S∈[0,1].

6. The method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler according to claim 5, It is characterized in that The corresponding relationship between the energy efficiency score and the energy efficiency level is as follows: Among them, S represents the energy efficiency score, S1 represents the first level, i.e. the optimal level, and its score range is [0.85, 1]; S2 represents the second level, and its score range is [0.7, 0.85); S3 represents the third level, and its score range is [0.55, 0.7); S4 represents the fourth level, and its score range is [0.4, 0.55); S5 represents the fifth level, i.e. the worst level, and its score range is [0, 0.4).

7. The method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler according to claim 4, It is characterized in that The energy efficiency evaluation of the usage scenario is as follows: First, in the use scenario of the large commercial building, the energy efficiency evaluation indicators and their corresponding weights are as follows: System thermal efficiency: reflects the thermal energy utilization efficiency of the heating system, with a weight of 40%; Energy consumption per unit area: reflects the energy consumption level per unit area of ​​the building, with a weight of 30%; Temperature control stability: evaluates the system's ability to stably control indoor temperature, with a weight of 30%; Combined with the calculation formula of energy efficiency score, we can get: S 1 =40%*S_te+30%*Ec+30%*TCS Among them, S 1 It represents the energy efficiency evaluation value of the heating system in the use scenario of large commercial buildings, S_te represents the system thermal efficiency, Ec represents the energy consumption per unit area, and TCS represents the temperature control stability; Second, in the usage scenario of the industrial area, the energy efficiency evaluation indicators and their corresponding weights are as follows: Thermal efficiency and capacity ratio: Combined with the evaluation of system thermal efficiency and industrial production capacity, the weight is 45%; Energy consumption cost ratio: Evaluate the proportion of heating system energy consumption in the overall operating cost, with a weight of 30%; Equipment reliability: reflects the stability and reliability of the heating system in an industrial environment, with a weight of 25%; Combined with the calculation formula of energy efficiency score, we can get: S 2 =45%*TEPR+30%*ECCP+25%*ER Among them, S 2 It represents the energy efficiency evaluation value of the heating system in the use scenario of industrial area, TEPR represents the thermal efficiency to capacity ratio, ECCP represents the energy consumption cost ratio, and ER represents the equipment reliability; The third type, the energy efficiency evaluation indicators and their corresponding weights for public facilities and venues are as follows: System thermal efficiency: Same as large commercial buildings, reflecting the efficiency of thermal energy utilization, with a weight of 40%; Comfort index: evaluates the contribution of the heating system to the indoor environmental comfort, with a weight of 35%; Response time: reflects the system's response speed to temperature changes, with a weight of 25%; Combined with the calculation formula of energy efficiency score, we can get: S 3 =40%*S_te+35%*CI+25%*RT Among them, S 3 It represents the energy efficiency evaluation value of the heating system in the use scenario of large commercial buildings, S_te represents the system thermal efficiency, CI represents the comfort index, and RT represents the response time; Fourth, for residential areas, the energy efficiency evaluation indicators and their corresponding weights are as follows: Residents’ satisfaction: The residents’ satisfaction with the heating effect is investigated, with a weight of 35%; Energy consumption per household: reflects the energy consumption level of each household in the residential area, with a weight of 30%; Energy efficiency ratio: evaluates the energy efficiency performance of the heating system under different environmental conditions, with a weight of 35%; Combined with the calculation formula of energy efficiency score, we can get: S 4 =35%*RS+30%*UH_ec+35%*COP Among them, S 4 represents the energy efficiency evaluation value of the heating system in the residential area, RS represents the residents' satisfaction, UH_ec represents the unit household energy consumption, and COP represents the energy efficiency ratio; The fifth type, the energy efficiency evaluation indicators and their corresponding weights for remote areas and areas without central heating are as follows: Self-sufficiency rate: assesses the independence of the heating system in the region’s energy supply, with a weight of 30%; Energy efficiency ratio: Considering the limited energy supply in remote areas, energy efficiency ratio is particularly important, with a weight of 40%; Equipment durability and maintenance cost: reflects the stability and maintenance cost of the system in harsh environments, with a weight of 30%; Combined with the calculation formula of energy efficiency score, we can get: S 5 =30%*S_sr+40%*COP+30%*MC Among them, S 5 It represents the energy efficiency evaluation value of the heating system in remote areas and areas without central heating. S_sr represents the system thermal efficiency, COP represents the energy efficiency ratio, and MC represents the equipment durability and maintenance cost.

8. A heating system energy efficiency evaluation system based on a thorium-based molten salt boiler, applied to the heating system energy efficiency evaluation method based on a thorium-based molten salt boiler as claimed in any one of claims 1 to 7, It is characterized in that The system comprises: Scenario analysis module: responsible for determining the specific usage scenarios of the heating system and understanding the unique needs of each scenario; Data collection module: responsible for collecting relevant data according to the usage scenarios and needs of the heating system; The data processing module is responsible for processing the collected raw data and standardizing it; Indicator determination module: responsible for selecting appropriate energy efficiency evaluation indicators according to the characteristics of the scenario, and setting reasonable weights for each indicator based on its importance and impact on system energy efficiency; Evaluation and calculation module: responsible for processing the collected data, and calculating the energy efficiency score of the heating system in different scenarios according to the evaluation indicators and weights, and assessing the energy efficiency level of the heating system; Result analysis module: responsible for analyzing the energy efficiency performance of the system in different scenarios; System optimization module: responsible for identifying the energy efficiency bottlenecks or shortcomings of the heating system in specific scenarios, and formulating specific energy efficiency improvement plans based on the identified energy efficiency bottlenecks, optimizing the heating system or adjusting the operation mode; Effect tracking module: responsible for continuously tracking the energy efficiency performance of the heating system after the optimization plan is implemented, evaluating the optimization effect, and continuously improving and optimizing the heating system.

9. The energy efficiency evaluation system for a heating system based on a thorium-based molten salt boiler according to claim 8, It is characterized in that The data processing module specifically includes: Data sorting unit: used to preliminarily sort the collected raw data, classify, sort and archive the data according to preset rules and formats, and conduct preliminary checks on the data to identify and process possible outliers or duplicate data; Data cleaning unit: used to identify and correct errors, anomalies and missing values ​​in the data, including deleting invalid data, filling missing values, and correcting incorrect data types or formats; Standardized unit: used to convert data into a unified standard format or standard range, including data normalization, standardization or discretization operations to eliminate dimensional differences and order of magnitude differences between different data items, making the data more comparable and interpretable.

10. The energy efficiency evaluation system for a heating system based on a thorium-based molten salt boiler according to claim 9, It is characterized in that The system further comprises: A processor, configured to execute computer instructions to enable the system to execute: a scenario analysis module, a data collection module, a data processing module, an indicator determination module, an evaluation calculation module, a result analysis module, a system optimization module, and an effect tracking module; A memory for storing computer instructions, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the method for evaluating the energy efficiency of a heating system based on a thorium-based molten salt boiler as described in any one of claims 1 to 7 is implemented.

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