Solid waste treatment carbon emission dynamic monitoring method based on dynamic data fusion

Abstract

The invention discloses a dynamic monitoring method for carbon emission in solid waste treatment based on dynamic data fusion, which comprises the following steps of: acquiring an actual measurement value and a theoretical value of carbon emission according to working condition data, energy data and material data in a solid waste treatment process; the actual measurement value and the theoretical value of the carbon emission comprise the actual measurement value and the theoretical value of the dry waste incineration carbon emission and the wet waste treatment carbon emission; constructing a credibility evaluation mechanism according to the sensor precision score, the data integrity score and the working condition stability score; performing credibility evaluation on the actually measured value of the carbon emission based on the credibility evaluation mechanism, dynamically adjusting the weight ratio of the theoretical value of the carbon emission to the actually measured value, and calculating to obtain a final carbon emission numerical value; and performing visual display on the final carbon emission data through bidirectional linkage of a three-dimensional process flow and a carbon emission mulberry-based graph. According to the method, the data quality is evaluated in real time, the fusion weight of the theoretical value and the measured value is dynamically adjusted, and the carbon emission calculation precision is improved.

Description

Technical Field

[0001] The present invention belongs to the field of environmental informatization, and is particularly suitable for the digital management of carbon emissions throughout the entire life cycle of solid waste disposal scenarios such as garbage incineration and wet garbage treatment. It specifically relates to a dynamic monitoring method for carbon emissions from solid waste treatment based on dynamic data fusion. Background Art

[0002] As a key area for carbon emission reduction, the solid waste treatment industry faces an increasingly urgent need for intelligent transformation of its carbon emission monitoring and management systems. However, the current solid waste treatment carbon emission management field faces multiple challenges:

[0003] First, the construction of regional carbon emission factor databases is lagging behind, making it difficult to support accurate accounting needs; second, the traditional carbon verification model that relies on manual sampling and offline reporting has inherent defects such as delayed data updates and poor timeliness, making it difficult to adapt to the digital management needs of real-time operation of solid waste treatment facilities, seriously restricting the intelligent upgrade of carbon emission management in the solid waste treatment industry; third, in the monitoring of carbon emissions from solid waste treatment, traditional LCA methods usually use fixed weights or a single data source, such as relying solely on theoretical models or using only measured data, resulting in large errors in carbon emission calculations when data is missing, abnormal, or operating conditions change.

[0004] In view of the above problems in the existing technology, it is urgent to propose a dynamic monitoring method for carbon emissions from solid waste treatment based on dynamic data fusion. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method for dynamic monitoring of carbon emissions from solid waste treatment based on dynamic data fusion, comprising the following steps:

[0006] Obtaining measured and theoretical values ​​of carbon emissions based on operating data, energy data, and material data during solid waste treatment, including measured and theoretical values ​​of carbon emissions from dry waste incineration and theoretical values ​​of carbon emissions from wet waste treatment;

[0007] Establish a credibility assessment mechanism based on sensor accuracy score, data integrity score and working condition stability score;

[0008] Conducting a credibility assessment on the measured value of carbon emissions based on the credibility assessment mechanism, dynamically adjusting the weight ratio between the theoretical value and the measured value of carbon emissions, and calculating the final carbon emissions value;

[0009] The final carbon emission data is visualized through the two-way linkage of the three-dimensional process flow and the carbon emission Sankey diagram.

[0010] Optionally, the process of obtaining measured and theoretical carbon emission values ​​based on operating data, energy data, and material data during the solid waste treatment process includes:

[0011] The measured values ​​of carbon emissions are obtained through direct measurement and calculation by relevant sensors; the theoretical values ​​of carbon emissions are calculated through mathematical models based on garbage component parameters and common parameters, and are used to supplement the measured values ​​of carbon emissions, provide benchmark references, and dynamically calibrate them.

[0012] Among them, the garbage component parameters include the proportion of wet-based component types, the proportion of dry matter content in wet weight, the proportion of total carbon in dry weight, the proportion of mineral carbon in total carbon and the garbage degradation rate; the shared parameters include the emission factors of each substance, the calorific value of steam, the average lower calorific value of natural gas and the global warming potential of gas.

[0013] Optionally, the sensor accuracy score includes a calibration error score and a calibration timeliness score, wherein the calibration error score is calculated based on the ratio of the actual error to the maximum allowable error, and the calibration timeliness score is mapped in reverse order according to the length of the calibration time, and the sensor accuracy score is obtained by comprehensive calculation.

[0014] Optionally, the data integrity score includes a missing rate score and an outlier ratio score, wherein the missing rate score is calculated based on the ratio of the amount of missing data to the total amount of data to be transmitted, and the outlier ratio score is calculated based on the ratio of the number of outliers to the total amount of data, and the data integrity score is obtained by comprehensive calculation.

[0015] Optionally, the score of the operating condition stability is calculated based on the ratio of the standard deviation of the operating condition data to the design allowable fluctuation range. When the standard deviation exceeds the design allowable fluctuation range, the operating condition stability score is zero.

[0016] Optionally, the process of performing credibility assessment on the measured value of carbon emissions based on the credibility assessment mechanism includes:

[0017] Input the measured value of carbon emissions to obtain the corresponding sensor accuracy score, data integrity score, and operating condition stability score; assign weights to the obtained sensor accuracy score, data integrity score, and operating condition stability score, and use weighted summation to output the credibility score of the measured value of carbon emissions.

[0018] Optionally, the calculation formula for the weight factor for dynamically adjusting the weight ratio of the theoretical value and the measured value of carbon emissions is:

[0019]

[0020] Where: σtheoretical and σactual are the standard deviations between the theoretical and measured carbon emissions, respectively; βcredibility is a correction coefficient based on the credibility score.

[0021] Optionally, the weight factor is dynamically adjusted according to the credibility assessment result. When the credibility is greater than 90%, the weight factor approaches 1. When the credibility is less than 60%, an alarm is triggered and the mode is switched to the theoretical value dominant mode, and the weight factor approaches 0.

[0022] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method.

[0023] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0024] Compared with the prior art, the present invention has the following advantages and technical effects:

[0025] The dynamic monitoring method of carbon emissions from solid waste treatment based on dynamic data fusion proposed in this invention breaks through the limitation of the "hard switching" between theoretical models and measured data in traditional LCA. By evaluating data quality in real time and dynamically adjusting the fusion weights of theoretical values ​​and measured values, it improves the accuracy of carbon emission calculations and reduces the jumps in carbon emission calculations caused by operating condition fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0027] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of the carbon footprint calculation boundary of the waste incineration life cycle according to an embodiment of the present invention;

[0029] Figure 3 This is a wet garbage treatment flow chart and a life cycle carbon footprint accounting boundary diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0030] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0031] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Example 1

[0033] like Figure 1 As shown, this embodiment provides a method for dynamic monitoring of carbon emissions from solid waste treatment based on dynamic data fusion, comprising the following steps:

[0034] Obtaining measured and theoretical values ​​of carbon emissions based on operating data, energy data, and material data during solid waste treatment, including measured and theoretical values ​​of carbon emissions from dry waste incineration and theoretical values ​​of carbon emissions from wet waste treatment;

[0035] Establish a credibility assessment mechanism based on sensor accuracy score, data integrity score and working condition stability score;

[0036] Conducting a credibility assessment on the measured value of carbon emissions based on the credibility assessment mechanism, dynamically adjusting the weight ratio between the theoretical value and the measured value of carbon emissions, and calculating the final carbon emissions value;

[0037] The final carbon emission data is visualized through the two-way linkage of the three-dimensional process flow and the carbon emission Sankey diagram.

[0038] As a specific implementation method, the following describes the monitoring method using the Shanghai Songjiang Tianma Park as an example:

[0039] The process of obtaining measured and theoretical carbon emissions values ​​based on operating data, energy data, and material data during solid waste treatment includes:

[0040] Obtain measured and theoretical values ​​of carbon emissions. Carbon emission calculations include carbon emissions from dry waste incineration and carbon emissions from wet waste treatment. Combustion emissions are an important and large component of carbon incineration emissions. Due to the influence of the external data collection environment, the measured values ​​of combustion emissions in carbon incineration emissions are not entirely reliable. It is necessary to obtain the corresponding theoretical values. The measured values ​​are calculated from factory-collected data, and the theoretical values ​​are calculated from method parameters. Measured values ​​are related content obtained through direct measurement and direct calculation by relevant sensors, while theoretical values ​​are obtained by obtaining relevant data and using the relevant data as the relevant influencing factors to solve the problem.

[0041] Regarding incineration emissions, this section includes information on combustion emissions, energy procurement, leachate treatment, material consumption, and energy substitution. As a major influencing factor, the error in combustion emissions significantly impacts the final incineration emissions. This embodiment calculates the waste combustion parameters within the combustion emission parameters using either measured or theoretical values ​​to obtain the corresponding combustion emissions. The final carbon incineration emissions value is displayed in a single or integrated manner using both measured and theoretical values.

[0042] Among them, the theoretical value is the carbon emissions calculated by the mathematical model based on the method parameters. Its functions include: data supplement: providing reliable carbon emission estimates when the measured data is missing or abnormal; benchmark reference: comparing with the measured value to verify the rationality of the data (such as whether the incinerator efficiency deviates from the design value); dynamic calibration: introducing a real-time data credibility assessment mechanism, and realizing the correction of carbon emission data by dynamically adjusting the weight ratio of theoretical value and measured value to improve data accuracy.

[0043] Furthermore, the method parameters involved include the following:

[0044] These parameters are mainly divided into waste composition parameters and shared parameters. Waste composition parameters include the wet basis component type ratio, dry matter content as a percentage of wet weight, total carbon as a percentage of dry weight, mineral carbon as a percentage of total carbon, and waste degradation rate. Shared parameters include emission factors for each substance, steam calorific value, average lower calorific value of natural gas, and global warming potential of gases.

[0045] As a feasible implementation method, the calculation method for dry waste incineration and wet waste treatment includes the following:

[0046] The obtained values ​​are calculated using the full life cycle LCA method:

[0047] E 垃圾焚烧 =E 燃烧排放 +E 能源外购 +E 渗沥液处理 +E 物质消耗 -E 能源替代 ;

[0048] E 厌氧发酵 =E 沼气处理 +E 能源外购 +E 物质消耗 +E 残渣处理 -E 能源 / 产品替代 ;

[0049] This embodiment mainly focuses on E 燃烧排放 The waste combustion part in the part is adjusted, among which E 燃烧排放 =E 垃圾燃烧 +E 助燃剂燃烧 .

[0050] Regarding waste incineration, which is feasible:

[0051] Life cycle carbon footprint analysis adds implicit CO2 emissions caused by material consumption or supply within the plant to the basic type of carbon emissions. Figure 2The following table shows the lifecycle carbon footprint calculation boundary for waste incineration. This embodiment uses real-time monitoring data from the plant area as the core of the carbon footprint calculation for the lifecycle of waste incineration. In addition to using measured values ​​for mineral carbon emissions from waste incineration, i.e., waste combustion in combustion emissions, theoretically calculated values ​​using carbon content are also added. The lifecycle carbon footprint calculation can be performed according to the following formula:

[0052] E 垃圾焚烧 =E 燃烧排放 +E 能源外购 +E 渗沥液处理 +E 物质消耗 -E 能源替代 ;

[0053] Specifically, the CO2 emissions caused by energy purchase, leachate treatment, energy substitution, and combustion of fossil fuel accelerants are calculated as follows:

[0054] Calculation of carbon emissions from purchased energy:

[0055] The implicit CO2 emissions from purchased electricity for municipal waste incineration facilities can be calculated using the following formula:

[0056] E 能源外购 =EC d ×EF d ,

[0057] Where: E 能源外购 —CO2 emissions from production activities corresponding to purchased electricity consumption, unit: t CO2-eq; EC d —Purchased electricity consumption, in MWh; EF d - The CO2 emission factor of the power supply. In this embodiment, the emission factor of the power supply is 0.42t CO2-eq / MWh according to the data published by Shanghai. The power consumption can be obtained by actual measurement of the electricity meter in the incineration plant.

[0058] Calculation of carbon emissions from leachate treatment:

[0059] Since the leachate generated in this example is treated using an anaerobic process, emissions must be calculated according to the "Emissions and Leakages from Anaerobic Digestion" procedure. The most important aspect of this step is determining the amount of methane produced by the anaerobic digester. Since the biogas generated by the anaerobic digester in this example is sprayed back into the incinerator for recycling, the primary emissions from the leachate treatment process are methane leaks. CO2 emissions from this process can be calculated using the following two methods.

[0060] (1) Calculation using monitoring data:

[0061] If the treatment scale is large, in order to improve the accuracy of the calculation, the methane produced by the anaerobic digester of the leachate should be monitored and tracked in real time. This method can be calculated according to the following formula:

[0062]

[0063] Where: E 渗沥液处理 —CO2 emissions from leachate treatment, unit: tCO2-eq; V 渗沥液 —Methane produced by anaerobic treatment of leachate, in Nm 3 α—CH4 ratio in biogas, which is 60% in this embodiment; β—leakage ratio of biogas treatment, which is 5% in this embodiment with reference to the IPCC default value; ρ—density of CH4 under standard conditions, 0.77kg / Nm 3 ;GWP CH4 —Global Warming Potential of CH4, 28. The key to this approach is the installation of a volumetric flow meter in the anaerobic digester of the leachate.

[0064] (2) Calculation using theoretical methods:

[0065] In the case where a volume flow meter is not installed, if the processing scale is small or the accuracy of the calculation result is generally required, the calculation can be performed according to the following formula:

[0066]

[0067] Where: E 渗沥液处理 —CO2 emissions from leachate treatment, unit: tCO2-eq; Q 渗沥液 —Amount of leachate after anaerobic treatment, in m 3 ;P COD -COD content of the leachate produced. The COD content of the leachate in this embodiment is 0.076tCOD / m 3 ; B0—maximum methane production potential, valued at 0.25t CH4 / t COD; MCF—methane conversion factor, valued at 0.8.

[0068] Calculation of carbon emission reduction effect of energy substitution:

[0069] The energy substitution in this embodiment is mainly electricity substitution. After the normal operation of the plant is met (the actual power consumption rate of the target domestic waste incineration plant is about 20% according to actual measurement), the excess power is supplied to the outside through the power grid. The calculation method is as follows:

[0070] E 能源替代 =EG bd ×EF bd ×10 -3 ,

[0071] Where: E 能源替代 —Avoided CO2 emissions from domestic waste incineration power generation, unit: t CO2-eq; EG bd —On-grid electricity consumption of the plant area, in MWh; EF bd The CO2 emission factor for electricity emissions is 0.5896 tCO2-eq / MWh. The amount of electricity consumed can be measured by the factory's electricity meter.

[0072] Regarding direct emissions from combustion of oxidants:

[0073] When the boiler is ignited or shut down, or when the calorific value of the garbage is low and it is difficult to maintain stable combustion, it is necessary to add fossil fuel combustion aid to the incinerator to maintain stable combustion. The CO2 emissions of this process are calculated according to the following formula:

[0074]

[0075] Where: E 助燃剂燃烧 —Direct CO2 emissions from the combustion of fossil fuel-type combustion aids added to the incineration of domestic waste, in tons CO2-eq; FC i —Consumption of the i-th fossil fuel, in tons; NCV i —the average lower calorific value of the i-th fossil fuel, in kJ / kg; CC i —Carbon content per unit calorific value of the i-th fossil fuel, in TC / TJ; OF i = Carbon oxidation rate (%) of the i-th fossil fuel; i = type of fossil fuel; 44 / 12 = ratio of carbon converted to carbon dioxide. The combustion aid used in this example is natural gas. The average lower calorific value of natural gas is 38931 kJ / kg, the nominal carbon content per unit calorific value is 15.32 TC / TJ, and the carbon oxidation rate of natural gas is generally 99%.

[0076] Calculation of carbon emissions from material consumption:

[0077] Material consumption generally includes chemicals and other materials required to maintain normal plant operations. For example, the Tianma Incineration Phase I project includes natural gas required to maintain stable combustion; urea solution required for selective non-catalytic reduction in the incinerator; slaked lime for dry deacidification; sodium hydroxide for wet deacidification; activated carbon for dioxin adsorption; acetylene for regular boiler soot blowing; chelating agents for fly ash solidification after incineration; and tap water for the entire plant operation.

[0078] This report uses the emission factor method to calculate the implicit CO2 emissions caused by material consumption, which can be calculated using the following formula:

[0079]

[0080] Where: E 物质消耗 —CO2 emissions due to material consumption, measured in tons; i—the i-th material consumed; MC—the amount of material consumed, measured in tons; EF—the emission factor for the consumed material, measured in tons CO2-eq / ton. Material consumption varies among incineration facilities due to the different technologies they employ. Specific material consumption can be tracked and monitored at the target incineration facility.

[0081] Second, the emission factors for material consumption in the lifecycle carbon footprint calculations for all treatment units in this example are derived from SimaPro software, using the Ecoinvent database and the IPCC (2021) evaluation method. Table 1 shows the background emission data used for the main material consumption of the Tianma Incineration Phase I.

[0082] Table 1

[0083]

[0084]

[0085] Calculation of measured and theoretical carbon emissions from waste combustion that can be implemented:

[0086] (1) Calculation of measured values:

[0087] This method is mainly based on the measurement of CO2 concentration and flue gas volume flow in waste incineration flue gas by the continuous online flue gas monitoring system (CEMS). The calculation method is as follows:

[0088]

[0089] Where: E 垃圾燃烧 —CO2 emissions from the incineration of domestic waste mineral carbon, unit is t CO2-eq; Q—volume flow rate of waste incineration flue gas, unit is Nm 3 / s or Nm 3 / h; CO2—CO2 concentration in waste incineration (%); 44—CO2 molar mass, in g / mol; 22.4—volume of 1 mol of gas under standard conditions, in L / mol; —Average mineral carbon coefficient, used to distinguish between biogenic carbon and mineral carbon in waste incineration flue gas.

[0090] Since the CO2 concentration measured by the CEMS system cannot distinguish between biogenic carbon and mineral carbon, it is necessary to calculate the average mineral carbon content of the waste entering the furnace based on its physical and chemical properties for the final mineral carbon calculation of the waste incineration. The calculation formula for the average mineral carbon is as follows:

[0091]

[0092] Secondly, since the CO2 concentration measured by the CEMS system includes CO2 emissions generated by fossil fuel combustion, this part of emissions needs to be deducted during the calculation to avoid duplication of calculations.

[0093] (2) Theoretical value calculation:

[0094] This method requires testing the physical and chemical properties of domestic waste to obtain the carbon content in the waste. The calculation method is as follows:

[0095]

[0096] Where: E 垃圾燃烧 —CO2 emissions from the incineration of mineral carbon in municipal solid waste, in tons CO2-eq; M—amount of municipal solid waste incinerated, in tons; C—carbon content of mixed solid waste (%); OF—oxidation factor (%), taken as 100%; 44 / 12—conversion ratio of carbon to carbon dioxide. —Average mineral carbon coefficient, used to distinguish between biogenic carbon and mineral carbon in waste incineration flue gas.

[0097] Regarding wet waste treatment, the following measures are feasible:

[0098] The wet garbage treatment process cannot be directly quantified using actual measurement data. This example calculates carbon emissions from different aspects to obtain the carbon emissions from wet garbage treatment:

[0099] Theoretical calculations for wet garbage treatment include:

[0100] The carbon in kitchen waste is all from biological carbon sources, and the CO2 generated by its conversion is not counted as carbon emissions. Since the wet waste pretreatment system will crush and sort the wet waste entering the factory, there will be sorting residues, which will be incinerated, and the treatment of the sorting residues is also taken into account. Figure 3 The figure below shows the life cycle carbon footprint accounting boundary for wet waste treatment. The life cycle carbon footprint accounting can be calculated according to the following formula:

[0101] E 厌氧发酵 =E 沼气处理 +E 能源外购 +E 物质消耗 +E 残渣处理 -E 能源 / 产品替代 ;

[0102] Among them, carbon emissions from biogas treatment are calculated as follows:

[0103] The carbon in food waste is all biogenic, and the CO2 generated by its conversion is not counted as carbon emissions. However, CH4 emissions from such facilities due to process disturbances, unintentional leaks, or other unexpected events typically range from 0% to 10% of the CH4 generated. If the technical standards of anaerobic digestion facilities ensure that all unintentional CH4 emissions are flared, then CH4 emissions can be approximated to zero, assuming that emissions from this process are negligible. The main calculation methods are as follows:

[0104]

[0105] Where: E 沼气处理 —Carbon emissions caused by biogas leakage during biogas treatment, unit: t CO2-eq; V 湿垃圾 —Methane produced by anaerobic decomposition of wet garbage, unit: Nm 3 ; V 渗沥液 —Methane produced by anaerobic treatment of leachate, in Nm 3 α—CH4 ratio in biogas, which is 60% in this embodiment; β—leakage ratio of biogas treatment, which is 5% in this embodiment with reference to the IPCC default value; ρ—density of CH4 under standard conditions, 0.77kg / Nm 3 ;GWP CH4 — Global warming potential of CH4, 28. The biogas produced by anaerobic decomposition of wet garbage and anaerobic treatment of leachate were measured by gas flow meters in the plant area.

[0106] Calculation of carbon emissions from purchased energy:

[0107] Due to the many factors that affect the production process, the phenomenon of insufficient self-produced energy supply often occurs. Therefore, it is necessary to purchase energy to meet the normal operation of the factory. The main consumption of this factory is electricity. The calculation method is as follows:

[0108] E 能源外购 =EC d ×EF d ×10 -3 ,

[0109] Where: E 能源外购 —CO2 emissions from production activities corresponding to purchased electricity consumption, unit: t CO2-eq; EC d —Purchased electricity consumption, in kWh; EF d - The CO2 emission factor of the power supply. In this embodiment, the emission factor of the power supply is 0.42t CO2-eq / MWh according to the data published by Shanghai. The power consumption can be obtained by actual measurement of the power meter in the factory.

[0110] Calculation of carbon emission reduction effect of energy substitution:

[0111] The energy substitution in this embodiment is mainly electricity substitution. After the normal operation of the plant is met (the actual power consumption rate of the target wet garbage treatment plant is about 60% according to actual measurement), the excess power is supplied to the outside through the power grid. The calculation method is as follows:

[0112] E 能源替代 =EG bd ×EF bd ×10 -3 ,

[0113] E 能源替代 —Avoided CO2 emissions from anaerobic fermentation of wet garbage to generate electricity, unit: t CO2-eq; EG bd —The power consumption of the factory area, in kWh; EF bd The CO2 emission factor for electricity emissions is 0.5896 tCO2-eq / MWh. The amount of electricity consumed can be measured by the factory's electricity meter.

[0114] Calculation of carbon emissions from material consumption:

[0115] The implicit CO2 emissions caused by the consumption of materials for anaerobic treatment of wet garbage are still calculated using the emission factor method and can be calculated according to the following formula:

[0116]

[0117] Where: E 物质消耗 =CO₂ emissions due to substance consumption, measured in tons; i = the i-th substance consumed; MC = the amount of substance consumed, measured in tons; EF = the emission factor for the consumed substance, measured in tons CO₂-eq / ton. Background emission data for the main substances consumed in anaerobic waste treatment are shown in Table 2.

[0118] Table 2

[0119]

[0120] Calculation of carbon emissions from residue treatment:

[0121] The sorting residue in this embodiment is incinerated. The residue mainly consists of plastic and bamboo. The calculation of the residue incineration is performed according to the following formula:

[0122]

[0123] Where: E 残渣处理—CO₂ emissions from residue incineration, measured in tons CO₂-eq; R—the amount of sorted residue from wet waste pretreatment, measured in tons; i—the type of residue incinerated. In this example, the residue incinerated primarily consists of sorted plastics and bamboo. The parameters required for the calculation are shown in Table 3. Based on actual operational monitoring, the residue consists of 70% plastic and 30% bamboo.

[0124] Table 3

[0125]

[0126] Calculation of carbon emission reduction effect of energy / product substitution:

[0127] The wet garbage treatment in this embodiment mainly involves anaerobic fermentation to generate biogas for power generation, followed by the processing of waste grease to extract crude oil, which can be used to produce biodiesel to replace traditional fossil diesel. The power generation after the residue incineration treatment also needs to be taken into account. Therefore, the carbon emission reduction effect of the life cycle carbon footprint accounting in this embodiment is divided into three parts: biogas power generation, residue incineration power generation, and diesel substitution.

[0128] Among them, the carbon emission reduction effect of residue incineration power generation is:

[0129] The first step is to calculate the calorific value of the residue. The calorific value of the residue is mainly based on the total calorific value of various dry substances in the residue minus the latent heat of evaporation of water. The calculation formula is as follows:

[0130]

[0131] Where: H 残渣 —Heat value of the residue, in MJ; H 水 The latent heat of vaporization of water is 2435 MJ / t. The residue mainly contains plastic and bamboo. The calorific value of plastic is 30900 MJ / t, and the calorific value of bamboo is 15400 MJ / t.

[0132] The power generation from residue incineration is calculated according to the following formula:

[0133]

[0134] Where: EL 残渣焚烧 —The thermal energy generated after the residue is incinerated, in kWh; η—The power generation efficiency of the incineration, which is calculated as 30% in this embodiment; 3.6—The equivalent energy of the electrical energy, in MJ / kWh.

[0135] Among them, the carbon emission reduction effect of diesel substitution:

[0136] In this embodiment, 18.73 tons of crude oil can be extracted daily by processing waste oil. The crude oil can be processed to produce biodiesel to replace traditional fossil diesel. The carbon emission reduction effect of the replacement is calculated according to the following formula:

[0137] E 柴油替代 =O×η×EF D ,

[0138] Where: E 柴油替代 —carbon emission reduction effect of crude oil in biodiesel production, unit is t CO2-eq; O—crude oil yield, unit is t; η—conversion rate of crude oil to biodiesel, calculated as 87.5% in this example; EF D - The full life cycle emission coefficient of diesel (from diesel supply to combustion completion) is 3.695t CO2-eq / t as determined by the study in this embodiment.

[0139] As a specific implementation method, the process of constructing a credibility assessment mechanism based on sensor accuracy score, data integrity score, and working condition stability score includes:

[0140] The credibility assessment mechanism is as follows:

[0141] Sensor accuracy: factory calibration error range (e.g. ±1%), recent calibration records;

[0142] Data completeness: data missing rate in the past hour (points will be deducted if missing >30%);

[0143] Working condition stability: such as whether the incineration temperature remains stable within the design range (such as 800-1200℃).

[0144] The sensor accuracy, data integrity, and working condition stability are scored separately:

[0145] First, each sub-item is scored from 0 to 1:

[0146] Sensor accuracy:

[0147] 1) Calibration error score: 1-(actual error / maximum allowable error);

[0148] If the actual error exceeds the maximum allowable error, the score is 0;

[0149] 2) Calibration timeliness score: calibration days are mapped in reverse order;

[0150] If the calibration time is ≤ 30 days, the value is 1; 0.2 is deducted for every additional 30 days; if the calibration time is > 150 days, the value is 0;

[0151] 3) Comprehensive calculation: Sensor accuracy score = calibration error score × calibration timeliness score.

[0152] Data Integrity:

[0153] 1) Missing rate score = 1-(missing data amount / total data amount to be transmitted);

[0154] Missing rate > 30% is forced to 0;

[0155] 2) Outlier ratio = 1-(number of outliers / total data volume);

[0156] 3) Comprehensive calculation: Data completeness score = missing rate score × 0.7 + outlier ratio × 0.3.

[0157] Working condition stability:

[0158] Working condition stability score = 1-standard deviation / design allowable fluctuation range;

[0159] If the standard deviation is greater than the design allowable fluctuation range, it is 0.

[0160] Scoring model:

[0161] Each indicator is weighted and the weighted sum is used to output the credibility (0-100%). The weighting process is as follows: sensor accuracy, data integrity, and working condition stability are monitored and calculated. Data credibility = 30% sensor accuracy score + 50% data integrity score + 20% working condition stability score.

[0162] As a specific implementation, the process of performing credibility assessment on the measured value of carbon emissions based on the credibility assessment mechanism, dynamically adjusting the weight ratio between the theoretical value and the measured value of carbon emissions, and calculating the final carbon emissions value includes:

[0163] Credibility > 90%: The measured data is completely credible, and the weight α → 1;

[0164] 60% < confidence level < 90%: reduce α proportionally;

[0165] The weight factor α is dynamically adjusted with the data credibility, and the formula is optimized as follows:

[0166]

[0167] Where: 理论 , σ 实际 are the standard deviations of the theoretical value and the measured value (reflecting uncertainty), β 可信度 It is a correction coefficient based on the credibility score, such as β = 0.8 when the credibility is greater than 80%. Whenever the credibility is higher, the correction coefficient is lower. Based on the above, if the credibility is reduced by 10%, the credibility is reduced by 0.2, and vice versa. Credibility < 60%: trigger an alarm and switch to theoretical value dominance, at this time α→0.

[0168] According to the output results of the real-time data credibility assessment mechanism, the weight ratio of the theoretical value and the measured value is adjusted:

[0169] E=α·Eactual+(1-α)·Etheoretical,

[0170] Where E represents the carbon emission value, the subscript "actual" represents the measured value, and the subscript "theoretical" represents the theoretical value. The above formula is adjusted only for carbon emissions from waste incineration. If abnormal data is detected, the system automatically switches to theoretical value-based mode and triggers an alarm.

[0171] As a specific implementation method, the final carbon emission data is visualized through a two-way linkage between a three-dimensional process flow and a carbon emission Sankey diagram.

[0172] As an additional implementation, after storing measured and theoretical carbon emissions data, administrators can view or modify method parameters, flexibly adjusting parameter ranges to calculate more accurate results based on actual conditions. A function is also available for viewing historical data for a specific point, allowing users to freely select time dimensions to view the value of that point and monitor data in real time.

[0173] Example 2

[0174] This embodiment further discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first embodiment.

[0175] Example 3

[0176] This embodiment further discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first embodiment are implemented.

[0177] Example 4

[0178] This embodiment further discloses a computer program product, including a computer program, which implements the steps of the method described in the first embodiment when executed by a processor.

[0179] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for dynamic monitoring of carbon emissions from solid waste treatment based on dynamic data fusion, characterized in that: The following steps are involved: Obtaining measured and theoretical values ​​of carbon emissions based on operating data, energy data, and material data during solid waste treatment, including measured and theoretical values ​​of carbon emissions from dry waste incineration and theoretical values ​​of carbon emissions from wet waste treatment; Establish a credibility assessment mechanism based on sensor accuracy score, data integrity score and working condition stability score; Conducting a credibility assessment on the measured value of carbon emissions based on the credibility assessment mechanism, dynamically adjusting the weight ratio between the theoretical value and the measured value of carbon emissions, and calculating the final carbon emissions value; The final carbon emission data is visualized through the two-way linkage of the three-dimensional process flow and the carbon emission Sankey diagram.

2. The method according to claim 1, characterized in that The process of obtaining measured and theoretical carbon emissions values ​​based on operating data, energy data, and material data during solid waste treatment includes: The measured values ​​of carbon emissions are obtained through direct measurement and calculation by relevant sensors; the theoretical values ​​of carbon emissions are calculated through mathematical models based on garbage component parameters and common parameters, and are used to supplement the measured values ​​of carbon emissions, provide benchmark references, and dynamically calibrate them. Among them, the garbage component parameters include the proportion of wet-based component types, the proportion of dry matter content in wet weight, the proportion of total carbon in dry weight, the proportion of mineral carbon in total carbon and the garbage degradation rate; the shared parameters include the emission factors of each substance, the calorific value of steam, the average lower calorific value of natural gas and the global warming potential of gas.

3. The method according to claim 1, characterized in that The sensor accuracy score includes a calibration error score and a calibration timeliness score. The calibration error score is calculated based on the ratio of the actual error to the maximum allowable error, and the calibration timeliness score is mapped in reverse order based on the length of the calibration time. The sensor accuracy score is obtained by comprehensive calculation.

4. The method according to claim 3, characterized in that The data integrity score includes the missing rate score and the outlier ratio score. The missing rate score is calculated based on the ratio of the amount of missing data to the total amount of data to be transmitted, and the outlier ratio score is calculated based on the ratio of the number of outliers to the total amount of data. The data integrity score is obtained by comprehensive calculation.

5. The method according to claim 4, characterized in that The working condition stability score is calculated based on the ratio of the standard deviation of the working condition data to the design allowable fluctuation range. When the standard deviation exceeds the design allowable fluctuation range, the working condition stability score is zero.

6. The method according to claim 5, characterized in that The process of performing credibility assessment on the measured value of carbon emissions based on the credibility assessment mechanism includes: Input the measured value of carbon emissions to obtain the corresponding sensor accuracy score, data integrity score, and operating condition stability score; assign weights to the obtained sensor accuracy score, data integrity score, and operating condition stability score, and use weighted summation to output the credibility score of the measured value of carbon emissions.

7. The method according to claim 1, characterized in that The calculation formula for the weight factor that dynamically adjusts the weight ratio of the theoretical value and the measured value of carbon emissions is: Where: 理论 , σ 实际 are the standard deviations of the theoretical value of carbon emissions and the measured value of carbon emissions, β 可信度 is a correction factor based on the credibility score.

8. The method according to claim 7, characterized in that The weight factor is dynamically adjusted according to the credibility assessment results. When the credibility is greater than 90%, the weight factor approaches 1. When the credibility is less than 60%, an alarm is triggered and the system switches to the theoretical value dominant mode, and the weight factor approaches 0.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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